Introduction

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

Installation

Install RCSL package and other requirements

RCSL can be installed directly from GitHub with ‘devtools’.

library(devtools)
devtools::install_github("QinglinMei/RCSL")

Now we can load RCSL. We also load the SingleCellExperiment, ggplot2 and igraph package.

library(RCSL)
library(SingleCellExperiment)
library(ggplot2)
library(igraph)
library(umap)

Run RCSL

Load dataset (yan)

We illustrate the usage of RCSL on a human preimplantation embryos and embryonic stem cells(Yan et al., (2013)). The yan data is distributed together with the RCSL package, with 90 cells and 20,214 genes:

data(yan, package = "RCSL")
head(ann)

##                 cell_type1
## Oocyte..1.RPKM.     zygote
## Oocyte..2.RPKM.     zygote
## Oocyte..3.RPKM.     zygote
## Zygote..1.RPKM.     zygote
## Zygote..2.RPKM.     zygote
## Zygote..3.RPKM.     zygote

yan[1:3, 1:3]

##          Oocyte..1.RPKM. Oocyte..2.RPKM. Oocyte..3.RPKM.
## C9orf152             0.0             0.0             0.0
## RPS11             1219.9          1021.1           931.6
## ELMO2                7.0            12.2             9.3

origData <- yan
label <- ann$cell_type1

1. Pre-processing

In practice, we find it always beneficial to pre-process single-cell RNA-seq datasets, including: 1. Log transformation. 2. Gene filter

data <- log2(as.matrix(origData) + 1)
gfData <- GenesFilter(data)

2. Calculate the initial similarity matrix S

resSimS <- SimS(gfData)

## Calculate the Spearman correlation 
## Calculate the Nerighbor Representation 
## Find neighbors by KNN(Euclidean)

3. Estimate the number of clusters C

Estimated_C <- EstClusters(resSimS$drData,resSimS$S)

## ======== Calculate maximal strongly connected components ======== 
## ======== Calculate maximal strongly connected components ======== 
## ======== Calculate maximal strongly connected components ========

4. Calculate the block diagonal matrix B

resBDSM <- BDSM(resSimS$S, Estimated_C)

## ======== Calculate maximal strongly connected components ========

Calculate accuracy of the clustering

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

Trajectory analysis to time-series datasets

DataName <- "Yan"
res_TrajecAnalysis <- TrajectoryAnalysis(gfData, resSimS$drData, resSimS$S,
                                         clustRes = resBDSM$y, TrueLabel = label, 
                                         startPoint = 1, dataName = DataName)

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the RCSL package.
##   Please report the issue to the authors.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Display the 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 quick tour of RCSL