sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
b3324a20243e6386217a8f4979a79de2db97ec44a0afa2abe5f881991f1b751a | R | 901 | 26 | #' OpenSRA
#'
#' Open SRA file into FASTQ file. The script will create new directory called FASTQ with the fastq files.
#' @param File The Path to the SRA File
#' @param Library_Type "Paired" or "Single"
#' @param SRAPath path for the SRAtools BIN directory (example: SRAPath = "~/SRAtoolkit/sratoolkit.2.5.4-ubuntu64/bi... |
6e5711e915bd7382c4d0fb3892bc16f1bb45d9042bd1787100634f8ed8da590d | R | 908 | 26 | #' Prejudice Remover
#' @description Prejudice remover is an in-processing technique that adds a discrimination-aware regularization term to the learning objective
#' @param eta fairness penalty parameter
#' @param sensitive_attr name of protected attribute
#' @param class_attr label name
#' @usage prejudice_remover(et... |
99b00a8043d767e30a6fddc6b0d785df82f79ea198dc6977fe3ceb4e473348ab | R | 908 | 37 | # Ñ°ÕÒintergenicÇøÓòÉϵÄÔöÇ¿×Óλµã
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(rtracklayer)
library(BiocManager)
BiocManager::install("Biostrings")
library(Biostrings)
# ¶ÁÈ¡bedÎļþ
intergenic <- read.table(file = "01-data/10-GO_enrichment_analysis/intergenic_UCR_neighboring_gene.bed")
interge... |
120e961cb3920ba8b727c7989b8f8f338612ecb1c22c965d1c7a61915b42c678 | R | 913 | 25 | #Integrate all samples ###########################################################################################
#Load library
library(Seurat)
#Create directory to store integrated samples
dir.create("3_integrated_samples", showWarnings=T)
#List input files
input_files = list.files(path="2_results_norm", pattern="... |
89e35ed839da676be872710ff2d82b3b08974afb8bf443fadb2317c8539396fc | R | 923 | 30 | #' Function to download fetal midbrain atlas
#'
#' Function to download a seurat object containing the fetal midbrain atlas from the zenodo repository
#'
#' @param file path to save the file
#' @param mini True to download a downsampled atlas which contains 10\% of the cells in the fetal midbrain atlas. Default is FAL... |
29cf9bd52090c9aee510fa5bd102e33b050207ac89ac1ceaedace9c1ed10764f | R | 935 | 21 | rm(list = ls())
source('CVASL_RELIEF.R')
library(MASS)
library(Matrix)
options(repos = c(CRAN = "https://cran.r-project.org"))
install.packages("denoiseR", dependencies = TRUE, quiet = TRUE)
library(denoiseR)
ins... |
33f9c024f89040dba1fff4793d328f7dcc5d11c0a7dc4a176757e480b00975b5 | R | 936 | 30 | #' Function to download fetal whole brain atlas
#'
#' Function to download a seurat object containing the fetal whole brain atlas from the zenodo repository
#'
#' @param file path to save the file
#' @param mini True to download a downsampled atlas which contains 10\% of the cells in the fetal whole brain atlas. Defau... |
60881548ea913288a16cb3187f9bb022c74e54a52af6b1d5c342000c1214c6f2 | R | 940 | 25 | # Load required packages
required_packages <- c("ggplot2", "readr")
for (pkg in required_packages) {
if (!requireNamespace(pkg, quietly = TRUE)) install.packages(pkg)
library(pkg, character.only = TRUE)
}
# Load data
data <- read_csv("Data/shared_compound_data.csv")
# Generate bubble plot
shared_comp_plot <- ggpl... |
9ffce3889fee5c5adcc7975dbee02918f38371bbbea6d3b437ed2c2d135fb76f | R | 941 | 28 | ### MNT add 18Jun2021 =======
# Check Read 1 files for discrepant read lengths, as seen with the original Br5182-NAc:
# Should be exactly 28bp = 16 [BC] + 12 [UMI]
library(ShortRead)
library(jaffelab)
FASTQ.dir <- "/dcs04/lieber/lcolladotor/rawDataTDSC_LIBD001/raw-data/2021-10-15_Tran2021_published/Br5182_NAc_res... |
fd88bc62d56b7f113d9924b46f209a818591156ce8a2c008a3492a17cbc6437f | R | 942 | 30 | # libraries ---------------------------------------------------------------
#Load libraries
library(tidyverse)
library(Seurat)
#devtools::install_github('satijalab/seurat-data')
library(SeuratData)
library(BayesSpace)
library(patchwork)
# read in data ------------------------------------------------------------
# read... |
fab909592fa9a96e69dc13cdc06c576b6065f1bb6fb340ded7c7a751942b4416 | R | 944 | 37 | # FairAdapt called from R
wrapper <- function(train_data, test_data, adj_mat, res_vars = NULL, prot_attr,
outcome) {
prot_attr <- gsub("-", ".", prot_attr)
outcome <- gsub("-", ".", outcome)
train_data <- as.data.frame(
lapply(train_data, function(x) {
if (is.ordered(x)) class(x) <... |
ae362a09a7b4375300b25676534708d40e9a663440a99b041a6fa062170bf1c3 | R | 945 | 37 |
library(GenomicPlot)
library(TxDb.Dmelanogaster.UCSC.dm6.ensGene)
bed_file <- "/Volumes/T9/drosophila_nanopore/DRS_basic/results/control_m6A_loci_sort.bed"
names(bed_file) <- c("control")
bed_file2 <- "/Volumes/T9/drosophila_nanopore/DRS_basic/results/meclip_m6A_loci.bed"
names(bed_file2) <- c("control2")
suppre... |
181fdbd812e576c2e44cb73e10f4f897edd62b6c27c9d1c221081ac25be6bfcb | R | 949 | 20 | # createConfirmedCityTreemap <- function(dataset) {
# parent <- "県名"
# child <- "市名"
# value <- colnames(dataset)[ncol(dataset)]
# setnames(dataset, value, "累計")
# dataset %>%
# e_charts() %>%
# e_treemap(県名, 市名, 累計,
# upperLabel = list(show = T, color = "#222"),
# left = "1%", right = "1%... |
0192d57cc0043715e95b254f8a718ee1478a3d7932952d63861088894ddea00e | R | 967 | 32 |
library("argparse")
library("ggplot2")
library("patchwork")
library("tidyverse")
library("Seurat")
# Command line arguments ----
parser <- ArgumentParser(description = "Differential expression analyses")
parser$add_argument('--seurat', '-s',
help = 'Seurat object')
parser$add_argument('--results'... |
1c07ca09a44773cae23014203efd1afade599faea10333b4d3620bb5c2823e2c | R | 967 | 23 | library(misha.ext)
options(timeout = 900)
# Download and extract the core IQ paper data first
download.file("https://iq-paper.s3.ap-south-1.amazonaws.com/IQ-paper-data.tar.gz", "IQ-paper-data.tar.gz")
untar("IQ-paper-data.tar.gz", exdir = ".")
