README.Rmd

---
title: spatialDLPFC
output:
  github_document:
    html_preview: true
  html_document:
    toc: true
    toc_flot: true
    includes:
      in_header: header.html
      after_body: footer.html
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r 'setup', include = FALSE}
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>",
    fig.path = "img/",
    out.width = "100%"
)
```

[![DOI](https://zenodo.org/badge/314001778.svg)](https://zenodo.org/badge/latestdoi/314001778)

## Overview

<img src="http://research.libd.org/spatialDLPFC/img/Br6255_ant_Sp09.png" align="left" width="300px" />

Welcome to the `spatialDLPFC` project! This project involves 3 data types as well as several interactive websites, all of which you are publicly accessible for you to browse and download.

In this project we studied spatially resolved and single nucleus transcriptomics data from the dorsolateral prefrontal cortex (DLPFC) from postmortem human brain samples. From 10 neurotypical controls we generated spatially-resolved transcriptomics data using using [10x Genomics **Visium**](https://www.10xgenomics.com/products/spatial-gene-expression) across the anterior, middle, and posterior DLPFC (n = 30). We also generated single nucleus RNA-seq (**snRNA-seq**) data using [10x Genomics **Chromium**](https://www.10xgenomics.com/products/single-cell-gene-expression) from 19 of these tissue blocks. We further generated data from 4 adjacent tissue slices with [10x Genomics **Visium Spatial Proteogenomics** (SPG)](https://www.10xgenomics.com/products/spatial-proteogenomics), that can be used to benchmark spot deconvolution algorithms. This work is being was performed by the [Keri Martinowich](https://www.libd.org/team/keri-martinowich-phd/), [Leonardo Collado-Torres](http://lcolladotor.github.io/), and [Kristen Maynard](https://www.libd.org/team/kristen-maynard-phd/) teams at the [Lieber Institute for Brain Development](libd.org) as well as [Stephanie Hicks](https://www.stephaniehicks.com/)'s group from [JHBSPH's Biostatistics Department](https://publichealth.jhu.edu/departments/biostatistics).

This project involves the GitHub repositories [LieberInstitute/spatialDLPFC](https://github.com/LieberInstitute/spatialDLPFC) and [LieberInstitute/DLPFC_snRNAseq](https://github.com/LieberInstitute/DLPFC_snRNAseq).

If you tweet about this website, the data or the R package please use
the <code>\#spatialDLPFC</code> hashtag. You can find previous tweets
that way as shown
<a href="https://twitter.com/search?q=%23spatialDLPFC&src=typed_query">here</a>.


Thank you for your interest in our work!

## Study Design

<img src="http://research.libd.org/spatialDLPFC/img/study_overview.png" width="1000px" align="left" />

