2 - Introduction: Input data

In ELMER: Inferring Regulatory Element Landscapes and Transcription Factor Networks Using Cancer Methylomes

library(ELMER.data)
library(ELMER)
data(elmer.data.example)
data(LUSC_meth_refined)
data(LUSC_RNA_refined)
library(DT)

Input data

A Multi Assay Experiment object [@mae2017] is the input for all main functions of r BiocStyle::Biocpkg("ELMER") and can be generated by createMAE function.

To perform r BiocStyle::Biocpkg("ELMER") analyses, the Multi Assay Experiment needs:

• a DNA methylation matrix or SummarizedExperiment object from HM450K or EPIC platform for multiple samples;
• a gene expression matrix or SummarizedExperiment object for the same samples;
• a matrix mapping DNA methylation samples to gene expression samples
• a matrix with samples metadata (i.e. clinical data, molecular subtype information).

If TCGA data are used, the the last two matrices will be automatically generated. Based on the genome of reference selected, metadata for the DNA methylation probes, such as genomic coordinates, will be added from Wanding Zhou annotation [@zhou2016comprehensive]; and metadata for gene annotation will be added from ensemble database [@yates2015ensembl] using biomaRt [@durinck2009mapping].

DNA methylation data

DNA methylation data feeding to r BiocStyle::Biocpkg("ELMER") should be a matrix of DNA methylation beta ($\beta$) value for samples (column) and probes (row) processed from row HM450K array data. If TCGA data is used, processed data from GDC website will be downloaded and automatically transformed to the matrix by r BiocStyle::Biocpkg("ELMER"). The processed TCGA DNA methylation data were calculated as $\frac{M}{(M+U)}$, where M represents the methylated allele intensity and U the unmethylated allele intensity. Beta values range from 0 to 1, reflecting the fraction of methylated alleles at each CpG in the each tumor; beta values close to 0 indicates low levels of DNA methylation and beta values close to 1 indicates high levels of DNA methylation.

If user have raw HM450K data, these data can be processed by r BiocStyle::Biocpkg("Methylumi") or r BiocStyle::Biocpkg("minfi") generating DNA methylation beta ($\beta$) value for each CpG site and multiple samples. The getBeta function in r BiocStyle::Biocpkg("minfi") can be used to generate a matrix of DNA methylation beta ($\beta$) value to feed in r BiocStyle::Biocpkg("ELMER"). And we recommend to save this matrix as meth.rda since createMAE can read in files by specifying their path which will help to reduce memory usage.

# Example of DNA methylation data input
datatable(Meth[1:10, 1:10], 
          options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
          rownames = TRUE)

Gene expression data

Gene expresion data feeding to r BiocStyle::Biocpkg("ELMER") should be a matrix of gene expression values for samples (column) and genes (row). Gene expression value can be generated from different platforms: array or RNA-seq. The row data should be processed by other software to get gene or transcript level gene expression calls such as mapping by tophat, calling expression value by cufflink, RSEM or GenomeStudio for expression array. It is recommended to normalize expression data making gene expression comparable across samples such as quantile normalization. User can refer TCGA RNA-seq analysis pipeline to do generate comparable gene expression data. Then transform the gene expression values from each sample to the matrix for feeding into r BiocStyle::Biocpkg("ELMER"). If users want to use TCGA data, r BiocStyle::Biocpkg("ELMER") has functions to download the RNA-Seq Quantification data (HTSeq-FPKM-UQ) from GDC website and transform the data to the matrix for feeding into r BiocStyle::Biocpkg("ELMER"). It is recommended to save this matrix as RNA.rda since createMAE can read in files by specifying the path of files which will help to reduce memory usage.

