其他
比较不同的对单细胞转录组数据聚类的方法
背景介绍
聚类之前必须要对表达矩阵进行normalization，而且要去除一些批次效应等外部因素。通过对表达矩阵的聚类，可以把细胞群体分成不同的状态，解释为什么会有不同的群体。不过从计算的角度来说，聚类还是蛮复杂的，各个细胞并没有预先标记好，而且也没办法事先知道可以聚多少类。尤其是在单细胞转录组数据里面有很高的噪音，基因非常多，意味着的维度很高。
对这样的高维数据，需要首先进行降维，可以选择PCA或者t-SNE方法。聚类的话，一般都是无监督聚类方法，比如：hierarchical clustering, k-means clustering and graph-based clustering。算法略微有一点复杂，略过吧。
这里主要比较6个常见的单细胞转录组数据的聚类包：
SINCERA
pcaReduce
SC3
tSNE + k-means
SEURAT
SNN-Cliq
所以需要安装并且加载一些包,安装代码如下；
install.packages('pcaReduce')
## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("SC3")
biocLite("Seurat")
install.packages("devtools")
library("devtools")
install_github("BPSC","nghiavtr")
install_github("hemberg-lab/scRNA.seq.funcs")
devtools::install_github("JustinaZ/pcaReduce")
加载代码如下：
library(pcaMethods)
library(pcaReduce)
library(SC3)
library(scater)
library(pheatmap)
set.seed(1234567)
加载测试数据
这里选取的是数据，加载了这个scater包的SCESet对象，包含着一个23730 features, 301 samples 的表达矩阵。
供11已知的种细胞类型，这样聚类的时候就可以跟这个已知信息做对比，看看聚类效果如何。
可以直接用plotPCA来简单PCA并且可视化。
pollen <- readRDS("../pollen/pollen.rds")
pollen
# SCESet (storageMode: lockedEnvironment)
# assayData: 23730 features, 301 samples
# element names: exprs, is_exprs, tpm
# protocolData: none
# phenoData
# rowNames: Hi_2338_1 Hi_2338_2 ... Hi_GW16_26 (301 total)
# varLabels: cell_type1 cell_type2 ... is_cell_control (33 total)
# varMetadata: labelDescription
# featureData
# featureNames: A1BG A1BG-AS1 ... ZZZ3 (23730 total)
# fvarLabels: mean_exprs exprs_rank ... feature_symbol (11 total)
# fvarMetadata: labelDescription
# experimentData: use 'experimentData(object)'
# Annotation:
head(fData(pollen))
# mean_exprs exprs_rank n_cells_exprs total_feature_exprs
# A1BG 0.56418762 12460 79 169.82048
# A1BG-AS1 0.31265010 10621 37 94.10768
# A1CF 0.05453986 6796 59 16.41650
# A2LD1 0.22572953 9781 28 67.94459
# A2M 0.15087563 8855 21 45.41356
# A2M-AS1 0.02428046 5366 3 7.30842
# pct_total_exprs pct_dropout total_feature_tpm
# A1BG 1.841606e-03 73.75415 481.37
# A1BG-AS1 1.020544e-03 87.70764 538.18
# A1CF 1.780276e-04 80.39867 13.99
# A2LD1 7.368203e-04 90.69767 350.65
# A2M 4.924842e-04 93.02326 1356.63
# A2M-AS1 7.925564e-05 99.00332 88.61
# log10_total_feature_tpm pct_total_tpm is_feature_control
# A1BG 2.683380 1.599256e-04 FALSE
# A1BG-AS1 2.731734 1.787996e-04 FALSE
# A1CF 1.175802 4.647900e-06 FALSE
# A2LD1 2.546111 1.164965e-04 FALSE
# A2M 3.132781 4.507134e-04 FALSE
# A2M-AS1 1.952356 2.943891e-05 FALSE
# feature_symbol
# A1BG A1BG
# A1BG-AS1 A1BG-AS1
# A1CF A1CF
# A2LD1 A2LD1
# A2M A2M
# A2M-AS1 A2M-AS1
table(pData(pollen)$cell_type1)
#
# 2338 2339 BJ GW16 GW21 GW21+3 hiPSC HL60 K562 Kera
# 22 17 37 26 7 17 24 54 42 40
# NPC
# 15
plotPCA(pollen, colour_by = "cell_type1")
