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A flexible model for correlated count data, with application to analysis of gene expression differences in multi-condition experiments

ABSTRACT: Detecting differences in gene expression is an important part of RNA sequencing (RNA-seq) experiments, and many statistical methods have been developed for this aim. Most differential expression analyses focus on comparing expression between two groups (e.g., treatment vs. control). But there is increasing interest in multi-condition differential expression analyses in which expression is measured in many conditions, and the aim is to accurately detect and estimate expression differences in all conditions. We show that directly modeling the RNA-seq counts in all conditions simultaneously, while also inferring how expression differences are shared across conditions, leads to greatly improved estimates of expression differences, particularly when the power to detect expression differences is low in the individual conditions (e.g., due to small sample sizes). We illustrate the potential of this new multi-condition differential expression analysis in analyzing data from a single-cell experiment for studying the effects of cytokine stimulation on gene expression. We call our new method “Poisson multivariate adaptive shrinkage,” and it is implemented in the R package poisson.mash.alpha, available at https://github.com/stephenslab/poisson.mash.alpha.

ORGANISM(S): Mus musculus

PROVIDER: GSE214633 | GEO | 2022/10/05

REPOSITORIES: GEO

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Project description:Similar to other droplet-based single cell assays, single nucleus ATAC-seq (snATAC-seq) data harbor multiplets that confound downstream analyses. Detecting multiplets in snATAC-seq data is particularly challenging due to data sparsity and limited dynamic range (0 reads: closed chromatin, 1: open on in one parental chromosome allele, 2: open on in both alleles chromosomes). Yet, these unique data features offer an opportunity to identify multiplets. ATAC-DoubletDetector (https://ucarlab.github.io/ATAC-DoubletDetector/) AMULET (Atac MULtiplet Estimation Tool) exploits these unique features to detect multiplets by studying enumerates the number of regions with >2 uniquely aligned reads across the genome to effectively detect multiplets - an effective alternative to methods based on artificially-generated multiplets. We evaluated the method by generating snATAC-seq data (e.g., state-of-the-art ArchR). For benchmarking we generated snATAC-seq data and generated data fromeasured the efficacy of AMULET inm in two primary human tissues: peripheral human blood mononuclear cells (PBMCs) and pancreatic islet samples. AMULET detects had high multiplets with an estimated precision (estimated via donor-based multiplexing) and high recall (estimated via simulated doublets) compared to alternatives 0.57 precision and achieves 0.85 recall. When and was the most effective when a certain read depth is achieved (a certain read depth per nucleus is achieved samples are sequenced deeply (e.g., median read count per nucleus >20K25K) reads per nucleus in PBMCs), ATAC-DoubletDetector captured 85% of simulated doublets (i.e., recall), significantly outperforming ArchR (24%). For lower read depth, ATAC-DoubletDetector and ArchR produced complementary results. Moreover, ATAC-DoubletDetector was equally effective in identifying homotypic multiplets (i.e., multiplets from the same cell type), which are missed by simulation-based methods. Cell-specific marker peaks enabled accurate (85%) tracing of cellular origins of snATAC-seq multiplets. Accordingly, more abundant cells within a tissue are more likely to form multiplets and the majority of multiplets are homotypic. ATAC-DoubletDetector is a fast and effective multiplet detection/annotation tool for improved single cell epigenomic data analyses across diverse biological systems and conditions.

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A flexible model for correlated count data, with application to analysis of gene expression differences in multi-condition experiments

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