**Study design to generate paired single nucleus RNA-sequencing (snRNA-seq) and spatially-resolved transcriptomic data across DLPFC**. (**A**) DLPFC tissue blocks were dissected across the rostral-caudal axis from 10 adult neurotypical control postmortem human brains, including anterior (Ant), middle (Mid), and posterior (Post) positions (n=3 blocks per donor, n=30 blocks total). The same tissue blocks were used for snRNA-seq (10x Genomics 3’ gene expression assay, n=1-2 blocks per donor, n=19 samples) and spatial transcriptomics (10x Genomics Visium spatial gene expression assay, n=3 blocks per donor, n=30 samples). (**B**) Paired snRNA-seq and Visium data were used to identify data-driven spatial domains (SpDs) and cell types, perform spot deconvolution, conduct cell-cell communication analyses, and spatially register companion PsychENCODE snRNA-seq DLPFC data. (**C**) _t_-distributed stochastic neighbor embedding (t-SNE) summarizing layer resolution cell types identified by snRNA-seq. (**D**) Tissue block orientation and morphology was confirmed by hematoxylin and eosin (H&E) staining and single molecule fluorescent in situ hybridization (smFISH) with RNAscope (_SLC17A7_ marking excitatory neurons in pink, _MBP_ marking white matter (WM) in green, _RELN_ marking layer (L)1 in yellow, and _NR4A2_ marking L6 in orange). Scale bar is 2mm. Spotplots depicting log transformed normalized expression (logcounts) of _SNAP25_, _MBP_, and _PCP4_ in the Visium data confirm the presence of gray matter, WM, and cortical layers, respectively. (**E**) Schematic of unsupervised SpD identification and registration using _BayesSpace_ SpDs at _k_=7. Enrichment _t_-statistics computed on _BayesSpace_ SpDs were correlated with manual histological layer annotations from ([Maynard, Collado-Torres et al., 2021, _Nat Neuro_](https://doi.org/10.1038/s41593-020-00787-0)) to map SpDs to known histological layers. The heatmap of correlation values summarizes the relationship between BayesSpace SpDs and classic histological layers. Higher confidence annotations (⍴ > 0.25, merge ratio = 0.1) are marked with an “X”.

## Introductory material

If you prefer to watch a video overview of this project, check the following journal club presentations of the main and supplementary results, respectively.

<iframe width="560" height="315" src="https://www.youtube.com/embed/EhP5-mhw29w?si=4voRLMPS4JS1RmS0" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen data-external="1"></iframe>

<iframe width="560" height="315" src="https://www.youtube.com/embed/vSjXCni8Ndc?si=ku8_3IgnG2nHvv21" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen data-external="1"></iframe>

L. A. Huuki-Myers also wrote an [overview blog post](https://lahuuki.github.io/posts/2024-05-23-spatialDLPFC/) summarizing some of the key results from this `spatialDLPFC` project.

## Interactive Websites

All of these interactive websites are powered by open source software, namely:

* 🔭 [`spatialLIBD`](https://doi.org/10.1186/s12864-022-08601-w)
* 🔍 [`samui`](http://dx.doi.org/10.1017/S2633903X2300017X) 
* 👀 [`iSEE`](https://doi.org/10.12688%2Ff1000research.14966.1)

We provide the following interactive websites, organized by dataset with software labeled by emojis:

* Visium (n = 30)
  - 🔭 [spatialDLPFC_Visium_Sp09](https://libd.shinyapps.io/spatialDLPFC_Visium_Sp09): [`spatialLIBD`](https://doi.org/10.1186/s12864-022-08601-w) website showing the spatially-resolved Visium data (n = 30) with statistical results comparing the Sp09 domains.
