├── .Rbuildignore ├── .gitignore ├── DESCRIPTION ├── LICENSE ├── LICENSE.md ├── NAMESPACE ├── R ├── MyLDheatMap.R ├── convertToNumeric.R ├── get_gene_from_snp.R ├── getsnpInfo.R ├── getsnpMat.R ├── ggmanhattan.R └── search.names.R ├── README.md ├── data └── test_vcf.rda ├── inst └── extdata │ ├── test.gff │ ├── test.vcf │ └── test_sig_snp.txt ├── man ├── MyLDheatMap.Rd ├── convertToNumeric.Rd ├── get_gene_from_snp.Rd ├── getsnpInfo.Rd ├── getsnpMat.Rd ├── ggmanhattan.Rd └── search.names.Rd ├── ttplot.Rproj └── vignettes ├── .gitignore └── tutorial.Rmd /.Rbuildignore: -------------------------------------------------------------------------------- 1 | ^data-raw$ 2 | ^.*\.Rproj$ 3 | ^\.Rproj\.user$ 4 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | inst/doc 2 | .Rproj.user 3 | -------------------------------------------------------------------------------- /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: ttplot 2 | Title: Tao Yan's Plot Toolkit 3 | Version: 0.0.0 4 | Authors@R: person("Tao", "Yan", email = "tyan@zju.edu.cn", role = c("aut", "cre")) 5 | Description: Just some scripts I used in my PhD project, most of them are NOT stable enough. 6 | Depends: R (>= 3.4.0) 7 | Imports: LDheatmap, genetics, vcfR, writexl, tidyverse, future.apply 8 | Encoding: UTF-8 9 | LazyData: true 10 | ByteCompile: true 11 | License: MIT + file LICENSE 12 | RoxygenNote: 6.1.1 13 | Suggests: knitr, rmarkdown 14 | VignetteBuilder: knitr 15 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | YEAR: 2019 2 | COPYRIGHT HOLDER: Tao Yan 3 | -------------------------------------------------------------------------------- /LICENSE.md: -------------------------------------------------------------------------------- 1 | # MIT License 2 | 3 | Copyright (c) 2019 Tao Yan 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | # Generated by roxygen2: do not edit by hand 2 | 3 | export(MyLDheatMap) 4 | export(convertToNumeric) 5 | export(get_gene_from_snp) 6 | export(getsnpInfo) 7 | export(getsnpMat) 8 | export(ggmanhattan) 9 | import(ggplot2) 10 | import(gtools) 11 | import(writexl) 12 | -------------------------------------------------------------------------------- /R/MyLDheatMap.R: -------------------------------------------------------------------------------- 1 | #' Get the LDhaetmap from the vcf file (plink format) directly 2 | #' 3 | #' This function defined to obtain the LDheatmap from the vcf file directly. 4 | #' @param vcffile The plink format vcf file. More detail can see View(test_vcf) 5 | #' @param file.output a logical, if file.output=TRUE, the result will be saved. 6 | #' if file.output=FALSE, the result will be printed. The default is TRUE 7 | #' @param file a character, users can choose the different output formats of plot, so far, "jpeg", "pdf", "png", "tiff" can be selected by users. The default is "png". 8 | #' @param title a character, the title of the LDheatmap will be "The LDheatmap of title". 9 | #' the default is "region:". I suggest users use your own title. 10 | #' @param dpi a number, the picture element for .jpeg, .png and .tiff files. The default is 300. 11 | #' @param verbose whether to print the reminder. 12 | #' 13 | #' @author Tao Yan <\email{tyan@zju.edu.cn}> | 14 | #' <\href{https://taoyan.netlify.com/}{https://taoyan.netlify.com/}> 15 | #' 16 | #' @return the LDheatmap. 17 | #' 18 | #' @export MyLDheatMap 19 | #' 20 | #' @examples 21 | #' MyLDheatMap(system.file("extdata","test.vcf", package = "ttplot"), title="your title") 22 | 23 | 24 | MyLDheatMap <- function( 25 | vcffile, 26 | file.output=TRUE, 27 | file="png", 28 | output="region", 29 | title="region:", 30 | verbose=TRUE, 31 | dpi=300,... 32 | ) 33 | { 34 | if(!require(LDheatmap)) BiocManager::install("LDheatmap") 35 | suppressWarnings(suppressMessages(library(LDheatmap, quietly = T))) 36 | gdat_snp <- ttplot::getsnpMat(vcffile) 37 | info <- ttplot::getsnpInfo(vcffile) 38 | snp_dist <- as.numeric(info$POS) 39 | rgb.palette <- colorRampPalette(rev(c("yellow","red")), space="rgb") 40 | if (!file.output){ 41 | if (verbose) print("The Ldheatmap is Plotting! Please wait for a moment...") 42 | LDheatmap(gdat_snp, 43 | genetic.distances = snp_dist, 44 | color = rgb.palette(100), 45 | flip = TRUE,title = paste0("The LDheatmap of ",title)) 46 | } 47 | if (file.output){ 48 | if(verbose) print("The Ldheatmap is Plotting! Please wait for a moment...") 49 | if(file=="jpg") jpeg(paste("LDheatmap of ", output, ".jpg", sep=""), width = 9*dpi, height=7*dpi, res=dpi, quality = 100) 50 | if(file=="pdf") pdf(paste("LDheatmap of ", output, ".pdf", sep=""), width = 9, height=7) 51 | if(file=="tiff") tiff(paste("LDheatmap of ", output, ".tiff", sep=""), width = 9*dpi, height=7*dpi, res=dpi) 52 | if(file=="png") png(paste("LDheatmap of ", output, ".png", sep=""), width = 9*dpi, height=7*dpi, res=dpi) 53 | par(xpd=TRUE) 54 | }else{ 55 | if(is.null(dev.list())) dev.new(width =9, height=7) 56 | par(xpd=TRUE) 57 | } 58 | LDheatmap(gdat_snp, 59 | genetic.distances = snp_dist, 60 | color = rgb.palette(100), 61 | flip = TRUE, 62 | title = paste0("The LDheatmap of ",title)) 63 | if(file.output) dev.off() 64 | if(file.output & verbose) print(paste("Plot is stored in: ", getwd(), sep="")) 65 | } 66 | -------------------------------------------------------------------------------- /R/convertToNumeric.R: -------------------------------------------------------------------------------- 1 | #' Convert the value of genotype into 0,1,2 2 | #' 3 | #' This function converts the value of genotype into 0,1,2 form 0/0,0/1,1/1. 4 | #' @param snp_data The snp_data matrix. 5 | #' @return The genodata with the value of 0,1,2. 6 | #' @export 7 | #' @examples 8 | #' convertTonumeric(snp_data) 9 | 10 | convertToNumeric <- function(snp_data){ 11 | genodata <- matrix(NA, nrow = nrow(snp_data), ncol = ncol(snp_data)) 12 | for (a in 1:nrow(snp_data)){ 13 | for (b in 1:ncol(snp_data)){ 14 | m <- as.numeric(unlist(strsplit(snp_data[a,b], "/"))[1]) 15 | n <- as.numeric(unlist(strsplit(snp_data[a,b], "/"))[2]) 16 | genodata[a,b] <- m+n 17 | } 18 | } 19 | rownames(genodata) <- rownames(snp_data) 20 | colnames(genodata) <- colnames(snp_data) 21 | return(genodata) 22 | } 23 | -------------------------------------------------------------------------------- /R/get_gene_from_snp.R: -------------------------------------------------------------------------------- 1 | #' Get the candidate genes in regions based on significant SNPs of GWAS results. 2 | #' 3 | #' This function is developed to get the candidate genes in regions based on significant SNPs of GWAS results. 4 | #' @param gff a data frame of all the gene (transcript), must have column names. 5 | #' @param sig.snp a data frame of significant SNPs. 6 | #' @param distance numeric (bp), it is to define the region. The default is 50000, you need to choose it based on the LD distance in your study. 