sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
caac52cf04119ca9fa8f16fe98880305ce4ed7ea2a288d8aab6f07c67162afff | R | 5,153 | 70 | #' Filter modules based on their tree representations
#'
#' Filters modules using two tree-based statistics (the total tree length and within-species diversity) by comparing the values of the actual modules to the values of the random modules.
#'
#' The within-species diversity measures the variability of module connec... |
d5a23c58571d77b2132773e402467b3a922d2be0a14dddd909dc9d1fbae9f702 | R | 5,159 | 151 | ##############################################################
# Script: 03_DE_analysis.R
# Purpose: SVA correction and differential expression analysis
# using limma for IMP-1088 proteomics data
##############################################################
# ========================
# Load required p... |
fa916ac5da15eae00ea032f6265711e2499eb2d3a48b6cfc6d61d1975c759424 | R | 5,168 | 137 | #' Create SQLite database for prediction models
#' @param brain.region Brain region name
#' @param file_input_summ Directory containing summary files
#' @param file_input_weights Directory containing weights files
#' @param drv_file_path Directory for database files
#' @param dataset_training Dataset identifier
#... |
f0643d6b8ff3a079d286fa8ffcc61d4e51c73755cdcd67f9d2c2093761a96610 | R | 5,170 | 104 | ## ----setup, include=FALSE----------------------------------------------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)
# setwd("/Users/warri/OneDrive/Documents/Denver/Research/Data/ashad_n110")
sessionInfo()
# RDAC R packages location '/home/rcorcoran/R/x86_64-pc-linux-gnu-librar... |
6f19123d4d4be0ea44d7d80a761d11c80ce7bf5687a3740d0fb409e096f5e34d | R | 5,172 | 125 | #' Normalizes and merges downsampled query and database expression data
#'
#' This function performs normalization and merging on the downsampled query and database expression data stored within an `ann` object. It assumes the data is gene expression for RNA-seq (assay_db = "RNA").
#'
#' @param ann An object containing... |
d9f29b99843d15f57a16b5cfefe55becb86db931f9ca036ebd0e492327089258 | R | 5,175 | 125 | ---
title: "XGBoost result plotter"
author: "Joonatan Palmu"
date: "`r format(Sys.time(), '%d.%m.%Y')`"
editor_options:
chunk_output_type: console
---
# Init
```{r options, include = FALSE}
knitr::opts_chunk$set(include = TRUE, echo = TRUE, message = FALSE, results='asis', cache=FALSE, warning=FALSE, collapse = TR... |
b9ccc56738509adde22aa65bacc6f2744f7a1dedef90980df52d7854e727de0b | R | 5,178 | 112 | library(ggplot2)
rm(list=ls())
options(digits = 4) # by default, print results to four decimal digits
set.seed(1823) #for replication - the year Trinity College was founded!
#replace the empty quotes with the file path to the location where you downloaded the files from Zenodo below
path = ""
load("CDVMBG_BRM_TargPre... |
6de69340d0003226fc5e8800c490c37f969f4f29ede5c57dfde390f4268fe125 | R | 5,179 | 157 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
# Cluster DNs, use various distances and explore ... |
037cceb462755b44ec3a340b755c7ee13f982b9e530fbcf24f82f45d559b2b59 | R | 5,180 | 140 | # Load libraries
library(ChIPseeker)
library(org.Hs.eg.db)
library(GenomicFeatures)
library(dplyr)
library(rtracklayer)
library(clusterProfiler)
library(stringr)
library(ggplot2)
library(purrr)
txdb_ensembl <- makeTxDbFromEnsembl(organism="Homo sapiens", release='113')
# Load CpG island annotation (see https://github.... |
ba36f3edf3d712374d95be2b4e333ac785c8fd1940d0759957ce4d7cd5877ef9 | R | 5,203 | 155 | #------------------------------------------------------SETUP-----------------------------------------------------#
# Load libraries
library(tidyverse)
library(data.table)
library(BuenColors)
library(Seurat)
library(irlba)
library(Matrix)
library(future)
library(reticulate)
library(ggrastr)
library(tidytext)
library(ma... |
fb2b6cc2e1663bdd89423ec0c9fb3bcaf67dcad64c029595abcb225fb45ea32a | R | 5,206 | 142 |
render_paneleditor_ui <- function(working.directory, ...) {renderUI({
fluidPage(
fluidRow(
column(6,
textInput("paneleditorui_output_folder", label = "Output folder name", value = "renamed")
),
column(3,
actionButton("paneleditorui_load_... |
437424008d2b01f5ab8a46004531339aa63752507de24af8d74f7d505de56f1a | R | 5,213 | 141 | #------------------------------------------------------SETUP-----------------------------------------------------#
# Load libraries
library(tidyverse)
library(data.table)
library(BuenColors)
library(Seurat)
library(irlba)
library(Matrix)
library(future)
library(reticulate)
library(ggrastr)
library(tidytext)
library(ma... |
3352de0fa63876e6ad1c8d197f397ca8a37c4d6a094cc1e3ad03b449ef70f717 | R | 5,215 | 157 | #------------------------------------------------------SETUP-----------------------------------------------------#
# Load libraries
library(tidyverse)
library(data.table)
library(BuenColors)
library(Seurat)
library(irlba)
library(Matrix)
library(future)
library(reticulate)
library(ggrastr)
library(tidytext)
library(ma... |
6334ed291ad95da41f125f37b4a176e8eab24a5c6d0816643bdc527da5f0190f | R | 5,216 | 119 | # 加载必需R包（未安装请先运行安装命令）
if (!requireNamespace(c("readxl", "ggplot2", "dplyr", "tidyr", "scales"), quietly = TRUE)) {
install.packages(c("readxl", "ggplot2", "dplyr", "tidyr", "scales"))
}
library(readxl) # 读取Excel文件
library(ggplot2) # 绘图核心
library(dplyr) # 数据处理
library(tidyr) # 数据重塑
library(scales)... |
874a7417913e239cd7d4de23a773834058efe48a0baa112f0b80f160685412e2 | R | 5,217 | 224 | test_that("Test computeCentroidWeights", {
coords <- readRDS(system.file("extdata", "MouseBrainCoords.rds",
package = "SpNeigh"
))
cells_c2 <- subset(coords, cluster == 2)$cell
weights <- computeCentroidWeights(data = coords, cell_ids = cells_c2)
expect_true(is.vector(weights))
expec... |
4957c2d50927fcfde2930f25b19b6c68611a5c0b1f103f9fa101ebb3394bc70f | R | 5,220 | 188 | library(MicrobiotaProcess)
library(phyloseq)
library(tidyverse)
library(ggtree)
library(ggtreeExtra)
library(patchwork)
library(ggnewscale)
library(vegan)
library(coin)
pdf("all_plots.pdf", width = 11.69, height = 11.69)
# -------------------------------------------------------------------------
# colors
# ---------... |
5e58fe2898f644cc7fc9cbe6778407fb6e97e983c452a7c7cf26060e12f42b7f | R | 5,223 | 120 | #!/usr/bin/env Rscript
#' banc-publish-segment-properties — Refresh per-dataset segment_properties JSON only.
