翻译 - TCGAbiolinks 8 - Case Studies
TCGAbiolinks：实例探究
在运行程序前，我们需要导入必要的包：
library(TCGAbiolinks)
library(SummarizedExperiment)
1. 介绍
在这里，我们展示了TCGAbiolinks的完整分析流程。分别在四个案例中展示相应的代码，这些案例是：
- Expression pipeline (BRCA)
- Expression pipeline (GBM)
- Integration of DNA methylation and RNA expression pipeline (COAD)
- ELMER pipeline (KIRC)
2. 案例研究 1：泛癌下游分析BRCA
在这个案例，我们对乳腺浸润癌（TCGA_BRCA）开展了分析，分析的内容包括：
- 计算差异表达基因；
- 差异基因的富集分析；
- 差异基因的生存分析；
- 差异基因的PPI网络分析
2.1. 数据下载及预处理
library(SummarizedExperiment)
library(TCGAbiolinks)
query.exp <- GDCquery(project = "TCGA-BRCA",
legacy = TRUE,
data.category = "Gene expression",
data.type = "Gene expression quantification",
platform = "Illumina HiSeq",
file.type = "results",
experimental.strategy = "RNA-Seq",
sample.type = c("Primary solid Tumor","Solid Tissue Normal"))
GDCdownload(query.exp)
brca.exp <- GDCprepare(query = query.exp, save = TRUE, save.filename = "brcaExp.rda")
# get subtype information
dataSubt <- TCGAquery_subtype(tumor = "BRCA")
# get clinical data
dataClin <- GDCquery_clinic(project = "TCGA-BRCA","clinical")
# Which samples are primary solid tumor
dataSmTP <- TCGAquery_SampleTypes(getResults(query.exp,cols="cases"),"TP")
# which samples are solid tissue normal
dataSmNT <- TCGAquery_SampleTypes(getResults(query.exp,cols="cases"),"NT")
2.2. 基因差异表达分析
使用TCGAnalyze_DEA，我们比较了114个正常样本和1097个BRCA样品，并鉴定了3,390个差异表达基因（DEGs, log fold change >=1 and FDR < 1%）。
dataPrep <- TCGAanalyze_Preprocessing(object = brca.exp, cor.cut = 0.6)
dataNorm <- TCGAanalyze_Normalization(tabDF = dataPrep,
geneInfo = geneInfo,
method = "gcContent")
dataFilt <- TCGAanalyze_Filtering(tabDF = dataNorm,
method = "quantile",
qnt.cut = 0.25)
dataDEGs <- TCGAanalyze_DEA(mat1 = dataFilt[,dataSmNT],
mat2 = dataFilt[,dataSmTP],
Cond1type = "Normal",
Cond2type = "Tumor",
fdr.cut = 0.01 ,
logFC.cut = 1,
method = "glmLRT")
2.3. 差异基因富集分析
为了理解这些差异表达基因的潜在的生物过程，我们使用TCGAnalyze_EA_complete函数进行了富集分析。
ansEA <- TCGAanalyze_EAcomplete(TFname="DEA genes Normal Vs Tumor",
RegulonList = rownames(dataDEGs))
TCGAvisualize_EAbarplot(tf = rownames(ansEA$ResBP),
GOBPTab = ansEA$ResBP,
GOCCTab = ansEA$ResCC,
GOMFTab = ansEA$ResMF,
PathTab = ansEA$ResPat,
nRGTab = rownames(dataDEGs),
nBar = 20)
最终TCGAbiolinks输出bar chart，其中包含GO分析的GO:biological process、GO:cellular component和GO:molecular function和Pathway的结果
上面代码得出的结果如下所示：
2.4. 差异基因生存分析
生存分析有两种，分别是单因素生存分析和多因素生存分析。下面我们分别介绍两种分析方法。
2.4.1. 单因素生存分析
我们使用Kaplan-Meier analysis来计算每一个基因的survival univariate curves，并使用TCGAanalyze_SurvivalKM函数，通过log-Ratio test来评估相应的统计显著性（statistical significance）;最终，我们从3,390个差异表达基因（DEGs）中获得555个对生存有影响的基因（p.value <0.05）
