sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
b2e9903692b66c24f2b5d073dbbb528ffa7f47d7700183b4a2fc418c6a3e7e2e | R | 2,848 | 72 | #' @title Get inputs from MRIcron/npm file
#'
#' @description
#' Function to load data from a previous analysis
#' in MRIcron/npm in a ready format for use in lesymap
#'
#' @param valfile mricron filename with extention *.val. The function
#' will search for images in the same folder where valfile
#' is located, unle... |
cb246165bb7be3de85fd128cb524350ab6b1e2b35fa9011a4e98c343022dac7a | R | 2,852 | 73 | library(dplyr)
train_dt <- read.csv("/home/data/sda/wt/Neodb_model/raw/train_data_iedb_2.csv")
all_weight <- data.table::fread("model/all_weight_mean.csv",data.table = F)
all_edge <- data.table::fread("model/all_edge_index.csv",data.table = F)
train_dt <- train_dt %>%
mutate(pep_len = nchar(pep),
hla_len = ... |
fefd15eb921306ae2c8cf98092f7452fd4079efe2595c82c6634834b3360b51c | R | 2,853 | 71 | #' @rdname createFolds
#' @title Create folds for k-fold validation
#'
#' @description Used to create balanced folds with respect to y input
#' for k-fold validation.
#'
#' @param y split sample by balancing y
#' @param k number of folds
#' @param list logical whether to return folds in a list
#' @param returnTrain log... |
65c45430e6fabe83f64d49a70e64f70cd964263651ffed5b19a1277d970a77dc | R | 2,855 | 84 |
# fedfast_marker_supp_table -----------------------------------------------
df <- fedfastmarkers
map <- c("0"= "EC1 Cldn5/Ly6c1/Ly6a",
"1"= "SGC1 Mmd2/Ednrb/Fabp7",
"2"= "SGC2 Cxcl10/Acsbg1/Phgdh",
"3"= "MGC1 Ncmap/Fam178b/Cldn19",
"4"= "SGC3 Bcan/Fbln2/Fbln5",
"5"= "NGN1 ... |
6ba3e22c31f57019ad3b623f7d9e1dfde9fbc6f1de0850e15619f4030349f922 | R | 2,855 | 77 | library(dplyr)
library(purrr)
# Set base directory for conditional analysis results
# base_dir = "path/to/conditional/results/"
base_dir = "./result/conditional.results/"
##### load Loci
load(file.path(base_dir, "Loci.RData"))
filter1 = names(table(Loci$Region))[table(Loci$Region) == 1]
filter2 = gsub("\\.R... |
adc413589302a5d5a6767dd1f39c05590a1eba064a17d298e56ba0628403de9a | R | 2,855 | 66 | library(Seurat)
library(dplyr)
#Use the markers identified by Wilcox (Seurat_Findallmarkers)
markers <- read.delim("251019_nodose_markers.txt")
markers$prop <- markers$avg_log2FC*(markers$pct.1 + 0.01)/(markers$pct.2 + 0.01)
markers$genesymbol <- gsub("\\..*","", markers$genesymbol)
top3 <- markers %>%
group_by(clu... |
7b2e954cf5e42f64c739beb8b4b32cfe103a5434e3c2413dfb924ac872afc391 | R | 2,856 | 55 | #' Creates output dataframes from a Seurat object
#'
#' This function generates variable features, average expression, and/or marker genes from a Seurat object, depending on the specified output option.
#'
#' @param obj A Seurat object.
#'
#' @param output A character string specifying the desired output. Options are ... |
e1cdf605b135d9433d09f553fe436a1ca2c7628ad564d35b9940985485dac095 | R | 2,861 | 73 | # 02_secretory_protein_venn.R
# Venn/Euler plots, protein specificity, point plots, GO enrichment
rm(list = ls())
library(openxlsx)
library(ggplot2)
library(eulerr)
library(clusterProfiler)
library(org.Mm.eg.db)
source("scripts/utils.R")
# Load preprocessed sample lists
samples <- readRDS("data/samples_li... |
d980fa709ebf701d88b27056c152a1153d358e16f93c73a67daadfd47961f62d | R | 2,863 | 117 | test_that("Test PlotExpression", {
df <- data.frame(
x = c(rnorm(100, 1), rnorm(100, 5)),
y = c(rnorm(100, 1), rnorm(100, 5)),
cell = 1:200,
cluster = rep(1:2, each = 100)
)
exp_mat <- data.frame(
gene1 = c(runif(100, 4, 6), runif(100, 0, 2)),
gene2 = c(runif... |
1445812e4062e3d8a351428726c2aa8e496e63f97d1c6c36e4bc0fa90b9ea7ba | R | 2,868 | 97 | library(scales)
get_thresholds <- function(prior_pr){
prior_odds = (prior_pr/(1-prior_pr))
BF = c(10,6,2)
A = exp(BF/2) * prior_odds
thr = A/ (A+1)
return(thr)
}
plot_mte <- function(logfile,color_trait=c(),prior_prob = 0.05){
pdf(file=paste(sub(".log","",logfile,fixed = T),".pdf",sep=""),width=10, height=7)... |
68871ac89548402188bee30dd9cbb656310ddb61e99077366f603e835d1268b6 | R | 2,869 | 73 | # When working with lM it is useful to design an "average and difference"
# contrast matrix, which for binary responses has a simple canonical from:
ADmat <- matrix(c(-1/2,1/2),ncol=1,dimnames=list(NULL,"d"))
# We also define a match function for lM
matchfun=function(d)d$S==d$lR
dat <- forstmann[forstmann$subjects %i... |
53ab9f611cab84b1fe84ed0ffff6a787a42a9300069eeeb8e1b11e48e9e129d9 | R | 2,870 | 89 | suppressPackageStartupMessages({
library(ggplot2)
library(ggrepel)
library(ggpubr)
library(UpSetR)
