--- title: "ELMER.data: Supporting data for the ELMER package" author: "Tiago Chedraoui Silva, Simon Coetzee, Nicole Gull, Lijing Yao, Peggy Farnham, Hui Shen, Peter Laird, Houtan Noushmehr, De-Chen Lin, Benjamin P. Berman" date: "`r Sys.Date()`" output: BiocStyle::html_document: highlight: tango toc: yes fig_caption: yes toc_depth: 3 toc_float: collapsed: yes number_sections: true editor_options: chunk_output_type: inline references: - id: ref1 title: HOCOMOCO a comprehensive collection of human transcription factor binding sites models author: - family: Kulakovskiy, Ivan V and Medvedeva, Yulia A and Schaefer, Ulf and Kasianov, Artem S and Vorontsov, Ilya E and Bajic, Vladimir B and Makeev, Vsevolod given: journal: Nucleic acids research volume: 41 number: D1 pages: D195--D202 issued: year: 2013 - id: heinz2010simple title: Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities author: - family: Heinz, Sven and Benner, Christopher and Spann, Nathanael and Bertolino, Eric and Lin, Yin C and Laslo, Peter and Cheng, Jason X and Murre, Cornelis and Singh, Harinder and Glass, Christopher K given: journal: Molecular cell volume: 38 number: 4 pages: 576--589 issued: year: 2010 - id: ELMER title: Inferring regulatory element landscapes and transcription factor networks from cancer methylomes author: - family: Yao, L., Shen, H., Laird, P. W., Farnham, P. J., & Berman, B. P. given: journal: Genome biology volume: 16 number: 1 pages: 105 issued: year: 2015 - id: wingender2014tfclass title: TFClass a classification of human transcription factors and their rodent orthologs author: - family: Wingender, E., Schoeps, T., Haubrock, M., & Dönitz, J given: journal: Nucleic acids research pages: gku1064 issued: year: 2014 - id: zhou2016comprehensive title: Comprehensive characterization, annotation and innovative use of Infinium DNA methylation BeadChip probes author: - family: Zhou, Wanding and Laird, Peter W and Shen, Hui given: journal: Nucleic Acids Research pages: gkw967 issued: year: 2016 - id: Lambert2018 title: The human transcription factors author: - family: Lambert, Samuel A and Jolma, Arttu and Campitelli, Laura F and Das, Pratyush K and Yin, Yimeng and Albu, Mihai and Chen, Xiaoting and Taipale, Jussi and Hughes, Timothy R and Weirauch, Matthew T given: journal: Cell pages: 650 issued: year: 2018 vignette: > \usepackage[utf8]{inputenc} %\VignetteIndexEntry{ELMER.data: Supporting data for the ELMER package} %\VignetteEngine{knitr::rmarkdown} editor_options: chunk_output_type: inline --- ```{r, echo = FALSE,hide=TRUE, message=FALSE,warning=FALSE} library(ELMER.data) library(DT) library(dplyr) ``` # Introduction This document provides an introduction of the `r BiocStyle::Biocpkg("ELMER.data")`, which contains supporting data for `r BiocStyle::Biocpkg("ELMER")` [@ELMER]. `r BiocStyle::Biocpkg("ELMER")` is package using DNA methylation to identify enhancers, and correlates enhancer state with expression of nearby genes to identify one or more transcriptional targets. Transcription factor (TF) binding site analysis of enhancers is coupled with expression analysis of all TFs to infer upstream regulators. `r BiocStyle::Biocpkg("ELMER.data")` provide 3 necessary data for `r BiocStyle::Biocpkg("ELMER")` analysis: 1. Probes information: files with DNA methylation platforms metadata retrieved from [http://zwdzwd.github.io/InfiniumAnnotation](http://zwdzwd.github.io/InfiniumAnnotation) [@zhou2016comprehensive]. 