| Title: | MethylMix: Identifying methylation driven cancer genes |
|---|---|
| Description: | MethylMix is an algorithm implemented to identify hyper and hypomethylated genes for a disease. MethylMix is based on a beta mixture model to identify methylation states and compares them with the normal DNA methylation state. MethylMix uses a novel statistic, the Differential Methylation value or DM-value defined as the difference of a methylation state with the normal methylation state. Finally, matched gene expression data is used to identify, besides differential, functional methylation states by focusing on methylation changes that effect gene expression. References: Gevaert 0. MethylMix: an R package for identifying DNA methylation-driven genes. Bioinformatics (Oxford, England). 2015;31(11):1839-41. doi:10.1093/bioinformatics/btv020. Gevaert O, Tibshirani R, Plevritis SK. Pancancer analysis of DNA methylation-driven genes using MethylMix. Genome Biology. 2015;16(1):17. doi:10.1186/s13059-014-0579-8. |
| Authors: | Olivier Gevaert |
| Maintainer: | Olivier Gevaert <[email protected]> |
| License: | GPL-2 |
| Version: | 2.37.0 |
| Built: | 2024-11-21 06:07:01 UTC |
| Source: | https://github.com/bioc/MethylMix |
This function uses the annotation for Illumina methylation arrays to map each probe to a gene. Then, for each gene, it clusters all its CpG sites using hierchical clustering and Pearson correlation as distance and complete linkage. If data for normal samples is provided, only overlapping probes between cancer and normal samples are used. Probes with SNPs are removed. This function is prepared to run in parallel if the user registers a parallel structure, otherwise it runs sequentially. This function also cleans up the sample names, converting them to the 12 digit format.
ClusterProbes(MET_Cancer, MET_Normal, CorThreshold = 0.4)ClusterProbes(MET_Cancer, MET_Normal, CorThreshold = 0.4)
MET_Cancer |
data matrix for cancer samples. |
MET_Normal |
data matrix for normal samples. |
CorThreshold |
correlation threshold for cutting the clusters. |
List with the clustered data sets and the mapping between probes and genes.
## Not run: # Optional register cluster to run in parallel library(doParallel) cl <- makeCluster(5) registerDoParallel(cl) # Methylation data for ovarian cancer cancerSite <- "OV" targetDirectory <- paste0(getwd(), "/") # Downloading methylation data METdirectories <- Download_DNAmethylation(cancerSite, targetDirectory, TRUE) # Processing methylation data METProcessedData <- Preprocess_DNAmethylation(cancerSite, METdirectories) # Saving methylation processed data saveRDS(METProcessedData, file = paste0(targetDirectory, "MET_", cancerSite, "_Processed.rds")) # Clustering methylation data res <- ClusterProbes(METProcessedData[[1]], METProcessedData[[2]]) # Saving methylation clustered data toSave <- list(METcancer = res[[1]], METnormal = res[[2]], ProbeMapping = res$ProbeMapping) saveRDS(toSave, file = paste0(targetDirectory, "MET_", cancerSite, "_Clustered.rds")) stopCluster(cl) ## End(Not run)## Not run: # Optional register cluster to run in parallel library(doParallel) cl <- makeCluster(5) registerDoParallel(cl) # Methylation data for ovarian cancer cancerSite <- "OV" targetDirectory <- paste0(getwd(), "/") # Downloading methylation data METdirectories <- Download_DNAmethylation(cancerSite, targetDirectory, TRUE) # Processing methylation data METProcessedData <- Preprocess_DNAmethylation(cancerSite, METdirectories) # Saving methylation processed data saveRDS(METProcessedData, file = paste0(targetDirectory, "MET_", cancerSite, "_Processed.rds")) # Clustering methylation data res <- ClusterProbes(METProcessedData[[1]], METProcessedData[[2]]) # Saving methylation clustered data toSave <- list(METcancer = res[[1]], METnormal = res[[2]], ProbeMapping = res$ProbeMapping) saveRDS(toSave, file = paste0(targetDirectory, "MET_", cancerSite, "_Clustered.rds")) stopCluster(cl) ## End(Not run)
Downloads DNA methylation data from TCGA.
