Fig. 4: Case study II: application of dsb to tri-modal TEA-seq data unmasks a MAIT cell population obscured by noise in CLR normalization.
From: Normalizing and denoising protein expression data from droplet-based single cell profiling
Analysis of TEA-seq (transcriptome, epitopes, and accessibility) tri-modal single cell assay data. a dsb normalization of protein data from TEA-seq showing the distribution of CD4 and CD14 with the same 3.5 threshold used throughout the study. b UMAP plot of single cells and clusters derived by WNN joint mRNA–protein clustering with protein data normalized using dsb. c Bi-axial distribution of the alpha beta and va7.2 T cell receptor (TCR) proteins in cluster 14 cells normalized by dsb and d the same cells’ CLR normalized values. e Similar to Fig. 3g but here for cluster 14 from (b) using dsb or f CLR normalized values (y-axis); in both plots Pearson correlation coefficients and p values (two sided) are shown between normalized values (y axis) and values in empty droplets (x axis). g Differential expression analysis (ROC test) of genes in cluster 14 vs. other clusters. h Gene set enrichment of a MAIT cell signature constructed from FACS-sorted TCR-va7.2+/MAIT cells compared to other T cells (RNA-seq data from Park et al. 2019) with genes ranked by log2 fold change in cluster 14 cells vs. other cells as in (g).
