CoCoA-diff: counterfactual inference for single-cell gene expression analysis.
Finding a causal gene is a fundamental problem in genomic medicine. We present a causal inference framework, CoCoA-diff, that prioritizes disease genes by adjusting confounders without prior knowledge of control variables in single-cell RNA-seq data. We demonstrate that our method substantially improves statistical power in simulations and real-world data analysis of 70k brain cells collected for dissecting Alzheimer's disease. We identify 215 differentially regulated causal genes in various cell types, including highly relevant genes with a proper cell type context. Genes found in different types enrich distinctive pathways, implicating the importance of cell types in understanding multifaceted disease mechanisms.
1. scREAD: A Single-Cell RNA-Seq Database for Alzheimer's Disease.
2. Optimal marker gene selection for cell type discrimination in single cell analyses.
3. scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses.
4. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics.
5. Deep learning of gene relationships from single cell time-course expression data.
1. Silencing of odorant receptor gene expression by G protein βγ signaling ensures the expression of one odorant receptor per olfactory sensory neuron
2. Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling [10x]
3. Novel Alzheimer risk genes determine the microglia response to amyloid-β but not to TAU pathology
4. Discovery and characterization of variance QTLs in human induced pluripotent stem cells
5. Single-cell RNA-sequencing of CD14+ monocyte differentiation with M-CSF stimulus