工作中常用到的一些基于R的功能，整理到一起了，方便重复使用。
You can install the development version of ZhangRtools from GitHub with:
library(devtools)
install_github("fentouxungui/ZhangRtools")library(ZhangRtools)以逐行读取方式，对文件里的内容进行字符串替换。
ModifyFile(file = "./scRNAseq/Fly/Ovary/Fly-Ovary-Jevitt-PlosBiology-2020/Parameters.R", #文件路径
LineMatchKeyWords = c("SplitBy.levels.max","15"), # 关键词向量，用于定位要被修改的行
LineMatch.ignore.case = FALSE, # 依据关键词定位行时，是否需要忽略关键词的大小写
WordOld = "SplitBy.levels.max <- 15", # 要被替换的词,一个字符串
wordold.Matchfixed = TRUE, # 匹配字符串时,是否需要完全匹配，FALSE为使用正则表达式
WordNew = "SplitBy.levels.max <- 50", # 新词,一个字符串
Replace = TRUE, # 是否执行替换,一个逻辑值
SaveOld = TRUE, # 是否要保存旧的文件,一个逻辑值
silence = FALSE, # 是否屏蔽信息输出,一个逻辑值
returnSummary = TRUE) # 是否输出替换统计结果,一个逻辑值,主要用于批量替换以批量形式，对某个目录下的某类文件进行字符串替换。
BatchModifyFile(Directory = "./shiny-server/PublicData/scRNAseq", # 目录
FileNamePattern = "^Parameters.R$", #目录下，所有符合此正则表达式的文件
LineMatchKeyWords = c("SplitBy.levels.max","15"), # 用于寻找要被修改的行,一个关键词向量
LineMatch.ignore.case = FALSE, # 依据关键词寻找要被修改的行时,是否需要忽略行关键词的大小写,一个逻辑值
WordOld = "SplitBy.levels.max <- 15", # 要被替换的词,一个字符串
WordNew = "SplitBy.levels.max <- 50", # 新词,一个字符串
Replace = TRUE, # 是否要执行替换,一个逻辑值
SaveOld = TRUE) # 是否保存旧文件,一个逻辑值建议，执行批量替换之前，先使用参数Replace = FALSE看一下替换是否正确！
合并某列的重复值，对其他所有行进行字符串拼接[压缩行]。压缩时，使用“; ”进行分隔。
dt <- data.frame(genotype = c("X1", "X2", "X3", "X1", "X2", "X3", "X1", "X2", "X3"),
variable = c("A", "A", "A", "B", "B", "B", "C", "C", "C"),
value = c(1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L), stringsAsFactors = FALSE)
dt
#> genotype variable value
#> 1 X1 A 1
#> 2 X2 A 1
#> 3 X3 A 2
#> 4 X1 B 2
#> 5 X2 B 3
#> 6 X3 B 3
#> 7 X1 C 4
#> 8 X2 C 4
#> 9 X3 C 5Aggregate_df(df = dt, id = colnames(dt)[1])
#> genotype variable value
#> 1 X1 A; B; C 1; 2; 4
#> 2 X2 A; B; C 1; 3; 4
#> 3 X3 A; B; C 2; 3; 5对某列进行字符串切割，复制其他所有行[扩展行]。
Expand_df(df,
id, # 要被切割的列
splitby = "/") # 字符串切割时的分隔符基于数据库，对ID向量进行转换，返回更新后的向量，其中NA值或空字符串会返回NA值。另外，ID向量里的单个元素可以是多个ID的组合。
db <- data.frame(old = c("1","2","3"), new = c("A", "B", "C"))
update_IDs(old = c("2","2","1"), # 要被转换的id向量，元素可以是多个id名的组合，用| ；或其它符号隔开。
db = db, # 注释数据库，包含旧名和新名
from = "old", # 注释数据库中对应原来ID的列名
to = "new", # 注释数据库中新ID的列名
split = NULL, # 如果ID向量中的元素为多个id组合，需要指定分割符号，默认为NULL，即为单个ID，无需分割
fixed = TRUE) # 默认TRUE，被strsplit函数继承的参数
#> [1] "B" "B" "A"update_IDs(old = c("2;2","3","1"),
db = db,
from = "old",
to = "new",
split = ";",
fixed = TRUE)
#> [1] "B;B" "C" "A"常用对数据框里的某一列基因ID进行更新或转换。
基于至多3个关键词的数据框更新
依据数据库信息，和至多3种ID，更新数据。返回1个list，包含两个数据框，第一个matched：更新后的数据框，第二个lost，不能被识别的行。注意：该函数值更新了第一个关键词，即by.input列。另外，该函数在后两轮匹配时，会输出重复匹配的条目。
