sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
e1bff85f6c94825036205313115d82912c8ef9cfbade97bac530284d86d5b337 | R | 3,315 | 106 | #####################################
## R script for reading in salmon quant.sf
## and analysis via DESeq2
## annotation via ensembl meta-data
#####################################
#####################################
## Load Libraries
#####################################
library(DESeq2)
library(gplots)
library(t... |
1a7246603ce53f535b09d4004e2f43494bc794bc60e98d139e8ac87fae7018ce | R | 3,317 | 108 | #' Create a factor with natural (human-friendly) ordering
#'
#' Converts a character or numeric vector into a factor where the levels are
#' ordered naturally (e.g., `a1`, `a2`, ..., `a10` instead of
#' lexicographically as `a1`, `a10`, `a2`, ...).
#' This is useful for plotting or labeling grouped data where numeric
#... |
c928e099d9f891f0252492c689f1bf57fa5a17690a9a455d99c688ff9ce2ce59 | R | 3,317 | 156 | ---
title: "Prepare significance results for input into Comb-p"
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: paged
cod... |
807e89816a68595b5e88f19bb2fb5d15da344996ac24ac0ff4777f4a63aceb51 | R | 3,319 | 101 | library(Seurat)
library(Matrix)
setwd("/home/joonho345/1_Epilepsy_RNA/scRNA_Human/")
#########################################################
#### Load Seurat Object (Deconv) ####
integration_name <- "GSE160189_186538_Integration"
seurat_rds <- "/data/project/1_Epilepsy_RNA/scRNA_Human/Deconv_objects/GSE160189_1865... |
084f8313af006abc8a9ce11d5f777f60dde6bb7604349fa500b693d1cb584225 | R | 3,320 | 106 | rm(list = ls())
gc(reset = T, verbose = F)
source('script/Functions/Functions.R')
pkgs[which(load.packages == F)]
rpid = Sys.getpid()
options(future.globals.maxSize = 1000 * 1024^3)
ids.file = 'results/04.RCTD/'
dir.exists(ids.file) || dir.create(ids.file)
script_start_time <- Sys.time()
cat("Start:", format(script... |
23e0819efe4f898b1af972d1ed11f211e983ac45ca987b978b30d728a61fec29 | R | 3,321 | 156 | ---
title: "Prepare significance results for input into Comb-p"
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: paged
cod... |
7493317421ccad88dfe82680a8a126a5a8532dc55f791b4a57c98061bbc96dc6 | R | 3,321 | 139 | ---
title: "Get statistics for probes and DMRs correcting for smoking"
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: paged
... |
6958ed4b378d5d76dbf372201f84032487fe067e3ac9a7ce5beba9db5ec8e399 | R | 3,323 | 98 | ## script to generate histogram of insert sizes from metrics file
## expecting 3 arguments:
## first is the metrics file with the histogram info
## second is the output file
## third is a name for the plot
args <- commandArgs(trailing=TRUE)
metricsFile <- args[1]
pdfFile <- args[2]
bamName <- args[3]
histoWidth <- ife... |
ebf969b1e82dc773e7086645d7cfc80b7719717b2fdaa64a25d00cc3f09e1e28 | R | 3,333 | 99 | ---
title: "Using New Shapes in Venn Diagram"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Using New Shapes in Venn Diagram}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
```{r setup... |
2aa38ca5a2f75866f38a2ada585234ec318e4801bbd70ffc8e4b9fd0b395f328 | R | 3,336 | 93 | #' Install Python ConnectomeInfluenceCalculator Library
#'
#' This function installs the Python ConnectomeInfluenceCalculator library
#' and its dependencies (PETSc and SLEPc) into the r-reticulate environment
#' using an optimized installation script.
#'
#' @param force_reinstall Logical. Whether to reinstall even if ... |
aa6c93c89929858a8e9e00774bad221d728265f1ba94d682e9fd41026c4949cf | R | 3,336 | 51 | rm(list=setdiff(ls(),c('params')))
basedir <- params$basedir # make this whatever you like, end with /
setwd(basedir)
source('code/aba/aba_fxns.R')
source('code/misc/miscfxns.R')
abadir <- paste0(basedir,'data/aba/')
dir.create(paste0(abadir,'ontologies'),recursive = T)
# now we need to get a list of relevant structur... |
309ac45212097f6edb04b9e55d1a113fd11de88609192f647f4fc35d10afdd99 | R | 3,338 | 107 | ---
title: "Fig_S4F_Tcell_proportion_by_sample"
output: html_document
date: "2026-02-17"
---
```{r}
library(Seurat)
library(tidyr)
library(ggplot2)
library(ggpubr)
library(dplyr)
library(harmony)
library(purrr)
library(cowplot)
library(rstatix)
library(ggbeeswarm)
library(ggbreak)
```
# Set up and adjust metadata
```... |
a76b746e5767572ef105fbd9c20eb83450689401f48a1ffcbb447c78874ba9f4 | R | 3,338 | 92 | #!/usr/bin/env Rscript
