Analysis of Cancer Genome Atlas in R
Tiago Maié
Fabio Ticconi
Martin Grasshoff
Martin Manolov
21 October 2022
1 Intro
This exercise will show how to obtain clinical and genomic data from the Cancer Genome Atlas (TGCA) and to perform classical analysis important for clinical data.
These include:
- Download the data (clinical and expression) from TGCA
- Processing of the data (normalization) and saving it locally using simple table formats.
- Unsupervised analysis includes differential expression, PCA and clustering.
- Build a machine learning model (classifier) to predict cancer.
- Perform survival analysis of molecular markers detected in previous analysis.
First, we start by loading all libraries necessary for this exercise. Please check their documentation if you want to know more.
# Load packages
library("TCGAbiolinks")
library("limma")
library("edgeR")
library("glmnet")
library("factoextra")
library("FactoMineR")
library("caret")
library("SummarizedExperiment")
library("gplots")
library("survival")
library("survminer")
library("RColorBrewer")
library("clusterProfiler")
library("genefilter")2 TCGA data
In this tutorial, we will focus on Liver Hepatocellular Carcinoma, which is identified in TCGA as LIHC. For LIHC, TCGA provides data for 377 patients including: clinical, expression, DNA methylation and genotyping data. In this tutorial, we will work with clinical and expression data (RNA-seq). Go to https://portal.gdc.cancer.gov/ and search for TCGA-LIHC if you want to understand the data deposited in TCGA. You can also try to find your way through the previous data to look for other data sets of your interest.
We will make use of the TCGAbiolinks library, which allows us to query, prepare and download data from the TCGA portal. TCGAbiolinks provides important functionality as matching data of same the donors across distinct data types (clinical vs expression) and provides data structures to make its analysis in R easy.
To download TCGA data with TCGAbiolinks, you need to follow 3 steps. First, you will query the TCGA database through R with the function GDCquery. This will allow you to investigate the data available at the TCGA database. Next, we use GDCdownload to download raw version of desired files into your computer. Finally GDCprepare will read these files and make R data structures so that we can further analyse them.
Before we get there however we need to know what we are searching for. We can check all the available projects at TCGA with the command bellow. Since there are many lets look at the first 6 projects using the command head().
GDCprojects = getGDCprojects()
head(GDCprojects[c("project_id", "name")])## project_id
## 1 TARGET-NBL
## 2 GENIE-GRCC
## 3 GENIE-DFCI
## 4 GENIE-NKI
## 5 GENIE-VICC
## 6 GENIE-UHN
## name
## 1 Neuroblastoma
## 2 AACR Project GENIE - Contributed by Institut Gustave Roussy
## 3 AACR Project GENIE - Contributed by Dana-Farber Cancer Institute
## 4 AACR Project GENIE - Contributed by Netherlands Cancer Institute
## 5 AACR Project GENIE - Contributed by Vanderbilt-Ingram Cancer Center
## 6 AACR Project GENIE - Contributed by Princess Margaret Cancer CentreAs a general rule in R (and especially if you are working in RStudio) whenever some method returns some value or table you are not familiar with, you should check its structure and dimensions. You can always use functions such as head() to only show the first entries and dim() to check the dimension of the data.
We already know that Liver Hepatocellular Carcinoma has as id TCGA-LIHC. We can use the following function to get details on all data deposited for TCGA-LIHC.
TCGAbiolinks:::getProjectSummary("TCGA-LIHC")## $file_count
## [1] 16965
##
## $data_categories
## file_count case_count data_category
## 1 2282 376 Copy Number Variation
## 2 2502 377 Sequencing Reads
## 3 5256 375 Simple Nucleotide Variation
## 4 1290 377 DNA Methylation
## 5 423 377 Clinical
## 6 1698 376 Transcriptome Profiling
## 7 1634 377 Biospecimen
## 8 184 184 Proteome Profiling
## 9 1696 371 Structural Variation
##
## $case_count
## [1] 377
##
## $file_size
## [1] 2.671516e+13Of note, not all patients were measured for all data types. Also, some data types have more files than samples. This is the case when more experiments were performed per patient, i.e. transcriptome profiling was performed both in mRNA and miRNA, or that data have been analysed by distinct computational strategies.
Let us start by querying all RNA-seq data from LIHC project. When using GDCquery we always need to specify the id of the project, i.e. “TCGA_LIHC”, and the data category we are interested in, i.e. “Transcriptome Profiling”. Here, we will focus on a particular type of data summarization for mRNA-seq data (workflow.type), which is based on raw counts estimated with HTSeq.
Note that performing this query will take a few of minutes
query_TCGA = GDCquery(
project = "TCGA-LIHC",
data.category = "Transcriptome Profiling", # parameter enforced by GDCquery
data.type = "Gene Expression Quantification",
experimental.strategy = "RNA-Seq",
#workflow.type = "HTSeq - Counts"
workflow.type = "STAR - Counts"
)To visualize the query results in a more readable way, we can use the command getResults.
lihc_res = getResults(query_TCGA) # make results as table
# head(lihc_res) # data of the first 6 patients.
colnames(lihc_res) # columns present in the table## [1] "id" "data_format"
## [3] "cases" "access"
## [5] "file_name" "submitter_id"
## [7] "data_category" "type"
## [9] "file_size" "created_datetime"
## [11] "md5sum" "updated_datetime"
## [13] "file_id" "data_type"
## [15] "state" "experimental_strategy"
## [17] "version" "data_release"
## [19] "project" "analysis_id"
## [21] "analysis_state" "analysis_submitter_id"
## [23] "analysis_workflow_link" "analysis_workflow_type"
## [25] "analysis_workflow_version" "sample_type"
## [27] "is_ffpe" "cases.submitter_id"
## [29] "sample.submitter_id"One interesting question is the tissue type measured at an experiment (normal, solid tissue, cell line). This information is present at column sample_type.
head(lihc_res$sample_type) # first 6 types of tissue.## [1] "Primary Tumor" "Primary Tumor" "Primary Tumor"
## [4] "Primary Tumor" "Solid Tissue Normal" "Primary Tumor"sample_type is a categorical variable, which can be better visualized with the table function, so that we can check how many different categories exist in the data.
table(lihc_res$sample_type) # summary of distinct tissues types present in this study##
## Primary Tumor Recurrent Tumor Solid Tissue Normal
## 371 3 50As you see, there are 50 controls (Solid Tissue Normal) and 371 cancer samples (Primary Tumors). For simplicity, we will ignore the small class of recurrent tumors. Therefore, we will redo the query as
query_TCGA = GDCquery(
project = "TCGA-LIHC",
data.category = "Transcriptome Profiling", # parameter enforced by GDCquery
data.type = "Gene Expression Quantification",
experimental.strategy = "RNA-Seq",
workflow.type = "STAR - Counts",
sample.type = c("Primary Tumor", "Solid Tissue Normal"))Next, we need to download the files from the query. Before, be sure that you set your current working directory to the place you want to save your data. TCGA will save the data in a directory structure starting with a directory “GDCdata”.
Let us now download the files specified in the query.
GDCdownload(query = query_TCGA)Given that you need to download many files, the previous operation might take some time. Often the download fails for one or another file. You can re-run the previous command until no error message is given. The method will recognize that the data has already been downloaded and won’t download the data again.
Finally, lets load the actual RNAseq data into R. Remember that the output directory set must be the same to where you downloaded the data.
tcga_data = GDCprepare(query_TCGA)We can then check the size of the object with the command.
dim(tcga_data)## [1] 60660 421There are 3 functions that allow us to access to most important data present in this object, these are: colData(), rowData(), assays(). Use the command ?SummarizedExperiment to find more details. colData() allows us to access the clinical data associated with our samples. The functions colnames() and rownames() can be used to extract the column and rows names from a given table respectively. But be careful if you have large objects. The colnames() and rownames() functions return all column and row names, even if you have tens of thousands. The combination header(colnames()) can make more sense in these cases.
# In R (and other programming languages) you can often
# chain functions to save time and space
colnames(colData(tcga_data))## [1] "barcode" "patient"
## [3] "sample" "shortLetterCode"
## [5] "definition" "sample_submitter_id"
## [7] "sample_type_id" "oct_embedded"
## [9] "sample_id" "submitter_id"
## [11] "state" "is_ffpe"
## [13] "sample_type" "tissue_type"
## [15] "tumor_descriptor" "composition"
## [17] "days_to_collection" "initial_weight"
## [19] "pathology_report_uuid" "synchronous_malignancy"
## [21] "ajcc_pathologic_stage" "days_to_diagnosis"
## [23] "treatments" "last_known_disease_status"
## [25] "tissue_or_organ_of_origin" "days_to_last_follow_up"
## [27] "age_at_diagnosis" "primary_diagnosis"
## [29] "prior_malignancy" "year_of_diagnosis"
## [31] "prior_treatment" "ajcc_staging_system_edition"
## [33] "ajcc_pathologic_t" "morphology"
## [35] "ajcc_pathologic_n" "ajcc_pathologic_m"
## [37] "classification_of_tumor" "diagnosis_id"
## [39] "icd_10_code" "site_of_resection_or_biopsy"
## [41] "tumor_grade" "progression_or_recurrence"
## [43] "alcohol_history" "exposure_id"
## [45] "race" "gender"
## [47] "ethnicity" "vital_status"
## [49] "age_at_index" "days_to_birth"
## [51] "year_of_birth" "demographic_id"
## [53] "year_of_death" "days_to_death"
## [55] "bcr_patient_barcode" "primary_site"
## [57] "project_id" "disease_type"
## [59] "name" "releasable"
## [61] "released" "sample.aux"This link provides a basic explanation about tcga_data. Note that both clinical and expression data are present in this object.