library(RColorBrewer)
library(tidyverse)
library(VennDiagram)
library(ggVennDiagram)
library(clusterProfiler)
})
# Gene Onto
d2 <- read.table("dge/Both_Dge_MouseID.txt",header=T)
d2 <- d2 %>% filter(Direction == "Both"... |
8d0ea00a5a658333029c5a1f3d00bb9f187e39aa635478cf81b9e22d669f790b | R | 2,871 | 83 | # open R
srun -n 10 -p interactive --pty /bin/bash
module load cluster/gsl/1.15
module load cluster/htslib/1.9-devel
module load cluster/build_tools/llvm/8.0
module load cluster/util/gcc/8.2
module load hdf5_18/1.8.21
conda activate r_env
#conda deactivate
R
library(dplyr)
library(stringr)
# --- Setup ---
disease... |
ea4c70046ec78d0cfbc2f7ff00fdfa25543fe218d3b7ad04605f87c940dd7f95 | R | 2,872 | 68 | library(DESeq2)
library(ggplot2)
library(pasilla)
### Choose one matrix to analyze ###
coldata_df_M_KAI_IA_IPSI_A_AC <- coldata_df_M %>% filter(TYPE_2 == "M_KAI_IA_IPSI_A_AC")
filtered_samples_M_KAI_IA_IPSI_A_AC <- rownames(coldata_df_M_KAI_IA_IPSI_A_AC)
adjusted_df_M_KAI_IA_IPSI_A_AC <- adjusted_df_M[, colnames(adju... |
c0dabb16319432fcaa6ef812e4af02b93189c459cd1509b15e4b0a456e8e3a64 | R | 2,873 | 58 | suppressMessages({
library(scrattch.hicat)
library(dendextend)
library(dplyr)
library(Seurat)
library(DESeq2)
})
parser = argparse::ArgumentParser(description="get DESeq2 normalised matrix")
parser$add_argument('-I','--input', help='input seurat object amniote/vertebrate and ortholog/metagene')
par... |
960d0542638a6dca1248a696f90c6d48454f619e2da2c1a72226698bbbe8aecc | R | 2,874 | 97 | # -----
# UMS IPSIT BIOINFO - Licence GPL-3
# https://github.com/fdumbioinfo/moal
# -----
#
# moal install
source("https://raw.githubusercontent.com/fdumbioinfo/rtools/main/moal-demo/0-moal-install-r-universe.r")
library(moal)
# DESeq install
if(!require("BiocManager",quietly=TRUE)){install.packages("BiocManager")}
if(... |
4ace840cbd6be93190f740cba2d89a0fb7b401ea317dc605a10292a0ab393153 | R | 2,876 | 95 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
# Script to look at top downstream targets of JO-... |
f10866a94246fb2ea3efdaca738c07b1416f82652cf9cc1d61eae904fab43a7a | R | 2,876 | 112 | test_that("Test RemoveOutliers", {
set.seed(123)
x1 <- rnorm(20, mean = 5, sd = 0.5)
y1 <- rnorm(20, mean = 5, sd = 0.5)
x2 <- runif(5, min = 10, max = 15)
y2 <- runif(5, min = 10, max = 15)
coords <- data.frame(x = c(x1, x2), y = c(y1, y2))
new_coords <- RemoveOutliers(coords, k = 5, dista... |
ee9dc5f9cda3c87c4369e689ec2601767a9590e97b270e1eb02f6696bdf6e796 | R | 2,882 | 68 | ### Concentration calculation
setwd(rstudioapi::getActiveProject())
rm(list=ls())
library(tidyverse)
library(stringi)
Exp2 <- read.csv("./Input_Data/Exp2_Reintegration_values_121725.peak.color.coded.csv", stringsAsFactors = F)
load("./Output_Data/Exp2.MS-Dial_IS.all.Rdata")
Exp2.rmv.poor.peaks <- Exp2 %>%
filter(... |
3717cc3654d63e14c77b92f97b7f23b8bad517922924abef9e0e265e2b1e1f9e | R | 2,886 | 97 | library(tidyverse)
library(lme4)
library(lmerTest)
library(ggplot2)
library(patchwork)
behav_long <- read.csv('C:/Users/mmatt/Desktop/Projects/NightOwls/night-owls/derivatives/data-outputs/behavioral_data.csv')
#Change across all obs
##All subj
pos_mood_mlm <- lmer(mood ~ obs + (1 | subj/ses), data = behav_long)
summ... |
14b0312f52e5e4fd9b756de6ed4a9e14d5ac43f299e273f31b30b88f5c6a27fd | R | 2,887 | 85 | # Extract bed files of cluster specific peaks for LDSC
library(GenomicRanges)
library(ArchR)
library(parallel)
addArchRThreads(threads = 20)
addArchRGenome("hg38")
library(ComplexHeatmap)
library(ggsignif)
library("RColorBrewer")
library(ggplot2)
library(ggpubr)
projdir <- "projrmSubset_subPeak"
proj <- loadArchRProje... |
e0fc09818671a297bc0fdc5d28b2652e660140647816fed76bf774134b73b170 | R | 2,892 | 46 | library(dplyr)
#### import RAW Count matrix adjusted_1 VERSION ####
adjusted_df <- read.table("/home/joonho345/1_Epilepsy_RNA/RNA_Human/02.Quantification/adjusted_merged_matrix_1.txt",
sep = "\t", header = TRUE, row.names = 1)
#### import TPM matrix adjusted_1 VERSION ####
adjusted_df <- rea... |
171dffad8700c0ff0551c3e34306210317cb1eff75c9df91b80e389841b16719 | R | 2,897 | 113 | ##### Converting for CellxGene Compatible Format #####
library(Seurat)
library(reticulate)
library(anndata)
library(Matrix)
library(dplyr)
anndata <- import("anndata")
#Load Seurat object
tanybase <- readRDS("/data/pharma_macrophages/Tanybase_integrated_080525.rds")
#Get normalized data (data slot)
norm... |
ac87616007b636c51017588cf2443037c35ff1332f089fc58770f8607352de83 | R | 2,898 | 72 | # jet.colors <- function (n){
