sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
a57569ac7a6f28f88a1be837e103301752e00ab5bd34c061f4433bde319128f0 | R | 1,236 | 22 | setwd("osmFISH_AllenVISp/")
library(Seurat)
allen <- read.table(file = "data/Allen_VISp/mouse_VISp_2018-06-14_exon-matrix.csv",
row.names = 1,sep = ',', stringsAsFactors = FALSE, header = TRUE)
allen <- as.matrix(x = allen)
genes <- read.table(file = "data/Allen_VISp/mouse_VISp_2018-06-14_gen... |
bfd41c67f709375a8c4a3de8b5f6e8d70057f7eeaf2f1b6138ed7466e843d719 | R | 1,236 | 22 | setwd("STARmap_AllenVISp/")
library(Seurat)
allen <- read.table(file = "data/Allen_VISp/mouse_VISp_2018-06-14_exon-matrix.csv",
row.names = 1,sep = ',', stringsAsFactors = FALSE, header = TRUE)
allen <- as.matrix(x = allen)
genes <- read.table(file = "data/Allen_VISp/mouse_VISp_2018-06-14_gen... |
f2f359c3d0c8136ef111bfae61a63a0bbcbf88f4c2b9b3633d1705981f0d1419 | R | 1,236 | 63 | # Siwei 10 Feb 2025
# plot use Sun et al. 2023
# init ####
{
library(ggplot2)
library(stringr)
library(lsr)
library(RColorBrewer)
library(ggpubr)
library(readr)
library(readxl)
library(stats)
library(rstatix)
library(agricolae)
library(DescTools)
# library
library(data.table)
}
df_raw... |
299a49f060f12c8b780654ae7b7388718fd4d1ddafe2823cc83828d45768b1c9 | R | 1,237 | 22 | # CytoTRACE
cytotraceresult <- CytoTRACE::CytoTRACE(mat = as.matrix(cDC@assays$RNA@counts),ncores = 20,
enableFast = F, batch=as.character(cDC$Batch))
# Monocle2
library(monocle)
diff_test_res <- differentialGeneTest(monocle_data,
fullModelF... |
ebdd5810ad4ac4c265f491548bb902e44a9007072af685a595885a92b9d65341 | R | 1,243 | 50 | # Siwei 22 Feb 2024
# plot Jubao's lysosome genes
# init ####
library(ggplot2)
library(RColorBrewer)
library(scales)
library(readxl)
df_raw <-
read_excel(path = "plot_lysosome_genes.xlsx")
df_2_plot <-
df_raw
df_2_plot$`-log10P` <-
0 - log10(df_2_plot$`p-value`)
df_2_plot$Gene <-
factor(df_2_plot$Gene,
... |
6b83fe66a8adcf7e081dbfa3a4796b5cf7612abfcf051e32afbf40a991c4b630 | R | 1,255 | 49 | # Siwei 01 Feb 2024
# Plot Jubao's Diseases bar plot
# init ####
library(readxl)
library(reshape2)
library(ggplot2)
library(scales)
library(RColorBrewer)
# load data #####
df_raw <-
read_excel("Jubao_Diseases_bar_plot_v2.xlsx")
df_melt <-
reshape2::melt(data = df_raw,
id = c(1, 10))
colnames(... |
5c9a4c5faef54fce48c3ec0a4156a197425ca6be9d6a98312901e43fa5f80350 | R | 1,256 | 38 | library(tidyverse)
# Because had accidentally kept events cryptic in only intermediate curve KDs, need to remove from BED to get accurate junction count
jncs <- read_tsv("processed/2023-09-12_papa_as_ale_cryptic.le.end_shift.junctions.bed",
col_names = c("chrom", "start", "end", "name", "score", "st... |
51cd4f53abcbe6b125674bb52d4af29c80641848e2254e2e96e79c6e294a991c | R | 1,259 | 51 | library(FKF)
# Observations
df <- read.csv("results_kalman_filter_stata.csv")
gdp = df$value
lgdp = log(gdp)
dlgdp = diff(lgdp)
# Stata parameters
params <- c(
0.40725515, 0.18782621, -0.01514009, -0.01027267, -0.03642297, 0.11576416,
0.02573029, -.00766572, 0.13506498, 0.08649569, 0.06942822, -0.10685783,
0.00... |
d0cdb066b92d94478f4f974236ca542694d76db474f545ae294004b2e65c21c8 | R | 1,270 | 46 |
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)
cor()
library(reshape2)
# creating correlation matrix
corr_mat <- round(cor(dfs[,12:17]),2)
# reduce the size of correlation matrix
... |
80969ec4ee66b0c7bc482eefc2d3a9c816c2a6d53fed424967730deab02531bd | R | 1,277 | 50 | # 18 Mar 2022
# make the input file of each disease (.ldct)
# init
library(stringr)
library(readr)
ld_files_list <-
read_delim("ldscore_file_list.ldcts",
delim = "\t", escape_double = FALSE,
col_names = FALSE, trim_ws = TRUE)
ld_files_list <- unlist(ld_files_list)
# make name column
ld_... |
57eea728371859a26106c41fff34808e48c80ae1c5add7274e5f6eb5ab099ae9 | R | 1,286 | 48 | # =============================================================================
# pySCENIC输出处理脚本
# =============================================================================
# 功能：处理pySCENIC转录因子分析结果
print("Hello world!")