Lets look at some potentially interesting features. The table() function (in this context) produces a small summary with the sum of each of the factors present in a given column.
table(tcga_data@colData$vital_status)##
## Alive Dead Not Reported
## 255 164 2table(tcga_data@colData$ajcc_pathologic_stage)##
## Stage I Stage II Stage III Stage IIIA Stage IIIB Stage IIIC Stage IV
## 189 97 6 73 8 10 3
## Stage IVA Stage IVB
## 1 2table(tcga_data@colData$definition)##
## Primary solid Tumor Solid Tissue Normal
## 371 50table(tcga_data@colData$tissue_or_organ_of_origin)##
## Liver
## 421table(tcga_data@colData$gender)##
## female male
## 143 278table(tcga_data@colData$race)##
## american indian or alaska native asian
## 2 164
## black or african american not reported
## 24 13
## white
## 218Is there a particular column (feature) that allows you to distinguish tumor tissue from normal tissue?
What about the RNA-seq data? We can use the assay function to obtain the RNA-seq count matrices and rowData to see gene mapping information. Can you tell how many genes and how many samples are included there?
dim(assay(tcga_data)) # gene expression matrices.## [1] 60660 421head(assay(tcga_data)[,1:10]) # expression of first 6 genes and first 10 samples## TCGA-DD-AACJ-01A-11R-A41C-07 TCGA-DD-A4NE-01A-11R-A27V-07
## ENSG00000000003.15 3311 3132
## ENSG00000000005.6 2 0
## ENSG00000000419.13 1216 1596
## ENSG00000000457.14 758 1241
## ENSG00000000460.17 156 530
## ENSG00000000938.13 91 66
## TCGA-ED-A8O6-01A-11R-A36F-07 TCGA-MI-A75I-01A-11R-A32O-07
## ENSG00000000003.15 7510 5443
## ENSG00000000005.6 0 0
## ENSG00000000419.13 1904 1217
## ENSG00000000457.14 719 432
## ENSG00000000460.17 233 138
## ENSG00000000938.13 106 133
## TCGA-FV-A3I0-11A-11R-A22L-07 TCGA-G3-AAV4-01A-11R-A38B-07
## ENSG00000000003.15 3957 839
## ENSG00000000005.6 0 18
## ENSG00000000419.13 873 1475
## ENSG00000000457.14 316 568
## ENSG00000000460.17 57 201
## ENSG00000000938.13 278 64
## TCGA-BC-4072-01B-11R-A155-07 TCGA-2Y-A9H6-01A-11R-A39D-07
## ENSG00000000003.15 4823 8519
## ENSG00000000005.6 0 5
## ENSG00000000419.13 2426 937
## ENSG00000000457.14 773 536
## ENSG00000000460.17 220 122
## ENSG00000000938.13 958 343
## TCGA-DD-A1EC-11A-11R-A131-07 TCGA-CC-5260-01A-01R-A131-07
## ENSG00000000003.15 3414 4241
## ENSG00000000005.6 5 1
## ENSG00000000419.13 819 786
## ENSG00000000457.14 416 535
## ENSG00000000460.17 466 200
## ENSG00000000938.13 274 507head(rowData(tcga_data)) # ensembl id and gene id of the first 6 genes.## DataFrame with 6 rows and 10 columns
## source type score phase gene_id
## <factor> <factor> <numeric> <integer> <character>
## ENSG00000000003.15 HAVANA gene NA NA ENSG00000000003.15
## ENSG00000000005.6 HAVANA gene NA NA ENSG00000000005.6
## ENSG00000000419.13 HAVANA gene NA NA ENSG00000000419.13
## ENSG00000000457.14 HAVANA gene NA NA ENSG00000000457.14
## ENSG00000000460.17 HAVANA gene NA NA ENSG00000000460.17
## ENSG00000000938.13 HAVANA gene NA NA ENSG00000000938.13
## gene_type gene_name level hgnc_id
## <character> <character> <character> <character>
## ENSG00000000003.15 protein_coding TSPAN6 2 HGNC:11858
## ENSG00000000005.6 protein_coding TNMD 2 HGNC:17757
## ENSG00000000419.13 protein_coding DPM1 2 HGNC:3005
## ENSG00000000457.14 protein_coding SCYL3 2 HGNC:19285
## ENSG00000000460.17 protein_coding C1orf112 2 HGNC:25565
## ENSG00000000938.13 protein_coding FGR 2 HGNC:3697
## havana_gene
## <character>
## ENSG00000000003.15 OTTHUMG00000022002.2
## ENSG00000000005.6 OTTHUMG00000022001.2
## ENSG00000000419.13 OTTHUMG00000032742.2
## ENSG00000000457.14 OTTHUMG00000035941.6
## ENSG00000000460.17 OTTHUMG00000035821.9
## ENSG00000000938.13 OTTHUMG00000003516.3Finally, we can use some core functionality of R to save the TCGA_data as a .RDS file. This is faster than repeating the previous operations and useful if you need to work in your data for several days.
# Save the data as a file, if you need it later, you can just load this file
# instead of having to run the whole pipeline again
saveRDS(object = tcga_data,
file = "tcga_data.RDS",
compress = FALSE)The data can be loaded with the following command
tcga_data = readRDS(file = "tcga_data.RDS")3 RNASeq Normalization
3.1 Defining a pipeline
Basics
A typical task on RNA-Seq data is differential expression (DE) analysis, based on some clinical phenotypes. This, in turn, requires normalization of the data, as in its raw format it may have batch effects and other artifacts.
A common approach to such complex tasks is to define a computational pipeline, performing several steps in sequence, allowing the user to select different parameters.
We will now define and run one such pipeline, through the use of an R function.
The function is called limma_pipeline(tcga_data, condition_variable, reference_group), where tcga_data is the data object we have gotten from TCGA and condition_variable is the interesting variable/condition by which you want to group your patient samples. You can also decide which one of the values of your conditional variable is going to be the reference group, with the reference_group parameter.
This function returns a list with three different objects:
- A complex object, resulting from running
voom, this contains theTMM+voom normalizeddata; - A complex object, resulting from running
eBayes, this contains the the fitted model plus a number of statistics related to each of the probes; - A simple table, resulting from running
topTable, which contains the top 100 differentially expressed genes sorted byp-value. This is how the code of this function:
limma_pipeline = function(
tcga_data,
condition_variable,
reference_group=NULL){
design_factor = colData(tcga_data)[, condition_variable, drop=T]
group = factor(design_factor)
if(!is.null(reference_group)){group = relevel(group, ref=reference_group)}
design = model.matrix(~ group)
dge = DGEList(counts=assay(tcga_data),
samples=colData(tcga_data),
genes=as.data.frame(rowData(tcga_data)))
# filtering
keep = filterByExpr(dge,design)
dge = dge[keep,,keep.lib.sizes=FALSE]
rm(keep)
# Normalization (TMM followed by voom)
dge = calcNormFactors(dge)
v = voom(dge, design, plot=TRUE)
# Fit model to data given design
fit = lmFit(v, design)
fit = eBayes(fit)
# Show top genes
topGenes = topTable(fit, coef=ncol(design), number=100, sort.by="p")
return(
list(
voomObj=v, # normalized data
fit=fit, # fitted model and statistics
topGenes=topGenes # the 100 most differentially expressed genes
)
)
}Please read the Details tab for a step by step explanation of what this pipeline does. For now, copy the limma_pipeline function in your R console, so we can start using it on the TCGA data.