#'
#' Sources just the helper functions from `banc-ngl-upload.R`, reads each
#' compiled_data feather from GCS (FANC uses local CSV), builds the
#' tag-rich `segment_properties` JSON, and pushes to the per-data... |
e2d63e84ee8f4401e04b067a2bb0179417d407bc838aeaff79b956a902876da7 | R | 5,226 | 110 | #' Creation of SAHA object for analysis
#'
#' The `SAHA` class is used to store and manage data throughout the SAHA analysis workflow. It holds information about query and database data, matched markers for different analyses, and final analysis results.
#'
#' @slot query A list containing data frames for both marker (... |
24ea95a8fb4cc3b6d6eb65843b67964389a076c9e224760cf9236ff36b186c82 | R | 5,228 | 203 | #single-cell analysis package
library(Seurat)
# plotting and data science packages
library(tidyverse)
library(cowplot)
library(patchwork)
# co-expression network analysis packages:
library(WGCNA)
library(hdWGCNA)
# using the cowplot theme for ggplot
theme_set(theme_cowplot())
# set random seed for reproducibility
s... |
e35ba4f2a3d9c5e407efb2a651a2a1920cb8ba0af015f5c5e7de2fcd50bdff9e | R | 5,236 | 146 | ### In vitro #2 - Analysis on overlapping sets of lipids that are increased or decreased in each KO compared to control
rm(list = ls())
library(tidyverse)
library(ggthemes)
library("scales")
library(ggpubr)
library(UpSetR)
setwd(rstudioapi::getActiveProject())
source("./Scripts/Function_scripts/Effect_size_functions... |
4e1411d88074f8bb0022131a214668a0cde58887d21961ef297b280598e7fde9 | R | 5,240 | 190 | OrderDataBySd <- function(exp_mat){
# compute sds for each row
sds <- matrixStats::rowSds(exp_mat)
sdsSorted <- order(sds, decreasing = TRUE)
# order by most variable probes on top
exp_mat[sdsSorted ,]
}
calculate_dnam_pca <- function(dnam_data, pc_count = 20) {
beta_data <- SummarizedExperiment::assays... |
1ed6b4adcf0d5fd9d5c0a6f1b84e9951fca18ad93c5e68ae7705ef68eb85b68c | R | 5,246 | 167 | ---
title: "tumor_preprocessing_8_manual_annotations_miller_myeloid.rmd"
output: html_document
date: "2025-12-18"
---
```{r}
library(Seurat)
library(readr)
library(tibble)
library(dplyr)
library(viridis)
library(presto) # used to greatly improve FindAllMarkers efficiency
```
# define funcitons
```{r}
# Define a func... |
8570a3cd0ec255e4c4547926658b9d2281983950fbf3e8021a81ab5b6a75e88d | R | 5,250 | 235 | ---
title: "Check genes associated with case-control status"
author: "Wei Sun"
date: "`r Sys.Date()`"
output:
html_document:
theme: journal
highlight: tango
toc: true
toc_depth: 3
toc_float:
collapsed: true
smooth_scroll: false
number_sections: false
df_print: paged
params:
gr... |
aa83becb784fbc7eac05476dfea1d8483db0b68ba561d4815a3b0dbf25c244ec | R | 5,255 | 144 | flyem_dataset <- function(dataset) {
cds=clio_datasets()
dataset=match.arg(dataset, names(cds))
cds[[dataset]]
}
#' Choose active male VNC / FlyEM dataset
#'
#' @description \code{choose_malevnc_dataset} chooses the dataset that will be
#' used by the male VNC package.