rm(list = ls())
# 加载配置文件
source("../config.R")
# 设置工作目录
setwd(ENV_DIR)
lf <- list.files("./")
... |
659d34386ebf0592c0d94d138040712ec4e8ba754502094bfcad7dcdd756618e | R | 1,290 | 48 | #null simulation: take input data set
#1: expression data sets
#2: gene set directory. By default
#3: output file directory
args <- commandArgs(trailingOnly = TRUE)
options(warn = -1)
#load packages
suppressMessages(library("here"))
suppressMessages(library("magrittr"))
suppressMessages(library("tidyverse"))
#argume... |
a52c05f2e0f27ea500e6088ae687c080bf3662e99e2575d3fb4df977a7cf467b | R | 1,290 | 34 | 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(),
... |
0129e5e6b84a00363694e002546d7fb9fbb34b3c441245dea2ce88a0ddcf6291 | R | 1,295 | 39 | # find marker genes for clusters (e.g. cell types)
library(scran)
#-----------------------------
# example from tran et al 2021
#-----------------------------
# load data
sce <- get(load("SCE_DLPFC-n3_tran-etal_processed.rda"))
# subset cell types
ctv <- sce$cellType
which.ct <- sce$cellType %in% c("Astro", "OPC", "O... |
f9f520be21010564fc19ece5474161178c4e486a3cc8d9567dcaef2c33bdc27f | R | 1,298 | 42 | # quantify VPS45 at rs2027349 site
# Siwei 19 Dec 2019
# init
library(stats)
#
VPS45_isogenic_counts_samples <- VPS45_isogenic_counts_all_45M[, c(7:14)]
# VPS45_read <- data.frame(c(119, 106, 122, 113, 117, 107, 130, 50.7))
VPS45_read <- data.frame(raw_reads = 1:ncol(VPS45_isogenic_counts_samples))
VPS45_read$raw_re... |
5d816574449bc0c8556997c7f6822ce515f6da4baa7b66d64728a38816639b3c | R | 1,300 | 50 | setwd("STARmap_AllenVISp/")
library(Seurat)
starmap <- readRDS("data/seurat_objects/20180505_BY3_1kgenes.rds")
allen <- readRDS("data/seurat_objects/allen_brain.rds")
# remove HPC from starmap
class_labels <- read.table(
file = "data/Starmap/visual_1020/20180505_BY3_1kgenes/class_labels.csv",
sep = ",",
... |
228b7f0ee87f6000f30e10085207ef898ae829b346f4002dbe86559cfd2c1ccf | R | 1,313 | 40 | #### Figure 4A ####
#### upset plot for astrocytes ####
library(stringr)
library(ggplot2)
library(UpSetR)
library(here)
# pairwise comparisons:
# human vs chimp:
h_c <- read.table(here("data/astrocytes", "Astro_human_vs_chimp_sig_genes.csv"), sep=",", header=TRUE)
h_c <- h_c[h_c$padj<0.01 & (h_c$log2FoldChange<(-0.5... |
d5e9248bfffe634034e30c43212d7455ba10a30b6a6965ac7e486baa01de7ac8 | R | 1,316 | 38 | #' dmrff.sites
#'
#' Link genomic locations to a set of genomic regions.
#'
#' @param regions Data frame of genomic regions providing
#' chromosome (chr), start and end coordinates.
#' @param chr A vector providing the chromosome of each location.
#' @param pos A vector providing the chromosomal position of each locati... |
ef3b397be56ab73e0af3fcf07401f2b895f363983aa9858e09ef48a87fb28be4 | R | 1,319 | 32 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
list2env(rjson::fromJSON(file = "00_configs.json"), envir = .GlobalEnv)
library(Seurat)
library(qs)
library(foreach)
library(Signac)
library(ClustAssess)
objects_folder <- file.path(project_folder, "objects", "R", "seurat")
qc_folder <- file.path(project_fold... |
6c8119d8d8c4d618fabfbb723877b75ab44224b66ad2bde9fbb1d3cc4025e21f | R | 1,324 | 52 | #!/usr/bin/env R
#
# Author: Sean Maden
#
# Get the cell type statistic using findmarkers.
# this is a comparison combining subjects and replicates
# load data
# load marker genes
markers <- get(load("markerlist_celltypebroadk_dlpfc-ro1.rda"))
# load marker gene summary expr
lct <- get(load("lct-sstat-mean-median_nma... |
b1da4f8a4e5138c7228d0fa305e83c06c11cbebaf72f842455e2e901615b8640 | R | 1,333 | 54 | # Siwei 14 Feb 2024
# load HiC Data RDS files and export links
# init ####
library(Chicago)
# load data ####
CD11_0hr_5kb <-
readRDS("/data/FASTQ/HiC_RData/CD11-0_5000bp.Rds")
CD11_1hr_5kb <-
readRDS("/data/FASTQ/HiC_RData/CD11-1_5000bp.Rds")
CD11_6hr_5kb <-
readRDS("/data/FASTQ/HiC_RData/CD11-6_5000bp.Rds")
... |
7373fa917693ef788874eb3086fc0fbb56bb5e034bc16f0fda33b90b7cb9f5ca | R | 1,334 | 37 | library("utils")
# Package list generated by local grateful report
library("argparse")
library("base")
library("circlize")
library("ComplexHeatmap")
library("hdf5r")
library("janitor")
library("Matrix")
library("patchwork")
library("rhdf5")
library("scales")
library("scRepertoire")
library("Seurat")
library("SeuratDi... |
09e00ce615e3d4b84173866f8bbef7f43254dbcd91d3117ef66a4a6ed839b69b | R | 1,335 | 45 | #Normalize samples ##############################################################################################
#Load libraries
library(Seurat)
library(tidyverse)
#Create directory to store results of normalized samples
dir.create("2_results_norm", showWarnings=T)
#Create function to normalize samples