With the following command, we can obtain the DE analysis comparing Primary solid Tumor samples against Solid Tissue Normal. This will be used in the next section, on the classification task.
limma_res = limma_pipeline(
tcga_data=tcga_data,
condition_variable="definition",
reference_group="Solid Tissue Normal"
)
Let’s save this object to file, like we did with tcga_data:
# Save the data as a file, if you need it later, you can just load this file
# instead of having to run the whole pipeline again
saveRDS(object = limma_res,
file = "limma_res.RDS",
compress = FALSE)As an example, we also show here how we can use limma_pipeline to perform DE analysis by grouping patients by gender instead of by tissue type.
gender_limma_res = limma_pipeline(
tcga_data=tcga_data,
condition_variable="gender",
reference_group="female"
)Details
In our pipeline function, we use the package limma. We will select a particular clinical feature of the data to use as class for grouping the samples as either tumor vs normal tissue. This data is available under the column definition for tcga_data, but needs the use of the function colData to be accessed. In addition, limma requires this data to be a factor, so we convert it as such:
clinical_data = colData(tcga_data)
group = factor(clinical_data$definition)As seen before, we have 2 distinct groups of tissues defined in this column, Solid Tissue Normal (our control samples) and Primary solid Tumor (our samples of interest). In this factor, we also want to define Solid Tissue Normal as being our base or reference level.
group = relevel(group, ref="Solid Tissue Normal")Next, we need to create a design matrix, which will indicate the conditions to be compared by the DE analysis. The ~ symbol represents that we are constructing a formula.
design = model.matrix(~group)
head(design)## (Intercept) groupPrimary solid Tumor
## 1 1 1
## 2 1 1
## 3 1 1
## 4 1 1
## 5 1 0
## 6 1 1Before performing DE analysis, we remove genes, which have low amount of counts. We transform our tcga_data object as DGEList, which provides functions for filtering. By default genes with counts with less than 10 reads are removed.
dge = DGEList( # creating a DGEList object
counts=assay(tcga_data),
samples=colData(tcga_data),
genes=as.data.frame(rowData(tcga_data)))
# filtering
keep = filterByExpr(dge,design) # defining which genes to keep
dge = dge[keep,,keep.lib.sizes=FALSE] # filtering the dge object
rm(keep) # use rm() to remove objects from memory if you don't need them anymoreBefore we fit a model to our data, we normalize the data to minimize batch effects and technical variation with the TMM (trimmed mean of M-values) normalization method. Moreover, to apply limma on RNA-seq, we need to convert the data to have a similar variance as arrays. This is done with the VOOM method.
dge = calcNormFactors(dge, method = "TMM")
v = voom(dge, design, plot=TRUE)
Finally, using lmFit lets fit a series of linear models, one to each of the probes. These data will then be fed to eBayes to produce a complex object which holds a number of statistics that we can use to rank the differentially expressed genes.
fit = lmFit(v, design)
fit = eBayes(fit)Using the function topTable we can check the top10 genes classified as being differentially expressed. ’
topGenes = topTable(fit, coef = 1, sort.by = "p")
print(topGenes)## source type score phase gene_id gene_type
## ENSG00000009307.16 HAVANA gene NA NA ENSG00000009307.16 protein_coding
## ENSG00000011304.22 HAVANA gene NA NA ENSG00000011304.22 protein_coding
## ENSG00000022840.16 HAVANA gene NA NA ENSG00000022840.16 protein_coding
## ENSG00000044115.21 HAVANA gene NA NA ENSG00000044115.21 protein_coding
## ENSG00000048828.17 HAVANA gene NA NA ENSG00000048828.17 protein_coding
## ENSG00000054118.15 HAVANA gene NA NA ENSG00000054118.15 protein_coding
## ENSG00000057608.17 HAVANA gene NA NA ENSG00000057608.17 protein_coding
## ENSG00000067560.13 HAVANA gene NA NA ENSG00000067560.13 protein_coding
## ENSG00000068697.7 HAVANA gene NA NA ENSG00000068697.7 protein_coding
## ENSG00000070831.17 HAVANA gene NA NA ENSG00000070831.17 protein_coding
## gene_name level hgnc_id havana_gene logFC
## ENSG00000009307.16 CSDE1 1 HGNC:29905 OTTHUMG00000012060.6 8.426555
## ENSG00000011304.22 PTBP1 2 HGNC:9583 OTTHUMG00000181789.13 7.266500
## ENSG00000022840.16 RNF10 1 HGNC:10055 OTTHUMG00000168999.17 6.839278
## ENSG00000044115.21 CTNNA1 1 HGNC:2509 OTTHUMG00000163502.5 7.433791
## ENSG00000048828.17 FAM120A 2 HGNC:13247 OTTHUMG00000020252.4 7.202252
## ENSG00000054118.15 THRAP3 2 HGNC:22964 OTTHUMG00000007866.8 6.646039
## ENSG00000057608.17 GDI2 1 HGNC:4227 OTTHUMG00000017607.10 7.941541
## ENSG00000067560.13 RHOA 1 HGNC:667 OTTHUMG00000156838.8 7.947602
## ENSG00000068697.7 LAPTM4A 2 HGNC:6924 OTTHUMG00000090750.3 8.189098
## ENSG00000070831.17 CDC42 2 HGNC:1736 OTTHUMG00000002753.10 7.009241
## AveExpr t P.Value adj.P.Val B
## ENSG00000009307.16 8.203491 125.3471 0 0 759.8161
## ENSG00000011304.22 7.489407 159.3929 0 0 857.5700
## ENSG00000022840.16 6.882460 136.6479 0 0 795.1348
## ENSG00000044115.21 7.801750 121.1519 0 0 746.2174
## ENSG00000048828.17 6.997776 129.3667 0 0 772.8610
## ENSG00000054118.15 6.365231 130.6175 0 0 776.8190
## ENSG00000057608.17 7.627046 131.7043 0 0 779.9930
## ENSG00000067560.13 8.089706 158.9324 0 0 856.2014
## ENSG00000068697.7 7.866955 125.1309 0 0 759.1753
## ENSG00000070831.17 6.925490 125.9530 0 0 762.0301topTable returns a table with the top genes sorted by P.value expressing if the gene is differentially expressed.
3.2 Visualization
We have also prepared a function that produces PCA plots given the voom object created by the limma_pipeline function. You can inspect this function and try to figure out how it works.
plot_PCA = function(voomObj, condition_variable){
group = factor(voomObj$targets[, condition_variable])
pca = prcomp(t(voomObj$E))
# Take PC1 and PC2 for the plot
plot(pca$x[,1:2],col=group, pch=19)
# include a legend for points
legend("bottomleft", inset=.01, levels(group), pch=19, col=1:length(levels(group)))
return(pca)
}By calling the function plot_PCA, we get a plot of the first two principal components:
res_pca = plot_PCA(limma_res$voomObj, "definition")
This plot shows that the two sample groups (tumor tissue and healthy tissue) have a well-separated RNA expression profile.
With all the information already available to us created with the limma_pipeline function, we can also, for example, create a heatmap picturing the expression of the top20 differentially expressed genes on our samples.
# first lets get the normalized expression matrix from our limma_res object
expr_mat = as.matrix(t(limma_res$voomObj$E))
# then lets get gene names that are easier to look at
gene_names = limma_res$voomObj$genes[,"gene_name"]
# and use these to rename the genes in our expression matrix
colnames(expr_mat) = gene_names
# we want to get the top20 DE genes
# topGenes is already sorted by the adjusted p-value (in ascending order)
head(limma_res$topGenes,20)## source type score phase gene_id gene_type
## ENSG00000104938.18 HAVANA gene NA NA ENSG00000104938.18 protein_coding
## ENSG00000165682.14 HAVANA gene NA NA ENSG00000165682.14 protein_coding
## ENSG00000263761.3 HAVANA gene NA NA ENSG00000263761.3 protein_coding
## ENSG00000163217.2 HAVANA gene NA NA ENSG00000163217.2 protein_coding
## ENSG00000136011.15 HAVANA gene NA NA ENSG00000136011.15 protein_coding
## ENSG00000164619.10 HAVANA gene NA NA ENSG00000164619.10 protein_coding
## ENSG00000145708.11 HAVANA gene NA NA ENSG00000145708.11 protein_coding
## ENSG00000160339.16 HAVANA gene NA NA ENSG00000160339.16 protein_coding
## ENSG00000019169.10 HAVANA gene NA NA ENSG00000019169.10 protein_coding
## ENSG00000251049.2 HAVANA gene NA NA ENSG00000251049.2 lncRNA
## ENSG00000184374.3 HAVANA gene NA NA ENSG00000184374.3 protein_coding
## ENSG00000142748.13 HAVANA gene NA NA ENSG00000142748.13 protein_coding
## ENSG00000145824.13 HAVANA gene NA NA ENSG00000145824.13 protein_coding
## ENSG00000160323.19 HAVANA gene NA NA ENSG00000160323.19 protein_coding
## ENSG00000164100.9 HAVANA gene NA NA ENSG00000164100.9 protein_coding
## ENSG00000114812.13 HAVANA gene NA NA ENSG00000114812.13 protein_coding
## ENSG00000164161.10 HAVANA gene NA NA ENSG00000164161.10 protein_coding
## ENSG00000181072.11 HAVANA gene NA NA ENSG00000181072.11 protein_coding
## ENSG00000160801.14 HAVANA gene NA NA ENSG00000160801.14 protein_coding
## ENSG00000183287.14 HAVANA gene NA NA ENSG00000183287.14 protein_coding
## gene_name level hgnc_id havana_gene logFC
## ENSG00000104938.18 CLEC4M 2 HGNC:13523 OTTHUMG00000182432.4 -9.230079
## ENSG00000165682.14 CLEC1B 1 HGNC:24356 OTTHUMG00000168502.1 -7.672067
## ENSG00000263761.3 GDF2 2 HGNC:4217 OTTHUMG00000188320.2 -9.252944
## ENSG00000163217.2 BMP10 2 HGNC:20869 OTTHUMG00000129573.2 -7.967087
## ENSG00000136011.15 STAB2 2 HGNC:18629 OTTHUMG00000170056.2 -6.135552
## ENSG00000164619.10 BMPER 2 HGNC:24154 OTTHUMG00000128675.24 -6.068348
## ENSG00000145708.11 CRHBP 2 HGNC:2356 OTTHUMG00000102133.3 -6.477126
## ENSG00000160339.16 FCN2 2 HGNC:3624 OTTHUMG00000020892.2 -7.521944
## ENSG00000019169.10 MARCO 2 HGNC:6895 OTTHUMG00000131400.4 -7.222983
## ENSG00000251049.2 AC107396.1 2 <NA> OTTHUMG00000162168.2 -4.878924
## ENSG00000184374.3 COLEC10 2 HGNC:2220 OTTHUMG00000164971.2 -5.970541
## ENSG00000142748.13 FCN3 2 HGNC:3625 OTTHUMG00000005722.2 -5.866426
## ENSG00000145824.13 CXCL14 2 HGNC:10640 OTTHUMG00000129139.7 -6.679269
## ENSG00000160323.19 ADAMTS13 2 HGNC:1366 OTTHUMG00000020876.4 -3.143669
## ENSG00000164100.9 NDST3 2 HGNC:7682 OTTHUMG00000132959.5 -5.221459
## ENSG00000114812.13 VIPR1 1 HGNC:12694 OTTHUMG00000131792.9 -4.663159
## ENSG00000164161.10 HHIP 2 HGNC:14866 OTTHUMG00000161428.3 -6.529694
## ENSG00000181072.11 CHRM2 2 HGNC:1951 OTTHUMG00000155658.3 -5.542917
## ENSG00000160801.14 PTH1R 2 HGNC:9608 OTTHUMG00000133515.6 -4.167186
## ENSG00000183287.14 CCBE1 2 HGNC:29426 OTTHUMG00000180087.6 -5.430651
## AveExpr t P.Value adj.P.Val B
## ENSG00000104938.18 -3.29792212 -45.73718 7.192465e-166 1.634847e-161 367.9031
## ENSG00000165682.14 -3.36630698 -38.85242 1.182238e-141 1.343613e-137 312.4139
## ENSG00000263761.3 -2.97511698 -38.49714 2.451977e-140 1.857781e-136 309.7851
## ENSG00000163217.2 -3.56629765 -37.47196 1.687964e-136 9.591858e-133 300.6683
## ENSG00000136011.15 -0.87316796 -35.47037 7.669389e-129 3.486504e-125 283.6029
## ENSG00000164619.10 -1.73614838 -32.47381 5.699022e-117 2.158980e-113 256.2907
## ENSG00000145708.11 0.50819057 -31.81064 2.807408e-114 9.116056e-111 250.3490
## ENSG00000160339.16 -0.71253149 -31.77862 3.792417e-114 1.077521e-110 250.0199
## ENSG00000019169.10 0.02106692 -30.75429 6.095353e-110 1.539415e-106 240.3967
## ENSG00000251049.2 -5.58008401 -29.74075 1.000158e-105 2.273359e-102 229.2715
## ENSG00000184374.3 0.41556403 -28.60462 6.106193e-101 1.261761e-97 219.7624
## ENSG00000142748.13 1.97353779 -28.11263 7.540447e-99 1.428286e-95 214.9809
## ENSG00000145824.13 -0.50761409 -26.98018 5.418231e-94 9.473569e-91 203.8152
## ENSG00000160323.19 2.63404340 -26.81833 2.707198e-93 4.395329e-90 202.2345
## ENSG00000164100.9 -4.20542375 -26.40795 1.617918e-91 2.451686e-88 197.4752
## ENSG00000114812.13 0.75603499 -26.26335 6.863429e-91 9.750358e-88 196.7036
## ENSG00000164161.10 -1.63232306 -25.69212 2.107948e-88 2.818450e-85 190.9591
## ENSG00000181072.11 -4.42726426 -25.33904 7.370354e-87 9.307120e-84 186.8836
## ENSG00000160801.14 1.07293354 -25.07932 1.013444e-85 1.212399e-82 184.8461
## ENSG00000183287.14 -1.45879734 -24.92830 4.664442e-85 5.301138e-82 183.2542# therefore we can just go ahead and select the first 20 values
top_de_genes = limma_res$topGenes$gene_name[1:20]
# we'll get the sample type data so that we can show the groups in the heatmap
sample_type = factor(limma_res$voomObj$targets$sample_type)
# define the color palette for the plot
hmcol = colorRampPalette(rev(brewer.pal(9, "RdBu")))(256)
# perform complete linkage clustering
clust_func = function(x) hclust(x, method="complete")
# use the inverse of correlation as distance.
dist_func = function(x) as.dist((1-cor(t(x)))/2)
# A good looking heatmap involves a lot of parameters and tinkering
gene_heatmap = heatmap.2(
t(expr_mat[,top_de_genes]),
scale="row", # scale the values for each gene (row)
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
col=hmcol, # define the color map
labCol=FALSE, # Not showing column labels
ColSideColors=as.character(as.numeric(sample_type)), # Show colors for each response class
dendrogram="both", # Show dendrograms for both axis
hclust = clust_func, # Define hierarchical clustering method
distfun = dist_func, # Using correlation coefficient for distance function
cexRow=1.0, # Resize row labels
keysize = 1.25, # Size of the legend
margins=c(1,6) # Define margin spaces
)
3.3 Exercise
Produce a PCA plot using gender as condition (hint: remember that we already run the pipeline for gender). How does it look like? Are the tissues well separated or not?
Can you think about a more interesting feature to group patients? Choose one and pass it to the condition_variable argument of the limma_pipeline function. Make sure to save the result to a variable.
Remember that you can see all available clinical features/phenotypes with the following command:
colnames(colData(tcga_data))You can also run the plot_PCA function with this new condition_variable that you chose.
Extra challenge for the brave: change the limma_pipeline function so that it returns an arbitrary number of topGenes (defined by the user), instead of always 100.
4 Survival Analysis
One analysis often performed on TCGA data is survival analysis. In short, this boils down to answering the following question: how more likely is a certain group of patients to live longer than another?
The techniques we are going to see are not, however, limited to survival (to death events) but can be applied to any experiment where patients can be divided into groups and there is an event, something happening at a specific time point.
Before we start, if you don’t have all the libraries we used previously, as well as the objects limma_res and tcga_data already loaded, please do so:
# Load packages
library("TCGAbiolinks")
library("limma")
library("edgeR")
library("glmnet")
library("factoextra")
library("FactoMineR")
library("caret")
library("SummarizedExperiment")
library("gplots")
library("survival")
library("survminer")
library("RColorBrewer")
library("gProfileR")
library("genefilter")
# NB: make sure you set the working directory of RStudio correctly
setwd("/home/grasshoff/Documents/BIR_course_2022/D4_TGCA/practice/")
tcga_data = readRDS(file = "tcga_data.RDS")
limma_res = readRDS(file = "limma_res.RDS")To warm up and explain the method, we will start with an easy question: does gender influence survival in liver cancer patients?
TCGA, as mentioned before, provides a lot of clinical data for each patient. We need to extract the gender variable, and a few more besides:
# extract clinical data
clinical = tcga_data@colData
dim(clinical)## [1] 421 62# we are only interested in the "Primary solid Tumor" cases for survival
clin_df = clinical[clinical$definition == "Primary solid Tumor",
c("patient",
"vital_status",
"days_to_death",
"days_to_last_follow_up",
"gender",
"ajcc_pathologic_stage")]Now we have a new dataframe, clin_df, containing only the information that is relevant to survival analysis. In addition to gender, we have added vital_status (whether patient is alive or dead), ajcc_pathologic_stage (from stage 1 to 4) and two important variables: days_to_death, that is the number of days passed from the initial diagnosis to the patient’s death (clearly, this is only relevant for dead patients), and days_to_last_follow_up that is the number of days passed from the initial diagnosis to the last visit.
Before we can proceed, we need to change part of this information in a way that is acceptable to the methods from the survival package we are using:
# create a new boolean variable that has TRUE for dead patients
# and FALSE for live patients
clin_df$deceased = clin_df$vital_status == "Dead"
# create an "overall survival" variable that is equal to days_to_death
# for dead patients, and to days_to_last_follow_up for patients who
# are still alive
clin_df$overall_survival = ifelse(clin_df$deceased,
clin_df$days_to_death,
clin_df$days_to_last_follow_up)
# show first 10 samples
head(clin_df)## DataFrame with 6 rows and 8 columns
## patient vital_status days_to_death
## <character> <character> <integer>
## TCGA-DD-AACJ-01A-11R-A41C-07 TCGA-DD-AACJ Alive NA
## TCGA-DD-A4NE-01A-11R-A27V-07 TCGA-DD-A4NE Dead 660
## TCGA-ED-A8O6-01A-11R-A36F-07 TCGA-ED-A8O6 Dead 56
## TCGA-MI-A75I-01A-11R-A32O-07 TCGA-MI-A75I Alive NA
## TCGA-G3-AAV4-01A-11R-A38B-07 TCGA-G3-AAV4 Dead 27
## TCGA-BC-4072-01B-11R-A155-07 TCGA-BC-4072 Dead 1490
## days_to_last_follow_up gender
## <integer> <character>
## TCGA-DD-AACJ-01A-11R-A41C-07 2102 male
## TCGA-DD-A4NE-01A-11R-A27V-07 NA female
## TCGA-ED-A8O6-01A-11R-A36F-07 6 female
## TCGA-MI-A75I-01A-11R-A32O-07 630 male
## TCGA-G3-AAV4-01A-11R-A38B-07 NA female
## TCGA-BC-4072-01B-11R-A155-07 NA female
## ajcc_pathologic_stage deceased overall_survival
## <character> <logical> <integer>
## TCGA-DD-AACJ-01A-11R-A41C-07 Stage II FALSE 2102
## TCGA-DD-A4NE-01A-11R-A27V-07 Stage IIIA TRUE 660
## TCGA-ED-A8O6-01A-11R-A36F-07 Stage IIIA TRUE 56
## TCGA-MI-A75I-01A-11R-A32O-07 NA FALSE 630
## TCGA-G3-AAV4-01A-11R-A38B-07 Stage I TRUE 27
## TCGA-BC-4072-01B-11R-A155-07 Stage IIIA TRUE 1490Let’s now see if male and female patients have had different prognosis (in this dataset).