#' @details For \code{choose_malevnc_data... |
f3c7bb6975e8281cdfac10e33bca35c02baa910331e6b886efa6478b98c0bbcd | R | 5,255 | 113 | #!/usr/bin/env Rscript
#-------------------------------------------------
# Title: combine all the single data metadatas
# Author: Christina Meier
# Date: 2025-06-02
# Email: 19cm51@queensu.ca
#-------------------------------------------------
# Notes ------------------------------------------
# module load StdEnv/2023... |
7e17bdbf3f7ff94bbacaba7940201b5ac079f36c0e122e0ad80ae7139ded4e2d | R | 5,260 | 125 | ################################################################################
## Gene Ontology pathway enrichment of plasma differentially expressed proteins
##
## Companion code for:
## van Weperen et al. "Spinal nociceptive denervation impedes chronic autonomic
## remodeling after myocardial infarction"
##
## ... |
f2b4b25a14f76df7c0cc06eb246a7115aca4eba2887fac88a2c86aa775551ddb | R | 5,260 | 233 | ---
title: "Check genes associated with case-control status"
author: "Wei Sun"
date: "`r Sys.Date()`"
output:
html_document:
theme: journal
highlight: tango
toc: true
toc_depth: 3
toc_float:
collapsed: true
smooth_scroll: false
number_sections: false
df_print: paged
params:
ce... |
b4774d1bdee65a77a386fddc935e2c4dce3627df967aeda96bd4000d1d895e4a | R | 5,278 | 125 | library(Seurat)
library(BPCells)
library(tibble)
library(dplyr)
library(ggplot2)
library(ggrepel)
library(patchwork)
library(scater)
DGE_folder <- "./Pollard_lab_SSE/"
mat <- ReadParseBio(DGE_folder)
# Check to see if empty gene names are present, add name if so.
table(rownames(mat) == "")
rownames(mat)[rownames(mat... |
2b4330e5b6f9b30603aaff30cc015a97d4ec6c2fe92c37061d3ccc3f7f542abf | R | 5,279 | 63 | rm(list = ls()); setwd('/sc/arion/projects/DiseaseGeneCell/Huang_lab_project/ADASE/')
outdir <- 'ASE_individual_analysis/ASE_Var_RegionVsChrBand/ASE_Var_RegionVsChrBand'
# All sample in analysis clinical information
clinicals <- as.data.frame(readxl::read_xlsx('data/SampleInAnalysis/SampleInAnalysis_TenReads.xlsx'... |
cc91f289ce75cffdfbc158062be17f643565ef85bce4face7ea6e8ca46233409 | R | 5,280 | 135 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
.emc2_build_info <- function() {
invisible(.Call(`_EMC2_emc2_build_info`))
}
EMC2_call_custom_trend <- function(trend_pars, input, funptrSEXP) {
.Call(`_EMC2_EMC2_call_custom_trend`, t... |
dac2237b116e4633944648bd2c40b7352feb3a3dc6612d0fd2c09bb7dbf15175 | R | 5,287 | 144 | ##################################
# Preparing the snRNAseq data from CAREmut dataset for NMF
# Author: Kevin Johnson
# Date: 2026.03.23
##################################
library(tidyverse)
library(RColorBrewer)
library(viridis)
library(Seurat)
library(harmony)
library(Matrix)
library(ggpubr)
library(openxlsx)
librar... |
af78c21fc276091bc3bda94de43edbf90bdae020ab0bf00ee0280a8f7c9deec6 | R | 5,288 | 155 | library(AnnotationDbi)
library(org.Hs.eg.db)
library(clusterProfiler)
library(dplyr)
library(ggplot2)
library(ReactomePA)
library(data.table)
line_of_interest = "3110_uqcrc2"
df <- read.csv("C:/Users/Jiawan/Dropbox/Jiawan/Analysis/Deseq2/comparison/CRISPRa_fam3_new_result/3110_uqcrc2_versus_3110_ev/npc_3110_uqcrc2_ver... |
dbb6341d285c92dd51d336b2a6dacdf770b288a88da39573c6c8f8783aeb834e | R | 5,291 | 251 | ##########################################################################################
# SparseMatrix Utilities
##########################################################################################
.normalizeCols <- function(mat = NULL, colSm = NULL, scaleTo = NULL){
if(is.null(colSm)){
colSm <- M... |
c84f38f7846f28158c8a1c65665a01565e4b58a28ea19273aa5be7d32da65dcb | R | 5,298 | 186 | library(testthat)
library(openxlsx)
library(dplyr)
library(readxl)
library(tidyr)
library(zoo)
library(purrr)
source("../R/module_upload_helpers.R")
test_that("convert_wide_to_long behaves correctly across 20 Excel scenarios", {
dir.create("test_excels", showWarnings = FALSE)
write_test_excel <- function(da... |
b2a417649c632035bd0fbfdfa14cd0b15c821dde7734373f383aa448245eda1b | R | 5,299 | 118 | ##
##Load regNet package
##
#- See https://github.com/seifemi/regNet for details how to install regNet.
library( regNet )
##
##Set the following paths according to your system
##
#- Must be modified to fit to your own system
#- Central project path prefix for regNet to store outputs of regNet
centralProjectPathPrefix... |
a9098007c5ebf4f35a4b2c30fc36a732345dcca19c8cfd483184bf7d0dd52bec | R | 5,300 | 147 | # ============================================================================
# Figure 1A: Venn Diagram - FIGURE GENERATION SCRIPT
# ============================================================================
# This script generates the Venn diagram showing overlap between three
# ISR gene list sources: GO Consortium... |
f5dac9b60781d0e4ddd4921e8e77b28613d6dae44c47d898120b79ba8cab1170 | R | 5,301 | 150 | calc_ll_R <- function(p_vector, model, dadm){
if(!is.null(model$transform)){
pars <- get_pars_matrix_oo(p_vector, dadm, model)
} else{
pars <- p_vector
}
ll <- model$log_likelihood(pars, dadm, model)
return(ll)
}
log_likelihood_race <- function(pars,dadm,model,min_ll=log(1e-10))
# Race model summe... |
5a6c4ee4ce71ba92ceda006f3fa1e80c3eace3ba360c75a4fa786598d9b0dcd2 | R | 5,306 | 155 | # Description: Plot a prognostic curve under the dynamic progression of the
# disease in reference patients
library(dplyr)
library(mstate)
library(ggplot2)
library(patchwork)
msT2dComp2ndEd <- "D:\\work2\\t2dAndComplicationsUkb\\data\\processed\\integComp2ndEd\\msT2dComp2ndEd.rds" %>%
readRDS(.)