#-------------------------------------------------
# Title: Write individual Hisat2 scripts
# Author: Amanda Zacharias
# Date: 2023-06-30
# Email: 16amz1@queensu.ca
#-------------------------------------------------
# Notes -------------------------------------------
# module load nixpkgs/16.09 g... |
4721b95c69e044bf3bcdd807caa417a68dc30c3575674ea5085fda50bfcb07e9 | R | 3,340 | 95 | #' Plot cells located within spatial boundaries or ring regions
#'
#' Visualizes cells that fall within defined spatial regions
#' (boundaries or rings), typically obtained using
#' the `getCellsInside()` function. The cells are colored by cluster,
#' and the function offers two plotting modes: using `geom_sf()`
#' (wi... |
df42f6fe68f1d3bfd2c8902e05912f568067da2f02fb7291a43bb0da25255420 | R | 3,347 | 96 | ########################################################################################
### Metabolomics Volcano Plot Generation
########################################################################################
# Clean workspace
rm(list=ls())
# Define required packages
required_packages <- c("ggplot2", "RCol... |
f474b33fca5ac1b2e74add819cff4d89dc9c4ace3cf5209223d514d354395907 | R | 3,347 | 133 | library(ggplot2)
library(dplyr)
#input df and group prder
# Define custom order
group_order
# Convert group to factor with specified order
df$group <- factor(df$group, levels = group_order)
# Recalculate summary with ordered factor
df_summary <- df %>%
group_by(group) %>%
summarise(
mean_value = mean(Nkx2.1,... |
9c5301a3b34fcecd0e1ac4784f57f1a516b573305ebdfde59e70b7c101b8d41e | R | 3,350 | 160 | ---
title: "Compare significant probes to Miami-AD results"
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: paged
code_do... |
91766c239ffe30eb4814ef54f9647f76699ce02a637dba020e51aa9fed620490 | R | 3,353 | 96 | ########################################################################################
### Metabolomics Volcano Plot Generation
########################################################################################
# Clean workspace
rm(list=ls())
# Define required packages
required_packages <- c("ggplot2", "RCol... |
0b647b8b0a8a28c546de97aa3781e3b1292df9a6aa8401f749c901186005df03 | R | 3,354 | 156 | ---
title: "Prepare significance results for input into Comb-p"
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: paged
cod... |
fedbdadb3132357a7fe857eb521280d61f4445091ff8cbc3d24d76d03dd5ac7a | R | 3,358 | 69 | context("test PANDA result")
test_that("panda function works", {
# Skip if Python or required modules are not available
skip_if_not(reticulate::py_module_available("pandas"), "Python pandas module not available")
skip_if_not(reticulate::py_module_available("numpy"), "Python numpy module not available")
# tes... |
b3e5e2d2ef40f639b08c21d4ec3c04cd4bf4d3f88121a25a7a45691174587af0 | R | 3,367 | 111 | #' Extract significant genes for all modeling results
#'
#' This function combines the output of [sig_genes_extract()] from all the
#' layer-level (group-level) modeling results and builds the data required for
#' functions such as [layer_boxplot()].
#'
#' @inheritParams sig_genes_extract
#'
#' @return A [DataFrame-cla... |
1cb5dfa49e3a4f7ef48095a1e410d693a8c68668a68d5a07f3d6fb837d3c98ef | R | 3,370 | 84 | library("methods")
library("glmnet")
library("stringr")
library("data.table")
library("dplyr")
get_gene_annotation <- function(gene_annot_file_name, chrom)
{
gene_df <- read.table(gene_annot_file,header=F,stringsAsFactors =F,sep="\t",fill=T)
gene_df1 <- filter(gene_df,V3 %in% "gene")
geneid <- str_extract... |
e4310953be3ce2a293793fe79fdd041f2f5d2de0329ec602140506609d069d3f | R | 3,373 | 134 | context("Identification of Marker Genes by COSG")
library(COSG)
library(SeuratObject)
# Load test data from SeuratObject package (compatible with v5)
data("pbmc_small", package = "SeuratObject")
test_that("COSG works with default parameters", {
expect_error(cosg(object = pbmc_small), NA)
})
test_that("COSG works ... |
f8ed2dae89a06dbb30a5ae0f515417fc548ee2915c1427058fae568e6076a09b | R | 3,377 | 101 | #!/usr/bin/env Rscript
# -*-coding: utf-8 -*-
#-----------------------------------------------
# Title: scRNA of the naive human DRG from Renthal lab
# Author: Amanda Zacharias
# Date: 2025-07-22
# Email: 16amz1@queensu.ca
# Notes -----------------------------------------------
# module load StdEnv/2023 r/4.5.0
# Opti... |
7ac46d7b6878c7f885317369632c036cce381b9b1490febdf739c44972c7438c | R | 3,382 | 83 | ### Concentration calculation
