# #——回声= F ------------------------------------------------------------------- # 检查对象:# devtools: load_all (export_all = F);qwraps2::lazyload_cache_dir("vignettes/V5_slam_seq_cache/html") knitr::opts_chunk$set(cache=TRUE, autodep=TRUE) ## ----setup, cache=FALSE, warning=FALSE, message=FALSE------------------------- library(tidyverse) library(ComplexHeatmap) library(weitrix) # BiocParallel支持多后端。#如果默认挂起或出错，请尝试其他方法。#最可靠的支持是使用串行处理BiocParallel::注册(BiocParallel:: SerialParam () ) ## ---- 加载消息= FALSE ------------------------------------------------------ < -系统覆盖。file("GSE99970"， "GSE99970_T_coverage.csv.gz"， package="weitrix") %>% read_csv() %>% column_to_rownames("gene") %>% as.matrix()转换<- system. txt文件。file("GSE99970"， " gse99970_t_c_converversions .csv.gz"， package="weitrix") %>% read_csv() %>% column_to_rownames("gene") %>% as.matrix() #计算比例，创建weitrix wei <- as_weitrix(conversions/coverage, coverage) dim(wei) #我们将只使用观察到至少30个转换的基因good <- rowsum (conversions) >= 30 wei <- wei[good，] #从名称部分<- str_match(colnames(wei)，“(. *)_ (Rep_。*)”)colData(魏)元组< - fct_inorder(部分[2])colData(魏)美元代表< - fct_inorder (paste0(“Rep_”,部分[3]))rowData(魏)美元mean_coverage < - rowMeans (weitrix_weights(魏))魏colMeans (weitrix_x(魏)na.rm = TRUE) # #——校准 ---------------------------------------------------------------- # 计算初始适合提供剩余工资符合< - weitrix_components(魏、设计= ~组)卡尔< weitrix_calibrate_all(魏、设计=适合trend_formula = ~日志(重量)+偏移量(日志(μ*(1μ))),mu_min = 0.001, mu_max = 0.999)元数据(cal) weitrix all_coef # #美元——calplot_mu fig.height = 8 ------------------------------------------------- weitrix_calplot(魏,健康,猫=集团柯伐合金=μ,导游= FALSE) + coord_cartesian (xlim = c(0, - 0.1)) +实验室(title =“校准”)weitrix_calplot (cal,健康,猫=组,柯伐合金=μ)+ coord_cartesian (xlim = c(0, - 0.1)) +实验室校准后(标题= " ") ## ---- calplot_weight fig.height = 8 --------------------------------------------- weitrix_calplot(魏、健康,猫=组,柯伐合金=日志(weitrix_weights(魏)),导游= FALSE) +实验室(title =“校准”)weitrix_calplot (cal,健康,猫=组,柯伐合金=日志(weitrix_weights(魏)))+实验室校准后(标题= " ") ## ---- 广告样稿,消息= FALSE ------------------------------------------------------ comp < - weitrix_components (cal 2 n_restarts = 1) # #——showcomp ----------------------------------------------------------------- matrix_long (comp坳美元[1],row_info = colData (cal) % > % ggplot (aes (x =组,y =值)+ geom_jitter(高度宽度= 0.2,= 0)+ facet_grid (col ~.) ## ---- comp1文件 -------------------------------------------------------------------- 快< - weitrix_confects (cal comp坳美元,C1)快速的热图(weitrix_x (cal)[头(快表名称,美元10),),name =“转换比例”,cluster_columns = FALSE, cluster_rows = FALSE) # #——comp2 -------------------------------------------------------------------- < - weitrix_confects缓慢(cal comp坳美元,C2)缓慢的热图(weitrix_x (cal)[头(缓慢的表名称,美元10),),name =“转换比例”,cluster_columns = FALSE, cluster_rows = FALSE) # # - eval = FALSE --------------------------------------------------------------- # 库(tidyverse) # # download.file(“https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE99970&format=file”、“GSE99970_RAW.tar”)#压缩(“GSE99970_RAW.tar”,exdir =“GSE99970_RAW”)# #文件名< -列表。files("GSE99970_RAW"， full.names=TRUE) # samples <- str_match(文件名，"mESC_(.*)\\.tsv\\.gz")[，2] # dfs <- map(文件名，read_tsv, comment="#") # coverage <- do. txt文件覆盖范围call(cbind, map(dfs， "CoverageOnTs")) %>% # rowsum(dfs[[1]]$Name) # conversion <- do. call(cbind, map(dfs， "CoverageOnTs"))call(cbind, map(dfs, "ConversionsOnTs")) %>% # rowsum(dfs[[1]]$Name) # colnames(conversions) <- colnames(coverage) <- samples # # reorder <- c(1:3, 25:27, 4:24) # # coverage[,reorder] %>% # as.data.frame() %>% # rownames_to_column("gene") %>% # write_csv("GSE99970_T_coverage.csv.gz") # # conversions[,reorder] %>% # as.data.frame() %>% # rownames_to_column("gene") %>% # write_csv("GSE99970_T_C_conversions.csv.gz")