4.1 Kaplan-Meier plots
As a first step, we need to define a survival formula with the help of the Surv object.
In R, formulas are special constructs of the form y ~ x, and in the context of linear models you can see x as the independent variable and y as the dependent variable.
This works also for multivariate models: age ~ gender + height is a formula that can be used to predict age from gender and height. You can refer to the documentation of formula for further examples and explanations, by typing ?formula in a R shell.
Let’s get back to survival. We have a categorical variable, gender, that needs to be used to separate (or, more appropriately, stratify) the available death events.
The survival package provides us with an object, Surv, to form a dependent variable out of the overall_survival and deceased information:
Surv(clin_df$overall_survival, clin_df$deceased)## [1] 2102+ 660 56 630+ 27 1490 357+ 87 408+ 101 20+ 299
## [13] 784+ 2425+ 419 219+ 711 2017+ 79+ 438 1560 91 2116 97
## [25] 3437+ 14 1363+ 3478+ 279 20+ 31 2456 1718+ 1241+ 555 1562+
## [37] 1791 452 366+ 2412+ 472+ 698+ 1553+ 951+ 910+ 631+ 140 381
## [49] 643 430+ 768 1008+ 555+ 765 1085+ 1067+ 322+ 10+ 170+ 19
## [61] 291+ 453+ 500+ 409+ 354+ 1452+ 361+ 925+ 1085+ 698+ 260+ 347+
## [73] 1339+ 837 688 2415+ 373 103 1+ 183+ 1516+ 570+ 1168+ 917+
## [85] 810+ 535 520+ 1397 363+ 1694 802 564+ 655+ 898+ 1711+ 1731+
## [97] 303+ 328+ 9+ 562+ 538+ 1823+ 195 1088 575+ 372+ 296 425
## [109] 8+ 410 444+ 141+ 365 2245+ 3258 194 9+ 566+ 250+ 1295+
## [121] 1229 65 1970+ 412 406+ 587+ 52 638+ 12+ 1372 67 827
## [133] 658+ 0+ 728+ 608+ 848+ 1900+ 2028+ 1989+ 0+ 1091+ 211+ 11
## [145] 94+ 2455+ 6+ 365+ 671+ 556 315 359+ 588+ 2728+ 36 447+
## [157] 931 107 722+ 552+ 1852 612 341+ 585+ 2184+ 579+ 1450+ 2202+
## [169] 680+ 520+ 1005 395+ 2301+ 262 480+ 519+ 848 56 1855+ 303
## [181] 1115+ 137+ 278 1876+ 474+ 729+ 22+ 1779+ 91 382+ 425+ 601
## [193] 390+ 558 366 2752+ 752 171 942+ 644+ 2513+ 782+ 427+ 673+
## [205] 601+ 574+ 1685 899 65 46 1242+ 415+ 214 507+ 361+ 314+
## [217] 0+ 780+ 478+ 697+ 763+ 387+ 1066+ 23+ 330+ 1386 636+ 406+
## [229] 596 989+ 12 701+ 1210 1049+ 2317+ 2486 449+ 2131 412+ 633
## [241] 416 719+ 347 399+ 615+ 512+ 44+ 547 395+ 229+ 621+ 436+
## [253] 486+ 30+ 396+ 693+ 469 363+ 640+ 1030+ 300 1271 1231+ 879+
## [265] 300 272 387+ 928+ 468+ 554+ 693 649 432 0+ 327+ 9
## [277] 848+ 171 16 476+ 1624 829+ 2532 304 1633+ 262 2398+ 1233+
## [289] 115 2324+ 2746+ 906+ 799+ 756+ 344 0+ 400+ 1567+ 2301+ 247
## [301] 21+ 816+ 359 1531+ 632+ 1531+ 555+ 1098+ 724 1345+ 744+ 819+
## [313] 1351+ 415 854+ 672+ 3308+ 581 180+ 423+ 248+ 757 1424+ 1145+
## [325] 639 1636+ 458+ 345+ 662+ 233 349 2542 602+ 1135 849+ 394
## [337] 1149 129 1423 102 608+ 283 1939+ 594+ 770 1618+ 706+ 3125
## [349] NA+ 3675+ 898+ 308 2442+ 129 6+ 15+ 223 217 837 1804+
## [361] 1302+ 747+ 860+ 1495+ 1219+ 6+ 34 153 1622 627 2015+This modifies our overall survival vector by adding censoring information (the + just after the time), which requires a small digression.
This data is right censored, meaning that for some patients we only have the time of the last follow up but we don’t know if they died at a later date or not.
These patients are kept in the early stages of the analysis (eg, they are part of the survival curve) but they are dropped (or as it is said, censored) when the time of their last follow up arrives.
Now that the survival time has been tagged with the censoring, we can add the categorical independent variable gender, and effectively create a formula
Surv(clin_df$overall_survival, clin_df$deceased) ~ clin_df$genderWe now have a survival formula that can be passed to the survfit function to fit the survival model, and then to another function to produce the Kaplan-Meier plots. Actually, when executing the survival analysis with survfit, we can exclude the clin_df$ if we tell the function to use clin_df as data by using the data= parameter.
# fit a survival model
fit = survfit(Surv(overall_survival, deceased) ~ gender, data=clin_df)
print(fit)## Call: survfit(formula = Surv(overall_survival, deceased) ~ gender,
## data = clin_df)
##
## 1 observation deleted due to missingness
## n events median 0.95LCL 0.95UCL
## gender=female 121 51 1490 1005 2456
## gender=male 249 79 2486 1423 NA# we produce a Kaplan Meier plot
ggsurvplot(fit, data=clin_df)
This Kaplan-Meier plot shows two very similar trends until almost the 2000-day mark, where females seem to have a worse survival probability. But is there a significant difference?
The difference between two such “event curves” is best tested via the logrank test, which is, fundamentally, a repeated test of independence. survminer will add the p-value of such test if we tell it to do so:
ggsurvplot(fit, data=clin_df, pval=T)
The p-value is non-significant, so gender alone does not significantly sway prognosis in this dataset.
If you find this strange because the curves deviate at the 2000-day mark, you have to remember that the amount of patients involved matters. At that point, only a few patients remain, and any difference is likely to not be significant.
Can we see the number of patients dying (or being “censored”) as Time increases? Indeed we can, with what is called the “at risk table”.
ggsurvplot(fit, data=clin_df, pval=T, risk.table=T, risk.table.col="strata")
With the risk.table=T argument, we get the number of patients “at risk”, that is neither dead nor censored at a certain time point.
The argument risk.table.col="strata" tells survminer to colour the table in the same way as the strata, or groups, are coloured.
You can see that most of the patients die or are censored before the 2000-day mark, and therefore it makes sense that the p-value would not be significant.
Another question could be: how does tumor stage affect survival?
The ajcc_pathologic_stage variable that TCGA provides for this tumor contains both stages and sub-stages, eg stage iiia or stage ivb. We want to join together the sub-stages, to increase the group size and reduce complexity (and thus increase the power of the logrank statistics).
# remove any of the letters "a", "b" or "c", but only if they are at the end
# of the name, eg "stage iiia" would become simply "stage iii"
clin_df$ajcc_pathologic_stage = gsub("[ABC]$", "", clin_df$ajcc_pathologic_stage)
# let's see how many patients we have for each stage
print(table(clin_df$ajcc_pathologic_stage))##
## Stage I Stage II Stage III Stage IV
## 171 86 85 5# since we have too few Stage 4 patients, we put them together with Stage IIIs
clin_df[which(clin_df$ajcc_pathologic_stage == "Stage III"), "ajcc_pathologic_stage"] = "Stage III+"
clin_df[which(clin_df$ajcc_pathologic_stage == "Stage IV"), "ajcc_pathologic_stage"] = "Stage III+"
print(table(clin_df$ajcc_pathologic_stage))##
## Stage I Stage II Stage III+
## 171 86 90We can now fit a new survival model with the tumor stage groups (one to four, plus the “not reported”):
fit = survfit(Surv(overall_survival, deceased) ~ ajcc_pathologic_stage, data=clin_df)
# we can extract the survival p-value and print it
pval = surv_pvalue(fit, data=clin_df)$pval
print(pval)## [1] 3.081341e-06# we produce a Kaplan-Meier plot from the fitted model
ggsurvplot(fit, data=clin_df, pval=T, risk.table=T)
We get an overall p-value testing the null hypothesis that all the curves are similar at every time point. In this case, the p-value is small enough that we can reject the null hypothesis.
What we saw here is an easy way of producing Kaplan-Meier plots to investigate survival, as well as evaluating whether the survival curves are significantly different or not. A more interesting application to this is using, for example, gene expression to divide the patients into groups, to see whether up or down regulation of genes affects survival. We’ll see this in the next section.
