sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
de10e48b85f1df6ec154dd5cadcda9b63bd02a9e0cd4b438ea313393ac950711 | R | 496 | 25 | # ====感染確認情報====
output$news <- renderDataTable({
newsData <-
data.table(
paste0(
"<small>",
as.POSIXct(as.character(news$date), format = "%Y%m%d"),
"</small><br><a href='",
news$link,
"'>",
news$title,
"</a>"
)
)
datatable(
newsData[or... |
b9eb86bcbc38615a95feca732409fdad8ebc4acdd2c6e30be754b428f77d3319 | R | 501 | 41 | # http://enhanceratlas.org/
# ÐèÒªintergenicµÄbedÎļþ
rm(list = ls())
library(tidyverse)
intergenic <- read.table(file = "02-analysis/16-New_classification/03-UCR_intergenic.bed",
sep = "\t", header = FALSE)
intergenic$V1 <- paste("chr", intergenic$V1, sep = "")
write.table(intergenic, file... |
30f26556df1c50115daa3718ad6a1b590cf320f11e694e5ebbb89e07f7cda008 | R | 508 | 20 | # load packages
require(tidyverse)
require(Seurat)
# load Seurat objects (list)
sdata.list <- readRDS('sdata_mmCortex_snRNA-seq_list.rds')
# filter
sdata.list.filtered <- lapply(sdata.list, \(obj) {
# define percent_mito filter
mt.cutoff <- 1
# filter cells
obj <- obj %>%
subset(percent_mito <= mt.cutoff... |
a42610103eb509e770a1373a388e8c26eb06569e10aab6f3ad7f8c865b869d76 | R | 509 | 17 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly = TRUE)
library(tidyverse)
library(stringr)
te_embl_dir <- args[1]
te_misc_dir <- args[2]
te_meta_dir <- args[3]
te_misc <- read_csv(te_misc_dir, col_names = c("te_name", "type", "subtype"))
te_embl <- read_csv(te_embl_dir, col_names = c("embl_id", "fb_id", "sour... |
8a1a453bb30f04afc74e32b4cb9c3519c161855415fefea3c5029177ac213e48 | R | 510 | 15 | library(findPC)
find_PC <- function(sce, scale, nPC = c(10, 20, 30, 40, 50)){
#data(procdata) #读一个rowname是基因，colname是样本的表达矩阵
procdata <- as.matrix(sce@assays$RNA@counts)
sdev<-prcomp(t(procdata),scale. = scale)$sdev[1:50]
head(sdev)
p <- findPC(sdev = sdev, #需要降序排列
number = nPC, #输入PC范围，默认为1:... |
658c34d732ef29d1de14155727c7bb1f106d833ce7bdb24e098594b46e8ed7b0 | R | 519 | 21 | eaf2maf <- function(eaf = NULL) {
if (any(is.infinite(eaf))) {
warning("The 'eaf' vector contains infinite values. These will be turned into NAs.")
is.na(eaf) <- is.infinite(eaf)
}
if (is.null(eaf)) {
stop("No 'eaf' vector provided.")
}
if (is.character(eaf)) {
stop("'eaf' must be a numeric ve... |
e027327a0ae143332d9ef473e5fec61cf351777ad4c804cf7d4d20e1ba0d6e6e | R | 520 | 19 |
## 5. WGCNA - Combine pathway analyses and annotate with module name ##
modules <- c("turquoise", "blue", "brown", "yellow", "green", "red", "All")
df <- data.frame()
for(m in modules){
f <- paste0(outputPath,"missMethylPathwayAnalysis_nonlinear_fetalBrain_",m,".csv")
if(file.exists(f)){
res <- read.csv(f, row.... |
e27caf4179d8edb236d1d66eb1c0da5e5fcd5b4805b76d26c04e18571d0491eb | R | 528 | 20 | args = commandArgs(TRUE)
datafile = args[1]
species = args[2]
genNum = as.numeric(args[3])
k = as.numeric(args[4])
data = read.table(datafile)
#rep = scan("samples.txt")
#d = log(log(c(data[data$V2==species,3],sample(rep,genNum-length(data[data$V2==species,1]),replace=TRUE))))
#d = log(log(data[data$V2==species,3]))
... |
898f6bcd874dd8dd734e76ac9fe523a018cf646f0cbb7c9e78ef7872327d2b68 | R | 537 | 15 | data = read.csv("tests/data/test01.csv")
factor_range = 1:15
statistic = rep(0, length(factor_range))
dof = rep(0, length(factor_range))
pvalue = rep(0, length(factor_range))
for (i in 1:length(factor_range)) {
res = factanal(data, factors=factor_range[i], rotation="none")
statistic[i] = res$STATISTIC
dof[i] = r... |
620ed9e7335d72cb5a9231aabb8a5bcbbc80fb396148be8f3e7813a875429e07 | R | 540 | 19 | # plot BIN1 peak graph
plot_BIN1(chr = "chr2", start = 127100000, end = 127110000,
ylimit = 1200)
plot_BIN1(chr = "chr2", start = 127106846, end = 127107846,
ylimit = 1200)
plot_BIN1(chr = "chr16", start = 30936618, end = 31004664,
ylimit = 2000) # SETD1A 20k
plot_CLU(chr = "chr8", sta... |
0b663be1d11b4291112118b95f156373f1e4b2dda83554ae50b2cc572ad985c1 | R | 554 | 23 |
library("SummarizedExperiment")
library("variancePartition")
library("scater")
library("ggplot2")
library("here")
library("sessioninfo")
#### Set up ####
## dirs
plot_dir <- here("plots", "09_bulk_DE", "01_bulk_data_exploration")
if(!dir.exists(plot_dir)) dir.create(plot_dir, recursive = TRUE)
## load colors
load(h... |
591ac3f9187f01e47a7d30859a776d5393b40eb1a81234bed50b8681bcd7ce6f | R | 560 | 11 |
perf_data = read.delim("mechanical_perfusion_donor_data.tsv", sep = "\t")
c(perf_data$Histology...L.FCTX, perf_data$Histology...R.FCTX, na.rm = TRUE)
perfusion_cases_frontal = c(perf_data$Histology...L.FCTX, perf_data$Histology...R.FCTX)
perfusion_cases_frontal = perfusion_cases_frontal[!is.na(perfusion_cases_fronta... |
f1f0e5c1248ef54f814a309753b2bf19e02c968f785d8a7ea4a43aae385fac2a | R | 570 | 20 | # »ñµÃrandom fragmentsµÄbedÎļþ£¬È»ºóÈ¥linuxÓÃbedtools»ñµÃÏà¹Ø»ùÒò