cfull2ndE... |
a543fcfd2ef2ee1737d300cb3f277000d042a667e2d9dfab9be98169643be6a6 | R | 5,313 | 158 | #------------------------------------------------------SETUP-----------------------------------------------------#
# Load libraries
library(tidyverse)
library(data.table)
library(BuenColors)
library(Seurat)
library(irlba)
library(Matrix)
library(future)
library(reticulate)
library(ggrastr)
library(tidytext)
library(ma... |
199ce17d2e85050dc8266e84a56971345d3f0c6a7c0b7f85ff942419e8803dcd | R | 5,320 | 133 | #####################################################################
# INGENE Prediction Helper Functions
#####################################################################
# Helper functions for applying trained INGENE models to new data and
# averaging predictions across networks
###########################... |
f59d94341e424932cd2dc0a05d0fe192caf7a35702d26aa62589009ff0811192 | R | 5,320 | 143 | #------------------------------------------------------SETUP-----------------------------------------------------#
# Load libraries
library(tidyverse)
library(data.table)
library(BuenColors)
library(Seurat)
library(irlba)
library(Matrix)
library(future)
library(reticulate)
library(ggrastr)
library(tidytext)
library(ma... |
858e0d050ebaa5b26b64648c4b22bc5e2d73ad08606f685d939c3e7cb68c73dd | R | 5,323 | 70 |
## Annotate human insert ------------------------------------------------
# get human plasmid sequence (txt created based on /data/share/lab/lab specific overview and lists/Plasmid Sequences/p216_pAAVS1-TetOn-zKRAB-dCas9-P2A-mCherry/)
plasmid_seq_human <- read_tsv("pAAVS1-TetOn-zKRAB-dCas9-P2A-mCherry.txt", col_nam... |
269621ff349316a8ff0adfc0ef690c92b67751b2df23f6d054a1404cba00ff0e | R | 5,335 | 278 | # Tests for arrow
# change in random number generation in R3.6, this ensures tests will pass under older and newer Rs
# (this was implemented in Seurat's testthat)
library(ArchR)
library(testthat)
library(Matrix)
suppressWarnings(RNGversion(vstr = "3.5.3"))
set.seed(1)
#Context
context("test_arrow")
#Fragments
fragm... |
be3a78295b5111fd38c4dec2e195e1232455674501807d86e1d39304ff841c25 | R | 5,354 | 115 | #' @title importArboreto
#' @description Import output from Arboreto/GRNBoost (https://arboreto.readthedocs.io)
#' @param fileName File name of the link/adjacency list
#' @param reorder Whether to sort the links by decreasing weight/importance
#' @seealso pySCENIC (https://pyscenic.readthedocs.io)
#' @examples
#' GRNB... |
3e7338342c02d0bb75bc70ff14c7b135cc599bc3c2323c63de91e164ddbf461a | R | 5,355 | 239 | ---
title: "Ambra Villani's microglia RNA-seq, custom plots"
author: "Izaskun Mallona, Mark D. Robinson's lab, UZH"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
html_document:
toc: true
toc_float: true
code_folding: hide
code_download: true
number_sections: true
df_print: kable
them... |
f37b850da84a7da681376845427d29fbab1b29d5bb1cc69e7019d74b7c30d1b1 | R | 5,357 | 182 | ---
title: "Getting Started with influencer"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Getting Started with influencer}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/fi... |
84e1763bc0b1e814fdcd00e747a1eaa0c2a0fb87096bb2801985eef11a7899de | R | 5,361 | 156 | #------------------------------------------------------SETUP-----------------------------------------------------#
# Load libraries
library(tidyverse)
library(data.table)
library(BuenColors)
library(Seurat)
library(irlba)
library(Matrix)
library(future)
library(reticulate)
library(ggrastr)
library(tidytext)
library(ma... |
9b3afb704c7f50afe3dc3406c796354a856efc86f280eec8a3c1526e2f37494f | R | 5,361 | 168 | ---
title: "Fig4_NKcells"
output: html_document
date: "2025-04-22"
---
```{r}
library(Seurat)
library(tidyr)
library(dplyr)
library(tibble)
library(ggplot2)
library(ggnewscale)
library(gghalves)
library(ggpubr) # needs cmake
library(scales)
library(harmony)
library(patchwork)
library(stringr)
library(forcats)
```
# ... |
c681aa922ba958e71e6b5029dea847396b5617da2f938f046f8b8248214e7bc6 | R | 5,364 | 122 | #' banc-malecns-mesh-transform — Transform maleCNS meshes into BANC space (navis TPS).
#'
#' For each maleCNS bodyid, fetches the OBJ mesh and applies the
#' JRCFIB2022M → BANC TPS transform via Python `navis` + `flybrains`
#' (no R-native registration exists yet for this chain).