# GPMV data
setwd(rstudioapi::getActiveProject())
rm(list=ls())
library(tidyverse)
library(stringi)
GPMVdf <- read.csv("./Input_Data/GPMV_validated_120525_Full_integration_peak_graded.csv", stringsAsFactors = F)
load("./Output_Data/GPMV.IS.w.conc.Rdata")
GPMV.lpd.check <- GPMVdf %>%
... |
ca786167f59f291451f4161ee77a53ff9b437626d59cfc23bcd8a3524e989c52 | R | 3,384 | 86 |
setwd(rstudioapi::getActiveProject())
library(tidyverse)
library(ggrepel)
rm(list = ls())
source("./Scripts/Function_scripts/Pre-processing_functions.R")
source("./Scripts/Function_scripts/Effect_size_functions.R")
load("./Output_Data/M2OE_SC.es.by.OE.in.Old.Rdata")
### ==== Side chains that show opposing effects ... |
1ac0d124c9465628c5bc4c181e2d68805c85b91a14ca11fbe3506c96d37dffad | R | 3,388 | 100 | #Set QC cutoffs for individual samples
library(ArchR)
library(parallel)
addArchRThreads(threads = 10)
addArchRGenome("hg38")
outp <- "path_to_outdir"
setwd(outp)
# create new arrow files for samples
mysample <- read.csv("/home/xhan/atac/archrproject0318/list.qcsample", sep = "\t")
filename <- paste(mysample$sample, ... |
ec8c10b3d71040f1546a73b4fbd71cd066099d6d258581488be92717a298e4a7 | R | 3,394 | 72 | ##### ##### ##### ##### ##### ##### ##### ##### ##### #####
# Parameter settings
##### ##### ##### ##### ##### ##### ##### ##### ##### #####
setwd(file.path(getwd(), "R"))
source("00_directorySettings.R") # load baseDir <- "/WORKING_DIR"
outDir <- file.path(baseDir, "results")
section <- "RSC"
##### ##### ##### ####... |
e4093e703ce03a4f883d5e9379ec519594034ca96dcfec171b50d2dd6ee4efa9 | R | 3,402 | 105 | read_ng_raw_header <- function(con) {
if (is.character(con)) {
con = file(con, open = 'rb')
on.exit(close(con))
}
h=list()
h$header=readBin(con, what=raw(), n = 8)
# h$something=readBin(con, what=integer(), n = 1, size=4)
h$filenamelen=readBin(con, what=integer(), n = 1, size=4)
if(h$filenamelen>2... |
6bb0fe1d47b11b2d742aa63861f36326a68a0a45b1ff00d536a00b6b4b4f6364 | R | 3,406 | 104 | #!/usr/bin/env Rscript
#-------------------------------------------------
# Title: Write individual fastqc scripts
# Author: Amanda Zacharias
# Date: 2022-12-17
# Email: 16amz1@queensu.ca
#-------------------------------------------------
# Notes -------------------------------------------
# module load nixpkgs/16.09 g... |
7c61063bd64bd8f9193540e160a914271fcf88f31b5507ca61a3a332b167e906 | R | 3,410 | 65 | AnalysisSpatialNetworks<- function(object,
cluster_column = NULL,
selected_clusters = NULL,
selected_pairs = NULL,
networks = 'spatial_network_2median') {
cell_metadata = object@meta.data
nr_... |
4839a3bd715a9010c4d38bd4c086be3c48f91c2d26e24422c1ebea40be0d8a2e | R | 3,412 | 88 | ##### ##### ##### ##### ##### ##### ##### ##### ##### #####
# 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... |
eba4fc1565f3f3d6594ad378f2eef9d08fa30a22ffd191938c390e0c0d84ca7c | R | 3,415 | 102 | # Call DARs for individual cell types based on the downsampled dataset.
library(ArchR)
library(parallel)
addArchRThreads(threads = 5)
addArchRGenome("hg38")
library(ComplexHeatmap)
library(ggsignif)
library("RColorBrewer")
proj <- loadArchRProject(projdir)
outp <- "mydir"
dropSubCs <- c("undefined","ast.C6","mg.C8"... |
f3568bf288b226ac2231827c1bbb167c382d81ff7ec85db50b7f19c929243958 | R | 3,419 | 88 | #' Compute a spatial neighborhood interaction matrix using
#' K-nearest neighbors (KNN)
#'
#' Computes a spatial interaction matrix where each entry quantifies
#' the number of neighboring cells from a given cluster (columns) that are
#' among the `k`-nearest neighbors of cells in another cluster (rows).
#' This provid... |
3904ed5da275af44d244558770a7f8d1a405fdecd6c598abffff8a12d31f917f | R | 3,421 | 133 |
library(data.table)
#library(readr)
#library(readxl)
library(dplyr)
###### NCA #########
table3_NCA_AUC_CPTPSP <- readRDS("datasets/table3_NCA_AUC_CPTPSP.RDS")
table3_NCA_AUC_CPTPSP %>%
summarize(median_value = median(AucRatio, na.rm = TRUE))
table3_NCA_CLAST_CPTPSP <- readRDS("datasets/table3_NCA_CLAST_CPT... |
9048f0699803a12f154b483493583ad73ab0990a485c2d633deae02ac51a2377 | R | 3,421 | 103 | #' Import alignments from a .BAM file.
#' Optional filtering can be applied for the alignments, based on bam flags, mapping quality, and reference sequences.
#' LoRTIA ouptuts can be imported, along with LoRTIA tags for further filtering.