7 | #' @param geneid Name of the column containing the geneid in gff file; default is NA. 8 | #' 9 | #' @param gff.chrom Name of the column containing the chromosome identifers in the gff file; default is NA. 10 | #' @param snp.chrom Name of the column containing the chromosome identifers in the snp.sig file; default is NA. 11 | #' @param pvalue Name of the column containing the p values in snp.sig file; default is NA. 12 | #' @param snp_location Name of the column containing the snp position in snp.sig file; default is NA. 13 | #' @param file.save a logical, if file.output=TRUE, the result will be saved. 14 | #' if file.output=FALSE, the result will be printed. The default is TRUE. 15 | #' @param file.type a character, users can choose the different output formats, 16 | #' so far, "csv", "txt", "xlsx" can be selected by users. The default is "csv". 17 | #' 18 | #' @author Tao Yan <\email{tyan@zju.edu.cn}> | 19 | #' <\href{https://taoyan.netlify.com/}{https://taoyan.netlify.com/}> 20 | #' 21 | #' @import writexl 22 | #' @return a data.frame contain the candidate genes with start,end,genid etc 23 | #' 24 | #' @export 25 | #' 26 | #' @examples 27 | #' get_gene_from_snp(gff,sig.snp) 28 | 29 | get_gene_from_snp <- function( 30 | gff, 31 | sig.snp, 32 | distance=75000, 33 | file.save=TRUE, 34 | file.type="csv", 35 | gff.chrom=NA, 36 | snp.chrom=NA, 37 | geneid=NA, 38 | pvalue=NA, 39 | gene_start=NA, 40 | gene_end=NA, 41 | snp_location=NA, 42 | verbose=TRUE,...) 43 | { 44 | gff_names <- names(gff) 45 | snp_names <- names(sig.snp) 46 | if(is.na(gff.chrom)){ 47 | gff.chrom <- search.names(c("chr", "chrom", "chromosome"), gff_names) 48 | if(is.null(gff.chrom)){ 49 | stop("Couldn't find the chromosome column. Please specify the name of the column with chromosome ids(chr, chrom or chromosome)") 50 | } 51 | } 52 | 53 | if(is.na(gene_start)){ 54 | gene_start <- search.names(c("start", "gene_start", "genestart", "begin"), gff_names) 55 | if(is.null(gene_start)){ 56 | stop("Couldn't find the gene_start column. Please specify the name of the column with gene_start ids(start, gene_start, genestart or begin)") 57 | } 58 | } 59 | if(is.na(gene_end)){ 60 | gene_end <- search.names(c("end", "gene_end", "geneend"), gff_names) 61 | if(is.null(gene_end)){ 62 | stop("Couldn't find the gene_end column. Please specify the name of the column with gene_end ids(end, gene_end or geneend)") 63 | } 64 | } 65 | if(is.na(geneid)){ 66 | geneid <- search.names(c("gene", "geneid", "gene_id", "gene_name","genename","transcript","transcript_id","transcriptid"), gff_names) 67 | if(is.null(geneid)){ 68 | stop("Couldn't find the geneid column. Please specify the name of the column with geneid ids(gene, geneid, gene_id, gene_name, genename, transcript,transcript_id or transcriptid)") 69 | } 70 | } 71 | if(is.na(pvalue)){ 72 | pvalue <- search.names(c("p", "p-value", "pval", "pvalue"), snp_names) 73 | if(is.null(pvalue)){ 74 | stop("Couldn't find the pvalue column. Please specify the name of the column with pvalue ids(p, pvalue, pval or p-value)") 75 | } 76 | } 77 | if(is.na(snp_location)){ 78 | snp_location <- search.names(c("bp","position","pos","snp_location","location"), snp_names) 79 | if(is.null(snp_location)){ 80 | stop("Couldn't find the bp column. Please specify the name of the column with bp ids(bp, pos, snp-location, location or position)") 81 | } 82 | } 83 | if(is.na(snp.chrom)){ 84 | snp.chrom <- search.names(c("chr", "chrom", "chromosome"), snp_names) 85 | if(is.null(snp.chrom)){ 86 | stop("Couldn't find the chromosome column. Please specify the name of the column with chromosome ids(chr, chrom or chromosome)") 87 | } 88 | } 89 | gff$gff.chrom <- gff[ ,gff.chrom] 90 | gff$gene_start <- gff[ ,gene_start] 91 | gff$gene_end <- gff[ ,gene_end] 92 | gff$geneid <- gff[ ,geneid] 93 | sig.snp$pvalue <- sig.snp[ ,pvalue] 94 | sig.snp$snp_location <- sig.snp[ ,snp_location] 95 | sig.snp$snp.chrom <- sig.snp[ ,snp.chrom] 96 | 97 | r1 <- vector(mode = "numeric") 98 | r2 <- vector(mode = "numeric") 99 | 100 | for (i in 1:nrow(sig.snp)){ 101 | if (sig.snp[i,][["snp_location"]]>=distance){ 102 | r <- c(sig.snp[i,][["snp_location"]]-distance, sig.snp[i,][["snp_location"]]+distance) 103 | }else{ 104 | r <- c(sig.snp[i,][["snp_location"]], sig.snp[i,][["snp_location"]]+distance) 105 | } 106 | r1 <- append(r1,r[1]) 107 | r2 <- append(r2,r[2]) 108 | } 109 | sig.snp$r1 <- r1 110 | sig.snp$r2 <- r2 111 | 112 | ## search significant genes 113 | findgene <- function(i){ 114 | snp_gene <- gff[gff$gene_start >= sig.snp$r1[i] & gff$gene_end <= sig.snp$r2[i] & gff$gff.chrom==sig.snp$snp.chrom[i], ] 115 | if (nrow(snp_gene)==0) { 116 | return(NULL)}else{ 117 | snp_gene$snp_location <- sig.snp$snp_location[i] 118 | snp_gene$p_value <- sig.snp$pvalue[i] 119 | return(snp_gene) 120 | } 121 | } 122 | 123 | genes_data <- future.apply::future_lapply(seq(1,nrow(sig.snp)),findgene) 124 | gene_snp <- do.call(rbind, genes_data) 125 | 126 | 127 | 128 | if(!file.save) { 129 | if(verbose) { 130 | cat(paste("The distance you choose is ", distance, "bp!", sep = ""), sep = "\n") 131 | cat(paste("You have ", nrow(sig.snp), " significant SNPs and ", nrow(gff), " genes!", sep = ""),sep = "\n") 132 | cat("Now we will extract the genes in the significant regions! This will need some time, please wait for severals minutes! ...", sep = "\n") 133 | } 134 | } 135 | if(file.save){ 136 | if(verbose) { 137 | cat(paste("The distance you choose is ", distance, "bp!", sep = ""),sep = "\n") 138 | cat(paste("You have ", nrow(sig.snp), " significant SNPs and ", nrow(gff), " genes!", sep = ""),sep = "\n") 139 | cat("Now we will extract the genes in the significant regions! This will need some time, please wait! ...", sep = "\n") 140 | } 141 | } 142 | 143 | 144 | 145 | if(file.save){ 146 | if(file.type=="csv"){ 147 | write.csv(gene_snp,file = paste("genes_in_sig_regions","(",distance,"bp)",".csv"), row.names = FALSE, quote = FALSE) 148 | } 149 | if(file.type=="txt"){ 150 | write.table(gene_snp,file = paste("genes_in_sig_regions","(",distance,"bp)",".txt"), col.names = TRUE, row.names = FALSE, quote = FALSE) 151 | } 152 | if(file.type=="xlsx"){ 153 | writexl::write_xlsx(gene_snp,path = paste("genes_in_sig_regions","(",distance,"bp)",".xlsx")) 154 | } 155 | } 156 | if(file.save & verbose) { 157 | cat(" \n") 158 | cat(paste("The output is stored in: ", getwd(), sep = "")) 159 | } 160 | if(!file.save & verbose) { 161 | cat(" ","The result is : ", "-------------------------------------------------------------",sep = "\n") 162 | return(gene_snp) 163 | } 164 | } 165 | -------------------------------------------------------------------------------- /R/getsnpInfo.R: -------------------------------------------------------------------------------- 1 | #' Obtain the snp info from the vcf file 2 | #' 3 | #' This function defined to get the snp info (ID, POS) from the vcf file. 4 | #' @param file The vcf file name. 5 | #' @return The snp info (ID, POS). 