# Create genome databases
gdb.create_genome("mm10", here("data"))
gdb.cre... |
73952af1392b709accac543c51b17e130c3d7d34955388f12b956b04b2fdf974 | R | 967 | 26 | # 総計検査人数Sparkline ====
output$pcrSparkLine <- renderSparkline({
sparkline(
tail(mhlwSummary[, .(cumsum = sum(検査人数, na.rm = T)), by = "日付"][, .(diff = cumsum - shift(cumsum))], n = 28)[[1]],
type = "bar", width = 100, barColor = "white"
)
})
output$confirmedSparkLine <- renderSparkline({
value <- rowSums... |
146c9becb5fceb4e01f3b9e1b52c99d930b158b1079e968b60cbe41e30b669d3 | R | 973 | 44 | # ÔÚ¸»¼¯·ÖÎöÖ®ºó£¬²é¿´all coding genesÔÚËùÓÐÆ÷¹Ùµ±ÖеÄpattern
# ·¢ÏÖÔÚ´óÄÔµ±ÖÐAS pathwayµÄ»ùÒòÓÐspecific pattern£¨ÕâÀïÎÒÏë»æÖÆÒ»¸öÈÈͼ£¬²»Õ¹Ê¾»ùÒòÃû£¬ÒòΪ»ùÒòÌ«¶à£©
# ÔÚmouse¡¢ratµÄbrainµ±Öв鿴AS pathwayµÄ»ùÒòµÄpattern
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(pheatmap)
libr... |
37fc39c65cf7cceb63d126e733285f96e4912d676cc18184ef6b37aa875daa54 | R | 974 | 41 | # count the % of ASoC SNPs that resides within peaks
# that also falls into MicroC bait intervals whose target
# resides in TSS region (-2 kb ~ +1 kb)
# use separate npglut peak sets (0/1/6)
# this will not be necessary since ASoC SNPs are always inside peaks
# Siwei 16 Feb 2024
# init ####
library(Seurat)
library(S... |
9dbf106bf7c15326cbccc9d851e8cf8f954ff352ef4205b9aae0742c15995e44 | R | 975 | 18 | setwd("MERFISH_Moffit/")
library(Seurat)
library(Matrix)
MERFISH <- read.csv(file = "data/MERFISH/Moffitt_and_Bambah-Mukku_et_al_merfish_all_cells.csv", header = TRUE)
MERFISH_1 <- MERFISH[MERFISH['Animal_ID']==1,]
MERFISH_1 <- MERFISH_1[MERFISH_1['Cell_class']!='Ambiguous',]
MERFISH_meta <- MERFISH_1[,c(1:9)]
... |
c280adb1032e1deeca2b54685f161966804ae40834afb2e1846a7488f99bd868 | R | 981 | 29 | # load packages
require(tidyverse)
require(Seurat)
# load Seurat object and gene names
sdata.align <- readRDS('sdata_align_RefF_prelim.rds')
gene.names <- read_csv('gene_names.csv')
# set default assay to 'RNA'
DefaultAssay(sdata.align) <- 'RNA'
# reduce object size
sdata.align[['integrated']] <- NULL
# check, use ... |
ace43f83898c9bc1b59dac2ef60f3b7b4309dd403c997572850c850eaec2d743 | R | 988 | 20 | library(Seurat)
library(Rmisc)
load("CNVfiltered.IntegrateData.RData")
get.id = function(y){y = paste0("^",y,"-ENSG");grep(pattern = y,x = rownames(embryo.integrated_CNVdefined@assays$RNA@counts),value = T)}
chrom.state = c("E",paste0(c(1:22,"X","Y"),"T"),paste0(c(1:22,"X"),"M"))
term.names = c("Apoptotic","Stress res... |
6c2fc6383ff32362aec42096faae506b70ba68bcbf5f42bd12278473dc26ea82 | R | 995 | 33 | args=(commandArgs(TRUE))
library(dplyr)
library(survival)
library(survminer)
setwd(args[5])
table <- paste(args[1], args[4], "survival", sep="_")
label1 <- paste(args[4], "Low", sep="_")
label2 <- paste(args[4], "High", sep="_")
heading1 <- paste("Kaplan-Meier curve", args[3], "in", args[2], sep=" ")
heading2 <- past... |
a8766239f98b280a184599988ee52c7330b34acfd4b65e6616028a16b9ff6f60 | R | 999 | 48 | # Siwei 16 Jan 2024
# plot fig. 6b use plotly
# init ####
library(ggplot2)
library(RColorBrewer)
library(readxl)
library(stringr)
library(plotly)
# load data ####
df_raw <-
read_excel("Fig6b_12.xlsx")
# Fig 6b #1 #####
df_to_plot <-
df_raw[, c(1, 2)]
df_to_plot$Gene <-
factor(df_to_plot$Gene,
level... |
958a18582192d6bc5442a501f41513e0508ad83423336f006cb1132ec3bccb77 | R | 1,003 | 35 | #!/usr/bin/env Rscript
library(DESeq2)
df <- read.table('data/ercc.tsv', header=T)
sample_df <- data.frame(samplename = names(df)[2:ncol(df)])
sample_df$sample <- substr(sample_df$samplename, 1,1)
sample_df$batch <- substr(sample_df$samplename, 3,3)
row.names(sample_df) <- sample_df$samplename
sample_df$sample <- f... |
f2094ac776aeba613c5736e043b5b4109a5a63c65145418bd06400066b9f535a | R | 1,006 | 28 | library(zellkonverter)
library(here)
library(tidyverse)
library(SingleCellExperiment)
library(sessioninfo)
sce_ad_path = "/dcs04/lieber/lcolladotor/spatialDLPFC_LIBD4035/spatialDLPFC/raw-data/psychENCODE/version6/CMC_update_Feb2023/CMC_annotated.h5ad"
meta_path = '/dcs04/lieber/lcolladotor/spatialDLPFC_LIBD4035/spatia... |
8b1b16c5b04b207e2055eb6adc1479a7e9638d756898b3dd7c959e08815e739d | R | 1,010 | 43 |
getLatestWeekValue <- function(data, type = "week") {
if (type == "day") {
span <- 1
} else if (type == "week") {
span <- 6
} else {
stop("Unknown span type.")
