官网教程：Scran1.20.1（最新）。本教程使用的是Scran1.18.7，有几个函数不太一样。
# 查看安装的scran的网页版教程：
browseVignettes("scran")
1. 介绍
The scran package implements methods to perform low-level processing of scRNA-seq data, including cell cycle phase assignment, variance modelling and testing for marker genes and gene-gene correlations. This vignette provides brief descriptions of these methods and some toy examples to demonstrate their use.
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2. 数据准备
这个数据集是CEL-seq测的人类胰腺数据集，由SingleCellExperiment包提供。
library(scRNAseq)
sce <- GrunPancreasData()
sce
## class: SingleCellExperiment
## dim: 20064 1728
## metadata(0):
## assays(1): counts
## rownames(20064): A1BG-AS1__chr19 A1BG__chr19 ... ZZEF1__chr17
## ZZZ3__chr1
## rowData names(2): symbol chr
## colnames(1728): D2ex_1 D2ex_2 ... D17TGFB_95 D17TGFB_96
## colData names(2): donor sample
## reducedDimNames(0):
## mainExpName: endogenous
## altExpNames(1): ERCC
View(sce)
关于sce对象，参考： https://www.jianshu.com/p/533aa6c50a38
首先进行一些（粗略的）质控以去除counts过低或spike-in过高的细胞。
library(scuttle)
qcstats <- perCellQCMetrics(sce)
qcfilter <- quickPerCellQC(qcstats, percent_subsets="altexps_ERCC_percent")
sce <- sce[,!qcfilter$discard]
summary(qcfilter$discard)
## Mode FALSE TRUE
## logical 1291 437
3. 标准化（Normalizing cell-specific biases）
scran中的标准化使用computeSumFactors()函数，该过程是基于反卷积方法：先合并许多细胞的计数以避免drop-out问题，然后将基于池的量化因子(scale factor)“去卷积”为cell-based factors，以进行特定细胞的标准化。而标准化之前对细胞进行聚类(quickCluster)不是必需的，不过减少cluster中细胞之间的差异表达基因数量确实可以提高标准化的准确性。
library(scran)
clusters <- quickCluster(sce)
sce <- computeSumFactors(sce, clusters=clusters)
summary(sizeFactors(sce))
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.006722 0.442629 0.801312 1.000000 1.295809 9.227575
也可以使用spike-in来进行标准化
sce2 <- computeSpikeFactors(sce, "ERCC")
summary(sizeFactors(sce2))
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.01041 0.57760 0.88667 1.00000 1.27679 7.43466
不管使用哪种方法来计算量化因子，计算标准化的表达值都是使用scuttle包的logNormCounts()函数。标准化后的表达矩阵可以用于下游的聚类和降维等分析。
sce <- logNormCounts(sce)
4. 差异建模（Variance modelling）
dec <- modelGeneVar(sce)
plot(dec$mean, dec$total, xlab="Mean log-expression", ylab="Variance")
curve(metadata(dec)$trend(x), col="blue", add=TRUE)
dec2 <- modelGeneVarWithSpikes(sce, 'ERCC')
plot(dec2$mean, dec2$total, xlab="Mean log-expression", ylab="Variance")
points(metadata(dec2)$mean, metadata(dec2)$var, col="red")
curve(metadata(dec2)$trend(x), col="blue", add=TRUE)
# Turned off weighting to avoid overfitting for each donor.
dec3 <- modelGeneVar(sce, block=sce$donor, density.weights=FALSE)
per.block <- dec3$per.block
par(mfrow=c(3, 2))
for (i in seq_along(per.block)) {
decX <- per.block[[i]]
plot(decX$mean, decX$total, xlab="Mean log-expression",
ylab="Variance", main=names(per.block)[i])
curve(metadata(decX)$trend(x), col="blue", add=TRUE)
}
# Get the top 10% of genes.
top.hvgs <- getTopHVGs(dec, prop=0.1)
# Get the top 2000 genes.
top.hvgs2 <- getTopHVGs(dec, n=2000)
# Get all genes with positive biological components.
top.hvgs3 <- getTopHVGs(dec, var.threshold=0)
