diff --git a/CONTRIBUTORS.yaml b/CONTRIBUTORS.yaml index 9fbcd7a6aa97e4..07928b304db7ec 100644 --- a/CONTRIBUTORS.yaml +++ b/CONTRIBUTORS.yaml @@ -1858,7 +1858,11 @@ r1corre: twitter: r1corre orcid: 0000-0001-6354-2278 joined: 2019-04 - + +renu-pal: + name: Renu Pal + joined: 2024-07 + rikeshi: name: Erik Schill joined: 2021-05 diff --git a/topics/microbiome/images/Plot-Faecalibacterium prausnitzii-VS-age-Maaslin2.png b/topics/microbiome/images/Plot-Faecalibacterium prausnitzii-VS-age-Maaslin2.png new file mode 100644 index 00000000000000..8155b4d7591e9c Binary files /dev/null and b/topics/microbiome/images/Plot-Faecalibacterium prausnitzii-VS-age-Maaslin2.png differ diff --git a/topics/microbiome/images/Plot-Faecalibacterium.prausnitzii-VS-age-Maaslin2.png b/topics/microbiome/images/Plot-Faecalibacterium.prausnitzii-VS-age-Maaslin2.png new file mode 100644 index 00000000000000..8155b4d7591e9c Binary files /dev/null and b/topics/microbiome/images/Plot-Faecalibacterium.prausnitzii-VS-age-Maaslin2.png differ diff --git a/topics/microbiome/images/Plot-alistipes.shahii-VS-diagnosis-Maaslin2.png b/topics/microbiome/images/Plot-alistipes.shahii-VS-diagnosis-Maaslin2.png new file mode 100644 index 00000000000000..d88e1f57b756f9 Binary files /dev/null and b/topics/microbiome/images/Plot-alistipes.shahii-VS-diagnosis-Maaslin2.png differ diff --git a/topics/microbiome/images/heatmap_maaslin2.png b/topics/microbiome/images/heatmap_maaslin2.png new file mode 100644 index 00000000000000..b931a82642618b Binary files /dev/null and b/topics/microbiome/images/heatmap_maaslin2.png differ diff --git a/topics/microbiome/tutorials/multivariable-association/data-library.yaml b/topics/microbiome/tutorials/multivariable-association/data-library.yaml new file mode 100644 index 00000000000000..3bac947c2a9891 --- /dev/null +++ b/topics/microbiome/tutorials/multivariable-association/data-library.yaml @@ -0,0 +1,24 @@ +--- +destination: + type: library + name: GTN - Material + description: Galaxy Training Network Material + synopsis: Galaxy Training Network Material. See https://training.galaxyproject.org +items: +- name: Microbiome + description: Microbiome research has grown substantially over the past decade in terms of the range of biomes sampled, identified taxa, and the volume of data derived from the samples + items: + - name: Determining multivariable association between various meta’omic features + using MaAslin2l + items: + - name: 'DOI: 10.5281/zenodo.12614561' + description: latest + items: + - url: https://zenodo.org/records/12614561/files/HMP2_metadata.tsv + src: url + ext: tabular + info: https://zenodo.org/records/12614561 + - url: https://zenodo.org/records/12614561/files/HMP2_taxonomy.tsv + src: url + ext: tabular + info: https://zenodo.org/records/12614561 diff --git a/topics/microbiome/tutorials/multivariable-association/faqs/index.md b/topics/microbiome/tutorials/multivariable-association/faqs/index.md new file mode 100644 index 00000000000000..9ce3fe4fce824b --- /dev/null +++ b/topics/microbiome/tutorials/multivariable-association/faqs/index.md @@ -0,0 +1,3 @@ +--- +layout: faq-page +--- diff --git a/topics/microbiome/tutorials/multivariable-association/tutorial.bib b/topics/microbiome/tutorials/multivariable-association/tutorial.bib new file mode 100644 index 00000000000000..e6c8d0fcc215d1 --- /dev/null +++ b/topics/microbiome/tutorials/multivariable-association/tutorial.bib @@ -0,0 +1,125 @@ + +# This is the bibliography file for your tutorial. +# +# To add bibliography (bibtex) entries here, follow these steps: +# 1) Find the DOI for the article you want to cite +# 2) Go to https://doi2bib.org and fill in the DOI +# 3) Copy the resulting bibtex entry into this file +# +# To cite the example below, in your tutorial.md file +# use {% cite Batut2018 %} +# +# If you want to cite an online resourse (website etc) +# you can use the 'online' format (see below) +# +# You can remove the examples below + +@article{Batut2018, + doi = {10.1016/j.cels.2018.05.012}, + url = {https://doi.org/10.1016/j.cels.2018.05.012}, + year = {2018}, + month = jun, + publisher = {Elsevier{BV}}, + volume = {6}, + number = {6}, + pages = {752--758.e1}, + author = {B{\'{e}}r{\'{e}}nice Batut and Saskia Hiltemann and Andrea Bagnacani and Dannon Baker and Vivek Bhardwaj and + Clemens Blank and Anthony Bretaudeau and Loraine Brillet-Gu{\'{e}}guen and Martin {\v{C}}ech and John Chilton + and Dave Clements and Olivia Doppelt-Azeroual and Anika Erxleben and Mallory Ann Freeberg and Simon Gladman and + Youri Hoogstrate and Hans-Rudolf Hotz and Torsten Houwaart and Pratik Jagtap and Delphine Larivi{\`{e}}re and + Gildas Le Corguill{\'{e}} and Thomas Manke and Fabien Mareuil and Fidel Ram{\'{i}}rez and Devon Ryan and + Florian Christoph Sigloch and Nicola Soranzo and Joachim Wolff and Pavankumar Videm and Markus Wolfien and + Aisanjiang Wubuli and Dilmurat Yusuf and James Taylor and Rolf Backofen and Anton Nekrutenko and Bj\"{o}rn Gr\"{u}ning}, + title = {Community-Driven Data Analysis Training for Biology}, + journal = {Cell Systems} +} + +@online{gtn-website, + author = {GTN community}, + title = {GTN Training Materials: Collection of tutorials developed and maintained by the worldwide Galaxy community}, + url = {https://training.galaxyproject.org}, + note = "[Online; accessed Fri Nov 05 2021]" +} + +@article{Himel2021, + title = {Multivariable association discovery in population-scale meta-omics studies}, + author = {Himel Mallick and Ali Rahnavard and Lauren J. McIver and Siyuan Ma and Yancong Zhang and Long H. Nguyen and Timothy L. Tickle and + George Weingart and Boyu Ren and Emma H. Schwager and Suvo Chatterjee and Kelsey N. Thompson and Jeremy E. Wilkinson and + Ayshwarya Subramanian and Yiren Lu and Levi Waldron and Joseph N. Paulson and Eric A. Franzosa and Hector Corrada Bravo and + Curtis Huttenhower}, + year = {2021}, + month = {11}, + doi={https://doi.org/10.1371/journal.pcbi.1009442}, + journal = {PLOS COMPUTATIONAL BIOLOGY}, + url={https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009442#sec002} +} + +@article{Yang2023, + author = {Yang, Lu and Chen, Jun}, + title = {Benchmarking differential abundance analysis methods for correlated microbiome sequencing data}, + journal = {Briefings in Bioinformatics}, + volume = {24}, + number = {1}, + pages = {bbac607}, + year = {2023}, + month = {01}, + issn = {1477-4054}, + doi = {10.1093/bib/bbac607}, + url = {https://pubmed.ncbi.nlm.nih.gov/36617187/} +} + + +@article{Yez2021, + title = {Integrating Dietary Data into Microbiome Studies: A Step Forward for Nutri-Metaomics}, + volume = {13}, + ISSN = {2072-6643}, + url = {http://dx.doi.org/10.3390/nu13092978}, + DOI = {10.3390/nu13092978}, + number = {9}, + journal = {Nutrients}, + publisher = {MDPI AG}, + author = {Yáñez, Francisca and Soler, Zaida and Oliero, Manon and Xie, Zixuan and Oyarzun, Iñigo and Serrano-Gómez, Gerard + and Manichanh, Chaysavanh}, + year = {2021}, + month = {08}, + pages = {2978} +} + +@article{Reasoner2024, + title = {Longitudinal profiling of the intestinal microbiome in children with cystic fibrosis treated with elexacaftor-tezacaftor-ivacaftor}, + volume = {15}, + ISSN = {2150-7511}, + url = {http://dx.doi.org/10.1128/mbio.01935-23}, + DOI = {10.1128/mbio.01935-23}, + number = {2}, + journal = {mBio}, + publisher = {American Society for Microbiology}, + author = {Reasoner, Seth A. and Bernard, Rachel and Waalkes, Adam and Penewit, Kelsi and Lewis, Janessa and Sokolow, Andrew G. and Brown, + Rebekah F. and Edwards, Kathryn M. and Salipante, Stephen J. and Hadjifrangiskou, Maria and Nicholson, Maribeth R.}, + editor = {Pier, Gerald B. and O’Toole, George}, + year = {2024}, + month = {02} +} + +@article{Lebeaux2021, + title = {The infant gut resistome is associated with E. coli and early-life exposures}, + volume = {21}, + ISSN = {1471-2180}, + url = {http://dx.doi.org/10.1186/s12866-021-02129-x}, + DOI = {10.1186/s12866-021-02129-x}, + number = {1}, + journal = {BMC Microbiology}, + publisher = {Springer Science and Business Media LLC}, + author = {Lebeaux, Rebecca M. and Coker, Modupe O. and Dade, Erika F. and Palys, Thomas J. and Morrison, Hilary G. and Ross, + Benjamin D. and Baker, Emily R. and Karagas, Margaret R. and Madan, Juliette C. and Hoen, Anne G.}, + year = {2021}, + month = {07} +} + +@misc{IBDMDB, + author = {IBD MDB Consortium}, + title = {The Inflammatory Bowel Disease Multi-omics Database}, + url = {https://ibdmdb.org/}, + note = "[Online; accessed 2021-03-24]" +} + diff --git a/topics/microbiome/tutorials/multivariable-association/tutorial.md b/topics/microbiome/tutorials/multivariable-association/tutorial.md new file mode 100644 index 00000000000000..ef3c805d04db98 --- /dev/null +++ b/topics/microbiome/tutorials/multivariable-association/tutorial.md @@ -0,0 +1,455 @@ +--- +layout: tutorial_hands_on + +title: "Determining multivariable association of various meta’omic features using MaAslin2" +subtopic: metagenomics +tags: + - Heatmap + - metagenomics + - associations + - differential analysis + - statistics + +level: Introductory +zenodo_link: https://zenodo.org/records/12614561 +questions: +- How do I find associations between microbial features and specific metadata variables? +objectives: +- Identify statistically significant associations between microbial features and metadata variables (such as clinical conditions, environmental factors, or demographic information) in microbiome data. +- Uncover potential biomarkers associated with specific disease states. +time_estimation: 45M +contributions: + authorship: + - renu-pal + - paulzierep + editing: + - shiltemann +--- +# Microbiome Association Detection with MaAsLin2 + +The importance of identifying associations between microbial features and metadata variables using tools like MaAsLin2 lies in several key areas: + +- **Understanding Disease Mechanisms:** These associations can provide insights into how microbial composition changes may contribute to disease development or progression. This understanding is crucial for advancing knowledge of disease mechanisms. + +- **Potential Diagnostic Markers:** Identifying microbial biomarkers associated with specific diseases or conditions can potentially lead to the development of diagnostic tests. These tests could aid in earlier detection, more accurate diagnosis, and monitoring of disease progression. + +- **Personalized Medicine:** By identifying associations between gut microbes and individual characteristics (such as diet, medication use, or genetic factors), personalized treatment strategies can be developed. This approach may lead to more effective and tailored interventions for patients. + +- **Therapeutic Targets:** Associations can reveal potential targets for therapeutic interventions, such as probiotics, prebiotics, or dietary changes, aimed at modulating the gut microbiota to promote health or mitigate disease risks. + +- **Advancing Microbiome Research:** Building a comprehensive understanding of microbial associations with various factors enhances microbiome research. This knowledge can contribute to broader insights into microbial ecology, evolution, and interactions within the human body and the environment. + +In addition to MaAslin2, Galaxy offers several other differential analysis tools widely used in transcriptomics and microbiome studies. +These tools are designed to handle different types of data (e.g., RNA-seq, microbial count data), with varying strengths in terms of statistical power, +handling of sparsity, and treatment of compositional data. Some of them are mentioned below: + + +| Tool | Strengths | Weaknesses | Comparison to MaAsLin2 | +|---------------------|----------------------------------------------|------------------------------------------|------------------------------------------------| +| **ANCOM-BC** | Compositionality and bias correction | Computationally intensive for large datasets | ANCOM-BC is good for simpler designs, but MaAsLin2 handles complex metadata better. | +| **LEfSe** | Easy to interpret, focuses on effect size | No covariates, may overfit | LEfSe is simpler but lacks the flexibility and multivariable depth of MaAsLin2. | +| **ALDEx2** | Robust to sparsity, small sample sizes | Limited handling of complex metadata | ALDEx2 is suitable for small datasets, but MaAsLin2 is superior in handling multivariable data and covariates. | +| **MetagenomeSeq** | Handles zero-inflation, sparse data | Computationally heavy for large datasets | MetagenomeSeq is great for zero-inflated data, but lacks MaAsLin2's multivariable modeling capacity. | +| **Corncob** | Models both abundance and variability | Complex to use, requires R expertise | Corncob excels at overdispersion analysis, but MaAsLin2 is easier for broader multivariable models. | +| **DESeq2**| Originally developed for RNA-seq and transcriptomics | Lacks compositionality awareness | While DESeq2 works for microbiome data, MaAsLin2 offers more suitable options for compositional data and covariate handling. | +| **Limma-Voom** | Effective for RNA-seq and microarray data, handles low counts | Not tailored for compositional microbiome data | Limma-Voom is well-suited for gene expression, but MaAsLin2 better accounts for the unique characteristics of microbiome data. | + +- ANCOM-BC and MaAsLin2, outperform general-purpose tools like DESeq2 and limma-voom when it comes to microbiome data. This is due to their handling of the compositional nature of microbiome data and the sparsity typical of microbial datasets. ({% cite Yang2023 %}) +- While general methods like DESeq2 and limma-voom are reliable for gene expression analysis, they do not handle the unique properties of microbiome data as effectively as MaAsLin2. The latter provides a more accurate estimation of differential abundance in the presence of metadata confounders. + +![sensitivity and false discovery rate (FDR) across different tools](https://journals.plos.org/ploscompbiol/article/figure/image?size=large&id=10.1371/journal.pcbi.1009442.g004 "Source: sensitivity and false discovery rate (FDR) across different tools"){:width="60%"} + +The above figure compares various tools for differential abundance detection (Panel A) and multivariable association detection (Panel B) in microbiome studies, based on sensitivity and false discovery rate (FDR).