#' Gaps (Ns) in the alignments can be removed into distinct alignments for the sa... |
f853117c89d1247fc0911940b792b1426c7ca1efc46cb154a39ef0f5e09a560e | R | 3,423 | 85 |
setwd(rstudioapi::getActiveProject())
library(tidyverse)
library(ggrepel)
rm(list = ls())
source("./Scripts/Function_scripts/Pre-processing_functions.R")
source("./Scripts/Function_scripts/Effect_size_functions.R")
load("./Output_Data/Exp2_lpd.es.by.KO.in.old.compare.to.CTRL.Rdata")
### ==== old ====
# In KO dataset... |
b41d3c22404e2c21a8b13c7e1b3a400f8116a534459156b6abecea37d929b4b0 | R | 3,424 | 75 | ## Correlations with peak frequency and oscillatory power in alpha, and other bands
library(ggplot2)
library(ggpubr)
library(readr)
library(tidyverse)
library(psych)
library(reshape2)
library(flextable)
library(boot)
library(rempsyc)
library(ppcor)
settheme = theme(strip.text.x = element_text(size = 9.5, face = "bol... |
93c41f1fc5f1fa9602eda3a816c89d609eda694d7de3d835dc2933638667ae9d | R | 3,426 | 79 | # 1. 加载必备包
#install.packages(c("dplyr", "readr")) # 首次运行需安装
library(dplyr)
library(readr)
# 2. 读取输入文件（适配上传的CSV）
# 若读取后字段名与预期不同，需修改后续代码中的字段名（如background_size、degs_num等）
df <- read_csv("overlap_data_up_regulated.csv", show_col_types = FALSE)
# 查看数据结构（确认字段名和数据完整性）
cat("数据字段名：", colnames(df), "\n")
print(head... |
0843700033b106f184f939d41bbd96bb9b380b4c1efdf0166f3d8e84dd6d7c22 | R | 3,427 | 116 | # ======================================================================
# GLMM ANALYSIS NOTE
# ======================================================================
# This script implements a logistic generalized linear mixed-effects model
# (GLMM) to quantify how M1 social gaze primitives predict M2 social gaze
# be... |
5719f46cbfe927c384adffb1bbf428531088f61d09e0eacf3fa8d8e34d87c2c4 | R | 3,427 | 97 | library(tidyverse)
library(extrafont)
intros <- c("wel_and_sel_to_lpa", "lpa_to_wel", "lpa_to_sel")
intros_long <- c("Iberian lynx", "Western Eurasian lynx", "Southern Eurasian lynx")
get_obs_stat <- function(intro, stat){
return(read.table(paste0("data/introgression_scans/bed_files/", intro, "_intro.merged.", stat... |
5cdce96e704a9d61f73601799b7bb4f151b5f6196b4a1fb4c09e86360f2d23d3 | R | 3,429 | 48 | ##### ##### ##### ##### ##### ##### ##### ##### ##### #####
# Parameter settings
##### ##### ##### ##### ##### ##### ##### ##### ##### #####
setwd(file.path(getwd(), "R"))
source("00_directorySettings.R") # load baseDir <- "/WORKING_DIR"
source("00_settings.R")
dataDir <- file.path(baseDir, "data")
nmfDir <- file.path... |
15c0bbec2997c982f090926530d37bf8202f71287ae88f89b2023c2432b09b18 | R | 3,430 | 79 | # 1. 加载必备包
#install.packages(c("dplyr", "readr")) # 首次运行需安装
library(dplyr)
library(readr)
# 2. 读取输入文件（适配上传的CSV）
# 若读取后字段名与预期不同，需修改后续代码中的字段名（如background_size、degs_num等）
df <- read_csv("overlap_data_down_regulated.csv", show_col_types = FALSE)
# 查看数据结构（确认字段名和数据完整性）
cat("数据字段名：", colnames(df), "\n")
print(he... |
0d599674c333dd3d9b445fef44ce6024a7b3dae64062515d1a6a4585b4db2aba | R | 3,432 | 99 | library(MatrixEQTL)
## Settings
# Linear model to use, modelANOVA, modelLINEAR, or modelLINEAR_CROSS
useModel = modelLINEAR; # modelANOVA, modelLINEAR, or modelLINEAR_CROSS
args <- commandArgs(trailingOnly=TRUE)
n_args <- 7
if (length(args) < n_args) {
stop(paste0("Error: Too few arguments supplied (", length(arg... |
6ef3d5d3902d4c288b889337a3e490d8aa886a65128fea6b794c1aaf22e1a927 | R | 3,432 | 60 | #' Initializes marker sets for SAHA analysis
#'
#' Filters markers from query and database datasets based on specified thresholds.
#'
#' @param ann An object containing `query$Markers` and `db$Markers` data frames.
#' @param p_thresh Adjusted p-value threshold for marker selection.
#' @param FC_thresh Fold change thres... |
d31329a2370ef7e6eda137f3f172fcb2d8f7a6298cd0d56871b810828e4e5a43 | R | 3,441 | 90 | library(mgc)
library(foreach)
library(doParallel)
library(stringr)
## Calculate the mean of real, bootstrap and jackknife Discr
setwd('/home/huyang/Projects/HuYang/HY_20251111')
## Set parallel cluster to speed up
n.cores <- 20
my.cluster <- parallel::makeCluster(n.cores, type = "FORK")
doParallel::registerDoParallel(c... |
0cd566b98cb6a10e14fa1ebf4f1f0be55c4f5abe4b4eb68d28f16a3a16f217c9 | R | 3,442 | 115 | library(IsoformSwitchAnalyzeR)
dir <- "C:/Users/Jiawan/Dropbox/Jiawan/Analysis/isoform/abundance_mn"
patterns <- c("111", "131", "185", "CR001WT", "CR007WT")
# List and concatenate files matching any pattern
samples_all <- unlist(lapply(patterns, function(p) list.files(path = dir, pattern = p, full.names = FALSE)))
... |
fa0b80ce6cb2aa5ebb0d58ba064836e587e93b411805576850ee6d9d01d26d08 | R | 3,442 | 80 | # Load required libraries
library(dplyr) # Data manipulation
library(readr) # CSV reading and writing
library(tidyr) # Data reshaping
# Ensure the results directory exists; create it if missing
if (!dir.exists("results")) {
dir.create("results")
}
# Function to export single-variable raincloud data
export_sin... |
67011792c06f27b4442eeccfebd8204fd02a1dd6862c516b2db131a5c7f8b8f4 | R | 3,443 | 101 | here::i_am("scripts/3.brain_dataset/3.1.data_preparation/3.1.3.subsampling.R")
library(tidyverse)
library(Matrix)
library(SingleCellExperiment)
library(scran)
library(foreach)
library(doParallel)
library(transformGamPoi)
library(SummarizedExperiment)
library(here)
input_dir <- here("data/brain_dataset/processed_data/... |
815df29ee1718d2f1d4f3954a47f0c2f3b07217a3a8d4214612e12491cb5f329 | R | 3,445 | 103 | ---
title: "Code of geneFiltering()"
subtitle: Workflow run by geneFiltering()
output:
BiocStyle::html_document:
number_sections: no
toc: yes
toc_float: false
css: corrected.css
html_notebook:
toc: yes
pdf_document:
toc: yes
package: '`r pkg_ver("SCENIC")`'
vignette: >
%\VignetteIndexEn... |
90c65199e219c981814281fc0f21edcf53cbc1b05e9fb8c90db48cf4d35c3aef | R | 3,445 | 85 | ### `utils.R` – 公共函数
```r
# utils.R
# Common functions used across the analysis pipeline
#' Parse GeneRatio string (e.g., "10/100") to numeric
parse_ratio <- function(ratio_str) {
as.numeric(sapply(strsplit(ratio_str, "/"), function(x) as.numeric(x[1]) / as.numeric(x[2])))
}
#' Cluster enrichment analys... |
a3ce2b3938110acaa840dc02ff18fdac28b4ed8fb27afccdf1f27c2f7fb35af0 | R | 3,447 | 110 | ################################################################################
###############------ PREPROCESS FUNCTION FOR MODEL FITTING -----###############
################################################################################
## Proprocessing task data for cmd stan modelling --------------------------... |
81f3a8f6c9412c1b6264607fe6d68bd92731aadaeb99dab1b765f468ddba0e36 | R | 3,448 | 119 | #!/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="input snap format file")
parser$add_argument("--frag... |
70cc1eaee25f32efa5d5961501ed8bed506e554b5dcd25b1522167c49eebfdd4 | R | 3,450 | 98 | # ==============================================================================
# Logistic Regression Analysis with Elastic Net and Bootstrapping
# ==============================================================================
# Load required libraries
library(glmnet)
library(reshape)
library(ggplot2)
library(GGally)... |
7e9493365c95425ec8a275be2280ea1663014ef9252159e7b0f2ba17b4b64e89 | R | 3,451 | 78 | #' Find all merged segment ids for a given starting google segment id
#'
#' @param x a segment id or any other input that can be interpreted by
#' \code{\link{ngl_segments}}
#' @param return.groups Whether to return the merge groups as well as the
#' segment ids
#' @param return.segmentids.for.groups Whether to ret... |
353c233357c03479575a32456d40ebe3840a816b37471676d1730c745116f804 | R | 3,452 | 80 |
#install.packages
library(Seurat)
library(ggplot2)
library(DoubletFinder)
library(dplyr)
library(cowplot)
library(reshape2)
library(MAST)
setwd("/athena/ganlab/scratch/lif4001/LG815C/integration_mt5")
LG815C_integrated <- readRDS("LG815C_integrated_PCA_0.1.rds")
Idents(LG815C_integrated) <- "seurat_clusters"
#Remove 5... |
9baa6ebc2bcc7c4ae1402ace45af6565c8e1793aa91c3d053f930547156f4c84 | R | 3,452 | 104 | #### set up ####
setwd("/scratch/hhu/MethylCnT/04-analysis/ANA08-organoid_development_enrichment_overlayBS/")
library(methylKit)
library(dplyr)
library(rtracklayer)
library(ggplot2)
library(reshape2)
library(ChIPseeker)
library(org.Hs.eg.db)
library(GenomicFeatures)
library(biomaRt)
library(tidyverse)
library(ggalluvia... |
4fa278d734cdfcb33f64e53d7b47db76e7514196b2e475faec49bedad7e69f3a | R | 3,453 | 118 | # Clonal Diversity--Time-Scaled Phylogenetic Tree
rm(list=ls())
library(ggtree)
library(treeio)
library(phytools)
library(phangorn)
library(ggplot2)
library(ggpubr)
load('~/.../data/Phylotime/Phylotime_WholeMidgut.RData')
# Take the time-scaled phylogenetic tree of first sample obtained on day 3 (S1_3d... |
f3e01a5273d9e9b6213f6ea0439a5370512147f41b5a6911694802942f1efdaa | R | 3,454 | 90 | #' Obtain the colors for a set of cluster names
#'
#' This function returns a vector of colors based on a vector of cluster
#' names. It can be used to automatically assign colors.
#'
#' @param colors A vector of colors. If `NULL` then a set of default colors will
#' be used when `clusters` has less than 12 unique valu... |
8b9d5d90dea23da17a29260cea2fb9dbaab5695f308dde8b043b295c24098a82 | R | 3,456 | 97 | #' Compute Spatial Enrichment Index (SEI) for Each Gene
#'
#' Calculates the spatial enrichment index (SEI) for each gene based on
#' a user-supplied set of spatial weights. The SEI reflects the extent to
#' which gene expression is enriched in spatially weighted regions of
#' the tissue (e.g., near boundaries or centr... |
820555eedb1839d8b71c953ad693bb153a1c6a17d71ce8742a5d077da7b846e1 | R | 3,465 | 108 | library(dplyr)
library(stringr)
library(openxlsx)
library(forestploter)
library(grid)
library(ggplot2)
labName <- c("ner", "ren", "eye", "car", "per", "met", "mic", "mac", "int")
compAbb <- c(
"DN","DKD","DR","DCVD","DPVD",
"Metabolic Disorder","Microvascular",
"Macrovascular","Total complication"
)
... |
a94f4ecc5f46e9af5c4ec7364f5502d029fa217afc442990339371993facc7a4 | R | 3,466 | 81 | ### Concentration calculation
# In vivo sorted data
setwd(rstudioapi::getActiveProject())
rm(list=ls())
library(tidyverse)
library(stringi)
library(openxlsx)
load("./Output_Data/Invivo_IS_w_conc.Rdata")
Invivo.df <- read.csv("./Input_Data/Invivo_sorted_NSCs_integration_graded_120325.csv", stringsAsFactors = F)
Invivo... |
6e08ce4127e68494c70857c2e694a5eec8f02b136217e0a77ac11e2ee897689d | R | 3,467 | 91 | # R script for identifying CN and MCI converters based on longitudinal data
# Author: Maryam Hadji, University of Eastern Finland, Kuopio, Finland (maryamh@uef.fi)
# Last updated: 21-Feb-2025
#
# Description:
# This function identifies stable and converting individuals in the CN and MCI groups
# based on their longitud... |
4f234664372b826f2be99a4a6042e3a5fce648453abdf04822389f2198e030a2 | R | 3,469 | 69 | #' @keywords internal
#' @section Package Options: \itemize{
#'
#' \item{\code{fafbseg.sampleurl}} optionally set to a sample Neuroglancer URL
#' that will modified to point to arbitrary locations by
#' \code{\link{open_fafb_ngl}}.