normalizeDat... |
98b691667465ab65d63c52ae4d034b4739d8a0610b9964d9922b27036404693c | R | 1,338 | 31 | #! /work/home/sdxgroup01/00envs/anaconda3/envs/R4.2.0/bin/R
library(optparse)
library(Seurat)
library(ggplot2)
library(dplyr)
library(reshape2)
library(openxlsx)
library(tools)
library(grid)
op_list <- list(
make_option(c("-i", "--input"), type = "character", default = NULL, action = "store", help = "The input of S... |
2dc83e1ce5e3d5e21135a416db0068b69969265af048576d76117820e9a87b55 | R | 1,341 | 41 | # load packages
require(tidyverse)
require(Seurat)
require(DropletUtils)
# load Seurat objects (list)
sdata.list <- readRDS('sdata_filtered_list.rds')
# get meta.data
meta.data <- lapply(sdata.list, \(obj) obj@meta.data) %>% bind_rows()
# calculate scale factors
x <- meta.data %>%
group_by(time_point, sample_id) ... |
e805e716cf3188c7f4bf8c298a0165c5fe2f96846dbe35d676beba59afc32a83 | R | 1,341 | 39 | #### Figure S6B ####
#### Upset plot for microglia - great apes ####
library(stringr)
library(ggplot2)
library(UpSetR)
library(here)
# pairwise comparisons:
# human vs chimp:
h_c <- read.table(here("data/microglia", "Micro-PVM_human_vs_chimp_sig_genes.csv"), sep=",", header=TRUE)
h_c <- h_c[h_c$padj<0.01 & (h_c$log... |
322a6192335721c201bc95c62a0f7dffdcc527f763e54c99b4754e1875466ff5 | R | 1,350 | 36 | library(RCy3)
library(netZooR)
library(twice)
pbd_obj <- readRDS("data/pbd_obj.rds")
data("hg19rmsk_info")
# cluster 1
y_kznf <- kznf_infer %>%filter(age=="young")
hs_p <- pbd_obj$hmc1_corr %>% filter(coef>0) %>% mutate(species="Hs")
pp_n <- pbd_obj$ppc1_corr %>% filter(coef<0) %>% mutate(species="Pp")
intersect_hs... |
fa641b580d2186f89161a09abb63408fcdf03161d8648516826361e4fe075ae7 | R | 1,356 | 47 | ---
title: "GNN Enrich demo speed profiling"
output: html_document
params:
input: "perf.tsv"
---
```{r}
data <- read.table(params$input, header=TRUE, quote="", stringsAsFactors=FALSE, sep="\t")
data$percent_CPU <- as.integer(gsub("%$", "", data$percent_CPU))
data
```
Reshape to have each n_proc in a column:
```{r... |
833f210e788fec5afbad18f75c1c8d0934b1fd0a47e9fdcf57766b2b98536d9f | R | 1,357 | 39 | #### Figure S6I ####
#### Upset plot for oligodendrocytes - great apes ####
library(stringr)
library(ggplot2)
library(UpSetR)
library(here)
# pairwise comparisons:
# human vs chimp:
h_c <- read.table(here("data/oligodendrocytes", "Oligo_human_vs_chimp_sig_genes.csv"), sep=",", header=TRUE)
h_c <- h_c[h_c$padj<0.01 ... |
da8d73d648b37839f23655872526b7170eb380c47edf8dfaf529bf2c30ae2d43 | R | 1,357 | 19 | ```{r}
# MERFISH brain receptor map
suppressPackageStartupMessages(library(xfun))
pkgs = c("SingleCellExperiment","tidyverse","data.table","dendextend","fossil","gridExtra","gplots","metaSEM","foreach","Matrix","grid","spdep","diptest","ggbeeswarm","Signac","metafor",
"matrixStats","remotes","ggpubr","ggridge... |
e2c5d967b0777ef338f6dba78dbb40a88d1e40b027cdbc136ab99f4dcd47fe16 | R | 1,359 | 42 | 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]
min.read_counts = args[2]
... |
77de6f1fe0e9718ae35c5b09d3d4494277df05adddf50884c5b20c53866b39db | R | 1,360 | 46 | ## Generate PCCF reference values via OLS regression
##
## The partial cross-correlation at lag h between x and y is defined as
## the Pearson correlation between the residuals from regressing x[t]
## and y[t+h] on all intervening observations {x[t+j], y[t+j]: j=1..h-1}
## plus intercept. At lag 1 there are no interve... |
a7a595ef91b5119442aa86769afc4bd05c34b47cbf6144cbf3f939a692559c3c | R | 1,369 | 39 | options(stringsAsFactors = FALSE)
library(ggplot2)
library(reshape2)
library(dplyr)
library(stringr)
library(lme4)
library(lmerTest)
library(RColorBrewer)
library(ggpubr)
library(circlize)
#### Circos plot of CTE cases ####
# load PTA SNV calls in CTE
snv_list <- read.table("data/TableS4_PTA_calls_snv.tsv", sep="\t",... |
a1837dfb5ab06ffa98110c22a6f71f511d61ac86c0437db0d9ace4dfbbc1e508 | R | 1,375 | 52 | library("SingleCellExperiment")
library("rafalib")
library("iSEE")
library("lobstr")
library("here")
library("whisker")
library("usethis")
library("withr")
library("rsconnect")
library("sessioninfo")
load(here("rdas", "revision", "regionSpecific_DLPFC-n3_cleaned-combined_SCE_LAH2021.rda"), verbose = TRUE)
source(here... |
f77be8237a380eecc372bb13b0aad419550daa332d34561042ffbe28fd0dff57 | R | 1,375 | 49 | Component.Tendency <- function() {
box(
# 国内状況推移
title = tagList(icon("chart-line"), i18n$t("国内状況推移")),
closable = F,
collapsible = T,
collapsed = F,
enable_label = T,
label = boxLabel(
i18n$t("実況中"),
status = "info"
),
footer = tags$small(icon("lightbulb"), i18n$t("凡例... |
df9966e9f02348122501409730e6ce848aea37ee78a717e9add538df955f8954 | R | 1,379 | 42 |