#'
#' @section Reads:
#' - `<banc.met... |
f18165b96b45ddfd639c96811ca6d80bc5166eda1b571db6b22c608fe59a38d5 | R | 5,369 | 162 | #!/usr/bin/env Rscript
suppressPackageStartupMessages({
library(Seurat)
library(harmony)
})
# ============================================================
# Purpose
# ============================================================
# This script integrates per-sample Seurat objects (after QC) using:
# LogNormalize ... |
914ed5d5949e44257d7273a336fb4ec1226f1ebeaaff9c54d3ae5920a800a72e | R | 5,370 | 134 | #!/usr/bin/env Rscript
packs <- c("qqman","optparse","data.table","R.utils")
for (p in packs) {
if( !require(p, character.only = T)) {
print(p)
install.packages( p, repos = c(CRAN = "http://cran.r-project.org") )
}
}
option_list = list(
make_option(c("-f", "--file"), type="character", default=NULL,
... |
b3d5f386c507394304f6f935db8573dd14373aa6dc1d1a50a059386e30440ae4 | R | 5,381 | 172 | ##### ##### ##### ##### ##### ##### ##### ##### ##### #####
# GeoMx parameters
corMet = "spearman"
corrCoefThre <- 0.5 # correlation coefficient threshold values to include
seed <- 1118
niter <- 1000 # graph layout iteration
commWeight <- 100 # graph layout weights on community info
cohortWeight <- 1 # graph layout wei... |
fda2445ebf695f1f9e1fc9387bd9e2eb84865c1185fa1dc6365a912e9eaa924b | R | 5,383 | 141 | #' Annotated spatially-registered clusters
#'
#' Once you have computed the enrichment t-statistics for your sc/snRNA-seq data
#' using `registration_wrapper()` and related functions, you can then use
#' `layer_stat_cor()` and `layer_stat_cor_plot()` to perform the spatial
#' registartion of your sc/snRNA-seq data. Thi... |
edb25744faefacfb8ee3a1e3b9f0dc166a833a05e38d83538ad27d2c4982fcd9 | R | 5,389 | 150 | #------------------------------------------------------SETUP-----------------------------------------------------#
# Load libraries
library(tidyverse)
library(data.table)
library(BuenColors)
library(Seurat)
library(irlba)
library(Matrix)
library(future)
library(reticulate)
library(ggrastr)
library(tidytext)
library(ma... |
10408a99b3e9ad8e0c76a0463cfb359c310df525d3fddd0322cb0be66408e34d | R | 5,391 | 165 | # Hai Wang
# Ensemble Learning
library(data.table)
library(dplyr)
# load the main table
data <- fread('../5_prepare_data/data4.csv',header=T,sep=',',stringsAsFactors=F,nThread=40)
data$V1 <- NULL
data$index_main <- 0:(nrow(data)-1)
data$median <- log10(data$median+1)
# load indices
i20001000 <- read.csv('2000%1000.c... |
151825780bf0de7d4ffec048800fdaab0e83984875f79e536cd312bd80f31a42 | R | 5,398 | 156 | # Description: This script is used to plot the incidence probability of a patient.
# In the selection of patients, we selected four levels effective value for comparison
library(dplyr)
library(mstate)
library(patchwork)
library(ggplot2)
msT2dComp2ndEd <- "D:\\work2\\t2dAndComplicationsUkb\\data\\processed\\in... |
7196703970f1b76b45f8f229c9d606ddd8a19fc40bdcb57277035eca927286a5 | R | 5,414 | 161 | # Contains a collection of utility functions
#
#' Make cluster names in format 'study_id|cell_type'
#'
#' @param study_id Character vector containing study ids.
#' @param cell_type Character vector containing cell type names
# (must be same length as study_id).
#'
#' @return Character vector containing cluster names ... |
217e2eda9f172234d24909a9ed2605528ce2f1f574794ab1147be3832eb15d0b | R | 5,422 | 154 | # ==============================================================
# Script: Inhibitory neurons — merge clinical, QC, and DEG (wilcox)
# Criteria: logfc.threshold=0, min.pct=0, min.cells.feature = 1
# Output: saves CSV
# ==============================================================
suppressPackageStartupMessages({
li... |
37086f418577561c6ec7358780f7f68379bff6b301a309bfa2f42dc7356fd3a1 | R | 5,422 | 146 | ##################################
# Preparing the snRNAseq data from CAREmut dataset for NMF in the undifferentiated malignant population
# Author: Kevin Johnson
# Date: 2026.04.01
##################################
library(tidyverse)
library(Seurat)
library(Matrix)
## Specify directories:
proj_dir <- "/vast/pal... |
ffc584e2d109a7cbb4cfada193499b5a4fb2a917aa85170f4e83c5be2b6e2817 | R | 5,423 | 158 | library(tidyverse)
library(readr)
library(ggplot2)
library(ggpubr)
library(rstatix)
library(wesanderson)
raw <- read_csv("data/TREC_KREC_combine_Baby_4.csv")
data <- raw %>%
filter(!is.na(age_bin)) %>%
mutate(age_bin = factor(age_bin,
levels = c(
"CB", "0-3 Days", "4-14 Days", "15-30 Days",... |
17f23f172e20a3aa8c7e6175cad6573edc811881c77e3b0810f7fc7341a6d94c | R | 5,426 | 185 |
# Helper functions
#' Look up corresponding values from a lookup table
#'
#' @param list A string vector - keys
#' @param table A tibble - the lookup table
#' @param col_1 An integer - column index of the keys
#' @param col_2 An integer - column index of the values
#'
#' @return A string vector.
#' @export
#'
vlo... |
0c025785827c36599cc3c74433e91f2412839b304f2875dad59eb74e3cc271d5 | R | 5,427 | 117 | library(ggplot2)
rm(list=ls())
options(digits = 4) # by default, print results to four decimal digits
set.seed(1823) #for replication - the year Trinity College was founded!