# x <- ramp(seq.int(0, 1, length.out = n))
# if (ncol(x) == 4L)
# rgb(x[, 1L], x[, 2L], x[, 3L], x[, 4L], maxColorValue = 255)
# else rgb(x[, 1L], x[, 2L], x[, 3L], maxColorValue = 255)
# }
#' Compare a neuroglancer mesh object with a regular neuron
#'
#' @param x A n... |
4c902aa0ca76a14e3a493d522ef3c4cfabf8e50150136666ab2f6efef88d26fb | R | 2,902 | 127 | library(MicrobiotaProcess)
library(phyloseq)
library(tidyverse)
library(vegan)
library(coin)
library(ggnewscale)
library(patchwork)
library(ggtree)
library(ggtreeExtra)
data_dir <- file.path(proj_dir, "Data")
qiime_dir <- file.path(data_dir, "QIIME2")
micom_dir <- file.path(data_dir, "MICOM")
# ---------------------... |
fadf8249cca172d395f76501f28514448539cc78ed48cbe74bb1d05bf35046d6 | R | 2,903 | 73 | #tissue_species_mod.plot
library(ggplot2)
library(dplyr)
data <- read.table("all.motif.prob", header = FALSE, sep = "\t", col.names = c("Tissue", "Count", "Value"))
data <- data %>%
mutate(Tissue = recode(Tissue, "Li" = "liver", "Ki" = "kidney", "Br" = "brain"))
colors <- c("liver" = "#82B6CE", "kidney" = "#D6B36C"... |
05347764f59203f3de1c5fa1ea508acd118af4f0553b3ed46f5e97c44714a652 | R | 2,906 | 78 | #Written by Yapei Xie and CBIG under MIT license: https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
# Helper function to install and load packages
personal_lib <- path.expand("~/R/library")
dir.create(personal_lib, recursive = TRUE, showWarnings = FALSE)
.libPaths(c(personal_lib, .libPaths()))
install_if_mi... |
13d00d31be99393a30dffb9cc3871587446279888a3dddf67ef8a49831fecb1a | R | 2,908 | 112 | ---
title: "Fig_3J_MalignantCell_GSEA_HallmarkEMT"
output: html_document
date: "2026-02-17"
---
---
title: "programs_of_interest"
output: html_document
date: "2026-01-08"
---
```{r}
install.packages("msigdbr")
library(devtools)
install_github("alserglab/fgsea") # dependency for clusterprofiler
devtools::install_gith... |
2d61c16a42b0b7936db72063a347d5d75f6893dc2f2f24a520f434c0a2e593c0 | R | 2,909 | 69 | #####################################
## Write GATK segments to database for GLSS-LX cohort batch 2
## Author: Kevin Johnson
## Updated: 2022.03.21
#####################################
library(DBI)
library(tidyverse)
## Current project
projdir <- "/projects/verhaak-lab/USERS/johnsk/glass4"
setwd(projdir)
## Connect... |
8cc56111fb1be928d17d2dae87dfbc039a8166919bfc96b33ae1452a6c85649c | R | 2,914 | 91 | #' snpcov_mvmr
#'
#' Uses individual level genetic and exposure data to generate covariance matrices for estimated effects of individual genetic
#' variants on each exposure. The function returns a number of covariance matrices equal to the number of SNPs, where SNP and
#' row numbers reference ordered exposures.
#'
#'... |
f75a0b7f3705b1dd1199f5582c1e16880afa19f8f120485c1824005a6edc812c | R | 2,915 | 95 | ---
Author: Carrisa V. Cocuzza, PhD
Purpose: CND project, TCP dataset, Train/Test subset, dimensional phenotyping, hierarchical clustering checks.
Output: html_notebook
---
```{r}
#install.packages("NbClust")
```
```{r}
# LIBRARIES
library(dplyr)
library(bestNormalize)
library(parallel)
library(tibble)
library(tidy... |
5b6382d083faa6afba2e004f02555d399b343f21f9d50974b448bfcba803e7e7 | R | 2,918 | 69 | #################################
## Create blocklist for GLSS-LX WGS batch 2
## Updated: 2023.03.20
## Author: Kevin Johnson
#################################
library(DBI)
library(tidyverse)
## Connect to the latest version of the database that contains the GLSS-LX samples.
con <- DBI::dbConnect(odbc::odbc(), "Verha... |
284fa6d8ae752163c089224d7981e9088197ec4c8373f3618c26d63daf2f9850 | R | 2,929 | 80 | check_meshparty_reticulate <- memoise::memoise(function() {
check_reticulate()
tryCatch(
reticulate::import("meshparty"),
error = function(e) {
stop(
call. = F,
"Please install python meshparty module\n",
"We strongly recommend doing\n",
"fafbseg::simple_python('full')"... |
b55c84f66491a8e30b5c695551b52096e83fa660ba85aff6990fc2611d47cf2f | R | 2,929 | 67 | #' Create Self-Similarity Visualization
#'
#' This function creates a self-similarity visualization based on the provided
#' annotation object. It generates heatmaps for marker self-similarity or average
#' expression self-similarity, depending on the specified slot
#'
#' @param ann An object containing annotation data... |
21d42158aabd10a72b6e34041db4599cdff4f1c126c6fdf21758adf8ba99e05b | R | 2,930 | 70 | #!/usr/bin/env -S pixi run --manifest-path /app/pixi.toml Rscript
options(stringsAsFactors=F)
## load R libraries
#library(SAIGE, lib.loc="../../install_0.93")
library(SAIGE)
require(optparse) #install.packages("optparse")
print(sessionInfo())