}
index <- (nrow(data) - 6):nrow(data)
indexBefore <- index - 1
value <- colSums(data[index, 2:ncol(data)])
valueBefore <- colSum... |
ee4734884e9abd9727c3142fe91479ce6354a3b103232e01fe1d04e6f57cda9c | R | 1,011 | 23 | setwd("/project2/xinhe/xsun/neuron_simulation/2.torus")
df_snp <- readxl::read_xlsx("data/SNPs_for_Torus.xlsx",sheet = 3)
colnames(df_snp)[(ncol(df_snp)-2):ncol(df_snp)] <- c("chr","start","end")
df_snp <- df_snp[complete.cases(df_snp$chr),]
index <- seq(2,nrow(df_snp)*2, by =2)
chr <- unlist(strsplit(df_snp$chr, sp... |
8ecc009c2cc4a27bd570899be1597c104ccc5a035c703a523a27caad35c66063 | R | 1,013 | 47 | library("SingleCellExperiment")
library("rafalib")
library("iSEE")
library("pryr")
library("here")
library("whisker")
library("usethis")
library("withr")
library("sessioninfo")
load(here("rdas", "regionSpecific_DLPFC-n2_SCE_cellTypesSplit-fromST_Apr2020.rda"), verbose = TRUE)
source(here("shiny_apps", "00_clean_funct... |
0f070670b5735fe3a7d2a4f88b3f0afa1ac06c18cdb702f3afc6e79bdc1058fa | R | 1,021 | 40 | library(here)
# params
# load
#------
# paths
#------
# project
proj.dpath <- "Human_DLPFC_Deconvolution"
save.dname <- "008_revisit-halo-settings"
save.dpath <- file.path(here(), proj.dpath,
"processed-data", save.dname)
# new halo data
halo.new.dname <- "Algorithm_Check_20220920"
halo.dpath... |
e0628476215af72815e7c16d55f3b823d61a82d77e808cdd0d85eb1e3c58fee7 | R | 1,025 | 52 | library(fgsea)
library(tidyverse)
library(xlsx)
files <- list.files(#
pattern="*all_genes.csv", full.names = TRUE, recursive = FALSE)
i <- 0
for(file in files) {
print(file)
res <- read_csv(file)
celltype <- sub("./", "", str_split(file, "_")[[1]][1])
res2 <- res %>%
dplyr::s... |
0d55fa73f5cd636baaf44fde2c7d706a2935d509d45b27ef741b9d264a3ff810 | R | 1,027 | 27 | library(stats)
path2data = '/Volumes/server/Projects/attentionpRF/derivatives/amplitude_data'
#path2data = '/Users/ekintuncok/Desktop'
data_tbl = read.csv(paste(path2data, "amplitude_data_for_ANOVA.csv", sep = '/'))
data_tbl$id <- as.factor(data_tbl$id)
data_tbl$ROI <- as.factor(data_tbl$ROI)
data_tbl$Location <- as.f... |
3b44a5a7310aaa76204b7e0687053b36824e7316f3fe25331e732ff6e9fef618 | R | 1,027 | 29 | # load packages
require(tidyverse)
require(Seurat)
# load Seurat object (pre-doublet identification) and gene names
sdata.align <- readRDS('sdata_align_snRNA-seq_prelim.rds')
gene.names <- read_csv('gene_names.csv')
# set default assay to 'RNA'
DefaultAssay(sdata.align) <- 'RNA'
# reduce object size
sdata.align[['i... |
f5b9c4f4bd60389ec58c1ce35647fc812f67602c3603ac3e745811fa6ff08fbc | R | 1,028 | 31 | #!/usr/bin/env Rscript
suppressPackageStartupMessages({
library(dplyr)
library(optparse)
})
# importing
option_list = list(
make_option(c("-i", "--input"), type="character", default=NULL,
help="input JWR output file"),
make_option(c("-j", "--JAQ"), type="numeric", default=NULL,
he... |
8eb0fb1e7c7fc53d1c8bf8a44fc74633a9bab8c3e0040bfaaadacdb27c4d4d6d | R | 1,029 | 21 | ##' @importFrom utils packageDescription
.onAttach <- function(libname, pkgname) {
pkgVersion <- packageDescription(pkgname, fields="Version")
msg <- paste0(pkgname, " v", pkgVersion, " ",
"For help: https://dingruofan.github.io/xQTLbiolinks/", "\n\n")
# if (capabilities("libcurl")) {
# dl.m... |
3bdcf7160fdc234dde4c755eff8a9f77288941d9aaed9a568bc020ea5a1afa02 | R | 1,030 | 27 | #' Disparate Impact Remover
#' @description Disparate impact remover is a preprocessing technique that edits feature values increase group fairness while preserving rank-ordering within groups
#' @param repair_level Repair amount. 0.0 is no repair while 1.0 is full repair.