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
rf <- read.table(file = "01-data/UCR_raw/random_fragments_location.txt",
sep = "\t", header = TRUE)
rf <- rf[, c(5, 2, 3, 6)]
rf <- rf[!rf$rf_name %in%... |
a64894e763fb195e65795044d5cad5afadc24d26f55e5f016c726ca901f2ac96 | R | 574 | 20 | library(tidyverse)
df <- read_tsv("processed/2023-10-18_i3_cryptic_genes_riboseq_deseq_summary.tsv")
# get all diff translated ALEs
df_sig <- df %>% filter(diff_translated)
df_sig_ale <- df_sig %>%
filter(simple_event_type == "spliced") %>%
arrange(desc(abs(log2FoldChangeShrink)))
# remove TRAPPC12 as know ove... |
f86cf8cb61846987b87260e22416300d906d4c43790bb49739464d0b03bc00b1 | R | 580 | 12 | library(here)
### Palette taken from `scater`
tableau10medium = c("#729ECE", "#FF9E4A", "#67BF5C", "#ED665D",
"#AD8BC9", "#A8786E", "#ED97CA", "#A2A2A2",
"#CDCC5D", "#6DCCDA")
tableau20 = c("#1F77B4", "#AEC7E8", "#FF7F0E", "#FFBB78", "#2CA02C",
"#98DF8A", "#D62728"... |
bad506dfcb4441ac500036d8f53fe5b1b7237e0d10d1a4be4cea36436ed7edfa | R | 581 | 17 |
library(here)
markers.mathys.custom = list(
'neurons' = c('SYT1', 'SNAP25', 'GRIN1'),
'excitatory_neuron' = c('CAMK2A', 'NRGN','SLC17A7', 'SLC17A6', 'SLC17A8'),
'inhibitory_neuron' = c('GAD1', 'GAD2', 'SLC32A1'),
'oligodendrocyte' = c('MBP', 'MOBP', 'PLP1'),
'oligodendrocyte_precursor' = c('PDGFRA', 'VCAN',... |
2943643d773d8b084bd546ea7191e7d40df9ca77a26f4e65b4f08cd0058038fa | R | 590 | 23 | #.libPaths( c(.libPaths(), "~/R_libs/"))
suppressMessages(library(BandNorm))
args <- commandArgs(trailingOnly = TRUE)
interaction_dir <- as.character(args[1])
genes <- as.character(args[2])
out_file <- as.character(args[3])
depth_norm <- as.logical(args[4])
geneObj = hg19Annotations
if ( genes == "mm10"... |
eec1e6d79449ece9f6f843054a155bd16622b5491dd795c24aadad4c75d2070f | R | 593 | 23 | # load packages
require(monocle3)
require(tidyverse)
require(tictoc)
# load CDS files
message('loading CDS file')
cds_E16_filtered <- readRDS('cds_filtered/cds_filtered_E16.rds')
# run monocle::fit_models
tic('E16.0'); message('run monocle::fit_models')
cds_E16.fits <- cds_E16_filtered %>%
lapply(fit_models, model_... |
470c0cebc6cf6cf6fa9a3e5d1d1cc2458b5b0298e3efe6ef77a7d9d37170c79b | R | 594 | 27 | # Siwei 13 Sept 2023
# CellNet pre-configure file
# install packages ####
install.packages("devtools")
library(devtools)
install_github("pcahan1/CellNet",
ref = "master")
# ERROR: dependencies ‘GO.db’, ‘org.Hs.eg.db’
# are not available for package ‘cancerCellNet’
install_github("pcahan1/cancerCellNet@v... |
cc23d16be33dce0c0392b2f0ddc358db6b4d02c570eebf09088516c2850c4c55 | R | 594 | 15 | library(ggplot2)
hs_pp_276 <- read.csv("tables/hs_pp_276_c1.csv")
hs_pp_276_teFamily <- data.frame(t(table(hs_pp_276[,c(10, 11)])))
colnames(hs_pp_276_teFamily) <- c("TE_family", "link", "Count")
g_hs_pp_276 <- ggplot(hs_pp_276_teFamily, aes(x=TE_family, y=Count, fill=link)) +
geom_bar(stat="identity") +
sca... |
034debb7e358ad074e16f4df5a54ea5c14b897ab778ec9f2dcf06fe4b1bcdadb | R | 596 | 13 | require("ggplot2")
threshold <- function(y,e=0.001,epsilon=10^-3) {x=y+epsilon;qlnorm(p=1-e, sdlog = sd(log(x)), meanlog = mean(log(x)))}
plotdots = function(d, name=paste("figure",date(),"pdf",sep=".") ) {qplot(1:length(d$V1),d$V1,geom=c("point","line"))+geom_hline(yintercept=threshold(d$V1,e=0.01),color="red",linet... |
8632fc5f457efa38fbea0818723544c87fbd02b9fb31df3beb5b15673f0723de | R | 600 | 23 | # load packages
require(monocle3)
require(tidyverse)
require(tictoc)
# load CDS files
message('loading CDS file')
cds_P25_filtered <- readRDS('cds_filtered/cds_filtered_P25.rds')
# run monocle::fit_models
tic('P25'); message('run monocle::fit_models')
cds_P25.fits <- cds_P25_filtered %>%
lapply(fit_models, model_fo... |
376f7d02c163c69d6e15975ff0c4adf3c1e0505ff7bdb8853a7a98c3a70ff99a | R | 601 | 23 | # load packages
require(monocle3)
require(tidyverse)
require(tictoc)
# load CDS files
message('loading CDS file')
cds_E14_filtered <- readRDS('cds_filtered/cds_filtered_E14.rds')
# run monocle::fit_models
tic('E14.5'); message('run monocle::fit_models')
cds_E14.fits <- cds_E14_filtered %>%
lapply(fit_models, model_... |
92fc5ed45a2fcb7c4f67f93c5eaae81f5d54196c651e4282fef0b64161bfde3f | R | 601 | 23 | # load packages
require(monocle3)
require(tidyverse)
require(tictoc)
# load CDS files
message('loading CDS file')
cds_E12_filtered <- readRDS('cds_filtered/cds_filtered_E12.rds')
# run monocle::fit_models
tic('E12.5'); message('run monocle::fit_models')
cds_E12.fits <- cds_E12_filtered %>%
lapply(fit_models, model_... |
3d4ab1517558bffa3a71379c76f3103de84db9ee45dfee89d1d62920c7d68331 | R | 602 | 23 | # load packages
require(monocle3)
require(tidyverse)
require(tictoc)
# load CDS files
message('loading CDS file')
cds_E17_filtered <- readRDS('cds_filtered/cds_filtered_E17.rds')