#replace the empty quotes with the file path to the location where you downloaded the files from Zenodo below
path = ""
setwd(paste(path, "/data... |
575b0e19662246e82283abc4e96549b3c655e3edb02aae1ce92f4f26c30c1a1e | R | 5,427 | 234 | ---
title: "Single cell Cme-CUT&Tag data analysis"
output: html_notebook
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(warning = FALSE, message = FALSE, results = "markup")
```
# Load libraries
```{r, results = "hide"}
library(EnsDb.Hsapiens.v86) # Annotation database
library(Seurat)
library(Signac)
libr... |
b45bdff6bd218eb76925f16baa005ab429b57dfdf93e531ebd4ddd2a6e9beeff | R | 5,427 | 142 | test_that("R and Python implementations give similar results", {
# Skip if Python environment is not available
skip_if_no_python <- function() {
python_available <- tryCatch({
# Use r-reticulate environment
reticulate::use_condaenv("r-reticulate", required = TRUE)
ic_module <- reticulate::impo... |
b205f3de26643e69081db072650ef3f587eeb37fcdf56b7f0367d3595ceffa92 | R | 5,428 | 132 | #!/usr/bin/Rscript --slave
# LSR_example_code.R
# 16th January 2026
# Modified: 16th January 2026
# Author: Richard G. Carson (richard.carson@tcd.ie)
#################
# Read the data file
data <- read.csv("example_data.csv", header=TRUE, stringsAsFactors=FALSE)
# Ensure data are numeric
data["mep_amplitude"] <- ... |
af901afffa8cb33b005c75fe99030166eaad157b7e80e1c04990f8f6b7e67d61 | R | 5,435 | 132 | ##############################
## Visualize experimental PDGFRAi analyses
## Author: Kevin Johnson
##############################
# Listed as 637451 in the data but should be 673451
library(tidyverse)
library(ggpubr)
library(EnvStats)
library(openxlsx)
proj_dir <- "/vast/palmer/pi/verhaak/kcj28/care_idh_mut"
fig_... |
8dcd59fd4b5a4109a73dd1ad556d0180b247d20c6872613370c910e7d176a2b9 | R | 5,439 | 155 | library(dplyr)
library(ArchR)
library("stringr")
library(ggplot2)
library(ggpubr)
library(reshape2)
library(ggsignif)
outp <- "sn_mC"
colors.dx <- ArchR::ArchRPalettes$circus[1:4]
names(colors.dx) <- c("Control","AD","bvFTD","PSP_S")
#---load 50 subCs
projmeta <- readRDS("projATAC_50subC_meta_584904.rds")
my_subcs <- ... |
19431e2b04df62e9daf803648ef47cceb694bc6fabd07bdf0748d1b7b3da01d4 | R | 5,443 | 136 | #' banc-lr-nblast-images — Render per-query left-right BANC mirror NBLAST review PNGs.
#'
#' Restricted to BANC neurons with a strong FAFB or MANC NBLAST match.
#'
#' @section Reads:
#' - per-query mirror NBLAST CSVs, BANC meshes
#'
#' @section Writes:
#' - `<banc.nblast.mirror.save.path>/images/todo/*.png`
#'
#' @... |
efd700d2e782dd2a90f86c2ac76ad6919de6f402f202a24a75307d305e3cea13 | R | 5,443 | 110 | suppressMessages(suppressWarnings({
library(Seurat)
library(ggplot2)
library(igraph)
library(reshape2)
library(tidyverse)
library(ggpubr)
library(rstatix)
}))
parser = argparse::ArgumentParser(description="Test paralog/ohnolog shift")
parser$add_argument('-I','--input', help='input process... |
38c23db57134ff2eee410bc31196e333fa4f78c280c8aab8c65d6390baff681e | R | 5,444 | 135 | context("Model")
library("carrier")
testthat_model_name <- basename(tempfile("model_"))
teardown({
mlflow_clear_test_dir(testthat_model_name)
})
test_that("mlflow model creation time format", {
mlflow_clear_test_dir(testthat_model_name)
model <- lm(Sepal.Width ~ Sepal.Length, iris)
fn <- crate(~ stats::pred... |
60cde36915a6aacf4b0e031011f69c29fa92ac26ab7c57604ea4e7915027ebaf | R | 5,445 | 207 | ---
title: 'Epilepsy - plotting'
author: "Jon Thompson, Pers lab"
date: "`r Sys.time()`"
params:
date: !r substr(gsub("-","",as.character(Sys.Date())),3,1000)
randomSeed: !r 12345
pValThreshold: !r 0.05
padjMethod: "bonferroni"
output:
html_notebook:
df_print: paged
number_sections: yes
toc: yes
toc_dept... |
164c66b07c208bab1b7559fe073058d93a1fbb02de4716be210250ce99f2424a | R | 5,448 | 115 | ##
##Load regNet package
##
#- See https://github.com/seifemi/regNet for details how to install regNet.