## set list of cmd line arguments
option_list <- list(
make_option("--... |
6cc34cc5a1677eb6e8c27d9838fc2842b6de419af903b8bd361b7a7f50e7e703 | R | 2,933 | 100 | #################################
# Push sequenza seg + params for GLSS-LX-WGS into db
# Updated: 2023.03.20
# Author: Kevin Johnson
#################################
library(DBI)
library(odbc)
library(tidyverse)
rm(list=ls())
myDir1 <- "results/sequenza/seqzR/"
mytag <- dir(myDir1)
myDir1 <- paste(myDir1, mytag, se... |
d7c4c2ca26a860082fa940a645a009e3fb805a33089c544755462591235ac6fd | R | 2,933 | 93 | # this is very much still WIP
read_graphene_meshes <- function(segment,
cloudvolume.url=getOption("fafbseg.cloudvolume.url"),
dracodecode=c("auto", "dracor", "DracoPy")) {
baseurl=sub(".*(https://[^/]+)/.*", "\\1", cloudvolume.url)
manifesturl=sprint... |
2ae2f42b2ec80455cce8599cadb6b327c2083a4d6b8ffe4efcf46863445901d0 | R | 2,934 | 82 | #!/usr/bin/env Rscript
#-------------------------------------------------
# Title: prepare reference sequence for DRG deconvolution
# Author: Christina Meier
# Date: 2025-06-05
# Email: 19cm51@queensu.ca
#-------------------------------------------------
# Notes ------------------------------------------
# module load ... |
1a5c3df416f6afee849b5f3b381302b23f791e37b0968b918e14eba7155d70cd | R | 2,936 | 49 | ##### ##### ##### ##### ##### ##### ##### ##### ##### #####
# Parameter settings
##### ##### ##### ##### ##### ##### ##### ##### ##### #####
setwd(file.path(getwd(), "R"))
source("00_directorySettings.R") # load baseDir <- "/WORKING_DIR"
source("00_settings.R")
dataDir <- file.path(baseDir, "data", "NMF")
##### #####... |
33beaf1aabb0a042612000ef279e4bdd2905b9698fdfe5efb79509bbb574edb5 | R | 2,936 | 76 | library(lmerTest)
library(emmeans)
library(tidyr)
setwd("/Users/hyx/Downloads/hyx/Network-based_Brain_Ageing_ADNI/")
data <- read.csv('./adni_BA/lme_data_3group_6yr.csv', header = TRUE)
features <- c('VIS', 'SM', 'DAN', 'VAN', 'LIM', 'FP', 'DMN')
anova_results <- list()
pairwise_results <- list()
slopes_matrix <- da... |
55c80ce7e3dd70151c51ef75a0283e373ce7dc2aa348622b095d3e26be9be99d | R | 2,936 | 55 | library(ggplot2)
library(dplyr)
library(viridis)
library(treemapify)
# Your data in a data frame
data3 <- data.frame(
MainCategory = c(rep("Coral Species and Biology", 16),
rep("Reproductive Timing and Larval Release", 2),
rep("Larval Development and Competency", 4),
... |
8fee35e16292f8e49e624fb5b97d39ff0a9aba6f42aced913a5da0b96587780e | R | 2,939 | 107 | # 02_Analysis/Supp7.verify_fdr_impact.R
#
# Objective:
# 1. Verify the specific statistical claims about NNAT from the manuscript.
# 2. Quantify the number of DEGs at FDR < 0.05 vs. FDR < 0.1 for all contrasts.
# 3. Check the significance status of key calcium-related genes at both thresholds.
#
# Input:
# - DE... |
edfcc9eadc0dbdc8588f6d403dee23469862c21984570bb6ee30cafa0f68ceac | R | 2,942 | 100 | #################################
# Push sequenza seg + params into db
# Updated: 2022.07.06
# Author: Kevin Johnson
#################################
library(DBI)
library(odbc)
library(tidyverse)
rm(list=ls())
myDir1 <- "results/sequenza/seqzR/"
mytag <- dir(myDir1)
myDir1 <- paste(myDir1, mytag, sep="")
myinf1 <- ... |
425815aac0ce359c120bda2a4b3d5aad3c6d9bb1214eae89fe0a1b98183eefcf | R | 2,943 | 102 | ---
title: "Fig_3D_Tumor_Tcells_by_timepoint"
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
```... |
d04ed9b8cfeb9d7bdda2bba653eb071f21e2994c3404e2c12f2475a4da21a5e3 | R | 2,944 | 54 | print('This script would take 6 arguments, which are:')
print('Conditional clump 0.01 lead csv, respective FUMA snps.tx, std sumstats for TRAIT1')
print('std sumstats for TRAIT2, TRAIT1 name, TRAIT2 name')
print('to produce cond_0.01_TRAIT1_vs_TRAIT2.csv')
library(data.table)
args <- commandArgs(TRUE)
#lead1=read.csv('... |
16dca418616e022e17d16ddc74d7e95515874d759b464f0298622e4ab5f869f0 | R | 2,946 | 87 | library(Seurat)
library(tidyverse)
library(DoubletFinder)
library(glmGamPoi)
library(biomaRt)
DLPFC <- Read10X(data.dir = "/Barcodes/", gene.column = 1)
so <- CreateSeuratObject(counts = DLPFC, project = "DLPFC")
#Add QC info to the metadata
so$log10GenesPerUMI <- log10(so$nFeature_RNA) / log10(so$nCount_RNA)
so$mito... |
bfb22f313fa61cc2589967b2055d6ae6118f303e1250a9298c8cecb10dd95548 | R | 2,948 | 89 | #' @title T-tests for symptom mapping (slow)
#'
#' @description
#' Lesion to symptom mapping performed on a prepared matrix.
#' T-tests are performed using each column of the
#' matrix to split the behavioral scores in two groups. If
#' var.equal=TRUE the Welch test is performed instead.
