这里是佳奥，我们继续复现文章数据。
1 scater Package
rm(list = ls())
Sys.setenv(R_MAX_NUM_DLLS=999)
## 首先载入文章的数据
load(file='../input.Rdata')
counts=a
counts[1:4,1:4];dim(counts)
library(stringr)
meta=df
head(meta)
options(warn=-1) # turn off warning message globally
suppressMessages(library(scater))
## 创建 scater 要求的对象
sce <- SingleCellExperiment(
assays = list(counts = as.matrix(counts)),
colData = meta
)
sce
exprs(sce) <- log2( calculateCPM(sce ) + 1)
## 只有运行了下面的函数后才有各式各样的过滤指标
genes=rownames(rowData(sce))
genes[grepl('^MT-',genes)]
genes[grepl('^ERCC-',genes)]
sce <- calculateQCMetrics(sce,
feature_controls = list(ERCC = grep('^ERCC',genes)))
sce <- addPerCellQC(sce,
subsets=list(ERCC = grep('^ERCC',genes)))
keep_feature <- rowSums(exprs(sce) > 0) > 5
table(keep_feature)
sce <- sce[keep_feature,]
tf=sce$detected
boxplot(tf)
fivenum(tf)
table(tf>2000)
sce=sce[,tf > 2000 ]
sce
head(meta)
QQ截图20220902202443.png
## 基因表达，理论上应该是跟384孔板 这个变量无关
plotExpression(sce, rownames(sce)[1:6],
x = "plate",
exprs_values = "logcounts")
QQ截图20220902202601.png
# 展示高表达量基因, 绘图有点耗时
plotHighestExprs(sce, exprs_values = "counts")
QQ截图20220902203002.png
sce <- runPCA(sce)
plotPCA(sce)
reducedDimNames(sce)
QQ截图20220902203036.png
# colnames(as.data.frame(colData(sce)))
head(colData(sce))
## PCA分布图上面添加临床信息--------------
plotReducedDim(sce, "PCA",
shape_by= "plate",
colour_by= "g")
QQ截图20220902203244.png
##包更新了，没有feature_set功能了
## 考虑ERCC 影响后继续PCA
#sce2 <- runPCA(sce,
feature_set = rowData(sce)$is_feature_control)
#plotPCA(sce2)
## PCA分布图上面添加临床信息--------------
#plotReducedDim(sce2, use_dimred = "PCA",
shape_by= "plate",
colour_by= "g")
## 运行 tSNE 降维算法
set.seed(1000)
sce <- runTSNE(sce, perplexity=10)
plotTSNE(sce,
shape_by= "plate",
colour_by= "g")
QQ截图20220902210133.png
## 对tSNE降维后结果进行不同的聚类
##原始代码跑不了
#colData(sce)$tSNE_kmeans <- as.character(kmeans(sce@reducedDims$TSNE,
centers = 4)$clust)
##蛮试了一个
colData(sce)$tSNE_kmeans <- as.character(kmeans(sce@colData$detected,
centers = 4)$clust)
head(sce@colData$detected)
hc=hclust(dist( sce@colData$detected ))
clus = cutree(hc, 4)
colData(sce)$tSNE_hc <- as.character(clus)
plotTSNE(sce, colour_by = "tSNE_kmeans")
plotTSNE(sce, colour_by = "tSNE_hc")
table(colData(sce)$tSNE_hc , colData(sce)$tSNE_kmeans)##查看两种分组情况
1 2 3 4
1 107 51 0 0
2 0 188 0 30
3 0 0 110 179
4 0 0 28 0
QQ截图20220902210933.png
QQ截图20220902210943.png
##'runDiffusionMap'不再有用。
## 同样是一直降维方式，不同的算法
# sce <- runDiffusionMap(sce)
# plotDiffusionMap(sce,
shape_by= "plate",
colour_by= "g")
library(SC3) # BiocManager::install('SC3')
sce <- sc3_estimate_k(sce)
metadata(sce)$sc3$k_estimation
rowData(sce)$feature_symbol=rownames(rowData(sce))
# 耗费时间
kn=4
sc3_cluster="sc3_4_clusters"
# 非常耗时
sce <- sc3(sce, ks = kn, biology = TRUE)
sc3_plot_consensus(sce, k = kn, show_pdata = c("g",sc3_cluster))
sc3_plot_expression(sce, k = kn, show_pdata = c("g",sc3_cluster))
sc3_plot_markers(sce, k = kn, show_pdata = c("g",sc3_cluster))
plotPCA(sce, shape_by= "g" , colour_by = sc3_cluster )
sc3_interactive(sce)
QQ截图20220902212049.png
QQ截图20220902212104.png
QQ截图20220902212152.png
QQ截图20220902212209.png
QQ截图20220902212321.png
2 monocle Package
参考代码：
https://cloud.tencent.com/developer/article/1606574
rm(list = ls())
Sys.setenv(R_MAX_NUM_DLLS=999)
## 首先载入文章的数据
load(file='../input.Rdata')