# library("SummarizedExperiment")
library("tidyverse")
library("here")
library("sessioninfo")
# library("jaffelab")
#### Load data ####
## marker_stats
load(here("processed-data", "06_marker_genes", "03_find_markers_broad", "marker_stats_broad.Rdata"), verbose = TRUE)
marker_stats |>
filter(ratio > 1) |>
group_b... |
b534ff81948eaacf557cf995b3ca4c10c9bb759196af10672f17fbf5c88ba909 | R | 1,382 | 42 | #' @title plotbeta
#' perform beta coefficient barplot on Cox results
#' @param df results coxbycol function
#' @param nb number of covariates to plot
#' @param title title of the graph
#' @param size size of labels
#' @usage data(cancer)
#' @usage library(dplyr)
#' @usage cancer%>%select(3:7)->data
#' @usage df<-coxb... |
7754cfc02d3c071554257e3b7365460d904aabade17e25602fb731ba65522e72 | R | 1,392 | 46 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
list2env(rjson::fromJSON(file = "00_configs.json"), envir = .GlobalEnv)
library(Seurat)
library(ClustAssess)
library(qs)
objects_folder <- file.path(project_folder, "objects", "R")
so_folder <- file.path(objects_folder, "seurat")
ca_folder <- file.path(object... |
3ba1d48211ab5d2b6b73f3f74590a1a44eab6f863e98ded42cde533d0b195a7c | R | 1,395 | 54 | # install packages
install.packages("vegan")
# load libraries
library(vegan)
library(ggplot2)
library(stats)
library(readr)
# Load data
values <-read_csv("../data/multi-voxels_dataset.csv",col_names = FALSE)
# Create Gram matrices
DMN<-t(values)
DMN_XX<-crossprod(DMN)
# Standardize the data
DMN_XX.st... |
8ef55cd534c75e66c0fe155fcc35f0802ef9bbb3b673183990cf270eb93b5d63 | R | 1,395 | 48 | #Script with functions to modify function implemented in "DropSeq.util" package for reading in data of DropViz
#Original function did not work well
if (!require("Matrix")) {
install.packages("Matrix")
library("Matrix")
}
strStartsWith = function (theString, thePrefix)
{
return(strtrim(theString, nchar(thePrefi... |
27d71395eadb76173f2cfc4228c8747e3d823f9985d9183a576d3d55eaf465b7 | R | 1,399 | 41 | add_samplesize_info <- function(dat=dat,samplesize=samplesize)
{
load("/mnt/data/lijincheng/mGWAS/result/01UVMR_immune_BBB/immune_BBB_info.Rdata")
message("dat= bulk harmonised data")
message("samplesize= 'samplesize.exposure' or 'samplesize.outcome' ")
library(dplyr)
library(tidyverse)
BBB_immune_i... |
426c49c1fe1835a87e6fdcca1da93914d1bb5b1100d17a71d2c29dac3585ca36 | R | 1,402 | 49 |
#Find the perturbgenes which are shared across different cell types
cells=c("Astrocyte","Oligodendrocyte","Microglial_cell","Excitatory_neuron")
for (cell in cells){
for (el in c("Shared", "Contrasting")){
nam <- paste("Perturbator_genes", cell, el, sep = "_")
path_file= sprintf("./%s/%s/BruteForcePertur... |
6041fcc61b25808fdc6d39a839a3d9fb1b41ce1fda2fcfe0028d060a6d2e0420 | R | 1,404 | 46 | if (!require("here")) {
install.packages("here")
library("here")
}
if (!require("magrittr")) {
install.packages("magrittr")
library("magrittr")
}
if(!require("tidyverse")) {
install.packages("tidyverse")
library("tidyverse")
}
if (!require("SingleCellExperiment")) {
if (!requireNamespace("BiocManager", qu... |
3d9dc4cd37ffa611d56893072357b4d5d90066e582e0b1a7d084cfa530a82b72 | R | 1,406 | 49 | #Script used to retreive number of unclassified/low quality classified and total reads
load("Y:/Microbiome/NDcollect/HQ/tablesBS/2018-12-07workspace.RData")
#Number of sequences for each sample classified and retained for relative abundances computation
abbAmpSp=vector()
abbAmpGn=vector()
for (i in 1:length(matList... |
6691e4dea99c4ec0e998fd4be35de68fe7ccbe9109b1cde88790e406baabe1f9 | R | 1,411 | 40 | #!/usr/bin/env Rscript
#### Combining counts after running Kallisto
#### Authors: Somnath Tagore, PhD; Jana Biermann, PhD
library(dplyr)
library(tximport)
library(biomaRt)
library(readr)
library(rhdf5)
library(AnnotationDbi)
sample_dir <- 'data/celllines_RNAseq'
sample_names <- grep('-', x = list.files(sample_dir), ... |
8ed7c87d54a3e876569fbcb3461637da3f607b4b8ca83fd4fad5212ef21f2f37 | R | 1,414 | 39 | args <- commandArgs(trailingOnly = T)
library(tidyverse)
# gemma assoc is args[1];
# lmm output
# chr rs ps n_miss allele1 allele0 af beta se logl_H1 l_remle p_wald
assoc <- read.table(args[1], header = T, as.is = T)
## allele list is args[2]; no header; first 3 columns (SNP,A1,A2)
allele ... |
9b9085133de7990be9d7fd9e0a6f4cda66ff66a0cf1b281ba52158ad8d6549e6 | R | 1,414 | 46 | # =============================================================================
# 巨噬细胞分化轨迹分析脚本
# =============================================================================
# 功能：使用CytoTRACE方法计算细胞分化潜能，识别高/低分化潜能细胞亚群
print("Hello world!")
rm(list = ls())
# 加载配置文件
source("../config.R")
# 设置工作目录
setwd(ENV_DIR)
