单细胞转录组数据校正批次效应实战(中)
刘小泽写于19.6.24、29
和花花毕业流浪,武汉完事转向成都
继续进行剩下两个数据集
第二个数据--CEL-seq2, GSE85241
Muraro et al. (2016) 利用CEL-seq2技术并结合UMI、ERCC得到的
https://www.ncbi.nlm.nih.gov//geo/query/acc.cgi?acc=GSE85241
下面快速使用代码
读数据,看数据
gse85241.df <- read.table("GSE85241_cellsystems_dataset_4donors_updated.csv.gz", sep='\t', header=TRUE, row.names=1)
> dim(gse85241.df)
[1] 19140 3072
提取meta信息
# 还是先看一下
> head(colnames(gse85241.df))
[1] "D28.1_1" "D28.1_2" "D28.1_3" "D28.1_4" "D28.1_5" "D28.1_6"
# 依然是:点号前面的是donor信息
donor.names <- sub("^(D[0-9]+).*", "\\1", colnames(gse85241.df))
> table(donor.names)
donor.names
D28 D29 D30 D31
768 768 768 768
# 然后文章使用了8个96孔板,于是可以将点号和下划线之间的数字提取出来
plate.id <- sub("^D[0-9]+\\.([0-9]+)_.*", "\\1", colnames(gse85241.df)) #这句代码中注意使用了一个转义符\\,在R中需要用两个反斜线来表示转义
> table(plate.id)
plate.id
1 2 3 4 5 6 7 8
384 384 384 384 384 384 384 384
提取基因、ERCC信息
gene.symb <- gsub("__chr.*$", "", rownames(gse85241.df))
is.spike <- grepl("^ERCC-", gene.symb)
> table(is.spike)
is.spike
FALSE TRUE
19059 81
基因转换
library(org.Hs.eg.db)
gene.ids <- mapIds(org.Hs.eg.db, keys=gene.symb, keytype="SYMBOL", column="ENSEMBL")
gene.ids[is.spike] <- gene.symb[is.spike]
keep <- !is.na(gene.ids) & !duplicated(gene.ids)
gse85241.df <- gse85241.df[keep,]
rownames(gse85241.df) <- gene.ids[keep]
> summary(keep)
Mode FALSE TRUE
logical 1949 17191
# 去掉了快2000个重复或无表达的基因
创建单细胞对象
# 存储metadata作为colData、基因信息作为rawData、ERCC作为spike-in
sce.gse85241 <- SingleCellExperiment(list(counts=as.matrix(gse85241.df)),
colData=DataFrame(Donor=donor.names, Plate=plate.id),
rowData=DataFrame(Symbol=gene.symb[keep]))
isSpike(sce.gse85241, "ERCC") <- grepl("^ERCC-", rownames(gse85241.df))
质控和标准化
sce.gse85241 <- calculateQCMetrics(sce.gse85241, compact=TRUE)
QC <- sce.gse85241$scater_qc
low.lib <- isOutlier(QC$all$log10_total_counts, type="lower", nmad=3)
low.genes <- isOutlier(QC$all$log10_total_features_by_counts, type="lower", nmad=3)
high.spike <- isOutlier(QC$feature_control_ERCC$pct_counts, type="higher", nmad=3)
data.frame(LowLib=sum(low.lib), LowNgenes=sum(low.genes),
HighSpike=sum(high.spike, na.rm=TRUE))
# LowLib LowNgenes HighSpike
# 577 669 696
# 然后去掉低质量的细胞
discard <- low.lib | low.genes | high.spike
sce.gse85241 <- sce.gse85241[,!discard]
> summary(discard)
Mode FALSE TRUE
logical 2346 726
可以看到文库小的有577个,基因表达少的有669个,高spike-in的有696个,但是最后只去掉了726个,这是因为,有的细胞同时存在以上两种或三种低质量情况,因此并不能简单认为总共去除细胞数=577+669+696
聚类
clusters <- quickCluster(sce.gse85241, min.mean=0.1, method="igraph")
> table(clusters)
clusters
1 2 3 4 5 6
237 248 285 483 613 480
标准化
sce.gse85241 <- computeSumFactors(sce.gse85241, min.mean=0.1, clusters=clusters)
summary(sizeFactors(sce.gse85241))
sce.gse85241 <- computeSpikeFactors(sce.gse85241, general.use=FALSE)
summary(sizeFactors(sce.gse85241, "ERCC"))
sce.gse85241 <- normalize(sce.gse85241)
鉴定HVGs
block <- paste0(sce.gse85241$Plate, "_", sce.gse85241$Donor)
fit <- trendVar(sce.gse85241, block=block, parametric=TRUE)