Download_DNAmethylation(CancerSite, TargetDirectory, downloadData = TRUE)Download_DNAmethylation(CancerSite, TargetDirectory, downloadData = TRUE)
CancerSite |
character of length 1 with TCGA cancer code. |
TargetDirectory |
character with directory where a folder for downloaded files will be created. |
downloadData |
logical indicating if data should be downloaded (default: TRUE). If false, the url of the desired data is returned. |
list with paths to downloaded files for both 27k and 450k methylation data.
## Not run: # Optional register cluster to run in parallel library(doParallel) cl <- makeCluster(5) registerDoParallel(cl) # Methylation data for ovarian cancer cancerSite <- "OV" targetDirectory <- paste0(getwd(), "/") # Downloading methylation data METdirectories <- Download_DNAmethylation(cancerSite, targetDirectory, TRUE) # Processing methylation data METProcessedData <- Preprocess_DNAmethylation(cancerSite, METdirectories) # Saving methylation processed data saveRDS(METProcessedData, file = paste0(targetDirectory, "MET_", cancerSite, "_Processed.rds")) # Clustering methylation data res <- ClusterProbes(METProcessedData[[1]], METProcessedData[[2]]) # Saving methylation clustered data toSave <- list(METcancer = res[[1]], METnormal = res[[2]], ProbeMapping = res$ProbeMapping) saveRDS(toSave, file = paste0(targetDirectory, "MET_", cancerSite, "_Clustered.rds")) stopCluster(cl) ## End(Not run)## Not run: # Optional register cluster to run in parallel library(doParallel) cl <- makeCluster(5) registerDoParallel(cl) # Methylation data for ovarian cancer cancerSite <- "OV" targetDirectory <- paste0(getwd(), "/") # Downloading methylation data METdirectories <- Download_DNAmethylation(cancerSite, targetDirectory, TRUE) # Processing methylation data METProcessedData <- Preprocess_DNAmethylation(cancerSite, METdirectories) # Saving methylation processed data saveRDS(METProcessedData, file = paste0(targetDirectory, "MET_", cancerSite, "_Processed.rds")) # Clustering methylation data res <- ClusterProbes(METProcessedData[[1]], METProcessedData[[2]]) # Saving methylation clustered data toSave <- list(METcancer = res[[1]], METnormal = res[[2]], ProbeMapping = res$ProbeMapping) saveRDS(toSave, file = paste0(targetDirectory, "MET_", cancerSite, "_Clustered.rds")) stopCluster(cl) ## End(Not run)
Downloads gene expression data from TCGA.
Download_GeneExpression(CancerSite, TargetDirectory, downloadData = TRUE)Download_GeneExpression(CancerSite, TargetDirectory, downloadData = TRUE)
CancerSite |
character of length 1 with TCGA cancer code. |
TargetDirectory |
character with directory where a folder for downloaded files will be created. |
downloadData |
logical indicating if data should be downloaded (default: TRUE). If false, the url of the desired data is returned. |
This function downloads RNAseq data (file tag "mRNAseq_Preprocess.Level_3"), with the exception for OV and GBM, for which micro array data is downloaded since there is not enough RNAseq data
list with paths to downloaded files for both 27k and 450k methylation data.
## Not run: # Optional register cluster to run in parallel library(doParallel) cl <- makeCluster(5) registerDoParallel(cl) # Gene expression data for ovarian cancer cancerSite <- "OV" targetDirectory <- paste0(getwd(), "/") # Downloading gene expression data GEdirectories <- Download_GeneExpression(cancerSite, targetDirectory, TRUE) # Processing gene expression data GEProcessedData <- Preprocess_GeneExpression(cancerSite, GEdirectories) # Saving gene expression processed data saveRDS(GEProcessedData, file = paste0(targetDirectory, "GE_", cancerSite, "_Processed.rds")) stopCluster(cl) ## End(Not run)## Not run: # Optional register cluster to run in parallel library(doParallel) cl <- makeCluster(5) registerDoParallel(cl) # Gene expression data for ovarian cancer cancerSite <- "OV" targetDirectory <- paste0(getwd(), "/") # Downloading gene expression data GEdirectories <- Download_GeneExpression(cancerSite, targetDirectory, TRUE) # Processing gene expression data GEProcessedData <- Preprocess_GeneExpression(cancerSite, GEdirectories) # Saving gene expression processed data saveRDS(GEProcessedData, file = paste0(targetDirectory, "GE_", cancerSite, "_Processed.rds")) stopCluster(cl) ## End(Not run)
Cancer Gene expression data of glioblastoma patients from the TCGA project. A set of 14 genes that have been shown in the literature to be involved in differential methylation in glioblastoma were selected as an example to try out MethylMix.
data(GEcancer)data(GEcancer)
A numeric matrix with 14 rows (genes) and 251 columns (samples).
Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008 Oct 23; 455(7216):1061-8. doi: 10.1038/nature07385. Epub 2008 Sep 4. Erratum in: Nature. 2013 Feb 28;494(7438):506. PubMed PMID: 18772890; PubMed Central PMCID: PMC2671642.
TCGA: The Cancer Genome Atlas: http://cancergenome.nih.gov/
This function wraps the functions for downloading and pre-processing DNA methylation and gene expression data, as well as for clustering CpG probes.
GetData(cancerSite, targetDirectory)GetData(cancerSite, targetDirectory)
cancerSite |
character of length 1 with TCGA cancer code. |
targetDirectory |
character with directory where a folder for downloaded files will be created. |
Pre-process of DNA methylation data includes eliminating samples and genes with too many NAs, imputing NAs, and doing Batch correction. If there is both 27k and 450k data, and both data sets have more than 50 samples, we combine the data sets, by reducing the 450k data to the probes present in the 27k data, and bath correction is performed again to the combined data set. If there are samples with both 27k and 450k data, the 450k data is used and the 27k data is discarded, before the step mentioned above. If the 27k or the 450k data does not have more than 50 samples, we use the one with the greatest number of samples, we do not combine the data sets.
For gene expression, this function downloads RNAseq data (file tag "mRNAseq_Preprocess.Level_3"), with the exception for OV and GBM, for which micro array data is downloaded since there is not enough RNAseq data. Pre-process of gene expression data includes eliminating samples and genes with too many NAs, imputing NAs, and doing Batch correction.
For the clustering of the CpG probes, this function uses the annotation for Illumina methylation arrays to map each probe to a gene. Then, for each gene, it clusters all its CpG sites using hierchical clustering and Pearson correlation as distance and complete linkage. If data for normal samples is provided, only overlapping probes between cancer and normal samples are used. Probes with SNPs are removed.
This function is prepared to run in parallel if the user registers a parallel structure, otherwise it runs sequentially.
This function also cleans up the sample names, converting them to the 12 digit format.
The following files will be created in target directory:
gdac: a folder with the raw data downloaded from TCGA.
MET_CancerSite_Processed.rds: processed methylation data at the CpG sites level (not clustered).
GE_CancerSite_Processed.rds: processed gene expression data.
data_CancerSite.rds: list with both gene expression and methylation data. Methylation data is clustered and presented at the gene level. A matrix with the mapping from CpG sites to genes is included.
## Not run: # Get data for ovarian cancer cancerSite <- "OV" targetDirectory <- paste0(getwd(), "/") GetData(cancerSite, targetDirectory) # Optional register cluster to run in parallel library(doParallel) cl <- makeCluster(5) registerDoParallel(cl) cancerSite <- "OV" targetDirectory <- paste0(getwd(), "/") GetData(cancerSite, targetDirectory) stopCluster(cl) ## End(Not run)## Not run: # Get data for ovarian cancer cancerSite <- "OV" targetDirectory <- paste0(getwd(), "/") GetData(cancerSite, targetDirectory) # Optional register cluster to run in parallel library(doParallel) cl <- makeCluster(5) registerDoParallel(cl) cancerSite <- "OV" targetDirectory <- paste0(getwd(), "/") GetData(cancerSite, targetDirectory) stopCluster(cl) ## End(Not run)
Cancer Gene expression data of glioblastoma patients from the TCGA project. A set of 14 genes that have been shown in the literature to be involved in differential methylation in glioblastoma were selected as an example to try out MethylMix.
data(METcancer)data(METcancer)
A numeric matrix with 14 rows (genes) and 251 columns (samples).
Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008 Oct 23; 455(7216):1061-8. doi: 10.1038/nature07385. Epub 2008 Sep 4. Erratum in: Nature. 2013 Feb 28;494(7438):506. PubMed PMID: 18772890; PubMed Central PMCID: PMC2671642.
TCGA: The Cancer Genome Atlas: http://cancergenome.nih.gov/
MethylMix identifies DNA methylation driven genes by modeling DNA methylation data in cancer vs. normal and looking for homogeneous subpopulations. In addition matched gene expression data (e.g. from microarray technology or RNA sequencing) is used to identify functional DNA methylation events by requiring a negative correlation between methylation and gene expression of a particular gene. See references below.
MethylMix(METcancer, GEcancer, METnormal = NULL, listOfGenes = NULL, filter = TRUE, NoNormalMode = FALSE, OutputRoot = "")MethylMix(METcancer, GEcancer, METnormal = NULL, listOfGenes = NULL, filter = TRUE, NoNormalMode = FALSE, OutputRoot = "")
METcancer |
Matrix with the methylation data of cancer tissue with genes in rows and samples in columns. |
GEcancer |
Gene expression data for cancer tissue with genes in rows and samples in columns. |
METnormal |
Matrix with the normal methylation data of the same genes as in METcancer. Again genes in rows and samples in columns. The samples do not have to match with the cancer data. If this argument is NULL, MethylMix will run without comparing to normal samples. |
listOfGenes |
Vector with genes names to be evaluated, names must coincide with the names of the rows of METcancer. |
filter |
Logical indicating if the linear regression to select genes with significative linear negative relation between methylation and gene expression should be performed (default: TRUE). |
NoNormalMode |
Logical indicating if the methylation states found in the cancer samples should be compared to the normal samples (default: FALSE). |
OutputRoot |
Path to store the MethylMix results object. |
MethylMixResults is a list with the following components:
MethylationDrivers |
Genes identified as transcriptionally predictive and differentially methylated by MethylMix. |
NrComponents |
The number of methylation states found for each driver gene. |
MixtureStates |
A list with the DM-values for each driver gene. Differential Methylation values (DM-values) are defined as the difference between the methylation mean in one mixture component of cancer samples and the methylation mean in the normal samples, for a given gene. |
MethylationStates |
Matrix with DM-values for all driver genes (rows) and all samples (columns). |
Classifications |
Matrix with integers indicating to which mixture component each cancer sample was assigned to, for each gene. |
Models |
Beta mixture model parameters for each driver gene. |
Gevaert 0. MethylMix: an R package for identifying DNA methylation-driven genes. Bioinformatics (Oxford, England). 2015;31(11):1839-41. doi:10.1093/bioinformatics/btv020.
Gevaert O, Tibshirani R, Plevritis SK. Pancancer analysis of DNA methylation-driven genes using MethylMix. Genome Biology. 2015;16(1):17. doi:10.1186/s13059-014-0579-8.
Pierre-Louis Cedoz, Marcos Prunello, Kevin Brennan, Olivier Gevaert; MethylMix 2.0: an R package for identifying DNA methylation genes. Bioinformatics. doi:10.1093/bioinformatics/bty156.
# load the three data sets needed for MethylMix data(METcancer) data(METnormal) data(GEcancer) # run MethylMix on a small set of example data MethylMixResults <- MethylMix(METcancer, GEcancer, METnormal) ## Not run: # run in parallel library(doParallel) cl <- makeCluster(5) registerDoParallel(cl) MethylMixResults <- MethylMix(METcancer, GEcancer, METnormal) stopCluster(cl) ## End(Not run)# load the three data sets needed for MethylMix data(METcancer) data(METnormal) data(GEcancer) # run MethylMix on a small set of example data MethylMixResults <- MethylMix(METcancer, GEcancer, METnormal) ## Not run: # run in parallel library(doParallel) cl <- makeCluster(5) registerDoParallel(cl) MethylMixResults <- MethylMix(METcancer, GEcancer, METnormal) stopCluster(cl) ## End(Not run)
Model gene expression as a function of gene expression with a simple linear regression model. Genes with a significant negative linear association between DNA methylation and gene expression are returned.