5 Gene expression and survival
5.1 Overview
Ealier, we looked at the RNASeq data for this tumor. We found some genes that are differentially expressed between the healthy and disease samples, and we also trained an Elastic Net model and investigated those predictors that are important in discriminating healthy and disease tissue.
So, taken one of the selected genes, we know that they are either up-regulated in the tumor tissue tissue and not in the normal tissue, or viceversa. But do these genes also provide an advantage, or disadvantage, regarding prognosis?
We already have the top differentially expressed genes, ordered by significance, in the limma_res$topGenes dataframe, so we just have to take the first one.
# let's extract the table of differential expression we got earlier
genes_df = limma_res$topGenes
# print the first row, to see the gene name, the logFC value and the p-value
genes_df[1, ]## source type score phase gene_id gene_type
## ENSG00000104938.18 HAVANA gene NA NA ENSG00000104938.18 protein_coding
## gene_name level hgnc_id havana_gene logFC
## ENSG00000104938.18 CLEC4M 2 HGNC:13523 OTTHUMG00000182432.4 -9.230079
## AveExpr t P.Value adj.P.Val B
## ENSG00000104938.18 -3.297922 -45.73718 7.192465e-166 1.634847e-161 367.9031# get the ensembl gene id of the first row
gene_id = genes_df[1, "gene_id"]
# also get the common gene name of the first row
gene_name = genes_df[1, "gene_name"]We now have selected a gene. Let’s visualize how much differentially expressed it is:
# get the actual expression matrix from the limma result object.
# we transpose it because we want genes in the columns and patients in the rows
expr_df = t(limma_res$voomObj$E)
# let's show the first five genes for the first five patients
expr_df[1:5, 1:5]## ENSG00000000003.15 ENSG00000000419.13
## TCGA-DD-AACJ-01A-11R-A41C-07 5.792547 4.347799
## TCGA-DD-A4NE-01A-11R-A27V-07 5.831222 4.858820
## TCGA-ED-A8O6-01A-11R-A36F-07 6.993771 5.014274
## TCGA-MI-A75I-01A-11R-A32O-07 6.635428 4.474815
## TCGA-FV-A3I0-11A-11R-A22L-07 7.110740 4.931030
## ENSG00000000457.14 ENSG00000000460.17
## TCGA-DD-AACJ-01A-11R-A41C-07 3.666284 1.389297
## TCGA-DD-A4NE-01A-11R-A27V-07 4.495991 3.269332
## TCGA-ED-A8O6-01A-11R-A36F-07 3.609928 1.986356
## TCGA-MI-A75I-01A-11R-A32O-07 2.981666 1.338851
## TCGA-FV-A3I0-11A-11R-A22L-07 3.466428 1.005856
## ENSG00000000938.13
## TCGA-DD-AACJ-01A-11R-A41C-07 0.6149782
## TCGA-DD-A4NE-01A-11R-A27V-07 0.2734053
## TCGA-ED-A8O6-01A-11R-A36F-07 0.8537870
## TCGA-MI-A75I-01A-11R-A32O-07 1.2858051
## TCGA-FV-A3I0-11A-11R-A22L-07 3.2818997# visualize the gene expression distribution on the diseased samples (in black)
# versus the healthy samples (in red)
expr_diseased = expr_df[rownames(clin_df), gene_id]
expr_healthy = expr_df[setdiff(rownames(expr_df), rownames(clin_df)), gene_id]
boxplot(expr_diseased, expr_healthy,
names = c("Diseased", "Healthy"),
main = "Distribution of gene expression")
Quite so! To see if its expression also influences prognosis, we take all the expression values in the diseased samples, then take the median of them.
Patients with expression greater than the median we put in the up-regulated groups, and the others in the down-regulated group.
# get the expression values for the selected gene
clin_df$gene_value = expr_df[rownames(clin_df), gene_id]
# find the median value of the gene and print it
median_value = median(clin_df$gene_value)
median_value## [1] -5.043144# divide patients in two groups, up and down regulated.
# if the patient expression is greater or equal to them median we put it
# among the "up-regulated", otherwise among the "down-regulated"
clin_df$gene = ifelse(clin_df$gene_value >= median_value, "UP", "DOWN")
# we can fit a survival model, like we did in the previous section
fit = survfit(Surv(overall_survival, deceased) ~ gene, data=clin_df)
# we can extract the survival p-value and print it
pval = surv_pvalue(fit, data=clin_df)$pval
pval## [1] 0.8437825# and finally, we produce a Kaplan-Meier plot
ggsurvplot(fit, data=clin_df, pval=T, risk.table=T, title=paste(gene_name))
You can also save the plot to file instead of simply visualizing it:
This gene does not appear to make a difference for prognosis. But what about the other differentially expressed genes?
5.2 Exercise
Loops, plots and multiple testing
We are not limited to the top gene, though. We could, for example, look at the ten most differentially expressed genes, one by one, and save the survival plot to see if some of them provide a significant effect on survival.
To achieve this, we can place the previous code in a for loop, and save each result to a different file. Try it.
for (i in 1:10) {
# get the ensembl gene id of the i-th row
gene_id = genes_df[i, "gene_id"]
# also get the common gene name of the i-th row
gene_name = genes_df[i, "gene_name"]
# get the expression values for the selected gene
clin_df$gene_value = expr_df[rownames(clin_df), gene_id]
# find the median value of the gene
median_value = median(clin_df$gene_value)
# divide patients in two groups, up and down regulated.
# if the patient expression is greater or equal to them median we put it
# among the "up-regulated", otherwise among the "down-regulated"
clin_df$gene = ifelse(clin_df$gene_value >= median_value, "UP", "DOWN")
# we can now run survival like we did in the previous section
fit = survfit(Surv(overall_survival, deceased) ~ gene, data=clin_df)
survp = ggsurvplot(fit, data=clin_df, pval=T, risk.table=T, title=paste(gene_name))
# we can save the survival plot to file, try it by uncommenting it
# ggsave(file=paste0(gene_name, ".pdf"), print(survp))
# let's also save the p-value for later.. you'll see why :)
genes_df[i, "surv_pv"] = surv_pvalue(fit, data=clin_df)$pval
}Now we have generated ten plots, and likely found some interesting candidates with significant p-value. Since we performed a statistical test multiple times (a task called multiple testing), we need to correct all the p-values.
The function p.adjust helps us do this. Read how to use it with ?p.adjust, and make sure to select the Benjamini-Hochberg method (also called fdr).
After you do this, you will notice that some of the p-values that were significant now are not.
This is important to remember when applying statistical procedures multiple times: if you keep trying, there’s always a chance of getting a significant p-value when the null hypothesis is actually true.
You must, therefore, control for this chance via a multiple test correction procedure.
What is multiple testing?
Whenever we perform a statistical test, there is a chance to produce a “discovery”, or significant result, that does not in fact correspond to a ground truth (that is: despite a significant p-value, the null hypothesis is true).
That is because “significance” is an arbitrary probabilistic cutoff. In many fields, a 5% chance of rejecting the null hypothesis when, in fact, it is true is widely accepted. Other fields or applications require much stricter cutoffs.
Can you guess what would happen if you performed a multitude of such tests, each at a significance level of 5%? In the worst case (that is: all null hypotheses are true), you might get an incorrectly-significant test every 20 or so.
Those wrongly-rejected null hypotheses are called false positives, and if you don’t control for it they will impede your future research efforts. This is very relevant in genomics: when we check the expression of tens of thousands of genes for an effect on survival, for example, we don’t want to end up with a bunch of candidates that won’t hold up in replication experiments.
Correction methods like the Benjamini-Hochberg (also called the “False Discovery Rate” method) help us control the amount of false positives that we are willing to accept, by further rejecting the weakest “discoveries” we’ve made in a multiple-testing setup - like today’s Exercise.
5.3 Exercise
You should perform clustering of tissues with liver cancer. Tip: use code similar to the one seen in gene expression data (day 3). Since, we are interested in grouping patients, you can transpose the matrix with the function t.
Can you see nice clusters in the dendrogram?
What about genes associated to each group? Are they associated to some particular biological function? Use differential expression analysis and GO enrichment analysis to solve this task.
Check if groups of patients are associated to survival, tumour grade or any other clinical variable. You can use the table function for some of these analysis.
Good luck!
6 Classification (optional)
6.1 Train and test paradigm
Now, we will explore the use of a machine learning method to classify an unseen sample as being a tumor or not. To achieve this goal we are going to build first a simple linear model (with feature selection), and then an Elastic Net model. For this, we need to split the data into two sets: a training set, which we will use to train our model, and a testing set. The test data serves as an independent dataset to validate our results. It is important that you do not use test data to optimize your results or this will include bias in the classifier.
First let’s start by extracting the data that we are going to use to build our model. We want the expression data that has already been normalized and a clinical feature which divides our data into different groups, such as tumor vs. non-tumor or tumor stage. We can get the normalized expression values from limma_res$voomObj$E and the type of sample is determined by the definition column.
limma_res = readRDS(file = "limma_res.RDS")
# Transpose and make it into a matrix object
d_mat = as.matrix(t(limma_res$voomObj$E))
# As before, we want this to be a factor
d_resp = as.factor(limma_res$voomObj$targets$definition)With the data in the correct format we can now divide it into a train set and a test set. We will use the function createDataPartition which creates a vector of booleans (TRUE or FALSE) that we can then use to subset the matrix in this case leaving 75% of samples for training and 25% for testing.