group1 <- TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("NT"))
group2 <- TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("TP"))
dataSurv <- TCGAanalyze_SurvivalKM(clinical_patient = dataClin,
dataGE = dataFilt,
Genelist = rownames(dataDEGs),
Survresult = FALSE,
ThreshTop = 0.67,
ThreshDown = 0.33,
p.cut = 0.05, group1, group2)
2.4.2. 多因素生存分析
我们使用Cox-regression analysis来计算survival multivariate curves，并使用TCGAnalyze_SurvivalCoxNET函数计算cox p-value值来评估统计显着性。Survival multivariate analysis发现了160个具有显著意义的基因（cox p-value FDR 5.00e-02）。
2.5. 差异基因PPI网络
在差异表达基因中（DEGs)中，通过univariate and multivariate analyses，我们发现了与生存相关的差异表达基因。下面，我们基于这个信息，进行网络分析。
通过使用STRING.,org.Hs.string version 10 (Human functional protein association network)，我们构建出了PPI互作网络。并通过dnet包从PPI互作网络中提取出multivariate survival genes相关的网络，这个网络的的节点之间具备strong interaction (threshold = 700)。最终这个网络包含24个节点（nodes）和31条边（edges）。
require(dnet) # to change
org.Hs.string <- dRDataLoader(RData = "org.Hs.string")
TabCoxNet <- TCGAvisualize_SurvivalCoxNET(dataClin,
dataFilt,
Genelist = rownames(dataSurv),
scoreConfidence = 700,
org.Hs.string = org.Hs.string,
titlePlot = "Case Study n.1 dnet")
上面代码获得的图如下所示：
3. 案例研究 2：泛癌下游分析LGG
在这个案例中，我们专注于脑低级别胶质瘤（TCGA-LGG）样品的分析。特别是，我们使用TCGAbiolinks下载293个具有分子亚型的样本。在这一节中，我们会以下几个分析：
- 处理样本的表达数据
- 对样本进行分类
- 不同类别的样本对肿瘤生存的影响
- 不同类别的样本的基因表达情况
- 不同类别的样本中基因突变情况
3.1. 基因表达数据下载
library(TCGAbiolinks)
library(SummarizedExperiment)
query.exp <- GDCquery(project = "TCGA-LGG",
legacy = TRUE,
data.category = "Gene expression",
data.type = "Gene expression quantification",
platform = "Illumina HiSeq",
file.type = "results",
experimental.strategy = "RNA-Seq",
sample.type = "Primary solid Tumor")
GDCdownload(query.exp)
lgg.exp <- GDCprepare(query = query.exp, save = TRUE, save.filename = "lggExp.rda")
3.2. 样品聚类分析 - 基于基因表达数据
在这里，我们将排除数据异常的样本、样本表达数据标准化、基因过滤，最终进行样本聚类，将LGG样本进行分类
-
异常样本数据排除
首先，我们使用
TCGAanalyze_Preprocessing函数执行一次Array Array Intensity correlation AAIC来搜索可能的异常值。我们使用表达数据中包含了分子亚型信息的全部样本，然后过滤掉没有分子信息的样本，并且仅使用IDHmut-codel (n=85)、IDHmut-non-codel (n=141)、IDHwt (n=56) 和 NA (11)来定义定义所有样本中皮尔逊相关的方形对称矩阵（n = 293）。根据这个矩阵，我们发现没有样本可以被识别为可能的异常值（low correlation (cor.cut = 0.6)）。因此我们继续使用70个样本进行分析。 -
样本表达数据标准化
其次，我们使用
TCGAanalyze_Normalization函数借助EDASeq包对mRNA transcripts和miRNA进行标准化：1）该函数使用Within-lane normalization procedures来调整read counts中的GC-content effect（或者其他gene-level effects）：loess robust local regression、global-scaling、full-quantile normalization(Risso et al. 2011) ；2）使用between-lane normalization procedures来调整distributional differences between lanes（比如sequencing depth）：global-scaling、full-quantile normalization(Bullard et al. 2010). -