# run monocle::fit_models
tic('E17.5'); message('run monocle::fit_models')
cds_E17.fits <- cds_E17_filtered %>%
lapply(fit_models, model... |
e55691f8576e148ebb70dd2da1827e95f015457644fb07988e28daf82ec77ec3 | R | 605 | 22 | library(psych)
library(nFactors)
scriptsDir <- '/projects/f_mc1689_1/ReliableFC/docs/scripts'
indivDiffs <- read.csv(paste(scriptsDir,'/indivDiffs.csv',sep=""))
row.names(indivDiffs) <- indivDiffs[, 1]
indivDiffs <- indivDiffs[, -1]
nSubjs <- nrow(indivDiffs)
faOutput <- fa(indivDiffs, nfactors=1, covar=FALSE, fm="m... |
ed0e05c234767df285998fc22f2b51b5f8183afa6c686c901403b7185cca97f1 | R | 609 | 20 | Component.ComfirmedPyramid <- function() {
box(
title = tagList(icon("venus-mars"), i18n$t("歳代・性別")),
width = 4,
enable_label = T,
collapsible = T,
collapsed = T,
label = boxLabel("Archived", status = "danger"),
uiOutput("ageGenderOption"),
echarts4rOutput("genderBar"),
closable = ... |
442b8a77823c7a1af585c6198f6efe25e076d1f141be58607b7a4dbe86e8bab6 | R | 615 | 18 | library(tidyverse)
#####
bind_rows(dorothea_all = ferguson_ulm_dorothea_elks,
dorothea_nospl = ferguson_nospl_ulm_dorothea_elks,
collectri_all = ferguson_ulm_collectri_elks,
collectri_nospl = ferguson_nospl_ulm_collectri_elks,
.id = "targets_source") %>%
filter(source == "EL... |
6ef2e9a342d4273a3ebd56446a3011a228aa9b4a9f0a63867e78b55bd41e720a | R | 617 | 13 | # cran packages
install.packages(c("rmarkdown", "markdown",
"shiny", "shinyjs", "bs4Dash", "shinyWidgets", "shinybrowser", "shinyvalidate",
"ggplot2", "gridExtra", "plotly", "heatmaply", "ggpubr", "ggupset", "ellipse", "waiter",
"stringr",
"rea... |
dd3bfd71eed7190e72021858f2a3b0bbdab615f2580c6e91c7659853bcc9ed2e | R | 619 | 24 | library(dendextend)
library(dplyr)
options(stringsAsFactors = F)
source("dend_functions.R")
# Load dendrogram
dataset_dir <- '/data/rnaseqanalysis/shiny/facs_seq/Mm_VISp_14236_20180912'
dend <- readRDS(file.path(dataset_dir,"dend.RData"))
# Add parent attribute
dend2 <- add_parent_attr(dend)
# Retrieve node table... |
985457bfb06018a9a3b8c88d6775b0c6d42844585e58d9830340899ac1048ec2 | R | 623 | 28 | tabPanel(
title = i18n$t("回復者数の推移"),
icon = icon("user-shield"),
value = "discharged",
fluidRow(
column(
width = 8,
tags$br(),
fluidRow(
column(
width = 6
)
),
echarts4rOutput("recoveredLine") %>% withSpinner()
),
column(
width = 4,
... |
98449cf48cf60a11637a984962e0a866862f438a784bd39bc0e17627d3a0ad0d | R | 625 | 16 | library(decoupleR)
library(progeny)
library(readr)
progeny_full <- progeny::model_human_full
# decoupleR vignette uses top 500
progeny_top500 <- get_progeny(organism = 'human', top = 500)
# progeny vignette recommends top 100 for each pathway
progeny_top100 <- get_progeny(organism = 'human', top = 100)
if (!dir.exis... |
a568e7485ad8013d88c305a29f353ee6979f3395fdbdeb46863d8c3555e059fd | R | 626 | 13 |
## Extract non variable probes from fetal brain DNAm data ##
source(paste0(scriptsPath, "1_2_extractNonVarProbes.r"))
# bulk fetal
extractNonVarProbes(path.betas=paste0(PathToBetas,"fetalBulk_EX3_23pcw_n91.rdat"), path.output=outputPath, name.output="fetalBulk_EX3_23pcw_n91.nonVarprobes.rds")
# fetal FANS
extract... |
26551e22c54e3d279473884209d207bee690a597c9c2a9a8e4fa22d33947521e | R | 643 | 19 | impute.matrix <- function(x, FUN=function(x) rowMeans(x, na.rm=T)) {
##idx <- which(is.na(x), arr.ind=T)
## the line above causes a 'long vectors not supported yet' error
## when x contains more elements the .Machine$integer.max
## the following 5 lines allows x to be much larger without generating this... |
224843f39d3b3af6ede0135801bc208689c4193902a05b8165b13aafed43f28e | R | 647 | 28 | #!/usr/bin/env Rscript
args <- base::commandArgs(trailingOnly=TRUE)
nomenclature <- read.table(
file = args[1],
header = T,
sep = "\t",
stringsAsFactors = F
)
library(flybaseR)
nomenclature_converted <- id.converter(x = nomenclature[[args[2]]], symbols = TRUE, convert.into = "g")
if(length(x = nomenclat... |
b07454a59c8166f0a4ad7b24dd318f14f88deda4e64d7556c70e1c59d14ecf9f | R | 649 | 31 | CRAN_PACKAGES <- c(
"tidyverse",
"tgutil",
"tgstat",
"glue",
"here",
"purrr",
"Matrix",
"patchwork",
"princurve"
)
# install packages if not already installed
if (length(setdiff(CRAN_PACKAGES, rownames(installed.packages()))) > 0) {
install.packages(setdiff(CRAN_PACKAGES, rownames(installed.pack... |
4c23ed4406ab4210dbcdb8892716d7af979fcbeea2b1bbb8b5a24ff4b56b4f5f | R | 650 | 42 | # https://home.kaessmannlab.org/resources#apps
# ¸ù¾ÝÊý¾Ý¿òµÄÊý¾Ý×öһЩ·¢ÓýÏà¹Ø·ÖÎö
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(dbplyr)
library(readxl)
DDG_lncRNA <- read_xlsx(path = "01-data/18-Development/development_dynamic_lncRNA.xlsx",
sheet = 1)
DDG_lncRNA <- DDG_l... |
e735f82305cffaa3d99c66f87ac6a8ae569d2ad065d1cc8e85ab603819d95882 | R | 651 | 20 | #' Package notice
#'
#' Prints a feedback notice once per session.
