4.13 实战十三 | 10X | 小鼠乳腺上皮细胞
刘小泽写于2020.7.21
library(scRNAseq)
sce.mam <- BachMammaryData(samples="G_1")
sce.mam
# class: SingleCellExperiment
# dim: 27998 2915
# metadata(0):
# assays(1): counts
# rownames: NULL
# rowData names(2): Ensembl Symbol
# colnames: NULL
# colData names(3): Barcode Sample Condition
# reducedDimNames(0):
# altExpNames(0):
# 样本信息
sapply(names(colData(sce.mam)), function(x) head(colData(sce.mam)[,x]))
# Barcode Sample Condition
# [1,] "AAACCTGAGGATGCGT-1" "G_1" "Gestation"
# [2,] "AAACCTGGTAGTAGTA-1" "G_1" "Gestation"
# [3,] "AAACCTGTCAGCATGT-1" "G_1" "Gestation"
# [4,] "AAACCTGTCGTCCGTT-1" "G_1" "Gestation"
# [5,] "AAACGGGCACGAAATA-1" "G_1" "Gestation"
# [6,] "AAACGGGCAGACGCTC-1" "G_1" "Gestation"
依然是整合行名 + 添加染色体信息
library(scater)
rownames(sce.mam) <- uniquifyFeatureNames(
rowData(sce.mam)$Ensembl, rowData(sce.mam)$Symbol)
library(AnnotationHub)
ens.mm.v97 <- AnnotationHub()[["AH73905"]]
rowData(sce.mam)$SEQNAME <- mapIds(ens.mm.v97, keys=rowData(sce.mam)$Ensembl,
keytype="GENEID", column="SEQNAME")
# 总共有13个线粒体基因
sum(grepl("MT",rowData(sce.mam)$SEQNAME))
# [1] 13
依然是备份一下，把unfiltered数据主要用在质控的探索上
unfiltered <- sce.mam
使用线粒体信息进行过滤
is.mito <- rowData(sce.mam)$SEQNAME == "MT"
stats <- perCellQCMetrics(sce.mam, subsets=list(Mito=which(is.mito)))
qc <- quickPerCellQC(stats, percent_subsets="subsets_Mito_percent")
colSums(as.matrix(qc))
## low_lib_size low_n_features high_subsets_Mito_percent
## 0 0 143
## discard
## 143
作图
colData(unfiltered) <- cbind(colData(unfiltered), stats)
unfiltered$discard <- qc$discard
gridExtra::grid.arrange(
plotColData(unfiltered, y="sum", colour_by="discard") +
scale_y_log10() + ggtitle("Total count"),
plotColData(unfiltered, y="detected", colour_by="discard") +
scale_y_log10() + ggtitle("Detected features"),
plotColData(unfiltered, y="subsets_Mito_percent",
colour_by="discard") + ggtitle("Mito percent"),
ncol=3
)
再看看线粒体含量与文库大小的关系
plotColData(unfiltered, x="sum", y="subsets_Mito_percent",
colour_by="discard") + scale_x_log10()
最后过滤
dim(unfiltered);dim(sce.mam)
# [1] 27998 2915
# [1] 27998 2772
使用去卷积的方法
library(scran)
set.seed(101000110)
clusters <- quickCluster(sce.mam)
sce.mam <- computeSumFactors(sce.mam, clusters=clusters)
sce.mam <- logNormCounts(sce.mam)
这里由于是10X的数据，所以会有UMI信息，因此可以用基于泊松分布的模型构建方法
set.seed(00010101)
dec.mam <- modelGeneVarByPoisson(sce.mam)
top.mam <- getTopHVGs(dec.mam, prop=0.1)
最后做个图
plot(dec.mam$mean, dec.mam$total, pch=16, cex=0.5,
xlab="Mean of log-expression", ylab="Variance of log-expression")
curfit <- metadata(dec.mam)
curve(curfit$trend(x), col='dodgerblue', add=TRUE, lwd=2)
library(BiocSingular)
set.seed(101010011)
sce.mam <- denoisePCA(sce.mam, technical=dec.mam, subset.row=top.mam)
sce.mam <- runTSNE(sce.mam, dimred="PCA")
# 检查PC的数量
ncol(reducedDim(sce.mam, "PCA"))
## [1] 15
有一个很重要的参数是k，含义是：the number of nearest neighbors used to construct the graph。如果k设置越大，得到的图之间联通程度越高，cluster也越大。因此这个参数也是可以不断尝试的
我们这里由于细胞数量比较多，所以设置的k就比较大，得到的cluster就少而大
snn.gr <- buildSNNGraph(sce.mam, use.dimred="PCA", k=25)
colLabels(sce.mam) <- factor(igraph::cluster_walktrap(snn.gr)$membership)
table(colLabels(sce.mam))
##
## 1 2 3 4 5 6 7 8 9 10
## 550 799 716 452 24 84 52 39 32 24
plotTSNE(sce.mam, colour_by="label")