# Get all genes with FDR below 5%.
top.hvgs4 <- getTopHVGs(dec, fdr.threshold=0.05)
# Running the PCA with the 10% of HVGs.
library(scater)
sce <- runPCA(sce, subset_row=top.hvgs)
reducedDimNames(sce)
5. 自动化PC选择
sced <- denoisePCA(sce, dec2, subset.row=getTopHVGs(dec2, prop=0.1))
ncol(reducedDim(sced, "PCA"))
## [1] 50
output <- getClusteredPCs(reducedDim(sce))
npcs <- metadata(output)$chosen
reducedDim(sce, "PCAsub") <- reducedDim(sce, "PCA")[,1:npcs,drop=FALSE]
npcs
## [1] 14
6. Graph-based clustering
# In this case, using the PCs that we chose from getClusteredPCs().
g <- buildSNNGraph(sce, use.dimred="PCAsub")
cluster <- igraph::cluster_walktrap(g)$membership
# Assigning to the 'colLabels' of the 'sce'.
colLabels(sce) <- factor(cluster)
table(colLabels(sce))
##
## 1 2 3 4 5 6 7 8 9 10 11 12
## 79 285 64 170 166 164 124 32 57 63 63 24
sce <- runTSNE(sce, dimred="PCAsub")
plotTSNE(sce, colour_by="label", text_by="label")
ratio <- clusterModularity(g, cluster, as.ratio=TRUE)
library(pheatmap)
pheatmap(log10(ratio+1), cluster_cols=FALSE, cluster_rows=FALSE,
col=rev(heat.colors(100)))
ass.prob <- bootstrapCluster(sce, FUN=function(x) {
g <- buildSNNGraph(x, use.dimred="PCAsub")
igraph::cluster_walktrap(g)$membership
}, clusters=sce$cluster)
pheatmap(ass.prob, cluster_cols=FALSE, cluster_rows=FALSE,
col=colorRampPalette(c("white", "blue"))(100))
subout <- quickSubCluster(sce, groups=colLabels(sce))
table(metadata(subout)$subcluster) # formatted as '<parent>.<subcluster>'
##
## 1.1 1.2 10.1 10.2 10.3 11.1 11.2 12 2.1 2.2 2.3 2.4 3.1 3.2 3.3 4.1
## 38 41 16 19 28 29 34 24 96 35 81 73 26 19 19 53
## 4.2 4.3 4.4 5.1 5.2 6.1 6.2 6.3 7.1 7.2 7.3 8 9.1 9.2
## 67 33 17 73 93 64 65 35 35 35 54 32 33 24
7. marker基因鉴定
# Uses clustering information from 'colLabels(sce)' by default:
markers <- findMarkers(sce)
markers[[1]][,1:3]
## DataFrame with 20064 rows and 3 columns
## Top p.value FDR
## <integer> <numeric> <numeric>
## CPE__chr4 1 1.72176e-62 1.43939e-59
## KCNQ1OT1__chr11 1 2.07621e-61 1.60220e-58
## LOC100131257__chr7 1 2.66948e-43 7.87654e-41
## PGM5P2__chr9 1 2.67376e-51 1.27730e-48
## SCG2__chr2 1 1.41986e-104 2.84880e-100
## ... ... ... ...
## TLX1NB__chr10 19918 1 1
## TNFRSF17__chr16 19946 1 1
## TSPAN32__chr11 19973 1 1
## WFDC11__chr20 20015 1 1
## XKR7__chr20 20025 1
wmarkers <- findMarkers(sce, test.type="wilcox", direction="up", lfc=1)
wmarkers[[1]][,1:3]