#'
#' @param lesmat binary matr... |
3a25ef46be73384949171a457f3148eb454f780e78971f9b0362238a1b8e2ada | R | 2,950 | 99 | library(tidyverse)
subj <- c('sub-101','sub-103','sub-104','sub-105')
#Read mood rating data
mood.csv <- sapply(subj,function(x) NULL)
for (sub in 1:length(subj)){
dir <- paste0('C:/Users/mmatt/Desktop/Projects/NightOwls/night-owls/stimuli/mood/logs/'
,subj[sub])
#sessions <- list.dirs(dir)
#f... |
afc333ba6573c6062c0340fa1dd4cf299c44599edaa300afb9777ca7ae7af5c7 | R | 2,954 | 69 |
##### Tany-seq Data Collection and Labelling #####
#Sullivan, A.I.; Potthoff, M.J.; Flippo, K.H. Tany-Seq: Integrated Analysis of the Mouse Tanycyte Transcriptome. Cells 2022, 11, 1565. https://doi.org/10.3390/cells11091565
#Data obtained from Flippo, Kyle (2022), “Tany-Seq: Integrated Analysis of the Mouse Tanyc... |
c167a899ae499a51f579ac30857898cbe5ef8c2f70513719d85c941a681c4a99 | R | 2,960 | 71 | #####################################
## Write GATK segments to database for CARE-MD and CARE-PS tumor only
## Author: Kevin Johnson
## Updated: 2022.08.26
#####################################
library(DBI)
library(tidyverse)
## Current project
projdir <- "/projects/verhaak-lab/USERS/johnsk/glass4"
setwd(projdir)
##... |
9311da618273d0465acb254dac5e23a37cc4723e19202d0c8f5b8b5dc5cdb476 | R | 2,961 | 100 | ## code to prepare `shapes` dataset goes here
rm(list = ls())
devtools::install_github("gaospecial/shapeMageR")
library(shapeMageR)
## code to prepare `ggVennDiagramShapes` dataset goes here
# construct a object for plotting Venn Diagram
# columns in `shapes`
# - shapeId: shape id, each shape has three components, a... |
5f85927137ffb27bd1faf967050e95e4d5b35d740dec79bd01ee22973d0ddd28 | R | 2,963 | 88 | # When working with lM it is useful to design an "average and difference"
# contrast matrix, which for binary responses has a simple canonical from:
ADmat <- matrix(c(-1/2,1/2),ncol=1,dimnames=list(NULL,"d"))
# We also define a match function for lM
matchfun=function(d)d$S==d$lR
# Drop most subjects
dat <- forstmann[... |
f7f669bde003f7e659bc100a281b1c32bdb719621f84d9093f1485231c0095ef | R | 2,968 | 148 | ---
title: "Get signif counts in correlation tests"
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_download: ... |
5ec8f297b499440487943614b4ebc9d0b15595b912835447f435a718de65d2d8 | R | 2,971 | 79 | library(dplyr)
library(openxlsx)
library(stringr)
t2dCompMststy2ndEd <- "D:\\work2\\t2dAndComplicationsUkb\\data\\processed\\t2dCompMststy2ndEd.rds" %>%
readRDS(.)
diseaseNameVsIcd10_2nd_ed <- "D:\\work2\\t2dAndComplicationsUkb\\data\\vs\\diseaseNameVsIcd10_2nd_ed.xlsx" %>%
read.xlsx(., sheet = 1)
t2dCo... |
c3f6221997ae51dbcb1708c604f8544b1d2b7eebe32de31787080fc2fbfae4c3 | R | 2,976 | 86 | library(dplyr)
library(survival)
library(car)
library(glmnet)
library(stringr)
all_file_paths <- "D:\\work2\\t2dAndComplicationsUkb\\data\\processed\\LassoCox" %>%
list.files(path = ., pattern = ".*", full.names = TRUE, recursive = TRUE)
compIndex <- which(str_detect(all_file_paths, "Comp.rds"))
comp_path <... |
fb8b4c655b68c25f00aa865f1a9b8fd4a1495b082702cb44b2d34fc833b6ba2c | R | 2,978 | 104 | #' Check input spe
#'
#' This function checks that the `spe` object has the appropriate structure.
#' For more details please check the vignette documentation.
#'
#' @inheritParams sce_to_spe
#' @inheritParams vis_clus
#' @param variables A `character()` vector of variable names expected to
#' be present in `colData(sp... |
9cb066d418b88ca8f878146aae3f734d29d63b34abe279f1d2c99dabc5efc997 | R | 2,985 | 66 | RNGkind("L'Ecuyer-CMRG")
set.seed(123)
matchfun <- function(d) d$S == d$lR
d_ord_probit <- design(Rlevels = c("low", "mid", "high"),
factors = list(subjects = 1, S = c("low", "mid", "high")),
formula = list(location ~ 0 + S, scale ~ 1, cut ~ 1),
matc... |
775777025185bedf1d5ca56ef088d9cdec0da4db31514e7e84f7fcc0691a170e | R | 2,989 | 115 | test_that("Test computeSpatialEnrichmentIndex", {
coords <- readRDS(system.file("extdata", "MouseBrainCoords.rds",
package = "SpNeigh"
))
logNorm_expr <- readRDS(system.file("extdata", "LogNormExpr.rds",
package = "SpNeigh"
))
bon_c0 <- getBoundary(data = coords, one_cluster = 0)
... |
90d207d84985af3ee231d2018a8d8dd3cc436fa43a909d4afeb7a8ac21c16269 | R | 2,990 | 66 |
setwd('/sc/arion/projects/roussp01a/liting/Olf/figures/downstream_iOSN/')
scdrs_outps <- list.files('./', pattern = 'scdrs_group.N_types_stage')
cor_traits <- c()
for (trait in scdrs_outps){
cor_trait <- read.table(trait, header = T)
cor_trait$trait <- trait
cor_traits <- rbind(cor_traits, cor_trait )
}
mytr... |
832ea46fe165f1dd61895d9cc835e8b1ea06d0a3f92749e8d0b331e3055effb2 | R | 2,992 | 81 | ###Compare NodoMap with Buchanan dataset
library(Seurat)
library(ggplot2)
library(dplyr)
library(stringr)
#Load data
data <- readRDS("diet_nodomap_metadata_updated_2510.rds")
#Read Buchanan annotation
buchanan <- read.csv("GSE185173_nodoseMerged.meta 2.csv")
#extract metadata and cell names
x <- data@meta.data
x$ori... |
03cd7ab3b418342da0ef02a008ca12be12a90dd88c962c5f799cb404c24aee97 | R | 2,994 | 103 | #' @keywords internal
"_PACKAGE"
## usethis namespace: start
#' @importFrom abind abind
#' @importFrom Brobdingnag as.brob
#' @importFrom coda acfplot
#' @importFrom coda as.mcmc
#' @importFrom coda cumuplot
#' @importFrom coda effectiveSize
#' @importFrom coda gelman.diag
#' @importFrom coda gelman.plot
#' @importFro... |
0a735f810b3485bf17203f4c1604e7a1940afb0a517efd2527f31179735d957e | R | 2,995 | 112 | start_parallel <- function(parallel, cores) {
if (parallel &&