\ +**Sensitivity** measures how well the methods detect true signals, higher values lead to better performance.\ +**False discovery rate (FDR)** measures the proportion of false positives among detected signals (lower FDR is better).\ +MaAsLin2 is the clear standout for both differential abundance detection and multivariable association detection, showing high sensitivity and maintaining a low FDR. + +> +> +> In this tutorial, we will cover: +> +> 1. TOC +> {:toc} +> +{: .agenda} + +# Get the data +MaAsLin2 requires the following input files: + +- **Features file**: \ + This file is tab-delimited.\ + Formatted with features as columns and samples as rows.\ + Possible features in this file include microbes, genes, pathways, etc. +- **Metadata file** : \ + This file is tab-delimited.\ + Formatted with features as columns and samples as rows. + +The Features file can contain samples not included in the metadata file (or vice versa). For both cases, those samples not included in both files will be removed from the analysis. +Also, the samples do not need to be in the same order in the two files. + +## Origin +In this tutorial, the two input files used are: +- `HMP2_taxonomy.tsv` or features file +- `HMP2_metadata.tsv` or metadata file + +The files provided were generated from the HMP2 data. To download [Click here](https://ibdmdb.org/) + +The **HMP2_taxonomy.tsv** and **HMP2_metadata.tsv** files are part of the **Human Microbiome Project 2 (HMP2)**, which is a key component of the Inflammatory Bowel Disease Multi'omics Database ({% cite IBDMDB %}). The IBDMDB is a large-scale, multi-omic research initiative aimed at understanding the microbiome's role in IBD progression by integrating various omics data like metagenomics, metabolomics, and host genetics. + +The **HMP2_taxonomy.tsv** file contains microbiome data (species abundances) collected from IBD patients and healthy controls, while the **HMP2_metadata.tsv** file includes clinical and demographic metadata for these samples, such as IBD diagnosis (non-IBD, ulcerative colitis(UC), or Crohn’s disease(CD)), dysbiosis state, and treatments like antibiotics. + +The above two files were utilized prominently in the research study, **"Multivariable Association Discovery in Population-Scale Meta-Omics Studies."** ({% cite Himel2021 %}) + +> Getting the data +> 1. Create and name a new history for this tutorial. +> +> {% snippet faqs/galaxy/histories_create_new.md %} +> +> 2. Import the files from [Zenodo](https://zenodo.org/records/12614561) or from the data library: +> +> ``` +> +> https://zenodo.org/records/12614561/files/HMP2_taxonomy.tsv +> https://zenodo.org/records/12614561/files/HMP2_metadata.tsv +> +> ``` +> +> > Importing data via links +> > +> > * Copy the link location (Right-click on the filename then "Copy Link Address") +> > * Open the Galaxy Upload Manager +> > * Select **Paste/Fetch Data** +> > * Paste the link into the text field +> > * Press **Start** +> {: .tip} +> +> 3. Change the name of the files to `feature` and `metadata` . +> +> As a default, Galaxy uses the link as the name of the new dataset. It also does not link the dataset to a database or a reference genome. +> +> {% snippet faqs/galaxy/datasets_rename.md %} +> +> 4. Inspect the content of a file. +> +> > Inspecting the content of a dataset +> > +> > * Click on the {% icon galaxy-eye %} (eye) icon next to the relevant history entry +> > * View the content of the file in the central panel +> {: .tip} +> +> > +> > +> > 1. What is the main difference between the two files? +> > +> > +> > > +> > > 1. The metadata file describes sample characteristics (e.g., clinical data, demographics) while the features file contains microbial data (e.g., taxa abundance) used for analysis in microbiome studies. +> > {: .solution } +> {: .question} +> +{: .hands_on} +## Data that can be analysed with MaAsLin2 + +MaAsLin2 is designed to analyze various types of **tabular data** for multivariable association studies. The following table formats are compatible: + +**OTU/ASV Abundance Tables:** These tables represent the abundance of Operational Taxonomic Units (OTUs) or Amplicon Sequence Variants (ASVs) across different samples, allowing for analysis of microbial diversity in relation to metadata. + +**MAGs Abundance Matrices:** These matrices reflect the abundance of Metagenome-Assembled Genomes (MAGs) in samples, facilitating the exploration of associations between reconstructed genomes and various metadata. + +**Taxonomy Tables:** These tables contain taxonomic classifications of microbes, enabling researchers to investigate how different taxonomic levels relate to associated metadata variables. + +**Gene Count Matrices:** These matrices provide counts of specific genes across samples, useful for examining associations between gene abundances and metadata, particularly in metagenomics or transcriptomics studies. +## Galaxy tools that can generate output for MaAsLin2 + +Several tools available on Galaxy can generate outputs that are compatible with MaAsLin2, particularly in the context of microbiome data analysis. Following are some Galaxy tools that produce outputs that can be used as input for MaAsLin2: +1. [**QIIME2**](https://training.galaxyproject.org/training-material/search2?query=Qiime) is a popular tool for processing microbiome data, and Galaxy offers a suite of QIIME2 tools.\ +**Outputs compatible with MaAsLin2**:\ + - Feature Table (OTU/ASV table): Generated after steps like DADA2 or Deblur, this table includes the abundance of microbial features in each sample. + - Taxonomy Assignment: The feature table can be combined with taxonomy information to link ASVs/OTUs to microbial taxa. + - Metadata Files: QIIME2 requires metadata to perform analyses, and the same metadata file can be formatted and used in MaAsLin2. + +2. [**Mothur**](https://training.galaxyproject.org/training-material/search2?query=mothur) is another widely used microbiome data analysis tool available in Galaxy, and it can be used to produce inputs for MaAsLin2.\ +**Outputs compatible with MaAsLin2**: + - Shared File (OTU Table): Mothur generates a "shared" file that contains the abundance of OTUs across samples, which can be reformatted and used as input to MaAsLin2. + - Features File: Mothur also outputs taxonomy assignments for OTUs, which can be paired with the shared file. + - Metadata File: As in QIIME2, metadata is used during the Mothur workflow and can be reused in MaAsLin2. +3. [**MetaPhlAn (Metagenomic Phylogenetic Analysis)**](https://training.galaxyproject.org/training-material/by-tool/iuc/metaphlan.html) is a tool that provides taxonomic profiling of microbial communities from metagenomic sequencing data. It is available in Galaxy and can generate outputs that are directly compatible with MaAsLin2.\ +**Outputs compatible with MaAsLin2**: + - Taxonomic Profiles: MetaPhlAn generates taxonomic abundance tables (e.g., relative abundance of microbial taxa at various taxonomic levels) that can be directly used as the feature table for MaAsLin2. + - Metadata File: As with other tools, the sample metadata used in MetaPhlAn analysis can also be reused in MaAsLin2. + + + + +# Find associations between the two groups +Now we will find significant associations between microbial features(features file) and metadata variables (metadata file) using the **MaAslin2** tool + +> Task description +> +> 1. {% tool [MaAsLin 2](toolshed.g2.bx.psu.edu/repos/iuc/maaslin2/maaslin2/1.16.0+galaxy0) %} with the following parameters: +> - *"Interactions: Fixed effects"*: `c3:age`, `c4:diagnosis` +> - *"Random effects"*: `c5:subject` +> - *"Reference"*: `diagnosis,CD` +> +> Keep the rest of the default values as it is. +{: .hands_on} + +## Understanding parameters in the tool + +Let's now understand the role of each parameter in the tool. + +1. **Interactions: Fixed effects**: Fixed effects are the factors in your model that you want to study and draw conclusions about. These are the variables you hypothesize have a direct and consistent influence on the outcome. For example, you are studying how different diets affect gut microbiome composition, then diet would be a fixed effect because you’re specifically interested in understanding how different diets influence the microbiome. You might also include other fixed effects like age and gender to control for their impact. + +3. **Random effects**: In some studies, like those following people over time or studying families, samples from the same group can be similar. MaAsLin2 helps handle this by letting researchers choose a grouping factor. This helps make sure the statistical analysis is more accurate. For example, setting random_effects = "Subject_ID" helps control for the correlation between samples that come from the same individual. + +4. **Reference**: It allows researchers to establish a baseline or standard feature file category against which other categories are compared, helping to interpret and understand the effects of different variables on microbial features. + + > + > - In MaAslin2, the reference level is required for variables with more than two levels to avoid errors. + > - Reference for a variable with more than two levels is provided as a string of `variable, reference`. + > - Reference for more than one variable having more than two levels each is provided as a string of `variable1,reference1,variable2,reference2`. + > - Example, both diagnosis and site variable have more than two levels hence reference can be provided as `diagnosis, CD, site, Cedars-Sinai`. + {: .comment} + +**Additional options** : + +4. **min_abundance** [ Default: 0 ] +- The minimum abundance for each feature within a single sample. +- If a feature's abundance (or presence) is lower than this threshold, it won't be included in the analysis. +- For example, if you set min_abundance to 0.01, only features that make up at least 1% of the total abundance in at least one + sample will be analyzed. +- Setting the min_abundance parameter to 0 means that no abundance threshold is applied. In other words, all features (regardless + of how rare or abundant they are) will be included in the analysis, as long as they are present in the data. +5. **min_prevalence** [ Default: 0.1 ] : +- The minimum proportion of samples in which a feature must be detected to be included in the analysis. +- It filters features based on their frequency across the entire dataset. +- For example, if min_prevalence is set to 0.1 (10%), only features that appear in at least 10% of the samples are kept, + regardless of how abundant they are in those samples. +6. **max_significance** [ Default: 0.25 ] : +- When you set a value for max_significance, you are specifying the maximum q-value a feature can have to be + considered significant. +- A max_significance of 0.25 means you are considering results with a q-value of 0.25 or lower as + statistically significant. +7. **normalization**: [ Default: "TSS" ] [ Options: "TSS", "CLR", "CSS", "TMM", "NONE"] +- Ensures that features or samples with different scales or units are brought to a comparable level. +- Prevents features with larger scales or more abundant counts from dominating the analysis. +- Helps in making accurate comparisons and interpretations by standardizing data. +- Options:\ + 1. Total Sum Scaling (TSS) : Each count is divided by the total count for that sample, often multiplied by a constant to transform it into a percentage or proportion. + 2. Centered Log-Ratio (CLR) : Each feature count is divided by the geometric mean of the counts in the same sample, and then the logarithm is taken. Useful for data where ratios between features are of interest, and it helps deal with the compositional nature of microbiome data. + 3. Cumulative Sum Scaling (CSS) : Does the same basic conversion as TSS but it might include extra adjustments to deal with specific data patterns, giving a potentially more accurate normalization. + 4. Trimmed Mean of M-values(TMM) : TMM normalizes data so you can accurately compare gene or feature counts across samples that may have +different total counts or distributions.\ +For each feature (like a gene), TMM computes the log-fold change (M-value) between each sample and a metadata sample.\ +It then removes extreme values (outliers) that could skew the results. This trimming helps focus on more typical values and reduces the impact of any unusual data points.\ +The weighted mean of the remaining M-values is calculated to determine the overall adjustment factor for each sample.\ +Finally, this adjustment factor is used to normalize the counts in each sample, making them more comparable.\ + +8. **transform** [ Default: "LOG" ] [Options: "LOG", "LOGIT", "AST", "NONE" ] +- The transform to apply to the datasets. +- This is done to make the data more suitable for the linear models used in MaAslin2, helping to improve the accuracy and reliability of the results. +- Options: \ + 1. LOG: The log transformation applies the natural logarithm (log base e) to the data. Used When your data has a wide range of values or +is heavily skewed, such as microbiome abundance data where some taxa are much more abundant than others. + 2. LOGIT: The logit transformation is used for data that represent proportions or probabilities, where the values lie between 0 and 1. It is defined as logit(x) = log(x / (1 - x)). Used when dealing with data that represents proportions, such as relative abundances that are expressed as fractions or percentages.\ +The logit transformation is only applicable to data within the open interval (0, 1), so values exactly at 0 or 1 need to be adjusted (e.g., adding a small constant like 0.001). + 3. Arcsine Square Root Transformation (AST): It is a statistical transformation used primarily on proportion or percentage data.\ +The transformation starts by taking the square root of the proportion value. This step reduces the impact of extreme values.\ +Next, it applies the arcsine function (the inverse of the sine function) to the square root result. The arcsine function helps to normalize the distribution further.