sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
087224497d3d7a8b2d43e6f7796073456dc513f6f77f9e3a61e4b51b34a9848f | R | 3,851 | 87 | ##################################
# Purpose: Analyze all publicly available IDH-mutant glioma sc/snRNAseq data for malignant state abundance across tumor grade
# Author: Kevin Johnson
# Date: 2026-03-11
# Input: data/public_all_scrna_snrna_malignant_cell_state_abundance.tsv
# Output: figures/edf7f_public_tumor_type_gr... |
faf95a58d13533610327cbbc8707c9822b5707062b86bec26af6b2ea9629891d | R | 3,852 | 95 | library(dplyr)
library(data.table)
library(GenomicRanges)
library(ggplot2)
outp <- "QTL_fsVars/out"
majorCs <- c("ast","mg","EX","IN","odc","opc")
Dxs <- c("Control","AD","bvFTD","PSP_S")
p1 <- "resources"
colors.peakType <- ArchR::ArchRPalettes$summerNight[1:5]
names(colors.peakType) <- c("Distal","Enhancer","Promoter... |
0e327dd73d4b1a8e61f90636cdec066b4401723e9d19dc864dfcd4c4eb600e16 | R | 3,853 | 100 | # img_update <- function(spe, sampleid, image_id, new_image_id = paste0("edited_", image_id), brightness = 100, saturation = 100, hue = 100, enhance = FALSE, contrast_sharpen = NA, quantize_max = NA, quantize_dither = TRUE, equalize = FALSE, transparent_color = NA, transparent_fuzz = 0, median_radius = NA, negate = FAL... |
a7bc7364257e06530fcf5dfa5bbbe50a7233090ca7ba08bb82ab80b2f470be36 | R | 3,853 | 174 | ---
title: "Evaluate gene sets by cell type-specific gene expression"
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
---... |
5995b71d627f6a58f1f91d52123cf21899419395d0ad93a2ced7ec55ce289513 | R | 3,854 | 86 | library(readr)
library(tidyverse)
library(dplyr)
#### import TPM Count matrix with gene id ####
adjusted_TPM_id_df <- read.table("/home/joonho345/1_Epilepsy_RNA/RNA_Human/08.Enrichment/GSEA_01.Matrix_GTF/adjusted_merged_matrix_1_id_TPM.txt",
sep = "\t", header = TRUE, row.names = 1)
#... |
e31b11cdeec4490c2d7a4a56734f5180b090e621428eb814d2eb46bfb9365f00 | R | 3,854 | 79 | #' Forstmann et al.'s Data
#'
#' A dataset containing the speed or accuracy manipulation for a Random Dot
#' Motion experiment.
#'
#' Details on the dataset can be found in the following paper:
#'
#' \strong{Striatum and pre-SMA facilitate decision-making under time pressure}
#'
#' Birte U. Forstmann, Gilles Dutilh, Sc... |
beaf48def060542e20bea5e1683e28b7e8ded26133981ea34284176974765fe3 | R | 3,855 | 71 | # Used this script to look at the distribution of mean coverage across all WXS samples when adding the H2 cohort
library(tidyverse)
## Load WGS metrics
wgsmetricsf = list.files("results/align/wgsmetrics", recursive = T, pattern = "WgsMetrics.txt", full.names = T)
wgsmetrics = wgsmetricsf %>%
map(function(f) data.tab... |
aaf51edd5b6f7fa6c25fd68ac5fba58b2f78343671e1e2e2d94650e5927660c1 | R | 3,856 | 110 | ##### ##### ##### ##### ##### ##### ##### ##### ##### #####
# Parameter settings
##### ##### ##### ##### ##### ##### ##### ##### ##### #####
setwd(file.path(getwd(), "R"))
source("00_directorySettings.R") # load baseDir, xeniumDir
source("00_settings.R")
dataDir <- file.path(baseDir, "data")
outDir <- file.path(baseDi... |
77624e003b753eedd9e6dfd1505c75a39a2ac28c8918d4c5c3d34f1dcea86e05 | R | 3,858 | 133 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
# Script to explore oviDNs cluster (cluster 15)
#... |
3e2b33bfdfd7b45831c72540b7ed11f9484c7359befbc725085b7ccdbe4f44c1 | R | 3,859 | 99 | ###############################################################################
## Extract SynGO Ribosome Gene Lists ##
## Script to extract presynaptic and postsynaptic ribosome genes ##
###############################################################################
l... |
f91619dc9f313af7e63da6925ef089bba9211c8e1b62929d2976879bfd1c980e | R | 3,859 | 65 | # Load libraries
library(bancr)
library(tidyverse)
# get GT
poss.nts <- c("acetylcholine","gaba","glutamate","dopamine","histamine","octopamine","serotonin")
gt.data <- readr::read_csv("gt_sources/bates_2024/202508-gt_data.csv") %>%
dplyr::select(cell_type, neurotransmitter_verified, neurotransmitter_verified_source... |
49ce374f7eec3ddd2e8385c96d4c08987952d9266cade00cd1b4bf336b5381a2 | R | 3,863 | 70 | print('This script would take 6 arguments, which are:')
print('Conjuntional clump 0.05 lead csv, respective FUMA snps.tx, std sumstats for TRAIT1')
print('std sumstats for TRAIT2, TRAIT1 name, TRAIT2 name')
print('to produce conj_0.05_TRAIT1_vs_TRAIT2_lead.csv')
library(data.table)
args <- commandArgs(TRUE)
#lead1=read... |
d82cf7f63b311057175edf1d992411272a3796f3f7f062406024046e99f1d600 | R | 3,863 | 116 | # ============================================================