## DataFrame with 20064 rows and 3 columns
## Top p.value FDR
## <integer> <numeric> <numeric>
## FBLIM1__chr1 1 7.55507e-30 2.16550e-26
## KCNQ1OT1__chr11 1 1.30379e-37 1.51912e-33
## PGM5P2__chr9 1 1.46543e-35 9.80080e-32
## UGDH-AS1__chr4 1 1.51428e-37 1.51912e-33
## LOC100131257__chr7 2 7.66839e-33 3.07717e-29
## ... ... ... ...
## TLX1NB__chr10 19918 1 1
## TNFRSF17__chr16 19946 1 1
## TSPAN32__chr11 19973 1 1
## WFDC11__chr20 20015 1 1
## XKR7__chr20 20025 1
markers <- findMarkers(sce, pval.type="all")
markers[[1]][,1:2]
## DataFrame with 20064 rows and 2 columns
## p.value FDR
## <numeric> <numeric>
## UGDH-AS1__chr4 8.36195e-20 1.67774e-15
## KCNQ1OT1__chr11 2.72694e-19 2.42652e-15
## LOC100131257__chr7 3.62817e-19 2.42652e-15
## TFDP2__chr3 5.98639e-19 3.00277e-15
## LOC643406__chr20 1.09251e-18 4.38402e-15
## ... ... ...
## ZP1__chr11 1 1
## ZP3__chr7 1 1
## ZPBP__chr7 1 1
## ZSCAN1__chr19 1 1
## ZSCAN20__chr1 1 1
8. Detecting correlated genes
# Using the first 200 HVs, which are the most interesting anyway.
# Also turning down the number of iterations for speed.
of.interest <- top.hvgs[1:200]
cor.pairs <- correlatePairs(sce, subset.row=of.interest, iters=1e5)
cor.pairs
## DataFrame with 19900 rows and 6 columns
## gene1 gene2 rho p.value FDR
## <character> <character> <numeric> <numeric> <numeric>
## 1 UGDH-AS1__chr4 KCNQ1OT1__chr11 0.830410 1.99998e-05 3.83241e-05
## 2 GCG__chr2 TTR__chr18 0.798357 1.99998e-05 3.83241e-05
## 3 REG1A__chr2 PRSS1__chr7 0.784339 1.99998e-05 3.83241e-05
## 4 REG1A__chr2 SPINK1__chr5 0.780994 1.99998e-05 3.83241e-05
## 5 REG1A__chr2 REG1B__chr2 0.760322 1.99998e-05 3.83241e-05
## ... ... ... ... ... ...
## 19896 TM4SF1__chr3 HYDIN2__chr1 0.000115737 0.99877 0.998971
## 19897 SLC30A8__chr8 MUC13__chr3 0.000169530 0.99919 0.999341
## 19898 KRT7__chr12 FLJ30403__chr19 0.000159983 0.99957 0.999670
## 19899 C15orf48__chr15 FLJ30403__chr19 0.000144531 0.99973 0.999780
## 19900 RBP4__chr10 DUSP1__chr5 0.000153488 0.99993 0.999930
## limited
## <logical>
## 1 TRUE
## 2 TRUE
## 3 TRUE
## 4 TRUE
## 5 TRUE
## ... ...
## 19896 FALSE
## 19897 FALSE
## 19898 FALSE
## 19899 FALSE
## 19900 FALSE
cor.pairs2 <- correlatePairs(sce, subset.row=of.interest,
block=sce$donor, iters=1e5)
cor.genes <- correlateGenes(cor.pairs)
cor.genes
## DataFrame with 200 rows and 5 columns
## gene rho p.value FDR limited
## <character> <numeric> <numeric> <numeric> <logical>
## 1 UGDH-AS1__chr4 0.830410 7.23629e-05 9.39778e-05 TRUE
## 2 GCG__chr2 0.798357 2.94812e-05 6.17910e-05 TRUE
## 3 REG1A__chr2 0.784339 2.88403e-05 6.17910e-05 TRUE
## 4 TTR__chr18 0.798357 2.72600e-05 6.17910e-05 TRUE
## 5 CHGB__chr20 0.751585 2.70746e-05 6.17910e-05 TRUE
## ... ... ... ... ... ...
## 196 FN1__chr2 0.164284 1.59198e-04 1.60806e-04 TRUE
## 197 MAFA__chr8 0.362235 6.12302e-05 8.33063e-05 TRUE
## 198 F3__chr1 0.378165 3.82688e-05 6.31239e-05 TRUE
## 199 SRXN1__chr20 0.340372 2.09472e-04 2.09472e-04 TRUE
## 200 KDM4A-AS1__chr1 0.330622 9.47610e-05 1.03564e-04 TRUE
9. 转换成其他数据形式
y <- convertTo(sce, type="edgeR")
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