#'
#' @return Invisible NULL.
#' @keywords internal
.ukbrapr_startup_notice <- function() {
# Feedback notice once per session
if (!isTRUE(getOption("ukbrapR.feedback_notice_shown", FALSE))) {
options(ukbrapR.feedback_notice_shown = TRUE)
c... |
1af77b64f099407115c83b92def0fd8370af1b83cf7046dd9570337a2cda119a | R | 662 | 30 | setwd("osmFISH_AllenVISp/")
library(Seurat)
osmFISH <- readRDS("data/seurat_objects/osmFISH_Cortex.rds")
allen <- readRDS("data/seurat_objects/allen_brain.rds")
#Project on allen labels
i2 <- FindTransferAnchors(
reference = allen,
query = osmFISH,
features = rownames(osmFISH),
reduction = 'cca',
... |
d4e48c8f2d8327d5eec4b30bd1111a01b8489a569faf1d83a8c4327e7cc24bae | R | 663 | 30 | setwd("osmFISH_Ziesel/")
library(Seurat)
osmFISH <- readRDS("data/seurat_objects/osmFISH_Cortex.rds")
Zeisel <- readRDS("data/seurat_objects/Zeisel_SMSC.rds")
#Project on Zeisel labels
i2 <- FindTransferAnchors(
reference = Zeisel,
query = osmFISH,
features = rownames(osmFISH),
reduction = 'cca',
... |
0b4fc7dbd23f804f2630e58fe0fe1bd240b4a9043137fe37924f912847ad5006 | R | 664 | 24 | # Siwei 09 Jun 2022
# prepare data and plot for MAGMA analysis
# init
# load data
library(readr)
library(ggplot2)
library(RColorBrewer)
Ast_MG_RGlut_magma_Gene_list <-
read_delim("Ast_MG_RGlut_magma_Gene_list.txt",
delim = "\t", escape_double = FALSE,
col_names = FALSE, trim_ws = TRUE... |
bc987760198988257b12601d8455c4af2f62d7c5f99ce425abb1e242c1c5c720 | R | 665 | 30 | setwd("osmFISH_AllenSSp/")
library(Seurat)
osmFISH <- readRDS("data/seurat_objects/osmFISH_Cortex.rds")
allen <- readRDS("data/seurat_objects/allen_brain_SSp.rds")
#Project on allen labels
i2 <- FindTransferAnchors(
reference = allen,
query = osmFISH,
features = rownames(osmFISH),
reduction = 'cca'... |
380229652bc8fdf83bbdb69ba324581f4c16ea413d2018bdedda37f151e0b1c4 | R | 678 | 23 | options(stringsAsFactors = FALSE)
library(ggplot2)
library(reshape2)
library(dplyr)
library(stringr)
library(lme4)
library(lmerTest)
library(RColorBrewer)
library(ggpubr)
library(MutationalPatterns)
#### COSMIC decomposition of META-CS Indel signatures ####
# load COSMIC signatures
cosmic_signatures <- as.matrix(read... |
ff91512a3edcff8efeb4a226eca396bd2b7719d68662d2f10a7a448182e091d6 | R | 678 | 15 | library(quantreg)
library(strucchange)
macrodata <- read.csv('/Users/fulton/projects/statsmodels/statsmodels/datasets/macrodata/macrodata.csv')
y <- macrodata$cpi
X <- cbind(rep(1, nrow(macrodata)), macrodata$m1)
rec_beta <- lm.fit.recursive(X, y, int=FALSE)
rec_resid <- recresid(cpi ~ 1 + m1, data=macrodata)
cusum <... |
6a3db44289cadea47081799c87bb1a50f9d0632377e799771aca34dce0b50cdf | R | 679 | 20 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly = TRUE)
library(tidyverse)
library(stringr)
# blast_dir <- "/Users/shunhua/work/S2/te_library_0521/embl_repbase_best.tsv"
# te_table_dir <- "/Users/shunhua/git/transposons/misc/te_id_table_new.csv"
# out_dir="/Users/shunhua/git/transposons/misc/embl_repbase_match.... |
aa2de11df9ed662e60e3a17b96019a01b6ea843c920830f7b07e94c2f414c1ec | R | 689 | 21 | dta <- read.csv("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/datasets/macrodata/macrodata.csv")
cpi <- dta$cpi
/* this automatically suppresses the constant */
mod111 <- arima(cpi, order=c(1,1,1), method="CSS")
/*you can use xreg=1:length(cpi)*/
dcpi <- diff(cpi)
mod111 <- arima(dcpi, order=c(1,0,1), ... |
b15094415382ff42a15993bd2e0c7a7758225c621f034b0e96a69fe9974752f1 | R | 697 | 18 | library(readr)
library(dplyr)
library(stringr)
variants <-
read_tsv(file = "sample_minqiao_SCZGWAS.zscore",
col_names = F,
col_types = 'ccd')
colnames(variants) = c('variant', 'ld', 'zscore')
var_info <- str_split(variants$variant, ":")
variants <- mutate(variants, chr = paste0("chr", sapply(v... |
7dd36171a8bcf0cf619753dca46a4e0c968613dc0a676ca2a8bc95e175a5c785 | R | 699 | 21 | ---
title: "License"
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
What license are you using for your code? See [choosealicense.com][choose] for
help deciding. It's a convention to save a file `LICENSE` in the root of your
Git repo that contains the license text.