可以看到简单的PCA也是可以区分部分细胞类型的，只不过在某些细胞相似性很高的群体区分力度不够，所以需要开发新的算法来解决这个聚类的问题。
SC聚类
pollen <- sc3_prepare(pollen, ks = 2:5)
pollen <- sc3_estimate_k(pollen)
pollen@sc3$k_estimation
# [1] 11
# 准备 SCESet对象 数据给 SC3方法，先预测能聚多少个类，发现恰好是11个。
# 这里是并行计算，所以速度还可以
pollen <- sc3(pollen, ks = 11, biology = TRUE)
pollen
# SCESet (storageMode: lockedEnvironment)
# assayData: 23730 features, 301 samples
# element names: exprs, is_exprs, tpm
# protocolData: none
# phenoData
# rowNames: Hi_2338_1 Hi_2338_2 ... Hi_GW16_26 (301 total)
# varLabels: cell_type1 cell_type2 ... sc3_11_log2_outlier_score
# (35 total)
# varMetadata: labelDescription
# featureData
# featureNames: A1BG A1BG-AS1 ... ZZZ3 (23730 total)
# fvarLabels: mean_exprs exprs_rank ... sc3_11_de_padj (16 total)
# fvarMetadata: labelDescription
# experimentData: use 'experimentData(object)'
# Annotation:
head(fData(pollen))
# mean_exprs exprs_rank n_cells_exprs total_feature_exprs
# A1BG 0.56418762 12460 79 169.82048
# A1BG-AS1 0.31265010 10621 37 94.10768
# A1CF 0.05453986 6796 59 16.41650
# A2LD1 0.22572953 9781 28 67.94459
# A2M 0.15087563 8855 21 45.41356
# A2M-AS1 0.02428046 5366 3 7.30842
# pct_total_exprs pct_dropout total_feature_tpm
# A1BG 1.841606e-03 73.75415 481.37
# A1BG-AS1 1.020544e-03 87.70764 538.18
# A1CF 1.780276e-04 80.39867 13.99
# A2LD1 7.368203e-04 90.69767 350.65
# A2M 4.924842e-04 93.02326 1356.63
# A2M-AS1 7.925564e-05 99.00332 88.61
# log10_total_feature_tpm pct_total_tpm is_feature_control
# A1BG 2.683380 1.599256e-04 FALSE
# A1BG-AS1 2.731734 1.787996e-04 FALSE
# A1CF 1.175802 4.647900e-06 FALSE
# A2LD1 2.546111 1.164965e-04 FALSE
# A2M 3.132781 4.507134e-04 FALSE
# A2M-AS1 1.952356 2.943891e-05 FALSE
# feature_symbol sc3_gene_filter sc3_11_markers_clusts
# A1BG A1BG TRUE 5
# A1BG-AS1 A1BG-AS1 TRUE 4
# A1CF A1CF TRUE 2
# A2LD1 A2LD1 FALSE NA
# A2M A2M FALSE NA
# A2M-AS1 A2M-AS1 FALSE NA
# sc3_11_markers_padj sc3_11_markers_auroc sc3_11_de_padj
# A1BG 7.740802e-10 0.8554452 1.648352e-18
# A1BG-AS1 1.120284e-03 0.6507985 5.575777e-03
# A1CF 5.007946e-23 0.8592113 1.162843e-17
# A2LD1 NA NA NA
# A2M NA NA NA
# A2M-AS1 NA NA NA
# 可以看到SC3方法处理后的SCESet对象的基因信息增加了5列，比较重要的是sc3_gene_filter信息，决定着该基因是否拿去聚类，因为基因太多了，需要挑选
sc3_gene_filter)
#
# FALSE TRUE
# 11902 11828
## 只有一半的基因被挑选去聚类了
# 后面是一些可视化
sc3_plot_consensus(pollen, k = 11, show_pdata = "cell_type1")
sc3_plot_silhouette(pollen, k = 11)
sc3_plot_expression(pollen, k = 11, show_pdata = "cell_type1")
sc3_plot_markers(pollen, k = 11, show_pdata = "cell_type1")
plotPCA(pollen, colour_by = "sc3_11_clusters")
# 还支持shiny的交互式聚类，暂时不显示
sc3_interactive(pollen)
很明显可以看到SC3聚类的效果要好于普通的PCA
pcaReduce
