muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data

Abstract

Single-cell RNA sequencing (scRNA-seq) has become an empowering technology to profile the transcriptomes of individual cells on a large scale. Early analyses of differential expression have aimed at identifying differences between subpopulations to identify subpopulation markers. More generally, such methods compare expression levels across sets of cells, thus leading to cross-condition analyses. Given the emergence of replicated multi-condition scRNA-seq datasets, an area of increasing focus is making sample-level inferences, termed here as differential state analysis; however, it is not clear which statistical framework best handles this situation. Here, we surveyed methods to perform cross-condition differential state analyses, including cell-level mixed models and methods based on aggregated pseudobulk data. To evaluate method performance, we developed a flexible simulation that mimics multi-sample scRNA-seq data. We analyzed scRNA-seq data from mouse cortex cells to uncover subpopulation-specific responses to lipopolysaccharide treatment, and provide robust tools for multi-condition analysis within the muscat R package.

Whole Animal Multiplexed Single-Cell RNA-Seq Reveals Plasticity ofClytiaMedusa Cell Types

“…We used a likelihood ratio test (LRT) with negative-binomial based models of each gene's expression in DESeq2 68,69 , with the reduced model not including the condition label (fed or starved) and single-cells treated as individual replicates. There are other approaches that can be used 70,71 ; we found that this method yielded many biologically relevant genes, which we could validate via the in situ experiments. All nonzero-expression genes were used for analysis.…”

Confronting false discoveries in single-cell differential expression

“…Ground-truth datasets. Previous benchmarks of DE analysis methods for single-cell transcriptomics have relied heavily on simulated data, or else have compared the results of different methods in scenarios where no ground truth was available 9,18 . We reasoned that the best possible approximation to the biological ground truth in a scRNA-seq experiment would consist of a matched bulk RNA-seq dataset in the same purified cell type, exposed to the same perturbation under identical experimental conditions, and sequenced in the same laboratory.…”

“…Having established that the performance of DE methods is contingent on their ability to account for biological replicates, we asked why mixed models failed to match the performance of pseudobulk methods. In addition to the linear mixed model described above, we implemented generalized linear mixed models (GLMMs) based on the negative binomial or Poisson distributions, adapting implementations provided in the 'muscat' R package 9 . For each of these models, we evaluated the impact of incorporating the library size factors as an offset term, and compared the Wald test of model coefficients to a likelihood ratio test against a reduced model, yielding a total of four GLMMs from each distribution.…”

Role of hypothalamic MAPK/ERK signaling in diabetes remission induced by the central action of fibroblast growth factor 1

“…Using the Muscat package() pre-defined clusters were split by sample requiring at least 10 cells per sample-cluster combination 34 . Raw counts were summed across each gene-sample-cluster combination to generate a single column per sample-cluster.…”