#' @param sensitive_attribute Single protected... |
b4ccb58d7304291bdc66b8b570f65d06e9616b40f64cc0e80437ea5a0f846dba | R | 1,030 | 13 | #!/usr/bin/env Rscript
### title: Load results of rctd pipe line for all spatial data, and convert their cell type assignments into txt format
### author: Yiping Wang date: 03/29/2022
pats = c("MBM05_rep1_slide","MBM06_slide","MBM07_slide","MBM08_slide","MBM11_rep1_slide","MBM18_slide","MBM13_slide","MPM08_pre_slide"... |
c8a6e57a00a3f115bb2696b30d6ab97f9031323be9da4f2523eca5b3aed0e8f4 | R | 1,030 | 41 | options(digits=15, scipen=999)
# Example 3.3.2 from Brockwell and Davis
ar_params = c(1, -0.25)
ma_params = c(1)
print(ARMAacf(ar_params, ma_params, lag.max=9))
# Custom example 1
ar_params = c(1, -0.25)
ma_params = c(1, 0.2)
print(ARMAacf(ar_params, ma_params, lag.max=9))
# Custom example 2
ar_params = c(1, -0.25)
... |
9aa0c17b1d110992e5e8ebb7dbaab5ea4d7344ea1645534d51f580b677a967b7 | R | 1,036 | 29 | # load packages
require(scDblFinder)
require(Seurat)
require(tidyverse)
# load Seurat object
sdata.align <- readRDS('sdata_align_snRNA-seq_label.rds')
# set Idents to scDblFinder-compatible cluster names
Idents(sdata.align) <- sdata.align$seurat_clusters <- sdata.align$integrated_snn_res.0.5
# convert Seurat object ... |
c3d6769096428ef5d47b44e7f729d07d77e1a01c56015e604dd317223e8b7ac0 | R | 1,038 | 43 | #' List of Subjects
#'
#' Get subid and sesid of a set of subjects, given a csv. TODO: Add filtering by conditions?
#'
#' @description This function takes a table of subjects in csv format and returns a list of (subid, sesid) tuples(lists)
#'
#' @param table: input table
#'
#' @return list of lists with the duple (subi... |
a2c7cb5798c34b0e6acf2663b16a65842d54fea136f4a12e0545d76029d93d45 | R | 1,040 | 50 | # organize the classification of UCR
setwd(dir = "D:/R_project/UCR_project/")
options(stringsAsFactors = FALSE)
rm(list = ls())
library(tidyverse)
library(ggplot2)
codingUCR <- read.table(
file = "02-analysis/16-New_classification/01-UCR_from_protein_coding_gene.bed",
sep = "\t"
)
length(unique(codingUCR$V4)) #... |
b068156a6cb2e6176e9c54eff97de38f86a9bd681a383e334c7d60219978fb13 | R | 1,043 | 34 | # this script exports the counts matrices to CSV files for collaborators
# output is sent to a temporary dir as we do not need to keep these long-term
library(DESeq2)
library(tidyverse)
dds <- readRDS("results/DESeqDataSet/dds_gene_regular.rds")
# get the gene names
gene_meta <- rowData(dds) |>
as_tibble(rownames... |
db877940a71eb77644992265d26562f37bb264f1df9e176fa2c41d025a3764dc | R | 1,049 | 40 | library(glmnet)
library(R2nparray)
# Run the glmnet lasso (elastic net) on all the test data sets
data = read.csv("lasso_data.csv", header=FALSE)
ik = 0
rslt = list()
for (n in c(100, 200, 300)) {
for (p in c(2, 3, 5)) {
endog = data[1:n, 1]
exog = data[1:n, 2:(p+1)]
exog = as.matrix(ex... |
75b12c1008aaf982110a38c7f2528cf358b1a743bc7c2cac65041aea81032e45 | R | 1,053 | 29 | # This script was run interactively to investigate whether MuSiC involved
# randomness (and thus must be run with a seed for reproducibility). It does
# not involve randomness.
library(MuSiC)
library(SingleCellExperiment)
GSE50244.bulk.eset = readRDS(url('https://xuranw.github.io/MuSiC/data/GSE50244bulkeset.rds... |
7fec32ffcfd420339fa46456a10373270c2e495bf9d5634e84b9dc359c815d1d | R | 1,053 | 19 | # script to divide log files properly
library(tidyverse)
# figure out which files need to be converted
fl.path = paste('/home/emba/Documents/EMBA', 'BVET', sep = "/") # file path
fls.log = list.files(path = fl.path, pattern = "*\\.log$", full.names = F) # get all log files
fls.log = substr(fls.log... |
663e11ded50dbb972adaa57e6c7a1e2d72fbc692c59ced4173fe05bbf902f2f5 | R | 1,055 | 67 | # ClinPred Score
setwd(dir = "/home/wangxiangting/baiyun0216/projects/UCR/data/41-SNPsPathogenicityEvaluation")
options(stringsAsFactors = FALSE)
rm(list = ls())
library(tidyverse)
library(stringr)
## load all SNPs info
# load("passed_UCR_SNPs.Rdata")
# load("passed_UCR_left_SNPs.Rdata")
# load("passed_UCR_right_SNP... |
e50b9217291cae30221c3d615bbf329661c0898e64ccfb4b9b58a991a6810c06 | R | 1,059 | 34 | #S2
library(ggplot2)
hg38TE <- read.csv("~/github/pBrain/data/hg38rmsk_info.csv")
young_TE <- read.csv("~/github/pBrain/data/Dfam_TE_simiiformes.csv")
hg38TE_age <- hg38TE %>%
mutate(age = ifelse(repName %in% young_TE$NM, "young", "old")) %>%
select(c(3,4))
hg38_old <- hg38TE_age %>%
filter(age=="old") %>... |
65abab78d1e17c5ae77308ca178bbcb3769e7fbb1354ef4155f4aef37498c1f1 | R | 1,060 | 33 | # ÓÃlncRNAÏà¹ØµÄµ°°×±àÂë»ùÒò×ö¸»¼¯·ÖÎö
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(biomaRt)
ncRNA_UCR <- read.table(file = "02-analysis/16-New_classification/ncRNA_UCR(66)_ensembl_id.txt", sep = "\t")
ncRNA_UCR <- unique(ncRNA_UCR)
# ÀûÓÃuseMartÁ´½Óµ½ÈËÀàµÄÊý¾Ý¿â£¬ÆäËûÊý¾Ý¿âÊ... |
5e80518fcbff4d10afdf1adbb4819907627014de7aa24cd663ed9959535fe060 | R | 1,065 | 53 | #Environment and relevant functions for sc analysis
# Single-Cell Analysis
library(Seurat)
library(SeuratWrappers)
library(Signac)
