scMC: Integrating and comparing multiple single cell genomic datasets

Capabilities

• scMC is an R toolkit for integrating and comparing multiple single cell genomic datasets from single cell RNA-seq and ATAC-seq experiments across different conditions, time points and tissues.
• scMC exhibits superior performance in detecting context-shared and -specific biological signals, particularly noticeable for the datasets with imbalanced cell population compositions across interrelated biological conditions.
• scMC learns a shared reduced dimensional embedding of cells that retains the biological variation while removing the technical variation. This shared embedding can enhance a variety of single cell analysis tasks, such as low-dimensional visualization, cell clustering and pseudotemporal trajectory inference.

Installation

scMC R package can be easily installed from Github using devtools:

devtools::install_github("jinworks/scMC_SeuratWrapper")

Installation of other dependencies

• Install Leiden python pacakge for identifying cell clusters: pip install leidenalg. Please check here if you encounter any issue.

Tutorials

Please check the tutorial directory of the repo.

• R Markdown: Demo of scMC Seurat Wrapper (in-house, SJ2020)

• R code: Demo of scMC Seurat Wrapper (in-house, SJ2020)

Other great tools for single-cell data processing and downstream analysis

R package

1. Seurat (https://satijalab.org/seurat/articles/get_started_v5_new)
2. SCP (https://github.com/zhanghao-njmu/SCP) ( Very nice visualization; Pipelines embedded with multiple integration methods for scRNA-seq or scATAC-seq data, including Uncorrected, Seurat, scVI, MNN, fastMNN, Harmony, Scanorama, BBKNN, CSS, LIGER, Conos, ComBat.; Multiple single-cell downstream analyses such as identification of differential features, enrichment analysis, GSEA analysis, identification of dynamic features, PAGA, RNA velocity, Palantir, Monocle2, Monocle3, etc.)