6 | #' @export 7 | #' @examples 8 | #' snpInfo(sample.vcf) 9 | 10 | getsnpInfo <- function(file){ 11 | if(!require(vcfR)) BiocManager::install("vcfR") 12 | suppressWarnings(suppressMessages(library(vcfR, quietly = T))) 13 | snp <- read.vcfR(file,verbose = FALSE) 14 | snp_info <- snp@fix 15 | snp_info <- as.data.frame(snp_info) 16 | snp_info$ID <- paste(snp_info$CHROM, snp_info$POS, sep = "_") 17 | snp_info <- snp_info%>%dplyr::select(ID, POS) 18 | snp_info$POS <- as.integer(as.character(snp_info$POS)) 19 | return(snp_info) 20 | } 21 | -------------------------------------------------------------------------------- /R/getsnpMat.R: -------------------------------------------------------------------------------- 1 | #' Get the snpMatrix from the vcf file (plink format) 2 | #' 3 | #' This function defined to obtain the snpMatrix(with snp ID) from the vcf file 4 | #' @param vcffile The vcf file (plink format). 5 | #' @return The snpMAtrix for the visulization of LDheatmap. 6 | #' @export 7 | #' @examples 8 | #' getsnpMat(vcffile) 9 | 10 | getsnpMat <- function(vcffile){ 11 | if(!require(vcfR)) BiocManager::install("vcfR") 12 | if(!require(genetics)) BiocManager::install("genetics") 13 | suppressWarnings(suppressMessages(library(vcfR,quietly = T))) 14 | suppressWarnings(suppressMessages(library(genetics, quietly = T))) 15 | snp_data <- read.vcfR(vcffile, verbose = FALSE) 16 | snp_gt <- snp_data@gt 17 | snp_gt <- as.data.frame(snp_gt[,-1]) 18 | info <- as.data.frame(snp_data@fix) 19 | info$ID <- paste(info$CHROM,info$POS,sep = "_") 20 | snp_geno <- cbind(info[,c(4,5)],snp_gt) 21 | snp_geno[,1:ncol(snp_geno)] <- lapply(snp_geno[,1:ncol(snp_geno)], as.character) 22 | for (i in 1:nrow(snp_geno)){ 23 | snp_geno[i,] <- ifelse(snp_geno[i,]=="0/0",paste(snp_geno[i,1],snp_geno[i,1], sep = "/"),ifelse(snp_geno[i,]=="0/1",paste(snp_geno[i,1],snp_geno[i,2], sep = "/"),ifelse(snp_geno[i,]=="1/1",paste(snp_geno[i,2],snp_geno[i,2], sep = "/"),snp_geno[i,]))) 24 | } 25 | snpMat <- t(snp_geno[,-c(1,2)]) 26 | snpNames <- info$ID 27 | colnames(snpMat) <- snpNames 28 | rownames(snpMat) <- NULL 29 | snpMat <- as.data.frame(snpMat) 30 | snpMat[,1:ncol(snpMat)] <- lapply(snpMat[,1:ncol(snpMat)], genetics::as.genotype) 31 | return(snpMat) 32 | } 33 | -------------------------------------------------------------------------------- /R/ggmanhattan.R: -------------------------------------------------------------------------------- 1 | # global variables to escape r cmd check 2 | utils::globalVariables(c("index","marker","chrom_alt","xbreaks")) 3 | #' Make manhattan plot with full ggplot customizability 4 | #' 5 | #' This function is provided to make manhattan plot with full ggplot customizability. So next 6 | #' we can customize the manhattan plot with kinds of functions of ggplot2 and add additional layers. 7 | #' @param gwasres a data frame of gwas results. 8 | #' @param snp Name of the column containing SNP identifiers; default is NA. 9 | #' @param bp Name of the column containing the SNP positions; default is NA. 10 | #' @param chrom Name of the column containing the chromosome identifers; default is NA. 11 | #' @param pvalue Name of the column containing the p values; default is NA. 12 | #' @param vlinetype the type of vline (geom_vline()). The default is "solid". 13 | #' @param vlinesize the size of the vline. The default is 0.75. 14 | #' @param title the title of manhattan plot. The default is "Manhattan Plot". 15 | #' @param color the colors of alternate chromosome. The default is "#FF8C00" and "#556B2F" 16 | #' @param pointsize the size of point. The default is 1.25. 17 | #' @param file.output a logical, if file.output=TRUE, the result will be saved. 18 | #' if file.output=FALSE, the result will be printed. The default is TRUE. 19 | #' @param file a character, users can choose the different output formats of plot, 20 | #' so far, "jpeg", "pdf", "png", "tiff" can be selected by users. The default is "png". 21 | #' @param dpi a number, the picture element for .jpeg, .png and .tiff files. The default is 300. 22 | #' @param output a character, the name of your trait. The default is "Trait" 23 | #' 24 | #' @author Tao Yan <\email{tyan@zju.edu.cn}> | 25 | #' <\href{https://taoyan.netlify.com/}{https://taoyan.netlify.com/}> 26 | #' 27 | #' @import ggplot2 28 | #' @import gtools 29 | #' @return a manhattan plot based on ggplot2. 30 | #' 31 | #' @export 32 | #' 33 | #' @examples 34 | #' ggmanhattan(gwas_test) 35 | 36 | ggmanhattan <- function( 37 | gwasres, 38 | snp=NA, 39 | bp=NA, 40 | chrom=NA, 41 | pvalue=NA, 42 | index=NA, 43 | file.output=FALSE, 44 | file="png", 45 | output="Trait", 46 | dpi=300, 47 | vlinetype="solid", 48 | vlinesize=0.75, 49 | title="Manhattan Plot", 50 | color= c("#FF8C00", "#556B2F"), 51 | pointsize= 1.25, 52 | verbose=TRUE,...) 53 | { 54 | dfnames <- names(gwasres) 55 | if(is.na(chrom)){ 56 | chrom <- search.names(c("chr","chrom","chromosome"), dfnames) 57 | if(is.null(chrom)){ 58 | stop("Couldn't find the chromosome column. Please specify the name of the column with chromosome ids(chr,chrom or chromosome)") 59 | } 60 | } 61 | if(is.na(snp)){ 62 | snp <- search.names(c("snp","snpid","rs","rsid"), dfnames) 63 | if(is.null(snp)){ 64 | stop("Couldn't find the snp column. Please specify the name of the column with snp ids(snp, snpid, rs or rsid)") 65 | } 66 | } 67 | if(is.na(bp)){ 68 | bp <- search.names(c("bp","pos","position"), dfnames) 69 | if(is.null(bp)){ 70 | stop("Couldn't find the bp column. Please specify the name of the column with bp ids(bp, pos or position)") 71 | } 72 | } 73 | if(is.na(pvalue)){ 74 | pvalue <- search.names(c("p", "p-value", "pvalue", "pval"), dfnames) 75 | if(is.null(chrom)){ 76 | stop("Couldn't find the pvalue column. Please specify the name of the column with pvalue ids(p, pvalue, pval or p-value)") 77 | } 78 | } 79 | df <- as.data.frame(gwasres) 80 | df$chrom <- df[ ,chrom] 81 | df$chrom <- as.character(df$chrom) 82 | df$bp <- as.numeric(as.character(df[ ,bp])) 83 | df$pvalue <- as.numeric(as.character(df[ ,pvalue])) 84 | df$snp <- df[ ,snp] 85 | 86 | suppressMessages(require(gtools, quietly = TRUE)) 87 | if(is.na(index)){ 88 | df <- df[order(df$bp), ] 89 | df <- df[mixedorder(df$chrom), ] 90 | df$index <- 1:nrow(df) 91 | } 92 | else { 93 | df$index <- df[ ,index] 94 | df <- df[order(df$index), ] 95 | df <- df[mixedorder(df$chrom), ] 96 | } 97 | 98 | #calculate the numbers of chromosome 99 | chrnum <- data.frame(table(df$chrom)) 100 | chrnum$Var1 <- as.character(chrnum$Var1) 101 | chrnum <- chrnum[mixedorder(chrnum$Var1), ] 102 | 103 | #marker the odd and even chromosome 104 | chrom_odd <- as.character(chrnum$Var1[seq(1,nrow(chrnum),2)]) 105 | df$chrom_alt <- replace(df$chrom, df$chrom %in% chrom_odd, 0) 106 | df$chrom_alt <- replace(df$chrom_alt, df$chrom_alt !