Introduction

tiotemporal transcriptomics sequencing can reveal cellular heterogeneity at a specified resolution. Differential expression analysis is used to identify genes (Differential Expression Gene, DEG) that show significant changes in expression between different cell populations or conditions. It provides a geneset basis for various downstream analyses, including gene set enrichment analysis, protein interaction network analysis and public bulk database validation.

According to data characteristics and analysis objectives, commonly used differential analysis methods are mainly divided into two categories: single-cell level methods and pseudobulk methods.

• Single-cell level methods (t-test, Wilcoxon, MAST): Suitable for scenarios without biological replicates, focusing on differences between cell subpopulations, or when sample size is limited.

• Pseudobulk methods (DESeq2, edgeR): When there are multiple biological replicates, the expression levels of cells within the same group can be aggregated (e.g., summed or averaged) to form a "pseudobulk" expression matrix. Bulk RNA-seq differential analysis methods (DESeq2, edgeR) can then be used, which more accurately reflect biological variation and reduce the false positive rate.

Module overview

Module	Slice number	DEG plan	which de_method can be used
DEG	single slice	1-vs-1	t-test, wilcoxcon, MAST
DEG	single slice	1-vs-n	t-test, wilcoxcon, MAST
DEG	multiple slices without replicates	1-vs-1	t-test, wilcoxcon, MAST
DEG	multiple slices without replicates	1-vs-n	t-test, wilcoxcon, MAST
DEG	multiple slices with replicates	1-vs-1	DESeq2, edgeR
DEG	multiple slices with replicates	1-vs-n	DESeq2

Reference

• Finak, G., McDavid, A., Yajima, M., Deng, J., Gersuk, V., Shalek, A. K., ... & Gottardo, R. (2015). MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome biology, 16(1), 278.

• Hao, Y., Stuart, T., Kowalski, M. H., Choudhary, S., Hoffman, P., Hartman, A., ... & Satija, R. (2024). Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nature biotechnology, 42(2), 293-304.

• Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. bioinformatics, 26(1), 139-140.

• Wolf, F. A., Angerer, P., & Theis, F. J. (2018). SCANPY: large-scale single-cell gene expression data analysis. Genome biology, 19(1), 15.