lf <- li... |
7358a308cf41cba801e5516f3e86918178e59a708b3421320eaa0f50a4dec9fa | R | 1,416 | 45 | library(magrittr)
library(data.table)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggrepel)
library(Hmisc)
library(cowplot)
library(DescTools)
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
meth <- 'spearman'
sanger_dat <- rbindlist(lapply(seq(5)-1, function(x) read.csv(paste0('../results/dat... |
f39b30e7ab458658cb6d25206d1291ef62b4c6651bfb88eaa6c1146608120dc3 | R | 1,416 | 58 | #!/usr/bin/env Rscript
print("##################################################")
print("# cisTopic #")
print("##################################################")
# Loading dependencies scripts
library("optparse")
parser <- OptionParser(
prog = "create_cistopic_object.R",
description = "Create cisTopic object ... |
eccf20beec89c02b63c8718a436fb443595ff9ac2b207840e2d9b4b3bbec3b6f | R | 1,417 | 25 | context("compareRhythms")
load("test_data_ma.rda")
test_that("input validation works", {
expect_error(compareRhythms(expr, NULL))
expect_error(compareRhythms(expr, exp_design[-1,]))
expect_error(compareRhythms(expr, exp_design[,-2]))
expect_error(compareRhythms(expr, exp_design[,-4]))
expect_error(compareRh... |
cb9c617f8ab76408a6a6560ea5ee00913d13fe6baab2bcecca0a84f85ef4ef8d | R | 1,422 | 35 | ### MNT add 07Apr2021 =======
# Check Read 1 files for discrepant read lengths, as seen with Br5182-NAc:
# Should be exactly 28bp = 16 [BC] + 12 [UMI]
library(ShortRead)
library(jaffelab)
FASTQ.dir <- "/dcs04/lieber/lcolladotor/rawDataTDSC_LIBD001/raw-data/2021-10-15_Tran2021_published/"
### Read in preprint 'sa... |
fcd9fa7808232f0675393320fcf86bce3fcfe20f203e7f0cc317b7958bb33f60 | R | 1,422 | 34 |
## TEST
# test_markers <- list(A = paste0("Gene_000", 1:4),
# B = paste0("Gene_000", 5:8))
#
# plotExpressionCustom(sce = example_sce, features = test_markers$A, "A", anno_name = "Cell_Cycle")
plotExpressionCustom <- function(sce, features, features_name, anno_name = "cellType",
... |
a3d61580fc4b382631d30076e8122ebcc592ae9d26057a1574a875021bb63053 | R | 1,423 | 52 | if(!require(limma))
if(!require(Seurat))
if(!require(dplyr))
if(!require(magrittr))
if(!require(devtools))
if(!require(ggplot2))
if(!require(scater))
if(!require(dplyr))
#BiocManager::install("org.Mm.eg.db")
#BiocManager::install("org.Hs.eg.db")
#BiocManager::install("org.Rn.eg.db")
if(!require(msigdbr))
if(!require(fg... |
f33eecc5abc1378dea3abe1fe3dfd6dfb266c958d63907a48b57b83731625ef5 | R | 1,426 | 52 | library("SingleCellExperiment")
library("rafalib")
library("iSEE")
library("pryr")
library("here")
library("whisker")
library("usethis")
library("withr")
library("sessioninfo")
load(here("rdas", "regionSpecific_HPC-n3_cleaned-combined_SCE_MNTFeb2020.rda"), verbose = TRUE)
source(here("shiny_apps", "00_clean_functions... |
e4c4b232a5364b33da8d3fbae7ac445feeba730800c7ea5f9bcdedb6dea8e8f4 | R | 1,428 | 53 | # »æÖƽ«¼¸×é»ùÒòµÄ¸»¼¯·ÖÎö½á¹û½øÐбȽϵÄÆøÅÝͼ
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(ggplot2)
library(readxl)
library(ggsci)
compare_GO <- read_xlsx(path = "02-analysis/10-GO_enrichment_analysis/compare_GO_table.xlsx", sheet = 1)
lwd_pt <- .pt*72.27/96
p1 <- ggplot(dat... |
9dcd56f555e9eb01520066dba02667156527651261c6de267312372f78dab4a4 | R | 1,429 | 44 | # Usage: Rscript 01.count_GeneReadNumber_ERCC.R gencode.v19.chr_patch_hapl_scaff.annotation.gtf xxx/bamfile/*.bam gencode.v19.chr_patch_hapl_scaff.annotation.gene.length.txt output.genecount.txt
args= commandArgs(T)
library(GenomicFeatures)
library(GenomicAlignments)
library(data.table)
gtf=args[1]
bam=args[2]
geneL... |
e45897b79ffde8b59b7b10d0fd0cce4872105cd39ffb51a05481886fb7fef8bc | R | 1,430 | 67 | # lncRNAµÄ¸»¼¯·ÖÎöÏÔʾºÍgland·¢Óý£¬ÑõÆøÓ¦´ðµÈÓйØ
# ÔÚ¸÷×éÖ¯Æ÷¹Ù¿´¿´ÕâЩlncRNAµÄ±í´ïpattern£¬ÍƲâÆ书ÄÜ
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(biomaRt)
library(curl)
ncRNA_UCR_ensembl_id <- read.table(file = "02-analysis/16-New_classification/ncRNA_UCR_ensembl_id.txt")
nc... |
4d225b9612171e5bf47cbaeb56438c63b5a98d3e6af7c9386b75db7f44e0bd66 | R | 1,431 | 45 | # quantify VPS45 at rs2027349 site
# quantify multiple regions
# Siwei 20 Dec 2019
# init
library(stats)
#
VPS45_isogenic_counts_samples <- VPS45_isogenic_counts_all_45M[, c(7:14)]
# VPS45_read <- data.frame(c(119, 106, 122, 113, 117, 107, 130, 50.7))
VPS45_read <- data.frame(base_1 = 1:ncol(VPS45_isogenic_counts_sa... |
64ef19b4ddad78a4415d0d319c4168f576bd62b391f0a02033e47079074429a0 | R | 1,442 | 42 | load('MSBB_methylation_EPIC_n201.rdata')
load("MSBB_Mayo_RNA_bulk_count_meta.rdata")
rm(bulk_mayo, meta_mayo, meth_msbb, meth_meta_msbb)
library(readxl)
multiplesheets <- function(fname) {
sheets <- readxl::excel_sheets(fname)