=0, 1) 107 | 108 | #-Log10 transform the pvalue 109 | 110 | df$marker <- -log10(df$pvalue) 111 | 112 | #specify the y limit 113 | ymax <- max(df$marker)+1.5 114 | 115 | #specify x axis tick points 116 | 117 | df_split <- split(df, df$chrom) 118 | xbreaks <- sapply(df_split, function(x){ 119 | midpoint <- length(x$index)/2 120 | if(midpoint < 1) midpoint <- 1 121 | return(x$index[midpoint]) 122 | }) 123 | 124 | #calculate the number of SNPs 125 | 126 | snp_num <- cumsum(chrnum$Freq) 127 | 128 | #make the manhattan plot 129 | 130 | p1 <- ggplot(df, aes(x=index, y=marker, colour=as.factor(chrom_alt)))+ 131 | geom_point(size=pointsize)+ 132 | scale_x_continuous(breaks = xbreaks, labels = names(xbreaks), expand = c(0,0))+ 133 | scale_y_continuous(expand = c(0,0), limits = c(0, ymax))+ 134 | guides(colour=FALSE)+ 135 | labs(x="Chromosomes",y=expression(bold(-log[10]~(pvalue))), title = "Manhattan Plot")+ 136 | geom_vline(xintercept = snp_num, colour="#C0C0C0", size=vlinesize, linetype=vlinetype)+ 137 | scale_color_manual(values = color)+ 138 | theme(panel.grid.major = element_blank(), 139 | panel.grid.minor = element_blank(), 140 | panel.background = element_blank(), 141 | panel.border = element_blank(), 142 | axis.line.x = element_line(color="black", size = 1), 143 | axis.line.y = element_line(color="black", size=1), 144 | axis.title = element_text(face = "bold",size = 20), 145 | axis.text = element_text(face = "bold",size=16, colour = "black"), 146 | plot.title = element_text(hjust = 0.5, size = 20, face = "bold")) 147 | class(p1) <- append(class(p1),"ggman") 148 | 149 | #whether to save the plot or not 150 | 151 | if(!file.output){ 152 | if(verbose) cat("The Manhattan Plot is Plotting! Please wait for a moment...", sep = "\n") 153 | return(p1) 154 | } 155 | if (file.output){ 156 | if(verbose) cat("The Manhattan Plot is Plotting! Please wait for a moment...", sep = "\n") 157 | if(file=="jpg") jpeg(paste("The_Manhattan_Plot_of_", output, ".jpg", sep=""), width = 15*dpi, height=7*dpi, res=dpi, quality = 100) 158 | if(file=="pdf") pdf(paste("The_Manhattan_Plot_of_", output, ".pdf", sep=""), width = 15, height=7) 159 | if(file=="tiff") tiff(paste("The_Manhattan_Plot_of_", output, ".tiff", sep=""), width = 15*dpi, height=7*dpi, res=dpi) 160 | if(file=="png") png(paste("The_Manhattan_Plot_of_", output, ".png", sep=""), width = 15*dpi, height=7*dpi, res=dpi) 161 | par(xpd=TRUE) 162 | }else{ 163 | if(is.null(dev.list())) dev.new(width =9, height=7) 164 | par(xpd=TRUE) 165 | } 166 | print(p1) 167 | if(file.output) dev.off() 168 | if(file.output & verbose) cat(paste("The Manhattan Plot is stored in: ", getwd(), sep = ""), sep = "\n") 169 | } 170 | -------------------------------------------------------------------------------- /R/search.names.R: -------------------------------------------------------------------------------- 1 | #' search.names 2 | #' 3 | #' @keywords internal 4 | #' 5 | #' @return Nothing; internal function 6 | #' 7 | search.names <- function(term,dfnames){ 8 | for(i in 1:length(term)){ 9 | res <- grep(paste0("\\b",term[i],"\\b"),dfnames, ignore.case = TRUE) 10 | if(length(res)>0){ 11 | if(length(res)==1){ 12 | return(dfnames[res]) 13 | } 14 | } 15 | } 16 | } 17 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## Overview 2 | 3 | This package is just developed to finish my project. So most of scripts are **NOT stable** enough. By wrapping some functions from other useful packages, it's easy for us to finish our jobs. 4 | 5 | ## Installation 6 | 7 | This package dependents on some other packages:[**vcfR**](https://github.com/knausb/vcfR),[**dplyr**](https://github.com/tidyverse/dplyr),[**genetics**](https://cran.r-project.org/web/packages/genetics/index.html),[**LDheatmap**](https://github.com/SFUStatgen/LDheatmap), ... 8 | 9 | The dependent packages will be installed at the time you install the package [**ttplot**](https://github.com/YTLogos/ttplot) 10 | 11 | ``` 12 | #install the ttplot package from Github 13 | if(!require(devtools)) { 14 | install.packages("devtools"); 15 | require(devtools) 16 | } 17 | devtools::install_github("YTLogos/ttplot") 18 | ``` 19 | 20 | ## Usage 21 | 22 | Until now there are just several functions 23 | 24 | ### Draw the LDheatmap from the [**VCF**](https://en.wikipedia.org/wiki/Variant_Call_Format) format file ([**plink**](https://www.cog-genomics.org/plink2/) format) 25 | 26 | **VCF** (Variant Call Format) is a text file format. It contains meta-information lines, a header line, and then data lines each containing information about a position in the genome. There is an example how to draw LDheatmap from data in VCF file(**plink** format). The **VCF** file looks like: 27 | 28 | ``` 29 | #This is a test vcf file (test.vcf) 30 | 31 | ##fileformat=VCFv4.3 32 | ##fileDate=20090805 33 | ##source=myImputationProgramV3.1 34 | ##reference=file:///seq/references/1000GenomesPilot-NCBI36.fasta 35 | ##contig= 36 | ##phasing=partial 37 | ##INFO= 38 | ##INFO= 39 | ##INFO= 40 | ##INFO= 41 | ##INFO= 42 | ##INFO= 43 | ##FILTER= 44 | ##FILTER= 45 | ##FORMAT= 46 | ##FORMAT= 47 | ##FORMAT= 48 | ##FORMAT= 49 | #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA00001 NA00002 NA00003 50 | 20 14370 rs6054257 G A 29 PASS NS=3;DP=14;AF=0.5;DB;H2 GT:GQ:DP:HQ 0|0:48:1:51,51 1|0:48:8:51,51 1/1:43:5:.,. 51 | 20 17330 . T A 3 q10 NS=3;DP=11;AF=0.017 GT:GQ:DP:HQ 0|0:49:3:58,50 0|1:3:5:65,3 0/0:41:3 52 | 20 1110696 rs6040355 A G,T 67 PASS NS=2;DP=10;AF=0.333,0.667;AA=T;DB GT:GQ:DP:HQ 1|2:21:6:23,27 2|1:2:0:18,2 2/2:35:4 53 | 20 1230237 . T . 47 PASS NS=3;DP=13;AA=T GT:GQ:DP:HQ 0|0:54:7:56,60 0|0:48:4:51,51 0/0:61:2 54 | 20 1234567 microsat1 GTC G,GTCT 50 PASS NS=3;DP=9;AA=G GT:GQ:DP 0/1:35:4 0/2:17:2 1/1:40:3 55 | ``` 56 | 57 | This kind of **VCF** is very large, so first we can use **plink** to recode the **VCF** file 58 | 59 | ``` 60 | $ plink --vcf test.vcf --recode vcf-iid --out Test -allow-extra-chr 61 | ``` 62 | 63 | So the final **VCF** file we will use looks like： 64 | 65 | ``` 66 | ##fileformat=VCFv4.2 67 | ##fileDate=20180905 68 | ##source=PLINKv1.90 69 | ##contig= 70 | ##INFO= 71 | ##FORMAT= 72 | #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT R4157 R4158 73 | chrC07 31076164 . T G . . PR GT 0/0 0/1 74 | chrC07 31076273 . G A . . PR GT 0/0 0/1 75 | chrC07 31076306 . G T . . PR GT 0/0 0/0 76 | ``` 77 | 78 | In the `extdata` directory there is one test file: `test.vcf`. We can test the function: 79 | 80 | ``` 81 | library(ttplot) 82 | test <- system.file("extdata", "test.vcf", package = "ttplot") 83 | ttplot::MyLDheatMap(vcffile = test, title = "My gene region") 84 | ``` 85 | ![LDheatmap](https://raw.githubusercontent.com/YTLogos/Pic_blog/master/LDheatmap.png) 86 | 87 | ### More 88 | 89 | #### **Usage:** 90 | 91 | ``` 92 | MyLDheatMap(vcffile, file.output = TRUE, file = "png", 93 | output = "region", title = "region:", verbose = TRUE, dpi = 300) 94 | ``` 95 | 96 | #### Arguments 97 | 98 | * **vcffile:** The plink format vcf file. More detail can see `View(test_vcf)`. 99 | * **file.output:** a logical, if `file.output=TRUE`, the result will be saved. if `file.output=FALSE`, the result will be printed. The default is `TRUE`. 100 | * **file:** a character, users can choose the different output formats of plot, so far, **"jpeg"**, **"pdf"**, **"png"**, **"tiff"** can be selected by users. The default is **"png"**. 101 | * **title:** a character, the title of the LDheatmap will be "The LDheatmap of title". the default is "region:". I suggest users use your own title. 102 | * **verbose:** whether to print the reminder. 103 | * **dpi:** a number, the picture element for **.jpeg**, **.png** and **.tiff** files. The default is `300`. 