Sydney Bioformatics Webinar: Ho and Chau -- A Statistical Method to Identify Cell Types with Differential Abundance in Single Cell RNA-seq Data

Advances in single cell RNA-seq (scRNA-seq) technology has enabled identification and
quantification of cell type composition of complex tissues across multiple samples.  It
is increasingly common to ask questions such as ’Is the proportion of this cell type
significantly altered between conditions?’ In other words, we want to perform
differential proportion analysis on the cell count matrix based on scRNA-seq data to
identify cell types that have a significant increase or decrease in proportion.  Our
initial work showed that a simple statistical test such as Fisher’s exact test produces
high false positive rate, suggesting that there are additional variability beyond random
sampling.  We reason that a possible source of variation is cell-type
mis-classification, which can be estimated by cell-to-cell similarity matrix computed
during the clustering process.  We implemented our idea in an R package and tested it
using simulation and real scRNA-seq data sets.  In this seminar we will introduce our
method and share our initial evaluation results.