April 2023 bioRxiv paper

New analysis focus on new datasets including Robinson2023, Pelka2021 and Zhang2021

bioRxiv May 2021 paper

This release supports our bioRxiv May 2021 paper "CSI-Microbes: Identifying cell-type specific intracellular microbes from single-cell RNA-seq data".

Release v0.1.0

This release provides a standardized approach for normalizing microbial reads from scRNA-seq datasets to identify cell-type specific intracellular microbes. First, microbial read data (in the PathSeq format) are downloaded (this is done manually right now). Next, this data is parsed into a cell-by-microbe matrix for a given microbial kingdom (Bacteria, Archaea, Viruses, Fungi) and taxonomic level. Next, this cell-by-microbe matrix is further divided by any applicable division (patient, timing, etc.). Finally, we test for differential microbial abundance across different cell-types. For bacteria and Smart-seq2-like data, we identify differential microbial abundance using the Wilcoxon rank-sum test. For bacteria or viruses on 10x data, we identify differential microbial abundance using the binomial test.

Included in this release is the tested code to generate results for our five validation datasets.