返回一个list，包含两个数据框，第一个matched：更新后的数据框，第二个lost，不能被识别的行.可能的问题：1. 如果两个ensembl id对应同一个HGNC，那么第二轮 用HGNC匹配时，可能仅能匹配到一个Ensebmbl id。同理第三轮匹配也是。
mapping_update(inputDF = tf.full.database,
db = gtf,
by.input = "Ensembl.ID", by.db = "Ensembl",
by.input.2 = "HGNC.symbol", by.db.2 = "Symbol",
by.input.3 = "EntrezGene.ID", by.db.3 = "EntrezID")demo_df1 <- data.frame(A = c("a", "b", "c", "d", "e", "f","g"),
B = c("A", "B", "C", "D", "E", "F", "G"),
C = c(11, 12, 13, 14, 15, 6, 7))
demo_df2 <- data.frame(a = c("a", "b", "c", "dd", "ee", "ff", "h"),
b = c("A", "BB", "C", "D", "E", "FF", "H"),
c = c(21, 22, 23, 24, 25, 6, 77))
mapping_update(inputDF = demo_df1,
db = demo_df2,
by.input = "A", by.db = "a",
by.input.2 = "B", by.db.2 = "b",
by.input.3 = "C", by.db.3 = "c")
#> 1. 依据input里的A列和数据库里的a列进行数据比对:
#> 4行未被对应上。
#> 2. 依据input里的B列和数据库里的b列对未匹配的数据再次进行比对:
#> 请手动检查以下替换是否正确！
#> 替换前：
#> A B C
#> 4 d D 14
#> 5 e E 15
#> 替换后：
#> A B C
#> 4 dd D 14
#> 5 ee E 15
#> 2行未被对应上。
#> 3. 依据input里的C列和数据库里的c列对未匹配的数据再次进行比对:
#> 请手动检查以下替换是否正确！
#> 替换前：
#> A B C
#> 6 f F 6
#> 替换后：
#> A B C
#> 6 ff F 6
#> 1行未被对应上。
#> $matched
#> A B C label
#> 1 a A 11 First
#> 2 b B 12 First
#> 3 c C 13 First
#> 4 dd D 14 Second
#> 5 ee E 15 Second
#> 6 ff F 6 Third
#>
#> $lost
#> A B C
#> 7 g G 7基于至多3对关键词，合并两个数据框
与mapping_update的区别，mapping_join不会修改原来的数据，只是在原来数据的基础上，添加新数据到新的列中。
返回1个list，包含两个数据框，第一个matched：合并后的数据框，第二个lost，不能被识别的行。
注意：该函数的第一个关键词，即by.input列，应为绝大多数能被匹配上的。关键词可以重复使用，每次的关键词对不同即可。另外，该函数在后两轮匹配时，会输出重复匹配的条目。
关键词匹配是有优先顺序的，第一次被匹配上了，后续就不会再去做匹配。
mapping_join(inputDF = tf.full.database,
db = gtf,
by.input = "Ensembl.ID", by.db = "Ensembl",
by.input.2 = "HGNC.symbol", by.db.2 = "Symbol",
by.input.3 = "EntrezGene.ID", by.db.3 = "EntrezID")mapping_join(inputDF = demo_df1,
db = demo_df2,
by.input = "A", by.db = "a",
by.input.2 = "B", by.db.2 = "b",
by.input.3 = "C", by.db.3 = "c")
#> 1. 依据input里的A列和数据库里的a列进行数据比对:
#> 4行未被对应上。
#> 2. 依据input里的B列和数据库里的b列对未匹配的数据再次进行比对:
#> 请手动检查以下替换是否正确！
#> 替换前：
#> A B C
#> 4 d D 14
#> 5 e E 15
#> 替换后：
#> A B C
#> 4 d D 14
#> 5 e E 15
#> 2行未被对应上。
#> 3. 依据input里的C列和数据库里的c列对未匹配的数据再次进行比对:
#> 请手动检查以下替换是否正确！
#> 替换前：
#> A B C
#> 6 f F 6
#> 替换后：
#> A B C
#> 6 f F 6
#> 1行未被对应上。
#> $matched
#> A B C a b c label
#> 1 a A 11 a A 21 First_A_a
#> 2 b B 12 b BB 22 First_A_a
#> 3 c C 13 c C 23 First_A_a
#> 4 d D 14 dd D 24 Second_A_a
#> 5 e E 15 ee E 25 Second_A_a
#> 6 f F 6 ff FF 6 Third_A_a
#>
#> $lost
#> A B C
#> 7 g G 7修正HGNC的Multi-symbol checker工具的输出结果
HGNC 提供的Multi-symbol checker在线工具可以将基因名更新到最新,但是会输出所有匹配的结果,也就是说单个input gene可能会有多个hits.此工具可保留主要匹配结果,去除可能的错误hits以及未必对上的input.