# Example of Gene expression data input
datatable(GeneExp[1:10, 1:2], 
          options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
          rownames = TRUE)

Sample information

Sample information should be stored as a data.frame object containing sample ID, group labels (control and experiment). Sample ID and groups labels are required. Other information for each sample can be added to this data.frame object. When TCGA data were used, samples information will be automatically generated by createMAE function by specifying option TCGA=TRUE. A columns name TN will create the groups Tumor and Normal using the following samples to each group:

Tumor samples are:

• Primary solid Tumor
• Recurrent Solid Tumor
• Primary Blood Derived Cancer - Peripheral Blood
• Recurrent Blood Derived Cancer - Bone Marrow
• Additional - New Primary
• Metastatic
• Additional Metastatic
• Human Tumor Original Cells
• Primary Blood Derived Cancer - Bone Marrow

Normal samples:

• Blood Derived Normal
• Solid Tissue Normal
• Buccal Cell Normal
• EBV Immortalized Normal
• Bone Marrow Normal

library(MultiAssayExperiment)
data <- createMAE(exp = GeneExp, 
                  met = Meth,
                  met.platform = "450K",
                  genome = "hg19",
                  save = FALSE,
                  TCGA = TRUE)
data
as.data.frame(colData(data)[,c("patient","definition","TN")]) %>% 
  datatable(options = list(scrollX = TRUE,pageLength = 5)) 

# Adding sample information for non TCGA samples
# You should have two objects with one for DNA methylation and 
# one for gene expression. They should have the same number of samples and the names of the 
# sample in the gene expression object and in hte DNA methylation matrix 
# should be the same
not.tcga.exp <- GeneExp # 234 samples
colnames(not.tcga.exp) <- substr(colnames(not.tcga.exp),1,15)
not.tcga.met <- Meth # 268 samples
colnames(not.tcga.met) <- substr(colnames(not.tcga.met),1,15)
# Number of samples in both objects (234)
table(colnames(not.tcga.met) %in% colnames(not.tcga.exp)) 

# Our sample information must have as row names the samples information
phenotype.data <- data.frame(row.names = colnames(not.tcga.exp), 
                             primary = colnames(not.tcga.exp), 
                             group = c(rep("group1", ncol(GeneExp)/2),
                                       rep("group2", ncol(GeneExp)/2)))

data.hg19 <- createMAE(exp = not.tcga.exp, 
                       met =  not.tcga.met, 
                       TCGA = FALSE, 
                       met.platform = "450K",
                       genome = "hg19", 
                       colData = phenotype.data)
data.hg19

# The samples that does not have data for both DNA methylation and Gene exprssion will be removed even for the phenotype data
phenotype.data <- data.frame(row.names = colnames(not.tcga.met), 
                             primary = colnames(not.tcga.met), 
                             group = c(rep("group1", ncol(Meth)/4),
                                       rep("group2", ncol(Meth)/4),
                                       rep("group3", ncol(Meth)/4),
                                       rep("group4", ncol(Meth)/4)))

data.hg38 <- createMAE(exp = not.tcga.exp, 
                       met =  not.tcga.met, 
                       TCGA = FALSE, 
                       save = FALSE,
                       met.platform = "450K",
                       genome = "hg38", 
                       colData = phenotype.data)
data.hg38
as.data.frame(colData(data.hg38)[1:20,]) %>%
  datatable(options = list(scrollX = TRUE,pageLength = 5)) 

Probe information

Probe information is stored as a GRanges object containing the coordinates of each probe on the DNA methylation array and names of each probe. The default probe information is fetching from Wanding Zhou annotation [@zhou2016comprehensive]

library(SummarizedExperiment, quietly = TRUE)
rowRanges(getMet(data))[1:3,1:8]

Gene information

Gene information is stored as a GRanges object containing coordinates of each gene, gene id, gene symbol and gene isoform id. The default gene information is the ensembl gene annotation fetched from r BiocStyle::Biocpkg("biomaRt") by r BiocStyle::Biocpkg("ELMER") function.

rowRanges(getExp(data))

