marker基因的表达量可视化

生信技能树jimmy

发布于 2020-03-30 14:34:15

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发布于 2020-03-30 14:34:15

文章被收录于专栏：单细胞天地单细胞天地

前言

这次的任务是模仿原文的：

肿瘤组织的差异分析(Fig. 4a、c)

下载数据

之前的分析都是基于第一个病人的PBMC，这次将基于这位病人的tumor：GSE117988_raw.expMatrix_Tumor.csv.gz

start_time <- Sys.time()
raw_dataTumor <- read.csv('./GSE117988_raw.expMatrix_Tumor.csv.gz', header = TRUE, row.names = 1)
end_time <- Sys.time()
end_time - start_time
# Time difference of 47.00589 secs
dim(raw_dataTumor) # 21861基因，7431细胞- already filtered

注意：之前PBMC包含了四个时间点的样本,这里的Tumor包含了2个样本（治疗之前Pre和复发AR）

常规流程

step1: 归一化

dataTumor <- log2(1 + sweep(raw_dataTumor, 2, median(colSums(raw_dataTumor))/colSums(raw_dataTumor), '*'))
> head(colnames(dataTumor))
[1] "AAACCTGAGGATGTAT.1" "AAACCTGCAGCGATCC.1" "AAACCTGGTACGAAAT.1" "AAACGGGAGCTGGAAC.1" "AAACGGGAGGAGTTGC.1"
[6] "AAACGGGAGTTTAGGA.1"

step2: 自定义划分细胞类型

cellTypes <- sapply(colnames(dataTumor), function(x) ExtractField(x, 2, '[.]'))
cellTypes <-ifelse(cellTypes == '1', 'Tumor_Before', 'Tumor_AcquiredResistance')
> table(cellTypes)
cellTypes
Tumor_AcquiredResistance             Tumor_Before 
                    5188                     2243

step3: 表达矩阵质控

# 第一点：基因在多少细胞表达 
> fivenum(apply(dataTumor,1,function(x) sum(x>0) ))
   VP2 GPRIN2   EML3 ZNF140  RPLP1 
     1      8    103    566   7431
# 第二点：细胞中有多少表达的基因
> fivenum(apply(dataTumor,2,function(x) sum(x>0) ))
GGAACTTAGGAATCGC.1 TACGGTACAAGCCGCT.2 CCTACCAAGCGTGAGT.1 TGAGCCGAGACTAGAT.2 GATCGTAGTCATATGC.2 
             192.0             1059.0             1380.0             1971.5             5888.0

看到大部分基因在500多个细胞表达，细胞平均能表达1000个基因以上

step4: 创建Seurat对象

tumor <- CreateSeuratObject(dataTumor, 
                           min.cells = 1, min.features = 0, project = '10x_Tumor')
> tumor 
An object of class seurat in project 10x_Tumor 
 21861 genes across 7431 samples.

step5: 添加metadata (nUMI 和细胞类型)

tumor <- AddMetaData(object = tumor, metadata = apply(raw_dataTumor, 2, sum), col.name = 'nUMI_raw')
tumor <- AddMetaData(object = tumor, metadata = cellTypes, col.name = 'cellTypes')

step6: 聚类标准流程

start_time <- Sys.time()
tumor <- ScaleData(object = tumor, vars.to.regress = c('nUMI_raw'), model.use = 'linear', use.umi = FALSE)
tumor <- FindVariableGenes(object = tumor, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.0125, x.high.cutoff = 3, y.cutoff = 0.5)
tumor <- RunPCA(object = tumor, pc.genes = tumor@var.genes)
tumor <- RunTSNE(object = tumor, dims.use = 1:10, perplexity = 25)
end_time <- Sys.time()
end_time - start_time
# Time difference of 3.324982 mins
TSNEPlot(tumor, group.by = 'cellTypes', colors.use = c('#EF8A62', '#67A9CF'))
save(tumor,file = 'patient1.Tumor.V2.output.Rdata') # 3.6Gb 大小

接着进行基因可视化

rm(list = ls()) 
options(warn=-1) 
start_time <- Sys.time()
load('patient1.Tumor.V2.output.Rdata')
end_time <- Sys.time()
end_time - start_time
# Time difference of 19.83097 secs