Check_hgnc_hits(hgnc.hits)对Seurat Object里的各个单细胞，分别统计top表达的基因，即基因的reads比例大于所设定的阈值expt.cut，并将结果汇总到一起。返回Top表达的基因，包括细胞数目、平均值和中位值信息。
top_genes(SeuratObj,
expr.cut = 0.01) # 针对UMI counts比例所设定的cut off，用于定义高表达的基因。library(SeuratExplorerServer)
#> Warning: replacing previous import 'R.utils::validate' by 'shiny::validate'
#> when loading 'SeuratExplorerServer'
#> Warning: replacing previous import 'R.utils::setProgress' by
#> 'shiny::setProgress' when loading 'SeuratExplorerServer'
#> Warning: replacing previous import 'R.utils::timestamp' by 'utils::timestamp'
#> when loading 'SeuratExplorerServer'rds.file <- paste(c(system.file(package ="SeuratExplorerServer"), "extdata/demo/fly/Rds-file/G101_PC20res04.rds"), collapse = "/")
cds <- readRDS(rds.file)
cds
#> An object of class Seurat
#> 9319 features across 1000 samples within 1 assay
#> Active assay: RNA (9319 features, 737 variable features)
#> 2 layers present: counts, data
#> 2 dimensional reductions calculated: pca, tsneFeaturePlot_Single(cds, feature = 'NPF', SplitBy = 'RandomGroup',remove_axes = FALSE)
#> 载入需要的程序包：ggplot2
#> 载入需要的程序包：SeuratObject
#> 载入需要的程序包：sp
#>
#> 载入程序包：'SeuratObject'
#> The following objects are masked from 'package:base':
#>
#> intersect, t
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
FeaturePlot_Single(cds, feature = 'NPF', SplitBy = 'RandomGroup',remove_axes = TRUE)
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
FeaturePlot_Single(cds, feature = 'NPF', SplitBy = 'RandomGroup',remove_axes = FALSE, PointColor = c("gray", "red"))
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
requireNamespace("dplyr")
requireNamespace("ggplot2")
df <- read.delim(system.file("extdata", "David_outputs_GO.txt", package = "ZhangRtools"), stringsAsFactors = FALSE)
David_barplot(df, fill.color = c("#ff9999","#ff0000"),x = "Fold.Enrichment", xlabel = "Fold Enrichment")
David_barplot(df, x = "Fold.Enrichment", xlabel = "Fold Enrichment", arrange.by.x = TRUE)
df %>% dplyr::mutate(fdr = -log(FDR, base=10)) %>% David_barplot(x = "fdr", xlabel = "-log(10)FDR")
kegg.res <- read.delim(system.file("extdata", "David_outputs_KEGG.txt",package = "ZhangRtools"), stringsAsFactors = FALSE)
David_barplot(df = kegg.res, fill.color = c("#ff9999","#ff0000"),x = "Fold.Enrichment", xlabel = "Fold Enrichment")
requireNamespace("dplyr")
requireNamespace("ggplot2")
df <- read.delim(system.file("extdata", "David_outputs_KEGG.txt", package = "ZhangRtools"), stringsAsFactors = FALSE)
David_dotplot(df)
David_dotplot(df, arrange.by.x = TRUE)
df <- read.csv(system.file("extdata", "volcano-plot-data.csv", package = "ZhangRtools"), stringsAsFactors = FALSE)
plot.data <- data.frame(gene = df$Symbol,
pval = -log10(df$FDR),
lfc = df$logFC)
Volcano_plot_1(plot.data = plot.data, markers = c("cv-c","pbl","msi","shn","E(spl)mbeta-HLH","E(spl)m3-HLH","E(spl)m7-HLH","puc","Socs36E","geminin","Blm"))
#> Warning in transformation$transform(x): 产生了NaNs
#> Warning in scale_y_continuous(trans = "log1p"): log1p transformation introduced
#> infinite values.
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_hline()`).