# Divide data into training and testing set
# Set (random-number-generator) seed so that results are consistent between runs
set.seed(42)
train_ids = createDataPartition(d_resp, p = 0.75, list = FALSE)
x_train = d_mat[train_ids, ]
x_test = d_mat[-train_ids, ]
y_train = d_resp[train_ids]
y_test = d_resp[-train_ids]x_train and y_train are the data we will use to train our model, where x is the matrix with the normalized expression values and y is the vector with the response variable, Primary solid Tumor and Solid Tissue Normal.
Following the same logic, x_test and y_test are the matrix with normalized expression values and the response variable respectively. Again, we will only use these (*_test) to perform a prediction and evaluate how good this prediction was after the training process has finished.
6.2 Train Elastic Net model
We will train an Elastic Net model, which is a generalized linear model that combines the best of two other models: LASSO and Ridge Regression. Ridge Regression is often good at doing predictions but its results are not very interpretable. LASSO is good at picking up small signals from lots of noise but it tends to minimize redundancy so if there are two genes that are equally good predictors (features with high correlation) it will tend to select one. Elastic Net is a balance between both methods, it selects the genes or groups of genes (if they are correlated) that best predict each of the conditions and use these to build a model that will then be used for classification.
We can then look at these genes individually to see if some interesting gene of biological relevance for the classification problem is selected. When using Elastic Net there are other parameters than we have to set, specifically alpha. This parameter will define if the Elastic Net will behave more like LASSO (alpha = 1) or like Ridge Regression (alpha = 0). For simplicity we will set it to 0.5 however in a real setting we would probably vary this value in order to find the best model (minimizing the error).
# Train model on training dataset using cross-validation
res = cv.glmnet(
x = x_train,
y = y_train,
alpha = 0.5,
family = "binomial"
)6.3 Model Evaluation
After training the model, we can now evaluate it against our test-dataset. The result from cv.glmnet is a complex object that, between other data, holds the coefficients for our model and the mean error found during training.
# Test/Make prediction on test dataset
y_pred = predict(res, newx = x_test, type = "class", s = "lambda.min")A confusion matrix is a simple table that compares the predictions from our model against their real values. Therefore, it shows us the true positives, true negatives, false positives and false negatives. We can use it to compute a number of accuracy metrics that we can then use to understand how good our model actually is.
confusion_matrix = table(y_pred, y_test)
# Evaluation statistics
print(confusion_matrix)## y_test
## y_pred Primary solid Tumor Solid Tissue Normal
## Primary solid Tumor 91 0
## Solid Tissue Normal 1 12print(paste0("Sensitivity: ", sensitivity(confusion_matrix)))## [1] "Sensitivity: 0.989130434782609"print(paste0("Specificity: ", specificity(confusion_matrix)))## [1] "Specificity: 1"print(paste0("Precision: ", precision(confusion_matrix)))## [1] "Precision: 1"As you see, for this data and under the current parameters, Elastic Net has great accuracy.
We can now take a look at the genes (coefficients), that Elastic Net selected to build its model.
# Getting genes that contribute for the prediction
res_coef = coef(res, s = "lambda.min") # the "coef" function returns a sparse matrix
dim(res_coef)## [1] 22731 1head(res_coef) # in a sparse matrix the "." represents the value of zero## 6 x 1 sparse Matrix of class "dgCMatrix"
## s1
## (Intercept) -4.157877
## ENSG00000000003.15 .
## ENSG00000000419.13 .
## ENSG00000000457.14 .
## ENSG00000000460.17 .
## ENSG00000000938.13 .Of course, the number of coefficients is large (there are many genes!). We only want to consider coefficients with non-zero values, as these represent variables (genes) selected by the Elastic Net.
# get coefficients with non-zero values
res_coef = res_coef[res_coef[,1] != 0,]
# note how performing this operation changed the type of the variable
head(res_coef)## (Intercept) ENSG00000008300.17 ENSG00000019169.10 ENSG00000043355.12
## -4.1578774633 -0.0087299400 0.0052909288 -0.0257519835
## ENSG00000066279.18 ENSG00000086991.13
## -0.0004281957 -0.0503265120# remove first coefficient as this is the intercept, a variable of the model itself
res_coef = res_coef[-1]
relevant_genes = names(res_coef) # get names of the (non-zero) variables.
length(relevant_genes) # number of selected genes## [1] 94head(relevant_genes) # few select genes## [1] "ENSG00000008300.17" "ENSG00000019169.10" "ENSG00000043355.12"
## [4] "ENSG00000066279.18" "ENSG00000086991.13" "ENSG00000087586.18"You might not know your Ensembl gene annotation by heart, so we can get the common gene name from limma_res$voomObj$genes as in this table we can find the ensembl to gene name correspondence.
head(limma_res$voomObj$genes)## source type score phase gene_id gene_type
## ENSG00000000003.15 HAVANA gene NA NA ENSG00000000003.15 protein_coding
## ENSG00000000419.13 HAVANA gene NA NA ENSG00000000419.13 protein_coding
## ENSG00000000457.14 HAVANA gene NA NA ENSG00000000457.14 protein_coding
## ENSG00000000460.17 HAVANA gene NA NA ENSG00000000460.17 protein_coding
## ENSG00000000938.13 HAVANA gene NA NA ENSG00000000938.13 protein_coding
## ENSG00000000971.16 HAVANA gene NA NA ENSG00000000971.16 protein_coding
## gene_name level hgnc_id havana_gene
## ENSG00000000003.15 TSPAN6 2 HGNC:11858 OTTHUMG00000022002.2
## ENSG00000000419.13 DPM1 2 HGNC:3005 OTTHUMG00000032742.2
## ENSG00000000457.14 SCYL3 2 HGNC:19285 OTTHUMG00000035941.6
## ENSG00000000460.17 C1orf112 2 HGNC:25565 OTTHUMG00000035821.9
## ENSG00000000938.13 FGR 2 HGNC:3697 OTTHUMG00000003516.3
## ENSG00000000971.16 CFH 1 HGNC:4883 OTTHUMG00000035607.6relevant_gene_names = limma_res$voomObj$genes[relevant_genes, "gene_name"]
head(relevant_gene_names) # few select genes (with readable names now)## [1] "CELSR3" "MARCO" "ZIC2" "ASPM" "NOX4" "AURKA"Are there any genes of particular relevance?
Did limma and Elastic Net select some of the same genes? We can check the common genes between the two results by using the intersect function.
print(intersect(limma_res$topGenes$gene_id, relevant_genes))## [1] "ENSG00000104938.18" "ENSG00000165682.14" "ENSG00000136011.15"
## [4] "ENSG00000019169.10" "ENSG00000251049.2" "ENSG00000160323.19"
## [7] "ENSG00000223922.1" "ENSG00000126838.10" "ENSG00000183166.11"
## [10] "ENSG00000228842.3" "ENSG00000140835.10" "ENSG00000250266.2"
## [13] "ENSG00000100170.10" "ENSG00000260015.1" "ENSG00000184809.13"Of note, we do not expect a high overlap between genes selected by limma and Elastic net. The reason for this is the fact Elastic Net criteria bias the selection of genes, which are not highly correlated against each other, while not such bias is present in limma.
6.4 Hierarchical clustering
Finally we can take a look at how our samples cluster together by running an hierarchical clustering algorithm. We will only be looking at the genes Elastic Net found and use these to cluster the samples. The genes highlighted in green are the ones that limma had also selected as we’ve seen before. The samples highlighted in red are Solid Tissue Normal, the samples highlighted in black are Primary solid Tumor.
# define the color palette for the plot
hmcol = colorRampPalette(rev(brewer.pal(9, "RdBu")))(256)
# perform complete linkage clustering
clust = function(x) hclust(x, method = "complete")
# use the inverse of correlation as distance.
dist = function(x) as.dist((1 - cor(t(x)))/2)
# Show green color for genes that also show up in DE analysis
colorLimmaGenes = ifelse(
# Given a vector of boolean values
(relevant_genes %in% limma_res$topGenes$gene_id),
"green", # if true, return green for that value
"white" # if false, return white for that value
)
# As you've seen a good looking heatmap involves a lot of parameters
d_mat_use <- t(d_mat[,relevant_genes])
rownames(d_mat_use) <- relevant_gene_names
gene_heatmap = heatmap.2(
#t(d_mat[,relevant_genes]),
d_mat_use,
scale = "row", # scale the values for each gene (row)
density.info = "none", # turns off density plot inside color legend
trace = "none", # turns off trace lines inside the heat map
col = hmcol, # define the color map
labRow = relevant_gene_names, # use gene names instead of ensembl annotation
RowSideColors = colorLimmaGenes,
labCol = FALSE, # Not showing column labels
ColSideColors = as.character(as.numeric(d_resp)), # Show colors for each response class
dendrogram = "both", # Show dendrograms for both axis
hclust = clust, # Define hierarchical clustering method
distfun = dist, # Using correlation coefficient for distance function
cexRow = .6, # Resize row labels
margins = c(1,5) # Define margin spaces
)
As you’ve seen, selected genes group into two classes: genes highly expressed in tumors vs. genes in control. Interestingly, genes also detected by DE analysis are only associated to high expression in the control group. One interesting question is, are selected genes (up in control or up in tumor) associated to any type of common biological problem? Try to do a GO analysis on them. Note that the package we use has been updated. You can use the new package gprofiler2, but this will change your results.