#'
#' \item{\code{fafbseg.cave.datastack_name}} optionally set to the
#' \code... |
04defd1e37ec128dce940a55d747c837232967bc30bff88878e23a1c3bec67fb | R | 3,476 | 85 |
setwd(rstudioapi::getActiveProject())
rm(list = ls())
library(tidyverse)
library(ggpubr)
library(ggtext)
source("./Scripts/Function_scripts/Effect_size_functions.R")
source("./Scripts/Function_scripts/Pre-processing_functions.R")
load("./Output_Data/Lpd.MsD.Invivo_Age_ES.w.RT.Rdata")
load("./Output_Data/Lpd.MsD.Invit... |
e37a4f71a534a571713fafbd9bb4ae072e1a7e27ca6ab60aa334277093db523c | R | 3,477 | 98 | #####################################################################
# Brain Tissue Module Mapping Pipeline
#####################################################################
# This script performs gene module mapping for downsream coeQTL analysis for brain tissue data.
# It processes each brain region and gene... |
70751df44d345ad2a9c5b93ba20cf8af0dbeac404cafbd49441843864d7584e8 | R | 3,480 | 130 | ---
title: "BANC"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{BANC}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
have_fafbsegpy=!inherits(try(fafbseg:::check_fafbsegpy(), silent = TRUE),... |
e9070c009c3cdbfb5bf763f088034a4c2c637fba63bf71757ba4df8ac87a92f5 | R | 3,483 | 96 | #' Summarise the connectivity of FANC neurons
#'
#' @details note that the rootids you pass in must be up to date. See example.
#'
#' @param rootids Character vector specifying one or more flywire rootids. As a
#' convenience this argument is passed to \code{\link{fanc_ids}} allowing you
#' to pass in data.frames, ... |
c4ce5eb0812df77c21fd9f323a8a5c71417ffb1c5b4a2b63507236611bb61fe0 | R | 3,486 | 121 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
# Load banctable and save DN metadata, DN skeleto... |
680a891b8844eb0b85765c5857fab7a214bdac4466e313395ee10accacfd921b | R | 3,489 | 93 | #!/bin/env Rscript
library(tools)
args <- commandArgs(TRUE)
verbose = TRUE
tranchesFile = args[1]
targetTITV = as.numeric(args[2])
targetSensitivity = as.numeric(args[3])
suppressLegend = ! is.na(args[4])
# -----------------------------------------------------------------------------------------------
# Useful gene... |
c348f54ce74569848457f83892af4a2e7f904cff56be88d365fb896c830b4c2a | R | 3,489 | 157 | ---
title: "Process nested CV results for ECG"
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, co... |
c92ace6ff383cd7b17591b12d33cd65370f08050253390d967782c92f5268590 | R | 3,489 | 70 | library("here")
library("sessioninfo")
outdir <- "spatialLIBD_files"
pkgname <- "spatialLIBD"
meta <- data.frame(
Title = c(
"Visium_SPG_AD_Visium_wholegenome_spe",
"Visium_SPG_AD_Visium_targeted_spe",
"Visium_SPG_AD_Visium_wholegenome_pseudobulk_spe",
"Visium_SPG_AD_Visium_whole... |
da49f61299751998147893f3feb61947a5c5ea3ca58cc7a4591a590709128b58 | R | 3,489 | 87 | library(ggplot2)
#### Define TPM matrices to process ####
tpm_matrices <- c(
"merged_matrix_TPM",
"adjusted_merged_matrix_1_TPM"
)
#### Read coldata (once, before loop) ####
coldata_df <- read.table("/home/joonho345/1_Epilepsy_RNA/RNA_Human/02.Quantification/filtered_coldata.txt",
sep = "... |
8979b647d90b2818f638a80c72b82535981aba954d9ca64ed661fa830b7ccb3d | R | 3,491 | 115 | ##############################################################
# Script: 04_enrichment_analysis.R
# Purpose: Perform GO (enrichGO) and pathway (Enrichr) enrichment
# analyses for differentially expressed proteins
##############################################################
# ========================
... |
1283fd9f6e8540ee4d6b499ec4110ed10168325804f84e128a8d7153b54610cc | R | 3,494 | 91 |
library(epicalc)
#######################
#Read PB final results#
#######################
#PB_complete<-read.table('sorted_complete_primeend.txt',sep="@")
#colnames(PB_complete)<-c("Queryid", "complete_start", "complete_end", "gi_complete", "complete_Strain")
#PB_complete<-PB_complete[,1:5]
#PB_complete<-PB_complete[,... |
14a6d38666af540025dba99dc277b2f6559983e5413de03d7ef383695a30ac13 | R | 3,497 | 106 | suppressPackageStartupMessages({
library(ggplot2)
library(ggrepel)
library(ggpubr)
library(UpSetR)
library(RColorBrewer)
library(tidyverse)
library(VennDiagram)
library(ggVennDiagram)
library(clusterProfiler)
})