  - 🔭 [spatialDLPFC_Visium_Sp16](https://libd.shinyapps.io/spatialDLPFC_Visium_Sp16): similar but with the Sp16 domains.
  - 🔭 [spatialDLPFC_Visium_Sp09_position](https://libd.shinyapps.io/spatialDLPFC_Visium_Sp09_position): similar to _spatialDLPFC_Visium_Sp09_ but with statistical results across the _position_ (anterior, middle, posterior) adjusting for the Sp09 domains.
  - 🔭 [spatialDLPFC_Visium_Sp09_position_noWM](https://libd.shinyapps.io/spatialDLPFC_Visium_Sp09_position_noWM): similar to _spatialDLPFC_Visium_Sp09_position_ but after dropping the `SP28D06`, `SP28D16`, `SP28D17`, `SP28D20` and `SP28D28` spots which correspond to white matter (hence the `noWM` acronym).
  - 👀 [spatialDLPFC_Visium_Sp09_pseudobulk](https://libd.shinyapps.io/spatialDLPFC_Visium_Sp09_pseudobulk): [`iSEE`](https://doi.org/10.12688%2Ff1000research.14966.1) website showing the pseudo-bulked Sp09 domains spatial data.
  - 👀 [spatialDLPFC_Visium_Sp16_pseudobulk](https://libd.shinyapps.io/spatialDLPFC_Visium_Sp16_pseudobulk): similar to _spatialDLPFC_Visium_Sp09_pseudobulk_ but with the Sp16 domains data.
  - 👀 [spatialDLPFC_Visium_Sp28_pseudobulk](https://libd.shinyapps.io/spatialDLPFC_Visium_Sp28_pseudobulk): similar to _spatialDLPFC_Visium_Sp09_pseudobulk_ but with the Sp28 domains data.
  - 🔍 [spatialDLPFC Visium on Samui](https://samuibrowser.com/from?url=data.samuibrowser.com/spatialDLPFC/&s=Br2720_ant&s=Br2720_mid&s=Br2720_post&s=Br2743_ant&s=Br2743_mid&s=Br2743_post&s=Br3942_ant&s=Br3942_mid&s=Br3942_post&s=Br6423_ant&s=Br6423_mid&s=Br6423_post&s=Br6432_ant&s=Br6432_mid&s=Br6432_post&s=Br6471_ant&s=Br6471_mid&s=Br6471_post&s=Br6522_ant&s=Br6522_mid&s=Br6522_post&s=Br8325_ant&s=Br8325_mid&s=Br8325_post&s=Br8492_ant&s=Br8492_mid&s=Br8492_post&s=Br8667_ant&s=Br8667_mid&s=Br8667_post): [`samui`](https://github.com/chaichontat/samui) website that allows to zoom in at the spot or cell level.
* snRNA-seq (n = 19)
  - 👀 [spatialDLPFC_snRNA-seq](https://libd.shinyapps.io/spatialDLPFC_snRNA-seq): [`iSEE`](https://doi.org/10.12688%2Ff1000research.14966.1) website showing the n = 19 snRNA-seq samples at single nucleus resolution.
* Visium SPG (n = 4)
  - 🔭 [spatialDLPFC_Visium_SPG](https://libd.shinyapps.io/spatialDLPFC_Visium_SPG): [`spatialLIBD`](https://doi.org/10.1186/s12864-022-08601-w) website showing the spatially-resolved data Visium SPG (n = 4).
  - 🔍 [spatialDLPFC Visium SPG on Samui](https://samuibrowser.com/from?url=data.samuibrowser.com/VisiumIF/&s=Br2720_Ant_IF&s=Br6432_Ant_IF&s=Br6522_Ant_IF&s=Br8667_Post_IF): [`samui`](https://github.com/chaichontat/samui) website that allows to zoom in at the spot or cell level.

  
### Local `spatialLIBD` apps
  
If you are interested in running the [`spatialLIBD`](https://doi.org/10.1186/s12864-022-08601-w) applications locally, you can do so thanks to the [`spatialLIBD::run_app()`](http://research.libd.org/spatialLIBD/reference/run_app.html), which you can also use with your own data as shown in our [vignette for publicly available datasets provided by 10x Genomics](http://bioconductor.org/packages/release/data/experiment/vignettes/spatialLIBD/inst/doc/TenX_data_download.html).

