RCSL is an R toolkit for single-cell clustering and trajectory analysis using single-cell RNA-seq data.
This package can be installed through devtools in R:
$ R > library("devtools") > devtools::install_github("QinglinMei/RCSL")
Now RCSL can be loaded in R:
The input of RCSL is a normalized data matrix with columns being cells and rows being genes in log(CPM+1), log(RPKM+1), log(TPM+1) or log(FPKM+1) format; or a data file in RDS format.
We provide an example script to run RCSL in demo_RCSL.R.
The nine functions of RCSL can also be run independently.
Function | Description
GenesFilter | Perform genes filtering.
SimS | Calculate the initial similarity matrix S.
NeigRepresent | Calculate the neighbor representation of cells.
EstClusters | Estimate the optimal number of clusters C.
BDSM | Learn the block-diagonal matrix B.
PlotMST | Construct MST based on clustering results from RCSL.
PlotPseudoTime | Infer the pseudo-temporal ordering of cells.
getLineage | Infer the lineage based on the clustering results and the starting cell.
PlotTrajectory | Plot the developmental trajectory based on the clustering results and the starting cell.
> library(RCSL) > library(SingleCellExperiment) > library(ggplot2) > library(igraph)
Load Yan dataset:
> origData <- yan > data <- logcounts(origData+1) > label <- origData$cell_type1 > DataName <- "Yan"
Generating clustering result:
> res_RCSL <- RCSL(data)
Calculating Adjusted Rand Index:
> ARI_RCSL <- igraph::compare(res_RCSL$y, label, method = "adjusted.rand")
> label <- origData$cell_type1 > res_TrajecAnalysis <- TrajectoryAnalysis(res_RCSL$gfData, res_RCSL$drData, res_RCSL$S, clustRes = res_RCSL$y, TrueLabel = label, startPoint = 1, dataName = DataName)
Display the plot of constructed MST:
Display the plot of the pseudo-temporal ordering
Display the plot of the inferred developmental trajectory
A vignette in R Notebook format is available here
1) The RCSL package requires three extra packages: namely the SingleCellExperiment package (see https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) to read the SingleCellExperiment object, the igraph package (see https://igraph.org/) to find the strongest connected components and the ggplot2 package (see https://cran.r-project.org/web/packages/ggplot2/index.html) to plot the developmental trajectory and MST. 2) The data for the demonstration purpose in the directory Data was from https://hemberg-lab.github.io/scRNA.seq.datasets/. This data is stored in both RDS and text formats.
Please feel free to contact us if you have problems running our tool at email@example.com.
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