# Inhibitory neurons (ROSMAP): CHCHD10 per-donor + clinical
# - Aggregates per donor from Seurat
# - Merges multiple clinical columns (Braak, CERAD, age, etc.)
# - Outputs: CHCHD10_per_donor_inhibitory.csv
# ===========================================... |
3a50b8a810b77d2a8c71084f35193c2e09c0ba128c81a3f99bb630273183f242 | R | 3,865 | 98 | 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 in data ---... |
ef76ce14662d39ec603a1378cfef0e7fea33cd5f1b36eaf93f87f9315e0a75fb | R | 3,866 | 111 | #!/usr/bin/env Rscript
#-------------------------------------------------
# Title: Make volcano plot helper
# Author: Amanda Zacharias
# Date: 2023-04-09
# Email: 16amz1@queensu.ca
#-------------------------------------------------
# Notes -------------------------------------------
# module load nixpkgs/16.09 gcc/7.3.... |
61f24180dd3c58fd1b8070588a4a13d67b75a9b043b4db3a705ad92b82495995 | R | 3,867 | 141 | # ===============================================================
# 🧪 Table Analyzer — Standalone App
# ===============================================================
library(bslib)
library(bsicons)
library(car)
library(dplyr)
library(DT)
library(emmeans)
library(fitdistrplus)
library(flextable)
library(GGally)
libr... |
f8a8865d3d29fe373d867259d946b7cdc1211623c81d9af6eea22a97c3685352 | R | 3,867 | 158 | require(readxl)
root="//eu.boehringer.com/users/bib/users1/duhoffma/Desktop/AA_pipeline/aa_test/"
funcdir=paste(root,"track_func/",sep="") # r function folder
max_cue_dur=14.82 #in seconds
max_shock_time=10
tdir=paste(root,"rawtrack",sep="")
tfiles=list.files(tdir)
tdirttl=paste(root,"rawtrack_ttl",se... |
72dda677b2a25608010d3b580a3299b1e71379b370469f6626a798e98275b943 | R | 3,869 | 67 | print('This script would take 6 arguments, which are:')
print('Conjuntional clump 0.05 snps csv, respective FUMA snps.tx, std sumstats for TRAIT1')
print('std sumstats for TRAIT2, TRAIT1 name, TRAIT2 name')
print('to produce conj_0.05_TRAIT1_vs_TRAIT2_snps.csv')
library(data.table)
args <- commandArgs(TRUE)
#lead1=read... |
474505892bebdb34296909ab6704857e8e3b6bbd386f600fb694a3f614259b23 | R | 3,871 | 162 | ---
title: "Get statistics for probes and DMRs correcting for education"
author:
- David lukacsovich^[University of Miami]
- Lily Wang^[University of Miami]
date: "`r Sys.Date()`"
output:
rmarkdown::html_document:
highlight: pygments
theme: lumen
toc: true
number_sections: true
df_print: page... |
ddfaf22baa31c77cb1aa06dd3971a66efa34e6652a99e3d3f441da55c570ac40 | R | 3,871 | 80 | ---
title: "The summary plot"
output: html_notebook
date: "`r format(Sys.time(), '%d %B, %Y')`"
---
```{r, message=FALSE, warning=FALSE}
library(tidyverse)
library(magrittr)
library(cowplot)
devtools::load_all()
annotation <- MetadataPath("annotation.csv") %>% read_csv() %$% setNames(l4, cell)
neuron_type_per_type <-... |
c958f7de349f14d8cd48242b66026e95831e39a4169fdec83f691cb79e732a71 | R | 3,872 | 99 | ##########################################################################################
###############--------------- MODEL AGNOSTIC TASK ANALYSES ---------------###############
##########################################################################################
### In this script:
# (1) Mixed-effects ANOVA... |
0fab3414e6b883a8037976258df3b588c539d0a23124e2fcfa05f68511caacd6 | R | 3,878 | 111 | #!/usr/bin/env Rscript
#--------------------------------------------------
# Title: Make volcano plot helper
# Author: Amanda Zacharias
# Date: 2023-04-09
# Email: 16amz1@queensu.ca
#--------------------------------------------------
# Notes -------------------------------------------
# module load nixpkgs/16.09 gcc/7.... |
982c78f1b1d92b1d72166b3859710a7fa6c4ffec0bd20d0722a6ad8df8804552 | R | 3,881 | 85 | library(tidyverse)
# in the past, we used seurat objects as input
# now this was changed to single-cell experiment
seurat_obj = readRDS("C:/Users/rbrowaey/work/Research/NicheNet/seurat_obj_lite_hnscc.rds")
# usethis::use_data(seurat_obj,overwrite = T, compress = "bzip2")
# convert seurat to SCE object
sce = Seurat::as... |
e977f6eff3f3fa09d275d0074505424c967dc36a8def86dd02b7b71ff0db5a45 | R | 3,882 | 96 | #conda activate R4.3
library(CellChat)
library(patchwork)
library(Seurat)
options(stringsAsFactors = FALSE)
setwd("/histor/sun/wangyan/shixh/CellChat/15result/CON")
## Extract expression matrix and cell type information
scRNA <- readRDS("/histor/sun/wangyan/shixh/CellChat/data/scRNA-Choline-10samples-noHarmony-umap-m... |
6038d06ea09c79937463e92eee0e3b56921f8a6b583cb779238f73b951d025c8 | R | 3,883 | 85 | ###############################################################################
## Export per-sample GSVA scores in long format ##
###############################################################################
## #... |
a09b7dd8c24574671c676ca1617823c1965689dc748c7be86d86b08851b6e78a | R | 3,885 | 58 | # function to load metric for all dataset and calculate the mean metric for case and control
# input: mFISH seurat object, with cell identities columns, assay, slots, networks, data directories, case and control dataset name, shortlist cell identity pairs and ligand-receptor pairs
# output: Metrics for each ligand-rece... |
dcc2db00026c5d144f31f8807fa06c963595b12e5a48c50b65a7817ea29bc07c | R | 3,888 | 149 | root="C:/Users/Jun/Documents/R/AA_wSignal/"
max_cue_dur=15 #in seconds
require(readxl)