library( regNet )
##
##Set the following paths according to your system
##
#- Must be modified to fit to your own system
#- Central project path prefix for regNet to store outputs of regNet
centralProjectPathPrefix... |
6594d893bb1888b2511403a468e42166bc0390ce56b74626a79b5e9e3f3a629f | R | 5,451 | 186 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
######################
### load libraries ###
###... |
765d47891e92b86543ba22d14fd1ebf4e7e113faec5d73c2fe8a014decd950ad | R | 5,453 | 90 | library(ggExtra)
library(ggplot2)
library(tidyr)
library(MetaNeighbor)
library(conflicted)
library(magrittr)
library(reshape2)
library(dplyr)
library(here)
library(readr)
conflict_prefer("count", "dplyr")
conflict_prefer("select", "dplyr")
conflict_prefer("filter", "dplyr")
here()
Sys.setenv("VROOM_CONNECTION_SIZE"=10... |
fe7e1f9b385288cd7273fd7ddbdafc737d4a1ca3a231b155c4a158bca9f9cc43 | R | 5,460 | 124 | library(MetaNeighbor)
library(SingleCellExperiment)
library(magrittr)
library(dplyr)
library(S4Vectors)
library(here)
library(readr)
library(tibble)
library(conflicted)
conflicts_prefer(base::setdiff)
conflicts_prefer(base::intersect)
conflicts_prefer(dplyr::filter)
conflicts_prefer(dplyr::rename)
conflicts_prefer(dply... |
1a36570d69aba095103e1772d7e3b69c564ee60d04f66aa1bb9f64bc759eeb72 | R | 5,463 | 219 |
library(data.table)
library(readxl)
library(Matrix)
library(tidyr)
library(ggplot2)
library(viridis)
library(ggpointdensity)
library(ggpubr)
theme_set(theme_classic())
# cell_type = "cd8"
args = commandArgs(trailingOnly=TRUE)
args
for(i in 1:length(args)){
eval(parse(text=args[[i]]))
}
cell_type
data_dir = "..... |
f2c9b4d144d278902fe41a9a8d3d72fad4616cee15c616c54a1485be2611678f | R | 5,463 | 145 | library(Scillus)
library(Seurat)
library(magrittr)
library(tidyverse)
a <- list.files("./test/GSE128531_RAW", full.names = TRUE)
m <- tibble::tibble(file = a,
sample = factor(stringr::str_remove(basename(a), ".csv.gz")),
group = factor(rep(c("CTCL", "Normal"), each = 3), leve... |
2b04a88cca0f791f9dc8a6167c13354873359f239a7d751f85e4bb76b3680d4f | R | 5,467 | 121 | library(Seurat)
library(SeuratData)
library(ggplot2)
library(patchwork)
library(dplyr)
library(spacexr)
library(jsonlite)
library(png)
VisiumData<-read.VisiumSpatialRNA("location")
spatial = Load10X_Spatial(data.dir = paste0("location"))
barcodes <- colnames(VisiumData@counts)
plot1 <- VlnPlot(spatial, featu... |
252435b9f9ec9e410ec467478e0cebcc08d6f8c36845501eb155eb54e21436f8 | R | 5,468 | 160 | # Load required libraries
library(DBI)
library(RSQLite)
library(purrr)
library(dplyr)
library(limma)
library(parallel)
library(tidyr)
library(magrittr)
library(data.table)
# Set working directory to project root
# setwd("path/to/project/root")
###############
## PARAMS
###############
mod... |
71373806180833b7f4d7830ddeac82059f46888d525efb293764bd08635ed945 | R | 5,470 | 155 | #' Run MitoCarta GSEA Analysis and Visualization
#'
#' @param tbl Differential expression results table
#' @param contrast Name of the contrast for plot labeling
#' @param T2G Term to gene mapping dataframe
#' @param T2N Term to name mapping dataframe
#' @param n_pathways Number of top pathways to display (default: 30)... |
3a472d45a9bae2a4079341b2fbd44bfb3830da24e2e5cdf02a8088e92a0f0048 | R | 5,474 | 155 | #------------------------------------------------------SETUP-----------------------------------------------------#
# Load libraries
library(tidyverse)
library(data.table)
library(BuenColors)
library(Seurat)
library(irlba)
library(Matrix)
library(future)
library(reticulate)
library(ggrastr)
library(tidytext)
library(ma... |
d106eea9726394eeb798e934281b3ba17e5ffa3671445aa22759b63175178dc7 | R | 5,481 | 148 | #####################################################################
# Create Prediction Database Pipeline
#####################################################################
# This script creates SQLite databases from the training results
# It performs the following steps:
# 1. Processes summary and weights fi... |
6adeca52808cdfc35d12391d569e4f48a4419b38d4518b5cb9f4f5694cdc56cf | R | 5,482 | 136 | library(ggplot2)
library(ggpubr)
library(extrafont)
library(dplyr)
library(svglite)
#### Read TPM matrix ####
tpm_file <- "/home/joonho345/1_Epilepsy_RNA/RNA_Human/03.Normalization/adjusted_merged_matrix_1_TPM.txt"
Target_matrix <- read.table(tpm_file, sep = "\t", header = TRUE, row.names = 1, check.names = FALSE)
##... |
01fb85c7c3f8d1e2bf09eb8ec28d0732570be51474c18aa4f44b2fe26da011df | R | 5,490 | 177 | ################################################################################
# Volcano plot
#
# - No hard-coded absolute/local paths
# - Input/output provided via command-line arguments
# - Highlights user-specified genes with custom colors
# - Labels: top N significant genes + highlighted genes
#
# Usage:
# Rscr... |
4b3a64b664a2382c5cd27d0253fead2b5863261fbceb6209b918dded86c17a71 | R | 5,490 | 148 | #' Create SQLite database for prediction models
#' @param brain.region Brain region name
#' @param file_input_summ Directory containing summary files
#' @param file_input_weights Directory containing weights files
#' @param drv_file_path Directory for database files
#' @param net.names Vector of network names
#' ... |
0f3561b8652a5bf7e09c72a47be88fe4a4c2dc2d2949e8a0e3225942c2e84558 | R | 5,494 | 146 | #!/usr/bin/Rscript
# plot umi/cell or gene/cell
args <- commandArgs(); # print(args)
dir <- args[6]
Name <- args[7]
suppressMessages(library(matrixStats))
suppressMessages(library(reshape2))
suppressMessages(library(Matrix))
suppressMessages(library(dplyr))
suppressMessages(library(data.table))
suppressMessages(libra... |
4a10ac1b418f358f2202da9c2259b814202968f58ff07d0c5356d036703046d3 | R | 5,499 | 182 | #!/usr/bin/env Rscript
suppressPackageStartupMessages(library("argparse"))
# create parser object
parser <- ArgumentParser()
# specify our desired options
# by default ArgumentParser will add an help option
parser$add_argument("-i", "--input", required=TRUE, help="load clustered RData")
parser$add_argument("-g", "--... |
f82f648300a0fa46e9121bfd1fb049ff91506006b353f0764b383d3ef7dfb487 | R | 5,500 | 162 | #------------------------------------------------------SETUP-----------------------------------------------------#
# Load libraries
library(tidyverse)
library(data.table)
library(BuenColors)
library(Seurat)
library(irlba)
library(Matrix)
library(future)
library(reticulate)
library(ggrastr)
library(tidytext)
library(ma... |
fe34c4b05c85f8d97c499ff37a609516801ad2a362dd80d85b26239001174ee0 | R | 5,503 | 81 | ---
title: "An Introduction to the pandaR Package"
author: "Daniel Schlauch, Albert Young, Joseph N. Paulson"
date: "2016-05-01"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{An Introduction to the pandaR Package}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
---
## Introduc... |
5595931404c6b9516d833b0ba2cf2146507fc28b3defbead56ce9f3330903943 | R | 5,506 | 179 | #' get plot data
#'
#' @param venn a Venn object
#' @param nsets This parameter will be set automatically.