\ +Used when you are working with proportion data, such as relative abundances in microbiome studies, where the values are bounded between 0 and 1. + +9. **analysis_method** [ Default: "LM" ] [ Options: "LM", "CPLM", "NEGBIN", "ZINB" ] +- MaAsLin2 uses linear models by default, but the specific model it applies depends on the input data provided and the configuration of parameters. +- **Broader categories of models:** + 1. Linear Model (default):This is the default modeling approach. A linear model is used when the relationships between the metadata and the taxa abundance are assumed to be linear. + 2. Linear Mixed-Effects Model: Linear mixed models extend simple linear models by incorporating both fixed and random effects, making them particularly suitable for situations where data exhibit non-independence, such as those arising from a hierarchical structure. + 3. Generalized Linear Models (GLMs):Maaslin2 uses GLMs, which extend linear models to accommodate different types of input data (e.g., continuous, binary, or count data) +**Options**: \ + 1. Linear Model (LM): The default linear model of MaAsLin2. By default, MaAsLin2 uses linear regression for continuous metadata or logistic regression (a generalized linear model) for binary metadata as its association method. + 2. Conditional Poisson Linear Model (CPLM): A type of generalized linear model used for non-negative count data. It looks at how one or more factors (covariates) influence the counts while considering that the counts may be related to each other. This model assumes that the counts follow a Poisson distribution based on certain predictors, making it useful for analyzing data with different levels of exposure or biological processes. + 3. Negative Binomial Model (NEGBIN): A generalized linear model used for count data where there is overdispersion (variance exceeds the mean). + 4. Zero-Inflated Negative Binomial Model (ZINB): A generalized linear model that combines features of both zero-inflation and negative binomial models, useful for count data with both excess zeros and overdispersion. + +10. **correction or adjustment methods** [ Default: "BH" ] : +- When performing numerous statistical tests simultaneously, like testing the association of many microbial taxa with various metadata variables, the risk of finding false positives increases. +- Correction methods help control this risk to ensure that the results are reliable and that significant findings are not due to random chance. +- This is done by computing the q-value, which is a measure of how many false positives are expected among the significant results. +- Options: + 1. Benjamini & Hochberg(BH)(aka false discovery rate(fdr)): A common method used for FDR correction. It ranks the p-values from smallest to largest and adjusts them based on their rank and the total number of tests. + 2. Benjamini & Yekutieli(BY): Similar to Benjamini-Hochberg but includes a correction factor that accounts for the correlation between tests. + 3. Bonferroni correction: Divides the significance threshold (alpha level) by the number of tests performed and then compares each p-value to this adjusted significance level to determine if it is statistically significant. + 4. Hochberg : It is similar to the Bonferroni correction but is often more powerful, meaning it has better statistical power to detect true effects while controlling for false positives. + 5. Hommel: controls the Family-Wise Error Rate (FWER) by adjusting p-values in a step-down fashion, starting from the smallest p-value and progressively increasing the threshold. It is more powerful than Bonferroni while maintaining strict error control. + 6. Holm: controls the Family-Wise Error Rate (FWER) by sequentially adjusting p-values from smallest to largest, and then comparing them to adjusted critical value, given by $$ \frac{α}{m−i+1} $$​, where α is the significance level (e.g., 0.05), m is the total number of hypothesis, and i is the rank of the p-value in the ordered list.\ +The corresponding null hypothesis is rejected for p-values less than the adjusted critical value and the process stops as soon as p-values equals or exceeds the corresponding critical value. This stepwise approach is less conservative than the Bonferroni correction, offering greater statistical power.\ +**FWER** is the probability of finding at least one false positive among all the tests performed, assuming all null hypotheses are true.\ +**FWER Control** is used to minimize the risk of incorrectly claiming significant results when there are none, thus maintaining the overall reliability of the results.\ +For more information on correction methods, [click here](https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/p.adjust). + +11. **standardize**: Apply z-score so continuous metadata are on the same scale [ Default: TRUE ] +12. **plot_heatmap**: Generate a heatmap for the significant associations [ Default: TRUE ] +13. **heatmap_first_n**: In heatmap, plot top N features with significant associations [ Default: 50 ] +14. **plot_scatter**: Generate scatter plots for the significant associations [ Default: TRUE ] +15. **cores**: The number of R processes to run in parallel [ Default: 1 ] + + +# Reading Output Files +The tool generates the following five major files: +- **Data output files** + 1. `residuals.rds` + Residual output in Maaslin2 refers to the values that remain after fitting a model to the data. It represents the variation in the microbiome data of feature file that cannot be explained by the metadata (predictors like diet, age, or treatment conditions) in metadata file.\ + **Why is Residual Output Important?**\ + **Finding Other Influences** : Looking at the residuals can help us spot other factors that might affect the microbiome that we didn’t include in our model.\ + **Checking Model Fit**: By examining the residuals, we can see if our model is working well. If the residuals show a pattern, it means our model might need some adjustments.\ + **Exploring Further**: We can analyze these residuals to find new connections or patterns that we might want to study further. + + 2. `significant_results.tsv` + Provides the most important output from MaAsLin2 which is the list of significant associations. + 3. `all_results.tsv` + Same format as significant_results.tsv, but includes all association results (instead of just the significant ones). + +- **Visualization output files** + 4. `heatmap.pdf` + This file contains a heatmap of the significant associations. + ![heatmap](../../images/heatmap_maaslin2.png "heatmap of significant associations") + + 5. `plots :` + A plot is generated for each significant association.\ + Scatter plots are used for continuous metadata.\ + Box plots are for categorical data.\ + Data points plotted are after normalization, filtering, and transformation.\ + For example, observe the output file diagnosis_3.png located in the 'Plots' directory: + + ![Plot1](../../images/Plot-alistipes.shahii-VS-diagnosis-Maaslin2.png "Plot: Alistipes.shahii VS diagnosis ") + +The above plot displays the relative abundance of the microbial species **Alistipes shahii** across different **diagnosis** groups: CD (Crohn's Disease), nonIBD (non-Inflammatory Bowel Disease), and UC (Ulcerative Colitis).\ +**Key Components of the Plot:** +- **X-axis (Diagnosis)**:\ + Represents the three diagnostic categories:\ + - CD: Crohn’s Disease (n = 474 samples)\ + - nonIBD: Individuals without Inflammatory Bowel Disease (n = 277 samples)\ + - UC: Ulcerative Colitis (n = 257 samples) + +- **Y-axis (Alistipes shahii)**:\ + Shows the relative abundance of Alistipes shahii in the samples from each diagnostic group.\ + The scale goes from 0.00 to 0.06, indicating that most of the abundance values are relatively low. + +- **Boxplots and Scatter Points**:\ + Each boxplot represents the distribution of Alistipes shahii abundance within each diagnosis group.\ + The box shows the interquartile range (IQR), with the horizontal line inside the box representing the median abundance value for each group. + Whiskers extend to 1.5 times the IQR, showing the spread of the data.\ + The individual points represent the relative abundance of Alistipes shahii for each sample, giving a more granular view of the data distribution. + +- **FDR (False Discovery Rate): 7.642e-02 (or 0.076)**\ +This value indicates the statistical significance of the association between diagnosis and the abundance of Alistipes shahii after adjusting for multiple testing. + +- **Coefficient: 4.57e+00 (~4.57)**\ +This is the effect size representing how strongly the abundance of Alistipes shahii differs across the diagnostic categories. The positive coefficient here is associated with the nonIBD group (as indicated by the "Value: nonIBD" label), meaning this group tends to have higher relative abundances compared to the other groups. + +- **Diagnosis Groups:**\ +CD (Crohn's Disease): The boxplot shows a very low abundance of Alistipes shahii in most samples, with the median near zero and very few outliers showing higher values.\ +nonIBD (non-IBD individuals): This group shows a wider distribution, with a higher median and more spread in the abundance of Alistipes shahii. Some samples have noticeably higher abundance compared to the disease groups.\ +UC (Ulcerative Colitis): Similar to CD, the UC group shows low median abundance with some variation, but overall the values are lower compared to the nonIBD group. + +- **Conclusion:** + This plot suggests that Alistipes shahii is more abundant in healthy individuals (nonIBD) compared to those with IBD (CD and UC). + + > + > + > 1. Open the heatmap.pdf output file and observe the heatmap. What do you notice? + > 2. What do you observe in the significant.tsv file? + > 3. What does residuals.rds output convey? + > 4. Try explaining the following plot. + > ![Plot1](../../images/Plot-Faecalibacterium.prausnitzii-VS-age-Maaslin2.png "Plot: Faecalibacterium prausnitzii VS age ") + > > + > > 1. You will observe the association of the microbes with the fixed effects variables as `age` and `diagnosis`. + > > - Observe how setting the reference value as `CD` for the categorical variable `diagnosis` in MaAsLin2 implies that this reference level will be used as the baseline for comparison against other levels of the variable, i.e, `nonIBD` and `UC`. + > > - The effects of other levels will be interpreted relative to this reference level, helping to understand their impact on microbial features. + > > - The **colors** of the heatmap represent the magnitude and direction of associations between microbial features and metadata variables. + > > - **Color Intensity**: The intensity of the color indicates the strength of the association. Darker or more vivid colors usually represent stronger associations. + > > - **Color Hue**: The hue (e.g., red, blue) typically indicates the direction of the association. For instance, red represents positive associations (where an increase in the metadata variable is associated with an increase in the microbial feature) and another color blue represents negative associations (where an increase in the metadata variable is associated with a decrease in the microbial feature). + > > + > > For example, if you look for `Bifidobacterium longum` in the heatmap, you'll notice that its occurrence in the human gut is least affected by the individual's age and shows a neutral effect in relation to their diagnosis of UC (Ulcerative Colitis) and non-IBD (non-Inflammatory Bowel Disease). + > > 2. The **significant.tsv** file shows statistically significant associations between microbial features and metadata variables that meet a specified threshold (in our case, the default `Maximum significance = 0.25`). It includes effect sizes, p-values, and adjusted p-values (q-values) to indicate the strength, direction, and reliability of each association. This file helps identify meaningful relationships in the microbiome data. + > > 3. To read and interpret the **residual.rds** file from Maaslin2, first load the file into R using the **readRDS()** function, which will typically return a matrix or data frame. + > > - The rows of this matrix represent different features, such as taxa or other variables in your microbiome dataset. + > > - The columns correspond to individual samples, identified by unique sample IDs (e.g., MSM9VZHN), which are used to match each sample to its metadata (e.g., patient, treatment, or environmental condition). + > > - The values in the matrix are the residuals, which show the difference between the observed microbiome abundance and the values predicted by the model for each sample and feature. + > > - Positive residuals indicate that the observed value is higher than the model’s prediction, while negative residuals indicate lower observed values. For example, a residual of 0.12 indicates that the observed value was slightly higher than predicted, -0.15 shows it was lower, and 0.02 suggests the observed and predicted values are nearly identical. + > > - Maaslin2 applies statistical thresholds (such as q-value for multiple testing correction or p-value) to filter results. If a taxon’s abundance is well predicted by the metadata, it may not meet the criteria for inclusion in the residual output. Essentially, only taxa with residuals large enough to be considered meaningful might be included. Interpreting these residuals allows you to assess how well the model has captured the variability in your data and explore any patterns or deviations not explained by the model's predictors. + > > 4. The mentioned plot shows the relationship between **age** (on the x-axis) and the relative abundance of the bacterial species **Faecalibacterium prausnitzii** (on the y-axis).\ + > > **Key Components:** + > > - **Y-axis (Faecalibacterium prausnitzii):**\ + > > Represents the relative abundance of the species Faecalibacterium prausnitzii across samples.\ + > > The range goes from 0 to approximately 0.6, indicating that the abundance values vary, but most are below 0.4. + > > - **X-axis (Age):**\ + > > Represents the age of the individuals from whom the samples were taken. The ages range from about 10 to 70. + > > - **Scatter Points:**\ + > > Each green dot represents a single sample, showing the relative abundance of Faecalibacterium prausnitzii for that specific individual's age.