4 Post analysis functions
RTCGAToolbox has analyze functions to provide basic information about datasets. Analyze function includes differential gene expression analyze, correlation analysis between CN and GE data, univariate survival analysis, mutation frequency table and report figure.
4.1 Toy dataset
Since downloading new dataset takes some time and requires enough space on hard drive, I will use nonsense data set for following steps.
You can use following code snippet to load toy dataset.
data(RTCGASample)
RTCGASample## TEST FirehoseData objectStandard run date: NA
## Analysis running date: NA
## Available data types:
## clinical: A data frame of phenotype data, dim: 100 x 7
## RNASeqGene: A matrix of count or normalized data, dim: 800 x 80
## GISTIC: A FirehoseGISTIC for copy number data
## Mutation: A data.frame, dim: 2685 x 30
## To export data, use the 'getData' function.4.2 Differential gene expression
RTCGAToolbox hires voom(Law, C. W. and Chen, Y. and Shi, W. and Smyth, G. K 2014) and limma(Smyth, G. K 2004) package functions to preform differential gene expression analysis between “Normal” and “Cancer” tissue samples. Every sample which is processed by TCGA project(Cancer Genome Atlas Research Network 2008) has a structured barcode number which includes the source of the tissue. RTCGAToolbox uses the barcode information to divide samples into “Normal” or “Tumor” groups and perform DGE analysis. Since “voom”(Law, C. W. and Chen, Y. and Shi, W. and Smyth, G. K 2014) requires raw count for RNASeq data, normalized RNASeq data cannot be used for the analysis. This function uses all gene expression datasets and returns a list which each member is “DGEResult” object. Each result object keeps top table from the genes that have 2 log fold change expression difference and significant adjusted p value. This function filters the results as a deafult behaviour using raw p value, adjusted p value and log fold change. Users can change “adj.pval”, “raw.pval” and “logFC” parameters to refine their results. Also function uses Benjamini & Hochberg adjustment for p values. For more details about adjment users can check base adjustment methods by calling “?p.adjust”. In addition to filter as a default behaviour function only draws heatmap for top 100 up and down regulated genes. Users can also adjust these values by using “hmTopUpN” and “hmTopDownN” parameters.
# Differential gene expression analysis for gene level RNA data.
diffGeneExprs <- getDiffExpressedGenes(dataObject=RTCGASample, DrawPlots=TRUE,
adj.method="BH", adj.pval=0.05, raw.pval=0.05, logFC=2, hmTopUpN=10,
hmTopDownN=10)
# Show head for expression outputs
diffGeneExprs## [[1]]## Dataset:RNASeq## DGEResult object, dim: 4 6showResults(diffGeneExprs[[1]])## Dataset: RNASeq## logFC AveExpr t P.Value adj.P.Val B
## TAP2 5.288573 1.743410 76.38279 7.496531e-76 4.760297e-73 150.8231
## GRTP1 6.187648 2.193494 48.25410 2.030875e-60 6.448029e-58 123.4407
## ENPP5 7.215676 2.707012 45.79069 1.110692e-58 2.350964e-56 120.1123
## APH1B 8.118533 3.158063 37.31744 5.796089e-52 6.134194e-50 106.2072toptableOut <- showResults(diffGeneExprs[[1]])## Dataset: RNASeq## logFC AveExpr t P.Value adj.P.Val B
## TAP2 5.288573 1.743410 76.38279 7.496531e-76 4.760297e-73 150.8231
## GRTP1 6.187648 2.193494 48.25410 2.030875e-60 6.448029e-58 123.4407
## ENPP5 7.215676 2.707012 45.79069 1.110692e-58 2.350964e-56 120.1123
## APH1B 8.118533 3.158063 37.31744 5.796089e-52 6.134194e-50 106.2072If “DrawPlots” set as FALSE, running code above won’t provide any output figure.
Voom + limma: To voom (variance modeling at the observational level) is to estimate the mean-variance relationship robustly and non-parametrically from the data. Voom works with log-counts that are normalized for sequence depth, in particular with log-counts per million (log-cpm). The mean-variance is fitted to the gene-wise standard deviations of the log-cpm, as a function of the average log-count. This method incorporates the mean-variance trend into a precision weight for each individual normalized observation. The normalized log-counts and associated precision weights can then be entered into the limma analysis pipeline, or indeed into any statistical pipeline for microarray data that is precision weight aware(Smyth, G. K 2004; Law, C. W. and Chen, Y. and Shi, W. and Smyth, G. K 2014). Users can check the following publications for more information about methods:
4.3 Correlation between gene expression and copy number data
“getCNGECorrelation” function provides correlation coefficient and adjusted p value between copy number and gene expression data for each dataset. This function takes main dataobject as an input (uses gene copy number estimates from GISTIC2 (Mermel, C. H. and Schumacher, S. E. and Hill, B. and Meyerson, M. L. and Beroukhim, R. and Getz, G 2011) algorithm and gen expression values from every platform (RNAseq and arrays) to prepare return lists. List object stores “CorResult” object that contains results for each comparison.)