require("changepoint")
funcdir=paste(root,"track_func/",sep="") # r function folder
tdir=paste(root,"rawtrack",sep="")
tfiles=list.files(tdir)
tdirttl=paste(root,"rawtrack_ttl",sep="")
ttlfl=list.files(tdirttl)
for... |
5188a5524967c3ab95a9d8e2d7f0e05b9e0c879ef9cf3c7c7c38d62be8b142fc | R | 3,890 | 116 | library(ggplot2)
library(tidyr)
library(cowplot)
#V3A
horizontal_data_V3A <- data.frame(
Category = factor(c(">80cm", "80-60cm", "60-40cm", "40-20cm", "<20cm"),
levels = c(">80cm", "80-60cm", "60-40cm", "40-20cm", "<20cm")),
Value = c(58.6, 112.9, 106.93, 110.93, 111.53)
)
vertical_data_V3A <... |
ae42d339faa06f5192b02f1f305a04a8aff4701440f8ca1ca97ba14a91c70e75 | R | 3,891 | 119 | #title: "Post-hoc ANOVA test Enhancing tumor area"
#author: "Mona Zimmermann"
#date: ""
#output: html_document
#### Email: m.l.m.zimmermann@amsterdamumc.nl
#### Purpose of script: Statistical analysis for the post hoc test of clinical
#### relevance in activity changes for the enhancing tumor area
#### Stat... |
782b01bf36b0efc71fafdeefdb62e008ad4fbcb6f85e50368bd000a5a27fcddb | R | 3,895 | 114 | # compute the likelihood of which_model, then return the original object with
# new element: obj$fits[[which_model]]$likelihood
compute_likelihood <- function(obj, which_model) {
stopifnot(is(obj, "sleuth"))
model_exists(obj, which_model)
# we basically do lapply on all of the models
#
# the fitted values ar... |
45d263064be6ca6f0071c54df22cc7dff07c457d2cc80c825078fe3e2fbaa653 | R | 3,902 | 93 | ## KO efficiency assessed by specific substrate and product level of target enzyme
rm(list=ls())
library(tidyverse)
library(ggpubr)
setwd(rstudioapi::getActiveProject())
load(file = "./Output_Data/Exp2_FFA_concentration.rdata")
load(file = "./Output_Data/KO_LUT_paper.order2024-02-08.Rdata")
## Elovl5 and Fads2 first... |
94907ccbc1bc0e84f8cb084a53b3a5d790067de0d6ae16df5ccf83dfbf5396d5 | R | 3,904 | 121 | #!/usr/bin/env Rscript
library(tidyverse)
makeGT <- function(alt) {
print(alt)
n_alts <- str_count(alt, ",") + 1
print(n_alts)
if (n_alts > 1) {
GTs <- 1:n_alts
return(paste(GTs, collapse = "/"))
} else {
return("1/1")
}
}
DepthFromCounts <- function(counts) {
counts_split <- str_split(coun... |
9446da69634354bbcc2a3b977350866f0b85bf5ec94c5c7105a137376869d19c | R | 3,908 | 106 | RNGkind("L'Ecuyer-CMRG")
set.seed(123)
calc_lls <- function(emc, n_particles=1e3) {
model <- emc[[1]]$model()
p_types <- names(model$p_types)
dadm <- emc[[1]]$data[[1]]
designs <- list()
for(p in p_types){
designs[[p]] <- attr(dadm,"designs")[[p]][attr(attr(dadm,"designs")[[p]],"expand"),,drop=FALSE]
... |
1f9801c10c3509fb509ee78ed827637521bc3eb0d04d1c6737179ca1f9762faa | R | 3,914 | 73 | here::i_am("scripts/2.validations/2.4.sequence_divergence/2.4.4.sequence_divergence_VS_network_divergence.R")
library(tidyverse)
library(here)
library(ape)
library(ggbeeswarm)
wd <- here("data/validations/sequence_divergence/")
fig_dir <- here(wd, "figures/")
# load module conservation ranking
module_conservation_o... |
d02c15aefc4566fc98c50d217d3d665b3081dbdef1303bc473096dc0486666e5 | R | 3,915 | 88 | #' @keywords internal
splitVectorByNodes <- function(vec, nodes, n.nodes) {
res <- lapply(1:n.nodes, function(x) list())
splitted <- split(vec, nodes)
res[as.integer(names(splitted))] <- splitted
return(res)
}
#' @keywords internal
graphToAdjList <- function(graph) {
edge.list.fact <- igraph::as_edgelist(gra... |
30344c9c57fbc39f0f239c48dacbff669f36ff5b6bfa1006eddbcea94c36baeb | R | 3,916 | 122 | #------------------------------------------------------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... |
a75f4b535a0974bcf2b7cdd3e37144c65e10656563275293cd28d3231e81880a | R | 3,920 | 110 | library(JASPAR2024)
library(TFBSTools)
library(RSQLite)
library(motifmatchr)
library(GenomicRanges)
library(BSgenome.Mmusculus.UCSC.mm10)
library(Biostrings)
library(stringr)
setwd("./SpaDC/GRN")
# Select appropriate CREs for downstream analysis
peak_file <- "CREs.bed"
output_dir <- "peak_motif_perturbation"
dir.creat... |
a5ad119bf9a11bf059c8d45eed3d0e0f7ef916e9a7e9fde42097038703468c6b | R | 3,922 | 95 | ---
title: "DunedinPACE: A DNA methylation biomarker of the pace of aging"
author: Vignette created by - Calen P. Ryan, PhD
output:
BiocStyle::html_document:
toc: true
vignette: >
% \VignetteIndexEntry{DunedinPACE: A DNA methylation biomarker of the pace of aging}
% \VignetteEngine{knitr::rmarkdown}
% \Vign... |
817526ee403caadde1f8891220803165d6ed1df6daa2bb38d3f3983b490921e7 | R | 3,924 | 88 |
#' @title geneFiltering
#' @description Gene filtering
#' @param exprMat Expression matrix
#' @param scenicOptions scenicOptions object (slots used: RcisTarget databases and genesKept)
#' @param minCountsPerGene Minimum counts per gene required
#' @param minSamples Minimum number of samples (cells) in which the gene s... |
4b2f05b7c638ff3ce90857447031380c1dbea1a30d1e7f74bc64907ca4904614 | R | 3,926 | 100 | # function to aggregate and load the data for the MSYNC project (c) IS Plank
# INPUT:
# * df.indi: data frame with one row per subject, must contain the columns listed in ls.vars and grp.var
# * ls.vars: list of column names in df.indi to be included in overview; referenced columns must contain doubles
# * grp.v... |
4c905fdf01b828ea340dc5bef6d6e38392de2792fcb8bd676d108322338e4f68 | R | 3,926 | 95 | #' banc-manc-mesh-transform — Transform MANC meshes into BANC space.
