A quick tour of RCSL
2021-05-19
Contents
1 Introduction
RCSL is an R toolkit for single-cell clustering and trajectory analysis using single-cell RNA-seq data.
2 Installation
2.0.1 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)
#> Loading required package: SummarizedExperiment
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library(ggplot2)
library(igraph)
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library(umap)3 Run RCSL
3.1 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:
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$cell_type13.2 1. Pre-processing
In practice, we find it always beneficial to pre-process single-cell RNA-seq datasets, including: 1. Log transformation. 2. Gene filter
data <- log2(as.matrix(origData) + 1)
gfData <- GenesFilter(data)3.3 2. Calculate the initial similarity matrix S
resSimS <- SimS(gfData)
#> Calculate the Spearman correlation
#> Calculate the Nerighbor Representation
#> Find neighbors by KNN(Euclidean)3.4 3. Estimate the number of clusters C
Estimated_C <- EstClusters(resSimS$drData,resSimS$S)
#> ======== Calculate maximal strongly connected components ========
#> ======== Calculate maximal strongly connected components ========
#> ======== Calculate maximal strongly connected components ========3.5 4. Calculate the block diagonal matrix B
resBDSM <- BDSM(resSimS$S, Estimated_C)
#> ======== Calculate maximal strongly connected components ========4 Calculate accuracy of the clustering
ARI_RCSL <- igraph::compare(resBDSM$y, label, method = "adjusted.rand")5 Trajectory analysis to time-series datasets
DataName <- "Yan"
res_TrajecAnalysis <- TrajectoryAnalysis(gfData, resSimS$drData, resSimS$S,
clustRes = resBDSM$y, TrueLabel = label,
startPoint = 1, dataName = DataName)6 Display the constructed MST
res_TrajecAnalysis$MSTPlot
7 Display the plot of the pseudo-temporal ordering
res_TrajecAnalysis$PseudoTimePlot
8 Display the plot of the inferred developmental trajectory
res_TrajecAnalysis$TrajectoryPlot