# Data Visualization
library(ggplot2)
library(patchwork)
library(ggpubr)
library(viridis)
library(cowplot)
library(ComplexHeatmap)
library(circlize)
# Data Manipulation
library(dplyr)
lib... |
85d9156635cc24cf9af82fcd15bfce45529cc8d49891ff4939078a3e2f6c2e17 | R | 1,067 | 22 | #' Effect of high fat diet on the liver transcriptome - Microarray
#'
#' Time-series data of the mouse liver transcriptome measured under normal chow
#' read using custom CDF file from Brainarray version 24.0.0 annotated by
#' Ensembl Gene ID followed by RMA normalization. The expression data are in the
#' log2 scale. ... |
93be36fca541e8c9f02da3208138efe0f8c7d75b77e0bc86b7420d35a0ed52fc | R | 1,067 | 36 | context("Comprehensive Test for Inprocessing Adversarial DebiasingAlgorithm")
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_p... |
369fea565adabb1bdf087f78bae36e8ef6bcaa1ba1d369382152486a82fe5f45 | R | 1,068 | 29 | library(dplyr)
library(Seurat)
library(Matrix)
library(sva)
library(ggplot2)
library(harmony)
read_data <- function(folder_path) {
cells <- read.csv(file.path(folder_path, "Cells.csv"), row.names = 1)
genes <- read.table(file.path(folder_path, "Genes.txt"), header = FALSE)
mtx_files <- list.files(folder_path, pa... |
5b0dead35f9cf145e50b307b8984d223ead6426ac05f835f7ef73fa00356c938 | R | 1,071 | 20 | #script to download, unpack, and read in data from Massri 2021
#make new directory for these data
dir.create(here::here("objects", "Massri_data"), showWarnings = FALSE)
#extend timeout for large download
options(timeout = max(1200, getOption("timeout"))) #20 minute timeout (default is 1 minute)
#download from SRA (~... |
bb4ac4fe592db526aaca7a419bee79aca79ef6774aa7ec9b4af226f6e2267378 | R | 1,072 | 40 |
# install packages if needed
BiocManager::install("GenomicPlot")
BiocManager::install("TxDb.Dmelanogaster.UCSC.dm6.ensGene")
library(GenomicPlot)
library(TxDb.Dmelanogaster.UCSC.dm6.ensGene)
txb <-
bed_file <- "/Volumes/T9/drosophila_nanopore/DRS_basic/results/control_m6A_loci_sort.bed"
names(bed_file) <- c("Co... |
27cfbba8b24bf0f6bf1c3ec05d8579e00dd6cff9d1c84cdffdcce913a26398e5 | R | 1,078 | 33 | library(tidyverse)
library(ggplot2)
library(moonBook)
library(webr)
# Setting working directory -----------------------------------------------
this.dir <- dirname(parent.frame(2)$ofile)
setwd(this.dir)
# Data paths --------------------------------------------------------------
project_path <- file.path(getwd(),'... |
97db9089b57f818ecaad521cae19242965a9644df500485b9df21e0fc5b18c07 | R | 1,088 | 38 | # plot practise
# Siwei 6 Feb 2019
# init
library(Gviz)
# data("cpgIslands")
library(rtracklayer)
library(BSgenome)
library(BSgenome.Hsapiens.UCSC.hg38)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(ensembldb)
library(org.Hs.eg.db)
library(grDevices)
##########
options(ucscChromosomeNames = F)
make_assembled_pl... |
599d9a4f59dff88e0f1c39126e2a6e1cfb807ac497c6b1e22d1c0cd96acff3b2 | R | 1,090 | 48 | library("SingleCellExperiment")
library("rafalib")
library("iSEE")
library("pryr")
library("here")
library("whisker")
library("usethis")
library("withr")
library("sessioninfo")
load(here("rdas", "ztemp_Amyg-n2_SCE-with-tSNEonOptPCs-minus-PC5_MNT.rda"), verbose = TRUE)
source(here("shiny_apps", "00_clean_functions.R")... |
74d5357f25ba054cba4b72e1e21dfb8f8224a9228b6bab50995d101b55bdf4dc | R | 1,091 | 29 |
## Calculate neuron (NeuN), oligodendrocyte (Sox10), microglia (IRF8) and astrocyte-enriched (TN) proportions in bulk postnatal samples ##
# https://github.com/ejh243/CETYGO
library(CETYGO)
#1. Load data ==============================================================================================================... |
318e2508e8d0910b749778c79c9562561cf15dd43f3c5f244d21256647bec8fc | R | 1,092 | 23 | # get all file names
# hidden files are not selected
incl_files <- list.files(path = "./",
recursive = TRUE)
# remove certain files
incl_files <- incl_files[!grepl(x = incl_files,
pattern = "^(240220.*|neurolipid_meta.csv)$")]
incl_files <- incl_files[!grepl(x =... |
5bb3cf66c0790f73e058dddb4f45f962ebdce4bc361f75e400d4834f27dbfa8b | R | 1,092 | 25 | #' Reweighing
#' @description Reweighing is a preprocessing technique that weights the examples in each (group, label) combination differently to ensure fairness before classification
#' @param unprivileged_groups a list with two values: the column of the protected class and the value indicating representation for unp... |
fdefe91a65e5946efd4dd542cc7d3573f76b0a4cfccfe46ec3d4ed20c38c48f3 | R | 1,101 | 32 | # load packages
require(tidyverse)
require(Seurat)
# load Seurat object (singlet-only dataset) and gene names
sdata.align <- readRDS('sdata_align_snRNA-seq_singlet.rds')
gene.names <- read_csv('gene_names.csv')
# set default assay to 'RNA'
DefaultAssay(sdata.align) <- 'RNA'
# set Idents to cluster labels
Idents(sda... |
c06b7d680871f19beb51b2beb5acc16c7f4aa200b6ecce9af99fc345bba44e7b | R | 1,107 | 32 | # load packages
require(tidyverse)
require(jmotif)
# load data
bin.data <- readRDS('bin_data_filtered.rds')
# scale data
bin.data.scale <- bin.data %>%