MethylMix_ModelGeneExpression(METcancer, GEcancer, CovariateData = NULL)MethylMix_ModelGeneExpression(METcancer, GEcancer, CovariateData = NULL)
METcancer |
matrix with methylation data for cancer samples (genes in rows, samples in columns). |
GEcancer |
matrix with gene expression data for cancer samples (genes in rows, samples in columns). |
CovariateData |
vector (numeric or character) indicating a covariate to be included in the model to adjust for it. Not used in an standard run of MethylMix. It can be used if samples can from different tissue type, for example. |
vector with the names of the genes for which there is a significant linear and negative association between methylation and gene expression.
# load data sets data(METcancer) data(GEcancer) # model gene expression MethylMixResults <- MethylMix_ModelGeneExpression(METcancer, GEcancer)# load data sets data(METcancer) data(GEcancer) # model gene expression MethylMixResults <- MethylMix_ModelGeneExpression(METcancer, GEcancer)
Produces plots to represent MethylMix's output.
MethylMix_PlotModel(GeneName, MixtureModelResults, METcancer, GEcancer = NULL, METnormal = NULL)MethylMix_PlotModel(GeneName, MixtureModelResults, METcancer, GEcancer = NULL, METnormal = NULL)
GeneName |
Name of the gene for which to create a MethylMix plot. |
MixtureModelResults |
List returned by MethylMix function. |
METcancer |
Matrix with the methylation data of cancer tissue with genes in rows and samples in columns. |
GEcancer |
Gene expression data for cancer tissue with genes in rows and samples in columns (optional). |
METnormal |
Matrix with the normal methylation data of the same genes as in METcancer (optional). Again genes in rows and samples in columns. |
a list with MethylMix plots, a histogram of the methylation data (MixtureModelPlot) and a scatterplot between DNA methylation and gene expression (CorrelationPlot, is NULL if gene expression data is not provided). Both plots show the different mixture components identified.
# Load the three data sets needed for MethylMix data(METcancer) data(METnormal) data(GEcancer) # Run methylmix on a small set of example data MethylMixResults <- MethylMix(METcancer, GEcancer, METnormal) # Plot the most famous methylated gene for glioblastoma MethylMix_PlotModel("MGMT", MethylMixResults, METcancer) # Plot MGMT also with its normal methylation variation MethylMix_PlotModel("MGMT", MethylMixResults, METcancer, METnormal = METnormal) # Plot a MethylMix model for another gene MethylMix_PlotModel("ZNF217", MethylMixResults, METcancer, METnormal = METnormal) # Also plot the inverse correlation with gene expression (creates two separate plots) MethylMix_PlotModel("MGMT", MethylMixResults, METcancer, GEcancer, METnormal) # Plot all functional and differential genes for (gene in MethylMixResults$MethylationDrivers) { MethylMix_PlotModel(gene, MethylMixResults, METcancer, METnormal = METnormal) }# Load the three data sets needed for MethylMix data(METcancer) data(METnormal) data(GEcancer) # Run methylmix on a small set of example data MethylMixResults <- MethylMix(METcancer, GEcancer, METnormal) # Plot the most famous methylated gene for glioblastoma MethylMix_PlotModel("MGMT", MethylMixResults, METcancer) # Plot MGMT also with its normal methylation variation MethylMix_PlotModel("MGMT", MethylMixResults, METcancer, METnormal = METnormal) # Plot a MethylMix model for another gene MethylMix_PlotModel("ZNF217", MethylMixResults, METcancer, METnormal = METnormal) # Also plot the inverse correlation with gene expression (creates two separate plots) MethylMix_PlotModel("MGMT", MethylMixResults, METcancer, GEcancer, METnormal) # Plot all functional and differential genes for (gene in MethylMixResults$MethylationDrivers) { MethylMix_PlotModel(gene, MethylMixResults, METcancer, METnormal = METnormal) }
Given a new data set with methylation data, this function predicts the mixture component for each new sample and driver gene. Predictions are based on posterior probabilities calculated with MethylMix'x fitted mixture model.