\ + > > There is considerable variability, with some individuals showing relatively high abundance, while others have low values. + > > - **Trend Line (Blue Line):**\ + > > A blue trend line indicates the overall relationship between age and the abundance of Faecalibacterium prausnitzii.\ + > > The line shows a slight downward slope, suggesting a negative correlation between age and the abundance of this species. As age increases, the relative abundance of Faecalibacterium prausnitzii tends to decrease slightly. + > > - **FDR (False Discovery Rate): 6.144e-02 (or ~0.061)**\ + > > This FDR value (0.061) is slightly above the commonly accepted threshold of 0.05, indicating that the observed association between age and Faecalibacterium prausnitzii is not statistically significant after correcting for multiple comparisons. + > > - **Coefficient: -1.20e+00 (~-1.20)**\ + > > The coefficient indicates the effect size of age on the abundance of Faecalibacterium prausnitzii. A negative coefficient (-1.20) implies that as age increases, the abundance of this species tends to decrease. + > > - **N = 1008:**\ + > > The number of samples analyzed is 1008, providing a large dataset to examine the relationship. + > > - **Conclusion:**\ + > > Faecalibacterium prausnitzii tends to decrease in abundance with age. + > {: .solution } + {: .question} + + + +# Conclusion + +In essence, uncovering associations between microbial features and metadata variables through tools like MaAsLin2 not only deepens our understanding of microbiome dynamics but also holds promise for clinical applications, personalized health strategies, and advancing the field of microbiome research. + +The results obtained from MaAslin2 can further be visualized using tools like [**phyloseq**](https://training.galaxyproject.org/training-material/by-tool/interactive_tool_phyloseq.html).\ +Tools such as phyloseq necessitate that users prepare and structure their data prior to visualization. This preparation may include subsampling features to retain only those that are statistically significant using tools like [**ampvis**](https://usegalaxy.eu/?tool_id=toolshed.g2.bx.psu.edu%2Frepos%2Fiuc%2Fampvis2_subset_taxa%2Fampvis2_subset_taxa%2F2.8.9%2Bgalaxy0&version=latest), as well as organizing the data into appropriate formats, such as an OTU/ASV table and accompanying metadata.\ +Once the data is prepared, phyloseq can be used to create a range of plots, such as: + +**Barplots**: Show the relative abundance of significant taxa across different metadata groups (e.g., diagnosis).\ +**Ordination plots**: Show how samples cluster based on beta diversity, focusing on the significant taxa identified.\ +**Heatmaps**: Show the abundance of significant taxa across samples, grouped by metadata variables. + +## Studies involving MaAslin2 tool for analysis + +- **Instegrating Dietary Data into Microbiome Studies: A Step Forward for Nutri-Metaomics** ({% cite Yez2021 %}) : \ +The study explores the enhancement of microbiome research through the incorporation of dietary data. The research emphasizes that integrating detailed dietary information with microbiome analyses provides a more comprehensive understanding of how diet influences gut microbiota composition and function. By applying advanced techniques in nutri-metaomics, the study aims to link specific dietary patterns with microbial changes, revealing insights into the interactions between diet, the microbiome, and health outcomes. This approach improves the ability to identify diet-related biomarkers and tailor personalized nutrition interventions based on microbial profiles.\ +MaAsLin2 was used to assess how specific dietary patterns influence the abundance and diversity of gut microbiota by integrating detailed dietary data with microbiome profiles. \ +MaAsLin2 was set up with the following parameters: \ +1. **normalization**: TMM +2. **transform**: LOG +3. **correction**: BH +4. **analysis_method**: LM +5. **max_significance**: 0.25 (default significance threshold) +6. **min_abundance**: 0.0001 +7. **min_prevalence**: 0.1 +8. **fixed effects**: Age, gender, and other characteristics of the participants as well as dietary data were added as fixed effects. +9. **random effects**: as participant samples from two time points were included, the participant identification number was added as a random effect.\ +All models were adjusted for gender. \ +Results with a false-discovery rate (FDR) lower than 0.25 were considered significant. + +- **Longitudinal profiling of the intestinal microbiome in children with cystic fibrosis treated with elexacaftor-tezacaftor-ivacaftor**( {% cite Reasoner2024 %} ) :\ +The study investigated how the intestinal microbiome of children with cystic fibrosis (CF) evolves over time while being treated with the elexacaftor-tezacaftor-ivacaftor (ETI) combination therapy. MaAsLin2 was employed to detect shifts in microbial abundance associated with this treatment. By performing longitudinal microbiome profiling, the research tracked changes in gut microbial diversity and composition in response to ETI treatment. The findings highlighted significant shifts in the microbiome, which could impact gut health and inform future CF treatment approaches. +MaAsLin2 was set up with the following parameters: + +1. **fixed effects**: treatment(ELX/TEZ/IVA), age, and recent antibiotic exposure +2. **random effects**: Subject ID was specified as a random effect due to multiple samples from the same subject +3. **min_prevalence**: The minimum prevalence threshold was set to 0.1, indicating that features must be present in at least 10% of the samples to be included. +4. **transform**: LOG transformed +5. **Analysis method**: The general linear “LM” model was used. +6. **Correction method**: The Benjamini-Hochberg procedure was used to correct P values +7. **Normalization method**: A Centered Log-Ratio (CLR) normalization approach was used instead of default normalization methods + +- **The infant gut resistome is associated with E. coli and early-life exposures** ({% cite Lebeaux2021 %}):\ +The study investigated how the infant gut resistome—the collection of antibiotic resistance genes (ARGs) in the gut microbiome—associates with E. coli and early-life exposures using MaAsLin2. +The analysis, which utilized additive boosting of generalized linear models for feature reduction, revealed significant associations between ARGs and E. coli presence, as well as early-life factors such +as antibiotic use and other exposures. Key parameters included CLR normalization of compositional abundance data, no standardization of continuous variables, and a strict significance threshold +(q-value < 0.01) using Benjamini-Hochberg correction. This approach highlighted how early exposures influence the resistome and its relationship with E. coli in the infant's gut. + + +Congratulations! You have successfully completed the tutorial. Now, feel free to explore the tool further by running it with different input files from other Galaxy tools and experimenting with various parameter settings. Enjoy the process! :) diff --git "a/topics/microbiome/tutorials/multivariable-association/workflows/Determining-multivariable-association-between-various-meta\342\200\231omic-features-using-MaAslin2-tests.yml" "b/topics/microbiome/tutorials/multivariable-association/workflows/Determining-multivariable-association-between-various-meta\342\200\231omic-features-using-MaAslin2-tests.yml" new file mode 100644 index 00000000000000..eb27d29527abf7 --- /dev/null +++ "b/topics/microbiome/tutorials/multivariable-association/workflows/Determining-multivariable-association-between-various-meta\342\200\231omic-features-using-MaAslin2-tests.yml" @@ -0,0 +1,17 @@ +- doc: "Test outline for Determining-multivariable-association-between-various-meta\u2019\ + omic-features-using-MaAslin2" + job: + HMP2_taxonomy.tsv: + location: https://zenodo.org/records/12614561/files/HMP2_taxonomy.tsv + class: File + filetype: tabular + HMP2_metadata.tsv: + location: https://zenodo.org/records/12614561/files/HMP2_metadata.tsv + class: File + filetype: tabular + outputs: + significant_results: + asserts: + has_n_columns: + n: 9 + diff --git a/topics/microbiome/tutorials/multivariable-association/workflows/index.md b/topics/microbiome/tutorials/multivariable-association/workflows/index.md new file mode 100644 index 00000000000000..e092e0ae66ddd4 --- /dev/null +++ b/topics/microbiome/tutorials/multivariable-association/workflows/index.md @@ -0,0 +1,3 @@ +--- +layout: workflow-list +--- diff --git a/topics/microbiome/tutorials/multivariable-association/workflows/test-data/HMP2_metadata.tsv.tabular b/topics/microbiome/tutorials/multivariable-association/workflows/test-data/HMP2_metadata.tsv.tabular new file mode 100644 index 00000000000000..a4e42a2502ba78 --- /dev/null +++ b/topics/microbiome/tutorials/multivariable-association/workflows/test-data/HMP2_metadata.tsv.tabular @@ -0,0 +1,1596 @@ +ID site age diagnosis subject antibiotics dysbiosis_binary dysbiosis dysbiosisnonIBD dysbiosisUC dysbiosisCD collection +CSM5FZ3N_P Cedars-Sinai 43 CD C3001 No No 0.792314801112388 No No No 1 +CSM5FZ3R_P Cedars-Sinai 43 CD C3001 No No 0.837923415694291 No No No 2 +CSM5FZ3T_P Cedars-Sinai 76 CD C3002 No Yes 0.924296928419748 No No Yes 1 +CSM5FZ3V_P Cedars-Sinai 43 CD C3001 No No 0.830400526852384 No No No 4 +CSM5FZ3X_P Cedars-Sinai 76 CD C3002 No Yes 0.910966992923041 No No Yes 2 +CSM5FZ3Z_P Cedars-Sinai 76 CD C3002 No Yes 0.912281677355966 No No Yes 3 +CSM5FZ42_P Cedars-Sinai 76 CD C3002 No Yes 0.903049136702093 No No Yes 4 +CSM5FZ44_P Cedars-Sinai 76 CD C3002 No Yes 0.87825476936449 No No Yes 5 +CSM5FZ46_P Cedars-Sinai 76 CD C3002 No Yes 0.92542872181954 No No Yes 6 +CSM5FZ4A_P Cedars-Sinai 47 UC C3004 No No 0.775160480913896 No No No 1 +CSM5FZ4C_P Cedars-Sinai 43 CD C3001 No No 0.836244630681399 No No No 5 +CSM5FZ4E_P Cedars-Sinai 43 UC C3003 Yes No 0.787616535915359 No No No 2 +CSM5FZ4G_P Cedars-Sinai 43 UC C3003 No No 0.68128445477854 No No No 3 +CSM5FZ4K_P Cedars-Sinai 43 UC C3003 Yes No 0.717164790084925 No No No 5 +CSM5FZ4M Cedars-Sinai 43 UC C3003 No No 0.761300183426806 No No No 6 +CSM5LLGB_P MGH 30 CD M2014 No No 0.730525895151516 No No No 8 +CSM5MCTZ_P Cedars-Sinai 32 UC C3006 No No 0.690477602078951 No No No 1 +CSM5MCU4_P Cedars-Sinai 53 CD C3008 Yes Yes 0.960340510520076 No No Yes 1 +CSM5MCU8_P Cedars-Sinai 47 UC C3004 No No 0.7487518327033 No No No 3 +CSM5MCUA_P Cedars-Sinai 47 UC C3004 No No 0.764756113674023 No No No 4 +CSM5MCUC_P Cedars-Sinai 47 UC C3004 No No 0.753471972559145 No No No 5 +CSM5MCUE_P Cedars-Sinai 47 UC C3004 No No 0.76616120085356 No No No 6 +CSM5MCUG_P Cedars-Sinai 32 UC C3006 No No 0.631995641824807 No No No 2 +CSM5MCUK_P Cedars-Sinai 32 UC C3006 No No 0.659833643343466 No No No 4 +CSM5MCUO Cedars-Sinai 32 UC C3006 No No 0.632952326253226 No No No 6 +CSM5MCUQ_P Cedars-Sinai 76 UC C3005 No No 0.727873670271853 No No No 2 +CSM5MCUS_P Cedars-Sinai 76 UC C3005 No No 0.698587884579809 No No No 3 +CSM5MCUW_P Cedars-Sinai 76 UC C3005 No No 0.759108845355999 No No No 5 +CSM5MCUY_P Cedars-Sinai 76 UC C3005 No No 0.75050127138637 No No No 6 +CSM5MCV1_P Cedars-Sinai NA CD C3007 No Yes 0.918640365469221 NA NA NA 2 +CSM5MCV5_P Cedars-Sinai NA CD C3007 No No 0.650022501781325 NA NA NA 4 +CSM5MCVB_P Cedars-Sinai NA CD C3007 No Yes 0.960878306358498 NA NA NA 1 +CSM5MCVD_P Cedars-Sinai 43 CD C3001 No No 0.790514760450371 No No No 7 +CSM5MCVF_P Cedars-Sinai 43 CD C3001 No Yes 0.853225777127933 No No Yes 8 +CSM5MCVJ_P Cedars-Sinai 76 CD C3002 No Yes 0.929706609618933 No No Yes 7 +CSM5MCVL Cedars-Sinai 76 CD C3002 No Yes 0.912606413860137 No No Yes 8 +CSM5MCVN Cedars-Sinai 76 CD C3002 No Yes 0.916894652604198 No No Yes 9 +CSM5MCVV_P Cedars-Sinai 43 CD C3001 No No 0.817227676926419 No No No 9 +CSM5MCVZ_P Cedars-Sinai 53 CD C3008 Yes Yes 0.971182046181404 No No Yes 3 +CSM5MCW4_P Cedars-Sinai 53 CD C3008 Yes Yes 0.969989533466459 No No Yes 4 +CSM5MCW6 Cedars-Sinai 53 CD C3008 Yes Yes 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0.987829091114291 No No Yes 8 +HSM6XRSE Cincinnati 15 CD H4015 No Yes 0.884843308733964 No No Yes 12 +HSM6XRSG Cincinnati 14 CD H4004 No Yes 0.942191919676421 No No Yes 13 +HSM6XRSI Cincinnati 14 CD H4004 No Yes 0.94208498299571 No No Yes 14 +HSM6XRSN Cincinnati 15 CD H4007 No No 0.717579325038175 No No No 9 +HSM6XRST Cincinnati 15 CD H4007 No No 0.697727865656788 No No No 12 +HSM6XRSX Cincinnati 13 nonIBD H4008 No No 0.724901387291857 No No No 7 +HSM6XRSX_P Cincinnati 13 nonIBD H4008 No No 0.725427909376552 No No No 7 +HSM6XRSZ_P Cincinnati 13 nonIBD H4008 No No 0.719211943722504 No No No 9 +HSM6XRT2_P Cincinnati 13 nonIBD H4008 No No 0.727366146181992 No No No 11 +HSM6XRT4_P Cincinnati 13 nonIBD H4008 No No 0.766377882577964 No No No 10 +HSM6XRT6_P Cincinnati 13 nonIBD H4008 No No 0.751264861190106 No No No 8 +HSM6XRT8 Cincinnati 6 nonIBD H4009 No No 0.668161089908291 No No No 7 +HSM6XRTA_P Cincinnati 6 nonIBD H4009 No No 0.711448640733247 No No No 8 +HSM6XRTC_P Cincinnati 6 nonIBD 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Cincinnati 11 nonIBD H4024 No No 0.694397109940266 No No No 23 +HSM7J4O9 Cincinnati 11 nonIBD H4024 No No 0.647161929432386 No No No 24 +HSM7J4OB Cincinnati 12 CD H4031 No No 0.7081145940388 No No No 13 +HSM7J4OE Cincinnati 12 CD H4031 No No 0.760862266756412 No No No 14 +HSM7J4OL Cincinnati 12 CD H4031 No No 0.807966828522506 No No No 18 +HSM7J4ON Cincinnati 16 CD H4030 No No 0.805605548775798 No No No 13 +HSM7J4OP Cincinnati 16 CD H4030 No No 0.793600119390078 No No No 14 +HSM7J4OT Cincinnati 16 CD H4030 No Yes 0.977207171127246 No No Yes 16 +HSM7J4OV Cincinnati 16 CD H4030 No Yes 0.988773839406603 No No Yes 17 +HSM7J4OX Cincinnati 16 CD H4030 No Yes 0.886178550644423 No No Yes 18 +HSM7J4OZ Cincinnati 12 CD H4032 No No 0.768875356867372 No No No 13 +HSM7J4P2 Cincinnati 12 CD H4032 No No 0.769003087958939 No No No 14 +HSM7J4PA Cincinnati 12 CD H4031 No No 0.785560285946999 No No No 6 +HSM7J4PC Cincinnati 11 UC H4019 No No 0.670920981859956 