#'
#' For each MANC bodyid, fetches the OBJ mesh and applies the
#' MANC → JRCVNC2018F → BANC registration chain. Saves the transformed OBJ.
#'
#' @section Reads:
#' - `<banc.meta.save.path>/manc_meta.csv`
#' - MANC meshes via `malevnc`
#'
#' @sec... |
1741723a4c644ef51793ec1303148ac1ad7d643fd38b2031889f107ae73c68af | R | 3,934 | 124 | library(shiny)
library(ggplot2)
library(dplyr)
# Load the dataframe
df <- read.csv("dfDesign.csv")
df$outcome <- ifelse(is.na(df$outcome), "Negative Affect", df$outcome)
df$outcome <- case_match(df$outcome,
"ERLookDiff" ~ "Task-based Ratings",
"neoN" ~ "Neuroticism",
"neoN1" ~ "N1:Anx... |
9f32e93386a8c612cbe48a4db4dbe99e7e07d24d9249cdb243adbbc7d26b7b2f | R | 3,936 | 132 | ### Heatmaps
setwd(rstudioapi::getActiveProject())
rm(list = ls())
library(tidyverse)
library(ComplexHeatmap)
source("./Scripts/Function_scripts/Effect_size_functions.R")
source("./Scripts/Function_scripts/Pre-processing_functions.R")
##====Plotting function====
heatmap.plot <- function(plot.df, dataset){
pdf(paste0... |
e6bb9efccdd76e463bcc22d76d2c7ad32fd5548c86456914764597eb38c7eb91 | R | 3,936 | 143 | #kalliso abundance files has been input as txi
library("tximport")
library("DESeq2")
library("AnnotationHub")
library("ensembldb")
library( "pheatmap" )
library( "RColorBrewer" )
library( "ggplot2" )
library( "biomaRt" )
library("dplyr")
colnames(txi$counts)
sampleTable <- data.frame(sample = colnames(df),
... |
104d31f3d34eabc7717ec35a65fd9644cb582a99a90073802b7733a83918d7cd | R | 3,938 | 81 | library(ggplot2)
library(tidyr)
library(conflicted)
library(magrittr)
library(reshape2)
library(dplyr)
library(here)
library(readr)
conflict_prefer("count", "dplyr")
conflict_prefer("select", "dplyr")
conflict_prefer("filter", "dplyr")
Sys.setenv("VROOM_CONNECTION_SIZE"=1000000)
base_folder <- here("results/full_run_Z... |
6a7607c579c2385287050a7cac5e93e4828ab95473d6685b14cb3447b2add49f | R | 3,938 | 116 | ###############################################################################
## parse_mitocarta_gmx.R – Parse MitoCarta GMX file for GSEA ##
###############################################################################
## Purpose: Convert GMX (Gene Matrix Transposed) format to TERM2GENE and
## ... |
fdb7018dae1311c388e7ddd2ac60cbce27682aa023a234e9866ede32040f147b | R | 3,938 | 92 |
library(epicalc)
args<-commandArgs(TRUE)
viruses_csv = sprintf("%s", args[1])
custom_db_out = sprintf("%s", args[2])
output_dir = sprintf("%s", args[3])
vir_all<-read.csv(viruses_csv)
vir_all<-vir_all[!is.na(vir_all$Queryid),]
new_vir<-vir_all[vir_all$identity<90,]
known_vir<-vir_all[vir_all$identity>=90,]
#####... |
026ef4e75a55d6d9a40e2fdc567bb03c6e4d3cd863a06bba1680ab14f53a120f | R | 3,949 | 117 | #!/usr/bin/env Rscript
#-------------------------------------------------
# Title: Write individual bash scripts
# Author: Amanda Zacharias
# Date: 2023-08-11
# Email: 16amz1@queensu.ca
#-------------------------------------------------
# Notes -------------------------------------------
# module load nixpkgs/16.09 gcc... |
de1a67a7b9a40ebc4bb9261a4c8675ad19b093bfc1e5b928610c5eebd164dedb | R | 3,949 | 115 | #------------------------------------------------------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... |
36244d5fbef98e6678f5e21f8bfc48bdd15b74a30d413f9f2baa4d197df7ec8e | R | 3,958 | 83 | ## ----setup, include=FALSE----------------------------------------------------------------------------------------
#knitr::opts_chunk$set(echo = TRUE)
# setwd("/Users/warri/OneDrive/Documents/Denver/Research/Data/ashad_n110")
#sessionInfo()
# RDAC R packages location '/home/rcorcoran/R/x86_64-pc-linux-gnu-libr... |
6ebcb00407a828b015072a396c4d37536c8ee8c866c8fc3c58a2e5eb430ca709 | R | 3,958 | 102 |
neodb_switch <- tabsetPanel(
id = "neo_switch",
type = "hidden",
selected = "overview",
tabPanel("overview",icon = icon("database"),
br(),
br(),
br(),
div(
fluidRow(
column(12,
div(slickROutput("OV",height = "400px",w... |
ab1a0e90d7191def54cc6adccf61170893da5ec80daf1c775653a8001273e46a | R | 3,958 | 101 | #' test-data-sources — testthat suite for alignment-data-sources.R.
