README.md

In RCSL: Rank Constrained Similarity Learning for Single Cell RNA Sequencing Data

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 installed through devtools in R:

$ R
> library("devtools")
> devtools::install_github("QinglinMei/RCSL")

Now RCSL can be loaded in R:

> library(RCSL)

Input

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.

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-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.

Example:

Load packages:

> 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")

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 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.

DEBUG

Please feel free to contact us if you have problems running our tool at [e-mail].

RCSL documentation built on April 19, 2021, 9:06 a.m.