取出log归一化后的表达矩阵

count_matrix=tumor@data
> count_matrix[1:4,1:4]
              AAACCTGAGGATGTAT.1 AAACCTGCAGCGATCC.1 AAACCTGGTACGAAAT.1 AAACGGGAGCTGGAAC.1
VP2                    0.0000000                  0                  0                  0
largeTAntigen          0.9670525                  0                  0                  0
smallTAntigen          0.0000000                  0                  0                  0
RP11-34P13.7           0.0000000                  0                  0                  0

取出细胞分群信息

cluster=tumor@meta.data$cellTypes
> table(cluster)
cluster
Tumor_AcquiredResistance             Tumor_Before 
                    5188                     2243

提取基因信息

文章主要探索了治疗前和复发后的HLA-A和HLA-B的变化，于是我们先看看有没有这两个基因

allGenes = row.names(tumor@raw.data)
> allGenes[grep('HLA',allGenes)]
 [1] "HHLA3"    "HLA-F"    "HLA-G"    "HLA-A"    "HLA-E"    "HLA-C"    "HLA-B"    "HLA-DRA"  "HLA-DRB5"
[10] "HLA-DRB1" "HLA-DQA1" "HLA-DQB1" "HLA-DQA2" "HLA-DQB2" "HLA-DOB"  "HLA-DMB"  "HLA-DMA"  "HLA-DOA" 
[19] "HLA-DPA1" "HLA-DPB1"

对HLA-A操作

FeaturePlot(object = tumor, 
            features.plot ='HLA-A', 
            cols.use = c("grey", "blue"), 
            reduction.use = "tsne")

看HLA-A表达量

> table(count_matrix['HLA-A',]>0, cluster)
       cluster
        Tumor_AcquiredResistance Tumor_Before
  FALSE                     2282         1057
  TRUE                      2906         1186

对HLA-B操作

FeaturePlot(object = tumor, 
            features.plot ='HLA-B', 
            cols.use = c("grey", "blue"), 
            reduction.use = "tsne")

可以看到治疗前HLA-A基因有1186个表达，1057个不表达；复发后这个基因表达和不表达的数量也相近

> table(count_matrix['HLA-B',]>0, cluster)
       cluster
        Tumor_AcquiredResistance Tumor_Before
  FALSE                     4794         1258
  TRUE                       394          985

对HLA-B来讲，不管是治疗前还是复发后，它的表达和不表达差异就很明显。另外从治疗前到复发，这个基因的表达数量的变化更显著

小问题：HLA基因这么多，我们怎么找到全部的具有相似模式的基因？

所谓相似表达模式，就是像HLA-B基因一样，在一个群表达很多，另一个群表达很少，通过卡方检验（http://rpubs.com/chixinzero/490992）就能看出区别

>  chisq.test(table(count_matrix['HLA-A',]>0, cluster))

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(count_matrix["HLA-A", ] > 0, cluster)
X-squared = 6.1069, df = 1, p-value = 0.01347

>  chisq.test(table(count_matrix['HLA-B',]>0, cluster))

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(count_matrix["HLA-B", ] > 0, cluster)
X-squared = 1364.4, df = 1, p-value < 2.2e-16

看p值，HLA-B显著性相比HLA-A就非常强，我们就是要挑出和HLA-B类似的，也就是极显著的，设定p值的阈值为0.01

HLA_genes <- allGenes[grep('HLA',allGenes)]
# 将输出结果保存在向量中
HLA_result <- c()
for (gene in HLA_genes) {
  tmp <- chisq.test(table(count_matrix[gene,]>0, cluster))
  if (tmp$p.value<0.01) {
    HLA_result[gene] <- gene
  }
}
> names(HLA_result)
 [1] "HLA-F"    "HLA-E"    "HLA-C"    "HLA-B"    "HLA-DRA"  "HLA-DRB5" "HLA-DRB1" "HLA-DQA1" "HLA-DQB1"
[10] "HLA-DQA2" "HLA-DMB"  "HLA-DMA"  "HLA-DOA"  "HLA-DPA1" "HLA-DPB1"
> length(HLA_result)
[1] 15

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