# Gene Onto
d2 <- read.table("dge/TETA_Dge_MouseID.txt",header=T)
d2 <- d2 %>% filter(Direction == "TETA_... |
13c8e333f97d2e1f86081e64f26406dba8a2d7edcf73a0f79c24d21ca8fcfdea | R | 3,501 | 92 | ##### ##### ##### ##### ##### ##### ##### ##### ##### #####
# Parameter settings
##### ##### ##### ##### ##### ##### ##### ##### ##### #####
setwd(file.path(getwd(), "R"))
source("00_directorySettings.R") # load baseDir <- "/WORKING_DIR"
source("00_settings.R")
dataDir <- file.path(baseDir, "data")
nmfDir <- file.path... |
1d706021adf99c8cfca9151f53e2a577dc5a0dd8d0730b5d6d7942b9d4a94530 | R | 3,502 | 128 | library(ggdmc)
library(tidyverse)
# options for summarise
options(dplyr.summarise.inform = FALSE)
# set cores
options(mc.cores = parallel::detectCores())
# set options for brms
options(brms.backend = "cmdstanr")
setwd("/Users/vilya/Downloads/ddm-scripts")
# Setup caching of results
ddm_dir = "./_ddm_models-exp"
if... |
447dbcd6cb9afb92c398eb1b755145805bce7094400b820062a9ff6034e26cf4 | R | 3,502 | 108 | context("Model xgboost")
idx <- withr::with_seed(3809, sample(nrow(mtcars)))
predictors <- c(
"mpg", "cyl", "disp", "hp", "drat", "wt", "qsec", "vs",
"gear", "carb"
)
rownames(mtcars) <- NULL
train <- mtcars[idx[1:25], ]
train <- list(data = train[, predictors], label = train$am)
test <- mtcars[idx[26:32], ]
test ... |
f23126bc2e079f2fafbaaf507a15ab99dffe0db01ec6d388c813a6cbab86843f | R | 3,502 | 73 | #' Run an MLflow Project
#'
#' Wrapper for the `mlflow run` CLI command. See
#' https://www.mlflow.org/docs/latest/cli.html#mlflow-run for more info.
#'
#' @examples
#' \dontrun{
#' # This parametrized script trains a GBM model on the Iris dataset and can be run as an MLflow
#' # project. You can run this script (assum... |
4240052c0d1049e89c420bf930e208c29c76d83012d4fa5676d12f3f205e4f65 | R | 3,511 | 89 | #' Run COBRA in R
#'
#' Description:
#' COBRA decomposes a (partial) gene co-expression matrix as a
#' linear combination of covariate-specific components.
#' It can be applied for batch correction, differential co-expression
#' analysis controlling for variab... |
7c0530edec9250bbaebdefb59adb7597526453939e61b217e4360395c1510d98 | R | 3,512 | 105 | ---
title: "Fig_3H_CD8_CART_CSF_Cytox_Exh"
output: html_document
date: "2026-02-17"
---
```{r}
library(Seurat)
library(tidyr)
library(ggplot2)
library(ggpubr)
library(dplyr)
library(harmony)
library(purrr)
library(cowplot)
library(rstatix)
library(ggbeeswarm)
library(ggbreak)
```
# 8) Import CSF data and compute Cyt... |
1338120d6235fabf5b0c7bab81e29171d3c29f4fd5ffa897b2440b764db77a4a | R | 3,519 | 124 | # =============================================================================
# CHCHD10 Interactome
# Enrichment Ranking Plot (Strong candidates only)
#
# PURPOSE
# plot the CHCHD10 IP enrichment ranking using the ranking table generated
# in Step 1b, while excluding Report-style candidates from the figure.... |
c207ba6b6d9b55967098fcfeb1eaaa92df3441940eb1ca3a8fb4105695cbc867 | R | 3,520 | 166 | ---
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... |
5e9bdb6ca5855202d7f3a03c5af2257e1394fdd9f577bb12bc019bed7e4148a6 | R | 3,524 | 87 | ### PCA
library(tidyverse)
rm(list=ls())
########Import color LUT for all future plots with KO #######################################################################
load("./Output_Data/KO_LUT_paper.order2024-02-08.Rdata")
######PCA on individual lipids################################################################... |
f45b4ab322828c947a3bda2219bcb7c0432ebf86315e773d5169a4790ba7c423 | R | 3,524 | 80 | # R script for generating ROC curves and AUC values for CN and MCI groups based on hippocampal/Ventricle/TGM volume and other models
# Author: Maryam Hadji, University of Eastern Finland, Kuopio, Finland (maryamh@uef.fi)
# Last updated: 21-Feb-2025
#
# Description:
# This function loads ROC values for CN and MCI groups... |
a5ea6b294f251f9052a8f401ce6957120b0c2f79ce39c03995a09dd2fb045397 | R | 3,526 | 99 | #' Plot a heatmap of a row-scaled spatial interaction matrix
#'
#' Visualizes a spatial interaction matrix using a heatmap,
#' where rows represent focal clusters and columns represent neighbor clusters.
#' Each row is scaled using z-scores to highlight
#' relative enrichment patterns across neighbor types.