Multi Assay Experiment object

A Multi Assay Experiment object from the r BiocStyle::Biocpkg("MultiAssayExperiment") package is the input for multiple main functions of r BiocStyle::Biocpkg("ELMER"). It contains the above components and making a Multi Assay Experiment object by createMAE function will keep each component consistent with each other. For example, althougth DNA methylation and gene expression matrixes have different rows (probe for DNA methylation and gene id for gene expression), the column (samples) order should be same in the two matrixes. The createMAE function will keep them consistent when it generates the Multi Assay Experiment object.

data <- createMAE(exp = GeneExp, 
                  met = Meth,
                  genome = "hg19",
                  save = FALSE,
                  met.platform = "450K",
                  TCGA = TRUE)

# For TGCA data 1-12 represents the patient and 1-15 represents the sample ID (i.e. primary solid tumor samples )
all(substr(colnames(getExp(data)),1,15) == substr(colnames(getMet(data)),1,15))

# See sample information for data
as.data.frame(colData(data)) %>% datatable(options = list(scrollX = TRUE)) 

# See sample names for each experiment
as.data.frame(sampleMap(data)) %>% datatable(options = list(scrollX = TRUE)) 

You can also use your own data and annotations to create Multi Assay Experiment object.

# NON TCGA example: matrices has different column names
gene.exp <- S4Vectors::DataFrame(sample1.exp = c("ENSG00000141510"=2.3,"ENSG00000171862"=5.4),
                                 sample2.exp = c("ENSG00000141510"=1.6,"ENSG00000171862"=2.3))
dna.met <- S4Vectors::DataFrame(sample1.met = c("cg14324200"=0.5,"cg23867494"=0.1),
                                sample2.met =  c("cg14324200"=0.3,"cg23867494"=0.9))
sample.info <- S4Vectors::DataFrame(primary =  c("sample1","sample2"), 
                                    sample.type = c("Normal", "Tumor"))
sampleMap <- S4Vectors::DataFrame(
  assay = c("Gene expression","DNA methylation","Gene expression","DNA methylation"),
  primary = c("sample1","sample1","sample2","sample2"), 
  colname = c("sample1.exp","sample1.met","sample2.exp","sample2.met"))
mae <- createMAE(exp = gene.exp, 
                 met = dna.met, 
                 sampleMap = sampleMap, 
                 met.platform ="450K",
                 colData = sample.info, 
                 genome = "hg38") 
# You can also use sample Mapping and Sample information tables from a tsv file
# You can use the createTSVTemplates function to create the tsv files
readr::write_tsv(as.data.frame(sampleMap), path = "sampleMap.tsv")
readr::write_tsv(as.data.frame(sample.info), path = "sample.info.tsv")
mae <- createMAE(exp = gene.exp, 
                 met = dna.met, 
                 sampleMap = "sampleMap.tsv", 
                 met.platform ="450K",
                 colData = "sample.info.tsv", 
                 genome = "hg38") 
mae

# NON TCGA example: matrices has same column names
gene.exp <- S4Vectors::DataFrame(sample1 = c("ENSG00000141510"=2.3,"ENSG00000171862"=5.4),
                                 sample2 = c("ENSG00000141510"=1.6,"ENSG00000171862"=2.3))
dna.met <- S4Vectors::DataFrame(sample1 = c("cg14324200"=0.5,"cg23867494"=0.1),
                                sample2=  c("cg14324200"=0.3,"cg23867494"=0.9))
sample.info <- S4Vectors::DataFrame(primary =  c("sample1","sample2"), 
                                    sample.type = c("Normal", "Tumor"))
sampleMap <- S4Vectors::DataFrame(
  assay = c("Gene expression","DNA methylation","Gene expression","DNA methylation"),
  primary = c("sample1","sample1","sample2","sample2"), 
  colname = c("sample1","sample1","sample2","sample2")
)
mae <- createMAE(exp = gene.exp, 
                 met = dna.met, 
                 sampleMap = sampleMap, 
                 met.platform ="450K",
                 colData = sample.info, 
                 genome = "hg38") 
mae

Bibliography

ELMER documentation built on Nov. 8, 2020, 4:59 p.m.