104 | 105 | 106 | ## ggmanhattan(): make manhattan plot based on **ggplot2** 107 | 108 | Here, we will use the data `gwasResults` from R package [**qqman**](https://github.com/stephenturner/qqman). This function is provided to make manhattan plot with full ggplot customizability. So next we can customize the manhattan plot with kinds of functions of **ggplot2** and add additional layers. The data need be reshaped as following: 109 | 110 | ``` 111 | > head(gwasResults) 112 | SNP CHR BP P 113 | 1 rs1 1 1 0.9148060 114 | 2 rs2 1 2 0.9370754 115 | 3 rs3 1 3 0.2861395 116 | 4 rs4 1 4 0.8304476 117 | 5 rs5 1 5 0.6417455 118 | 6 rs6 1 6 0.5190959 119 | ``` 120 | 121 | Then use the function `ggmanhattan` from [**ttplot**](https://github.com/YTLogos/ttplot) to make a manhattan plot: 122 | 123 | ``` 124 | ttplot::ggmanhattan(gwasResults) 125 | ``` 126 | 127 | ![](https://raw.githubusercontent.com/YTLogos/Pic_blog/master/The%20Manhattan%20Plot%20of%20gwas.png) 128 | 129 | ### More 130 | ``` 131 | ggmanhattan(gwasres, snp = NA, bp = NA, chrom = NA, pvalue = NA, 132 | index = NA, file.output = FALSE, file = "png", output = "Trait", 133 | dpi = 300, vlinetype = "solid", vlinesize = 0.75, 134 | title = "Manhattan Plot", color = c("#FF8C00", "#556B2F"), 135 | pointsize = 1.25, verbose = TRUE, ...) 136 | ``` 137 | 138 | #### Arguments 139 | 140 | * **gwasres** a data frame of gwas results. 141 | * **snp** Name of the column containing `SNP` identifiers; default is 'snp'. 142 | * **bp** Name of the column containing the `SNP` positions; default is `'bp'`. 143 | * **chrom** Name of the column containing the chromosome identifers; default is `'chrom'`. 144 | * **pvalue** Name of the column containing the p values; default is `'pvalue'`. 145 | * **file.output** a logical, if file.output=`TRUE`, the result will be saved. if file.output=`FALSE`, the result will be printed. The default is `TRUE`. 146 | * **file** a character, users can choose the different output formats of plot, so far, `"jpeg"`, `"pdf"`, `"png"`, `"tiff"` can be selected by users. The default is `"png"`. 147 | * **dpi** a number, the picture element for .jpeg, .png and .tiff files. The default is 300. 148 | * **vlinetype** the type of vline (`geom_vline()`). The default is `"solid"`. 149 | * **vlinesize** the size of the vline. The default is 0.75. 150 | * **title** the title of manhattan plot. The default is `"Manhattan Plot"`. 151 | * **color** the colors of alternate chromosome. The default is c(`"#FF8C00"`, `"#556B2F"`). 152 | * **pointsize** the size of point. The default is 1.25. 153 | 154 | ## Get genes bsaed on the locations of significant SNPs 155 | 156 | This function need two input files: gff, snp. 157 | 158 | ``` 159 | library(ttplot) 160 | gff <- read.table(system.file("extdata", "test.gff", package = "ttplot"), header= TRUE) 161 | head(gff) 162 | ``` 163 | ``` 164 | chr start end gene 165 | 1 436789 437474 chrA01g000077 166 | 1 439907 442623 chrA01g000078 167 | 1 448692 449999 chrA01g000079 168 | 1 454920 456931 chrA01g000080 169 | 1 457568 460619 chrA01g000081 170 | 1 461129 462693 chrA01g000082 171 | ``` 172 | 173 | ``` 174 | snp <- read.table(system.file("extdata", "test_sig_snp.txt", package = "ttplot"), header= TRUE) 175 | head(snp) 176 | ``` 177 | 178 | ``` 179 | SNP CHR BP P 180 | 1_500000 1 500000 5.53e-11 181 | 1_650000 1 650000 7.04e-09 182 | 2_1880000 2 1880000 3.84e-09 183 | 3_30500000 3 30500000 7.57e-08 184 | ``` 185 | 186 | Then we can get the candidate genes in regions based on significant SNPs of GWAS results. 187 | 188 | ``` 189 | ttplot::get_gene_from_snp(gff = gff, sig.snp = snp, distance = 20000, file.save = FALSE) 190 | The distance you choose is 20000bp! 191 | You have 4 significant SNPs and 130 genes! 192 | Now we will extract the genes in the significant regions! This will need some time, please wait for severals minutes! ... 193 | 194 | The result is : 195 | ------------------------------------------------------------- 196 | # A tibble: 9 x 5 197 | chrom geneid gene_start gene_end snp_location 198 | 199 | 1 chrA01g000088 479923 485997 500000 200 | 1 chrA01g000089 495531 497917 500000 201 | 1 chrA01g000090 498488 499689 500000 202 | 1 chrA01g000091 499749 501301 500000 203 | 1 chrA01g000092 503622 507079 500000 204 | 1 chrA01g000093 507659 510321 500000 205 | 1 chrA01g000094 515103 516863 500000 206 | 1 chrA01g000095 517335 518776 500000 207 | 1 chrA01g000096 519020 520668 500000 208 | ``` 209 | 210 | ### More 211 | 212 | ``` 213 | Usage 214 | get_gene_from_snp(gff, sig.snp, distance = 50000, file.save = TRUE, 215 | file.type = "csv", gff.chrom = NA, snp.chrom = NA, geneid = NA, 216 | pvalue = NA, gene_start = NA, gene_end = NA, snp_location = NA, 217 | verbose = TRUE, ...) 218 | ``` 219 | 220 | #### Arguments 221 | * gff: a data frame of all the gene (transcript), must have column names. 222 | * sig.snp: a data frame of significant SNPs. 223 | * distance: numeric (bp), it is to define the region. The default is 50000, you need to choose it based on the LD distance in your study. 224 | * file.save: a logical, if file.output=TRUE, the result will be saved. if file.output=FALSE, the result will be printed. The default is TRUE. 225 | * file.type: a character, users can choose the different output formats, so far, "csv", "txt", "xlsx" can be selected by users. The default is "csv". 226 | * gff.chrom: Name of the column containing the chromosome identifers in the gff file; default is NA. 227 | * snp.chrom: Name of the column containing the chromosome identifers in the snp.sig file; default is NA. 228 | * geneid: Name of the column containing the geneid in gff file; default is NA. 229 | * pvalue: Name of the column containing the p values in snp.sig file; default is NA. 230 | * snp_location: Name of the column containing the snp position in snp.sig file; default is NA. 231 | 232 | 233 | -------------------------------------------------------------------------------- /data/test_vcf.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YTLogos/ttplot/a86f35db9922f7e219af9ed95ed38d5e8f909ba4/data/test_vcf.rda -------------------------------------------------------------------------------- /inst/extdata/test.gff: -------------------------------------------------------------------------------- 1 | chr start end gene 2 | 1 436789 437474 chrA01g000077 3 | 1 439907 442623 chrA01g000078 4 | 1 448692 449999 chrA01g000079 5 | 1 454920 456931 chrA01g000080 6 | 1 457568 460619 chrA01g000081 7 | 1 461129 462693 chrA01g000082 8 | 1 463230 464184 chrA01g000083 9 | 1 464284 466180 chrA01g000084 10 | 1 467128 469508 chrA01g000085 11 | 1 470497 472284 chrA01g000086 12 | 1 477428 479529 chrA01g000087 13 | 1 479923 485997 chrA01g000088 14 | 1 495531 497917 chrA01g000089 15 | 1 498488 499689 chrA01g000090 16 | 1 499749 501301 chrA01g000091 17 | 1 503622 507079 chrA01g000092 18 | 1 507659 510321 chrA01g000093 19 | 1 515103 516863 chrA01g000094 20 | 1 517335 518776 chrA01g000095 21 | 1 519020 520668 chrA01g000096 22 | 1 521739 524630 chrA01g000097 23 | 1 527930 529132 chrA01g000098 24 | 1 533729 535441 chrA01g000099 25 | 1 540417 541595 chrA01g000100 26 | 1 546675 548401 chrA01g000101 27 | 1 548915 552044 chrA01g000102 28 | 1 551446 553945 chrA01g000103 29 | 1 556806 560831 chrA01g000104 