#' This is ... |
8bc2159aac98930d8bab13a6a885ea0e9471b3dd06f0a56e70c03f76144d2008 | R | 3,531 | 124 | # ============================================================
# Pre-image reconstruction–based dimension selection
#
# This function selects the truncation level of KPCA
# components using cross-validated pre-image reconstruction
# errors.
#
# IMPORTANT:
# - This procedure is NOT intended for reconstructing
#... |
969c9e02914d1b963f64aa4646d0d41cf9fbe3e4c1deb1f9d7a3cbae799d46b7 | R | 3,533 | 150 | # Script from Avishay Spitzer
# mdata - the metadata table that contains for each cell the CellID, Patient, Timepoint, Sample, State etc
# umi_data_all - a named list per sample of UMI count matrices
euclidean_dist <- function(x, y) {
sqrt(sum((x - y)^2))
}
# Helper function that normalizes UMI counts to per-millio... |
4a7276685584b3a2cb852d9f6d4fd5da361f36ce873a220b8b57d8109873c361 | R | 3,534 | 88 | library(ggplot2)
#### Define count matrices to process ####
count_matrices <- c(
"merged_matrix",
"adjusted_merged_matrix_1"
)
#### Read coldata (once, before loop) ####
coldata_df <- read.table("/home/joonho345/1_Epilepsy_RNA/RNA_Human/02.Quantification/filtered_coldata.txt",
sep = "\t",... |
0ef93be85548d23a40ec4f9aa28046cc1b26cf4b1c318f88b71f918e26d5cea1 | R | 3,547 | 189 | ---
title: "Tutorial: Using ggVennDiagram"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Tutorial: Using ggVennDiagram}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width = 7,
fig.a... |
4ed3b49f44f34caa626ed615774bdd54e10dbbdc6a36a562d365a42c1188ee41 | R | 3,547 | 116 | ############ Class Venn #####################
#
# The Venn class is defined originally in RVenn package by *Turgut Yigit Akyol*.
# It set a good model in calculating the subsets between different sets.
# However, RVenn has not been updated since 2019. I communicate with
# its author and he agreed to transfer some of th... |
4fe5e020cd5a03643a3bd458f68eb5dad2167386cc2671051bd078489013d367 | R | 3,559 | 179 | ---
title: "PBMC10K tutorial for COSG"
author: "Min Dai"
date: "2021/6/18"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{PBMC10K tutorial for COSG}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
### L... |
5676aca904763d27f75f8c4fb7d6fa823064d7b96abd8c1ab5f63646e56d87b1 | R | 3,560 | 125 | ---
title: "Global_CSF_Analysis"
output: html_document
date: "2025-03-20"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## R Markdown for global CSF (white and grey matter) analysis for PEACE study (Colwell et al., N.D.).
```{r cars}
library(dplyr)
library(tidyverse)
library(gtools)
library(... |
7b46fe5f52347d951ad9f9f78cb29d160e2aacbaf738f495085b7de37925f86c | R | 3,560 | 71 |
library(epicalc)
HPV<-read.csv("HPV.csv");
#start
if (length(readLines("seqment_start_id.txt"))>0 && length(readLines("seqment_end_id.txt"))>0 && length(readLines("end_ALL_TAXONOMY.txt"))>0 && length(readLines("start_ALL_TAXONOMY.txt"))>0) {
start_id<-read.table("seqment_start_id.txt")
c... |
c50f97567a588dcf0d9d3c56c2197696018d8e28b5fcdf933c8bc6655ec1822c | R | 3,561 | 79 | #' Identify differential edges in two PANDA networks
#'
#'To determine the probability that an edge is "different" between the networks,
#'we first subtracted the z-score weight values estimated by PANDA for the two networks and then determined the value of the inverse cumulative distribution for this difference.
#'T... |
fc71d14452be419303bcc0b9b4bdcbcd4eae610e55b98b3723699a61f247f60b | R | 3,564 | 102 | ##### ##### ##### ##### ##### ##### ##### ##### ##### #####
# Parameter settings
##### ##### ##### ##### ##### ##### ##### ##### ##### #####
setwd(file.path(getwd(), "R"))
source("00_directorySettings.R") # load baseDir <- "/WORKING_DIR"
source("00_settings.R")
dataDir <- file.path(baseDir, "data")
outDir <- file.path... |
82d97797c656f26810fef54d9f6148a399a0ca169a865ee464dcbb2f6c2dc2f8 | R | 3,565 | 89 | # Hai Wang
# Explore the relationship between p-values and importance scores
library(data.table)
args <- commandArgs(trailingOnly=T)
tissue <- args[1]
############################################################################################
# load eQTL data
##############################################... |
2e04451226fbfe77b6fbbdb56ab17bf4369223cf3fcac1022e588055dfbbd6c3 | R | 3,568 | 99 |
## Figure S3 ####
tell_user('done.\nCreating Figure S3...')
resx=300
png('display_items/figure-s3.png',width=13*resx,height=16*resx,res=resx)
layout_matrix = matrix(1:40, nrow=10, byrow=F)
layout(layout_matrix, heights=rep(c(0.3,1),5))
loq = read_tsv('data/loq.tsv', col_types=cols())
reference_dilution = 12
loq %... |
840cb5789625189a44384afada685a7bfd980b8d057c3df80ba1e05f346f21e4 | R | 3,569 | 86 | ##### Plots for the Publication #####
tany_integrated1 <- subset(x=tany_integrated, subset = Diet == "Chow" & Age == "6+ weeks") #Figures were plotted using Chow diet fed 6+ weeks old adult mice.
#Plot for the Fig 1c.
desired_order <- c("beta 2","beta 1","alpha 2","alpha 1")
tany_integrated1$label <- factor(... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.