No No No 13 +HSM7J4PE Cincinnati 11 UC H4019 No No 0.684489835836072 No No No 14 +HSM7J4PG Cincinnati 11 UC H4019 No No 0.59624005120402 No No No 15 +HSM7J4PI Cincinnati 11 UC H4019 No No 0.691065138222764 No No No 16 +HSM7J4PM Cincinnati 11 UC H4019 No No 0.662333866267353 No No No 18 +HSM7J4PO Cincinnati 13 CD H4020 No No 0.749679786745518 No No No 13 +HSM7J4PQ Cincinnati 13 CD H4020 No No 0.813113153379318 No No No 14 +HSM7J4PS Cincinnati 13 CD H4020 No No 0.781551550109095 No No No 15 +HSM7J4PU Cincinnati 13 CD H4020 No No 0.817271319724075 No No No 16 +HSM7J4PW Cincinnati 13 CD H4020 No No 0.731946184686108 No No No 17 +HSM7J4PY Cincinnati 13 CD H4020 No No 0.771455259267481 No No No 18 +HSM7J4Q1 Cincinnati 12 CD H4032 No No 0.702310226341119 No No No 2 +HSM7J4Q3_P Cincinnati 12 CD H4032 No No 0.710488782623297 No No No 3 +HSM7J4Q7 Cincinnati 12 CD H4032 No No 0.709959965198531 No No No 5 +HSM7J4Q9 Cincinnati 12 CD H4032 No No 0.756788142158943 No No No 6 +HSM7J4QB Cincinnati 15 CD H4015 No No 0.798485865140332 No No No 19 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nonIBD H4045 No No 0.769769690562487 No No No 3 +HSMA33J5 Cincinnati 14 nonIBD H4045 No No 0.732481111731324 No No No 4 +HSMA33J7 Cincinnati 14 nonIBD H4045 No No 0.676745467674546 No No No 5 +HSMA33J9 Cincinnati 14 nonIBD H4045 No No 0.680413665861674 No No No 6 +HSMA33JB Cincinnati 12 CD H4031 No No 0.747214514514551 No No No 19 +HSMA33JD Cincinnati 12 CD H4031 No No 0.710639586521725 No No No 20 +HSMA33JN Cincinnati 12 CD H4032 No No 0.764818381518806 No No No 19 +HSMA33JP Cincinnati 12 CD H4032 No No 0.772041011718907 No No No 20 +HSMA33JR Cincinnati 12 CD H4032 No No 0.729837989408424 No No No 21 +HSMA33KE Cincinnati 15 CD H4038 No No 0.756630216125637 No No No 13 +HSMA33KM Cincinnati 15 CD H4038 No No 0.767035562801538 No No No 18 +HSMA33KO Cincinnati 15 CD H4038 No No 0.779253045044073 No No No 19 +HSMA33KQ Cincinnati 12 CD H4039 No No 0.810221698506196 No No No 13 +HSMA33KS Cincinnati 12 CD H4039 No Yes 0.838769319602251 No No Yes 14 +HSMA33KU Cincinnati 12 CD H4039 No No 0.786943714815081 No No No 15 +HSMA33L1 Cincinnati 12 CD H4039 No No 0.769109396610491 No No No 18 +HSMA33LB Cincinnati 16 UC H4044 No No 0.701909174591227 No No No 9 +HSMA33LH Cincinnati 16 UC H4044 No No 0.706184864175025 No No No 12 +HSMA33LH_TR Cincinnati 16 UC H4044 No No 0.708929165967802 No No No 12 +HSMA33LJ Cincinnati 16 UC H4044 No No 0.6751016692408 No No No 13 +HSMA33LP Cincinnati 14 CD H4043 No No 0.675997045655961 No No No 9 +HSMA33LX Cincinnati 17 UC H4040 No No 0.700551648109873 No No No 18 +HSMA33LZ Cincinnati 17 UC H4040 No Yes 0.848203869819542 No Yes No 19 +HSMA33M2 Cincinnati 17 UC H4040 No No 0.735661433551774 No No No 20 +HSMA33M8 Cincinnati 17 UC H4040 No Yes 0.874600246222154 No Yes No 23 +HSMA33MA Cincinnati 16 UC H4035 No No 0.70877590106226 No No No 19 +HSMA33MC Cincinnati 16 UC H4035 Yes No 0.66715193262067 No No No 20 +HSMA33ME Cincinnati 16 UC H4035 Yes No 0.766954740485648 No No No 21 +HSMA33MG Cincinnati 16 UC H4035 No No 0.742296193550236 No No No 22 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H4040 No Yes 0.873664784342261 No Yes No 14 +HSMA33OR Cincinnati 16 UC H4035 No No 0.716371903908068 No No No 14 +HSMA33OT Cincinnati 16 UC H4035 No No 0.69000147542343 No No No 15 +HSMA33OV Cincinnati 16 UC H4035 No No 0.638558542940652 No No No 16 +HSMA33OX Cincinnati 16 UC H4035 No No 0.655901282237838 No No No 17 +HSMA33OZ Cincinnati 16 UC H4035 No No 0.711141467430522 No No No 18 +HSMA33P2_P Cincinnati 16 UC H4044 No No 0.68086299951102 No No No 2 +HSMA33P6 Cincinnati 16 UC H4044 No No 0.644003452699968 No No No 4 +HSMA33PL Cincinnati 12 CD H4039 No No 0.802071865003015 No No No 19 +HSMA33PN Cincinnati 12 CD H4039 No No 0.778626616646925 No No No 20 +HSMA33PX Cincinnati 15 CD H4038 No No 0.798366255510378 No No No 20 +HSMA33PZ Cincinnati 15 CD H4038 Yes No 0.817181846949149 No No No 21 +HSMA33Q6 Cincinnati 15 CD H4038 No No 0.793244722241131 No No No 24 +HSMA33QM Cincinnati 14 nonIBD H4045 No No 0.648250732777587 No No No 13 +HSMA33QO Cincinnati 14 nonIBD H4045 No No 0.795680609424151 No No No 14 +HSMA33QY Cincinnati 14 CD H4043 No No 0.657870055424649 No No No 12 +HSMA33R1 Cincinnati 14 CD H4043 No No 0.741357352076747 No No No 13 +HSMA33R5 Cincinnati 14 CD H4043 No No 0.669768239413585 No No No 16 +HSMA33R7 Cincinnati 14 CD H4043 No No 0.716798277759015 No No No 17 +HSMA33R9 Cincinnati 14 CD H4043 No No 0.64293225621677 No No No 19 +HSMA33RD Cincinnati 15 UC H4042 No No 0.752395361482268 No No No 18 +HSMA33RF Cincinnati 15 UC H4042 No No 0.739240874961002 No No No 20 +HSMA33RR Cincinnati 16 UC H4044 No No 0.753184499235358 No No No 15 +HSMA33RT Cincinnati 16 UC H4044 No No 0.738755167495708 No No No 20 +HSMA33RX Cincinnati 16 UC H4044 No No 0.744337524234432 No No No 21 +HSMA33RX_TR Cincinnati 16 UC H4044 No No 0.767058980879024 No No No 21 +HSMA33S4 Cincinnati 14 CD H4043 No No 0.702145508288558 No No No 20 +HSMA33SE Cincinnati 14 nonIBD H4045 No No 0.693072287674751 No No No 18 +HSMA33SG Cincinnati 14 nonIBD H4045 No No 0.640363185351797 No No No 19 +HSMA33SI Cincinnati 14 nonIBD H4045 No No 0.743927439909007 No No No 20 +HSMA33SK Cincinnati 14 nonIBD H4045 No No 0.659371111826753 No No No 21 +MSM5FZ9N_P MGH 18 CD M2010 No Yes 0.860107798282985 No No Yes 4 +MSM5FZ9X_P MGH 30 CD M2014 No Yes 0.898181021457974 No No Yes 3 +MSM5FZA2_P MGH 30 CD M2014 No Yes 0.906327193533616 No No Yes 5 +MSM5LLD6_P MGH 30 CD M2008 No No 0.74217331874092 No No No 16 +MSM5LLDA MGH 30 CD M2008 No No 0.709178754866006 No No No 19 +MSM5LLDC MGH 30 CD M2008 No No 0.70988152656019 No No No 17 +MSM5LLDE MGH 30 CD M2008 No No 0.706125373907079 No No No 18 +MSM5LLDI MGH 30 CD M2008 No No 0.827384206082804 No No No 1 +MSM5LLDK MGH 30 CD M2008 No No 0.788640850850464 No No No 2 +MSM5LLDM MGH 30 CD M2008 No No 0.770130515527347 No No No 3 +MSM5LLDQ MGH 30 CD M2008 No No 0.767456741782109 No No No 5 +MSM5LLDS MGH 30 CD M2008 No No 0.82638242341889 No No No 6 +MSM5LLE3_P MGH 18 CD M2010 No Yes 0.959144717012238 No No Yes 8 +MSM5LLE9_P MGH 18 CD M2010 No Yes 0.96552158007934 No No Yes 11 +MSM5LLEP MGH 26 CD M2021 No No 0.6710881 No No No 10 +MSM5LLER MGH 26 CD M2021 No No 0.736569215445179 No No No 11 +MSM5LLF2_P MGH 22 CD M2034 No Yes 0.91404411302832 No No Yes 4 +MSM5LLF4 MGH 22 CD M2034 No Yes 0.97287840058613 No No Yes 5 +MSM5LLF6 MGH 22 CD M2034 No Yes 0.841774010154803 No No Yes 6 +MSM5LLF8 MGH 22 CD M2034 No Yes 0.918725053674054 No No Yes 7 +MSM5LLFG_P MGH 24 CD M2028 No No 0.683015902046434 No No No 1 +MSM5LLFK_P MGH 21 UC M2026 No No 0.785662559557014 No No No 2 +MSM5LLFM_P MGH 21 UC M2026 No No 0.782992173366146 No No No 3 +MSM5LLFO_P MGH 21 UC M2026 No No 0.759614763429311 No No No 4 +MSM5LLFU_P MGH 21 UC M2026 No No 0.826386214683657 No No No 7 +MSM5LLGD_P MGH 30 CD M2014 No No 0.732317648651849 No No No 11 +MSM5LLGF_P MGH 30 CD M2014 No No 0.738731989730732 No No No 10 +MSM5LLGH_P MGH 30 CD M2014 No No 0.690782084670605 No No No 9 +MSM5LLGJ_P MGH 30 CD M2014 No No 0.708605589575437 No No No 12 +MSM5LLGL MGH 30 CD M2014 No No 0.674329568716167 No No No 13 +MSM5LLGN_P MGH 24 CD M2028 No No 0.75673355171055 No No No 2 +MSM5LLGR_P MGH 24 CD M2028 No No 0.711870796232438 No No No 4 +MSM5LLH2_P MGH 41 CD M2027 No No 0.780086776694762 No No No 2 +MSM5LLH4_P MGH 41 CD M2027 No No 0.803657947329482 No No No 3 +MSM5LLH8_P MGH 41 CD M2027 No Yes 0.842275503607467 No No Yes 5 +MSM5LLHA_P MGH 41 CD M2027 No No 0.775984392139549 No No No 6 +MSM5LLHC MGH 41 CD M2027 No No 0.756244699741344 No No No 7 +MSM5LLHG_P MGH 43 CD M2025 No No 0.698526762836011 No No No 3 +MSM5LLHI_P MGH 43 CD M2025 Yes No 0.716928721435924 No No No 4 +MSM5LLHO_P MGH 43 CD M2025 Yes No 0.68399474829315 No No No 7 +MSM5LLHQ_P MGH 30 CD M2008 No No 0.783114558907914 No No No 14 +MSM5LLHR_P MGH 26 CD M2021 No No 0.684061579232953 No No No 1 +MSM5LLHV_P MGH 38 UC M2024 No No 0.770893191047284 No No No 1 +MSM5LLHX_P MGH 30 CD M2008 No No 0.701907407860941 No No No 8 +MSM5LLI2_P MGH 30 CD M2008 No No 0.787064159206199 No No No 10 +MSM5LLI4_P MGH 30 CD M2008 No No 0.706778437235954 No No No 13 +MSM5LLI6_P MGH 30 CD M2008 No No 0.801597506917711 No No No 12 +MSM5LLI8_P MGH 30 CD M2008 No No 0.809486670951785 No No No 11 +MSM5LLIC_P MGH 43 CD M2025 No No 0.662239310807937 No No No 1 +MSM5LLIE_P MGH 26 CD M2021 No No 0.717544781878636 No No No 2 +MSM5LLIG_P MGH 26 CD M2021 No No 0.669740279879045 No No No 3 +MSM5LLIK_P MGH 26 CD M2021 Yes No 0.703713782521445 No No No 5 +MSM5LLIM_P MGH 26 CD M2021 No No 0.684088101283507 No No No 6 +MSM5LLIQ_P MGH 21 UC M2026 No Yes 0.908695075901589 No Yes No 1 +MSM5LLIS_P MGH 41 CD M2027 No Yes 0.841977851754695 No No Yes 1 +MSM5ZOJY_P MGH 30 CD M2014 No Yes 0.897053692567603 No No Yes 6 +MSM633FF_P MGH 30 CD M2014 No Yes 0.860554667688666 No No Yes 7 +MSM6J2HD MGH 21 UC M2026 No Yes 0.840194558037474 No Yes No 9 +MSM6J2HF MGH 21 UC M2026 No No 0.807510373926801 No No No 10 +MSM6J2HH MGH 21 UC M2026 No No 0.72620783032245 No No No 11 +MSM6J2HJ MGH 21 UC M2026 No No 0.774569941103647 No No No 12 +MSM6J2HL MGH 21 UC M2026 No No 0.837101882363026 No No No 13 +MSM6J2HN MGH 40 nonIBD M2039 No No 0.675835384813232 No No No 2 +MSM6J2HP MGH 40 nonIBD M2039 No No 0.627817535788027 No No No 3 +MSM6J2HR MGH 40 nonIBD M2039 No No 0.688601576639778 No No No 4 +MSM6J2HT MGH 40 nonIBD M2039 No No 0.747698509716577 No No No 5 +MSM6J2IE MGH 24 CD M2028 Yes No 0.7559479876058 No No No 8 +MSM6J2IG MGH 24 CD M2028 Yes No 0.795826863628643 No No No 9 +MSM6J2II MGH 24 CD M2028 No No 0.715029118196076 No No No 10 +MSM6J2IK MGH 24 CD M2028 No No 0.655930364141558 No No No 11 +MSM6J2IM MGH 24 CD M2028 No No 0.701527111067621 No No No 12 +MSM6J2IO MGH 24 CD M2028 No No 0.707868365953741 No No No 13 +MSM6J2IQ MGH 30 CD M2014 No No 0.700644669609161 No No No 14 +MSM6J2IY MGH 30 CD M2014 No No 0.692662424085503 No No No 18 +MSM6J2J1 MGH 30 CD M2014 No No 0.66557837463668 No No No 19 +MSM6J2J3 MGH 43 CD M2025 No No 0.656387265638727 No No No 8 +MSM6J2J5 MGH 43 CD M2025 No No 0.694969511563407 No No No 9 +MSM6J2JB MGH 43 CD M2025 No No 0.72894876648247 No No No 11 +MSM6J2JD MGH 43 CD M2025 No No 0.704539586348871 No No No 12 +MSM6J2JF_P MGH 55 nonIBD M2041 No No 0.742056567756231 No No No 1 +MSM6J2JH_P MGH 44 nonIBD M2042 No No 0.768340167748113 No No No 1 +MSM6J2JN MGH 55 nonIBD M2041 No No 0.776616098799286 No No No 2 +MSM6J2JP MGH 55 nonIBD M2041 No No 0.828646797365893 No No No 3 +MSM6J2JR MGH 55 nonIBD M2041 No No 0.812806068920174 No No No 4 +MSM6J2JT MGH 55 nonIBD M2041 No No 0.800017346718992 No No No 5 +MSM6J2JZ MGH 44 nonIBD M2042 No No 0.804860011508763 No No No 2 +MSM6J2K2 MGH 44 nonIBD M2042 No No 0.833949190586966 No No No 3 +MSM6J2K4 MGH 44 nonIBD M2042 No No 0.738706304505798 No No No 4 +MSM6J2K6 MGH 44 nonIBD M2042 No No 0.788817980425453 No No No 5 +MSM6J2K8 MGH 44 nonIBD M2042 No No 0.665988326479264 No No No 6 +MSM6J2KA MGH 44 nonIBD M2042 No No 0.688641298701872 No No No 7 +MSM6J2KC MGH 41 CD M2027 No No 0.79747560954545 No No No 8 +MSM6J2KE MGH 41 CD M2027 No No 0.809370383889017 No No No 9 +MSM6J2KM MGH 41 CD M2027 No No 0.745327136554979 No No No 13 +MSM6J2LB MGH 30 CD M2008 No No 0.72614109942215 No No No 22 +MSM6J2LH MGH 22 CD M2034 No Yes 0.84711814610648 No No Yes 8 +MSM6J2LJ MGH 22 CD M2034 No Yes 0.884772152278689 No No Yes 9 +MSM6J2LL MGH 22 CD M2034 No Yes 0.869008474678064 No No Yes 10 +MSM6J2LN MGH 22 CD M2034 No No 0.82366022017354 No No No 11 +MSM6J2LR MGH 22 CD M2034 No Yes 0.884304979344146 No No Yes 13 +MSM6J2LT MGH 26 CD M2021 No No 0.725719059403035 No No No 17 +MSM6J2LV MGH 55 nonIBD M2041 No No 0.766608983425841 No No No 8 +MSM6J2LW MGH 28 nonIBD M2048 No No 0.684182090760064 No No No 2 +MSM6J2LY MGH 28 nonIBD M2048 No No 0.663174997312663 No No No 3 +MSM6J2M3 MGH 28 nonIBD M2048 No No 0.692544179010364 No No No 5 +MSM6J2M3_P MGH 28 nonIBD M2048 No No 0.682145806064912 No No No 5 +MSM6J2MB MGH 22 CD M2034 No Yes 0.840836711785416 No No Yes 14 +MSM6J2MD MGH 22 CD M2034 No Yes 0.841627230885121 No No Yes 15 +MSM6J2MF MGH 22 CD M2034 No Yes 0.846647070721087 No No Yes 16 +MSM6J2MH MGH 22 CD M2034 No No 0.834947230990973 No No No 17 +MSM6J2MJ MGH 22 CD M2034 No No 0.812291509238353 No No No 18 +MSM6J2ML MGH 22 CD M2034 No Yes 0.953691132682482 No No Yes 19 +MSM6J2N6_P MGH NA UC M2059 Yes Yes 0.891638203032624 NA NA NA 1 +MSM6J2OH MGH 21 UC M2026 No No 0.780599881373295 No No No 14 +MSM6J2OJ MGH 21 UC M2026 No No 0.744970299436701 No No No 15 +MSM6J2OL MGH 21 UC M2026 No Yes 0.841241448548386 No Yes No 16 +MSM6J2ON MGH 21 UC M2026 No No 0.832965249051351 No No No 17 +MSM6J2OP MGH 21 UC M2026 No No 0.806476082960197 No No No 21 +MSM6J2PK MGH 43 CD M2025 Yes No 0.761421286725102 No No No 17 +MSM6J2PM MGH 43 CD M2025 No No 0.78075156636973 No No No 19 +MSM6J2PO MGH 57 nonIBD M2047 No No 0.679258953064682 No No No 1 +MSM6J2PQ_P MGH 57 nonIBD M2047 No No 0.769292722563979 No No No 2 +MSM6J2PS MGH 57 nonIBD M2047 No No 0.687738986738669 No No No 3 +MSM6J2PU MGH 57 nonIBD M2047 No No 0.750186470743458 No No No 4 +MSM6J2PW MGH 57 nonIBD M2047 No No 0.664471824417989 No No No 5 +MSM6J2Q1 MGH 28 nonIBD M2048 No No 0.708949326865501 No No No 1 +MSM6J2Q3 MGH 24 CD M2028 No No 0.72426821597183 No No No 14 +MSM6J2Q5 MGH 24 CD M2028 Yes No 0.768222954428084 No No No 15 +MSM6J2Q7 MGH 24 CD M2028 Yes No 0.77159945680187 No No No 16 +MSM6J2Q9 MGH 24 CD M2028 Yes Yes 0.847519675927169 No No Yes 17 +MSM6J2QB MGH 24 CD M2028 No No 0.759129235879686 No No No 18 +MSM6J2QD MGH 24 CD M2028 No No 0.74911424692123 No No No 19 +MSM6J2QF MGH 40 nonIBD M2039 No No 0.722481042241183 No No No 13 +MSM6J2QH MGH 40 nonIBD M2039 No No 0.642559933406708 No No No 9 +MSM6J2QJ MGH 40 nonIBD M2039 No No 0.622993303945127 No No No 12 +MSM6J2QL MGH 40 nonIBD M2039 No No 0.679310348021025 No No No 8 +MSM6J2QP MGH 40 nonIBD M2039 No No 0.701385396961143 No No No 10 +MSM6J2QR MGH 30 CD M2014 No No 0.68928809459151 No No No 20 +MSM6J2R2 MGH 41 CD M2027 No No 0.809706043329743 No No No 14 +MSM6J2R8 MGH 41 CD M2027 No No 0.766361028652913 No No No 17 +MSM6J2RA MGH 41 CD M2027 No No 0.749236975228908 No No No 18 +MSM6J2RC MGH 41 CD M2027 No No 0.71396910197541 