MethylMix_Predict(newBetaValuesMatrix, MethylMixResult)MethylMix_Predict(newBetaValuesMatrix, MethylMixResult)
newBetaValuesMatrix |
Matrix with new observations for prediction, genes/cpg sites in rows, samples in columns. Although this new matrix can have a different number of genes/cpg sites than the one provided as METcancer when running MethylMix, naming of genes/cpg sites should be the same. |
MethylMixResult |
Output object from MethylMix |
A matrix with predictions (indices of mixture component), driver genes in rows, new samples in columns
# load the three data sets needed for MethylMix data(METcancer) data(METnormal) data(GEcancer) # run MethylMix on a small set of example data MethylMixResults <- MethylMix(METcancer, GEcancer, METnormal) # toy example new data, of same dimension of original METcancer data newMETData <- matrix(runif(length(METcancer)), nrow = nrow(METcancer)) rownames(newMETData) <- rownames(METcancer) colnames(newMETData) <- paste0("sample", 1:ncol(METcancer)) predictions <- MethylMix_Predict(newMETData, MethylMixResults)# load the three data sets needed for MethylMix data(METcancer) data(METnormal) data(GEcancer) # run MethylMix on a small set of example data MethylMixResults <- MethylMix(METcancer, GEcancer, METnormal) # toy example new data, of same dimension of original METcancer data newMETData <- matrix(runif(length(METcancer)), nrow = nrow(METcancer)) rownames(newMETData) <- rownames(METcancer) colnames(newMETData) <- paste0("sample", 1:ncol(METcancer)) predictions <- MethylMix_Predict(newMETData, MethylMixResults)
Normal tissue DNA methylation data of glioblastoma patients. These normal tissue samples were run on the same platform and are described in the publication referenced below.
data(METnormal)data(METnormal)
A numeric matrix with 14 rows (genes) and 4 columns (samples).
Noushmehr H, Weisenberger DJ, Diefes K, Phillips HS, Pujara K, Berman BP, Pan F, Pelloski CE, Sulman EP, Bhat KP, Verhaak RG, Hoadley KA, Hayes DN, Perou CM, Schmidt HK, Ding L, Wilson RK, Van Den Berg D, Shen H, Bengtsson H, Neuvial P, Cope LM, Buckley J, Herman JG, Baylin SB, Laird PW, Aldape K; Cancer Genome Atlas Research Network. Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell. 2010 May 18;17(5):510-22. doi: 10.1016/j.ccr.2010.03.017. Epub 2010 Apr 15. PubMed PMID: 20399149; PubMed Central PMCID: PMC2872684
Auxiliar function. Given a new vector of beta values, this function calculates a matrix with posterior prob of belonging to each mixture commponent (columns) for each new beta value (rows), and return the number of the mixture component with highest posterior probabilit
predictOneGene(newVector, mixtureModel)predictOneGene(newVector, mixtureModel)
newVector |
vector with new beta values |
mixtureModel |
beta mixture model object for the gene being evaluated. |
A matrix with predictions (indices of mixture component), driver genes in rows, new samples in columns
Pre-processes DNA methylation data from TCGA.
Preprocess_DNAmethylation(CancerSite, METdirectories, MissingValueThreshold = 0.2)Preprocess_DNAmethylation(CancerSite, METdirectories, MissingValueThreshold = 0.2)
CancerSite |
character of length 1 with TCGA cancer code. |
METdirectories |
character vector with directories with the downloaded data. It can be the object returned by the Download_DNAmethylation function. |
MissingValueThreshold |
threshold for removing samples or genes with missing values. |
Pre-process includes eliminating samples and genes with too many NAs, imputing NAs, and doing Batch correction. If there is both 27k and 450k data, and both data sets have more than 50 samples, we combine the data sets, by reducing the 450k data to the probes present in the 27k data, and bath correction is performed again to the combined data set. If there are samples with both 27k and 450k data, the 450k data is used and the 27k data is discarded, before the step mentioned above. If the 27k or the 450k data does not have more than 50 samples, we use the one with the greatest number of samples, we do not combine the data sets.
List with the pre-processed data matrix for cancer and normal samples.