```{r run_app, eval = FALSE}
## Run this web application locally with:
spatialLIBD::run_app()

## You will have more control about the length of the session and memory usage.
## See http://research.libd.org/spatialLIBD/reference/run_app.html#examples
## for the full R code to run https://libd.shinyapps.io/spatialDLPFC_Visium_Sp09
## locally. See also:
## * https://github.com/LieberInstitute/spatialDLPFC/tree/main/code/deploy_app_k09
## * https://github.com/LieberInstitute/spatialDLPFC/tree/main/code/deploy_app_k09_position
## * https://github.com/LieberInstitute/spatialDLPFC/tree/main/code/deploy_app_k09_position_noWM
## * https://github.com/LieberInstitute/spatialDLPFC/tree/main/code/deploy_app_k16
## * https://github.com/LieberInstitute/spatialDLPFC/tree/main/code/analysis_IF/03_spatialLIBD_app

## You could also use spatialLIBD::run_app() to visualize your
## own data given some requirements described
## in detail in the package vignette documentation
## at http://research.libd.org/spatialLIBD/.
```

## Contact

We value public questions, as they allow other users to learn from the answers. If you have any questions, please ask them at [LieberInstitute/spatialDLPFC/issues](https://github.com/LieberInstitute/spatialDLPFC/issues) and refrain from emailing us. Thank you again for your interest in our work!


## Citing our work

Please cite this [manuscript](https://doi.org/10.1126/science.adh1938) if you use data from this project. 

> A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex
> Louise A. Huuki-Myers, Abby Spangler, Nicholas J. Eagles, Kelsey D. Montgomery, Sang Ho Kwon, Boyi Guo, Melissa Grant-Peters, Heena R. Divecha, Madhavi Tippani, Chaichontat Sriworarat, Annie B. Nguyen, Prashanthi Ravichandran, Matthew N. Tran, Arta Seyedian, PsychENCODE Consortium, Thomas M. Hyde, Joel E. Kleinman, Alexis Battle, Stephanie C. Page, Mina Ryten, Stephanie C. Hicks, Keri Martinowich, Leonardo Collado-Torres, Kristen R. Maynard
> _Science_ 384, eadh1938 (2024).; doi: https://doi.org/10.1126/science.adh1938

Below is the citation in [`BibTeX`](http://www.bibtex.org/) format.

```
@article {Huuki-Myers2024.eadh1938,
	author = {Huuki-Myers, Louise A. and Spangler, Abby and Eagles, Nicholas J. and Montgomery, Kelsey D. and Kwon, Sang Ho and Guo, Boyi and Grant-Peters, Melissa and Divecha, Heena R. and Tippani, Madhavi and Sriworarat, Chaichontat and Nguyen, Annie B. and Ravichandran, Prashanthi and Tran, Matthew N. and Seyedian, Arta and , and Hyde, Thomas M. and Kleinman, Joel E. and Battle, Alexis and Page, Stephanie C. and Ryten, Mina and Hicks, Stephanie C. and Martinowich, Keri and Collado-Torres, Leonardo and Maynard, Kristen R.},
	title = {A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex},
	year = {2024},
	doi = {10.1126/science.adh1938},
	publisher = {American Association for the Advancement of Science (AAAS)},
	URL = {https://doi.org/10.1126/science.adh1938},
	journal = {Science}
}
```

### Cite `spatialLIBD`

Below is the citation output from using `citation('spatialLIBD')` in R. Please run this yourself to check for any updates on how to cite __spatialLIBD__.

```{r 'citation_spatialLIBD'}
print(citation("spatialLIBD")[1], bibtex = TRUE)
```

Please note that the `spatialLIBD` was only made possible thanks to many other R and bioinformatics software authors, which are cited either in the vignettes and/or the paper(s) describing the package.

### Cite `Samui`

To cite [`samui`](https://github.com/chaichontat/samui) please use:

> Performant web-based interactive visualization tool for spatially-resolved transcriptomics experiments
> Chaichontat Sriworarat, Annie Nguyen, Nicholas J. Eagles, Leonardo Collado-Torres, Keri Martinowich, Kristen R. Maynard, Stephanie C. Hicks
> Biological Imaging; doi: https://doi.org/10.1017/S2633903X2300017X


Below is the citation in [`BibTeX`](http://www.bibtex.org/) format.