30 | 1 561076 564015 chrA01g000105 31 | 1 567479 569311 chrA01g000106 32 | 1 570861 574130 chrA01g000107 33 | 1 580932 583917 chrA01g000108 34 | 2 17847704 17848332 chrA02g002775 35 | 2 17855497 17856616 chrA02g002776 36 | 2 17861369 17873407 chrA02g002777 37 | 2 17874543 17875080 chrA02g002778 38 | 2 17875259 17876971 chrA02g002779 39 | 2 17902947 17904928 chrA02g002780 40 | 2 17931868 17932137 chrA02g002781 41 | 2 17944690 17945043 chrA02g002782 42 | 2 17948009 17948404 chrA02g002783 43 | 2 17949983 17950493 chrA02g002784 44 | 2 17959490 17960131 chrA02g002785 45 | 2 17961244 17962654 chrA02g002786 46 | 2 17978145 17980062 chrA02g002787 47 | 2 17994975 17997465 chrA02g002788 48 | 2 18000802 18001459 chrA02g002789 49 | 2 18005923 18007542 chrA02g002790 50 | 2 18014988 18015856 chrA02g002791 51 | 2 18038189 18038410 chrA02g002792 52 | 2 18043218 18044087 chrA02g002793 53 | 2 18048790 18049008 chrA02g002794 54 | 2 18052611 18052829 chrA02g002795 55 | 2 18098173 18102150 chrA02g002796 56 | 2 18108888 18110008 chrA02g002797 57 | 2 18121041 18123156 chrA02g002798 58 | 2 18126547 18127018 chrA02g002799 59 | 2 18127664 18128230 chrA02g002800 60 | 2 18171387 18172399 chrA02g002801 61 | 2 18224672 18226083 chrA02g002802 62 | 2 18229258 18233690 chrA02g002803 63 | 2 18261608 18262450 chrA02g002804 64 | 2 18264688 18265721 chrA02g002805 65 | 2 18267191 18267615 chrA02g002806 66 | 2 18280671 18281182 chrA02g002807 67 | 2 18289888 18291563 chrA02g002808 68 | 2 18299129 18300707 chrA02g002809 69 | 2 18313499 18322886 chrA02g002810 70 | 2 18320567 18321363 chrA02g002811 71 | 2 18339579 18341791 chrA02g002812 72 | 2 18354772 18354963 chrA02g002813 73 | 2 18369030 18373241 chrA02g002814 74 | 2 18374105 18375882 chrA02g002815 75 | 2 18381756 18382189 chrA02g002816 76 | 3 30561952 30566057 chrA03g005360 77 | 3 30566169 30568065 chrA03g005361 78 | 3 30568386 30570004 chrA03g005362 79 | 3 30586607 30587757 chrA03g005363 80 | 3 30587893 30589100 chrA03g005364 81 | 3 30589314 30597913 chrA03g005365 82 | 3 30604649 30605377 chrA03g005366 83 | 3 30611675 30612832 chrA03g005367 84 | 3 30620236 30624626 chrA03g005368 85 | 3 30632940 30633155 chrA03g005369 86 | 3 30633614 30634107 chrA03g005370 87 | 3 30636645 30638054 chrA03g005371 88 | 3 30671465 30674263 chrA03g005372 89 | 3 30693656 30694441 chrA03g005373 90 | 3 30695838 30697126 chrA03g005374 91 | 3 30698684 30699058 chrA03g005375 92 | 3 30700762 30702097 chrA03g005376 93 | 3 30703317 30704946 chrA03g005377 94 | 3 30706525 30708012 chrA03g005378 95 | 3 30708182 30711453 chrA03g005379 96 | 3 30711940 30712209 chrA03g005380 97 | 3 30722346 30724297 chrA03g005381 98 | 3 30733850 30736043 chrA03g005382 99 | 3 30736519 30738010 chrA03g005383 100 | 3 30750303 30751007 chrA03g005384 101 | 3 30753479 30755522 chrA03g005385 102 | 3 30755870 30756755 chrA03g005386 103 | 3 30757929 30758189 chrA03g005387 104 | 3 30768177 30769558 chrA03g005388 105 | 3 30771557 30772142 chrA03g005389 106 | 3 30780397 30781170 chrA03g005390 107 | 3 30782148 30783941 chrA03g005391 108 | 3 30785534 30787398 chrA03g005392 109 | 3 30794119 30794627 chrA03g005393 110 | 3 30795281 30796680 chrA03g005394 111 | 3 30800774 30801470 chrA03g005395 112 | 3 30801835 30802118 chrA03g005396 113 | 3 30810097 30811364 chrA03g005397 114 | 3 30817788 30819041 chrA03g005398 115 | 3 30828694 30833168 chrA03g005399 116 | 3 30833914 30835133 chrA03g005400 117 | 3 30835191 30838262 chrA03g005401 118 | 3 30839512 30839733 chrA03g005402 119 | 3 30840192 30845488 chrA03g005403 120 | 3 30845944 30847713 chrA03g005404 121 | 3 30847680 30853176 chrA03g005405 122 | 3 30866770 30869429 chrA03g005406 123 | 3 30871026 30873188 chrA03g005407 124 | 3 30880725 30881205 chrA03g005408 125 | 3 30917631 30921967 chrA03g005409 126 | 3 30946844 30951310 chrA03g005410 127 | 3 30951563 30954854 chrA03g005411 128 | 3 30956804 30960085 chrA03g005412 129 | 3 30962860 30963150 chrA03g005413 130 | 3 30971624 30972775 chrA03g005414 131 | 3 30977421 30979331 chrA03g005415 -------------------------------------------------------------------------------- /inst/extdata/test.vcf: -------------------------------------------------------------------------------- 1 | ##fileformat=VCFv4.2 2 | ##fileDate=20180905 3 | ##source=PLINKv1.90 4 | ##contig= 5 | ##INFO= 6 | ##FORMAT= 7 | #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT R4157 R4158 R4163 R4168 R4171 R4175 R4176 R4177 R4179 R4180 R4182 R4187 R4191 R4194 R4199 R4202 R4210 R4213 R4215 R4218 R4219 R4220 R4222 R4227 R4229 R4234 R4236 R4239 R4245 R4247 R4258 R4260 R4268 R4272 R4275 R4283 R4284 R4302 R4310 R4311 R4312 R4314 R4316 R4324 R4325 R4330 R4341 R4342 R4345 R4349 R4350 R4353 R4360 R4365 R4368 R4373 R4374 R4385 R4386 R4398 R4405 R4406 R4415 R4420 R4422 R4434 R4437 R4439 R4440 R4441 R4450 R4451 R4452 R4453 R4455 R4463 R4464 R4465 R4472 R4474 R4480 R4482 R4484 R4487 R4489 R4500 R4507 R4508 R4510 R4514 R4518 R4523 R4525 R4533 R4543 R4558 R4572 R4576 R4580 R4583 R4586 R4593 R4601 R4618 R4627 R4628 R4635 R4637 R4638 R4640 R4641 R4646 R4647 R4654 R4655 R4664 R4665 R4667 R4678 R4680 R4684 R4689 R4690 R4695 R4696 R4698 R4707 R4716 R4720 R4722 R4723 R4725 R4728 R4731 R4734 R4737 R4743 R4747 R4748 R4749 R4755 R4756 R4757 R4762 R4763 R4764 R4766 R4767 R4773 R4775 R4776 R4779 R4783 R4784 R4786 R4792 R4794 R4795 R4796 R4797 R4798 R4801 R4804 R4810 R4812 R4815 R4817 R4822 R4825 R4839 R4843 R4849 R4855 R4859 R4861 R4862 R4868 R4870 R4873 R4875 R4877 R4878 R4879 R4880 R4887 R4888 R4894 R4897 R4900 R4904 R4905 R4906 R4907 R4917 R4920 R4921 R4924 R4942 R4944 R4945 R4950 R4955 R4971 R4977 R4978 R4983 R4987 R4996 R4997 R4999 R5000 R5002 R5005 R5006 R5008 R5009 R5012 R5014 R5015 R5018 R5019 R5022 R5025 R5027 R5029 R5030 R5032 R5033 R5038 R5039 R5040 R5044 R5045 R5047 R5048 R5049 R5057 R5058 R5059 R5064 R5067 R5073 R5087 R5088 R5091 R5094 R5096 R5103 R5110 R5111 R5112 R5114 R5120 R5124 R5128 R5131 R5137 R5138 R5142 R5145 R5148 R5149 R5152 R5153 R5155 R5158 R5159 R5161 8 | chrC07 31076164 . 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A G . . 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C T . . 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T A . . 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G C . . 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T C . . 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G T . . 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T C . . 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T G . . 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A G . . 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C T . . PR GT 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/1 0/0 0/0 ./. 0/0 0/0 0/0 0/0 0/0 0/0 0/1 0/0 0/0 0/0 0/0 0/0 0/0 0/0 ./. 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 ./. 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 ./. 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 ./. 0/0 0/0 ./. 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 ./. 0/0 ./. 0/0 0/0 0/0 0/0 0/0 0/0 0/0 ./. 0/0 0/0 ./. 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 ./. 0/0 ./. ./. 0/0 0/1 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 ./. 