2. Probes.motif: motif occurences within $\pm 250bp$ of probe sites on HM450K/EPIC array aligned against hg19/hg38. ## Installing and loading ELMER.data To install this package, start R and enter ```{r, eval = FALSE} devtools::install_github(repo = "tiagochst/ELMER.data") library("ELMER.data") library("GenomicRanges") ``` # Contents ## ENSEMBL gene and TSS information Data from GRCh38.p12 and GRCh37.p13 accessed via biomart. ```{r, eval=FALSE, include=TRUE} getTranscripts <- function(genome = "hg38"){ tries <- 0L msg <- character() while (tries < 3L) { tss <- tryCatch({ host <- ifelse(genome == "hg19", "grch37.ensembl.org","www.ensembl.org") message("Accessing ", host, " to get TSS information") ensembl <- tryCatch({ useEnsembl("ensembl", dataset = "hsapiens_gene_ensembl", host = host) }, error = function(e) { message(e) for(mirror in c("asia","useast","uswest")){ x <- useEnsembl("ensembl", dataset = "hsapiens_gene_ensembl", mirror = mirror, host = host) if(class(x) == "Mart") { return(x) } } return(NULL) }) if(is.null(host)) { message("Problems accessing ensembl database") return(NULL) } attributes <- c("chromosome_name", "start_position", "end_position", "strand", "ensembl_gene_id", "transcription_start_site", "transcript_start", "ensembl_transcript_id", "transcript_end", "external_gene_name") chrom <- c(1:22, "X", "Y","M","*") db.datasets <- listDatasets(ensembl) description <- db.datasets[db.datasets$dataset=="hsapiens_gene_ensembl",]$description message(paste0("Downloading transcripts information from ", ensembl@host, ". Using: ", description)) filename <- paste0(gsub("[[:punct:]]| ", "_",description),"_tss.rda") tss <- getBM(attributes = attributes, filters = c("chromosome_name"), values = list(chrom), mart = ensembl) tss <- tss[!duplicated(tss$ensembl_transcript_id),] save(tss, file = filename, compress = "xz") }) } return(tss) } getGenes <- function (genome = "hg19"){ tries <- 0L msg <- character() while (tries < 3L) { gene.location <- tryCatch({ host <- ifelse(genome == "hg19", "grch37.ensembl.org", "www.ensembl.org") message("Accessing ", host, " to get gene information") ensembl <- tryCatch({ useEnsembl("ensembl", dataset = "hsapiens_gene_ensembl", host = host) }, error = function(e) { message(e) for (mirror in c("asia", "useast", "uswest")) { x <- useEnsembl("ensembl", dataset = "hsapiens_gene_ensembl", mirror = mirror, host = host) if (class(x) == "Mart") { return(x) } } return(NULL) }) if (is.null(host)) { message("Problems accessing ensembl database") return(NULL) } attributes <- c("chromosome_name", "start_position", "end_position", "strand", "ensembl_gene_id", "entrezgene", "external_gene_name") db.datasets <- listDatasets(ensembl) description <- db.datasets[db.datasets$dataset == "hsapiens_gene_ensembl", ]$description message(paste0("Downloading genome information (try:", tries, ") Using: ", description)) filename <- paste0(gsub("[[:punct:]]| ", "_", description), ".rda") if (!file.exists(filename)) { chrom <- c(1:22, "X", "Y") gene.location <- getBM(attributes = attributes, filters = c("chromosome_name"), values = list(chrom), mart = ensembl) } gene.location }, error = function(e) { msg <<- conditionMessage(e) tries <<- tries + 1L }) if (!is.null(gene.location)) break } if (tries == 3L) stop("failed to get URL after 3 tries:", "\n error: ", msg) return(gene.location) } Human_genes__GRCh37_p13__tss <- getTranscripts(genome = "hg19") Human_genes__GRCh38_p12__tss <- getTranscripts(genome = "hg38") Human_genes__GRCh37_p13 <- getGenes("hg19") Human_genes__GRCh38_p12 <- getGenes("hg38") save(Human_genes__GRCh37_p13__tss, file = "Human_genes__GRCh37_p13__tss.rda", compress = "xz") save(Human_genes__GRCh38_p12, file = "Human_genes__GRCh38_p12.rda", compress = "xz") save(Human_genes__GRCh38_p12__tss, file = "Human_genes__GRCh38_p12__tss.rda", compress = "xz") save(Human_genes__GRCh37_p13, file = "Human_genes__GRCh37_p13.rda", compress = "xz") ``` ## Probes information