```
@article{sriworarat_performant_2023,
	title = {Performant web-based interactive visualization tool for spatially-resolved transcriptomics experiments},
	volume = {3},
	issn = {2633-903X},
	url = {https://www.cambridge.org/core/journals/biological-imaging/article/performant-webbased-interactive-visualization-tool-for-spatiallyresolved-transcriptomics-experiments/B66303984D10B9E5A23D3656CB8537C0},
	doi = {10.1017/S2633903X2300017X},
	language = {en},
	urldate = {2024-04-19},
	journal = {Biological Imaging},
	author = {Sriworarat, Chaichontat and Nguyen, Annie and Eagles, Nicholas J. and Collado-Torres, Leonardo and Martinowich, Keri and Maynard, Kristen R. and Hicks, Stephanie C.},
	month = jan,
	year = {2023},
	keywords = {georeferencing, interactive image viewer, multi-dimensional image, single-cell transcriptomics, spatially resolved transcriptomics, web-based browser},
	pages = {e15}
}
```

### Cite `VistoSeg`

To cite [`VistoSeg`](http://research.libd.org/VistoSeg/) please use:

> VistoSeg: {Processing utilities for high-resolution images for spatially resolved transcriptomics data.
> Madhavi Tippani, Heena R. Divecha, Joseph L. Catallini II, Sang Ho Kwon, Lukas M. Weber, Abby Spangler, Andrew E. Jaffe, Thomas M. Hyde, Joel E. Kleinman, Stephanie C. Hicks, Keri Martinowich, Leonardo Collado-Torres, Stephanie C. Page, Kristen R. Maynard
> Biological Imaging ; doi: https://doi.org/10.1017/S2633903X23000235

Below is the citation in [`BibTeX`](http://www.bibtex.org/) format.

```
@article{tippani_vistoseg_2023,
	title = {{VistoSeg}: {Processing} utilities for high-resolution images for spatially resolved transcriptomics data},
	volume = {3},
	issn = {2633-903X},
	shorttitle = {{VistoSeg}},
	url = {https://www.cambridge.org/core/journals/biological-imaging/article/vistoseg-processing-utilities-for-highresolution-images-for-spatially-resolved-transcriptomics-data/990CBC4AC069F5EDC62316919398404B},
	doi = {10.1017/S2633903X23000235},
	language = {en},
	urldate = {2024-04-19},
	journal = {Biological Imaging},
	author = {Tippani, Madhavi and Divecha, Heena R. and Catallini, Joseph L. and Kwon, Sang H. and Weber, Lukas M. and Spangler, Abby and Jaffe, Andrew E. and Hyde, Thomas M. and Kleinman, Joel E. and Hicks, Stephanie C. and Martinowich, Keri and Collado-Torres, Leonardo and Page, Stephanie C. and Maynard, Kristen R.},
	month = jan,
	year = {2023},
	keywords = {hematoxylin and eosin, immunofluorescence, MATLAB, segmentation, spatially resolved transcriptomics, Visium, Visium-Spatial Proteogenomics},
	pages = {e23}
}
```


## Data Access

We highly value open data sharing and believe that doing so accelerates science, as was the case between our [`HumanPilot`](https://doi.org/10.1038/s41593-020-00787-0) and the external [`BayesSpace`](https://doi.org/10.1038/s41587-021-00935-2) projects, documented [on this slide](https://speakerdeck.com/lcolladotor/hca-la-2022?slide=18).

### Processed Data

[`spatialLIBD`](https://doi.org/10.1186/s12864-022-08601-w) also allows you to access the data from this project as ready to use R objects. That is, a:

* [`SpatialExperiment`](https://doi.org/10.1093/bioinformatics/btac299) object for the Visium samples (n = 30)
* [`SpatialExperiment`](https://doi.org/10.1093/bioinformatics/btac299) object for the Visium SPG samples (n = 4)
* [`SingleCellExperiment`](https://www.nature.com/articles/s41592-019-0654-x) object for the snRNA-seq samples (n = 19)

You can use the [`zellkonverter`](https://bioconductor.org/packages/zellkonverter/) Bioconductor package to convert any of them into Python [`AnnData`](https://anndata.readthedocs.io/en/latest/) objects. If you browse our code, you can find examples of such conversions.

If you are unfamiliar with these tools, you might want to check the [LIBD rstats club](http://research.libd.org/rstatsclub/#.Y4hWlOzMJUM) (check and search keywords on the [schedule](https://docs.google.com/spreadsheets/d/1is8dZSd0FZ9Qi1Zvq1uRhm-P1McnJRd_zxdAfCRoMfA/edit?usp=sharing)) videos and resources.

#### Installing spatialLIBD

Get the latest stable `R` release from [CRAN](http://cran.r-project.org/). Then install `spatialLIBD` from [Bioconductor](http://bioconductor.org/) with the following code:

```{r 'install', eval = FALSE}
## Install BiocManager in order to install Bioconductor packages properly
if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}

## Check that you have a valid R/Bioconductor installation
BiocManager::valid()

## Now install spatialLIBD from Bioconductor
## (this version has been tested on macOS, winOS, linux)
BiocManager::install("spatialLIBD")

## If you need the development version from GitHub you can use the following:
# BiocManager::install("LieberInstitute/spatialLIBD")
## Note that this version might include changes that have not been tested
## properly on all operating systems.
```

### R objects

Using `spatialLIBD` you can access the spatialDLPFC
transcriptomics data from the 10x Genomics Visium platform. For example,
this is the code you can use to access the spatially-resolved data. For more
details, check the help file for `fetch_data()`.

```{r 'access_data', message=FALSE, fig.height = 8, fig.width = 9, eval = FALSE}
## Check that you have a recent version of spatialLIBD installed
stopifnot(packageVersion("spatialLIBD") >= "1.11.6")

## Download the spot-level data
spe <- spatialLIBD::fetch_data(type = "spatialDLPFC_Visium")

## This is a SpatialExperiment object
spe
#> class: SpatialExperiment 
#> dim: 28916 113927 
#> metadata(1): BayesSpace.data
#> assays(2): counts logcounts
#> rownames(28916): ENSG00000243485 ENSG00000238009 ... ENSG00000278817 ENSG00000277196
#> rowData names(7): source type ... gene_type gene_search
#> colnames(113927): AAACAACGAATAGTTC-1 AAACAAGTATCTCCCA-1 ... TTGTTTGTATTACACG-1 TTGTTTGTGTAAATTC-1
#> colData names(155): age array_col ... VistoSeg_proportion wrinkle_type
#> reducedDimNames(8): 10x_pca 10x_tsne ... HARMONY UMAP.HARMONY
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
#> imgData names(4): sample_id image_id data scaleFactor

## Note the memory size
lobstr::obj_size(spe)
#> 6.97 GB

## Set the cluster colors
colors_BayesSpace <- Polychrome::palette36.colors(28)
names(colors_BayesSpace) <- seq_len(28)

## Remake the logo image with histology information
p09 <- spatialLIBD::vis_clus(
    spe = spe,
    clustervar = "BayesSpace_harmony_09",
    sampleid = "Br6522_ant",
    colors = colors_BayesSpace,
    ... = " spatialDLPFC Human Brain\nSp09 domains -- made with spatialLIBD"
)
p09
```

<a href="https://libd.shinyapps.io/spatialDLPFC_Visium_Sp09"><img src="http://research.libd.org/spatialDLPFC/img/Br6255_ant_Sp09.png" width="800px" align="center" /></a>

```{r "p16", fig.height = 8, fig.width = 9, eval = FALSE}
## Repeat but for Sp16
p16 <- spatialLIBD::vis_clus(
    spe = spe,
    clustervar = "BayesSpace_harmony_16",
    sampleid = "Br6522_ant",
    colors = colors_BayesSpace,
    ... = " spatialDLPFC Human Brain\nSp16 domains -- made with spatialLIBD"
)
p16
```