0/0 0/0 0/0 0/0 0/0 0/0 ./. 0/0 0/0 ./. 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 ./. 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 ./. 0/0 0/0 0/0 0/0 ./. 0/1 0/1 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/1 0/0 0/0 0/0 0/0 ./. 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 ./. 0/0 0/0 0/0 0/0 ./. 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 ./. ./. 0/0 ./. 0/0 0/0 0/0 0/0 0/0 ./. 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 ./. 0/0 0/0 0/0 0/0 0/0 0/0 ./. 0/0 0/0 1/1 0/0 0/0 0/1 95 | -------------------------------------------------------------------------------- /inst/extdata/test_sig_snp.txt: -------------------------------------------------------------------------------- 1 | SNP CHR BP P 2 | 1_500000 1 500000 5.53E-11 3 | 1_650000 1 650000 7.04E-09 4 | 2_1880000 2 1880000 3.84E-09 5 | 3_30500000 3 30500000 7.57E-08 6 | -------------------------------------------------------------------------------- /man/MyLDheatMap.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/MyLDheatMap.R 3 | \name{MyLDheatMap} 4 | \alias{MyLDheatMap} 5 | \title{Get the LDhaetmap from the vcf file (plink format) directly} 6 | \usage{ 7 | MyLDheatMap(vcffile, file.output = TRUE, file = "png", 8 | output = "region", title = "region:", verbose = TRUE, dpi = 300, 9 | ...) 10 | } 11 | \arguments{ 12 | \item{vcffile}{The plink format vcf file. More detail can see View(test_vcf)} 13 | 14 | \item{file.output}{a logical, if file.output=TRUE, the result will be saved. 15 | if file.output=FALSE, the result will be printed. The default is TRUE} 16 | 17 | \item{file}{a character, users can choose the different output formats of plot, so far, "jpeg", "pdf", "png", "tiff" can be selected by users. The default is "png".} 18 | 19 | \item{title}{a character, the title of the LDheatmap will be "The LDheatmap of title". 20 | the default is "region:". I suggest users use your own title.} 21 | 22 | \item{verbose}{whether to print the reminder.} 23 | 24 | \item{dpi}{a number, the picture element for .jpeg, .png and .tiff files. The default is 300.} 25 | } 26 | \value{ 27 | the LDheatmap. 28 | } 29 | \description{ 30 | This function defined to obtain the LDheatmap from the vcf file directly. 31 | } 32 | \examples{ 33 | MyLDheatMap(system.file("extdata","test.vcf", package = "ttplot"), title="your title") 34 | } 35 | \author{ 36 | Tao Yan <\email{tyan@zju.edu.cn}> | 37 | <\href{https://taoyan.netlify.com/}{https://taoyan.netlify.com/}> 38 | } 39 | -------------------------------------------------------------------------------- /man/convertToNumeric.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/convertToNumeric.R 3 | \name{convertToNumeric} 4 | \alias{convertToNumeric} 5 | \title{Convert the value of genotype into 0,1,2} 6 | \usage{ 7 | convertToNumeric(snp_data) 8 | } 9 | \arguments{ 10 | \item{snp_data}{The snp_data matrix.} 11 | } 12 | \value{ 13 | The genodata with the value of 0,1,2. 14 | } 15 | \description{ 16 | This function converts the value of genotype into 0,1,2 form 0/0,0/1,1/1. 17 | } 18 | \examples{ 19 | convertTonumeric(snp_data) 20 | } 21 | -------------------------------------------------------------------------------- /man/get_gene_from_snp.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/get_gene_from_snp.R 3 | \name{get_gene_from_snp} 4 | \alias{get_gene_from_snp} 5 | \title{Get the candidate genes in regions based on significant SNPs of GWAS results.} 6 | \usage{ 7 | get_gene_from_snp(gff, sig.snp, distance = 75000, file.save = TRUE, 8 | file.type = "csv", gff.chrom = NA, snp.chrom = NA, geneid = NA, 9 | pvalue = NA, gene_start = NA, gene_end = NA, snp_location = NA, 10 | verbose = TRUE, ...) 11 | } 12 | \arguments{ 13 | \item{gff}{a data frame of all the gene (transcript), must have column names.} 14 | 15 | \item{sig.snp}{a data frame of significant SNPs.} 16 | 17 | \item{distance}{numeric (bp), it is to define the region. The default is 50000, you need to choose it based on the LD distance in your study.} 18 | 19 | \item{file.save}{a logical, if file.output=TRUE, the result will be saved. 20 | if file.output=FALSE, the result will be printed. The default is TRUE.} 21 | 22 | \item{file.type}{a character, users can choose the different output formats, 23 | so far, "csv", "txt", "xlsx" can be selected by users. The default is "csv".} 24 | 25 | \item{gff.chrom}{Name of the column containing the chromosome identifers in the gff file; default is NA.} 26 | 27 | \item{snp.chrom}{Name of the column containing the chromosome identifers in the snp.sig file; default is NA.} 28 | 29 | \item{geneid}{Name of the column containing the geneid in gff file; default is NA.} 30 | 31 | \item{pvalue}{Name of the column containing the p values in snp.sig file; default is NA.} 32 | 33 | \item{snp_location}{Name of the column containing the snp position in snp.sig file; default is NA.} 34 | } 35 | \value{ 36 | a data.frame contain the candidate genes with start,end,genid etc 37 | } 38 | \description{ 39 | This function is developed to get the candidate genes in regions based on significant SNPs of GWAS results. 40 | } 41 | \examples{ 42 | get_gene_from_snp(gff,sig.snp) 43 | } 44 | \author{ 45 | Tao Yan <\email{tyan@zju.edu.cn}> | 46 | <\href{https://taoyan.netlify.com/}{https://taoyan.netlify.com/}> 47 | } 48 | -------------------------------------------------------------------------------- /man/getsnpInfo.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/getsnpInfo.R 3 | \name{getsnpInfo} 4 | \alias{getsnpInfo} 5 | \title{Obtain the snp info from the vcf file} 6 | \usage{ 7 | getsnpInfo(file) 8 | } 9 | \arguments{ 10 | \item{file}{The vcf file name.} 11 | } 12 | \value{ 13 | The snp info (ID, POS). 14 | } 15 | \description{ 16 | This function defined to get the snp info (ID, POS) from the vcf file. 17 | } 18 | \examples{ 19 | snpInfo(sample.vcf) 20 | } 21 | -------------------------------------------------------------------------------- /man/getsnpMat.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/getsnpMat.R 3 | \name{getsnpMat} 4 | \alias{getsnpMat} 5 | \title{Get the snpMatrix from the vcf file (plink format)} 6 | \usage{ 7 | getsnpMat(vcffile) 8 | } 9 | \arguments{ 10 | \item{vcffile}{The vcf file (plink format).} 11 | } 12 | \value{ 13 | The snpMAtrix for the visulization of LDheatmap. 14 | } 15 | \description{ 16 | This function defined to obtain the snpMatrix(with snp ID) from the vcf file 17 | } 18 | \examples{ 19 | getsnpMat(vcffile) 20 | } 21 | -------------------------------------------------------------------------------- /man/ggmanhattan.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/ggmanhattan.R 3 | \name{ggmanhattan} 4 | \alias{ggmanhattan} 5 | \title{Make manhattan plot with full ggplot customizability} 6 | \usage{ 7 | ggmanhattan(gwasres, snp = NA, bp = NA, chrom = NA, pvalue = NA, 8 | index = NA, file.output = FALSE, file = "png", output = "Trait", 9 | dpi = 300, vlinetype = "solid", vlinesize = 0.75, 10 | title = "Manhattan Plot", color = c("#FF8C00", "#556B2F"), 11 | pointsize = 1.25, verbose = TRUE, ...) 