tibble <- lapply(sheets, function(x) readxl::read_excel(fname, sheet = x))
data... |
943f46f647d3ea77e633674764c858c2a6742400738cc7a1e9a85b0c36ce0dc0 | R | 1,442 | 42 | #' Run differential rhythmicity analysis for RNA-seq data using limma-voom
#'
#' @param counts A matrix of expression counts
#' @inheritParams compareRhythms
#' @keywords internal
compareRhythms_voom <- function(counts, exp_design, period, rhythm_fdr,
compare_fdr, amp_cutoff, just_clas... |
542b6720730dcdc43dcafaf7f5f186949c1959cde9cd433cc3c555349bcf3772 | R | 1,445 | 46 |
library("SummarizedExperiment")
library("spatialLIBD")
library("here")
library("sessioninfo")
## load our 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_h... |
acf9deaccc98600009a6611f43ff5802d5b894c7576a7b199f18c819b7c04e8c | R | 1,447 | 48 | ---
title: "Home"
site: workflowr::wflow_site
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
Laura Cook
# Workflow and analysis of comparative craniofacial ChIP-seq work in the fat-tailed dunnart and mouse
This project involves a number of analysis for the generation... |
be4d2d9e669be7dcf8167e205343854ee5a16bee3b04e259bca1e9eafb1f24f8 | R | 1,447 | 24 | get_UVMR_sig <- function(input = input )
{
message("input = output of get_UVMR_collected function, a dataframe ")
#significant association criteria:
##1. cML as primary analysis for quantify the effect due to its robustness to both correlated and uncorrelated pleiotropic effects and consideration of potential bai... |
e5adaf0cf8facf10267eae32fbb2819f83c8a647c0514a1d58d9bedc30bd0c67 | R | 1,447 | 43 | #' @title plotnlphr
#' perform negative log10 pval. barplot on Cox results
#' @param df results coxbycol function
#' @param nb number of covariates to plot
#' @param title title of the graph
#' @param size size of labels
#' @usage data(cancer)
#' @usage library(dplyr)
#' @usage cancer%>%select(3:7)->data
#' @usage df<... |
8e00ec0ca4d2038ac3c8368c993723027d9118dcc636873ef18fca24c2ad1686 | R | 1,453 | 34 |
## Resave ASD GWAS summary statistics for input into MAGMA gene set analysis ##
library(data.table)
#1. Load ASD GWAS results =======================================================================================================
res <- fread(paste0(MAGMApath, "iPSYCH-PGC_ASD_Nov2017"), data.table=F)
dim(res)
#9112... |
a2a934761e3aaf2b453f97cc83935fa269a95a1f13f7fcdfc3ba8677828b884a | R | 1,458 | 52 |
#### =========================================================
#### metaNeighbor
#### =========================================================
#### Load Packages
library(MetaNeighbor)
library(SummarizedExperiment)
library(stringr)
library(ComplexHeatmap)
#### 1. Load Data
setwd("~/dat/Immune/03heatmap")
sce <- read... |
d283146991bcdee3ecf15acb02261a1a9a38dac428381055edebaa3f7408976d | R | 1,458 | 30 | library(data.table)
number_check <- function(cases_sum_source, path) {
cases_sum_source <- cases_sum_source[2:length(cases_sum_source)]
cases <- fread("50_Data/byDate.csv")
cases_sum_project <- colSums(cases, na.rm = TRUE)
cases_sum_project <- cases_sum_project[2:length(cases_sum_project)]
# クルーズ船
ca... |
272db1f7b80feec4b43bf148abdf904373ffa6ad7e7a0af8c877f8ab3843ca13 | R | 1,463 | 49 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
list2env(rjson::fromJSON(file = "00_configs.json"), envir = .GlobalEnv)
library(Seurat)
library(Signac)
library(ClustAssess)
library(qs)
library(rhdf5)
library(dplyr)
library(future)
library(doParallel)
library(ComplexHeatmap)
library(ggplot2)
seurat_path <- ... |
9a0bcaa7e03e3b9bb8c893ef9a3fb03c0a42dcf71b5550e9b63e9a70b1cd5c9b | R | 1,464 | 36 | # ÏÈ»ñµÃÁ½¸ö´óÀࣺ1¡¢Óëµ°°×±àÂë»ùÒòÖصþµÄcoding_UCR£»2¡¢ºÍ·Ç±àÂëRNAÖصþµÄncRNA_UCR£¨Ö»ÓÐÒ»¸ö²»ÊÇlncRNA£©
#
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(dbplyr)
Gene_Non_Pr <- read.table(file = "01-data/16-New_classification/Gene_Non_Protein_Coding.bed",
sep = "\t")
Gen... |
d0706b1b9eb04948a07de4e51f2dc7dd5e49b2dc55e3edbe73bad4a2939ebdfc | R | 1,465 | 42 | library(pscl)
library(readr)
# Load and save biochemists data ------------------------------------------
data("bioChemists", package = "pscl")
head(bioChemists)
#encode design matrix
design <- cbind.data.frame(art=bioChemists$art, model.matrix(art~., bioChemists)[,-1])
colnames(design) <- colnames(bioChemists)
head(... |
9a92c1300312d151f1a5fb2786085c57ba6aeb46de98a30912d393c055c6e326 | R | 1,466 | 65 | # ÓÃLncRNAsPathwayÎÄÕÂÀï¸øµÄÊý¾Ý²âÊÔÒ»ÏÂÎÒʹÓõķ½·¨¶Ô²»¶Ô
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(LncPath)
library(stringr)
diff_exp_lnc <- read.table(file = "01-data/38.1-LncRNAs2Pathway_example_test/Table_S3_DELncRNA_prostate_cancer.txt",
sep... |
23d43135aaabca6c34c0b7fb23f4f7f08ca3bd2946cba94c4fb1c21add4edf28 | R | 1,469 | 46 | ##