Probes information were retrieved from [http://zwdzwd.github.io/InfiniumAnnotation](http://zwdzwd.github.io/InfiniumAnnotation) [@zhou2016comprehensive]. ```{r, eval=FALSE, include=TRUE} for(plat in c("450K","EPIC")) { for(genome in c("hg38","hg19")) { base <- "http://zwdzwd.io/InfiniumAnnotation/current/" path <- file.path(base,plat,paste(plat,"hg19.manifest.rds", sep =".")) if (grepl("hg38", genome)) path <- gsub("hg19","hg38",path) if(plat == "EPIC") { annotation <- paste0(base,"EPIC/EPIC.hg19.manifest.rds") } else { annotation <- paste0(base,"hm450/hm450.hg19.manifest.rds") } if(grepl("hg38", genome)) annotation <- gsub("hg19","hg38",annotation) if(!file.exists(basename(annotation))) { if(Sys.info()["sysname"] == "Windows") mode <- "wb" else mode <- "w" downloader::download(annotation, basename(annotation), mode = mode) } } } devtools::use_data(EPIC.hg19.manifest,overwrite = T,compress = "xz") devtools::use_data(EPIC.hg38.manifest,overwrite = T,compress = "xz") devtools::use_data(hm450.hg19.manifest,overwrite = T,compress = "xz") devtools::use_data(hm450.hg38.manifest,overwrite = T,compress = "xz") ``` ```{r, message=FALSE} data("EPIC.hg19.manifest") as.data.frame(EPIC.hg19.manifest)[1:5,] %>% datatable(options = list(scrollX = TRUE,pageLength = 5)) data("EPIC.hg38.manifest") as.data.frame(EPIC.hg38.manifest)[1:5,] %>% datatable(options = list(scrollX = TRUE,pageLength = 5)) data("hm450.hg19.manifest") as.data.frame(hm450.hg19.manifest)[1:5,] %>% datatable(options = list(scrollX = TRUE,pageLength = 5)) data("hm450.hg38.manifest") as.data.frame(hm450.hg38.manifest)[1:5,] %>% datatable(options = list(scrollX = TRUE,pageLength = 5)) ``` ## TF family and subfamily classifications ELMER uses the TFClass [@wingender2014tfclass], a classification of eukaryotic transcription factors based on the characteristics of their DNA-binding domains, to identify which are the TF that might be binding to the same region. For example, if a FOXA1 motif is found in a region, there is FOXA2 would also be able to bind to that region. For that ELMER uses two classifications, Family and sub-family. TFClass schema is shown below. TFClass schema is below: | Level | Rank denomination | Definition | Example | |-------|-------------------|------------------------------------------------|---------------------------------------------------------------| | 1 | Superclass | General topology of the DNA-binding domain | Zinc-coordinating DNA-binding domains (Superclass 2) | | 2 | Class | Structural blueprint of the DNA-binding domain | Nuclear receptors with C4 zinc fingers (Class 2.1) | | 3 | Family | Sequence & functional similarity | Thyroid hormone receptor-related factors (NR1) (Family 2.1.2) | | 4 | Subfamily | Sequence-based subgroupings | Retinoic acid receptors (NR1B) (Subfamily 2.1.2.1) | | 5 | Genus | Transcription factor gene | RAR-α (Genus 2.1.2.1.1) | | 4 | Species | TF polypeptide | RAR-α1 (Species 2.1.2.1.1.1) The following code was used to create the objects: ```{r, eval=FALSE, include=TRUE} library(xml2) library(httr) library(dplyr) library(rvest) createMotifRelevantTfs <- function(classification = "family"){ message("Accessing hocomoco to get last version of TFs ", classification) file <- paste0(classification,".motif.relevant.TFs.rda") # Download from http://hocomoco.autosome.ru/human/mono tf.family <- "http://hocomoco11.autosome.ru/human/mono?full=true" %>% read_html() %>% html_table() tf.family <- tf.family[[1]] # Split TF for each family, this will help us map for each motif which are the some ones in the family # basicaly: for a TF get its family then get all TF in that family col <- ifelse(classification == "family", "TF family","TF