#'
#' Covers `parse_alignment_data_args` argument parsing (all CLI forms +
#' env-var fallback + validation errors). `resolve_alignment_paths` tests
#' are TODO — would need a `withr::with_tempdir` filesystem mock because
#' the function pokes real O2 ... |
b94bbf1875949d028bc214e85170ab3b7a2b4699b2a0740305393e4fc5558767 | R | 3,962 | 100 | #' banc-sync-influence — Validate + rsync influence parquet shards to GCS.
#'
#' Run AFTER all `banc-build-influence.R` array shards have completed.
#' Repeats the chunk completeness check + spot-check + gsutil rsync that
#' the un-sharded build script does at the end.
#'
#' @section Reads:
#' - `<banc.versioned.save... |
228ed3f490f6e852bdd2643c77b8c99881cf20cc802c5ec4e8416ae21064f52b | R | 3,964 | 78 | # Framework for modeling Ne dynamics using random forest
rm(list=ls())
library(ggplot2)
library(RColorBrewer)
library(scales)
library(randomForest)
load('~/.../code/Ne_WholeMidgut_data.RData')
# Take the samples obtained on day 3 (S1_3d, S2_3d and S3_3d) as an example
data_1 <- data[data$age=='3',]
... |
1c17fee98639d9d09ecb2957779ef75391d2e78ca5540c250c8c67e7bc7428c0 | R | 3,970 | 95 | 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 in data ----... |
7b74de37d73cbe91886a3c3e5e4b17f79e42e7e62df7e5513ced840da3ab3587 | R | 3,971 | 115 | ---
title: "neural_figures"
output: html_document
date: "2025-07-24"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
remotes::install_github('satijalab/seurat-wrappers')
```{r}
library(ggplot2)
library(dplyr)
library(Seurat)
library(monocle3)
library(SeuratWrappers)
neural <- LoadSeuratRds(fil... |
678388936402904996cd273de9e20377ca75317a409683c1c874e8bcdcdb3b0b | R | 3,979 | 42 | rm(list = ls())
tmp <- lapply(c('ggplot2','reshape2'),library,character.only=T)
indir <- 'C:/Users/wangz21/OneDrive - The Mount Sinai Hospital/Desktop/Subject/ADASE'
setwd('C:/Users/wangz21/Box Sync/Huang_lab/manuscripts/ADASE')
### Load related information
# variant mapped to gene for variants
tmpfile <- gzfil... |
e1aa1d786eb78a07482deaaee4d3684779db7f14e4774763f07944a5fc721cdb | R | 3,980 | 111 | library(Seurat)
library(tidyverse)
library(data.table)
library(biomaRt)
library(NatParksPalettes)
library(EnhancedVolcano)
library(ggthemes)
library(ggh4x)
library(speckle)
library(limma)
Astro_sub <- read_rds("Astro_SCT_integ_male_v2.rds")
# Remove batches with too few cells to integrate
Idents(object = Astro_sub) <... |
0530a589b4eec98f1c8bb37a7732f99fd1062e4196657daa61159ede9df18832 | R | 3,983 | 184 | ---
title: "Check DMR data against MIAMI-AD"
author:
- David Lukacsovich^[University of Miami]
- Lily Wang^[University of Miami]
date: "`r format(Sys.time(), '%d %B %Y')`"
output:
rmarkdown::html_document:
highlight: breezedark
theme: lumen
toc: true
number_sections: true
df_print: paged
c... |
7bb6f62e1aec5374954fa2a60c2af5f606be0a4a5b3c0a2f848a3e95201ecdae | R | 3,996 | 94 | # ==============================================================================
# Script 11: Figure 2F - CDH1 Allele Frequency in Cell Lines
# ==============================================================================
# Description: Analyzes and visualizes CDH1 mutation allele frequencies across
# ICL... |
b975dba3058da461cac977cc3cb7bf4a3cc5ecb46a6ff4b4a6f18ee904a99101 | R | 3,997 | 122 | #------------------------------------------------------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... |
a1b16e8a3d3dc3f06bd2b2146c4d60809701e61806e633258a995ccd9c28f464 | R | 4,000 | 105 | here::i_am("scripts/1.neural_differentiation_dataset/1.4.Spearman_network_inference_and_analysis/1.4.3.CroCoNet_analysis.R")
library(CroCoNet)
library(tidyverse)
library(ape)
library(SingleCellExperiment)
library(foreach)
library(here)
library(ggbeeswarm)
library(ggrepel)
wd <- here("data/neural_differentiation_datas... |
cb4249456c96bbf28339dec64fe2ec7d0b9f930dbfb74c97ef0d75d9423a7bbf | R | 4,001 | 124 | ---
title: "score_assumption"
author: "Marlene Lin"
date: "4/30/2025"
output: html_document
---
```{r}