基因过滤
第三，我们使用
TCGAanalyze_Filtering函数，通过3个过滤器去除样品中低信号（low signal）的features / mRNAs，第一次过滤后获得4578个mRNA，第二次过滤后进一步获得4284个mRNA，第三次过滤后最终获得1187个mRNA。 -
样本聚类
然后，我们在上述三次过滤后获得的1187个mRNA的基础上，应用了两个层次聚类（Hierarchical Cluster）：1）第一个聚类使用
ward.D2；2）第二个聚类使用ConsensusClusterPlus。
在两次聚类分析之后，使用cut.tree = 4，我们获得n = 4个表达簇（EC）。
library(dplyr)
dataPrep <- TCGAanalyze_Preprocessing(object = lgg.exp, cor.cut = 0.6)
dataNorm <- TCGAanalyze_Normalization(tabDF = dataPrep,
geneInfo = geneInfo,
method = "gcContent")
datFilt <- dataNorm %>% TCGAanalyze_Filtering(method = "varFilter") %>%
TCGAanalyze_Filtering(method = "filter1") %>% TCGAanalyze_Filtering(method = "filter2",foldChange = 0.2)
data_Hc2 <- TCGAanalyze_Clustering(tabDF = datFilt,
method = "consensus",
methodHC = "ward.D2")
# Add cluster information to Summarized Experiment
colData(lgg.exp)$groupsHC <- paste0("EC",data_Hc2[[4]]$consensusClass)
3.3. 四类样本的生存分析
接下来的步骤是可视化数据。首先，我们绘制四个表达簇的生存曲线：
TCGAanalyze_survival(data = colData(lgg.exp),
clusterCol = "groupsHC",
main = "TCGA kaplan meier survival plot from consensus cluster",
legend = "RNA Group",height = 10,
risk.table = T,conf.int = F,
color = c("black","red","blue","green3"),
filename = "survival_lgg_expression_subtypes.png")
结果如下所示：
3.4. 四类样本的基因表达热图
同时，我们还绘制基因表达的热图：
TCGAvisualize_Heatmap(t(datFilt),
col.metadata = colData(lgg.exp)[,c("barcode",
"groupsHC",
"subtype_Histology",
"subtype_IDH.codel.subtype")],
col.colors = list(
groupsHC = c("EC1"="black",
"EC2"="red",
"EC3"="blue",
"EC4"="green3")),
sortCol = "groupsHC",
type = "expression", # sets default color
scale = "row", # use z-scores for better visualization. Center gene expression level around 0.
title = "Heatmap from concensus cluster",
filename = "case2_Heatmap.png",
cluster_rows = TRUE,
color.levels = colorRampPalette(c("green", "black", "red"))(n = 11),
extrems =seq(-5,5,1),
cluster_columns = FALSE,
width = 1000,
height = 1000)
结果如下所示：
3.5. 基因突变分析
最后，我们将看一下突变基因情况。我们首先通过GDCquery_Maf函数下载MAF文件。在这个例子中，我们将研究基因“ATRX”的突变情况。
LGGmut <- GDCquery_Maf(tumor = "LGG", pipelines = "muse")
# Selecting gene
mRNAsel <- "ATRX"
LGGselected <- LGGmut[LGGmut$Hugo_Symbol == mRNAsel,]
dataMut <- LGGselected[!duplicated(LGGselected$Tumor_Sample_Barcode),]
dataMut$Tumor_Sample_Barcode <- substr(dataMut$Tumor_Sample_Barcode,1,12)
# Adding the Expression Cluster classification found before
dataMut <- merge(dataMut, cluster, by.y="patient", by.x="Tumor_Sample_Barcode")
dataMut <- dataMut[dataMut$Variant_Classification!=0,]
4. 案例研究 3：ACC的甲基化和表达的整合
近年来的研究，已经描述了DNA甲基化与基因表达之间的关系，并且这种关系的研究通常很难实现。
本案例研究将展示研究DNA甲基化与基因表达之间关系的步骤。