#Correlation between gene expression values and copy number
corrGECN <- getCNGECorrelation(dataObject=RTCGASample, adj.method="BH",
adj.pval=0.9, raw.pval=0.05)
corrGECN## [[1]]## Dataset:RNASeq## CorResult object, dim: 43 4showResults(corrGECN[[1]])## Dataset: RNASeq## GeneSymbol Cor adj.p.value p.value
## 2 SEPHS1 -0.3769382 0.6287624 0.01650501
## 9 MSMB -0.3404472 0.8472802 0.03158898
## 39 PRMT3 -0.3806078 0.6287624 0.01540088
## 85 HTR3A 0.3378158 0.8472802 0.03301479
## 91 PHLDB1 -0.3260183 0.8688328 0.04007191
## 101 EMP1 0.3814696 0.6287624 0.01515084corRes <- showResults(corrGECN[[1]])## Dataset: RNASeq## GeneSymbol Cor adj.p.value p.value
## 2 SEPHS1 -0.3769382 0.6287624 0.01650501
## 9 MSMB -0.3404472 0.8472802 0.03158898
## 39 PRMT3 -0.3806078 0.6287624 0.01540088
## 85 HTR3A 0.3378158 0.8472802 0.03301479
## 91 PHLDB1 -0.3260183 0.8688328 0.04007191
## 101 EMP1 0.3814696 0.6287624 0.01515084If the dataset has RNASeq data, data will be normalized for correlation analysis. Correlation function uses Benjamini & Hochberg adjustment for p values. For more details about adjment users can check base adjustment methods by calling “?p.adjust”. In addition, to filter results adjusted and raw p values are used. Users can change “adj.pval” and “raw.pval” parameters to refine results.
The RTCGAToolbox uses one of Pearson’s product moment correlation coefficient to test for associations between paired samples. Measures of association, all in the range [-1, 1] with 0 indicating no association, shows how copy number alterations affect gene expression changes. The test statistic follows a t-distribution, with length (x)-2 degrees of freedom if the samples follow independent normal distributions. Users can get detailed information by calling ?cor.test function
4.4 Mutation frequencies
“getMutationRate” function gets the data frame that stores mutation frequency for the genes. This function gets the mutation information for each sample that has data and calculates frequency for each gene.
# Mutation frequencies
mutFrq <- getMutationRate(dataObject=RTCGASample)
head(mutFrq[order(mutFrq[, 2], decreasing=TRUE), ])## Genes MutationRatio
## FCGBP FCGBP 0.46
## NF1 NF1 0.31
## ASTN1 ASTN1 0.24
## ODZ4 ODZ4 0.22
## BRWD1 BRWD1 0.22
## SYNE2 SYNE2 0.224.5 Univariate survival analysis
Survival analysis is considered as one of the methods that can provide clinically valuable information. To provide this information, the function creates 2 or 3 groups based on expression data.(If the dataset has RNASeq data, data will be normalized for survival analysis.). If function is triggered with 2 groups, RTCGAToolbox creates groups using the median expression level of individual genes. If group number is set to be 3, then the groups will be defined as: the samples in 1st. quartile (expression < 1st Q), the samples those have higher expression (expression > 3rd Q) and the samples lying in between these 2 groups.
This function also needs a survival data, which can be obtained using clinical data frame. Clinical data frames can be obtained from main data downloads. First column of the survival data frame should be sample barcodes, second column should be time and the last column should be event data. Below code chunk shows how survival data frame can be obtained from clinical data and how survival analysis can be done.
# Creating survival data frame and running analysis for
# FCGBP which is one of the most frequently mutated gene in the toy data
# Running following code will provide following KM plot.
clinicData <- getData(RTCGASample,"clinical")
head(clinicData)## Composite.Element.REF yearstobirth vitalstatus daystodeath
## TEST.00.0026 value 53 0 NA
## TEST.00.0052 value 50 0 NA
## TEST.00.0088 value 56 0 NA
## TEST.00.0056 value 56 0 NA
## TEST.00.0023 value 56 0 NA
## TEST.00.0092 value 52 0 NA
## daystolastfollowup neoplasm.diseasestage pathology.T.stage
## TEST.00.0026 1183 stage iiia stage iiia
## TEST.00.0052 897 stage iib stage iib
## TEST.00.0088 1000 stage iib stage iib
## TEST.00.0056 1134 stage iia stage iia
## TEST.00.0023 794 stage iib stage iib
## TEST.00.0092 1104 stage iia stage iiaclinicData <- clinicData[, 3:5]
clinicData[is.na(clinicData[, 3]), 3] <- clinicData[is.na(clinicData[, 3]), 2]
survData <- data.frame(Samples=rownames(clinicData),
Time=as.numeric(clinicData[, 3]), Censor=as.numeric(clinicData[, 1]))
getSurvival(dataObject=RTCGASample, geneSymbols=c("FCGBP"), sampleTimeCensor=survData)
