Last updated: 2023-06-06

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Knit directory: DEanalysis/

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Introduction

Motivation

Differential expression (DE) analysis in single-cell transcriptomics reveals cell-type-specific responses. Recent studies have raised concerns about current methods used to identify differentially expressed genes in this context.

scRNA sequencing provides the absolute abundances of RNA molecules in single cells, but normalization - a pre-processing step inherited from the bulk RNA-seq era - reduces this information and returns data as relative abundances, which may mask important differences among cell types
The majority of single-cell DE analysis methods are susceptible to generating false discoveries. This is mainly due to the lack of accounting for variations between biological replicates, commonly referred to as “donor effects”.
- Batch effects are often estimated from leading principal components, representing a consensus from most genes.
- Pseudo-bulk analysis ignores within-sample heterogeneity by treating donor effects as a fixed effect and assuming that each cell from the same donor is equally affected.
Clustering and DE analysis are different problems. The current commonly workflow works well in clustering, but cannot guarantee success in downstream analysis.
Excessive zeros are usually considered as “drop-outs”, while they are actually informative in cell-type heterogeneity. Ignoring zeros in single-cell gene expression data discards valuable information for any analysis.

We provide a generalized linear mixed model framework (GLMM) to detect differentially expressed genes (DEGs) between two given cell types. The model takes donor-specific variations as random effects, and uses raw UMI counts to prevent biases in DE analysis.

GLMM for DE analysis

Poisson GLMM

For each count $X_{cgk}$ sampled from cell $c$ , donor $k$ , and gene $g$ ,

$\begin{aligned} X_{cgk}|\lambda_{cgk} & \sim Poisson(\lambda_{cgk})\\ \log \lambda_{cgk} & = \mu_g + X_c{\beta_g} + \epsilon_{gk}\\ \end{aligned}$ where $X_c$ is the indicator for different cell types, and $\epsilon_{gk}\sim N(0,\sigma_{g}^2)$ represents the random effects for donor $k$ . Our goal is to test $H_0: \beta_g = 0$ . Here $e^{\beta_g}$ represents the fold change of gene $g$ between two cell types.

Binomial GLMM

$\begin{aligned} \mathbb{1}_{X_{cgk}=0}|p_{cgk} & \sim Bernoulli(p_{cgk})\\ \log \frac{p_{cgk}}{1-p_{cgk}} & = \mu_g + X_c\beta_{g} + \epsilon_{gk}\\ \end{aligned}$ where $X_c$ is the indicator for different cell types, and $\epsilon_{gk}\sim N(0,\sigma_{g}^2)$ represents the random effects for donor $k$ . Our goal is to test $H_0: \beta_g = 0$ .

new criteria

Application

Data summary

Data

Analysis and Methods comparison

Methods details
group12_13
group2_19
group8_17&2_19

DE analysis on scRNA data with poisson GLMM