4.1. DNA甲基化数据和基因表达数据下载
首先，我们下载ACC DNA甲基化数据（HumanMethylation450k platforms），以及ACC RNA表达数据（Illumina HiSeq platform）。
TCGAbiolinks将默认添加了研究人员已经发布的亚型分类。我们将使用这些分类作为示例。因此，选择CIMP-low和CIMP-high组进行RNA表达和DNA甲基化比较。
library(TCGAbiolinks)
library(SummarizedExperiment)
dir.create("case3")
setwd("case3")
#-----------------------------------
# STEP 1: Search, download, prepare |
#-----------------------------------
# 1.1 - DNA methylation
# ----------------------------------
query.met <- GDCquery(project = "TCGA-ACC",
legacy = TRUE,
data.category = "DNA methylation",
platform = "Illumina Human Methylation 450")
GDCdownload(query.met)
acc.met <- GDCprepare(query = query.met,
save = TRUE,
save.filename = "accDNAmet.rda",
summarizedExperiment = TRUE)
#-----------------------------------
# 1.2 - RNA expression
# ----------------------------------
query.exp <- GDCquery(project = "TCGA-ACC",
legacy = TRUE,
data.category = "Gene expression",
data.type = "Gene expression quantification",
platform = "Illumina HiSeq",
file.type = "results")
GDCdownload(query.exp)
acc.exp <- GDCprepare(query = query.exp, save = TRUE, save.filename = "accExp.rda")
4.2. DNA甲基化差异分析
对于DNA甲基化，我们进行DMR (different methylated region) 分析，获得探针在不同组的DNA甲基化差异及其显着性值。输出的结果可以在火山图中看到。
注意：该函数会运行
wilcoxon test，因此根据样品的数量，函数的运行可能非常慢，需要数小时到数天。
# na.omit
acc.met <- subset(acc.met,subset = (rowSums(is.na(assay(acc.met))) == 0))
# Volcano plot
acc.met <- TCGAanalyze_DMR(acc.met, groupCol = "subtype_MethyLevel",
group1 = "CIMP-high",
group2="CIMP-low",
p.cut = 10^-5,
diffmean.cut = 0.25,
legend = "State",
plot.filename = "CIMP-highvsCIMP-low_metvolcano.png")
上面代码得到的火山图如下所示：
4.3. 基因表达差异分析
对于表达分析，我们进行差异表达分析（DEA, differential expression analysis），其将给出基因表达的fold change及其显着性值。
#-------------------------------------------------
# 2.3 - DEA - Expression analysis - volcano plot
# ------------------------------------------------
acc.exp.aux <- subset(acc.exp,
select = colData(acc.exp)$subtype_MethyLevel %in% c("CIMP-high","CIMP-low"))
idx <- colData(acc.exp.aux)$subtype_MethyLevel %in% c("CIMP-high")
idx2 <- colData(acc.exp.aux)$subtype_MethyLevel %in% c("CIMP-low")
dataPrep <- TCGAanalyze_Preprocessing(object = acc.exp.aux, cor.cut = 0.6)
dataNorm <- TCGAanalyze_Normalization(tabDF = dataPrep,
geneInfo = geneInfo,
method = "gcContent")
dataFilt <- TCGAanalyze_Filtering(tabDF = dataNorm,
qnt.cut = 0.25,
method='quantile')
dataDEGs <- TCGAanalyze_DEA(mat1 = dataFilt[,idx],
mat2 = dataFilt[,idx2],
Cond1type = "CIMP-high",
Cond2type = "CIMP-low",
method = "glmLRT")
TCGAVisualize_volcano(x = dataDEGs$logFC,
y = dataDEGs$FDR,