No No No 19 +MSM6J2RG_P MGH 25 CD M2058 No No 0.734274621974886 No No No 1 +MSM6J2RK MGH 44 nonIBD M2042 No No 0.733838757094541 No No No 8 +MSM6J2RM MGH 44 nonIBD M2042 No No 0.693859323894817 No No No 9 +MSM6J2RO MGH 44 nonIBD M2042 No No 0.790553648000978 No No No 10 +MSM6J2RQ MGH 44 nonIBD M2042 No No 0.72658052603939 No No No 11 +MSM6J2RS MGH 44 nonIBD M2042 No Yes 0.910206076170865 Yes No No 13 +MSM6J2RU MGH 44 nonIBD M2042 No No 0.703984991819548 No No No 12 +MSM6J2SE MGH 55 nonIBD M2041 No No 0.749796434916889 No No No 9 +MSM6J2SI MGH 55 nonIBD M2041 No No 0.745731664898108 No No No 11 +MSM6J2SK MGH 55 nonIBD M2041 No No 0.811317720465123 No No No 12 +MSM79H52_P MGH 62 nonIBD M2060 No No 0.809195271662851 No No No 2 +MSM79H54 MGH 62 nonIBD M2060 No No 0.826308640586717 No No No 3 +MSM79H58 MGH 62 nonIBD M2060 No No 0.805877984321134 No No No 5 +MSM79H5A MGH 62 nonIBD M2060 No No 0.819955094157288 No No No 6 +MSM79H5E MGH 56 nonIBD M2061 No No 0.691174002859393 No No No 2 +MSM79H5G MGH 56 nonIBD M2061 No No 0.665766275403718 No No No 3 +MSM79H5K MGH 56 nonIBD M2061 No No 0.680846376993123 No No No 5 +MSM79H5M MGH 56 nonIBD M2061 No No 0.692684266987557 No No No 6 +MSM79H5Q MGH 40 nonIBD M2039 No No 0.725658470462528 No No No 16 +MSM79H5S MGH 40 nonIBD M2039 No No 0.637857562569306 No No No 19 +MSM79H5U MGH 40 nonIBD M2039 No No 0.655482811771822 No No No 17 +MSM79H5Y MGH 40 nonIBD M2039 No No 0.635616114076108 No No No 18 +MSM79H63 MGH 24 CD M2028 No No 0.732870418527937 No No No 20 +MSM79H65 MGH 24 CD M2028 No No 0.763930430967525 No No No 21 +MSM79H67 MGH 24 CD M2028 No No 0.742762775279816 No No No 22 +MSM79H69 MGH 24 CD M2028 No No 0.724794025776174 No No No 23 +MSM79H6B MGH 24 CD M2028 No No 0.708121801115757 No No No 24 +MSM79H6D MGH 44 nonIBD M2042 No No 0.715230558013768 No No No 14 +MSM79H6F MGH 44 nonIBD M2042 No No 0.675488084958408 No No No 15 +MSM79H6H MGH 44 nonIBD M2042 No No 0.791923801232063 No No No 17 +MSM79H6J MGH 44 nonIBD M2042 No No 0.740504964188836 No No No 16 +MSM79H6L MGH 44 nonIBD M2042 No No 0.744480393377225 No No No 18 +MSM79H6N MGH 44 nonIBD M2042 No No 0.722940843305913 No No No 19 +MSM79H6Y MGH 28 nonIBD M2048 No No 0.679735575768504 No No No 15 +MSM79H7C MGH 26 UC M2071 No No 0.665975520333137 No No No 2 +MSM79H7E MGH 26 UC M2071 No No 0.658449887671129 No No No 3 +MSM79H7G MGH 26 UC M2071 No No 0.70658784861913 No No No 6 +MSM79H7M MGH 26 UC M2071 Yes No 0.683567281739165 No No No 4 +MSM79H7O MGH 57 nonIBD M2047 No No 0.641632313785877 No No No 13 +MSM79H7Q MGH 57 nonIBD M2047 No No 0.615202027267754 No No No 14 +MSM79H7W MGH 57 nonIBD M2047 No No 0.66163664365256 No No No 18 +MSM79H7Y MGH 57 nonIBD M2047 No No 0.624545025399764 No No No 19 +MSM79H81 MGH 51 nonIBD M2072 No No 0.774706960971378 No No No 7 +MSM79H83 MGH 51 nonIBD M2072 No No 0.771862721253743 No No No 8 +MSM79H85 MGH 51 nonIBD M2072 No No 0.798023156638005 No No No 9 +MSM79H87 MGH 51 nonIBD M2072 No Yes 0.839861136883589 Yes No No 10 +MSM79H89 MGH 51 nonIBD M2072 No No 0.751258078813067 No No No 11 +MSM79H8B MGH 51 nonIBD M2072 No No 0.808012906895127 No No No 12 +MSM79H8D MGH 61 nonIBD M2075 No No 0.67798873502042 No No No 1 +MSM79H8F MGH 61 nonIBD M2075 No No 0.799194761545501 No No No 2 +MSM79H8H MGH 61 nonIBD M2075 No No 0.66134475020053 No No No 3 +MSM79H8J_P MGH 61 nonIBD M2075 No No 0.692800150223888 No No No 4 +MSM79H8L MGH 61 nonIBD M2075 No No 0.731131638678983 No No No 5 +MSM79H8N MGH 61 nonIBD M2075 No No 0.706666459659336 No No No 6 +MSM79H94_P MGH 32 nonIBD M2077 No Yes 0.85010182832413 Yes No No 1 +MSM79H98 MGH 19 CD M2068 Yes No 0.723155754917133 No No No 8 +MSM79H9A MGH 62 nonIBD M2060 No No 0.807082375368172 No No No 8 +MSM79H9C MGH 62 nonIBD M2060 No No 0.822827440150011 No No No 9 +MSM79H9G MGH 62 nonIBD M2060 No No 0.823456973302784 No No No 12 +MSM79H9K MGH 62 nonIBD M2060 No No 0.825817191956341 No No No 13 +MSM79H9M MGH 56 nonIBD M2061 No No 0.721053752179709 No No No 8 +MSM79H9Q MGH 56 nonIBD M2061 No No 0.774263783055668 No No No 10 +MSM79H9U_P MGH 35 UC M2067 No No 0.818024136124409 No No No 6 +MSM79H9W MGH 56 nonIBD M2061 No No 0.681301793347022 No No No 12 +MSM79H9Y MGH 44 nonIBD M2042 No No 0.706827370137322 No No No 20 +MSM79HA1 MGH 44 nonIBD M2042 No No 0.745151519753859 No No No 21 +MSM79HA3 MGH 44 nonIBD M2042 No No 0.827955524508796 No No No 22 +MSM79HA7 MGH 44 nonIBD M2042 No No 0.755378454859692 No No No 23 +MSM79HAH MGH 40 nonIBD M2039 No No 0.668687172530559 No No No 24 +MSM79HAJ MGH 74 UC M2064 No No 0.726341721259873 No No No 7 +MSM79HAL MGH 74 UC M2064 No No 0.718772341723385 No No No 8 +MSM79HAN MGH 74 UC M2064 No No 0.747339579688707 No No No 9 +MSM79HAR MGH 74 UC M2064 Yes No 0.692205988640212 No No No 11 +MSM79HAT MGH 74 UC M2064 No No 0.728559041523744 No No No 10 +MSM79HB6 MGH 55 nonIBD M2041 No No 0.763089805850452 No No No 24 +MSM79HBB MGH 28 nonIBD M2048 No No 0.668806889306416 No No No 18 +MSM79HBL MGH 28 nonIBD M2048 No No 0.705851666418372 No No No 8 +MSM79HBN MGH 22 CD M2034 No Yes 0.948214565093416 No No Yes 20 +MSM79HBP MGH 22 CD M2034 No No 0.737981837207499 No No No 21 +MSM79HBR MGH 22 CD M2034 Yes Yes 0.989086654890999 No No Yes 22 +MSM79HBT MGH 22 CD M2034 Yes Yes 0.990216977267555 No No Yes 23 +MSM79HBV MGH 22 CD M2034 No Yes 0.990297972447543 No No Yes 24 +MSM79HBX_P MGH 19 CD M2068 Yes No 0.817634787830115 No No No 1 +MSM79HBZ MGH 29 UC M2069 Yes Yes 0.879428770892086 No Yes No 1 +MSM79HC4 MGH 57 nonIBD M2047 No No 0.67175045285667 No No No 9 +MSM79HC8 MGH 57 nonIBD M2047 No No 0.672599882259847 No No No 11 +MSM79HCG MGH 51 nonIBD M2072 No No 0.777319843223698 No No No 1 +MSM79HCI MGH 51 nonIBD M2072 No No 0.822019748647618 No No No 2 +MSM79HCK MGH 51 nonIBD M2072 No No 0.827219892575846 No No No 3 +MSM79HCN_P MGH 51 nonIBD M2072 No No 0.838180836263053 No No No 4 +MSM79HCP MGH 51 nonIBD M2072 No No 0.800549378717576 No No No 5 +MSM79HCR MGH 51 nonIBD M2072 No Yes 0.880836148675644 Yes No No 6 +MSM79HD6_P MGH 26 UC M2071 No No 0.698153613326036 No No No 1 +MSM79HD8_P MGH 29 UC M2069 Yes Yes 0.900383559329064 No Yes No 2 +MSM79HDA MGH 29 UC M2069 No Yes 0.881726964608089 No Yes No 3 +MSM79HDC MGH 29 UC M2069 No No 0.829183857653367 No No No 4 +MSM79HDE MGH 29 UC M2069 No Yes 0.85236228987681 No Yes No 5 +MSM79HDG MGH 29 UC M2069 No Yes 0.844585951242744 No Yes No 6 +MSM79HDG_TR MGH 29 UC M2069 No Yes 0.852294678375315 No Yes No 6 +MSM79HDI MGH 29 UC M2069 No No 0.828008017615337 No No No 7 +MSM79HDK MGH 19 CD M2068 Yes No 0.764197576927693 No No No 2 +MSM79HDM MGH 19 CD M2068 Yes No 0.687963356057301 No No No 3 +MSM79HDO MGH 19 CD M2068 Yes No 0.688975063543645 No No No 4 +MSM79HDQ MGH 19 CD M2068 No No 0.687113531514376 No No No 5 +MSM79HDQ_TR MGH 19 CD M2068 No No 0.683075153110513 No No No 5 +MSM79HDS MGH 19 CD M2068 No No 0.70617955526176 No No No 6 +MSM79HDU MGH 19 CD M2068 Yes No 0.755897549580643 No No No 7 +MSM79HEA MGH 21 UC M2026 No No 0.752766074598166 No No No 23 +MSM79HES MGH 55 nonIBD M2041 No No 0.740374068782992 No No No 16 +MSM79HEU MGH 55 nonIBD M2041 No No 0.782650731771337 No No No 17 +MSM79HEW MGH 55 nonIBD M2041 No No 0.793013654051077 No No No 18 +MSM79HEY MGH 55 nonIBD M2041 No No 0.765322431543659 No No No 19 +MSM79HF1_P MGH 74 UC M2064 No No 0.707826233478814 No No No 1 +MSM79HF3 MGH 74 UC M2064 No No 0.727598956366044 No No No 2 +MSM79HF5 MGH 74 UC M2064 No No 0.692117989933078 No No No 3 +MSM79HF7 MGH 74 UC M2064 No No 0.7812870389221 No No No 4 +MSM79HF9 MGH 74 UC M2064 Yes No 0.746966955405312 No No No 5 +MSM79HF9_TR MGH 74 UC M2064 Yes No 0.757376316844858 No No No 5 +MSM79HFB MGH 74 UC M2064 No No 0.659854600460804 No No No 6 +MSM79HFW MGH 28 nonIBD M2048 No No 0.673943189072176 No No No 11 +MSM79HFY MGH 28 nonIBD M2048 No No 0.689111021964136 No No No 12 +MSM7J16J MGH 19 CD M2068 No No 0.787361744496448 No No No 14 +MSM7J16L MGH 19 CD M2068 No No 0.797745349955236 No No No 15 +MSM7J16N MGH 19 CD M2068 No No 0.712448789909045 No No No 16 +MSM7J16P MGH 19 CD M2068 No No 0.754813794389076 No No No 17 +MSM7J16R MGH 19 CD M2068 No No 0.7846301742446 No No No 18 +MSM9VZEK MGH 19 CD M2068 Yes No 0.787060974815092 No No No 9 +MSM9VZEK_TR MGH 19 CD M2068 Yes No 0.808717321060475 No No No 9 +MSM9VZEM MGH 19 CD M2068 Yes No 0.818319747094218 No No No 10 +MSM9VZEO MGH 19 CD M2068 Yes No 0.822851797262234 No No No 11 +MSM9VZEQ MGH 19 CD M2068 No No 0.789415682378248 No No No 12 +MSM9VZES MGH 19 CD M2068 No No 0.745223657602879 No No No 13 +MSM9VZEU MGH 29 UC M2069 No No 0.792835353214408 No No No 8 +MSM9VZEW MGH 29 UC M2069 No No 0.782156307948777 No No No 9 +MSM9VZEY MGH 29 UC M2069 No No 0.79055532976457 No No No 10 +MSM9VZF1 MGH 29 UC M2069 No No 0.781470794512605 No No No 11 +MSM9VZF3 MGH 29 UC M2069 No No 0.79685284990346 No No No 12 +MSM9VZF5 MGH 29 UC M2069 No No 0.781440906724747 No No No 13 +MSM9VZF7 MGH 62 nonIBD M2060 No No 0.81939463100437 No No No 14 +MSM9VZFF MGH 62 nonIBD M2060 No No 0.835908922287777 No No No 18 +MSM9VZFH MGH 62 nonIBD M2060 No No 0.786661138810587 No No No 19 +MSM9VZFJ_P MGH 29 nonIBD M2079 No No 0.732531546041599 No No No 1 +MSM9VZFL MGH 29 nonIBD M2079 No No 0.648010022165966 No No No 2 +MSM9VZFN MGH 29 nonIBD M2079 No No 0.656008013441279 No No No 3 +MSM9VZFR MGH 29 nonIBD M2079 No No 0.670259858583873 No No No 5 +MSM9VZFT MGH 29 nonIBD M2079 No No 0.637866679323458 No No No 6 +MSM9VZGO MGH 56 nonIBD M2061 No No 0.686265217792074 No No No 14 +MSM9VZGS MGH 56 nonIBD M2061 No No 0.745440030144764 No No No 16 +MSM9VZGU MGH 56 nonIBD M2061 No No 0.727086508354272 No No No 17 +MSM9VZGW MGH 61 nonIBD M2075 No No 0.743866299294058 No No No 13 +MSM9VZGY MGH 61 nonIBD M2075 No No 0.689921284169434 No No No 14 +MSM9VZH7 MGH 61 nonIBD M2075 No No 0.698921355995464 No No No 18 +MSM9VZHB MGH 56 nonIBD M2061 No Yes 0.854222521704735 Yes No No 21 +MSM9VZHF MGH 56 nonIBD M2061 No No 0.767629779761237 No No No 23 +MSM9VZHJ MGH 51 nonIBD M2072 No No 0.769611392888318 No No No 19 +MSM9VZHL MGH 51 nonIBD M2072 No No 0.747053184025601 No No No 20 +MSM9VZHN MGH 51 nonIBD M2072 No No 0.763122377974717 No No No 21 +MSM9VZHP MGH 51 nonIBD M2072 No No 0.764444085249789 No No No 22 +MSM9VZHR MGH 51 nonIBD M2072 No No 0.755211801866991 No No No 23 +MSM9VZHT MGH 51 nonIBD M2072 No No 0.744198013836384 No No No 24 +MSM9VZHX MGH 32 nonIBD M2077 No No 0.789956813840871 No No No 9 +MSM9VZHZ MGH 32 nonIBD M2077 Yes No 0.817116491191284 No No No 10 +MSM9VZI2 MGH 32 nonIBD M2077 No No 0.79743396646104 No No No 11 +MSM9VZI6 MGH 32 nonIBD M2077 Yes No 0.729556191857135 No No No 13 +MSM9VZIM MGH 25 UC M2083 No Yes 0.867168973014595 No Yes No 7 +MSM9VZIO MGH 25 UC M2083 No No 0.784911986505888 No No No 8 +MSM9VZIQ MGH 25 UC M2083 No No 0.83501292487364 No No No 9 +MSM9VZIS MGH 25 UC M2083 No Yes 0.875037103910737 No Yes No 10 +MSM9VZIU MGH 25 UC M2083 No No 0.827283268740972 No No No 11 +MSM9VZIW MGH 25 UC M2083 No Yes 0.845309070781517 No Yes No 12 +MSM9VZIY MGH 23 CD M2085 No No 0.659694674525781 No No No 8 +MSM9VZJ3 MGH 23 CD M2085 No No 0.787670164853321 No No No 10 +MSM9VZJB MGH 19 CD M2068 No No 0.763959313778023 No No No 19 +MSM9VZJF_P MGH 56 UC M2091 No No 0.735773694844247 No No No 1 +MSM9VZJJ_P MGH 22 CD M2086 No No 0.755072244817209 No No No 3 +MSM9VZJZ MGH 61 nonIBD M2075 No No 0.713555897914515 No No No 9 +MSM9VZKC MGH 29 nonIBD M2079 No No 0.605618371511714 No No No 8 +MSM9VZKE MGH 29 nonIBD M2079 No No 0.634310528767848 No No No 9 +MSM9VZKI MGH 29 nonIBD M2079 No No 0.619157010738492 No No No 11 +MSM9VZL5 MGH 21 nonIBD M2097 No No 0.662306369479768 No No No 1 +MSM9VZL7 MGH 23 nonIBD M2084 No No 0.682571437945218 No No No 7 +MSM9VZL9 MGH 23 nonIBD M2084 No No 0.66995241618228 No No No 8 +MSM9VZLB MGH 23 nonIBD M2084 No No 0.639856385882237 No No No 9 +MSM9VZLD MGH 23 nonIBD M2084 No No 0.660567458785132 No No No 10 +MSM9VZLF MGH 23 nonIBD M2084 No No 0.662608543151197 No No No 11 +MSM9VZLH MGH 23 nonIBD M2084 No No 0.652333852759084 No No No 12 +MSM9VZLJ MGH 56 nonIBD M2061 No No 0.716233266230705 No No No 18 +MSM9VZLL MGH 74 UC M2064 Yes No 0.810979973141134 No No No 12 +MSM9VZLN MGH 74 UC M2064 No No 0.716381237728282 No No No 13 +MSM9VZLP MGH 74 UC M2064 No No 0.705154997804936 No No No 14 +MSM9VZLR MGH 74 UC M2064 No No 0.754845754014667 No No No 15 +MSM9VZLT MGH 74 UC M2064 No No 0.758452774363359 No No No 18 +MSM9VZLV MGH 74 UC M2064 No No 0.736069451037067 No No No 16 +MSM9VZLX_P MGH NA CD M2082 No No 0.720093613106276 NA NA NA 1 +MSM9VZMA MGH 32 nonIBD M2077 No No 0.742090339836846 No No No 2 +MSM9VZMA_TR MGH 32 nonIBD M2077 No No 0.736392701526446 No No No 2 +MSM9VZMC MGH 32 nonIBD M2077 No No 0.742715154511506 No No No 4 +MSM9VZME MGH 32 nonIBD M2077 No No 0.761993355186609 No No No 3 +MSM9VZMI MGH 32 nonIBD M2077 No Yes 0.839858502579397 Yes No No 6 +MSM9VZMM MGH 23 nonIBD M2084 No No 0.672572962291856 No No No 1 +MSM9VZMO MGH 23 nonIBD M2084 No No 0.683491631390423 No No No 2 +MSM9VZMS MGH 23 nonIBD M2084 No No 0.668352098956351 No No No 4 +MSM9VZMU MGH 23 nonIBD M2084 No No 0.665768918934396 No No No 5 +MSM9VZMW MGH 23 nonIBD M2084 No No 0.727338796883153 No No No 6 +MSM9VZN4_P MGH 39 UC M2081 No No 0.78410195731066 No No No 4 +MSM9VZNH MGH 57 nonIBD M2047 No No 0.756601644682663 No No No 20 +MSM9VZNH_TR MGH 57 nonIBD M2047 No No 0.750074041837798 No No No 20 +MSM9VZNL MGH 57 nonIBD M2047 No No 0.697752136173629 No No No 22 +MSM9VZNR MGH 25 UC M2083 No Yes 0.870136304984003 No Yes No 2 +MSM9VZNX MGH 25 UC M2083 No Yes 0.880459318182044 No Yes No 4 +MSM9VZNZ MGH 25 UC M2083 No Yes 0.851024965473939 No Yes No 5 +MSM9VZO2 MGH 25 UC M2083 No Yes 0.871420488507305 No Yes No 6 +MSM9VZOG MGH 51 nonIBD M2072 No No 0.833491678616986 No No No 13 +MSM9VZOI MGH 51 nonIBD M2072 No Yes 0.869815856481188 Yes No No 14 +MSM9VZOK MGH 51 nonIBD M2072 No No 0.77066930105966 No No No 15 +MSM9VZOM MGH 51 nonIBD M2072 No No 0.807161790322183 No No No 16 +MSM9VZOO MGH 51 nonIBD M2072 No No 0.804784807698215 No No No 17 +MSM9VZOQ MGH 51 nonIBD M2072 No No 0.777464051033579 No No No 18 +MSM9VZOS MGH 29 UC M2069 No No 0.821357550670677 No No No 17 +MSM9VZOU MGH 29 UC M2069 No No 0.812452593471843 No No No 15 +MSM9VZOW MGH 29 UC M2069 No No 0.815891319928586 No No No 16 +MSM9VZOY MGH 29 UC