subfamily") family <- split(tf.family,f = tf.family[[col]]) motif.relevant.TFs <- plyr::alply(tf.family,1, function(x){ f <- x[[col]] if(f == "") return(x$`Transcription factor`) # Case without family, we will get only the same object return(unique(family[as.character(f)][[1]]$`Transcription factor`)) },.progress = "text") #names(motif.relevant.TFs) <- tf.family$`Transcription factor` names(motif.relevant.TFs) <- tf.family$Model # Cleaning object attr(motif.relevant.TFs,which="split_type") <- NULL attr(motif.relevant.TFs,which="split_labels") <- NULL return(motif.relevant.TFs) } updateTFClassList <- function(tf.list, classification = "family"){ col <- ifelse(classification == "family","family.name","subfamily.name") TFclass <- getTFClass() # Hocomoco tf.family <- "http://hocomoco11.autosome.ru/human/mono?full=true" %>% read_html() %>% html_table() tf.family <- tf.family[[1]] tf.members <- plyr::alply(unique(TFclass %>% pull(col)),1, function(x){ TFclass$Gene[which(x == TFclass[,col])] },.progress = "text") names(tf.members) <- unique(TFclass %>% pull(col)) attr(tf.members,which="split_type") <- NULL attr(tf.members,which="split_labels") <- NULL for(i in names(tf.list)){ x <- tf.family[tf.family$Model == i,"Transcription factor"] idx <- which(sapply(lapply(tf.members, function(ch) grep(paste0("^",x,"$"), ch)), function(x) length(x) > 0)) if(length(idx) == 0) next members <- tf.members[[idx]] tf.list[[i]] <- sort(unique(c(tf.list[[i]],members))) } return(tf.list) } getTFClass <- function(){ # get TF classification file <- "TFClass.rda" if(file.exists(file)) { return(get(load(file))) } file <- "http://tfclass.bioinf.med.uni-goettingen.de/suppl/tfclass.ttl.gz" downloader::download(file,basename(file)) char_vector <- readLines(basename(file)) # Find TF idx idx <- grep("genus",char_vector,ignore.case = T) # get TF names TF <- char_vector[sort(c( idx +1, idx + 2, idx + 4))] TF <- TF[-grep("LOGO_|rdf:type",TF)] TF <- gsub(" rdfs:label | ;| rdfs:subClassOf ","",TF) TF <- stringr::str_trim(gsub('"', '', TF)) TF <- tibble::as.tibble(t(matrix(TF,nrow = 2))) colnames(TF) <- c("Gene", "class") # Get family and subfamily classification family.pattern <- "^" idx <- grep(family.pattern,char_vector) family.names <- char_vector[ sort(c(idx,idx+ 2))] family.names <- gsub(" rdfs:label | ;| rdfs:subClassOf |" idx <- grep(subfamily.pattern,char_vector) subfamily.names <- char_vector[ sort(c(idx,idx+ 2))] subfamily.names <- gsub(" rdfs:label | ;| rdfs:subClassOf | 0] aux <- TFclass[rep(i,length(gene)),] aux$Gene <- gene df <- rbind(df,aux) } TFclass <- rbind(TFclass,df) TFclass <- TFclass[!duplicated(TFclass),] # Break ( into multiple cases) m <- grep("-",TFclass$Gene) df <- NULL for(i in m){ gene <- gsub("-","",sort(stringr::str_trim(unlist(stringr::str_split(TFclass$Gene[i],"\\(|,|\\)|/"))))) gene <- gene[stringr::str_length(gene) > 0] aux <- TFclass[rep(i,length(gene)),] aux$Gene <- gene df <- rbind(df,aux) } TFclass <- rbind(TFclass,df) df <- NULL for(i in 1:length(TFclass$Gene)){ m <- TFclass$Gene[i] gene <- unique(c(toupper(alias2Symbol(toupper(m))),toupper(m),toupper(alias2Symbol(m)))) if(all(gene %in% TFclass$Gene)) next aux <- TFclass[rep(i,length(gene)),] aux$Gene <- gene df <- rbind(df,aux) } TFclass <- rbind(TFclass,df) TFclass <- TFclass[!duplicated(TFclass),] TFclass <- TFclass[TFclass$Gene %in% human.TF$external_gene_name,] save(TFclass,file = "TFClass.rda") return(TFclass) } TF.family <- createMotifRelevantTfs("family") TF.family <- updateTFClassList(TF.family,"family") TF.subfamily <- createMotifRelevantTfs("subfamily") TF.subfamily <- updateTFClassList(TF.subfamily,classification = "subfamily") save(TF.family,file = "~/ELMER.data/data/TF.family.rda", compress = "xz") save(TF.subfamily,file = "~/ELMER.data/data/TF.subfamily.rda", compress = "xz") ``` ```{r, eval=FALSE, include=TRUE} hocomoco.table <- "http://hocomoco11.autosome.ru/human/mono?full=true" %>% read_html() %>% html_table() hocomoco.table <- hocomoco.table[[1]] save(hocomoco.table,file = "data/hocomoco.table.rda", compress = "xz") ``` ## Probes.motif Probes.motif provides information for motif occurences within$\pm 250bp$ of probe sites on HM450K/EPIC array. HOMER [@heinz2010simple] was used with a p-value < 0.0001 to scan a $\pm 250bp$ region around each probe on HM450K/EPIC using HOCOMOCO V11 motif position weight matrices (PWMs) which provides transcription factor (TF) binding models for more than 600 human TFs. This data set is used in get.enriched.motif function in `r BiocStyle::Biocpkg("ELMER")` to calculate Odds Ratio of motif enrichments for a given set of probes. This data is storaged in a sparse matrix with wuth 640 columns, there is one matrix for HM450K aligned to hg19, one for HM450K aligned to hg38, one for EPIC aligned to hg19, one for EPIC aligned to hg38. Each row is each probe regions (annotation of the regions used can be found in [this repository](http://zwdzwd.github.io/InfiniumAnnotation)) and each column is motif from [http://hocomoco.autosome.ru/](HOCOMOCO) [@ref1]. The value 1 indicates the occurrence of a motif in a particular probe and 0 means no occurrence. ```{r} data("Probes.motif.hg19.450K") dim(Probes.motif.hg19.450K) str(Probes.motif.hg19.450K) ``` ```{r} data("Probes.motif.hg38.450K") dim(Probes.motif.hg38.450K) str(Probes.motif.hg38.450K) ``` ```{r} data("Probes.motif.hg19.EPIC") dim(Probes.motif.hg19.EPIC) str(Probes.motif.hg19.EPIC) ``` ```{r} data("Probes.motif.hg38.EPIC") dim(Probes.motif.hg38.EPIC) str(Probes.motif.hg38.EPIC) ``` The following code was used to create the objects: ```{r, eval=FALSE, include=TRUE} getInfiniumAnnotation <- function(plat = "450K", genome = "hg38"){ message("Loading object: ",file) newenv <- new.env() if(plat == "EPIC" & genome == "hg19") data("EPIC.hg19.manifest", package = "ELMER.data",envir=newenv) if(plat == "EPIC" & genome == "hg38") data("EPIC.hg38.manifest", package = "ELMER.data",envir=newenv) if(plat == "450K" & genome == "hg19") data("hm450.hg19.manifest", package = "ELMER.data",envir=newenv) if(plat == "450K" & genome == "hg38") data("hm450.hg38.manifest", package = "ELMER.data",envir=newenv) annotation <- get(ls(newenv)[1],envir=newenv) return(annotation) } # To find for each probe the know motif we will use HOMER software (http://homer.salk.edu/homer/) # Step: # 1 - get DNA methylation probes annotation with the regions # 2 - Make a bed file from it # 3 - Execute section: Finding Instance of Specific Motifs from http://homer.salk.edu/homer/ngs/peakMotifs.html to the HOCOMOCO TF motifs # Also, As HOMER is using more RAM than the available we will split the files in to 100k probes. # Obs: for each probe we create a winddow of 500 bp (-size 500) around it. This might lead to false positives, but will not have false negatives. # The false posives will be removed latter with some statistical tests. TFBS.motif <- "http://hocomoco11.autosome.ru/final_bundle/hocomoco11/full/HUMAN/mono/HOCOMOCOv11_full_HUMAN_mono_homer_format_0.0001.motif" if(!file.exists(basename(TFBS.motif))) downloader::download(TFBS.motif,basename(TFBS.motif)) for(plat in c("EPIC","450K")){ for(gen in c("hg38","hg19")){ file <- paste0(plat,gen,".txt") print(file) if(!file.exists(file)){ # STEP 1 gr <- getInfiniumAnnotation(plat = plat,genome = gen) # This will remove masked probes. They have poor quality and might be arbitrarily positioned (Wanding Zhou) gr <- gr[!gr$MASK_general] df <- data.frame(seqnames=seqnames(gr), starts=as.integer(start(gr)), ends=end(gr), names=names(gr), scores=c(rep(".", length(gr))), strands=strand(gr)) step <- 10000 # nb of lines in each file. 10K was selected to not explode RAM n <- nrow(df) pb <- txtProgressBar(max = floor(n/step), style = 3) for(j in 0:floor(n/step)){ setTxtProgressBar(pb, j) # STEP 2 file.aux <- paste0(plat,gen,"_",j,".bed") if(!file.exists(gsub(".bed",".txt",file.aux))){ end <- ifelse(((j + 1) * step) > n, n,((j + 1) * step)) write.table(df[((j * step) + 1):end,], file = file.aux, col.names = F, quote = F,row.names = F,sep = "\t") # STEP 3 use -mscore to get scores cmd <- paste("source ~/.bash_rc; annotatePeaks.pl" ,file.aux, gen, "-m", basename(TFBS.motif), "-size 500 -cpu 12 >", gsub(".bed",".txt",file.aux)) system(cmd) } } } close(pb) # We will merge the results from each file into one peaks <- NULL pb <- txtProgressBar(max = floor(n/step), style = 3) for(j in 0:floor(n/step)){ setTxtProgressBar(pb, j) aux <- readr::read_tsv(paste0(plat,gen,"_",j,".txt")) colnames(aux)[1] <- "PeakID" if(is.null(peaks)) { peaks <- aux } else { peaks <- rbind(peaks, aux) } } close(pb) print("Writing file...") readr::write_tsv(peaks,path=file,col_names = TRUE) print("DONE!") gc() } } getMatrix <- function(filename) { motifs <- readr::read_tsv(file) # From 1 to 21 we have annotations matrix <- Matrix::Matrix(0, nrow = nrow(motifs), ncol = ncol(motifs) - 21 ,sparse = TRUE) colnames(matrix) <- gsub(" Distance From Peak\\(sequence,strand,conservation\\)","",colnames(motifs)[-c(1:21)]) rownames(matrix) <- motifs$PeakID matrix[!is.na(motifs[,-c(1:21)])] <- 1 matrix <- as(matrix, "nsparseMatrix") return(matrix) } for(plat in c("EPIC","450K")){ for(gen in c("hg19","hg38")){ file <- paste0(plat,gen,".txt") if(file == "450Khg19.txt"){ if(file.exists("Probes.motif.hg19.450K.rda")) next Probes.motif.hg19.450K <- getMatrix(file) save(Probes.motif.hg19.450K, file = "Probes.motif.hg19.450K.rda", compress = "xz") rm(Probes.motif.hg19.450K) } if(file == "450Khg38.txt"){ if(file.exists("Probes.motif.hg38.450K.rda")) next Probes.motif.hg38.450K <- getMatrix(file) save(Probes.motif.hg38.450K, file = "Probes.motif.hg38.450K.rda", compress = "xz") rm(Probes.motif.hg38.450K) } if(file == "EPIChg19.txt"){ if(file.exists("Probes.motif.hg19.EPIC.rda")) next Probes.motif.hg19.EPIC <- getMatrix(file) save(Probes.motif.hg19.EPIC, file = "Probes.motif.hg19.EPIC.rda", compress = "xz") rm(Probes.motif.hg19.EPIC) } if(file == "EPIChg38.txt"){ if(file.exists("Probes.motif.hg38.EPIC.rda")) next Probes.motif.hg38.EPIC <- getMatrix(file) save(Probes.motif.hg38.EPIC, file = "Probes.motif.hg38.EPIC.rda", compress = "xz") rm(Probes.motif.hg38.EPIC) } } } ``` ```{r} data("Probes.motif.hg19.450K") as.data.frame(as.matrix(Probes.motif.hg19.450K[1:20,1:20])) %>% datatable(options = list(scrollX = TRUE,pageLength = 5)) ``` ## TFclass classification ## TF list A curated list of TF was retrieved from Lambert, Samuel A., et al. "The human transcription factors." Cell 172.4 (2018): 650-665 [@Lambert2018] with the following code. ```{r, eval=FALSE, include=TRUE} human.TF <- readr::read_csv("http://humantfs.ccbr.utoronto.ca/download/v_1.01/DatabaseExtract_v_1.01.csv") human.TF <- human.TF[human.TF$`Is TF?` == "Yes",] colnames(human.TF)[1:2] <- c("ensembl_gene_id","external_gene_name") save(human.TF,file = "~/ELMER.data/data/human.TF.rda",compress = "xz") ``` ```{r} data("human.TF") as.data.frame(human.TF) %>% datatable(options = list(scrollX = TRUE,pageLength = 5)) ```
Homer versions
- Software: v4.9.1 - Genome hg19: v5.10 - Genome hg38: v5.10
# Session Information ****** ```{r sessionInfo} sessionInfo() ``` # References