library(TCGAbiolinks)
# 读取之前保存的数据
dataPrep <- readRDS("dataprep.rds")
dataPrep <- TCGAanalyze_Preprocessing(object = dataPrep,
cor.cut = 0.6)Number of outliers: 0差异分析
本笔记的目的在于对下载好的 TCGA 乳腺癌数据集进行指控以及差异分析（肿瘤 vs 正常）。
下载数据的质量控制
TCGAanalyze_Preprocessing()对数据进行预处理：使用 spearman 相关系数去除数据中的异常值：
导出：
write.csv(dataPrep,file = "TCGA_BRCA.csv",quote = FALSE)TCGAanalyze_Preprocessing()中的参数：
| 参数 | 用法 |
|---|---|
| object | 来自TCGAprepare的结果 |
| cor.cut | 设置阈值，根据样本中各个样本之间的spearman相关系数进行过滤。默认为0 |
| filename | 设置生成图片文件的名称，默认为PreprocessingOutput.png |
| width | 生成图片的宽度 |
| height | 生成图片的高度 |
| datatype | 描述RangedSummarizedExperiment 数据类型的字符串 |
处理得到的表达矩阵
进行表达矩阵标准化和过滤，得到用于差异分析的表达矩阵：
TCGAanalyze_Normalization()使用EDASeq软件包标准化 mRNA 转录本和 miRNA。 TCGAanalyze_Normalization()执行EDASeq包中的如下功能：
EDASeq::newSeqExpressionSetEDASeq::withinLaneNormalizationEDASeq::betweenLaneNormalizationEDASeq::counts
在此之前需要先安装EDASeq软件包：
BiocManager::install("EDASeq")dataNorm <- TCGAanalyze_Normalization(
tabDF = dataPrep,
geneInfo = geneInfoHT
)I Need about 887 seconds for this Complete Normalization Upper Quantile [Processing 80k elements /s] Step 1 of 4: newSeqExpressionSet ...Step 2 of 4: withinLaneNormalization ...Step 3 of 4: betweenLaneNormalization ...Step 4 of 4: exprs ...基因百分位筛选：
dataFilt <- TCGAanalyze_Filtering(
tabDF = dataNorm,
method = "quantile",
qnt.cut = 0.25
)提取癌组织及正常组织：
# selection of normal samples "NT"
samplesNT <- TCGAquery_SampleTypes(
barcode = colnames(dataFilt),
typesample = c("NT")
)
# selection of tumor samples "TP"
samplesTP <- TCGAquery_SampleTypes(
barcode = colnames(dataFilt),
typesample = c("TP")
)edgeR 差异分析
TCGAanalyze_DEA函数使用以下 R 函数执行差异分析：
edgeR::DGEList将计数矩阵转换为edgeR对象。edgeR::estimateCommonDisp每个基因被分配相同的离散度估计。edgeR::exactTest对两组之间的差异表达进行成对检验。edgeR::topTags获取exactTest()的输出，使用 FDR 校正调整原始 p 值，并返回前几个差异表达基因。
在此之前需要先安装edgeR软件包：
BiocManager::install("edgeR")这个函数接受如下参数：
mat1第一组的矩阵（在本例中，第一组是正常样本）mat2第二组的矩阵（在本例中，第二组是肿瘤样本）Cond1type第一组的标签Cond2type第二组的标签
# Diff.expr.analysis (DEA)
dataDEGs <- TCGAanalyze_DEA(
mat1 = dataFilt[,samplesNT],
mat2 = dataFilt[,samplesTP],
Cond1type = "Normal",
Cond2type = "Tumor",
fdr.cut = 0.01 ,
logFC.cut = 1,
method = "glmLRT"
)Batch correction skipped since no factors provided----------------------- DEA -------------------------------o 113 samples in Cond1type Normalo 1111 samples in Cond2type Tumoro 26540 features as miRNA or genes This may take some minutes...----------------------- END DEA -------------------------------# DEGs table with expression values in normal and tumor samples
# 输出结果
dataDEGsFiltLevel <- TCGAanalyze_LevelTab(
FC_FDR_table_mRNA = dataDEGs,
typeCond1 = "Tumor",
typeCond2 = "Normal",
TableCond1 = dataFilt[,samplesTP],
TableCond2 = dataFilt[,samplesNT]
)看看输出的结果：
head(dataDEGs) logFC logCPM LR PValue FDR
ENSG00000000005 -3.459760 1.345418 352.86249 1.008787e-78 2.754444e-77
ENSG00000000460 1.295790 3.652530 217.64219 2.955933e-49 4.062685e-48
ENSG00000000971 -1.347594 5.925204 232.26403 1.912521e-52 2.867701e-51
ENSG00000001617 1.121932 6.776507 145.12346 2.018223e-33 1.662950e-32
ENSG00000002079 1.054519 -1.570113 39.04656 4.138172e-10 1.038947e-09
ENSG00000002726 3.418085 1.968093 122.54263 1.756018e-28 1.181062e-27
gene_name gene_type
ENSG00000000005 TNMD protein_coding
ENSG00000000460 C1orf112 protein_coding
ENSG00000000971 CFH protein_coding
ENSG00000001617 SEMA3F protein_coding
ENSG00000002079 MYH16 transcribed_unitary_pseudogene
ENSG00000002726 AOC1 protein_codinghead(dataDEGsFiltLevel) mRNA logFC FDR Delta Tumor
ENSG00000108821 ENSG00000108821 2.250877 6.552169e-48 929155.8 412797.3
ENSG00000115414 ENSG00000115414 2.927516 2.736040e-78 912481.3 311691.3
ENSG00000153002 ENSG00000153002 4.652968 2.759898e-16 502990.8 108101.1
ENSG00000164692 ENSG00000164692 1.602106 3.985857e-29 500681.0 312514.2
ENSG00000211896 ENSG00000211896 3.063492 1.424533e-22 499798.5 163146.7
ENSG00000168542 ENSG00000168542 1.426295 5.324314e-23 404047.3 283284.5
Normal start end
ENSG00000108821 84650.053 50184101 50201632
ENSG00000115414 39882.327 215360440 215436073
ENSG00000153002 3887.947 148791102 148860187
ENSG00000164692 102432.071 94394895 94431227
ENSG00000211896 19985.912 105736343 105743071
ENSG00000168542 104408.619 188974373 189012746保存：
write.csv(dataDEGs, "BRCA_dataDEGs.csv")
write.csv(dataDEGsFiltLevel, "BRCA_dataDEGsFiltLevel.csv")工作环境
devtools::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.3.1 (2023-06-16 ucrt)
os Windows 11 x64 (build 22621)
system x86_64, mingw32
ui RTerm
language (EN)
collate Chinese (Simplified)_China.utf8
ctype Chinese (Simplified)_China.utf8
tz Asia/Hong_Kong
date 2023-11-04
pandoc 3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
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