The human immune system is governed by a complex interplay of molecules encoded by highly diverse genetic loci. Immune genes such as B and T cell receptors, human leukocyte antigens (HLAs), and killer Ig-like receptors (KIRs) exhibit remarkable allelic diversity across populations. However, conventional single-cell analysis methods often overlook this diversity, leading to erroneous quantification of immune mediators and compromised inter-donor comparability.
To address these challenges and unlock deeper insights from single-cell studies, we present a comprehensive workflow comprising two software and one data packages (Figure 1):
-
scIGD (single-cell ImmunoGenomic Diversity): A Snakemake workflow designed to automate allele-typing processes for immune genes, with a focus on key targets like HLAs. In addition, it facilitates allele-specific quantification from scRNA-seq data using donor-specific references.
-
SingleCellAlleleExperiment: This R/Bioconductor package maximizes the analytical potential of results obtained from scIGD. It offers a versatile multi-layer data structure, allowing representation of immune genes at various levels, from alleles to genes to functionally similar gene groups. This enables comprehensive analysis across different layers of immunologically-relevant annotation.
-
scaeData: An R/ExperimentHub data package housing three 10x datasets processed by scIGD. These datasets can be utilized to perform exploratory and downstream analysis using the novel SingleCellAlleleExperiment data structure.
Figure 1: Overview of the scIGD workflow for unraveling immunogenomic diversity in single-cell data, highlighting the integration of the SingleCellAlleleExperiment package for comprehensive data analysis.
Preliminary findings demonstrate accurate quantification of different HLA allele groups in (amplicon-based and whole-transcriptome-based) scRNA-seq datasets from diverse sources, including cancer patients and human atlas samples. This not only enhances the comparability of immune profiles across donors but also sheds light on population-specific susceptibilities to infections. Our work lays the groundwork for precise immunological analysis of multi-omics data, particularly in elucidating allele-specific interactions.
I intend to showcase all three tools, emphasizing the utilization of SingleCellAlleleExperiment and its functionalities on one of the example datasets available in scaeData, for exploratory and downstream analysis across the three layers offered by the data structure.
- Basic knowledge of R syntax
- Familiarity with single-cell transcriptomic analyses, such as OSCA
- Familiarity with SingleCellExperiment and/or SummarizedExperiment
The format is a 45 minute session consisting of hands-on demos, exercises and Q&A.
Questions are welcome at any time. Contact details are listed at the bottom of the page.
SingleCellAlleleExperiment: https://bioconductor.org/packages/SingleCellAlleleExperimentscaeData: https://bioconductor.org/packages/scaeData
| Activity | Time |
|---|---|
| Introduction | 10m |
| Overview of scIGD | 5m |
| Overview of scaeData | 5m |
| SingleCellAlleleExperiment + data analysis | 25m |
- Learn the constraints inherent in traditional single-cell analysis techniques and the importance of HLA allele-specific quantification
- Understand the difference between
SingleCellExperimentandSingleCellAlleleExperiment - Demonstrate how these tools can be applied and adopted to enhance existing workflows
- Perform allele typing to identify HLA alleles from genetic sequences in scRNA-seq data
- Achieve allele-specific quantification using donor-specific references
- Navigate through distinct layers within the data object for diverse representations of HLA genes
- Conduct exploratory data and downstream analyses across any of the layers offered by the data object
- Ahmad Al Ajami <alajami at med.uni-frankfurt.de>