12 | } 13 | \arguments{ 14 | \item{gwasres}{a data frame of gwas results.} 15 | 16 | \item{snp}{Name of the column containing SNP identifiers; default is NA.} 17 | 18 | \item{bp}{Name of the column containing the SNP positions; default is NA.} 19 | 20 | \item{chrom}{Name of the column containing the chromosome identifers; default is NA.} 21 | 22 | \item{pvalue}{Name of the column containing the p values; default is NA.} 23 | 24 | \item{file.output}{a logical, if file.output=TRUE, the result will be saved. 25 | if file.output=FALSE, the result will be printed. The default is TRUE.} 26 | 27 | \item{file}{a character, users can choose the different output formats of plot, 28 | so far, "jpeg", "pdf", "png", "tiff" can be selected by users. The default is "png".} 29 | 30 | \item{output}{a character, the name of your trait. The default is "Trait"} 31 | 32 | \item{dpi}{a number, the picture element for .jpeg, .png and .tiff files. The default is 300.} 33 | 34 | \item{vlinetype}{the type of vline (geom_vline()). The default is "solid".} 35 | 36 | \item{vlinesize}{the size of the vline. The default is 0.75.} 37 | 38 | \item{title}{the title of manhattan plot. The default is "Manhattan Plot".} 39 | 40 | \item{color}{the colors of alternate chromosome. The default is "#FF8C00" and "#556B2F"} 41 | 42 | \item{pointsize}{the size of point. The default is 1.25.} 43 | } 44 | \value{ 45 | a manhattan plot based on ggplot2. 46 | } 47 | \description{ 48 | This function is provided to make manhattan plot with full ggplot customizability. So next 49 | we can customize the manhattan plot with kinds of functions of ggplot2 and add additional layers. 50 | } 51 | \examples{ 52 | ggmanhattan(gwas_test) 53 | } 54 | \author{ 55 | Tao Yan <\email{tyan@zju.edu.cn}> | 56 | <\href{https://taoyan.netlify.com/}{https://taoyan.netlify.com/}> 57 | } 58 | -------------------------------------------------------------------------------- /man/search.names.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/search.names.R 3 | \name{search.names} 4 | \alias{search.names} 5 | \title{search.names} 6 | \usage{ 7 | search.names(term, dfnames) 8 | } 9 | \value{ 10 | Nothing; internal function 11 | } 12 | \description{ 13 | search.names 14 | } 15 | \keyword{internal} 16 | -------------------------------------------------------------------------------- /ttplot.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: No 4 | SaveWorkspace: No 5 | AlwaysSaveHistory: Default 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 2 10 | Encoding: UTF-8 11 | 12 | RnwWeave: knitr 13 | LaTeX: XeLaTeX 14 | 15 | AutoAppendNewline: Yes 16 | StripTrailingWhitespace: Yes 17 | 18 | BuildType: Package 19 | PackageUseDevtools: Yes 20 | PackageInstallArgs: --no-multiarch --with-keep.source 21 | PackageRoxygenize: rd,collate,namespace 22 | -------------------------------------------------------------------------------- /vignettes/.gitignore: -------------------------------------------------------------------------------- 1 | *.html 2 | *.R 3 | -------------------------------------------------------------------------------- /vignettes/tutorial.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "A simple tutorial of ttplot" 3 | author: "Tao Yan" 4 | date: "`r Sys.Date()`" 5 | output: 6 | rmarkdown::html_vignette: 7 | toc: true 8 | fig_width: 6 9 | fig_height: 6 10 | vignette: > 11 | %\VignetteIndexEntry{A simple tutorial of ttplot} 12 | %\VignetteEngine{knitr::rmarkdown} 13 | %\VignetteEncoding{UTF-8} 14 | --- 15 | 16 | ```{r setup, include = FALSE} 17 | knitr::opts_chunk$set( 18 | collapse = TRUE, 19 | comment = "#>" 20 | ) 21 | ``` 22 | 23 | ## Overview 24 | 25 | This package is just developed to finish my project. So most of scripts are NOT stable enough. By wrapping some functions from other useful packages, it's easy for us to finish our jobs. 26 | 27 | ## Installation 28 | 29 | This packages dependent on some other packages:[**vcfR**](https://github.com/knausb/vcfR),[**dplyr**](https://github.com/tidyverse/dplyr),[**genetics**](https://cran.r-project.org/web/packages/genetics/index.html),[**LDheatmap**](https://github.com/SFUStatgen/LDheatmap), ... 30 | 31 | The dependent packages will be installed at the time you install the package [**ttplot**](https://github.com/YTLogos/ttplot) 32 | 33 | ``` 34 | #install the ttplot package from Github 35 | devtools::install_github("YTLogos/ttplot") 36 | ``` 37 | 38 | ## Usage 39 | 40 | Until now there are just several functions 41 | 42 | ### Draw the LDheatmap from the [**VCF**](https://en.wikipedia.org/wiki/Variant_Call_Format) format file ([**plink**](https://www.cog-genomics.org/plink2/) format) 43 | 44 | **VCF** (Variant Call Format) is a text file format. It contains meta-information lines, a header line, and then data lines each containing information about a position in the genome. There is an example how to draw LDheatmap from data in VCF file(**plink** format). The **VCF** file looks like: 45 | 46 | ``` 47 | #This is a test vcf file (test.vcf) 48 | 49 | ##fileformat=VCFv4.3 50 | ##fileDate=20090805 51 | ##source=myImputationProgramV3.1 52 | ##reference=file:///seq/references/1000GenomesPilot-NCBI36.fasta 53 | ##contig= 54 | ##phasing=partial 55 | ##INFO= 56 | ##INFO= 57 | ##INFO= 58 | ##INFO= 59 | ##INFO= 60 | ##INFO= 61 | ##FILTER= 62 | ##FILTER= 63 | ##FORMAT= 64 | ##FORMAT= 65 | ##FORMAT= 66 | ##FORMAT= 67 | #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA00001 NA00002 NA00003 68 | 20 14370 rs6054257 G A 29 PASS NS=3;DP=14;AF=0.5;DB;H2 GT:GQ:DP:HQ 0|0:48:1:51,51 1|0:48:8:51,51 1/1:43:5:.,. 69 | 20 17330 . T A 3 q10 NS=3;DP=11;AF=0.017 GT:GQ:DP:HQ 0|0:49:3:58,50 0|1:3:5:65,3 0/0:41:3 70 | 20 1110696 rs6040355 A G,T 67 PASS NS=2;DP=10;AF=0.333,0.667;AA=T;DB GT:GQ:DP:HQ 1|2:21:6:23,27 2|1:2:0:18,2 2/2:35:4 71 | 20 1230237 . T . 47 PASS NS=3;DP=13;AA=T GT:GQ:DP:HQ 0|0:54:7:56,60 0|0:48:4:51,51 0/0:61:2 72 | 20 1234567 microsat1 GTC G,GTCT 50 PASS NS=3;DP=9;AA=G GT:GQ:DP 0/1:35:4 0/2:17:2 1/1:40:3 73 | ``` 74 | 75 | This kind of **VCF** is very large, so first we can use **plink** to recode the **VCF** file 76 | 77 | ``` 78 | $ plink --vcf test.vcf --recode vcf-iid --out Test -allow-extra-chr 79 | ``` 80 | 81 | So the final **VCF** file we will use is looks like： 82 | 83 | ``` 84 | ##fileformat=VCFv4.2 85 | ##fileDate=20180905 86 | ##source=PLINKv1.90 87 | ##contig= 88 | ##INFO= 89 | ##FORMAT= 90 | #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT R4157 R4158 91 | chrC07 31076164 . T G . . PR GT 0/0 0/1 92 | chrC07 31076273 . G A . . PR GT 0/0 0/1 93 | chrC07 31076306 . G T . . PR GT 0/0 0/0 94 | ``` 95 | 96 | In the `extdata` directory there is one test file: test.vcf. We can test the function: 97 | 98 | ```{r,echo=TRUE, eval=TRUE, fig.align='center'} 99 | library(ttplot) 100 | test <- system.file("extdata", "test.vcf", package = "ttplot") 101 | ttplot::MyLDheatMap(vcffile = test, title = "My gene region") 102 | ``` 103 | 104 | 105 | ## SessionInfo 106 | 107 | ```{r} 108 | sessioninfo::session_info() 109 | ``` 110 | --------------------------------------------------------------------------------