filename = "Case3_volcanoexp.png",
x.cut = 3,
y.cut = 10^-5,
names = rownames(dataDEGs),
color = c("black","red","darkgreen"),
names.size = 2,
xlab = " Gene expression fold change (Log2)",
legend = "State",
title = "Volcano plot (CIMP-high vs CIMP-low)",
width = 10)
上面代码得到的图如下所示：
4.4. DNA甲基化和基因表达关联
最后，这个和前面两个分析结果，我们绘制一个starburst来选择具有重要生物学意义的基因。
观察：随着时间的推移，LGG样本的数量增加，临床数据也进行了更新。这个案例中，我们仅使用了示例中具有分类的样本。
#------------------------------------------
# 2.4 - Starburst plot
# -----------------------------------------
# If true the argument names of the geenes in circle
# (biologically significant genes, has a change in gene
# expression and DNA methylation and respects all the thresholds)
# will be shown
# these genes are returned by the function see starburst object after the function is executed
starburst <- TCGAvisualize_starburst(met = acc.met,
exp = dataDEGs,
genome = "hg19"
group1 = "CIMP-high",
group2 = "CIMP-low",
filename = "starburst.png",
met.platform = "450K",
met.p.cut = 10^-5,
exp.p.cut = 10^-5,
diffmean.cut = 0.25,
logFC.cut = 3,
names = FALSE,
height=10,
width=15,
dpi=300)
上面代码得到的starburst图如下所示：
5. 案例研究 4：Elmer管道 - KIRC
ELMER是进行DNA甲基化和RNA表达分析的一个实例包（L. Yao et al. 2015,Chedraoui Silva et al. (2017),Yao, Berman, and Farnham (2015)）。它旨在结合人类组织的DNA甲基化和基因表达数据，推断出多层次顺式调控网络（multi-level cis-regulatory networks）。ELMER使用DNA甲基化来识别远端探针（distal probes），并将它们与附近基因的表达相关联，以识别一个或多个转录靶标（transcriptional targets）。将这些反相关远端探针（anti-correlated distal probes）进行转录因子结合位点分析（Transcription factor (TF) binding site analysis），并欧联所有转录因子（TF）的表达分析，以此推断上游调节因子（upstream regulators）。该包可以很容易地应用于TCGA的癌症数据集，用于分析DNA甲基化和基因表达数据集。
ELMER分析包括以下步骤：
- 将数据转变为
MultiAssayExperiment对象 - 当比较两组样本的时候，鉴定具DNA甲基化显著差异的远端探针（distal probes）。
- 使用DNA甲基化与基因表达相关性（methylation vs. expression correlation），鉴定DNA甲基化显著差异的远端探针的假定靶基因（putative target genes）
- 从每一个significant probe-gene队中，鉴定在对应探针上的富集的基元（motifs）
- 确定主要调控转录因子（TF），其表达与DNA甲基化相关，而该DNA甲基化在多个调控区域发生变化。
下面，我们将通过整合TCGAbiolinks和ELMER进行肾脏透明肾细胞癌（KIRC）的研究。
ELMER包
有关更多信息，请参阅ELMER包：
以及下面几篇文章：
5.1. DNA甲基化数据下载处理
首先，我们从搜索并下载KIRC的DNA甲基化数据（HumanMethylation450），并将下载数据转化为SummarizedExperiment对象：
library(TCGAbiolinks)
library(SummarizedExperiment)
library(ELMER)
library(parallel)
dir.create("case4")
setwd("case4")
#-----------------------------------
# STEP 1: Search, download, prepare |
#-----------------------------------
# 1.1 - DNA methylation
# ----------------------------------
query.met <- GDCquery(project = "TCGA-KIRC",
data.category = "DNA Methylation",
platform = "Illumina Human Methylation 450")
GDCdownload(query.met)
kirc.met <- GDCprepare(query = query.met,
save = TRUE,