requireNamespace("doParallel", quietly = TRUE) &&
requireNamespace("parallel", quietly = TRUE)) {
if (Sys.info()["sysname"] == "Windows"){
cluster <- parallel::makeCluster(cores)
doParallel::registerDoParallel(cluster)
} els... |
419d4833dacf8856aad49747d95aa96a55e5895d0afe1dff436b2a6b80934b25 | R | 2,997 | 86 | source("01.param.R")
source("02.net_gen.R")
source("03.API_extensions.R")
hiv_vec <- get.node.attr(est1$newnetwork, "HIV") %>% as.numeric()
status_vec <- rep("s", pop)
status_vec[which(hiv_vec==1)] <- "i"
init <- init.net(status.vector = status_vec)
module.order <- c("setup.FUN",
"resim_nets.FUN",... |
a922bf24b3fd7221d34cf33c4ad88f676b4ad3660976063f9e380db2c786616a | R | 3,000 | 76 | # ========= CHCHD10 vs Age (Inhibitory neurons; AD vs NC only, no other covariates) =========
suppressPackageStartupMessages({
library(dplyr); library(readr); library(ggplot2)
})
BASE <- "/home/cxy605/Synapse/ROSMAP_scRNAseq/Inhibitory_Neurons"
OUT_DIR <- file.path(BASE, "CHCHD10_pathology_plots")
IN_CSV <- file... |
324d534f4212cab46ae8f363ca5b886a46c77d536e6095e43da2d6efd2b45f08 | R | 3,001 | 41 | rm(list = ls()); setwd('/sc/arion/projects/DiseaseGeneCell/Huang_lab_project/ADASE/')
# All sample in analysis clinical information
clinicals <- as.data.frame(readxl::read_xlsx('data/SampleInAnalysis/SampleInAnalysis_TenReads.xlsx'),stringsAsFactors=F)
#clinicals <- clinicals[clinicals$Sampleid%in%c('B18C014.hB_RN... |
b6444719c5f46d3899c1528563439acce8813cb8a8e347057d1407381400efd8 | R | 3,003 | 67 | #################################
## Create blocklist for batch2 of CARE
## Updated: 2022.07.05
## Author: Kevin Johnson
#################################
library(DBI)
library(tidyverse)
## Connect to the latest version of the database that contains the CARE samples.
con <- DBI::dbConnect(odbc::odbc(), "VerhaakDB4")
... |
cfd2fb934005cd4275385adee5fe5d8e15b3a6e49beef7ec882807aab1297c16 | R | 3,003 | 82 | library(ggExtra)
library(ggplot2)
library(tidyr)
library(MetaNeighbor)
library(conflicted)
library(magrittr)
library(reshape2)
library(dplyr)
library(here)
library(readr)
library(tmod)
conflict_prefer("count", "dplyr")
conflict_prefer("select", "dplyr")
conflict_prefer("filter", "dplyr")
conflicts_prefer(dplyr::rename)... |
8422d70f8855dcc4a11d8606c97312ca1976425144e8d15351daf51ab71cb056 | R | 3,008 | 86 | library(corrplot)
library(igraph)
library(RColorBrewer)
Over_1000 <- read.csv("PHQ9_cluster_networks.csv")
All_net <- readRDS("All_GTEx_DLPFC_networks.RData")
df_names <- names(All_net)
new_df_names <- sub("_+$", "", df_names)
names(All_net) <- new_df_names
process_dataframe <- function(df, df_name) {
df <- df[, ... |
e29d152cc161a4674a2cadc4bb261e96b9dd35ecd1a2dfc80a2bb16bb23f2c60 | R | 3,010 | 106 | # Script: config.R
# Purpose: Centralizes all configuration parameters for the linear modeling analysis pipeline
###########################
# Base Directory Settings #
###########################
# Set working directory and base path
# BASE_DIR should be set to the project root directory
# BASE_DIR = "path/... |
e40a4f9467f22b457a09f0d6b7a0ad47e569631897a21002009efa6c0978acc7 | R | 3,010 | 86 | library(ggplot2)
library(dplyr)
library(data.table)
library(GenomicRanges)
outp <- "QTL_fsVars/out"
majorCs <- c("ast","mg","EX","IN","odc","opc")
Dxs <- c("Control","AD","bvFTD","PSP_S")
##define MPRA fsVar by cutoff: P < 0.05
mpra_in_celltypePeaks <- read.csv(file.path(outp, "mpra_in_celltypePeaks.csv"), row.names =... |
1e1bc68d3d4f2c64c31a289192d02905821208d950fec4ea053140a33387f331 | R | 3,012 | 87 | #' @title Check headers of list of antsImages
#'
#' @description
#' Function to check that all antsImages in a list have
#' the same orientation, origin, and resolution.
#' The function stops with an error if one of the images
#' has unusual headers. This behavior can be overcome
#' by setting showError=F, and using th... |
0de903e33a9f62b14143b0e085961aa28cc965273ab1630c65c7617458523876 | R | 3,013 | 117 | #### Error helpers ####
format_safe_error_message <- function(title, details = NULL) {
if (is.null(title) || !nzchar(title)) {
title <- "Error"
}
if (inherits(details, "condition")) {
details <- conditionMessage(details)
}
if (is.null(details)) {
details <- ""
}
if (is.list(details)) {
... |
cdbb21f3cc980ba830367596a00b377f1b61fa6507e1c3cc25ea1137692f9465 | R | 3,014 | 95 | #################################
# Push sequenza seg into db
# Updated: 2022.03.01
# Author: Kevin Johnson
#################################
library(DBI)
library(odbc)
library(tidyverse)
rm(list=ls())
myDir1 <- "results/sequenza/seqzR/"
mytag <- dir(myDir1)
myDir1 <- paste(myDir1, mytag, sep="")
myinf1 <- sapply(my... |
4f4d3325791068de6c185d718e238f80129b8bfb756b8989a0b717a976f67f19 | R | 3,016 | 89 | # # Step.00 ----------------------------------------------------------------------
rm(list = ls())
gc(reset = T)
source('script/Functions/Functions.R')
# unload packages
pkgs[which(load.packages == F)]
rpid = Sys.getpid()
options(future.globals.maxSize = 1000 * 1024^3)
##################################################... |
ae5bd412377ce29323aeaf33593f15fe11bd37bdd11c3670f9c7b5b59860146d | R | 3,017 | 89 | context("Run")
teardown({
mlflow_clear_test_dir("mlruns")
})
test_that("mlflow can run and save model", {
mlflow_clear_test_dir("mlruns")
mlflow_source("examples/train_example.R")
expect_true(dir.exists("mlruns"))
expect_true(dir.exists("mlruns/0"))
expect_true(file.exists("mlruns/0/meta.yaml"))
})
te... |
f48051408f46b89e8a1082d53e10e39246a363e933b0f0febbfaa65212823f14 | R | 3,017 | 76 | ## IMPRO Imaris Sholl Analysis