# Install required packages (only need to do this once)
#install.packages(c("performance", "gvlma", "report", "car","bestNormalize"))
# Load the packages (do this every R session you work in)
library(performance)
lib... |
a9d45cdae9074c268af9c391dc8e50fc5c0610d295857a4b96a06d0c525690eb | R | 4,006 | 120 | # Elastic Net Model for Predicting Brain Atrophy
# Author: Maryam Hadji, University of Eastern Finland, Kuopio, Finland (maryamh@uef.fi)
# Last updated: 20.Feb.2025
# Requirements:
# R version 4.3.1 or later
# install.packages("glmnet")
# install.packages("caret", dependencies = c("Depends", "Suggests"))
# install.pac... |
87fa05b8d8205efcb7682162febdbad03407fe275b3b0e70609738085bf3d09e | R | 4,007 | 125 | mlflow_rest_path <- function(version) {
switch(
version,
"2.0" = "api/2.0/mlflow"
)
}
mlflow_rest_timeout <- function() {
httr::timeout(getOption("mlflow.rest.timeout", 60))
}
try_parse_response_as_text <- function(response) {
raw_content <- httr::content(response, type = "raw")
tryCatch({
rawTo... |
90c528fe8633979da6a8f0d49406300c3139bbc09606d35b65f6e95c26f8749f | R | 4,007 | 93 | ##### The following code will download the raw preprocessed data #####
#### Change Working Directory if Necessary ####
#setwd("~/FC-Network-Replicability-Effects")
#### (Install and) Load Packages ####
#install.packages("dplyr")
#install.packages("curl")
library(dplyr)
library(curl)
#### Import and Prepare Summar... |
1e7af372aa27999615498dbf25d1c5766479e37e0138601376d996c1936447fa | R | 4,008 | 95 | # Load required libraries
library(ggplot2) # For visualization
library(dplyr) # For data manipulation
library(rnaturalearth) # Provides world map data
library(rnaturalearthdata) # Supplementary world map dataset
library(sf) # Simple Features for spatial ops (centroids/points)
# Fun... |
0df58ba360d0b92fcaca7304c68b5ef33e0b4204742ac34817f69c55ba204b2b | R | 4,009 | 108 | # Script: 2.run_LM.R
# Purpose: Fits linear models to compare different combinations of predictors (MODULE, INGENE, EpiXcan, CIS)
# for gene expression prediction in testing data. This script:
# 1. Loads preprocessed testing data predictions
# 2. For each brain region and gene, fits various linear mode... |
3ce9af7d388fbb3579e91eaccd5d93ed8795ffa9723e7dc80cb4e18a3aa91690 | R | 4,016 | 69 | ########################################################################################################
#### Trial dataset for this R script can be downloaded from ####
#### https://data.mendeley.com/datasets/454s2vw255/1/files/b3800d61-6988-4fd4-97e5-b3f50bfbacc8. ####
##... |
5893c6554d27840721d47cc8af2c2677414710cdc3082159095e0cb46ca56b67 | R | 4,022 | 102 |
## Figure S6 Ki817 mouse ####
tell_user('done.\nCreating Figure S6...')
resx=300
png('display_items/figure-s6.png',width=resx*6.5,height=5*resx, res=resx)
layout_matrix = matrix(1:4, nrow=4, byrow=T)
layout(layout_matrix, heights=c(0.8, 0.2, 0.8, 0.2))
grch38_coding_offset = 4699605 - 4680251
depth = read_tsv('data/... |
1819c2021895f6605cab9f84d838124d3c0d3eac1d91208f156294f113ad3255 | R | 4,025 | 65 | ########################################################################################################
#### Trial dataset for this R script can be downloaded from ####
#### https://data.mendeley.com/datasets/454s2vw255/1/files/1392d1f9-f950-4d35-b3dd-4a9b4487b5a4 ####
##... |
c865dd1858d59b2a3f35bb24b2d6516dbfa136e75e4bb7c48a6bb42b3a8daa3c | R | 4,027 | 70 | BHR_meta <- function(ss_list, trait_list, annotations, num_blocks, genomewide_correction, fixed_genes,
overdispersion, all_models, num_null_conditions, slope_correction, gwc_exclusion,
custom_variant_variances, intercept, start_time){
if(length(ss_list) > 1 & length(trai... |
9b8c3cb39a3f251e3aedc14fe7600a8c00084062b280874bd87ed6ac366dbe44 | R | 4,029 | 134 | ---
title: "mtcojo"
output: html_document
author: "Megan Skelton"
---
GWAS summary data
Columns are SNP, the effect allele, the other allele, frequency of the effect allele, effect size, standard error, p-value and sample size.