unite('rowname', time_point, genotype, mouse_id, mouse_name) %>%
column_to_rownames() %>% t() %>% scale() %>% t() %>%
as_tibble(rownames = 'rowname') %>%
se... |
204cb8b9f63a0cec8c51c7ac887c4ebd297b559b8b59c2e4079be398af2c36b5 | R | 1,108 | 42 | #!/usr/bin/env Rscript
# to use nf variables: "${meta.id}"
# load libraries
library(anndataR)
library(SeuratObject)
library(SingleCellExperiment)
# read input
adata <- read_h5ad("${h5ad}")
# convert to Seurat
obj <- adata\$as_Seurat()
# save files
dir.create(file.path("$meta.id"), showWarnings = FALSE)
saveRDS(obj... |
ef365443ca7af02a35f842b7fec4aa54e021532d65f26e7dfde0c33dd3cef088 | R | 1,109 | 48 | library("SingleCellExperiment")
library("rafalib")
library("iSEE")
library("pryr")
library("here")
library("whisker")
library("usethis")
library("withr")
library("sessioninfo")
load(here("rdas", "regionSpecific_sACC-n2_cleaned-combined_SCE_MNTFeb2020.rda"), verbose = TRUE)
source(here("shiny_apps", "00_clean_function... |
6f7593e83d44bf6c2c3089bed594bc6b9ddee82f4d70302511f0852199ac4d35 | R | 1,116 | 47 | library(readxl)
library(writexl)
# User-defined inputs ---------------------------------------------------------
# Filenames
datafile = 'input_data_perform_SEnorm/brain_metabolites.xlsx'
factorfile = 'SE_factors.xlsx'
opfilename = 'output_SEnorm/SEnorm_brain_metabolites.xlsx'
# First column number of MID data
index... |
b4b26c349e310d2c6bd4beb264901ef1d942879c87150b8cf9c61035fe691298 | R | 1,116 | 27 | #### Extract indel candidates from indel_calls.vcf for pileup in unmerged bam ####
options(stringsAsFactors = FALSE)
library(stringr)
args <- commandArgs(trailingOnly=TRUE)
input_f <- args[1]
output_f <- args[2]
indel_calls <- read.table(pipe(paste("grep -v '^#'", input_f)), header=FALSE, sep="\t", na.strings="", quo... |
4c8ffa249038b457bc20de9a0f6946dc9cc59eca2da75152b579dc0fefe3bf56 | R | 1,119 | 41 | tabPanel(
title = i18n$t("感染者数の推移"),
icon = icon("procedures"),
value = "confirmed",
fluidRow(
column(
width = 8,
fluidRow(
tags$br(),
column(
width = 6,
radioGroupButtons(
inputId = "selectTendencyConfirmedMode",
justified = TRUE,
... |
a3e9dd113a7dcbd38a87e4f7c3b0dbd3723f3bf148f234a61beb006545e8deea | R | 1,124 | 51 | #!/usr/bin/env Rscript
# Command line argument processing
args = commandArgs(trailingOnly=TRUE)
if (length(args) < 2) {
stop("Usage: markdown_to_html.r <input.md> <output.html>", call.=FALSE)
}
markdown_fn <- args[1]
output_fn <- args[2]
# Load / install packages
if (!require("markdown")) {
install.packages("mark... |
9e76ecce837c21ee388a0baa3244312b73ebe3d3ad379b6b58c1c4c4b3646b86 | R | 1,125 | 32 | ---
title: "Lvar Single Cell Cell Marker bar plots"
output: html_document
date: "2024-01-03"
---
setwd("C:/Users/kate.castellano/OneDrive - Gloucester Marine Genomics Institute/Documents/KCastellano/SeaUrchin/SeaUrchin_Data/2023Aug_CellCulture_ManuscriptDrafts/Figures/")
```{r }
##Make bar plots for single c... |
ec37eea9ec6b231639947f7dd87189c1dc5116c4d02cb5bd6672a4a8757362c7 | R | 1,126 | 24 | res_MRPRESSO <- function(dat=dat, NbD=1000,SignifThreshold = 0.05) {
if(nrow(dat)<4){
resMRPRESSO <- data.frame(check.names = FALSE,
Exposure=c(NA,NA), "MR Analysis"=NA , "Causal Estimate"=NA,
Sd=NA, "T-stat"=NA ,"P-value"=NA,"Outlier"=NA,"global... |
c7e66be2dd6ea94471405abd272b04bc127db7fd4844afa456eb5610913eced9 | R | 1,133 | 32 | #' Get UK Biobank participant Electronic Medical Records (EMR) data in a RAP Spark environment
#'
#' @description
#'
#' This function is completely removed. Better to use `get_diagnoses()`. Use a historic release of this package if you really need it https://github.com/lcpilling/ukbrapR/releases
#'
#' @return Returns... |
19e3e87e4b81eb6a8bddca766ea04e01568b6e1a8d87d66d960f3cde7ab4a19a | R | 1,134 | 30 | # 01 Load Data.R
# 01 Load Data.R
# ================================
# Load or Install All Libraries
# ================================
packages <- c(
"readxl","conflicted", "readr", "tidyr", "tibble", "pheatmap", "ggbreak",
"imputeLCMD", "openxlsx", "limma", "writexl", "showtext", "jsonlite", "curl",
"ggplot2",... |
49710c54e11b780c65b34aa7f6e92346b4d2729a1116b9ecf393ca402f2e1af3 | R | 1,138 | 30 | # --- make_score.R
# Inputs a FUSION wgt.RDat file and prints to screen a plink format score file for individual-level prediction
# ---
arg = commandArgs(trailingOnly=T)
if ( length(arg) == 0 || !file.exists(arg[1]) ) {
cat("\nUsage: Rscript make_score.R [wgt.RDat file] > [SCORE_FILE]\n")
cat("The SCORE_FILE can be lo... |
3e3e0efbfbd2082e5eec6e2413883f6919b7bcc69a98e57c1dd6b42b7ad604d1 | R | 1,149 | 28 | #Map and annotate integrated dataset with reference cell types from paper; run PCA and UMAP analyses ##########################################
#Load library
library(Seurat)
#Read integrated Seurat object
df = LoadSeuratRds("3_integrated_samples/integrated.rds")
#Load Seurat object from reference paper and run FindV... |
759d71f15cf0e91991bc335559289592e368c1589333029da9ef67338732823b | R | 1,151 | 25 | # ------------------------------------------------------------------------------