dec <- decomposeVar(sce.gse85241, fit)
plot(dec$mean, dec$total, xlab="Mean log-expression",
ylab="Variance of log-expression", pch=16)
is.spike <- isSpike(sce.gse85241)
points(dec$mean[is.spike], dec$total[is.spike], col="red", pch=16)
curve(fit$trend(x), col="dodgerblue", add=TRUE)
这张图中的ERCC表达量就有一些比较高的,但是占比不高,另外总体波动不大
# 选出来这些基因
dec.gse85241 <- dec
dec.gse85241$Symbol <- rowData(sce.gse85241)$Symbol
dec.gse85241 <- dec.gse85241[order(dec.gse85241$bio, decreasing=TRUE),]
> head(dec.gse85241,2)
DataFrame with 2 rows and 7 columns
mean total bio
<numeric> <numeric> <numeric>
ENSG00000115263 7.66453729345785 6.66863456231166 6.63983282676052
ENSG00000089199 4.62375793902937 6.46558866721711 6.34422879524839
tech p.value FDR Symbol
<numeric> <numeric> <numeric> <character>
ENSG00000115263 0.0288017355511366 0 0 GCG
ENSG00000089199 0.12135987196872 0 0 CHGB
第三个数据--Smart-seq2, E-MTAB-5061
Segerstolpe et al. (2016)利用Smart-seq2,添加了ERCC,这个数据和上面两个不同,它存放在ArrayExpress数据库,当然也是用链接规律的:https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-5061/ (这个文件比较大,压缩文件151M,解压后700多M)
读入数据
文件较大,先读入样本,也就是第一行(nrow=1),看下数量
header <- read.table("pancreas_refseq_rpkms_counts_3514sc.txt",
nrow=1, sep="\t", comment.char="")
# 先看下header信息
> header[1,1:4]
V1 V2 V3 V4
#samples HP1502401_N13 HP1502401_D14 HP1502401_F14
# 然后将第一个(#samples)去掉
ncells <- ncol(header) - 1L #保存为整数
然后只加载基因名称和表达矩阵
# 这段代码需要再好好理解下
col.types <- vector("list", ncells*2 + 2)
col.types[1:2] <- "character"
col.types[2+ncells + seq_len(ncells)] <- "integer"
e5601.df <- read.table("pancreas_refseq_rpkms_counts_3514sc.txt",
sep="\t", colClasses=col.types)
# 最后将基因信息和表达矩阵分离
gene.data <- e5601.df[,1:2]
e5601.df <- e5601.df[,-(1:2)]
colnames(e5601.df) <- as.character(header[1,-1])
dim(e5601.df)
## [1] 26271 3514
判断ERCC
# gene.data[,2]对应测序数据中的基因ID,gene.data[,1]是相应的symbol ID
is.spike <- grepl("^ERCC-", gene.data[,2])
> table(is.spike)
is.spike
FALSE TRUE
26179 92
基因转换
library(org.Hs.eg.db)
gene.ids <- mapIds(org.Hs.eg.db, keys=gene.data[,1], keytype="SYMBOL", column="ENSEMBL")
gene.ids[is.spike] <- gene.data[is.spike,2]
# 去掉重复和无表达基因
keep <- !is.na(gene.ids) & !duplicated(gene.ids)
e5601.df <- e5601.df[keep,]
rownames(e5601.df) <- gene.ids[keep]
> summary(keep)
Mode FALSE TRUE
logical 3367 22904
提取metadata信息
metadata <- read.table("E-MTAB-5061.sdrf.txt", header=TRUE,
sep="\t", check.names=FALSE)
m <- match(colnames(e5601.df), metadata$`Assay Name`)
stopifnot(all(!is.na(m)))
metadata <- metadata[m,]
donor.id <- metadata[["Characteristics[individual]"]]
> table(donor.id)
donor.id
AZ HP1502401 HP1504101T2D HP1504901 HP1506401
96 352 383 383 383
HP1507101 HP1508501T2D HP1509101 HP1525301T2D HP1526901T2D
383 383 383 384 384
创建单细胞对象
sce.e5601 <- SingleCellExperiment(list(counts=as.matrix(e5601.df)),
colData=DataFrame(Donor=donor.id),
rowData=DataFrame(Symbol=gene.data[keep,1]))