M2069 No No 0.777693495316751 No No No 14 +MSM9VZP1 MGH 29 UC M2069 No No 0.836511344670405 No No No 18 +MSM9VZP3 MGH 29 UC M2069 No Yes 0.868341892160675 No Yes No 19 +MSM9VZPH MGH 23 CD M2085 No No 0.698852362377339 No No No 2 +MSM9VZPL MGH 23 CD M2085 No No 0.663222363474532 No No No 4 +MSM9VZPN MGH 23 CD M2085 No No 0.650263185595642 No No No 5 +MSM9VZPT MGH 62 nonIBD M2060 No No 0.770618782210365 No No No 20 +MSMA267V MGH 35 UC M2103 No No 0.69708381899664 No No No 2 +MSMA267X MGH 35 UC M2103 No No 0.728910306852638 No No No 3 +MSMA2684 MGH 35 UC M2103 No No 0.715022892306409 No No No 6 +MSMA2688 MGH 74 UC M2064 No No 0.829956863019038 No No No 23 +MSMA26AL MGH 19 CD M2068 No No 0.7603146512325 No No No 20 +MSMA26AN MGH 19 CD M2068 No No 0.784531930767758 No No No 21 +MSMA26AP MGH 19 CD M2068 No No 0.795626250714022 No No No 22 +MSMA26AR MGH 19 CD M2068 No No 0.754988967360482 No No No 23 +MSMA26AT MGH 19 CD M2068 No No 0.75668504814262 No No No 24 +MSMA26AV MGH 26 UC M2071 No No 0.639538177506261 No No No 9 +MSMA26AX MGH 26 UC M2071 No No 0.66777130749288 No No No 7 +MSMA26AZ MGH 26 UC M2071 No No 0.671473465744067 No No No 8 +MSMA26AZ_TR MGH 26 UC M2071 No No 0.646916235016185 No No No 8 +MSMA26BB MGH 23 nonIBD M2084 No No 0.683053680598987 No No No 13 +MSMA26BD MGH 23 nonIBD M2084 No No 0.650255905929486 No No No 14 +MSMA26BF MGH 23 nonIBD M2084 No No 0.648916960739009 No No No 15 +MSMA26BH MGH 23 nonIBD M2084 No No 0.671763266946522 No No No 16 +MSMA26BJ MGH 23 nonIBD M2084 No No 0.666361600004661 No No No 17 +MSMA26BL MGH 23 nonIBD M2084 No No 0.662490250942603 No No No 18 +MSMA26BN MGH 29 nonIBD M2079 No No 0.6331369768095 No No No 12 +MSMA26BR MGH 29 nonIBD M2079 No No 0.605515003095706 No No No 14 +MSMA26BT MGH 29 nonIBD M2079 No No 0.645552817437076 No No No 15 +MSMA26BV MGH 29 nonIBD M2079 No No 0.644125915809832 No No No 16 +MSMA26BX MGH 29 nonIBD M2079 No No 0.632559103808062 No No No 17 +MSMA26CV MGH 23 CD M2085 No No 0.696873863192874 No No No 11 +MSMA26CX MGH 23 CD M2085 No No 0.748554936296046 No No No 12 +MSMA26DG MGH 21 nonIBD M2097 No No 0.656856381265813 No No No 3 +MSMA26DI MGH 21 nonIBD M2097 No No 0.65899863631887 No No No 4 +MSMA26DK MGH 21 nonIBD M2097 No No 0.641550561312456 No No No 6 +MSMA26DM MGH 21 nonIBD M2097 No No 0.639252877276947 No No No 2 +MSMA26DO MGH 21 nonIBD M2097 No No 0.665139621249438 No No No 5 +MSMA26EJ MGH 29 UC M2069 No Yes 0.863562401934129 No Yes No 21 +MSMA26EL MGH 29 UC M2069 No Yes 0.84949101325919 No Yes No 22 +MSMA26EN MGH 29 UC M2069 No Yes 0.849800317283877 No Yes No 23 +MSMA26ER MGH 74 UC M2064 Yes Yes 0.873004808960504 No Yes No 19 +MSMA26ET MGH 74 UC M2064 No Yes 0.861654636322593 No Yes No 21 +MSMA26EZ MGH 74 UC M2064 No No 0.724449676864122 No No No 22 +MSMAPC55 MGH 25 UC M2083 No No 0.703501013580143 No No No 13 +MSMAPC57 MGH 25 UC M2083 No Yes 0.866859265870762 No Yes No 14 +MSMAPC59 MGH 25 UC M2083 No No 0.797728200778404 No No No 15 +MSMAPC5B MGH 25 UC M2083 No Yes 0.852284534365287 No Yes No 16 +MSMAPC5D MGH 25 UC M2083 No No 0.821693808175163 No No No 17 +MSMAPC5H MGH 21 nonIBD M2097 No No 0.656633350508949 No No No 10 +MSMAPC5L MGH 21 nonIBD M2097 No No 0.641063475681751 No No No 12 +MSMAPC5Z MGH 35 UC M2103 No No 0.720675069826416 No No No 8 +MSMAPC64 MGH 35 UC M2103 No No 0.781596244243595 No No No 11 +MSMAPC66 MGH 35 UC M2103 No No 0.69271756352045 No No No 10 +MSMAPC6A MGH 23 CD M2085 No No 0.674215495504911 No No No 13 +MSMAPC6C MGH 23 CD M2085 No No 0.725573922226089 No No No 14 +MSMAPC6E MGH 23 CD M2085 No No 0.706421758153026 No No No 15 +MSMAPC6G MGH 23 CD M2085 No No 0.662343956187589 No No No 17 +MSMAPC6K MGH 23 nonIBD M2084 No No 0.640092976914531 No No No 19 +MSMAPC6M MGH 23 nonIBD M2084 No No 0.664001407233101 No No No 20 +MSMAPC6O MGH 23 nonIBD M2084 No No 0.644834793168932 No No No 21 +MSMAPC7J MGH 61 nonIBD M2075 No No 0.722696626326046 No No No 19 +MSMAPC7P MGH 32 nonIBD M2077 No No 0.740505959445398 No No No 14 +MSMAPC7R MGH 32 nonIBD M2077 No No 0.768848295056232 No No No 15 +MSMAPC7T MGH 32 nonIBD M2077 No No 0.724240881128452 No No No 16 +MSMB4LXS MGH 21 nonIBD M2097 Yes No 0.760779100850317 No No No 16 +MSMB4LXW MGH 21 nonIBD M2097 No No 0.632124566459497 No No No 15 +MSMB4LXY MGH 23 CD M2085 No No 0.706062396934093 No No No 19 +MSMB4LYB MGH 23 CD M2085 No No 0.685424611254767 No No No 21 +MSMB4LYH MGH 35 UC M2103 No No 0.673332547362269 No No No 14 +MSMB4LZ4 MGH 26 UC M2071 No No 0.671583042394719 No No No 12 +MSMB4LZ8 MGH 29 nonIBD M2079 No No 0.637293711995131 No No No 21 +MSMB4LZC MGH 32 nonIBD M2077 No No 0.779149099420652 No No No 21 +MSMB4LZK MGH 21 nonIBD M2097 No No 0.779154622046326 No No No 17 +MSMB4LZP MGH 25 UC M2083 No No 0.831845658964523 No No No 18 +MSMB4LZR MGH 25 UC M2083 No Yes 0.886247301124328 No Yes No 19 +MSMB4LZV MGH 23 nonIBD M2084 Yes No 0.695254826826563 No No No 23 +MSMB4LZX MGH 23 nonIBD M2084 No No 0.658623881744935 No No No 24 +MSMB4LZZ MGH 23 nonIBD M2084 Yes No 0.682390540543397 No No No 22 +PSM6XBQM_P MGH Pediatrics 11 CD P6005 No No 0.702442959419867 No No No 1 +PSM6XBQS MGH Pediatrics 11 CD P6005 No No 0.692204660068723 No No No 2 +PSM6XBQU MGH Pediatrics 11 CD P6005 No No 0.627161584046488 No No No 3 +PSM6XBQY MGH Pediatrics 11 CD P6005 No No 0.639341530004375 No No No 5 +PSM6XBQY_TR MGH Pediatrics 11 CD P6005 No No 0.633090523915136 No No No 5 +PSM6XBR1 MGH Pediatrics 11 CD P6005 No No 0.653418522404914 No No No 6 +PSM6XBRK MGH Pediatrics 16 CD P6009 No No 0.7359948720443 No No No 1 +PSM6XBRK_P MGH Pediatrics 16 CD P6009 No No 0.749854132453384 No No No 1 +PSM6XBRK_TR MGH Pediatrics 16 CD P6009 No No 0.700215281135691 No No No 1 +PSM6XBRM_P MGH Pediatrics 10 CD P6010 No No 0.756661186206465 No No No 1 +PSM6XBS2_P MGH Pediatrics 16 CD P6009 No No 0.649060095516495 No No No 2 +PSM6XBS4 MGH Pediatrics 16 CD P6009 No No 0.697990836681334 No No No 3 +PSM6XBS4_P MGH Pediatrics 16 CD P6009 No No 0.721279196425371 No No No 3 +PSM6XBS8 MGH Pediatrics 16 CD P6009 No No 0.697869503012824 No No No 5 +PSM6XBSA MGH Pediatrics 16 CD P6009 No No 0.673289058754145 No No No 6 +PSM6XBSC_P MGH Pediatrics 16 CD P6009 No No 0.683831307660535 No No No 7 +PSM6XBSE MGH Pediatrics 16 UC P6012 No No 0.73943857146836 No No No 1 +PSM6XBSE_P MGH Pediatrics 16 UC P6012 No No 0.694988177909266 No No No 1 +PSM6XBSG_P MGH Pediatrics 6 UC P6013 No No 0.688835139701001 No No No 1 +PSM6XBSI MGH Pediatrics 10 CD P6010 No No 0.710148292262329 No No No 2 +PSM6XBSK MGH Pediatrics 10 CD P6010 No No 0.68064429699305 No No No 5 +PSM6XBSM MGH Pediatrics 10 CD P6010 No No 0.654787000075355 No No No 4 +PSM6XBSO MGH Pediatrics 10 CD P6010 No No 0.650573090076663 No No No 3 +PSM6XBSS MGH Pediatrics 11 CD P6005 No No 0.655265513886936 No No No 9 +PSM6XBSU MGH Pediatrics 11 CD P6005 No No 0.651333851198748 No No No 10 +PSM6XBT1 MGH Pediatrics 11 CD P6005 No No 0.607123169985404 No No No 13 +PSM6XBT3 MGH Pediatrics 6 UC P6013 No No 0.835760406791661 No No No 2 +PSM6XBT5 MGH Pediatrics 6 UC P6013 No No 0.829087966923382 No No No 3 +PSM6XBT7 MGH Pediatrics 6 UC P6013 No No 0.808772122343834 No No No 4 +PSM6XBT9 MGH Pediatrics 6 UC P6013 No Yes 0.847732899336425 No Yes No 5 +PSM6XBTB MGH Pediatrics 6 UC P6013 No No 0.818774818053571 No No No 6 +PSM6XBTF_P MGH Pediatrics 16 CD P6009 No No 0.649668160267823 No No No 8 +PSM6XBTH_P MGH Pediatrics 16 CD P6009 No No 0.606953355908164 No No No 9 +PSM6XBTL MGH Pediatrics 16 CD P6009 Yes No 0.674478033976168 No No No 11 +PSM6XBTN_P MGH Pediatrics 16 CD P6009 No No 0.670768483802092 No No No 12 +PSM6XBTP MGH Pediatrics 16 CD P6009 No No 0.650407129138965 No No No 13 +PSM6XBTR MGH Pediatrics 15 nonIBD P6014 Yes No 0.735071583167828 No No No 3 +PSM6XBTT MGH Pediatrics 15 nonIBD P6014 No No 0.709676177927146 No No No 4 +PSM6XBTX MGH Pediatrics 15 nonIBD P6014 No No 0.692404123911777 No No No 6 +PSM6XBTZ_P MGH Pediatrics 15 nonIBD P6014 No No 0.700967462276612 No No No 7 +PSM6XBU2 MGH Pediatrics 15 nonIBD P6014 No No 0.716304700152622 No No No 8 +PSM6XBUG MGH Pediatrics 10 CD P6010 Yes No 0.711719733509515 No No No 8 +PSM6XBUI MGH Pediatrics 10 CD P6010 Yes No 0.689602032104879 No No No 10 +PSM6XBUK MGH Pediatrics 10 CD P6010 No No 0.68067360445854 No No No 12 +PSM6XBUM MGH Pediatrics 10 CD P6010 No No 0.670643253156739 No No No 11 +PSM6XBUO MGH Pediatrics 10 CD P6010 No No 0.660094798432795 No No No 14 +PSM6XBUQ MGH Pediatrics 10 CD P6010 No No 0.6634988003761 No No No 13 +PSM6XBV2 MGH Pediatrics 10 CD P6010 No No 0.718231305001306 No No No 6 +PSM6XBV4 MGH Pediatrics 10 CD P6010 Yes No 0.810828587545 No No No 7 +PSM6XBVI MGH Pediatrics 16 UC P6012 No No 0.766393166366127 No No No 2 +PSM6XBVK MGH Pediatrics 16 UC P6012 No No 0.817364613087212 No No No 3 +PSM6XBVM MGH Pediatrics 16 UC P6012 No No 0.764142932640261 No No No 4 +PSM6XBVO MGH Pediatrics 16 UC P6012 No No 0.733306031824106 No No No 5 +PSM6XBVQ MGH Pediatrics 16 UC P6012 No No 0.701920982389435 No No No 6 +PSM6XBVS MGH Pediatrics 16 UC P6012 No No 0.754068135310182 No No No 7 +PSM6XBVY_P MGH Pediatrics 16 CD P6016 Yes No 0.743137918148702 No No No 1 +PSM6XBW1_P MGH Pediatrics 11 nonIBD P6017 No No 0.710711907406854 No No No 1 +PSM6XBW3 MGH Pediatrics 17 nonIBD P6018 No Yes 0.948844345404797 Yes No No 1 +PSM7J127 MGH Pediatrics 6 UC P6013 No Yes 0.846817347400996 No Yes No 15 +PSM7J12B MGH Pediatrics 6 UC P6013 No Yes 0.860695777999163 No Yes No 17 +PSM7J12D MGH Pediatrics 6 UC P6013 No No 0.802790286341871 No No No 18 +PSM7J12F MGH Pediatrics 6 UC P6013 No Yes 0.8438747800623 No Yes No 19 +PSM7J12J MGH Pediatrics 16 CD P6024 No Yes 0.98593519935536 No No Yes 9 +PSM7J12R MGH Pediatrics 16 CD P6024 No Yes 0.968855783108721 No No Yes 13 +PSM7J12V MGH Pediatrics 17 UC P6025 Yes No 0.730964655181534 No No No 9 +PSM7J12Z MGH Pediatrics 17 UC P6025 No No 0.658630403339741 No No No 11 +PSM7J136 MGH Pediatrics 11 CD P6005 No No 0.656185438031238 No No No 20 +PSM7J13E MGH Pediatrics 11 CD P6005 No No 0.618646675688101 No No No 24 +PSM7J13I MGH Pediatrics 15 CD P6033 No No 0.760272133164773 No No No 2 +PSM7J13K MGH Pediatrics 15 CD P6033 Yes No 0.786998923206193 No No No 3 +PSM7J13M MGH Pediatrics 15 CD P6033 No No 0.751586130508107 No No No 6 +PSM7J13Q MGH Pediatrics 15 CD P6033 No No 0.764526770856342 No No No 5 +PSM7J13U_P MGH Pediatrics 16 CD P6009 No Yes 0.886418638817326 No No Yes 20 +PSM7J13Y MGH Pediatrics 16 CD P6009 No No 0.765264482493751 No No No 22 +PSM7J13Y_P MGH Pediatrics 16 CD P6009 No No 0.793014762640974 No No No 22 +PSM7J141 MGH Pediatrics 16 CD P6009 No Yes 0.855937652240473 No No Yes 23 +PSM7J143_P MGH Pediatrics 16 CD P6009 No No 0.833307685525921 No No No 24 +PSM7J14L MGH Pediatrics 10 CD P6010 No Yes 0.999066060453061 No No Yes 20 +PSM7J14N MGH Pediatrics 10 CD P6010 No No 0.785879186591881 No No No 21 +PSM7J14P MGH Pediatrics 10 CD P6010 No No 0.689862176829493 No No No 22 +PSM7J14R MGH Pediatrics 10 CD P6010 No No 0.661730895108244 No No No 23 +PSM7J14T MGH Pediatrics 10 CD P6010 No No 0.720627421200999 No No No 24 +PSM7J14X MGH Pediatrics 15 nonIBD P6014 No No 0.663890456074159 No No No 14 +PSM7J154 MGH Pediatrics 15 nonIBD P6014 No No 0.636304755213644 No No No 16 +PSM7J156 MGH Pediatrics 15 nonIBD P6014 No No 0.660871750542838 No No No 17 +PSM7J158 MGH Pediatrics 16 CD P6016 No No 0.710374662836692 No No No 14 +PSM7J15A MGH Pediatrics 16 CD P6016 No No 0.74339958046829 No No No 15 +PSM7J15G MGH Pediatrics 16 CD P6016 No Yes 0.839558960323097 No No Yes 18 +PSM7J15I MGH Pediatrics 16 CD P6016 No No 0.752062388091019 No No No 19 +PSM7J15K MGH Pediatrics 17 nonIBD P6018 No Yes 0.851703410180755 Yes No No 14 +PSM7J15M MGH Pediatrics 17 nonIBD P6018 No Yes 0.856056852939928 Yes No No 15 +PSM7J15O MGH Pediatrics 17 nonIBD P6018 No Yes 0.855712111740244 Yes No No 16 +PSM7J15Q MGH Pediatrics 17 nonIBD P6018 No No 0.802069105292622 No No No 17 +PSM7J15S MGH Pediatrics 17 nonIBD P6018 No No 0.81275390179984 No No No 18 +PSM7J15U MGH Pediatrics 17 nonIBD P6018 No No 0.820761967253908 No No No 19 +PSM7J15W MGH Pediatrics 9 CD P6028 No No 0.680786315952207 No No No 8 +PSM7J161 MGH Pediatrics 9 CD P6028 No Yes 0.999365615748931 No No Yes 10 +PSM7J163 MGH Pediatrics 9 CD P6028 No Yes 0.999157186782494 No No Yes 11 +PSM7J169 MGH Pediatrics 6 UC P6013 No No 0.739741504742991 No No No 20 +PSM7J16B MGH Pediatrics 6 UC P6013 Yes Yes 0.959548185945132 No Yes No 23 +PSM7J16F MGH Pediatrics 6 UC P6013 No No 0.82466591905339 No No No 21 +PSM7J16U MGH Pediatrics 11 nonIBD P6017 No No 0.834819826805206 No No No 9 +PSM7J16W MGH Pediatrics 11 nonIBD P6017 No No 0.751666813878551 No No No 11 +PSM7J16Y MGH Pediatrics 11 nonIBD P6017 No No 0.660269093458867 No No No 12 +PSM7J171 MGH Pediatrics 11 nonIBD P6017 No No 0.809689489352849 No No No 13 +PSM7J173 MGH Pediatrics 11 nonIBD P6017 No No 0.72931110256967 No No No 14 +PSM7J179 MGH Pediatrics 9 CD P6028 Yes No 0.741978826948162 No No No 3 +PSM7J17B MGH Pediatrics 9 CD P6028 No Yes 0.99890869990459 No No Yes 4 +PSM7J17L MGH Pediatrics 16 CD P6016 No No 0.714743409671274 No No No 9 +PSM7J17T MGH Pediatrics 16 CD P6016 No No 0.719633478153787 No No No 13 +PSM7J17V MGH Pediatrics 17 nonIBD P6018 No Yes 0.943418863945858 Yes No No 8 +PSM7J17X MGH Pediatrics 17 nonIBD P6018 No Yes 0.960418950491006 Yes No No 9 +PSM7J17Z MGH Pediatrics 17 nonIBD P6018 No Yes 0.857807836075611 Yes No No 10 +PSM7J182 MGH Pediatrics 17 nonIBD P6018 No Yes 0.858533384883679 Yes No No 11 +PSM7J184 MGH Pediatrics 17 nonIBD P6018 No Yes 0.863558404514812 Yes No No 12 +PSM7J186 MGH Pediatrics 17 nonIBD P6018 No Yes 0.866380113625317 Yes No No 13 +PSM7J18E MGH Pediatrics 10 CD P6010 No No 0.69634078575825 No No No 15 +PSM7J18G MGH Pediatrics 10 CD P6010 No No 0.703791935622541 No No No 16 +PSM7J18I MGH Pediatrics 10 CD P6010 No No 0.709956204389145 No No No 17 +PSM7J18K MGH Pediatrics 10 CD P6010 No No 0.710944104170679 No No No 18 +PSM7J18M MGH Pediatrics 10 CD P6010 No No 0.664176784119696 No No No 19 +PSM7J18Q MGH Pediatrics 16 UC P6012 No No 0.784153550842286 No No No 18 +PSM7J193 MGH Pediatrics 6 UC P6013 No No 0.820088861331406 No No No 14 +PSM7J199 MGH Pediatrics 16 CD P6016 Yes No 0.699119352969938 No No No 2 +PSM7J19B MGH Pediatrics 16 CD P6016 Yes No 0.724034052996746 No No No 3 +PSM7J19F