## Load packages. If missing, try installing from CRAN and Bioconductor.
##
load_install_packages = function(package_list) {
# create local user library path if not present (not present by default)
r_libs_user_dir = Sys.getenv("R_LIBS_USER")
if (!dir.exists(r_libs_user_dir)) {
message("creating local R_... |
eba9526627fa39aa236db1514ed00a69f9dd95b4315e7ca7871c3b3ef4b5165b | R | 1,470 | 37 | ## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
require(dplyr)
## ----setup--------------------------------------------------------------------
library(SpliceFlow)
library(GenomicAlignments)
library(Rsamtools)
library(Summari... |
3307b75661754a13e8245461af7801632d82fa1b8ce0d945b194be9099d041be | R | 1,472 | 66 | rm(list = ls())
library(VennDiagram)
library(RColorBrewer)
library(ggplot2)
myCol <- brewer.pal(4, "Pastel2")
futile.logger::flog.threshold(futile.logger::ERROR, name = "VennDiagramLogger")
cog <- read.csv("output/BULK_ROS/all_cog_corrected_genes.csv")
cog <- cog[cog$p.adj < 0.1,]$gene
adres <- read.csv("output... |
3d93d2b40ebc93d763632340942aa0997e8401365bc1456e809fbf6f5c86ae26 | R | 1,489 | 42 | library("DeconvoBuddies")
library("SingleCellExperiment")
library("tidyverse")
library("here")
library("sessioninfo")
## load our 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 <- dropleve... |
64f17c235d3f15a0b32159f3255af2cd92706483892558dd428b71300116be5f | R | 1,490 | 32 | library(getopt)
commonds = matrix(c("stat","W",1,"character","output","O",1,"character","batch","B",1,"character"),byrow=TRUE, ncol=4)
opt = getopt(commonds)
list.file = list.files(opt$stat, pattern = ".txt")
list.file = list.file[grep("genecount",list.file)]
filelist <- lapply(as.list(paste0(opt$stat,"/",list.file))... |
39a4eb0dd19ae973d1372078cd38693298402c5fb426f7e4295df3e9621137ef | R | 1,492 | 31 | #### Figure panels: S6L ####
#### Gene expression in oligodendrocytes ####
library(ggplot2)
library(here)
# gene expression:
data <- readRDS(here("data/oligodendrocytes","Oligo1_human_vs_all_dds.RDS"))
norm.data <- DESeq2::counts(data, normalized=TRUE)
gene = "CNTNAP2" ##gene of interest
gene.counts <- data.frame(nor... |
9d6f4c1d0b6e456dd640aaf0c2e3ffc838cb3c30959e18fae1902c09c626c65c | R | 1,494 | 35 | prepareFlowRegions <- function(refAnnotation.path, junctions){
intron_features<- retrieveIntrons(refAnnotation.path, junctions)
regionsFlow <- RegionsFlow()
# Make pair regions and remove redundancy
# prepare metadata column
message("preparing regions to quantify quantFlow")
# prepare quantFlow regions
# ... |
eaa46e99b4316be28ee36b2071efc6dd6f084bc31558d8ff5e7614b1c3038e27 | R | 1,494 | 31 | #### Figure panels: S6G ####
#### Expression of disease risk genes in microglia ####
library(ggplot2)
library(here)
# gene expression:
data <- readRDS(here("data/microglia","MicroPVM1_human_vs_all_dds.RDS"))
norm.data <- DESeq2::counts(data, normalized=TRUE)
gene = "APP" ##gene of interest
gene.counts <- data.frame(n... |
2c56a5b1729737724a1a978768bdc44bfb35ee42f21f1f8bf6ae275d4c71c93a | R | 1,495 | 46 | #!/usr/bin/env Rscript
suppressPackageStartupMessages({
library(diffUTR)
library(SummarizedExperiment)
library(rtracklayer)
library(utils)
})
# extract arguments
args <- commandArgs(trailingOnly = T)
input.df <- read.csv(args[1])
bed.rds <- args[2]
out.file <- args[3]
bamfiles <- list.files(input.df$folder_t... |
1cb7bb72b8e98f4c27f4e91c178e4eedb06ad49a6c936d4b618ce5d80f5c05a6 | R | 1,502 | 31 | setwd("osmFISH_Ziesel/")
library(Seurat)
library(hdf5r)
library(methods)
osm <- H5File$new("data/osmFISH/osmFISH_SScortex_mouse_all_cells.loom")
mat <- osm[['matrix']][,]
colnames(mat) <- osm[['row_attrs']][['Gene']][]
rownames(mat) <- paste0('osm_', osm[['col_attrs']][['CellID']][])
x_dim <- osm[['col_attrs'... |
dcf4fa0db43fee852844be6dd1e02aecd26775e9e05c68e9bc84af8ca01c1c64 | R | 1,502 | 38 | local_clumb <- function (dat, clump_kb = 10000, clump_r2 = 0.001, clump_p1 = 1,
clump_p2 = 1, pop = "EUR")
{
pval_column <- "pval.exposure"
if (!is.data.frame(dat)) {
stop("Expecting data frame returned from format_data")
}
if ("pval.exposure" %in% names(dat) & "pval.outcome" %in%
... |
2c09cf7124624e2db297bc75da45666be696c24a9e091cde8466c461a5f7cd33 | R | 1,504 | 31 | setwd("osmFISH_AllenSSp/")
library(Seurat)
library(hdf5r)
library(methods)
osm <- H5File$new("data/osmFISH/osmFISH_SScortex_mouse_all_cells.loom")
mat <- osm[['matrix']][,]
colnames(mat) <- osm[['row_attrs']][['Gene']][]
rownames(mat) <- paste0('osm_', osm[['col_attrs']][['CellID']][])
x_dim <- osm[['col_attr... |
41a1b618e24421a2857cbd8315344732c09278e8e93a6cd4b407657be5c29989 | R | 1,505 | 31 | setwd("osmFISH_AllenVISp/")
library(Seurat)
library(hdf5r)
library(methods)
osm <- H5File$new("data/osmFISH/osmFISH_SScortex_mouse_all_cells.loom")
mat <- osm[['matrix']][,]
colnames(mat) <- osm[['row_attrs']][['Gene']][]
rownames(mat) <- paste0('osm_', osm[['col_attrs']][['CellID']][])
x_dim <- osm[['col_att... |
74ff06f092e441b59d7991176809c8d9756279b3ba37a31e0ca3cacdf18b59f1 | R | 1,515 | 45 | #S1
library(dplyr)
library(twice)
# loading tables
data("primateGRFs")
kznf_infer_new <- kznf_infer
gentree <- readxl::read_xlsx("~/Downloads/hg38_ver95_age(2).xlsx")
kznf_infer_new <- kznf_infer_new %>%
left_join(gentree[,c(1,3)], join_by(ensembl_gene_id==gene))
# 127 needs to be check
kznf_infer_check <- subse... |
1749b116e7d8da38a94b22623ec009df30dfc018a18235f9cb6e05df2fba63ce | R | 1,516 | 31 | #### Figure panels: 4G, 4I, S5F, and S5H ####
#### Expression of cell surface molecules in astrocytes ####
library(ggplot2)
library(here)