#' @param shape_id apply filter to internal shapes. i.e. shape_id = "601"
#' @param type apply filter to internal shapes. i.e. type = "polygon"
#'
#' @details
#'
#' This function will conduct set operations and co... |
9ead2ef9290036402a91443a9d628600d82aa02205ef2e775d308579fb427e64 | R | 5,507 | 122 | ## ============================================================================
## LEfSe (Linear discriminant analysis Effect Size) differential abundance
## - Builds a microeco microtable from feature counts, taxonomy, and metadata
## - Runs LEfSe with the standard Kruskal-Wallis screen + LDA effect-size step
## - Exp... |
e7cdef432e119ebd66d788f78a6f5160b3c7a6c245c08dd5762d017b580cf607 | R | 5,510 | 245 | ---
title: 'Epilepsy 10x samples - preprocess for WGCNA'
author: "Jon Thompson, Pers lab"
date: "`r Sys.time()`"
output:
html_notebook:
df_print: paged
number_sections: yes
toc: yes
toc_depth: 3
toc_float: yes
html_document:
df_print: paged
---
# Setup
## reference
conos vignette https://github.com/hms-dbmi/... |
1e8b09ba2cf1fa3cf18d931d44d70dfd246a290164e2cc29e6d0c9c77d76352b | R | 5,511 | 230 | ---
title: "Prepare scRNA-seq data from Yost et al. 2019"
author: "Wei Sun"
date: "`r Sys.Date()`"
output:
html_document:
theme: journal
highlight: tango
toc: true
toc_depth: 3
toc_float:
collapsed: true
smooth_scroll: false
number_sections: false
df_print: paged
---
```{r setu... |
68a2f521c1344fdf1454f1788d30b8696996915ed74525bd602b3484c8e586ff | R | 5,514 | 142 | ###基因集打分总-----------------
###活性基因集--------------
Neuronal_activation <- toupper(c("Arc", "Btg2", "Coq10b", "Crem", "Dusp1", "Dusp5", "Egr1", "Egr3", "Fbxo33", "Fos",
"Fosl2", "Gadd45g", "Gmeb2", "Grasp", "Junb", "Nr4a1", "Nr4a2", "Nr4a3", "Per1", "Rgs2",
... |
b7ef230a71e01471334d4a797470ff901fe3c865595bea230e8c71f4821bc8af | R | 5,516 | 143 | # Hello, world!
#
# This is an example function named 'hello'
# which prints 'Hello, world!'.
#
# You can learn more about package authoring with RStudio at:
#
# http://r-pkgs.had.co.nz/
#
# Some useful keyboard shortcuts for package authoring:
#
# Install Package: 'Cmd + Shift + B'
# Check Package: ... |
ac8a5002cb3b06ca88af32cc1fe462cbcb44b0a0cac671453ece73fc9327d02d | R | 5,518 | 141 | # =============================================================================
# 06_run_fMRI.R
# fMRI connectivity ~ Age*EXT + covariates: lm per edge, bootstrap CI, omega², heatmap
# =============================================================================
#
# For each between-network connectivity edge, fits lm(e... |
9b7180e6e99e20c23eec8f919ec66e9e281fdeb4b7438a5a2705850d93ee9b84 | R | 5,519 | 136 | # =============================================================================
# 04_run_clinical.R
# Clinical models: GFAP and NfL ~ Age*EXT + covariates (Gamma GLM, log link)
# Interaction plots, Johnson-Neyman, regression tables, post-hoc power.
# =====================================================================... |
c45513e02a81cc196fbb76fb47ec1cf73ac99acaba1ac82f454411b63f3324de | R | 5,521 | 120 | #' @title tsneAUC
#' @description Calculates the t-SNE based on the regulon activity. (i.e. binary/continuous AUC)
#' @param scenicOptions Fields used:
#' @param aucType "AUC" for continuous AUC, or "Binary" . By default it uses the value in \code{getSettings(scenicOptions,"defaultTsne/dims")}
#' @param nPcs Number of... |
c6996038d5fcbbea1c84d8236d845bf1ae0f4e81bd1c3c094f1683913b45ed5e | R | 5,522 | 149 | #' @export
GetPagoda <- function (cm, n.cores = 30, clustering.type = "leiden", embeding.type = "tSNE", verbose=TRUE,
n.pcs=100, distance="cosine", trim=5, n.odgenes=1000, graph.k=30,
od.genes=NULL, clustering.resolution=2, build.graph=TRUE, var.scale=TRUE, min.dist=0.5, sp... |
1288cf428d9fd5dcc334a5814bc1b36a47c1354d05bd8190a98d1666db7e24ec | R | 5,523 | 135 | library(ggplot2)
library(here)
library(readr)
library(readxl)
library(tidyr)
library(magrittr)
library(conflicted)
library(dplyr)
library(org.Mm.eg.db) #from bioconductor
library(GO.db) #from bioconductor
conflicted::conflict_prefer("select", "dplyr")
conflicted::conflict_prefer("filter", "dplyr")
conflicted::co... |
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