# Input your working directory below.
# The script assumes that the samples are identified by the first numbers in the file name.
# Specify the name length below for accurate results (e.g. if your samples are named "001, 002, ...", animal_name_length would be 3).
# This script works ind... |
210ab2556ff082f8b502c23737df0c8aee535bfce16e8a1646d503d858a50aea | R | 3,018 | 65 | library(fsbrain)
library(tidyr)
library(stringr)
library(e1071)
library(dplyr)
## Extract parcel-wise measures based on vertex data
setwd('/home/huyang/Projects/HuYang/HY_20250708')
fs_dir <- './PROCDATA/STATS/T1/FSDATA'
sublist <- scan('./PROCDATA/LIST/t1_sublist_postqc.txt', what = '')
atlas_names <- c('Schaefer.300P... |
b70581fa53fbc9e10624fe04d83364f26dd5bcadfdf64e43cbc12e237cef7e60 | R | 3,020 | 85 | ################################################################################
###############-- TEST-RETEST POSTERIOR PREDICTIVE PERFORMANCE --###############
################################################################################
## Prediction session a choice data from session b parameters --------------... |
81efecb95843a10e49955de408e1274dcf5592285cb42063be908c7f1c8ab25b | R | 3,021 | 73 | #' Pick the principal branch point of a neuron (useful for annotation tables)
#'
#' @description `key_point_from_neuron()` picks a good "key" point on an
#' in-memory neuron — the principal branch point of its skeleton — suitable
#' for hanging an annotation off (e.g. a point column in an annotation table).
#'
#' ... |
e741ced6f85274028eff9c94b602d482f7d811774004d565a685c3c3c287eea6 | R | 3,021 | 75 | # Moderate effect size genes project
# Author: Madison Caballero
# Desription: Testing SCQ question responses for MES gene carriers using X2 test
options(tidyverse.quiet = TRUE)
library(tidyverse)
args = commandArgs(trailingOnly=TRUE)
# MES genes
GENES = read.delim(file = "MES_genes.txt", header = FALSE)
# SPARK met... |
bba825f45a57b676b51186e1e74716481d2d8a657905b0bc6187b8d518af240e | R | 3,023 | 64 | lasso.proj<-function (x, y, family = "gaussian", standardize = TRUE,
multiplecorr.method = "holm", N = 10000, parallel = FALSE,
ncores = getOption("mc.cores", 2L), betainit = "cv lasso",
sigma = NULL, Z = NULL, verbose = FALSE, return.Z = FALSE,
suppress.grouptesting = FALSE,... |
e46d150c9c2dc0cae9ca9de229526691e0c9fccb9109ff6279eaaf22fe965910 | R | 3,026 | 77 | ############################################################
# MS-DAP analysis after filtering peptides derived from
# eGRASP constructs (pre- and post-eGRASP)
#
# Peptides originating from the engineered constructs are
# removed using in-silico trypsin digestion prior to
# differential expression analysis.
###########... |
4827f004dd1b25a08d223e88618e3001b91c1457712cb516e899740ff7529bf4 | R | 3,032 | 129 | test_that("Test plotExpression", {
df <- data.frame(
x = c(rnorm(100, 1), rnorm(100, 5)),
y = c(rnorm(100, 1), rnorm(100, 5)),
cell = 1:200,
cluster = rep(1:2, each = 100)
)
exp_mat <- data.frame(
gene1 = c(runif(100, 4, 6), runif(100, 0, 2)),
gene2 = c(runif... |
660832a1b037b2ed9cda4fc3e6528b49c9801293fa4cfccb374c946af51eae67 | R | 3,034 | 98 | #' Sample spatial gene visualization grid
#'
#' This function visualizes the gene expression stored in `assays(spe)` or any
#' continuous variable stored in `colData(spe)` for a set of samples at the
#' spot-level using (by default) the histology information on the background.
#' To visualize clusters (or any discrete ... |
ccfa838e9a564dda2501ca7bf39fadfb7bc452d6d3058775a2bb587682973b87 | R | 3,035 | 63 | here::i_am("scripts/1.neural_differentiation_dataset/1.2.network_inference/1.2.1.prepare_data.R")
library(tidyverse)
library(SingleCellExperiment)
library(foreach)
library(scran)
library(scuttle)
library(transformGamPoi)
library(glue)
wd <- here("data/neural_differentiation_dataset/network_inference/input/")
dir.crea... |
2109a9353152bd4c30a924a0f55b5bd18bd41559de84baadf3095fba00a3ec22 | R | 3,036 | 85 | ---
title: "Extract latent factor scores for continuous measures of anxiety"
author: "Genevieve Morneau-Vaillancourt"
output: html_document
---
Load packages (install packages first by removing '#' if needed)
```{r}
# install.packages('lavaan')
library(lavaan)
# install.packages('tidyverse')
library(tidyverse)
#inst... |
6bb6342e5de8b6f5bfd65478cf4653d9aefeb9c203d2e03208bd1b6f1259b7fb | R | 3,038 | 87 | # Description: This script is used to plot the cumulative probability of Patient
# with Q4 levela confirming T2D at four different time points after baseline
library(dplyr)
library(mstate)
library(patchwork)
library(ggplot2)
msT2dComp2ndEd <- "D:\\work2\\t2dAndComplicationsUkb\\data\\processed\\integComp2ndEd... |
e20ee472f9e134772d676db9bb0d9eb6172353b9bfe8b400867e0ae4a7ec1323 | R | 3,039 | 72 | # Moderate effect size genes project
# Author: Madison Caballero
# Desription: Testing comorbidities for MES gene carriers with ASD
options(tidyverse.quiet = TRUE)
library(tidyverse)
args = commandArgs(trailingOnly=TRUE)
#MES genes
GENES = read.delim(file = "MES_genes.txt", header = FALSE)
# SPARK metadata
FAM = rea... |
bbdc4672f95ab1bf1394ffee32af3c6f6bc94c9a19ddca3c08649bc37d867636 | R | 3,041 | 106 | # Script: config.R
# Purpose: Centralizes all configuration parameters for the linear modeling analysis pipeline
###########################
# Base Directory Settings #
###########################
# Set working directory and base path
# BASE_DIR should be set to the project root directory
# BASE_DIR = "path/... |
22a84837c5a864c12d2f63d0f1ad193ca95acaba4fbc02fedc4871f9bc381a9b | R | 3,042 | 64 | ##################################
### Upload batch2 NorLux WGS data
### Author: Kevin Johnson
### Date updated: 2022.03.17
##################################
library(tidyverse)
library(odbc)
library(DBI)
library(stringi)