# Preprocessing Workflow using example data from Sturm et al. (2023)
#
# This script uses MNPpreprocessIllumina (Capper et al., 2018) for preprocessing.
# If this function is not available, similar methods (e.g., preprocessIllumina)
# may... |
5cda0c9e4299a321a488d2961556704b9acaf9bdde32d57f6f6a15ae2b34b584 | R | 1,152 | 29 | setwd("osmFISH_Ziesel/")
library(Seurat)
Zeisel <- read.delim(file = "data/Zeisel/expression_mRNA_17-Aug-2014.txt",header = FALSE)
meta.data <- Zeisel[1:10,]
rownames(meta.data) <- meta.data[,2]
meta.data <- meta.data[,-c(1,2)]
meta.data <- as.data.frame(t(meta.data))
Zeisel <- Zeisel[12:19983,]
gene_name... |
56c0068ae3c3ba81e1318137fedf1438fb7443647d1356dc72c286781b648a3e | R | 1,162 | 32 | context("Comprehensive Test for Binary Label Dataset Metric")
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_attrib... |
96dce7773f60fa5f9058d6d44e03d6ef4fd0e3591752dcf02ccd7c5a0a2e16a7 | R | 1,162 | 36 | #!/usr/bin/env Rscript
#### Merge CD45pos and CD45neg batches for scRNA-seq samples
#### Author: Jana Biermann, PhD
library(dplyr)
library(Seurat)
library(gplots)
library(ggplot2)
library(ggrastr)
pat <- commandArgs()[6] #provide patient name, e.g. 'MBM02_sc'
print(pat)
# Read in
scCD45neg <- readRDS(paste0('data/... |
5e56adc861236bf715713120de2745d65c070e74b1f43b65be858b4dd88bce70 | R | 1,164 | 30 | raw.counts = readRDS("/da/raid/spwang/project/00.aneuploid.embryo/00.norm.scnorm/embryo.14082cells.raw.counts.rds")
meta14082 = read.table("/da/raid/spwang/project/00.aneuploid.embryo/00.norm.scnorm/embryo_14082cells.metadata.upload.txt",sep = "\t",header = T,stringsAsFactors = F)
rownames(meta14082) = meta14082$Cell.... |
54e816922a6fc9d916150e13e424089b463a0426a800a79b06f4ba06af0815f8 | R | 1,169 | 37 | library(knitr)
library(markdown)
knit.report <- function(input.filename, output.filename,
stylesheet=getOption("markdown.HTML.stylesheet"), ...) {
input.filename <- normalizePath(input.filename)
output.filename <- normalizePath(output.filename)
output.dir <- dirname(output.filename... |
22235601a5b51177d4c538e9a26c733c627841cebb01710f1bc3758646c9e7c0 | R | 1,171 | 34 | #Plot Venn diagrams with intersections between cell populations ################################################################
#Load library
library(VennDiagram)
#List files
up_files = list.files(path=".", pattern="^up_")
down_files = list.files(path=".", pattern="^down_")
files = c(up_files, down_files)
#For each... |
0976c03f04f2c2820fd0419063cee90d9f9e5c2030cb753fc8a7344dda4c0507 | R | 1,174 | 37 | # load packages
require(tidyverse)
require(Seurat)
require(pbapply)
# load Seurat object and gene names
sdata.hg <- readRDS('sdata_Polioudakis_pseudotime.rds')
gene.names <- read_csv('Polioudakis_ensembl88_genes_edit.csv')
# get metadata, remove cells with NA pseudotime value
meta.data <- sdata.hg@meta.data %>%
ro... |
29f72ab3106451565bbd17a9aef6f6b8e7604382c4e3f706039e2dfe4ab8ae25 | R | 1,180 | 44 | options(max.print = 1000)
options(stringsAsFactors = FALSE)
options(scipen = 999)
library(data.table)
library(keras)
library(openxlsx)
args = commandArgs(trailingOnly=TRUE) # bam file name, CpG names, MARLIN model, class annotations
# load CpG names, ordered
load(args[2])
# load MARLIN model
model <- load_model_h... |
c895228847841944bd536ace4e33b192db086e2a1d0dcb8c5cba4e4e802a5d51 | R | 1,181 | 37 |
library("spatialLIBD")
library("here")
library("sessioninfo")
## load snRNA-seq DLPFC data
load(here("processed-data", "sce", "sce_DLPFC.Rdata"), verbose = TRUE)
## drop Ambiguous nuc
sce <- sce[, sce$cellType_broad_hc != "Ambiguous"]
sce$cellType_broad_hc <- droplevels(sce$cellType_broad_hc)
table(sce$cellType_bro... |
334377c5f2865efd18fbbf245aff8ec9853e094eae6cb207a40da8739edb9c24 | R | 1,182 | 18 | library(harmony)
library(Seurat)
library(qs)
seurat_integrated <- *seurat object after all filtering has been done post scvi and clustering; see scVI_Clustering.txt*
seurat_integrated <- SCTransform(seurat_integrated, vars.to.regress = c("percent.mt","percent.ribo", "batchCorrect"), assay = "RNA",
... |
21b59c18ac9734298dc6254205af04f1782c1b4c207fafe8860f3d47725dce2f | R | 1,184 | 31 | #Map and annotate integrated dataset with reference cell types; run PCA + UMAP analyses ###############
#Load library
library(Seurat)
#Read integrated Seurat object
df = LoadSeuratRds("3_integrated_samples/integrated.rds")
#Load Seurat object from reference paper
ref = LoadSeuratRds("integrated_1.5M_2M_harmonizedOBJ... |
87df09e9e0b683ce80561f31483e4e12f45ed722b0b01fa34741219993ee4386 | R | 1,187 | 36 | get_bulk_expANDout_harmonised_data <- function(exposure_data = bulkexposure,
outcome_data = bulkoutcome
) {
message("exposure_data = bulk exposure data input, dataframe")
message("outcome_data = bulk outcome data input, dataframe")
sour... |
fd46e6b05a23b2dd377543b597e2511c5ee8b8aa749b5e48a7ca78eef372bf7f | R | 1,201 | 34 | ### MNT add 07Apr2021 =======
# Check Read 1 files in expansion dataset for discrepant read lengths
# (as seen with Br5182-NAc in preprint dataset)
# Should be exactly 28bp = 16 [BC] + 12 [UMI]
library(ShortRead)
library(jaffelab)