sessionInfo()
#> R version 4.4.1 (2024-06-14 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows 11 x64 (build 22631)
#>
#> Matrix products: default
#>
#>
#> locale:
#> [1] LC_COLLATE=Chinese (Simplified)_China.utf8
#> [2] LC_CTYPE=Chinese (Simplified)_China.utf8
#> [3] LC_MONETARY=Chinese (Simplified)_China.utf8
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=Chinese (Simplified)_China.utf8
#>
#> time zone: Asia/Shanghai
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] ggeasy_0.1.5 Seurat_5.1.0
#> [3] SeuratObject_5.0.2 sp_2.1-4
#> [5] ggpubr_0.6.0 ggplot2_3.5.1
#> [7] SeuratExplorerServer_0.0.1.0002 ZhangRtools_0.0.0.9000
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3 rstudioapi_0.16.0
#> [3] jsonlite_1.8.8 billboarder_0.4.1
#> [5] magrittr_2.0.3 spatstat.utils_3.0-5
#> [7] farver_2.1.2 rmarkdown_2.27
#> [9] vctrs_0.6.5 ROCR_1.0-11
#> [11] memoise_2.0.1 spatstat.explore_3.2-7
#> [13] askpass_1.2.0 rstatix_0.7.2
#> [15] htmltools_0.5.8.1 broom_1.0.6
#> [17] sass_0.4.9 sctransform_0.4.1
#> [19] parallelly_1.37.1 KernSmooth_2.23-24
#> [21] bslib_0.7.0 htmlwidgets_1.6.4
#> [23] ica_1.0-3 plyr_1.8.9
#> [25] plotly_4.10.4 zoo_1.8-12
#> [27] cachem_1.1.0 igraph_2.0.3
#> [29] mime_0.12 lifecycle_1.0.4
#> [31] pkgconfig_2.0.3 Matrix_1.7-0
#> [33] R6_2.5.1 fastmap_1.2.0
#> [35] fitdistrplus_1.1-11 future_1.33.2
#> [37] shiny_1.8.1.1 digest_0.6.36
#> [39] colorspace_2.1-0 patchwork_1.2.0
#> [41] tensor_1.5 RSpectra_0.16-1
#> [43] irlba_2.3.5.1 RSQLite_2.3.7
#> [45] labeling_0.4.3 progressr_0.14.0
#> [47] fansi_1.0.6 spatstat.sparse_3.1-0
#> [49] httr_1.4.7 polyclip_1.10-6
#> [51] abind_1.4-5 compiler_4.4.1
#> [53] withr_3.0.0 bit64_4.0.5
#> [55] backports_1.5.0 carData_3.0-5
#> [57] DBI_1.2.3 fastDummies_1.7.3
#> [59] highr_0.11 R.utils_2.12.3
#> [61] ggsignif_0.6.4 MASS_7.3-60.2
#> [63] openssl_2.2.0 tools_4.4.1
#> [65] lmtest_0.9-40 httpuv_1.6.15
#> [67] future.apply_1.11.2 goftest_1.2-3
#> [69] R.oo_1.26.0 glue_1.7.0
#> [71] nlme_3.1-164 promises_1.3.0
#> [73] grid_4.4.1 Rtsne_0.17
#> [75] cluster_2.1.6 reshape2_1.4.4
#> [77] generics_0.1.3 gtable_0.3.5
#> [79] spatstat.data_3.1-2 R.methodsS3_1.8.2
#> [81] tidyr_1.3.1 data.table_1.15.4
#> [83] car_3.1-2 utf8_1.2.4
#> [85] spatstat.geom_3.2-9 RcppAnnoy_0.0.22
#> [87] ggrepel_0.9.5 shinymanager_1.0.410
#> [89] RANN_2.6.1 pillar_1.9.0
#> [91] stringr_1.5.1 spam_2.10-0
#> [93] RcppHNSW_0.6.0 later_1.3.2
#> [95] splines_4.4.1 dplyr_1.1.4
#> [97] lattice_0.22-6 bit_4.0.5
#> [99] survival_3.6-4 deldir_2.0-4
#> [101] tidyselect_1.2.1 miniUI_0.1.1.1
#> [103] pbapply_1.7-2 knitr_1.47
#> [105] gridExtra_2.3 scattermore_1.2
#> [107] xfun_0.45 shinydashboard_0.7.2
#> [109] matrixStats_1.3.0 DT_0.33
#> [111] stringi_1.8.4 scrypt_0.1.6
#> [113] lazyeval_0.2.2 yaml_2.3.8
#> [115] shinyWidgets_0.8.6 evaluate_0.24.0
#> [117] codetools_0.2-20 tibble_3.2.1
#> [119] cli_3.6.3 uwot_0.2.2
#> [121] xtable_1.8-4 reticulate_1.38.0
#> [123] munsell_0.5.1 jquerylib_0.1.4
#> [125] Rcpp_1.0.12 SeuratExplorer_0.0.6.0000
#> [127] globals_0.16.3 spatstat.random_3.2-3
#> [129] png_0.1-8 parallel_4.4.1
#> [131] blob_1.2.4 dotCall64_1.1-1
#> [133] listenv_0.9.1 viridisLite_0.4.2
#> [135] scales_1.3.0 ggridges_0.5.6
#> [137] leiden_0.4.3.1 purrr_1.0.2
#> [139] rlang_1.1.4 cowplot_1.1.3