# gene expression:
data <- readRDS(here("data/astrocytes","Astro1_human_vs_all_dds.RDS"))
norm.data <- DESeq2::counts(data, normalized=TRUE)
gene = "SLIT1" ##gene of interest
gene.... |
4ac069ca67e1dd223c03d05c33adb28a7aba6f7f445b6b2a3f4519b9bbeca3de | R | 1,516 | 45 | # Preprocessing Quality Assurance
# Supplemental Table 2
# Author: E. Pedapati
# Version: 3/27/2022
pacman::p_load(labelled, compareGroups, tidyverse, BiocManager, filesstrings)
# source(file = "_Common.R")
source.demo <- "https://figshare.com/ndownloader/files/34517351"
source.qi <- "https://figshare.co... |
046682e49bb399b17b0613a520bee2cc4aadb871413af82a0803dec307f92874 | R | 1,518 | 48 | library(Seurat)
library(Signac)
library(BPCells)
library(ggplot2)
object_sub <- readRDS("merged_object.rds")
#ASTROCYTES
astro <- subset(object_sub, cell_type=="Astro")
astro <- FindVariableFeatures(astro)
astro <- ScaleData(astro)
astro <- RunPCA(astro)
astro <- FindNeighbors(astro, dims = 1:15)
ast... |
66dacb495e256c44e1608cbff4d7284ae39fe2007fc82e50154d64d7804de7a8 | R | 1,518 | 34 | library(data.table)
pref <- list("福岡県" = "Fukuoka")
signateDetail <- fread(paste0(DATA_PATH, "SIGNATE COVID-2019 Dataset - 罹患者.csv"), header = T)
positiveDetail <- signateDetail[受診都道府県 == names(pref)]
signateRelation <- fread(paste0(DATA_PATH, "/Signate/relation.csv"), header = T)
relationDt <- signateRelation[`都道府... |
8b7ef4e1f643594a55857f8c8791f0beea10e4bc14c0712e641dba724dd9c181 | R | 1,521 | 59 | library(tidyverse)
library(broom)
library(readxl)
library(rstatix)
#
n_df <- read_csv("data/number_foci_ratio.csv")
n_df
#
ratio_df <- read_csv("data/nuclear_cyto_ratio.csv")
ratio_df
n_green_ttest <- t.test(n_df$Green, mu = 1) %>%
broom::tidy()
n_red_ttest <- t.test(n_df$Red, mu = 1) %>%
broom::tidy()
ratio_... |
244413a162e47a35856fc7780a232154bf980ef8d37cc72f45e9988f8ad26d76 | R | 1,522 | 25 | get_UVMR_mainfdr <- function(input = input )
{
message("input = output of get_UVMR_collected function, a dataframe ")
#significant association criteria:
##1. cML as primary analysis for quantify the effect due to its robustness to both correlated and uncorrelated pleiotropic effects and consideration of potential... |
aa919309cad2d1d13b55f464b577ea2afa73b2fa234de2a836a69ec9a72cd99c | R | 1,525 | 49 | ---
title: "SpliceFlow"
author: "Carlos Alfonso Gonzalez"
date: "2024-06-12"
output: html_document
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
require(Rsamtools)
require(GenomicAlignments)
```
## Build intron database
The function `prepareFlowRegion... |
61d9a7aa22116ad68c2a4ac26b2bc3784b360baa6543d47d3357c750b0939f8c | R | 1,528 | 34 |
## Resave SCZ GWAS summary statistics for input into MAGMA gene set analysis ##
library(data.table)
#1. Load SZ GWAS results ========================================================================================================
res <- fread(paste0(MAGMApath, "PGC3_SCZ_wave3.primary.autosome.public.v3.vcf.tsv"), d... |
173d37a5bcad31273e7d679a575a699527b9a656645b604816a4d555d8b01cb8 | R | 1,532 | 25 | get_UVMR_mainbonf <- function(input = input )
{
message("input = output of get_UVMR_collected function, a dataframe ")
#significant association criteria:
##1. cML as primary analysis for quantify the effect due to its robustness to both correlated and uncorrelated pleiotropic effects and consideration of potentia... |
38e4d985547777b282be32d794a8fcb996a8c12bcad9a5582f42401b9ca208a6 | R | 1,534 | 55 | fluidPage(
fluidRow(
column(width = 6, style='padding:0px;'),
column(width = 6,
uiOutput('kanagawaValueBoxes') %>% withSpinner(proxy.height = '200px')
)
),
fluidRow(
box(
width = 12,
title = tagList(icon('chart-line'), '神奈川県の発生状況'),
closable = F,
fluidRow(
... |
64d0186af02e59a23dd14f2294dd44ec2630d1f233b90ba769b55fe096ff155a | R | 1,535 | 23 | Component.SymptomsProgression <- function() {
box(
title = tagList(icon("hospital"), i18n$t("症状の進行")),
width = 8,
closable = F,
collapsible = T,
collapsed = T,
label = boxLabel("Archived", status = "danger"),
dateInput(
inputId = "selectProcessDay",
label = i18n$t("日付選択"),
... |
5424351a2139f2f602c584acf795066a4c397670c8fe553d947a72a7a7ead8a3 | R | 1,546 | 55 | #!/usr/bin/env R
# Author: Sean Maden
#
# Violin plots of DAPI intensity by slide, with some color annotations for star
# slides.
#
library(ggplot2)
#----------
# load data
#----------
dpath <- file.path("HALO", "Deconvolution_HALO_analysis")
fnv <- list.files(dpath, recursive = T)
fnv <- fnv[grepl("\\.csv$", fnv)]
... |
28aeda0cc7724a9cc232479371d3d62b6f9c426b83334abc7d62df529a28b21b | R | 1,547 | 56 | fluidPage(
fluidRow(
column(width = 6, style='padding:0px;'
),
column(width = 6,
uiOutput('AomoriValueBoxes') %>% withSpinner(proxy.height = '200px')
)
),
fluidRow(
box(
width = 12,
closable = F,
title = tagList(icon('chart-line'), sprintf(i18n$t("%sの発生状況"), i18n$t... |
a8c628e7e136d697f6ca2df30628c60e96e7c9ddb7880f67a7719891102ef1f3 | R | 1,549 | 58 | fluidPage(
fluidRow(
column(
width = 6, style = "padding:0px;"
),
column(
width = 6,
uiOutput("MiyagiValueBoxes") %>% withSpinner(proxy.height = "200px")
)
),
fluidRow(
box(
width = 12,
closable = F,
title = tagList(icon("chart-line"), sprintf(i18n$t("%sの発生状... |
d3640ec82ac96a24418d3ba91c74862021b6a5dd346bd33cadd50c6c9aa9e4c8 | R | 1,550 | 51 | rankEnrich <- function(exp, genelist){
if (!is.matrix(exp) || is.null(rownames(exp)) || length(genelist) == 0) {
stop("Invalid input. 'exp' must be a non-empty matrix and 'genelist' must be a non-empty list.")
}
row_names = rownames(exp)
num_genes = nrow(exp)
num_samples = ncol(exp)
R = matrixS... |
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