Wh... |
16a94d313dd361ab37d10eeeb5e1f681d225c9819f6d18f913aef63cc9a610a3 | R | 703 | 29 | #!/usr/bin/env Rscript
# Get command line arguments
args <- commandArgs(trailingOnly = TRUE)
# Require at least one package argument
if (length(args) == 0) {
cat("Error: No packages specified.\n")
cat("Usage: Rscript install_r_packages.R package1 [package2 ...]\n")
quit(status = 1)
}
# Set CRAN mirror
options(... |
36e6b05902034f29c01df0bd2d063be66492f1c28ba3d7ea422dd733f27867d6 | R | 704 | 28 | context("Comprehensive Test for Prejudice Remover Algorithm")
test_that("running prejudice remover test", {
dd <- aif360::binary_label_dataset(
data_path = system.file("extdata", "data.csv", package="aif360"),
favor_label=0,
unfavor_label=1,
unprivileged_protected_attribute=0,
privileged_protect... |
8a2ec2c6e97927df5134dcbf342744f2acde94b57c01878a90dd798b69eb0767 | R | 708 | 22 | # load packages
require(Seurat)
# load Seurat objects (list)
sdata.list <- readRDS('sdata_snRNA-seq_filtered_list.rds')
# split by batch
sdata.batch <- merge(sdata.list[[1]], sdata.list[2:length(sdata.list)])
sdata.batch <- SplitObject(sdata.batch, split.by = 'batch')
# SCTransform
# Note: SCTransform replaces Norma... |
612921f7956c1eadbfbcf822e2281e12424f8bca811ba8fd5adc3b6b8c919fea | R | 710 | 31 | # run HOMER enrichment of iMG/non-iMG SNPs for Jubao, size +/-250
# Siwei 24 Apr 2024
# init #####
library(readxl)
# load iMG SNPs #####
df_raw <-
read_excel("AD_ASoC_iMG_for_HOMER_TF_motif_enrichment.xlsx",
sheet = 1)
df_writeout <-
df_raw
write.table(df_writeout,
file = "iMG_SNPs_24Ap... |
05267fba8a88b8a7f1804d39cd983647412346231f5ba431f235e6058938a343 | R | 712 | 23 | # Description: In this script we will annotate the SNPs in the variants file with the dbSNP database
library(tidyverse)
args <- commandArgs(trailingOnly = TRUE)
var_filename <- args[1]
dbsnp_filename <- args[2]
dbsnp <- as_tibble(readLines(dbsnp_filename)) %>%
mutate(SNP = "SNP")
variants <- read.table(var_filena... |
c54e9fec52d2e329b62e221666ae72bdde84e3ea20b816103d83b00b0cdd7cc1 | R | 712 | 31 | #' Adult Census Income Dataset
#' @export
adult_dataset <- function(){
return (datasets$AdultDataset())
}
#' Bank Dataset
#' @export
bank_dataset <- function(){
return (datasets$BankDataset())
}
#' Compas Dataset
#' @export
compas_dataset <- function(){
return (datasets$CompasDataset())
}
#' German Dataset
#' ... |
1a7187f3be4f34702bf77a43dbbccf6e47ef426cba46e541ea08dd5b6fab06d5 | R | 715 | 17 | ### for MAGMA with LIBD 10x pilot analyses
# UPDATE: re-running with new v1.08
# MNT 09:10Sep2020 ======================
sumStats.ptsd <- read.table("/dcl02/lieber/ptsd/GWAS/ncbi-23818/files/release/submission/sub20190709/MVP.TE.REX.txt.gz",
header=T)
class(sumStats.ptsd)
dim(sumStats.ptsd)... |
bc6a416b154f06cb3c9357d52f938caf57bd6c221e633278c9262d38cf0649f7 | R | 721 | 21 | library(tidyverse)
library(plyranges)
# Quick script - read in quantification GTF, collapse to unique intervals for every le_id and output to file
papa_quant_gtf <- read_gff2("data/novel_ref_combined.quant.last_exons.gtf")
outdir <- "processed"
# summarise coordinates to single string
le_id_coords <- papa_quant_gtf... |
86105c227504875783b8e90b97381ebf680be1c7f9e2d98e8b01662381c9128d | R | 729 | 30 | options(tidyverse.quiet = TRUE)
suppressPackageStartupMessages({
library(misha)
library(misha.ext)
library(prego)
library(iceqream)
library(mcATAC)
library(tgutil)
library(tgstat)
library(tidyverse)
library(purrr)
library(glue)
library(patchwork)
library(here)
library(Matrix)
})
options(gmax.... |
fa61562fd10f90dceb0231e1185d4cfd6dc78a50a0386cf6a09115b90b572bbd | R | 736 | 30 | library(openxlsx)
# Set the directory path
directory_path <- "gProfiler_analysis/gProfiler_APA/new"
for (file in file_names) {
data <- read.table(file, sep = "\t", header = TRUE)
sheet_name <- create_sheet_name(file)
addWorksheet(wb, sheet_name)
writeData(wb, sheet = sheet_name, x = data)
}
wb <- createWorkbook()
f... |
1cb399be3c9ccd2b255e9e2b2806a87117c4094f6fb5db74280f7b257380ab0a | R | 740 | 22 | library("tidyverse")
library("sessioninfo")
library("here")
## Set up plotting
#### Compile data ####
deconvo_data_paths <- list(
BipSeq = "/dcl01/lieber/ajaffe/lab/zandiHyde_bipolar_rnaseq/deconvolution/est_prop_Bisque.Rdata",
BSP3 = "/dcl01/lieber/RNAseq/Datasets/BrainSeq_hg38/Phase3/caudate_BS3P/est_prop.... |
ba6084c0f812fe4081292718409c5820132ba388041579c4f97391dcc7500f63 | R | 741 | 18 | # Imputation using SAVER ####
library(SAVER)
library(doParallel)
registerDoParallel(cores = 5)
sc <- read.csv("../data/francesconi/francesconi_withDropout.csv", row.names = 1)
sav <- saver(as.matrix(sc), parallel = TRUE)
sav <- sav$estimate
write.csv(sav, file = "../data/francesconi/francesconi_saver.csv", quote... |
9af83f0b1ea83c981ebf05df766488c0a5b5856fc30e409a5273669f627c0e54 | R | 753 | 25 | library(tidyverse)
library(here)
library(sessioninfo)
meta_path = here('processed-data', '11_raw_data_upload', 'metadata.tsv')
dest_dir = here('raw-data', 'bulkRNA', 'flat_dir')