# Using the same method as in Day-2, get the dendrogram from the heatmap
# and cut it to get the 2 classes of genes
# Extract the hierarchical cluster from heatmap to class "hclust"
hc = as.hclust(gene_heatmap$rowDendrogram)
# Cut the tree into 2 groups, up-regulated in tumor and up-regulated in control
clusters = cutree(hc, k = 2)
table(clusters)## clusters
## 1 2
## 60 34# Group 1, up in tumor
go_cluster_1 = enrichGO(names(clusters[clusters == 1]), OrgDb = "org.Hs.eg.db", keyType = "SYMBOL", pvalueCutoff = 2, qvalueCutoff = 2, ont = "ALL", pAdjustMethod = "BH")## Loading required package: org.Hs.eg.db## Loading required package: AnnotationDbi##
## Attaching package: 'AnnotationDbi'## The following object is masked from 'package:clusterProfiler':
##
## select## head(go_cluster_1@result)## ONTOLOGY ID
## GO:0000212 BP GO:0000212
## GO:0035563 BP GO:0035563
## GO:1901992 BP GO:1901992
## GO:0035561 BP GO:0035561
## GO:0090068 BP GO:0090068
## GO:0007088 BP GO:0007088
## Description GeneRatio
## GO:0000212 meiotic spindle organization 2/37
## GO:0035563 positive regulation of chromatin binding 2/37
## GO:1901992 positive regulation of mitotic cell cycle phase transition 3/37
## GO:0035561 regulation of chromatin binding 2/37
## GO:0090068 positive regulation of cell cycle process 4/37
## GO:0007088 regulation of mitotic nuclear division 3/37
## BgRatio pvalue p.adjust qvalue geneID
## GO:0000212 14/18722 0.0003406972 0.1325227 0.1058207 ASPM/AURKA
## GO:0035563 14/18722 0.0003406972 0.1325227 0.1058207 CDT1/EBF2
## GO:1901992 93/18722 0.0008157959 0.1325227 0.1058207 CDC25C/TERT/CDT1
## GO:0035561 22/18722 0.0008562759 0.1325227 0.1058207 CDT1/EBF2
## GO:0090068 236/18722 0.0011726949 0.1325227 0.1058207 AURKA/CDC25C/TERT/CDT1
## GO:0007088 110/18722 0.0013258598 0.1325227 0.1058207 AURKA/CDC25C/CDT1
## Count
## GO:0000212 2
## GO:0035563 2
## GO:1901992 3
## GO:0035561 2
## GO:0090068 4
## GO:0007088 3# Group 2, up in control
go_cluster_2 = enrichGO(names(clusters[clusters == 2]), OrgDb = "org.Hs.eg.db", keyType = "SYMBOL", pvalueCutoff = 2, qvalueCutoff = 2, ont = "ALL", pAdjustMethod = "BH")
head(go_cluster_2@result)## ONTOLOGY ID Description GeneRatio
## GO:0008643 BP GO:0008643 carbohydrate transport 3/20
## GO:0042742 BP GO:0042742 defense response to bacterium 3/20
## GO:1904659 BP GO:1904659 glucose transmembrane transport 2/20
## GO:0008645 BP GO:0008645 hexose transmembrane transport 2/20
## GO:0015749 BP GO:0015749 monosaccharide transmembrane transport 2/20
## GO:0050900 BP GO:0050900 leukocyte migration 3/20
## BgRatio pvalue p.adjust qvalue geneID
## GO:0008643 152/18722 0.0005404429 0.1503978 0.1101184 SLC5A1/SMPD3/PPBP
## GO:0042742 350/18722 0.0058315584 0.1503978 0.1101184 STAB2/PPBP/IGHV2-70D
## GO:1904659 110/18722 0.0060657035 0.1503978 0.1101184 SLC5A1/PPBP
## GO:0008645 114/18722 0.0065003449 0.1503978 0.1101184 SLC5A1/PPBP
## GO:0015749 116/18722 0.0067228676 0.1503978 0.1101184 SLC5A1/PPBP
## GO:0050900 369/18722 0.0067490091 0.1503978 0.1101184 SMPD3/CHST4/PPBP
## Count
## GO:0008643 3
## GO:0042742 3
## GO:1904659 2
## GO:0008645 2
## GO:0015749 2
## GO:0050900 3# An alternative way to conduct the enrichment analysis.
# selecting just a few columns so that its easier to visualize the table
gprofiler_cols = c("significant","p.value","overlap.size","term.id","term.name")
gprofiler2_cols = c("significant", "p_value", "intersection_size", "term_id", "term_name")
# make sure the URL uses https
library(gProfileR)
set_base_url("https://biit.cs.ut.ee/gprofiler")
# gProfileR
# Group 1, up in tumor
gprofiler(names(clusters[clusters %in% 1]))[, gprofiler_cols]## Warning: Please consider using the new package "gprofiler2". At the moment you are using a deprecated package relying on outdated data.
## More information at https://biit.cs.ut.ee/gprofiler/page/r-new. Feel free to contact us at biit.support@ut.ee for further help.## significant p.value overlap.size term.id
## 1 TRUE 0.02280 3 HPA:053040_21
## 2 TRUE 0.00976 3 HPA:053040_22
## term.name
## 1 testis; pachytene spermatocytes[Approved,Low]
## 2 testis; pachytene spermatocytes[Approved,Medium]# Group 2, up in control
gprofiler(names(clusters[clusters %in% 2]))[, gprofiler_cols]## Warning: Please consider using the new package "gprofiler2". At the moment you are using a deprecated package relying on outdated data.
## More information at https://biit.cs.ut.ee/gprofiler/page/r-new. Feel free to contact us at biit.support@ut.ee for further help.## significant p.value overlap.size term.id
## 1 TRUE 0.00603 10 GO:0006952
## 2 TRUE 0.03910 10 TF:M04340
## 3 TRUE 0.03910 10 TF:M04340_0
## 4 TRUE 0.02190 13 TF:M01464
## 5 TRUE 0.02190 13 TF:M01464_0
## term.name
## 1 defense response
## 2 Factor: HOXA10; motif: NGYMATAAAAN
## 3 Factor: HOXA10; motif: NGYMATAAAAN; match class: 0
## 4 Factor: HOXA10; motif: NNNGYAATAAAATNNW
## 5 Factor: HOXA10; motif: NNNGYAATAAAATNNW; match class: 0library(gprofiler2)##
## Attaching package: 'gprofiler2'## The following objects are masked from 'package:gProfileR':
##
## gconvert, get_base_url, get_tls_version, get_user_agent, gorth,
## set_base_url, set_tls_version, set_user_agent# gprofiler2
# Group 1, up in tumor
gost(names(clusters[clusters %in% 1]))[["result"]][, gprofiler2_cols]## No results to show
## Please make sure that the organism is correct or set significant = FALSE## NULL# Group 2, up in control
gost(names(clusters[clusters %in% 2]))[["result"]][, gprofiler2_cols]## significant p_value intersection_size term_id term_name
## 1 TRUE 0.003382673 10 GO:0006952 defense response
## 2 TRUE 0.018412282 13 GO:0006950 response to stressThere are other options to conduct this type of analysis. For example the package gprofiler2 will not provide any results in this case.
You can also try the gprofiler2 web interface: https://biit.cs.ut.ee/gprofiler/gost
6.5 Exercise
Another way to reduce dimensionality is through the use of variance filtering. We can use the varFilter function to do this.
# retain only a small subset of the genes (see documentation for ?varFilter)
d_mat = varFilter(limma_res$voomObj$E, var.func = IQR, var.cutoff = 0.95)
# transpose the matrix, so that it has the same shape as the d_mat we used at the beginning
d_mat = t(d_mat)
# show the dimensions of the matrix
print(dim(d_mat))## [1] 421 1137This function takes the normalized RNASeq data stored in the limma_res object, and returns a matrix of the same form. What varFilter does is calculate the interquartile range (IQR) for all genes. It then removes 95% of the genes, starting with those with lower IQR. Therefore, only 5% of the genes will be retained - those with the highest dispersion, and therefore possibly the most useful information. This can easily be understood that remembering that a gene that has the exact same expression across all samples, has a variance (and IQR) of zero. We definitely want to remove those.
Run now again the commands to train the elastic net model, and to evaluate it. Has the performance increased or decreased? Are there more or less variables selected?
Remember to also re-generate your x_* training and test data (it is not needed for the response, because that hasn’t changed) with the new d_mat:
# size before
print(dim(x_train))## [1] 317 22730print(dim(x_test))## [1] 104 22730x_train = d_mat[train_ids, ]
x_test = d_mat[-train_ids, ]
# size after
print(dim(x_train))## [1] 317 1137print(dim(x_test))## [1] 104 1137If you don’t do this, x_train and x_test will still contain all 22534 genes, instead of only the 1127 we have selected.