## Establish connection with GLASS 4 database.
con <- DBI::dbConnect(odbc::odbc(), "VerhaakDB4")
... |
edd6e2fa38629cd8cc43a09a13120dfc8567db80ee6e890313967b6e7b0baf18 | R | 3,045 | 114 | # evaluating part of the R coloc sensitivity.R functions
# accessed the code on 07-09-2021
# loading data
library(coloc)
#########################################################
# test data
# ### constant data
# snp = paste0('snp_', seq(1,5))
# n = 1000
# sdY=2.32924174
# event_rate=0.10
# maf = c(0.1349454, 0.1178... |
2d2f58f6dbc4633ab48aa1b41ee67f00e668bf9f9adf5c3d3546e8588c4bf293 | R | 3,047 | 98 | # Load packages
suppressPackageStartupMessages(library(Signac))
suppressPackageStartupMessages(library(Seurat))
suppressPackageStartupMessages(library(GenomicRanges))
suppressPackageStartupMessages(library(magrittr))
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(future))
suppre... |
dcdc8a5c0294f6eac9b7a1b0e532d19c7188882a54e2d81c80446d037152c745 | R | 3,051 | 66 | #' Initializes marker sets for SAHA analysis
#'
#' This function filters marker genes from both the query and database datasets
#' in a SAHA object based on user-defined thresholds. It prepares the data for
#' downstream marker-based annotation by applying cutoffs for adjusted p-value,
#' fold change, sensitivity, and ... |
6fab49a9bf70a92077069e083b5d148ec3fd610aa4e6d1d2577eefa5bbe851d8 | R | 3,053 | 111 | ######The code below is the pipeline for the spatial clustering
######on the Xenium DMD brain samples for one the slides of the
###### of the run
library(Seurat)
library(Banksy)
library(R.utils)
library(data.table)
library(dplyr)
library(tidyverse)
library(SeuratWrappers)
library(scater)
library(ggplot2)
###... |
be67515fba729dcf6b5f4120c368aa5c5f3d3d9eafb388beef9cdc37858099ae | R | 3,055 | 61 | ##################################
# Plot the malignant cell state differences across tumor grade by tumor type
# Author: Kevin Johnson
##################################
library(tidyverse)
library(ggpubr)
library(EnvStats)
proj_dir <- "/vast/palmer/pi/verhaak/kcj28/care_idh_mut/"
fig_dir <- file.path(proj_dir... |
6d034b0346ccd55d897eeb83d65fbd2202f704e61f5a3224d2a73207e397a890 | R | 3,056 | 66 | #' recombine package
#'
#' RECOMBINE: Recurrent Composite Markers for Biological Identities with Neighborhood Enrichment
#'
#' This package includes five novel algorithms for feature selection coupled with clustering. Briefly, we advance SHC (sparse hierarchical clustering) and obtain two algorithms: SHC-SSL (sparse hi... |
e70318c10881f04f61089f3c0e840043b8228d5ec35880ac74b8399871907c5d | R | 3,056 | 83 | #!/usr/bin/env Rscript
## Author: Luuk Harbers
## Date: 2022-10-22
## Script to make summary table and plots
## Load/install packages
packages = c("data.table", "argparser", "ggplot2", "cowplot", "ggsci", "scales", "patchwork", "gtools")
invisible(sapply(packages, function(x) suppressMessages(require(x, character.onl... |
1f308f0cddb56c3a8c766ce9b92f74bf20096b291589c48cc9e68e1f226ec39b | R | 3,057 | 68 | #' Create Self-Similarity Visualization
#'
#' This function creates a self-similarity visualization based on the provided annotation object. It generates heatmaps for marker self-similarity or average expression self-similarity, depending on the specified slot
#'
#' @param ann An object containing annotation data. It s... |
ce0602070acaf68c36e5b20e929ae41b5f9d5586cd8bc4f33a826043323fd999 | R | 3,057 | 71 | ### Concentration calculation
#Mboat2 overexpression and lipid supplementation data
setwd(rstudioapi::getActiveProject())
rm(list=ls())
library(tidyverse)
library(stringi)
M2PMdf <- read.csv("./Input_Data/M2OE_Validation_112225.csv", stringsAsFactors = F)
load("./Output_Data/M2PM.IS.MsD.w.conc.Rdata")
M2PM.lpd.check... |
506b6201486ba3e1b3e052f9d8232eb9e4e55d5e839b89f33ad09d35397b37b9 | R | 3,058 | 75 | library(tidyverse) # tibble stuff
#dt.path = paste('/home/emba/Documents/EMBA', 'BVET', sep = "/")
dt.path = paste('/home/emba/Documents/EMBA', 'BVET-explo', sep = "/")
# load the relevant data in long format
df.tsk = list.files(path = dt.path, pattern = "PAL-BV*", full.names = T) %>%
map_df(~read_csv(., show... |
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