FASTQ.dir <- "/dcs04/lieber/lcolladotor/rawDataTDSC_LIBD001/raw-data/2021-10-... |
5b605cc171dcef36f6454532e49b68b2e86c55e5e26a166b2c9548511fffc973 | R | 1,202 | 41 | library(Matrix)
library("UCell")
set.seed(123)
gene <- read.csv2('hallmark_surv_curated.csv', sep=',')
geneSets <- list()
for (col_name in names(gene)) {
geneSets[[col_name]] <- gene[[col_name]][!is.na(gene[[col_name]]) & gene[[col_name]] != ""]
}
calculate_and_save_scores <- function(folder_path) {
# Read fil... |
7d8c90ddd3051d227f3c0021b24da5c5cb24ece0ef9d93d60295d3de757e3d0a | R | 1,209 | 47 | # »æÖÆMEMEÔÚintergenic UCRµ±ÖÐÕÒµ½µÄ¸»¼¯µÄmotif logo
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(seqLogo)
library(tidyverse)
# CTCF <- read.table(file = "01-data/11.1-Get_intergenic_UCR_closest_pc_gene/MA0139.1.jaspar",
# header = FALSE)
# CTCF <- CTCF[, c(3:21)]
# rownames(C... |
bf4b7f1336ef8afd784f5d51c257c2fc23dfe955aa198b7154563fc648c2e007 | R | 1,209 | 42 | library(tidyverse)
# library(ggpubr)
# read in Halo-TDP quantification
df <- read_csv("data/2023_12_17_elk1_quantification_relative_normalised.csv")
# convert conditions in to single column
df <- df %>%
pivot_longer(cols = -sample, names_to = "condition", values_to = "rel_expr") %>%
# rename TDPKD to TDP43KD for ... |
0f4b0846f699d64d9a1d45ec33d4a94d332d86f6382819dd1c5c16b0d4f2adbe | R | 1,210 | 34 | tabPanel(
title = i18n$t("コールセンターの対応"),
icon = icon("headset"),
value = "callCenter",
fluidRow(
column(
width = 8,
echarts4rOutput("callCenter") %>% withSpinner()
),
column(
width = 4,
tagList(
tags$br(),
tags$b(i18n$t("これまでの主な相談内容")),
tags$li(i18n$t("... |
2f0bfa3e5c0bb9ea0a57e7e8c7f8ed9406bd09035170039598556a1d32f334f1 | R | 1,211 | 34 |
## Compare fetal and adult cell-type effect sizes ##
#1. Load results ================================================================================================================
# probes significant to either fetal or adult in Cell-type EWAS
res <- read.csv(paste0(AnalysisPath, "FACS_Cell_EWAS_fetal_anno_sig.c... |
3049c2a7b303c994671e18d77a3afc299bcb3076955ceb577a8f99f0aa9e527b | R | 1,213 | 36 | #' MajorMinorCalc
#'
#' Major to Minor Calculation function
#' @param Table The variable containing the table of SNPs
#' @param minDP minimal reading depth required from accepted SNP, usually set to 20
#' @param maxDP maximal reading depth required from accepted SNP, usually set to very high number
#' @param minAF mini... |
f378a9a5508ef589412acf908bdc9d75270f8b5bbe95cb0329f25ac7b9c2130d | R | 1,215 | 34 | # load packages
require(tidyverse)
require(Seurat)
# load Seurat objects (list)
sdata.list <- readRDS('sdata_mmCortex_list.rds')
# filter
sdata.list.filtered <- lapply(sdata.list, \(obj) {
# define percent_mito filter
mt.cutoff <- switch(unique(obj$time_point),
E12.5 = 2, E14.5 = 2, E16.0 = ... |
f95837f99eba24bf0ccb37019cc1406ef397823bf3050aa7a7ea9bda1efd2a51 | R | 1,220 | 35 | library("SummarizedExperiment")
library("spatialLIBD")
library("here")
library("sessioninfo")
library("scater")
plot_dir <- here("plots", "10_bulk_vs_sn_DE", "06_sn_reduced_dims")
if (!dir.exists(plot_dir)) dir.create(plot_dir)
load(here("processed-data", "00_data_prep", "cell_colors.Rdata"), verbose = TRUE)
my_them... |
ca15d5a00f839d275e0cca8a15fe5fc53bc238e856e57f53783a2a0300377ed4 | R | 1,223 | 33 | #!/usr/bin/env Rscript
#
# Author: Tim Sterne-Weiler, 2014
# tim.sterne.weiler@utoronto.ca
#
# Install/loading of required Packages.
loadPackages <- function(toLoad, local.lib="Rlib/") {
for(i in toLoad) {
writeLines(sprintf("Trying to load required package: %s", toString(i)), stderr())
if(!suppressWarning... |
35603026af437f80fe2daed027cefe152f5cbd0b00b3a8aab2fd61dd6de73f35 | R | 1,229 | 38 | suppressPackageStartupMessages(library(readr))
suppressPackageStartupMessages(library(dplyr))
args <- commandArgs(T)
# args = c(5e3, '~/Projects/circCenters/output2/visualize.circRNA/STAR_chimeric/STAR_chimeric/library/w1118_embryos_14_16_R1_RR.Chimeric.out.junction')
max.length = args[1]
## Test data
# STAR juncti... |
789a4e43007d276c27ca20ce517da91b9516cbd6e18c2de213c002701c912325 | R | 1,229 | 53 | # Siwei 15 Jan 2024
# plot fig. 5a
# init ####
library(gplots)
library(RColorBrewer)
library(readxl)
library(stringr)
# load data
df_raw <-
read_excel("Fig_5a.xlsx")
genotype_info <-
df_raw$Genotype
gene_names <-
df_raw$Gene_symbol
df_to_plot <-
df_raw
df_to_plot$Gene_symbol <- NULL
df_to_plot$Genotype <- ... |
13ee44022520f7aba4f0d5697adc91fa4910eea42611c6b18613da85d1fd145a | R | 1,234 | 36 | library("tidyverse")
library("scRepertoire")
vdj_dir <- "data/raw/VDJ"
figs <- "figures/scRepertoire/"
data_out <- "data/processed/"
contig_annots <- list.files(vdj_dir,
recursive = TRUE,
full.names = TRUE,
pattern = "all_contig_a... |
e40ea53c1774ab3a4cfcfebf38756446a89568e49fa675d191df0efc4479be30 | R | 1,235 | 38 | output$user <- renderUser({
dashboardUser(
name = "Su Wei",
image = "Icon/wei_su.jpg",
title = "Developer",
subtitle = "Front-End Engineer | ex-Bioinformatician",
footer = tagList(
actionButton(
inputId = "sponsorMe",
label = i18n$t("スポンサー"),
icon = icon("heart"),
... |
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