save.filename = "kircDNAmet.rda",
summarizedExperiment = TRUE)
5.2. 基因表达数据下载处理
对于基因表达，我们将使用的数据类型是：Gene Expression Quantification
# Step 1.2 download expression data
#-----------------------------------
# 1.2 - RNA expression
# ----------------------------------
query.exp <- GDCquery(project = "TCGA-KIRC",
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "HTSeq - FPKM-UQ")
GDCdownload(query.exp)
kirc.exp <- GDCprepare(query = query.exp,
save = TRUE,
save.filename = "kircExp.rda")
kirc.exp <- kirc.exp
5.3. DNA甲基化显著差异的远端探针鉴定
来自r BiocStyle::Biocpkg(“MultiAssayExperiment”)的一个MultiAssayExperiment对象作为r BiocStyle::Biocpkg(“ELMER”)的多个主要函数的输入
我们首先需要获得远端探针（2 KB away from TSS）
distal.probes <- get.feature.probe(genome = "hg38", met.platform = "450K")
然后，我们使用createMAE函数保留具有DNA甲基化和基因表达数据的样品。
library(MultiAssayExperiment)
mae <- createMAE(exp = kirc.exp,
met = kirc.met,
save = TRUE,
linearize.exp = TRUE,
filter.probes = distal.probes,
save.filename = "mae_kirc.rda",
met.platform = "450K",
genome = "hg38",
TCGA = TRUE)
# Remove FFPE samples
mae <- mae[,!mae$is_ffpe]
然后，我们执行ELMER以鉴定与正常样品相比在肿瘤样品中低甲基化（hypomethylated）的探针。
group.col <- "definition"
group1 <- "Primary solid Tumor"
group2 <- "Solid Tissue Normal"
direction <- "hypo"
dir.out <- file.path("kirc",direction)
dir.create(dir.out, recursive = TRUE)
#--------------------------------------
# STEP 3: Analysis |
#--------------------------------------
# Step 3.1: Get diff methylated probes |
#--------------------------------------
sig.diff <- get.diff.meth(data = mae,
group.col = group.col,
group1 = group1,
group2 = group2,
minSubgroupFrac = 0.2,
sig.dif = 0.3,
diff.dir = direction, # Search for hypomethylated probes in group 1
cores = 1,
dir.out = dir.out,
pvalue = 0.01)
#-------------------------------------------------------------
# Step 3.2: Identify significant probe-gene pairs |
#-------------------------------------------------------------
# Collect nearby 20 genes for Sig.probes
nearGenes <- GetNearGenes(data = mae,
probes = sig.diff$probe,
numFlankingGenes = 20, # 10 upstream and 10 dowstream genes
cores = 1)
pair <- get.pair(data = mae,
group.col = group.col,
group1 = group1,
group2 = group2,
nearGenes = nearGenes,
minSubgroupFrac = 0.4, # % of samples to use in to create groups U/M
permu.dir = file.path(dir.out,"permu"),
permu.size = 100, # Please set to 100000 to get significant results
raw.pvalue = 0.05,
Pe = 0.01, # Please set to 0.001 to get significant results
filter.probes = TRUE, # See preAssociationProbeFiltering function
filter.percentage = 0.05,
filter.portion = 0.3,
dir.out = dir.out,
cores = 1,
label = direction)