```{bash}
# Example reformatting for a file structured: SNP MARKERID CHR BP A1 A2 FREQ ... |
4f038d4fa26f472ccb8c5ba0bf59d01e458c646b4663d96a8f62f4c7e9d1242d | R | 4,032 | 135 | rm(list = ls())
############################################
# Packages
############################################
pkgs_cran <- c(
"ggplot2",
"dplyr",
"readxl",
"survival",
"openxlsx",
"broom",
"tibble"
)
for (pkg in pkgs_cran) {
if (!requireNamespace(pkg, quietly = TRUE)) {
install.packages(pkg... |
a697fca43b978ab9d518882e429dbcd254159fc9c6677707c4a7fc13de224a5c | R | 4,043 | 118 | #!/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 subset RData")
parser$add_argument("-b", "--bat... |
e00afeee634b2e40da4403d4aee866c4ffd4d4f6fa025a977268bc9a98389dda | R | 4,044 | 106 | #' @docType methods
#' @rdname mr_mvlasso
#'
setMethod(mr_mvlasso,
"MRMVInput",
function(object,
orientate = 1,
distribution = "normal",
alpha = 0.05,
lambda = numeric(0)){
K = dim(object@betaX)[2]
if (orientate %in% 1:K) { orientAte = orientate} else {orien... |
b0c14ae85be2ce49a4837e96fd98f9615553b944509544efdaeff06e3f1aec39 | R | 4,054 | 165 | library(tidyverse)
library(patchwork)
data_file <- "merged_data_shared_bacteria.tsv"
data <- read.table(
data_file,
comment.char = "!",
row.names = 1,
sep = "\t",
header = TRUE,
check.names = FALSE
)
data <- data |>
mutate(
group_mean_relative_x = group_mean_relative_x * 100,
group_mean_relati... |
1c3c0d8289637fa4c601848b1cb23011a81afbe1aa31f33ca0c53b08067e002d | R | 4,063 | 123 | library(ArchR)
library(parallel)
addArchRThreads(threads = 1)
addArchRGenome("hg38")
# load the main object
projss1 <- loadArchRProject("projrmSubset_subPeak")
celltype <- "MG" # or 'AST' 'ODC' 'OPC'
cellKeep <- rownames(projss1@cellColData[projss1@cellColData$Clusters_r1_iLSI6 %in% celltype,])
length(cellKeep) / ... |
d40760367ff1cbf6dd01291dbb45a125087f0d10aa8825bf19fea3479ff2557f | R | 4,063 | 135 | .perform_undersampling <- function(df, target, frac, replace) {
test_indices <- sample(
x = nrow(df),
size = floor(frac * nrow(df))
)
test <- df[test_indices, ]
train <- df[-test_indices, ]
n_minority = min(table(train[[target]]))
fct_levels <- levels(train[[target]])... |
3fadef06e505d23ca9bc3c3a758b87764bf9a230b02d31b44f537a390c837e80 | R | 4,066 | 160 |
data_GMFE_test_NCA <- readRDS("datasets/data_GMFE_test_NCA_CTPPSP.rds")
data_GMFE_test_3CMT <- readRDS("datasets/data_GMFE_test_3CMT_CTPPSP.rds")
data_GMFE_test_PINN_ThreeCMT <- readRDS("datasets/data_GMFE_test_PINN_ThreeCMT_CTPPSP.rds")
data_PURE_fold_gmfe <- readRDS("datasets/data_PURE_fold_gmfe_CTPPSP.rds")
... |
8b188ee781b1bf54591f99cd5ad1e1aa2d046de7e5220dbf992200dea55a0f23 | R | 4,078 | 134 | # ===============================================================
# ?? Regression validation helpers
# ===============================================================
validate_regression_inputs <- function(df, input, engine, strat_details) {
# ---- 0) Stratification variable cannot be a predictor ----
if (!is.null... |
399bbd8d49e79d8b85a42be28725c9d93abe2f359392dc6b25a84bfa3bbb6133 | R | 4,083 | 86 | ### Mboat2 OE effect assessed by specific substrate and product level of target enzyme
rm(list = ls())
library(tidyverse)
library(ggpubr)
setwd(rstudioapi::getActiveProject())
source("./Scripts/Function_scripts/Pre-processing_functions.R")
M2.LUT <- c("#938b72", "#E15759")
load("./Output_Data/Mboat2_OE_LIPID.Rdata")
... |
8f6b524460d33ec225d4f6ac8ae56bdf0058dc1cae6a51a95647a5d40b049d67 | R | 4,094 | 108 | ### Plot abundance on individual lipids in 2 in vitro studies
setwd(rstudioapi::getActiveProject())
rm(list = ls())
library(tidyverse)
library(ggpubr)
library(ggrepel)
library(ggbreak)
source("./Scripts/Function_scripts/Pre-processing_functions.R")
load("./Output_Data/Invitro_lpd.mol.pct.Rdata")
load("./Output_Data/I... |
42afbd0842be6df3053c043471182e6ed34c23d484d0398b219b55b361823e12 | R | 4,104 | 135 | library(data.table)
library(dplyr)
library(susieR)
library(ggplot2)
library(viridis)
library(ggnewscale)
data_path <- "data/finemapping/susie"
check_mkdir <- function(path) {
if (!dir.exists(path)) dir.create(path, recursive = TRUE)
}
is_symmetric_tolerance <- function(M, tol = 1e-8) {
max(abs(M - t(M))) < tol
}... |
8b0669bd8b852874aa379cd9915c679b19ae85f0f21236b5a7aad17c7de66767 | R | 4,104 | 101 | library(EPICATAC)
setwd("/data/lyx/LDA/benchmark/")
source("EPIC_fun.R") # https://github.com/GfellerLab/EPIC-ATAC
# 定义RMSE函数（输入：实际值、预测值；输出：RMSE）
calc_rmse <- function(actual, predicted) {
# 检查输入长度是否一致
if (length(actual) != length(predicted)) {
stop("实际值和预测值长度必须一致！")
}
# 检查是否有NA值
if (any(is.na(actual)) | ... |
68417d50b606650413d84e65aa41bb2c7edbbe25c8bd5e17f434761420618b8d | R | 4,105 | 154 |
data_NCA_fold_error <- readRDS("datasets/data_NCA_fold_error.RDS")
data_3CMT_fold_error <- readRDS("datasets/data_3CMT_fold_error.RDS")
data_3CMTPINN_fold_error <- readRDS("datasets/data_3CMTPINN_fold_error.RDS")
data_PURE_fold_error <- readRDS("datasets/data_PURE_fold_error.RDS")
####### NCA ###############
cou... |
d6af25c8698e146b798d088b8803289c691d3efc756a970b5d5d0b6b8b35552f | R | 4,105 | 158 | library(data.table)
library(tidyverse)
rt=fread('./Count.txt')%>%as.data.frame()
colnames(data)[1]='group'
str(data)
