Rank Constrained Similarity Learning (RCSL)

RCSL is an R toolkit for single-cell clustering and trajectory analysis using single-cell RNA-seq data.

Installation

This package can be insatlled through devtools in R:

$ R
> library("devtools")
> devtools::install_github("QinglinMei/RCSL",build_vignettes = T)

Now RCSL can be loaded in R:

> library(RCSL)

Input

The input to 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.

Usage

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-diognal 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.

Example:

Load packages:

> library(RCSL)
> library(SingleCellExperiment)
> library(ggplot2)
> library(igraph)

Load Goolam dataset:

> origData <- readRDS("./Data/Goolam.rds")
> data <- logcounts(origData)
> label <- origData$cell_type1
> DataName <- "Goolam"

Generating clustering result:

> res_RCSL <- RCSL(data)

Calculating Adjusted Rand Index:

> ARI_RCSL <- igraph::compare(res_RCSL$y, label, method = "adjusted.rand")

Trajectory analysis:

> 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:

> res_TrajecAnalysis$MSTPlot

Display the plot of the pseudo-temporal ordering

> res_TrajecAnalysis$PseudoTimePlot

Display the plot of the inferred developmental trajectory

> res_TrajecAnalysis$TrajectoryPlot

A vignette in R Notebook format is available here

Required annotations for RCSL

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 stronggest 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 dataset for the demonstration purpose in the directory Data was from https://hemberg-lab.github.io/scRNA.seq.datasets/. This dataset is stored in both RDS and text formats.

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Please feel free to contact us if you have problems running our tool at meiql@mail.sdu.edu.cn.