meta_df = meta_path |>
read_tsv()
# Create the destination directory and describe its purpose in a README
dir.create(dest_dir, showWa... |
667993dbd0a128bb27d365ee18d2733bffa14f9b8a218719a4e72e8c29d84a99 | R | 754 | 29 | library(readr)
library(tidyverse)
Neuro_AD=read_csv("EnrichGO_Neurons_cell-cell_comm_AD.csv")#brain Organoid/ AD / GSE
Neuro_PD=read_csv("EnrichGO_Neurons_cell-cell_comm_PD.csv")#brain organoid / PD
Top_AD=Neuro_AD %>% arrange(p.adjust) %>% slice(1:100)
Top_PD=Neuro_PD %>% arrange(p.adjust) %>% slice(1:100)
... |
011fe9b549d65d1e205bd450bcf367a23bd8c1d732fee392b48698969ebb3da3 | R | 756 | 23 | # load packages
require(Seurat)
# load Seurat objects (list)
sdata.list <- readRDS('sdata_filtered_list.rds')
# split by batch
sdata.batch <- merge(sdata.list[[1]], sdata.list[2:length(sdata.list)])
sdata.batch <- SplitObject(sdata.batch, split.by = 'batch')
# SCTransform
# Note: SCTransform replaces NormalizeData, ... |
924b1990a4dad43aec6ba6a804ddfb6222e76fd4ecce67e33bee82dd57b3ddbe | R | 758 | 25 | library(twice)
library(ggpubr)
library(ggplot2)
library(tidyverse)
data("hmKZNFs337")
data("hg19rmsk_info")
# import brain data
read_rds_files("../../paper_source_code/data/results_rdata/")
HmPtC1_resKZNFs <- HmPtC1$DEobject$gene_res %>%
data.frame() %>%
filter(rownames(.) %in% hmKZNFs337$external_gene_name)... |
0eead702e7bd1d1f06628d393e45abf2cf7acb1ff5a628c8577fd36fb0bb575e | R | 762 | 28 | # Siwei 20 Oct 2023
# convert logOR and P to Z-score
library(readr)
library(parallel)
library(future)
library(stringr)
df_input <-
read_delim("UKB_MDD_2019_2_convert.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)
clust_df_assembled <- makeCluster(type = "FORK", 20)
clusterExpo... |
182bb14df783b08cde446b74fc75abeff873f981f349d4f53a2593ce04f02409 | R | 764 | 19 | #Install and load UpSetR for visualization
if(!requireNamespace("UpSetR", quietly=TRUE)) install.packages("UpSetR")
library(UpSetR)
#Source create_binaryMtx function to prepare data format
source("~/AD_signature_predictions/Scripts/create_binaryMtx.R")
#Upload example data
df<-read.csv("~/AD_signature_predictions/Da... |
8c75829500d514773a5174a32e4d0749a8ae3f434576fdb3585cd40c51bba5f0 | R | 776 | 19 | #Integrate reference datasets (1.5 + 2 months) ######################################################################################
#Load library
library(Seurat)
#Create Seurat object with input references
seurat_objects=list()
seurat_objects[[1]] = LoadSeuratRds("1.5M_harmonizedOBJ.rds")
seurat_objects[[2]] = Load... |
1f4a97015aff0a5c2fe9e1dffb4f9b4817cd847efe55e2bee37be5d1a97c7403 | R | 786 | 36 |
library(tidyverse)
bed_file <- "/Volumes/T9/drosophila_nanopore/DRS_compairison/xpore_results.txt"
dfs <- read_tsv(bed_file)
# Stacked + percent
m_counts_summ_subcat %>%
ggplot( aes(fill=subcategory, y=normalized_subcategory_count, x=group)) +
geom_bar(position="fill", stat="identity") +
theme_bw()
df... |
ee987b10dec74ef34bd85dcfb6292d974b1443cf5074712ffb70b804e0e9fc47 | R | 788 | 23 | #' load functions
#' @export
#'
load_aif360_lib <- function() {
e <- globalenv()
bindings <- c("datasets", "metrics", "pre_algo", "in_algo", "post_algo", "tf")
if (!all(bindings %in% ls(e))){
e$datasets <- import("aif360.datasets")
e$metrics <- import("aif360.metrics")
e$pre_algo <- import("aif360.alg... |
23e28df6d6a911ba9cd781317c37d4d401b6829177f7535bc3eb3bf1bc584915 | R | 792 | 27 | library(itsmr)
options(digits=10)
# Test Yule-Walker method on lake dataset using itsmr::yw
# Python test is: test_yule_walker::test_itsmr
for(i in 1:5) {
res <- yw(lake, i)
print(res$phi)
print(res$sigma2)
}
# [1] 0.8319112104
# [1] 0.5098608429
# [1] 1.0538248798 -0.2667516276
# [1] 0.4790561946
# [1] 1.0887... |
cced6aee54dcdc8f09b9cb2eca852a564cdf6495e7fc0c4e0d0b25399a2c6caf | R | 792 | 26 | library(here)
library(tidyverse)
library(purrr)
library(readxl)
image_meta_path = here(
'raw-data', 'HALO', 'Deconvolution_HALO_Analysis.xlsx'
)
image_meta = read_excel(image_meta_path, sheet = 2) |>
as_tibble() |>
mutate(
raw_img_path = `File Path to Raw Image` |>
str_replace_all('\\\... |
9f275e00a97239229428e0649c2f776ac44dbaff9a086dea4b64f2698858c362 | R | 800 | 17 | PlotTheme = theme_bw() +
theme(axis.line = element_line(colour = "black"),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
... |
e20da2e52941ef2f7c1ebc4ea89e458f0009dce68fdb84c66b077d5ba79d683c | R | 806 | 22 | library("vars")
#data <- read.csv("/home/skipper/statsmodels/statsmodels-skipper/scikits/statsmodels/datasets/macrodata/macrodata.csv")
#data <- read.csv("/home/bart/statsmodels/scikits/statsmodels/datasets/macrodata/macrodata.csv")
data <- read.csv("C:\\statsmodels\\statsmodels-bartbkr\\scikits\\statsmodels\\datasets... |
4552f99f162fc78af9a1a555986fd0f068e7d892a5f57b5414f084c39a653550 | R | 808 | 23 |
library(tidyverse)
bed_file <- "/Volumes/T9/drosophila_nanopore/DRS_basic/results/NanoCount_output/flybase/tau_ctrl_results-nanocount_with_TPMs.csv"
dfs <- read_csv(bed_file)