isSpike(sce.e5601, "ERCC") <- grepl("^ERCC-", rownames(e5601.df))
后面的操作和之前保持一致了
sce.e5601 <- calculateQCMetrics(sce.e5601, compact=TRUE)
QC <- sce.e5601$scater_qc
low.lib <- isOutlier(QC$all$log10_total_counts, type="lower", nmad=3)
low.genes <- isOutlier(QC$all$log10_total_features_by_counts, type="lower", nmad=3)
high.spike <- isOutlier(QC$feature_control_ERCC$pct_counts, type="higher", nmad=3)
low.spike <- isOutlier(QC$feature_control_ERCC$log10_total_counts, type="lower", nmad=2)
data.frame(LowLib=sum(low.lib), LowNgenes=sum(low.genes),
HighSpike=sum(high.spike, na.rm=TRUE), LowSpike=sum(low.spike))
# LowLib LowNgenes HighSpike LowSpike
# 162 572 904 359
# 舍弃低质量细胞
discard <- low.lib | low.genes | high.spike | low.spike
sce.e5601 <- sce.e5601[,!discard]
> summary(discard)
Mode FALSE TRUE
logical 2285 1229
# 聚类
clusters <- quickCluster(sce.e5601, min.mean=1, method="igraph")
> table(clusters)
clusters
1 2 3 4 5 6
305 307 469 272 494 438
# 标准化
sce.e5601 <- computeSumFactors(sce.e5601, min.mean=1, clusters=clusters)
sce.e5601 <- computeSpikeFactors(sce.e5601, general.use=FALSE)
sce.e5601 <- normalize(sce.e5601)
因为这个数据中donor信息比较多,所以可视化也要特别对待
donors <- sort(unique(sce.e5601$Donor))
> donors
[1] "AZ" "HP1502401" "HP1504101T2D" "HP1504901"
[5] "HP1506401" "HP1507101" "HP1508501T2D" "HP1509101"
[9] "HP1525301T2D" "HP1526901T2D"
一共10个donor,作图可以设置这个参数,调整图片为2列
par(mfrow=c(ceiling(length(donors)/2), 2),
mar=c(4.1, 4.1, 2.1, 0.1))
代码作图,注意这段代码和之前的不同
collected <- list() # 第一行可以先不管,目的是创建一个空列表
# 下面进行一个循环,对10个donor进行循环:先取出第一个donor的列信息,然后使用if判断它是不是大于两列(也就是说:这个donor是不是有两个以上的细胞样本),如果只有一列那么就舍去;然后对这个donor的所有列进行标准化,去掉细胞文库差异;接着利用trendVar和decomposeVar鉴定HVGs,然后和之前一样进行可视化;最后将这个donor鉴定出来的HVGs信息放入collected这个列表中,留着以后用
for (x in unique(sce.e5601$Donor)) {
current <- sce.e5601[,sce.e5601$Donor==x]
if (ncol(current)<2L) { next }
current <- normalize(current)
fit <- trendVar(current, parametric=TRUE)
dec <- decomposeVar(current, fit)
plot(dec$mean, dec$total, xlab="Mean log-expression",
ylab="Variance of log-expression", pch=16, main=x)
points(fit$mean, fit$var, col="red", pch=16)
curve(fit$trend(x), col="dodgerblue", add=TRUE)
collected[[x]] <- dec
}
因为这个数据中donor信息比较多,因此我们需要将不同donor的HVGs整合成一个数据框(注意是更高级的SV4数据框)
dec.e5601 <- do.call(combineVar, collected)
dec.e5601$Symbol <- rowData(sce.e5601)$Symbol
dec.e5601 <- dec.e5601[order(dec.e5601$bio, decreasing=TRUE),]
> head(dec.e5601,3)
DataFrame with 3 rows and 7 columns
mean total bio
<numeric> <numeric> <numeric>
ENSG00000115263 9.79547495804957 24.9059740209558 24.693297105741
ENSG00000118271 10.3601718361198 19.0590510324402 18.9670050741979
ENSG00000089199 8.78499018265489 17.2605488560106 16.9971950283286
tech p.value FDR Symbol
<numeric> <numeric> <numeric> <character>
ENSG00000115263 0.212676915214769 0 0 GCG
ENSG00000118271 0.0920459582422512 0 0 TTR
ENSG00000089199 0.263353827682004 0 0 CHGB
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