# Identify enriched motif for significantly hypomethylated probes which
# have putative target genes.
enriched.motif <- get.enriched.motif(data = mae,
probes = pair$Probe,
dir.out = dir.out,
label = direction,
min.incidence = 10,
lower.OR = 1.1)
TF <- get.TFs(data = mae,
group.col = group.col,
group1 = group1,
group2 = group2,
minSubgroupFrac = 0.4,
enriched.motif = enriched.motif,
dir.out = dir.out,
cores = 1,
label = direction)
5.4. 远端探针与基因的关系
随后，更具这个分析结果，我们可以验证该探针附近的20个基因的表达与DNA甲基化之间的关系。其结果由ELMER散点图显示。
scatter.plot(data = mae,
byProbe = list(probe = sig.diff$probe[1], numFlankingGenes = 20),
category = "definition",
dir.out = "plots",
lm = TRUE, # Draw linear regression curve
save = TRUE)
结果如下所示：
5.5. Motif与基因、转录因子的关系
此外，我们还可以通过散点图观察KIRC样品中，具有UA6 motif的位点（sites）的平均DNA甲基化水平与转录因子（"RUNX1","RUNX2","RUNX3"）表达水平的关系。
scatter.plot(data = mae,
byTF = list(TF = c("RUNX1","RUNX2","RUNX3"),
probe = enriched.motif[[names(enriched.motif)[10]]]),
category = "definition",
dir.out = "plots",
save = TRUE,
lm_line = TRUE)
结果如下所示：
我们还可以通过绘制热图来查看反相关（anticorrelated）的基因和探针对：
heatmapPairs(data = mae,
group.col = "definition",
group1 = "Primary solid Tumor",
annotation.col = c("gender"),
group2 = "Solid Tissue Normal",
pairs = pair,
filename = "heatmap.pdf")
结果如下所示：
在下图中显示了选定的motif（lower boundary of OR above 1.8）的Odds Ratio（x轴），对应的Odds Ratio的下限高于1.8。并展示了每一个Odds Ratio的95％置信区间（95% confidence interval）：
6. 会话信息
sessionInfo()
## R version 3.5.3 (2019-03-11)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.6 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.8-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.8-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid parallel stats4 stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] TCGAbiolinks_2.10.5 png_0.1-7
## [3] DT_0.5 dplyr_0.8.0.1
## [5] SummarizedExperiment_1.12.0 DelayedArray_0.8.0
## [7] BiocParallel_1.16.6 matrixStats_0.54.0
## [9] Biobase_2.42.0 GenomicRanges_1.34.0
## [11] GenomeInfoDb_1.18.2 IRanges_2.16.0
## [13] S4Vectors_0.20.1 BiocGenerics_0.28.0
## [15] testthat_2.0.1
##
## loaded via a namespace (and not attached):
## [1] backports_1.1.3 circlize_0.4.5
## [3] AnnotationHub_2.14.5 aroma.light_3.12.0
## [5] plyr_1.8.4 selectr_0.4-1
## [7] ConsensusClusterPlus_1.46.0 lazyeval_0.2.2
## [9] splines_3.5.3 usethis_1.4.0
## [11] ggplot2_3.1.0 sva_3.30.1
## [13] digest_0.6.18 foreach_1.4.4
## [15] htmltools_0.3.6 magrittr_1.5
## [17] memoise_1.1.0 cluster_2.0.7-1
## [19] doParallel_1.0.14 limma_3.38.3
## [21] remotes_2.0.2 ComplexHeatmap_1.20.0
## [23] Biostrings_2.50.2 readr_1.3.1
## [25] annotate_1.60.1 sesameData_1.0.0
## [27] R.utils_2.8.0 prettyunits_1.0.2
## [29] colorspace_1.4-1 ggrepel_0.8.0
## [31] blob_1.1.1 rvest_0.3.2
## [33] xfun_0.5 callr_3.2.0
## [35] crayon_1.3.4 RCurl_1.95-4.12
## [37] jsonlite_1.6 genefilter_1.64.0
## [39] survival_2.43-3 zoo_1.8-4
## [41] iterators_1.0.10 glue_1.3.1
## [43] survminer_0.4.3 gtable_0.2.0
## [45] sesame_1.0.0 zlibbioc_1.28.0
## [47] XVector_0.22.0 GetoptLong_0.1.7
## [49] wheatmap_0.1.0 