tpms <- dfs[,c(1,9,12:17)] %>% dplyr::mutate(row_mean = rowMeans(3:8))
melted_corr_mat <- melt(tpms)
marker <- c("alrm","wrapper","Indy"... |
f74f8dbae7ec237877502eaf6f24d60b2e3043c87201e31185942d71f756f714 | R | 815 | 24 | library(tidyverse)
text = readLines("out_results.txt")
fln = text[seq(from=1, to=length(text), by=2)]
val = text[seq(from=2, to=length(text), by=2)]
df = data.frame(fln, val) %>%
# get rid of non-significant results
filter(val != "0 0.000000 ") %>%
mutate(
cov = if_else(grepl("-cov", fln), "cov", "non... |
67ae9cf8d2a2c83a749a933ed68a6aaecf15eb653b5b1c27bda709a96d5fa6ba | R | 820 | 27 | context("Comprehensive Test for Preprocessing Reweighing Algorithm")
test_that("running dataset test", {
dd <- aif360::binary_label_dataset(
data_path = system.file("extdata", "data.csv", package="aif360"),
favor_label=0,
unfavor_label=1,
unprivileged_protected_attribute=0,
privileged_protected_... |
881bce580dcba6c563881d82d013e6924b0c891538c8b56e58215dbd0a0d8bd0 | R | 824 | 26 | library(itsmr)
options(digits=10)
# Test Burg's method on lake dataset using itsmr::burg
# Python test is: test_burg::test_itsmr
res <- burg(lake, 10)
print(res$phi)
# [1] 1.05853631096 -0.32639150878 0.04784765122 0.02620476111 0.04444511374
# [6] -0.04134010262 0.02251178970 -0.01427524694 0.22223486915 -0.209... |
1c46b42cfc06b0cb52f04c041bd90a5deba645d4d52f353ef8b17004e7239020 | R | 834 | 31 | ---
title: "Install"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Install}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
## Installing PAWS [GitHub - Development Version]
To install the development version of PAWS, run the lines below in RStudio. The `devtools` package is... |
86ca0ac3dff460558bed172607f543d99ecde54004b096241f3492ae4d39318a | R | 836 | 33 | ############################################################# Single omics ####
#----------------------------------------------------- Help Single omics UI ----
help_single_omics_ui = function(id){
ns = shiny::NS(id)
bs4Dash::tabsetPanel(
type = "tabs",
shiny::tabPanel(
title = "Data preprocessing",... |
f456abde272f153fd64998db62b766ab910232d3bcfd02b271521ad20882be8d | R | 836 | 47 | # ÓÃGEOÊý¾Ý¿â²éµ½µÄHiCÊý¾Ý¿ÉÊÓ»¯£¬¿´¿´uc.157ºÍOtpµÄpromoterÖ®¼äÓÐÎÞÏ໥×÷ÓÃ
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
FB_hic <- read.table(
file = "01-data/32-HiC_embryonic_forebrain_GSE217078_FB/GSE217078_FB.ibed/GSE217078_FB.ibed",
sep = "\t", header = TRUE
)
FB_hic <- FB_hi... |
51d64f8523b3b7444e41b82f6317f568bdd52e3dcc329e9331d42839cb6e7b5a | R | 845 | 30 | fluidRow(
box(
title = tagList(icon("google"), i18n$t("コミュニティモビリティレポート")),
width = 12,
closable = F,
footer = tags$small(
tags$a(href = "https://www.google.com/covid19/mobility/",
icon("file-medical-alt"),
"Google Community Mobility Reports"),
"取り急ぎバージョン V 0.1"
... |
f05526a1dbde4ab111d6c3fdf389e371171d32c125356ae3b54a0b725314f294 | R | 848 | 19 | setwd("osmFISH_AllenSSp/")
library(Seurat)
allen <- read.table(file = "data/Allen_SSp/SSp_exons_matrix.csv",
row.names = 1,sep = ',', stringsAsFactors = FALSE, header = TRUE)
allen <- as.matrix(x = allen)
meta.data <- read.csv(file = "data/Allen_SSp/AllenSSp_metadata.csv",
... |
8a7197cb52adcbcdd695425277b07f189a870211e58a9fd955871cea530b2e75 | R | 852 | 30 | #!/usr/bin/env Rscript
##
## Try loading an R package. Install to local user library if not present.
## This standalone script allows for basic package management without modifying any code that requires those packages.
##
## usage: Rscript --vanilla test-package.R package
##
# get scripts directory (directory of t... |
c3f97b5dcb8f26fee07140426bd95d8c055a3d79305dbe1db08f0afa52c009d4 | R | 859 | 32 | #### Oligodendrocytes analysis - SCENIC/GENIE3 ####
library(Seurat)
library(SeuratObject)
library(ggplot2)
library(doParallel)
library(future)
library(cowplot)
library(patchwork)
library(SeuratWrappers)
library(Nebulosa)
library(dplyr)
library(SCENIC)
library(GENIE3)
library(doRNG)
library(SCopeLoomR)
set.seed(0)
regis... |
c60a7a52bbf79560a88bcbe0983ca2af2a159629a2735d5925a1a1ae83f7cef9 | R | 861 | 23 | library(PheWAS)
genes = c("YLPM1", "GIGYF1")
for (gene in genes) {
PROJECT_DIR <- "/Users/deeprobanerjee/Documents/bmi_project/BMI_monogenic"
filename <- paste0(PROJECT_DIR, "/data/phewas/", gene, "/phewas_meta.csv.gz")
phe_df <- read.csv(filename, colClasses = c("phenotype" = "character"))
phe_df["p"] <- phe... |
82e381326d9e4ad6c025687c2e035c81cb6fa028d1367a0706f755a78cf15e35 | R | 866 | 20 | if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
required_packages = c(
"Heatplus", "vegan", "viridis", "data.table", "ggplot2", "dplyr", "tidyr",
"GenomicRanges", "minet", "RColorBrewer", "ggsci", "ComplexHeatmap", "corrplot",
"tidyverse", "scales", "ggthemes", "Rtsne", ... |
2834fa8c5211a8cf15c234bb01d89d5ba6c878c3ed81519d6627386b90f4bf24 | R | 869 | 26 | context("Comprehensive Test for Disparate Impact Remover Algorithm")
test_that("running dataset test", {
dd <- aif360::binary_label_dataset(