# use the same gene filter as in SC3
input <- exprs(pollen[fData(pollen)$sc3_gene_filter, ])
# run pcaReduce 1 time creating hierarchies from 1 to 30 clusters
pca.red <- PCAreduce(t(input), nbt = 1, q = 30, method = 'S')[[1]]
## 这里对2~30种类别的情况都分别对样本进行分组。
## 我们这里取只有11组的时候，这些样本是如何分组的信息来可视化。
pData(pollen)$pcaReduce <- as.character(pca.red[,32 - 11])
plotPCA(pollen, colour_by = "pcaReduce")
tSNE + kmeans
scater包包装了 Rtsne 和 ggplot2 来做tSNE并且可视化。
pollen <- plotTSNE(pollen, rand_seed = 1, return_SCESet = TRUE)
## 上面的tSNE的结果，下面用kmeans的方法进行聚类，假定是8类细胞类型。
pData(pollen)$tSNE_kmeans <- as.character(kmeans(pollen@reducedDimension, centers = 8)$clust)
plotTSNE(pollen, rand_seed = 1, colour_by = "tSNE_kmeans")
SNN-Cliq
这个有一点难用，算了吧。
distan <- "euclidean"
par.k <- 3
par.r <- 0.7
par.m <- 0.5
# construct a graph
scRNA.seq.funcs::SNN(
data = t(input),
outfile = "snn-cliq.txt",
k = par.k,
distance = distan
)
# find clusters in the graph
snn.res <-
system(
paste0(
"python snn-cliq/Cliq.py ",
"-i snn-cliq.txt ",
"-o res-snn-cliq.txt ",
"-r ", par.r,
" -m ", par.m
),
intern = TRUE
)
cat(paste(snn.res, collapse = "\n"))
snn.res <- read.table("res-snn-cliq.txt")
# remove files that were created during the analysis
system("rm snn-cliq.txt res-snn-cliq.txt")
pData(pollen)$SNNCliq <- as.character(snn.res[,1])
plotPCA(pollen, colour_by = "SNNCliq")
SINCERA
至少是在这个数据集上面表现不咋地
# perform gene-by-gene per-sample z-score transformation
dat <- apply(input, 1, function(y) scRNA.seq.funcs::z.transform.helper(y))
# hierarchical clustering
dd <- as.dist((1 - cor(t(dat), method = "pearson"))/2)
hc <- hclust(dd, method = "average")
num.singleton <- 0
kk <- 1
for (i in 2:dim(dat)[2]) {
clusters <- cutree(hc, k = i)
clustersizes <- as.data.frame(table(clusters))
singleton.clusters <- which(clustersizes$Freq < 2)
if (length(singleton.clusters) <= num.singleton) {
kk <- i
} else {
break;
}
}
cat(kk)
# 14
pheatmap(
t(dat),
cluster_cols = hc,
cutree_cols = 14,
kmeans_k = 100,
show_rownames = FALSE
)
SEURAT
library(Seurat)
pollen_seurat <- new("seurat", raw.data = get_exprs(pollen, exprs_values = "tpm"))
pollen_seurat <- Setup(pollen_seurat, project = "Pollen")
pollen_seurat <- MeanVarPlot(pollen_seurat)
pollen_seurat <- RegressOut(pollen_seurat, latent.vars = c("nUMI"),
genes.regress = pollen_seurat@var.genes)
pollen_seurat <- PCAFast(pollen_seurat)
pollen_seurat <- RunTSNE(pollen_seurat)
pollen_seurat <- FindClusters(pollen_seurat)
TSNEPlot(pollen_seurat, do.label = T)
pData(pollen)$SEURAT <- as.character(pollen_seurat@ident)
sc3_plot_expression(pollen, k = 11, show_pdata = "SEURAT")
markers <- FindMarkers(pollen_seurat, 2)
FeaturePlot(pollen_seurat,
head(rownames(markers)),
cols.use = c("lightgrey", "blue"),
nCol = 3)