pkgbuild_1.0.2
## [51] shape_1.4.4 scales_1.0.0
## [53] DESeq_1.34.1 DBI_1.0.0
## [55] edgeR_3.24.3 ggthemes_4.1.0
## [57] Rcpp_1.0.1 xtable_1.8-3
## [59] progress_1.2.0 cmprsk_2.2-7
## [61] matlab_1.0.2 bit_1.1-14
## [63] preprocessCore_1.44.0 km.ci_0.5-2
## [65] htmlwidgets_1.3 httr_1.4.0
## [67] RColorBrewer_1.1-2 pkgconfig_2.0.2
## [69] XML_3.98-1.19 R.methodsS3_1.7.1
## [71] DNAcopy_1.56.0 locfit_1.5-9.1
## [73] labeling_0.3 later_0.8.0
## [75] tidyselect_0.2.5 rlang_0.3.1
## [77] AnnotationDbi_1.44.0 munsell_0.5.0
## [79] tools_3.5.3 downloader_0.4
## [81] cli_1.1.0 ExperimentHub_1.8.0
## [83] generics_0.0.2 RSQLite_2.1.1
## [85] devtools_2.0.1 broom_0.5.1
## [87] evaluate_0.13 stringr_1.4.0
## [89] yaml_2.2.0 processx_3.3.0
## [91] knitr_1.22 bit64_0.9-7
## [93] fs_1.2.7 survMisc_0.5.5
## [95] randomForest_4.6-14 purrr_0.3.2
## [97] EDASeq_2.16.3 nlme_3.1-137
## [99] mime_0.6 R.oo_1.22.0
## [101] xml2_1.2.0 biomaRt_2.38.0
## [103] compiler_3.5.3 rstudioapi_0.10
## [105] curl_3.3 interactiveDisplayBase_1.20.0
## [107] tibble_2.1.1 geneplotter_1.60.0
## [109] stringi_1.4.3 highr_0.7
## [111] ps_1.3.0 GenomicFeatures_1.34.6
## [113] desc_1.2.0 lattice_0.20-38
## [115] Matrix_1.2-16 KMsurv_0.1-5
## [117] pillar_1.3.1 BiocManager_1.30.4
## [119] GlobalOptions_0.1.0 data.table_1.12.0
## [121] bitops_1.0-6 httpuv_1.5.0
## [123] rtracklayer_1.42.2 R6_2.4.0
## [125] latticeExtra_0.6-28 hwriter_1.3.2
## [127] promises_1.0.1 ShortRead_1.40.0
## [129] gridExtra_2.3 codetools_0.2-16
## [131] sessioninfo_1.1.1 assertthat_0.2.0
## [133] pkgload_1.0.2 rprojroot_1.3-2
## [135] rjson_0.2.20 withr_2.1.2
## [137] GenomicAlignments_1.18.1 Rsamtools_1.34.1
## [139] GenomeInfoDbData_1.2.0 mgcv_1.8-27
## [141] hms_0.4.2 tidyr_0.8.3
## [143] rmarkdown_1.12 ggpubr_0.2
## [145] shiny_1.2.0
7. 参考
- Bullard, James H, Elizabeth Purdom, Kasper D Hansen, and Sandrine Dudoit. 2010. “Evaluation of Statistical Methods for Normalization and Differential Expression in mRNA-Seq Experiments.” BMC Bioinformatics 11 (1). BioMed Central Ltd:94.
- Chedraoui Silva, Tiago, Simon G. Coetzee, Lijing Yao, Dennis J. Hazelett, Houtan Noushmehr, and Benjamin P. Berman. 2017. “Enhancer Linking by Methylation/Expression Relationships with the R Package Elmer Version 2.” bioRxiv. Cold Spring Harbor Labs Journals. https://doi.org/10.1101/148726.
- Risso, Davide, Katja Schwartz, Gavin Sherlock, and Sandrine Dudoit. 2011. “GC-Content Normalization for Rna-Seq Data.” BMC Bioinformatics 12 (1). BioMed Central Ltd:480.
- Yao, Lijing, Benjamin P Berman, and Peggy J Farnham. 2015. “Demystifying the Secret Mission of Enhancers: Linking Distal Regulatory Elements to Target Genes.” Critical Reviews in Biochemistry and Molecular Biology 50 (6). Taylor & Francis:550–73.
- Yao, L, H Shen, PW Laird, PJ Farnham, and BP Berman. 2015. “Inferring Regulatory Element Landscapes and Transcription Factor Networks from Cancer Methylomes.” Genome Biology 16 (1):105–5.
更新时间：2019-05-25 17:30:30
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最后更新：2019-05-25 17:30:30