MGH Pediatrics 16 CD P6016 No No 0.747527735818476 No No No 5 +PSM7J19H MGH Pediatrics 16 CD P6016 No No 0.723048555808018 No No No 6 +PSM7J19J MGH Pediatrics 16 CD P6016 No No 0.78023987944547 No No No 7 +PSM7J19N MGH Pediatrics 11 nonIBD P6017 No No 0.705421938425865 No No No 3 +PSM7J19P MGH Pediatrics 11 nonIBD P6017 No No 0.742403102671158 No No No 4 +PSM7J19R MGH Pediatrics 11 nonIBD P6017 No No 0.651872410349999 No No No 5 +PSM7J19T MGH Pediatrics 11 nonIBD P6017 No No 0.744200093193988 No No No 6 +PSM7J19X_P MGH Pediatrics 17 nonIBD P6018 Yes Yes 0.947658400143828 Yes No No 2 +PSM7J19Z MGH Pediatrics 17 nonIBD P6018 Yes Yes 0.934129689532851 Yes No No 3 +PSM7J1A2 MGH Pediatrics 17 nonIBD P6018 No Yes 0.938746078486047 Yes No No 4 +PSM7J1A4 MGH Pediatrics 17 nonIBD P6018 No Yes 0.876149305193059 Yes No No 5 +PSM7J1A6 MGH Pediatrics 17 nonIBD P6018 Yes Yes 0.99598798853868 Yes No No 6 +PSM7J1A8 MGH Pediatrics 17 nonIBD P6018 No Yes 0.947629391218891 Yes No No 7 +PSM7J1AM MGH Pediatrics 6 UC P6013 No No 0.779684027488403 No No No 8 +PSM7J1AO MGH Pediatrics 6 UC P6013 No No 0.824422724882723 No No No 11 +PSM7J1AQ MGH Pediatrics 6 UC P6013 No No 0.832449569326255 No No No 12 +PSM7J1AS MGH Pediatrics 6 UC P6013 No No 0.829552533229322 No No No 9 +PSM7J1AU MGH Pediatrics 6 UC P6013 No No 0.820771941102692 No No No 10 +PSM7J1B3_P MGH Pediatrics 9 CD P6028 Yes Yes 0.855849295291026 No No Yes 1 +PSM7J1B7 MGH Pediatrics 16 UC P6012 No No 0.792748927060189 No No No 8 +PSM7J1B9 MGH Pediatrics 16 UC P6012 No No 0.750040026273283 No No No 9 +PSM7J1BB MGH Pediatrics 16 UC P6012 No No 0.728848219267264 No No No 11 +PSM7J1BD MGH Pediatrics 16 UC P6012 No No 0.739017058562628 No No No 12 +PSM7J1BF MGH Pediatrics 16 UC P6012 No No 0.725255200396217 No No No 13 +PSM7J1BJ MGH Pediatrics 16 CD P6024 No Yes 0.995397840918018 No No Yes 3 +PSM7J1BL MGH Pediatrics 16 CD P6024 Yes Yes 0.997058470737704 No No Yes 4 +PSM7J1BN_P MGH Pediatrics 16 CD P6024 Yes Yes 0.985643503747259 No No Yes 5 +PSM7J1BP MGH Pediatrics 16 CD P6024 Yes Yes 0.997346202074199 No No Yes 6 +PSM7J1BR MGH Pediatrics 16 CD P6024 Yes Yes 0.987176997218099 No No Yes 7 +PSM7J1BV MGH Pediatrics 17 UC P6025 No No 0.774717798044822 No No No 2 +PSM7J1BX MGH Pediatrics 17 UC P6025 No No 0.66158916340707 No No No 3 +PSM7J1C2_P MGH Pediatrics 17 UC P6025 No Yes 0.871098113872062 No Yes No 5 +PSM7J1C4 MGH Pediatrics 17 UC P6025 No No 0.699314923981486 No No No 6 +PSM7J1C8 MGH Pediatrics 11 CD P6005 No No 0.66776677258753 No No No 14 +PSM7J1CC MGH Pediatrics 11 CD P6005 No No 0.623344110480413 No No No 16 +PSM7J1CG MGH Pediatrics 11 CD P6005 Yes No 0.709136544957211 No No No 18 +PSM7J1CI MGH Pediatrics 11 CD P6005 No No 0.609974780746608 No No No 19 +PSM7J1CK_P MGH Pediatrics 16 CD P6009 No No 0.683280923750694 No No No 14 +PSM7J1CS_P MGH Pediatrics 16 CD P6009 No No 0.780374708764871 No No No 18 +PSM7J1CU MGH Pediatrics 16 CD P6009 No No 0.755376258110351 No No No 19 +PSM7J1DF MGH Pediatrics 15 nonIBD P6014 No No 0.704783808863503 No No No 10 +PSM7J1DL MGH Pediatrics 15 nonIBD P6014 No No 0.670177989828438 No No No 13 +PSM7J4EF MGH Pediatrics 15 CD P6037 No No 0.762246179526217 No No No 1 +PSMA263M MGH Pediatrics 16 CD P6024 No Yes 0.960744939491056 No No Yes 14 +PSMA263S MGH Pediatrics 16 CD P6024 No Yes 0.96351706392056 No No Yes 17 +PSMA263U MGH Pediatrics 16 CD P6024 No Yes 0.963318746215513 No No Yes 18 +PSMA263W MGH Pediatrics 16 CD P6024 No Yes 0.957420077702007 No No Yes 19 +PSMA264K MGH Pediatrics 17 UC P6025 No No 0.757045633040137 No No No 16 +PSMA264O MGH Pediatrics 16 UC P6035 No No 0.759685483544399 No No No 2 +PSMA264Q MGH Pediatrics 16 UC P6035 No No 0.781380677857354 No No No 3 +PSMA264S MGH Pediatrics 16 UC P6035 No No 0.791531326183874 No No No 4 +PSMA264U MGH Pediatrics 16 UC P6035 No No 0.695267589178597 No No No 5 +PSMA264W MGH Pediatrics 16 UC P6035 No No 0.677308857433583 No No No 6 +PSMA2651_P MGH Pediatrics 15 CD P6033 No No 0.694761370950797 No No No 8 +PSMA2653 MGH Pediatrics 15 CD P6033 No No 0.690954230738867 No No No 9 +PSMA2659 MGH Pediatrics 15 CD P6033 No No 0.718055665585006 No No No 12 +PSMA265B MGH Pediatrics 15 CD P6033 No No 0.736183771002547 No No No 13 +PSMA265D MGH Pediatrics 17 nonIBD P6018 No Yes 0.842407349407832 Yes No No 20 +PSMA265F MGH Pediatrics 17 nonIBD P6018 No Yes 0.838859754896309 Yes No No 21 +PSMA265H MGH Pediatrics 17 nonIBD P6018 No No 0.836683871894684 No No No 24 +PSMA265J MGH Pediatrics 17 nonIBD P6018 No No 0.83671278205524 No No No 23 +PSMA265J_TR MGH Pediatrics 17 nonIBD P6018 No No 0.83671278205524 No No No 23 +PSMA265L MGH Pediatrics 17 nonIBD P6018 No No 0.819823743743616 No No No 22 +PSMA265N MGH Pediatrics 16 CD P6016 No No 0.741223648483934 No No No 20 +PSMA265T MGH Pediatrics 16 CD P6016 No No 0.790027868251687 No No No 23 +PSMA265X MGH Pediatrics 16 UC P6038 No No 0.71231836162602 No No No 1 +PSMA2668 MGH Pediatrics 9 CD P6028 No Yes 0.999604254488082 No No Yes 15 +PSMA266I MGH Pediatrics 15 CD P6037 No No 0.719491155527537 No No No 2 +PSMA266M MGH Pediatrics 15 CD P6037 Yes No 0.731123627705743 No No No 4 +PSMA266O MGH Pediatrics 15 CD P6037 Yes No 0.745586402407911 No No No 5 +PSMA266Q MGH Pediatrics 15 CD P6037 Yes No 0.709162734571597 No No No 6 +PSMA266U MGH Pediatrics 16 UC P6038 No No 0.756402659712828 No No No 2 +PSMA266Y MGH Pediatrics 16 UC P6038 No No 0.71583332758601 No No No 4 +PSMA2671 MGH Pediatrics 16 UC P6038 No No 0.702035643033747 No No No 5 +PSMA2675 MGH Pediatrics 16 UC P6038 No No 0.653939747515284 No No No 7 +PSMA267D MGH Pediatrics 15 CD P6033 No No 0.742717159270931 No No No 17 +PSMA267F MGH Pediatrics 15 CD P6033 No No 0.771418335214622 No No No 18 +PSMA267H MGH Pediatrics 15 CD P6033 No No 0.747876595273697 No No No 19 +PSMA267J MGH Pediatrics 16 UC P6035 No No 0.731158902309119 No No No 8 +PSMA267P MGH Pediatrics 16 UC P6035 No No 0.801575972906687 No No No 11 +PSMA267R MGH Pediatrics 16 UC P6035 No No 0.770047699968093 No No No 12 +PSMA269G MGH Pediatrics 15 CD P6037 No No 0.775266691965913 No No No 8 +PSMA269O MGH Pediatrics 15 CD P6037 No No 0.718533251599801 No No No 12 +PSMA269S MGH Pediatrics 16 UC P6038 No No 0.663812474472784 No No No 8 +PSMA269W MGH Pediatrics 16 UC P6038 No No 0.709396968369554 No No No 10 +PSMA26A1 MGH Pediatrics 16 UC P6038 No No 0.679252999929992 No No No 12 +PSMA26A3 MGH Pediatrics 16 UC P6038 No No 0.682001418709998 No No No 13 +PSMB4MBI MGH Pediatrics 16 UC P6035 No No 0.749741766306635 No No No 17 +PSMB4MBK MGH Pediatrics 16 UC P6035 No No 0.698786574135189 No No No 16 +PSMB4MBS MGH Pediatrics 15 CD P6037 No No 0.732561466588752 No No No 16 +PSMB4MC1 MGH Pediatrics 16 UC P6038 No No 0.691453182231825 No No No 17 +PSMB4MC3 MGH Pediatrics 16 UC P6038 No No 0.719423017924537 No No No 18 +PSMB4MC5 MGH Pediatrics 16 UC P6038 No No 0.722597079037033 No No No 19 +PSMB4MC7 MGH Pediatrics 16 UC P6035 No No 0.699209149925812 No No No 18 diff --git a/topics/microbiome/tutorials/multivariable-association/workflows/test-data/HMP2_taxonomy.tsv.mothur.axes b/topics/microbiome/tutorials/multivariable-association/workflows/test-data/HMP2_taxonomy.tsv.mothur.axes new file mode 100644 index 00000000000000..50c9c0acd2cda9 --- /dev/null +++ b/topics/microbiome/tutorials/multivariable-association/workflows/test-data/HMP2_taxonomy.tsv.mothur.axes @@ -0,0 +1,1009 @@ +ID Bifidobacterium adolescentis Bifidobacterium bifidum Bifidobacterium longum Bifidobacterium pseudocatenulatum Collinsella aerofaciens Bacteroides caccae Bacteroides cellulosilyticus Bacteroides dorei Bacteroides eggerthii Bacteroides faecis Bacteroides finegoldii Bacteroides fragilis Bacteroides intestinalis Bacteroides massiliensis Bacteroides ovatus Bacteroides salyersiae Bacteroides stercoris Bacteroides thetaiotaomicron Bacteroides uniformis Bacteroides vulgatus Bacteroides xylanisolvens Bacteroidales bacterium ph8 Barnesiella intestinihominis Coprobacter fastidiosus Odoribacter splanchnicus Parabacteroides distasonis Parabacteroides goldsteinii Parabacteroides merdae Parabacteroides unclassified Paraprevotella clara Paraprevotella unclassified Prevotella copri Alistipes finegoldii Alistipes onderdonkii Alistipes putredinis Alistipes shahii Alistipes unclassified Streptococcus salivarius Clostridium bolteae Clostridium citroniae Clostridium clostridioforme Clostridium hathewayi Clostridium leptum Clostridium nexile Clostridium symbiosum Flavonifractor plautii Eubacterium eligens Eubacterium hallii Eubacterium rectale Eubacterium siraeum Eubacterium sp 3 1 31 Eubacterium ventriosum Ruminococcus gnavus Ruminococcus obeum Ruminococcus torques Coprococcus comes Dorea longicatena Lachnospiraceae bacterium 1 1 57FAA Lachnospiraceae bacterium 3 1 46FAA Roseburia hominis Roseburia intestinalis Roseburia inulinivorans Roseburia unclassified Oscillibacter unclassified Peptostreptococcaceae noname unclassified Faecalibacterium prausnitzii Ruminococcus bromii Ruminococcus callidus Ruminococcus lactaris Subdoligranulum unclassified Coprobacillus unclassified Acidaminococcus unclassified Dialister invisus Veillonella atypica Veillonella dispar Veillonella parvula Veillonella unclassified Burkholderiales bacterium 1 1 47 Parasutterella excrementihominis Sutterella wadsworthensis Bilophila unclassified Escherichia coli Escherichia unclassified Klebsiella pneumoniae Haemophilus parainfluenzae Akkermansia muciniphila C2likevirus unclassified +CSM5FZ4M 0 0 0 0 0 2.99E-05 0 0.130632 0 0 0 0 0 0 0.190863 0.0090411 0 0.0199315 0.325812 3.62E-05 0.011119 0 0 0 0 0.0026035 0 0 0.0605912 0 0 0 6.78E-05 0.0041528 0.0587475 0 0 0 3.69E-05 0 7.88E-05 0 0.0008177 0 0 0 0 0.0015772 0 0.0028149 0 0 0.0007802 0.0016194 0.0010076 0.0013645 0.00307 0 0 0 0 0 0 0.0041547 0 0.0936191 0.0071109 0 0 0.0156577 0 0.0287905 0 0 0 0 0 0 0 0 0.0004782 0.0010136 0 0 0 0 0 +CSM5MCUO 0.0002803 0 0.0002857 0 0.0002506 0.025743 0.0003977 0.0057939 0 0.0025964 0 0.0062334 0 0 0.0344354 0.0129821 0.0836427 0.0006236 0.0677076 0.169157 0.0009044 0.0024183 0.0195324 0 0.0030421 0 0 0.0026058 0 0.0071178 0.0203598 0 0 0.0034786 0.047193 0.0021057 0 0 6.96E-05 4.3E-06 0 1.94E-05 0 0 0 7.25E-05 0.0745387 0.0001715 0.0377607 0 0 0 0 0.0012169 0.002148 0.0013398 0.0081932 0.0001191 0.0016632 9.11E-05 0.067003 0.0075671 0 0 0.00194 0.160706 0 0 0.0005857 0.0320903 0 0 0 0 0 2.06E-05 4.24E-05 0.000971 0.0086767 0 0.0011601 0.0026399 0 0.0044638 0.000241 0 0 +CSM5MCVL 0 0 0 0 0.000644 0 0 3.04E-05 0 0 0 0.260366 0 0 2.46E-05 0 0 0.129383 0 0.314003 0.0232747 0 0 0 0 0 0 0 0.0001241 0 0 0 0 0 0 0 0 0 0.0043222 0 0.0276218 0.0071817 0 0.0012594 0 0.0047196 0 0 0.0145548 0 0 0 7.3E-05 0 0.000822 0 6.43E-05 0 2.08E-05 0.02246 0 0 0 0 0 0.0001213 0 0 0 0 0.0005083 0.0096519 0.0239 0 0 0 0 0 0 0 0 0.0112689 0 0.0138117 0 0 0.0141917 +CSM5MCVN 0 0 0 0 7.9E-06 0 0 6.2E-06 0 0 0 0.149041 0 1.25E-05 0 0 0 0.0832982 0 0.423798 0.0164535 0 0 0 0 0 0 0 0.0006701 0 0 0 0 0 0 0 0 0 0.001367 0 0.0884657 0.0005971 0 0.0018467 0 0.0012898 0 0.0002252 0.018187 0 0 0 0 1.57E-05 0 0 0.0053145 0 0.0010537 0.0009666 0 0 0 0 0 0 0 0 0 7.68E-05 2.18E-05 0.122568 0.0224342 0 0 0 0 0 0 0 0 0.0001853 0.0004242 0 0 0 0 +CSM5MCW6 0 0 0 0 0 0 0 0.797928 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0033702 0.0204998 0 0 0 0 0 0 0 0 0 0 0 0.0387423 0 0.0090233 0 0.0157132 0 0 0 0.0032695 0.0005009 0.0241049 0 0 0 0 0 0.000299 0.0005244 0.001166 0 0 0.0015111 0 0.0298091 0 0.0011846 0 0.0126023 0 0 0 0 0 0.0095345 0 0 0 0 0 0 1.29E-05 0.00037 0.005708 0 0 0 0 0 0 0 0 +CSM5MCWC 0 2.31E-05 0.0003162 0 0 1.44E-05 0 3.17E-05 0 0 0 0 0 0 6.6E-05 0 0.0002643 0 0.000198 0.0005533 0 0 0 0 0 0 0 0 0 0 6.9E-05 0 0 0 8.15E-05 0 0 0 0 0 0.0006028 0.0099797 0 0 0 0.0024009 0.0002178 0 0.0001344 0 0 0 0.0098838 0 0.0037154 0 0 0 0 0 0.718226 0 0 0.0040982 0 0.0004805 0 0 0 0.0518636 0.0003928 0 0 0 0 0.13466 0 0 0 0 0 0.0504913 0 0 0 0 0 +CSM5MCWE 0 0.00034 0.002193 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0348624 0.0070646 0 0 0 0.0021959 0 0 0 0 0 0 0.0397243 0 0.0048 0 0 0 0 0 0.259999 0 0 0.058234 0.0001926 0 0 0 0 0.0386629 0.0003843 0 0 1.01E-05 0 0.311084 0.0066505 0 0 0 0 0.216984 0 0.0002289 0 0 0 +CSM5MCWG 0 0 0.0003679 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.95E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5.32E-05 0 0 0 0 0 0 0 0 0 0 0 0.806009 0 0.027933 0 0 0 +CSM5MCWQ 0 0 0 0 0 8.17E-05 9.64E-05 0.0672288 0 0 0 0.0113786 0 0 0.122942 0.0201914 0.000105 0.0040688 0.217201 0.0008374 2.58E-05 0 0 0 0 0.0004571 0 5.4E-05 0 0 0 0 0.000363 0.0162975 0.114866 0 0 0 0.0001883 0 0.000379 0 0.0001216 0 0.00015 0 0 0.0046789 1.34E-05 0.0197896 0.0001966 0 0.002362 0.0255746 0.004419 0.0085504 0.0325097 0 0 0 0 0 0.0001466 0 0 0.122649 0.0333318 0 0 0.0606285 7.45E-05 0.0412816 0 0 0 0 0 0 0 0 0.0011062 0 0 0 0 0 0 +CSM5MCX3 0.0002539 0 0 0 0.0014928 0.0242821 0.0015386 0.0040416 0 0.0044463 0 0.0077786 0 0 0.0336512 0.0175735 0.0701398 0.0011342 0.0465973 0.179288 0.0012624 0.0006817 0.0336377 0 0.0092572 8.4E-06 0 0.0075155 0 0.006469 0.0206797 0 0 0.0048614 0.0298645 0.0034485 0 0 0 0 0 0 0 0 0 0.000124 0.0116151 0.000848 0.0815114 0 0 0 0 0.0018713 0.0099968 0.002291 0.0012411 0 0.0028484 0.0006999 0.0638985 0.0217855 0 0.0021544 0.0010134 0.146862 0 0 0.0020005 0.0256008 0.0003196 0 0 1.15E-05 0 0.0001387 0.0001265 0.0001856 0.0013029 0.0043194 0.0004984 0.0355164 0 0 0.0047487 0 0 +CSM5MCXD 0 0 0 0 0 0.0232205 0 0 0 0 0 0.0143431 0 0 0 0 0 0.143841 0.394913 0.0826526 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0188117 0.000241 0.0282565 0.000477 0.0030871 0 0 0.0028702 0 0.010184 0 0 7.31E-05 0 0.0004308 0.0023877 0.0188037 0 0.0014947 0 0.0003486 0.0004154 0 0.0081558 0.0127071 0.0512882 0 0.055412 0 0 0.0029844 0.0159726 0 0 0.0525671 0 0 0 6.31E-05 0 0 0 0 0.0010821 0.00017 0 0 0.0308464 0 +CSM5MCXH 0 0 1.74E-05 0 1.62E-05 0.0493889 0.0334967 0.0066452 0 0.0021273 0.006606 0 0 0 0.073836 0 0.141228 0.0001597 0.218669 0.220928 0.0001106 0 0 0 0 0.0139203 0 0.0313531 0 0 0 0 0.0139698 0 0 0 0.0128026 0 0.0062217 3.34E-05 0.0001771 7.33E-05 0.002021 0 0 0.0014415 0.0018687 0 0.0198946 0 0 0 0.0007365 0 0.0015047 0 0 0 0 0 0 0.0025246 0 0.0313484 0 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