# 原始表达矩阵
counts=a
# using raw counts is the easiest way to process data through Seurat.
counts[1:4,1:4];dim(counts)
library(stringr)
# 样本信息
meta=df
head(meta)
# 按行/基因检查：每个基因在多少细胞中有表达量
fivenum(apply(counts,1,function(x) sum(x>0) ))
boxplot(apply(counts,1,function(x) sum(x>0) ))
# 按列/样本检查：每个细胞中存在多少表达的基因
fivenum(apply(counts,2,function(x) sum(x>0) ))
hist(apply(counts,2,function(x) sum(x>0) ))
# 上面检测了 counts 和 meta 两个变量，后面需要使用
有50%的基因只在低于20个细胞中有表达量，还有许多没有表达量的
QQ截图20220903102950.png
QQ截图20220903102959.png
table(apply(counts,1,function(x) sum(x>0) )==0)
FALSE TRUE
17429 7153
# 存在7000多个基因在任何一个细胞中都没表达
##开始使用newCellDataSet()构建monocle对象
# ---------首先构建基因的注释信息(feature_data)-----------
gene_ann <- data.frame(
gene_short_name = row.names(counts),
row.names = row.names(counts)
)
sample_ann=meta
fd <- new("AnnotatedDataFrame",
data=gene_ann)
# ---------然后构建样本的注释信息(sample_data)---------
pd <- new("AnnotatedDataFrame",
data=sample_ann)
# ---------开始构建对象---------
#sc_cds <- newCellDataSet(
as.matrix(counts),
phenoData = pd,
featureData =fd,
expressionFamily = negbinomial.size(),
lowerDetectionLimit=1)
counts<-as(as.matrix(counts),"sparseMatrix")
sc_cds <- newCellDataSet(
counts,
phenoData = pd,
featureData =fd,
lowerDetectionLimit = 1,
expressionFamily = VGAM::negbinomial.size())
sc_cds
> sc_cds
CellDataSet (storageMode: environment)
assayData: 24582 features, 768 samples
element names: exprs
protocolData: none
phenoData
sampleNames: SS2_15_0048_A3 SS2_15_0048_A6 ... SS2_15_0049_P24 (768 total)
varLabels: g plate ... Size_Factor (5 total)
varMetadata: labelDescription
featureData
featureNames: 0610005C13Rik 0610007P14Rik ... ERCC-00171 (24582 total)
fvarLabels: gene_short_name
fvarMetadata: labelDescription
experimentData: use 'experimentData(object)'
Annotation:
##接下来质控过滤
cds=sc_cds
cds
## 起初是 24582 features, 768 samples
#---------首先是对基因的过滤-------------
cds <- detectGenes(cds, min_expr = 0.1)
print(head(cds@featureData@data))
expressed_genes <- row.names(subset(cds@featureData@data,
num_cells_expressed >= 5))
length(expressed_genes)
# 14442
# 这里需要去掉ERCC基因
# 去掉ERCC基因
is.ercc <- grepl("ERCC",expressed_genes)
length(expressed_genes[!is.ercc])
# 14362(看到去掉了80个ERCC)
cds <- cds[expressed_genes[!is.ercc],]
cds
# 过滤基因后是 14362 features, 768 samples
#---------然后是对细胞的过滤-------------
# 如果不支持使用pData()函数，可以使用cds@phenoData@data来获得各种细胞注释信息
cell_anno <- cds@phenoData@data
> head(cell_anno)
g plate n_g all Size_Factor num_genes_expressed
SS2_15_0048_A3 1 0048 2624 all 0.6693919 3069
SS2_15_0048_A6 1 0048 2664 all 0.6820532 3040
SS2_15_0048_A5 2 0048 3319 all 1.0759418 3743
SS2_15_0048_A4 3 0048 4447 all 0.7294812 5014
SS2_15_0048_A1 2 0048 4725 all 1.5658507 5128
SS2_15_0048_A2 3 0048 5263 all 1.6187569 5693
# 这里简单过滤细胞
valid_cells <- row.names(cell_anno[cell_anno$num_genes_expressed>2000,] )
cds <- cds[,valid_cells]
cds
# 最后剩下：14362 features, 693 samples
library(dplyr)
colnames(phenoData(sc_cds)@data)
## 接下来的分析，都是基于sc_cds对象
cds=sc_cds
cds
## 起初是 24582 features, 768 samples
cds <- detectGenes(cds, min_expr = 0.1)
#print(head(fData(cds)))
#expressed_genes <- row.names(subset(fData(cds),
num_cells_expressed >= 5))
print(head(cds@featureData@data))
expressed_genes <- row.names(subset(cds@featureData@data,
num_cells_expressed >= 5))
length(expressed_genes)
cds <- cds[expressed_genes,]
cds
# 过滤基因后是 14442 features, 768 samples
##然后进行归一化
library(dplyr)
colnames(phenoData(cds)@data)
## 必要的归一化
cds <- estimateSizeFactors(cds)
