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

To make it easy to run scMC in most common scRNA-seq data analysis pipelines, scMC is now implemented within Seurat V3 workflow. Please first install Seurat R pacakge (>= 3.2.1) via install.packages('Seurat'). For the standalone implementent of scMC and reproducing results from manuscript, please check out previous release.

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

devtools::install_github("amsszlh/scMC")

Installation of other dependencies

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

Tutorials

The implementent of scMC is now seamlessly compatible with the workflow of Seurat V3 package. The runtime is also significantly reduced now.

Please check out the full workflow

• Demo of scMC workflow

We also wrote a Seurat Wrapper function RunscMC to run scMC directly on Seurat objects. You can run scMC within your Seurat V3 workflow. You'll only need to make two changes to your code.

• Run scMC with the RunscMC() function

• In downstream analyses, use the scMC embeddings instead of PCA.

For example, run scMC and then UMAP in two lines.

combined <- RunscMC(seuratObj.list)
combined <- RunUMAP(combined, reduction = "scMC")

For details, please check out

• Demo of scMC Seurat Wrapper

Here we also showcase scMC’s superior performance in detecting context-shared and -specific biological signals by applying it to a mouse skin scRNA-seq dataset and comparing it with other methods (Seurat, Harmony and LIGER)

• Benchmarking of scMC against other methods

Help

If you have any problems, comments or suggestions, please contact us at Lihua Zhang (lihuaz1@uci.edu).