# =========================
# 1. 加载包
# =========================
#if (!requireNamespace("DESeq2", quietly = TRUE)) {
# if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("Bi... |
f70905d27a5c260e388f6dd6b8be2dcc8400109ec483bab571b83045c15725c4 | R | 4,117 | 131 | #' Sample spatial gene visualization workhorse function for Xenium data with
#' centroid based spatailCoords
#'
#' This function visualizes the gene expression stored in `assays(spe)` or any
#' continuous variable stored in `colData(spe)` for one given sample at the
#' spot-level using (by default) the histology inform... |
7ee2fdbb4a87aaa79fc98262531a5b0593afbed715f0ae568a2d58bd1bdb768a | R | 4,125 | 149 | rm(list = ls())
############################################
# Packages
############################################
pkgs_cran <- c("openxlsx", "readxl", "ggplot2", "tidyr", "dplyr", "reshape2", "rstatix")
for (pkg in pkgs_cran) {
if (!requireNamespace(pkg, quietly = TRUE)) {
install.packages(pkg, dependencies ... |
16093842823cd27cd4df784ce8c9ffb827109d34ed463ddc1419c7996b8eb828 | R | 4,130 | 128 | #------------------------------------------------------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... |
ef41cfac2cfb278570deeb0b67b84c7b66851cd1e35423b4ad478f05e52c9776 | R | 4,130 | 116 | # Load here package for path resolution
library(here)
source(here("src/utils/utils.R"))
source(here("src/utils/funcs.R"))
# Figures ####
source(here("src/figs/fig1.R"))
source(here("src/figs/figS1.R"))
source(here("src/figs/figS2.R"))
source(here("src/figs/figS3.R"))
source(here("src/figs/fig2.R"))
source(here("src... |
066d4e701a9b4a89c83b93fb7590db3464d7377ef6481f32e5ff776c557c155a | R | 4,133 | 165 | ---
title: 'Epilepsy 10x samples v2 - gene ontology analysis (using gProfiler)'
author: "Jon Thompson, Pers lab"
date: "`r Sys.time()`"
params:
date: !r Sys.time()
randomSeed: !r 12345
pValThreshold: !r 0.05
padjMethod: "bonferroni"
output:
html_notebook:
df_print: paged
number_sections: yes
toc: yes
toc... |
1793499beb4aade7081c335046e5fe8e3e5cdbfd5d26c3a76f303d8ac8ec3ac9 | R | 4,135 | 116 | # ==============================================================================
# Load RPPA (Reverse Phase Protein Array) Data
# ==============================================================================
# This script loads and prepares RPPA protein expression data
#
# Author: Osama Shiraz Shah
# =================... |
218f23cb57f3c39164cc5928829cbb087ce07f218fbaf17f073db7546c068ab8 | R | 4,135 | 117 | #functions needed to pull signal values from bigwigs and take median within the union of peaks for a tissue/cell type
#Author: Brian Roberts
require(rtracklayer)
read_and_bin_func_bigWig <- function(bin_GR,bigWig_path,bin_func = function(x){quantile(x,0.5)}, run_unstable=F){
#the which input to rtracklayer::import... |
b4f36a414601d7a5218814957a99461552d0cbc343434648d336c462cd948246 | R | 4,138 | 108 | #' banc-extract-synapse-lookups — Extract version-agnostic synapse → neuropil lookup.
#'
#' Synapse `id` is stable across BANC root_id versions; run ONCE before a
#' rebuild to preserve existing neuropil assignments.
#'
#' @section Reads:
#' - `banc_<ver>_synapses_<src>_neuropils.parquet`
#'
#' @section Writes:
#' ... |
afe9aee9c9ae692d1f96ff9af5c21e4322f0ad51248c755cbca336bda7316831 | R | 4,144 | 94 | ### Concentration calculation
setwd(rstudioapi::getActiveProject())
rm(list=ls())
library(tidyverse)
library(stringi)
Invitro.new.peak <- read.csv("./Input_Data/Exp3_Validated_Reintegration_121125_color.code.csv", stringsAsFactors = F)
Invitro <- Invitro.new.peak %>%
filter(!Peak.color.code == "orange") #404 lipi... |
d4cf6b26fbedd06f60bbdb66653686ee7c6b4cb95d74073e867ee1d6b4af44e6 | R | 4,149 | 106 | library("data.table")
library("R.utils")
library("vcfR")
Args <- commandArgs(TRUE)
dname <- as.character(Args[2])
chr <- Args[3]
phenoname <- as.character(Args[1])
# How many ordinal categories per pheno? zt2 = {0,1,2}, zt3 = {0,1,2,3} , etc.
zt2 <- c("LEWY_grp")
zt3 <- c("AMY_A","AMY_CERAD","B_SCORE","ADNC","REAG","W... |
7ec4671906fc9be73ea4d70d5ceacd975ce94287b08cad1448e4c53cd5793dec | R | 4,151 | 137 | .check_target <- function(df, target) {
if (!target %in% names(df)) {
stop("Target column not found in the dataframe.")
}
}
.check_factor <- function(df, target) {
if(!is.factor(df[[target]])) {
stop("Target must be a factor.")
}
}
.check_binary <- function(df, target) {
target_val... |
9b79708af14e3ce6eef4b59e576c42f4bba9a772ea811130c8f71945baf57e2a | R | 4,153 | 137 | #' Select Searchlight Domain and Create Masks
#'
#' Extracts the relevant subset of data for a given searchlight index.
#' Returns masked locations, gradients, subject indices, and mask.
#'
#' @param data List returned by load_data()
#' @param sl.idx Index of the searchlight to select
#' @return List with masked locati... |
b0effdb6903db3842cfbadeccbac16e5b101be604bb21a73f63aa97d8b5ffa75 | R | 4,159 | 101 | # Load required packages
if (!require(dplyr)) install.packages("dplyr")
library(dplyr)
# 1. Create original data frame
data <- data.frame(
Feature = c(rep("F1CON", 14), rep("F1GCS", 14)),
LM = c(5.2138,7.9386,5.1037,4.5447,4.321,4.8528,4.9567,5.1595,4.6176,4.2695,3.4082,3.4728,4.6216,4.4669,
5.336... |
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