Dataset Information

Differential gene expression analysis for multi-subject single cell RNA sequencing studies with aggregateBioVar.

ABSTRACT:

Motivation

Single cell RNA sequencing (scRNA-seq) studies provide more granular biological information than bulk RNA sequencing, but bulk RNA sequencing has remained popular due to relatively lower costs per sample, which has allowed investigators to process more biological replicates and design more powerful studies. As scRNA-seq costs have decreased, collecting data from more than one biological replicate has become more feasible, but careful modeling of different layers of biological variation remains challenging for many users. Here, we propose a statistical model for scRNA-seq gene counts, describe a simple method for estimating model parameters, and show that failing to account for additional biological variation in scRNA-seq studies can inflate false discovery rates of statistical tests.

Results

In a simulation study, we show that when the gene expression distribution of a population of cells varies between subjects, a naïve approach to differential expression analysis will inflate the false discovery rate. We also compare multiple differential expression testing methods on scRNA-seq data sets from human samples and from animal models. These analyses suggest that a naïve approach to differential expression testing could lead to many false discoveries; in contrast, an approach based on pseudobulk counts has better false discovery rate control.

Availability

A software package, aggregateBioVar, is freely available on Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/aggregateBioVar.html) to accommodate compatibility with upstream and downstream methods in scRNA-seq data analysis pipelines.

Supplementary information

Raw gene-by-cell count matrices for pig scRNA-seq data are available as GEO accession GSE150211. Supplementary data are available at Bioinformatics online.

SUBMITTER: Thurman AL

PROVIDER: S-EPMC8504643 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:MotivationWith the wide availability of single-cell RNA-seq (scRNA-seq) technology, population-scale scRNA-seq datasets across multiple individuals and time points are emerging. While the initial investigations of these datasets tend to focus on standard analysis of clustering and differential expression, leveraging the power of scRNA-seq data at the personalized dynamic gene co-expression network level has the potential to unlock subject and/or time-specific network-level variation, which is critical for understanding phenotypic differences. Community detection from co-expression networks of multiple time points or conditions has been well-studied; however, none of the existing settings included networks from multiple subjects and multiple time points simultaneously. To address this, we develop Multi-subject Dynamic Community Detection (MuDCoD) for multi-subject community detection in personalized dynamic gene networks from scRNA-seq. MuDCoD builds on the spectral clustering framework and promotes information sharing among the networks of the subjects as well as networks at different time points. It clusters genes in the personalized dynamic gene networks and reveals gene communities that are variable or shared not only across time but also among subjects.ResultsEvaluation and benchmarking of MuDCoD against existing approaches reveal that MuDCoD effectively leverages apparent shared signals among networks of the subjects at individual time points, and performs robustly when there is no or little information sharing among the networks. Applications to population-scale scRNA-seq datasets of human-induced pluripotent stem cells during dopaminergic neuron differentiation and CD4+ T cell activation indicate that MuDCoD enables robust inference for identifying time-varying personalized gene modules. Our results illustrate how personalized dynamic community detection can aid in the exploration of subject-specific biological processes that vary across time.Availability and implementationMuDCoD is publicly available at https://github.com/bo1929/MuDCoD as a Python package. Implementation includes simulation and real-data experiments together with extensive documentation.

Project description:BackgroundThe analysis of single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the detection of differentially expressed (DE) genes. scRNAseq data, however, are highly heterogeneous and have a large number of zero counts, which introduces challenges in detecting DE genes. Addressing these challenges requires employing new approaches beyond the conventional ones, which are based on a nonzero difference in average expression. Several methods have been developed for differential gene expression analysis of scRNAseq data. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to evaluate and compare the performance of differential gene expression analysis methods for scRNAseq data.ResultsIn this study, we conducted a comprehensive evaluation of the performance of eleven differential gene expression analysis software tools, which are designed for scRNAseq data or can be applied to them. We used simulated and real data to evaluate the accuracy and precision of detection. Using simulated data, we investigated the effect of sample size on the detection accuracy of the tools. Using real data, we examined the agreement among the tools in identifying DE genes, the run time of the tools, and the biological relevance of the detected DE genes.ConclusionsIn general, agreement among the tools in calling DE genes is not high. There is a trade-off between true-positive rates and the precision of calling DE genes. Methods with higher true positive rates tend to show low precision due to their introducing false positives, whereas methods with high precision show low true positive rates due to identifying few DE genes. We observed that current methods designed for scRNAseq data do not tend to show better performance compared to methods designed for bulk RNAseq data. Data multimodality and abundance of zero read counts are the main characteristics of scRNAseq data, which play important roles in the performance of differential gene expression analysis methods and need to be considered in terms of the development of new methods.