## Not run: # Optional register cluster to run in parallel library(doParallel) cl <- makeCluster(5) registerDoParallel(cl) # Methylation data for ovarian cancer cancerSite <- "OV" targetDirectory <- paste0(getwd(), "/") # Downloading methylation data METdirectories <- Download_DNAmethylation(cancerSite, targetDirectory, TRUE) # Processing methylation data METProcessedData <- Preprocess_DNAmethylation(cancerSite, METdirectories) # Saving methylation processed data saveRDS(METProcessedData, file = paste0(targetDirectory, "MET_", cancerSite, "_Processed.rds")) # Clustering methylation data res <- ClusterProbes(METProcessedData[[1]], METProcessedData[[2]]) # Saving methylation clustered data toSave <- list(METcancer = res[[1]], METnormal = res[[2]], ProbeMapping = res$ProbeMapping) saveRDS(toSave, file = paste0(targetDirectory, "MET_", cancerSite, "_Clustered.rds")) stopCluster(cl) ## End(Not run)## Not run: # Optional register cluster to run in parallel library(doParallel) cl <- makeCluster(5) registerDoParallel(cl) # Methylation data for ovarian cancer cancerSite <- "OV" targetDirectory <- paste0(getwd(), "/") # Downloading methylation data METdirectories <- Download_DNAmethylation(cancerSite, targetDirectory, TRUE) # Processing methylation data METProcessedData <- Preprocess_DNAmethylation(cancerSite, METdirectories) # Saving methylation processed data saveRDS(METProcessedData, file = paste0(targetDirectory, "MET_", cancerSite, "_Processed.rds")) # Clustering methylation data res <- ClusterProbes(METProcessedData[[1]], METProcessedData[[2]]) # Saving methylation clustered data toSave <- list(METcancer = res[[1]], METnormal = res[[2]], ProbeMapping = res$ProbeMapping) saveRDS(toSave, file = paste0(targetDirectory, "MET_", cancerSite, "_Clustered.rds")) stopCluster(cl) ## End(Not run)
Pre-processes gene expression data from TCGA.
Preprocess_GeneExpression(CancerSite, MAdirectories, MissingValueThresholdGene = 0.3, MissingValueThresholdSample = 0.1)Preprocess_GeneExpression(CancerSite, MAdirectories, MissingValueThresholdGene = 0.3, MissingValueThresholdSample = 0.1)
CancerSite |
character of length 1 with TCGA cancer code. |
MAdirectories |
character vector with directories with the downloaded data. It can be the object returned by the Download_DNAmethylation function. |
MissingValueThresholdGene |
threshold for missing values per gene. Genes with a percentage of NAs greater than this threshold are removed. Default is 0.3. |
MissingValueThresholdSample |
threshold for missing values per sample. Samples with a percentage of NAs greater than this threshold are removed. Default is 0.1. |
Pre-process includes eliminating samples and genes with too many NAs, imputing NAs, and doing Batch correction.
List with the pre-processed data matrix for cancer and normal samples.
## Not run: # Optional register cluster to run in parallel library(doParallel) cl <- makeCluster(5) registerDoParallel(cl) # Gene expression data for ovarian cancer cancerSite <- "OV" targetDirectory <- paste0(getwd(), "/") # Downloading gene expression data GEdirectories <- Download_GeneExpression(cancerSite, targetDirectory, TRUE) # Processing gene expression data GEProcessedData <- Preprocess_GeneExpression(cancerSite, GEdirectories) # Saving gene expression processed data saveRDS(GEProcessedData, file = paste0(targetDirectory, "GE_", cancerSite, "_Processed.rds")) stopCluster(cl) ## End(Not run)## Not run: # Optional register cluster to run in parallel library(doParallel) cl <- makeCluster(5) registerDoParallel(cl) # Gene expression data for ovarian cancer cancerSite <- "OV" targetDirectory <- paste0(getwd(), "/") # Downloading gene expression data GEdirectories <- Download_GeneExpression(cancerSite, targetDirectory, TRUE) # Processing gene expression data GEProcessedData <- Preprocess_GeneExpression(cancerSite, GEdirectories) # Saving gene expression processed data saveRDS(GEProcessedData, file = paste0(targetDirectory, "GE_", cancerSite, "_Processed.rds")) stopCluster(cl) ## End(Not run)
Data set with annotation from Illumina methylatin arrays mapping CpG sites to genes.