data_path = system.file("extdata", "data.csv", package="aif360"),
favor_label=0,
unfavor_label=1,
unprivileged_protected_attribute=0,
privileged_protected_... |
7ffccda39f7b305fb916dafee3de194ae0cbb04845e369f1606c4002187ef43f | R | 870 | 28 | library(fgsea)
library(tidyverse)
library(readxl)
library(enrichplot)
res <- read_csv("DESeq2/filtered2/results/DRD2MSNs_classification_AUD_vs_Control_all_genes.csv")
res2 <- res %>%
dplyr::select(gene, log2FoldChange) %>%
na.omit() %>%
distinct() %>%
group_by(gene)
ranks <- deframe(res2)
pathw... |
02ff4e1c78277cf5c6c443527620b748213d3f36b3a4f576e8790fdbd28290f4 | R | 875 | 38 | # Siwei 16 Oct 2023
# init ####
library(ggplot2)
library(readr)
library(stringr)
df_raw <-
read_table("SCZw3_summary_2_transform.txt",
col_names = FALSE)
df_2_plot <-
data.frame(annotation = str_split(string = df_raw$X1,
pattern = '\\.',
... |
77fa410bdc28b3a2465c858b6ce1642d0e5a0016f64a3e00cdfd98d5361ff0b9 | R | 876 | 31 | fluidPage(
fluidRow(box(
width = 12,
dataTableOutput("detail") %>% withSpinner(),
progressBar(
id = "detailProgress",
# 事例記録数 (更新中)
title = paste(lang[[langCode]][69], " (", lang[[langCode]][70], ")"),
value = nrow(detail),
total = TOTAL_WITHIN
)
)),
fluidRow(
box... |
370c884cfee43c70e0f5d60a4b50a17ae3446cd6d0dacc2e934eaf359275ae91 | R | 886 | 22 | library(rtracklayer)
library(GenomicRanges)
setwd("/project2/xinhe/xsun/neuron_simulation/2.torus")
df_snp <- readxl::read_xlsx("data/Bulk_DOPA_ASoC for TORUS.xlsx",sheet = 1)
gr_hg38 <- GRanges(seqnames = df_snp$CHROM,
ranges = IRanges(names = df_snp$ID, start = as.numeric(df_snp$POS), end = as.... |
e156995944ead1eef0f920626add20e4e3c451bef4c7466fabb3e3d741faa263 | R | 887 | 23 | suppressPackageStartupMessages(library(rtracklayer))
suppressPackageStartupMessages(library(GenomicFeatures))
suppressPackageStartupMessages(library(dplyr))
# Arguments: <input_gtf> <outdir>
args0 = commandArgs(TRUE)
# txdb0 <- makeTxDbFromGFF('./resources/dm6_ensembl96.gtf')
txdb0 <- makeTxDbFromGFF(args0[1])
# int... |
e266a83074614899cc2fddd107672e18970491285c763153bee8af00a71ca079 | R | 888 | 30 | get_bulk_harmonised_data <- function(exposure_data = bulkexposure,
outcome_data = outcome
) {
message("exposure_data = bulk exposure data input, dataframe")
message("outcome_data = one specific outcome data, dataframe")
source("/mnt/dat... |
679209e711f4dcbcab6b65384e6d75073233a44ecdcebb99d10ab2d4771377d5 | R | 890 | 13 | # Authors: Stephen Coleman, PhD; Lauren Rylaarsdam, PhD
# 2024-2025
############################################################################################################################
## amethyst -- R package for single cell methylation data analysis and visualization
.onAttach <- function(libname, pkgname) {... |
da386c73a6629630ee1d62aa5b4c5e7a492602f29b9555555ba028b9224ebefc | R | 890 | 35 | library(glmnet)
library(survival)
library(R2nparray)
# List containing (sample size, # covariates) for each data set
ixd = list(c(50,2), c(100,5))
res = list()
for (ix in ixd) {
fname = sprintf("results/survival_data_%d_%d.csv", ix[1], ix[2])
data = read.table(fname)
time = data[,1]
status = data[,... |
43fe603005bde1b5e315dad625ef0573cbd730c9b35654498033b0c9b50188db | R | 891 | 24 | tabPanel(
title = tagList(icon("chart-bar"), i18n$t("多次元比較")),
fluidRow(
column(
width = 6,
pickerInput(
inputId = "comparePref",
label = "",
choices = regionName[!regionName %in% c("都道府県", "チャーター便", "検疫職員", "クルーズ船", "伊客船")],
options = list(
`live-search` = ... |
f491de1a8711017e4385d36d3448db8fc9b5f0d44a776288decb2e210c779bc1 | R | 891 | 37 | library(glmnet)
library(R2nparray)
rslt = list()
for (dtype in c("binomial", "poisson")) {
ik = 0
data = read.csv(sprintf("enet_%s.csv", dtype))
endog = data[, 1]
exog = data[, 2:dim(data)[2]]
exog = as.matrix(exog)
for (k in 1:dim(exog)[2]) {
exog[,k] = exog[,k] - mean(exog[,k])
... |
000cab136e244af64195669fc5e813ef94bab2395d0351696c5632cab57955c7 | R | 892 | 22 | library(cellcall)
Cell_call <- function(object, cell_type, Org = "Mus musculus"){
library(cellcall)
test <- CreateObject_fromSeurat(Seurat.object= object, #seurat对象
slot="counts",
cell_type=cell_type, #细胞类型
data_s... |
f197be14024ddaaaf5377bdeb7ed5bc1929de8e261240714f93d0454a0bf1877 | R | 893 | 27 | #Filter differentially expressed protein-coding genes (|log2FC| > 0.1 & adj.pvalue < 0.05) ######################################
#List files
files = list.files(path=".", pattern="unfilt")
#For each file
for(file in files){
#Read file
df = read.delim(file, header=T)
#Filter diff exp protein-coding genes with ... |
4688736834dd96a6a2757f378a364e8c1b3d39310fc008533f29a56a2979521e | R | 899 | 31 | # FFERREIRA 11/27/2023
# Livia Project - NASA
# Edits MAP files so that we have only unique gene appearences
# Sets WS
wd <- getwd()
setwd(wd)
# General OBJs
sep <- "\t"
nas <- c("NA", "", NA)
# Reads MAP file between Ensembl Gene IDs and Symbols / Types
## PS: First COL must be set as "row.names"
map <- as.data.fr... |
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