cds <- estimateDispersions(cds)
##降维聚类
disp_table <- dispersionTable(cds)
unsup_clustering_genes <- subset(disp_table,
mean_expression >= 0.1)
cds <- setOrderingFilter(cds, unsup_clustering_genes$gene_id)
cds
plot_ordering_genes(cds)
# 图中黑色的点就是被标记出来一会要进行聚类的基因
QQ截图20220903134337.png
plot_pc_variance_explained(cds, return_all = F) # norm_method='log'
QQ截图20220903134941.png
关于聚类：monocle一共有三种方法：
densityPeak", "louvain", "DDRTree"
默认使用density peak的方法，但是对于大型数据（例如有5万细胞）推荐louvain方法
# ---------进行降维---------
cds <- reduceDimension(cds, max_components = 2, num_dim = 6,
reduction_method = 'tSNE', verbose = T)
# ---------进行聚类---------
# (这里先设置num_clusters，不一定最后真就分成5群；我们这里设置5，最后只能得到4群；如果设成4，结果就得到3群)
cds <- clusterCells(cds, num_clusters = 5)
# Distance cutoff calculated to 1.818667
# color使用的这些数据就在：cds$Cluster
plot_cell_clusters(cds, 1, 2, color = "Cluster")
QQ截图20220903135039.png
因为之前还做过层次聚类，所以还可以对比一下：
plot_cell_clusters(cds, 1, 2, color = "g")
看到monocle使用其他的聚类算法确实不如使用自己的结果得到的效果好
QQ截图20220903135059.png
再次对比不同分群结果的基因数量差异：
boxplot(cds@phenoData@data$num_genes_expressed~cds@phenoData@data$Cluster)
boxplot(cds@phenoData@data$num_genes_expressed~cds@phenoData@data$g)
QQ截图20220903135122.png
QQ截图20220903135143.png
去除一些影响因素
因为这几群的细胞中基因表达数量是有差别的，因此我们可以在聚类之前先去掉这部分影响，从而关注它们真正的生物学影响
## 去除检测到基因数量效应
cds <- reduceDimension(cds, max_components = 2, num_dim = 2,
reduction_method = 'tSNE',
residualModelFormulaStr = "~num_genes_expressed",
verbose = T)
cds <- clusterCells(cds, num_clusters = 5)
plot_cell_clusters(cds, 1, 2, color = "Cluster")
QQ截图20220903135252.png
##差异分析
start=Sys.time()
diff_test_res <- differentialGeneTest(cds,
fullModelFormulaStr = "~Cluster")
end=Sys.time()
end-start
# Time difference of 4.724045 mins
##挑差异基因
# 选择FDR-adjusted p-value(也就是q值) < 10%的基因作为差异基因
sig_genes <- subset(diff_test_res, qval < 0.1)
dim(sig_genes)
> head(sig_genes[,c("gene_short_name", "pval", "qval")] )
gene_short_name pval qval
0610007P14Rik 0610007P14Rik 3.377244e-03 1.277429e-02
0610010F05Rik 0610010F05Rik 1.243943e-02 3.924761e-02
0610011F06Rik 0610011F06Rik 2.338530e-03 9.285587e-03
1110004E09Rik 1110004E09Rik 2.487600e-02 7.003903e-02
1110020A21Rik 1110020A21Rik 2.318327e-02 6.618129e-02
1110059E24Rik 1110059E24Rik 5.193533e-09 4.856089e-08
##作图
cg=as.character(head(sig_genes$gene_short_name))
# 还能上色
plot_genes_jitter(cds[cg,],
grouping = "Cluster",
color_by = "Cluster",
nrow= 3,
ncol = NULL )
QQ截图20220903141430.png
##推断发育轨迹
##step1: 选合适基因
ordering_genes <- row.names (subset(diff_test_res, qval < 0.01))
cds <- setOrderingFilter(cds, ordering_genes)
plot_ordering_genes(cds)
QQ截图20220903141500.png
##step2: 降维
# 默认使用DDRTree的方法
cds <- reduceDimension(cds, max_components = 2,
method = 'DDRTree')
##step3: 细胞排序
cds <- orderCells(cds)
head(pData(cds))
##最后可视化
plot_cell_trajectory(cds, color_by = "Cluster")
QQ截图20220903141709.png
写在后面
orderCells会出现报错：
Error in if (class(projection) != "matrix") projection <- as.matrix(projection) :
the condition has length > 1
In addition: Warning message:
In graph.dfs(dp_mst, root = root_cell, neimode = "all", unreachable = FALSE, :
Argument `neimode' is deprecated; use `mode' instead
GitHub上解决办法是修改该包的代码：
https://github.com/cole-trapnell-lab/monocle-release/issues/434
from
if(class(projection) != 'matrix')
projection <- as.matrix(projection)
to
projection <- as.matrix(projection)
尚不确定是否有用。
本流程花了很多时间把所有代码都调试到可以顺利运行最新版本的package。
希望能有所帮助。
下一篇我们看一下作者的原始代码。
我们下一篇再见！
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