```{r "save_plots", include = FALSE, eval = FALSE}
ggplot2::ggsave(here::here("img", "Br6255_ant_Sp09.png"),
    p09,
    width = 8,
    height = 7
)
ggplot2::ggsave(here::here("img", "Br6255_ant_Sp16.png"),
    p16,
    width = 8,
    height = 7
)
```

<a href="https://libd.shinyapps.io/spatialDLPFC_Visium_Sp16"><img src="http://research.libd.org/spatialDLPFC/img/Br6255_ant_Sp16.png" width="800px" align="center" /></a>


### Raw data

The source data described in this manuscript are available from the [National Institute of Mental Health (NIMH) Data Archive](https://nda.nih.gov/) under [NDA Study 2619](https://nda.nih.gov/study.html?id=2619); doi: [10.15154/7893-6778](https://doi.org/10.15154/7893-6778).

You can also access all the raw data through [Globus](http://research.libd.org/globus/) (`jhpce#spatialDLPFC` and `jhpce#DLPFC_snRNAseq`). This includes all the input FASTQ files as well as the outputs from tools such as [`SpaceRanger`](https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/what-is-space-ranger) or [`CellRanger`](https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger). The files are _mostly_ organized following the [LieberInstitute/template_project](https://github.com/LieberInstitute/template_project) project structure.


## Internal

* JHPCE locations:
  - `/dcs04/lieber/lcolladotor/spatialDLPFC_LIBD4035/spatialDLPFC`
  - `/dcs04/lieber/lcolladotor/deconvolution_LIBD4030/DLPFC_snRNAseq`
* Slack channel: [`libd_dlpfc_spatial`](https://jhu-genomics.slack.com/archives/C01EA7VDJNT).

### Files: [`spatialDLPFC`](https://github.com/LieberInstitute/spatialDLPFC)

- `code`: R, python, and shell scripts for running various analyses.
  * `spot_deconvo`: cell-type deconvolution within Visium spots, enabled by tools like [`tangram`](https://doi.org/10.1038/s41592-021-01264-7), [`cell2location`](https://doi.org/10.1038/s41587-021-01139-4), [`cellpose`](https://cellpose.readthedocs.io/en/latest/), and [`SPOTlight`](https://doi.org/10.1093/nar/gkab043)
  * `spython`: older legacy testing scripts mostly replaced by `spot_deconvo`
- `plots`: plots generated by RMarkdown or R analysis scripts in `.pdf` or `.png` format
- `processed-data`
  * `images_spatialLIBD`: images used for running `SpaceRanger`
  * `NextSeq`: `SpaceRanger` output files
  * `rdata`: R objects
- `raw-data`
  * `FASTQ`: FASTQ files from `NextSeq` runs.
  * `FASTQ_renamed`: renamed symbolic links to the original FASTQs, with consistent nomenclature
  * `Images`: raw images from the scanner in `.tif` format and around 3 GB per sample.
  * `images_raw_align_json`
  * `psychENCODE`: external data from PsychENCODE (doi: [10.7303/syn2787333](https://doi.org/10.7303/syn2787333)).
  * `sample_info`: spreadsheet with information about samples (sample ID, sample name, slide serial number, capture area ID)
  
This GitHub repository is organized along the [_R/Bioconductor-powered Team Data Science_ group guidelines](https://lcolladotor.github.io/bioc_team_ds/organizing-your-work.html#.Yaf9fPHMIdk).  It aims to follow the [LieberInstitute/template_project](https://github.com/LieberInstitute/template_project) structure, though most of the `code/analysis` output is saved at `processed-data/rdata/spe` directory unlike what's specified in the template structure. This is due to historical reasons.

### Files: [`DLPFC_snRNAseq`](https://github.com/LieberInstitute/DLPFC_snRNAseq)

- `code`: R scripts for running various analyses.
- `plots`: plots generated by RMarkdown or R analysis scripts in `.pdf` or `.png` format
- `processed-data`
  * `cellranger`: `CellRanger` output files
- `raw-data`
  * `FASTQ`: FASTQ files.
  * `sample_info`: spreadsheet with information about samples (sample ID, sample name)

This GitHub repository is organized along the [_R/Bioconductor-powered Team Data Science_ group guidelines](https://lcolladotor.github.io/bioc_team_ds/organizing-your-work.html#.Yaf9fPHMIdk). It aims to follow the [LieberInstitute/template_project](https://github.com/LieberInstitute/template_project) structure.

### Other related files

- Reference transcriptome from 10x Genomics: `/dcs04/lieber/lcolladotor/annotationFiles_LIBD001/10x/refdata-gex-GRCh38-2020-A/`