sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
4d7280f4002a189cd89a371a2ef77fb26928dafa4d4a6c10c223e534a2762103 | R | 3,843 | 66 | bedgraph = function()
{
samples_df = tlx_read_samples("~/Workspace/Datasets/HTGTS/samples/All_samples.tsv", "~/Workspace/Datasets/HTGTS") %>%
dplyr::filter(celltype=="NPC" & tlx_exists) %>%
dplyr::filter(grepl("promoter/enhancer", experiment) & alleles==2 | "APH concentration"==experiment & concentration==0.4... |
5f31ceb2f1575b8c3034b5e9c87b220635aa56bdd924e5ec3435740e9cd9961d | R | 3,858 | 81 | #' Multiple variance Brownian motion estimation
#'
#' Computes rescaled branch lengths using multiple variance Brownian motion as described in Smaers et al. (2016)
#' @param data a vector of tip values for species; should be in the same order as tiplabels in the tree
#' @param tree an object of class "phylo".
#' @para... |
2dfd65ba5c42c064c38c02fd7fa09a8a29380db0f103d05f900d29381bc8c7d9 | R | 3,861 | 56 | # Independent arithmetic verification using base R. No fitted health model.
args <- commandArgs(trailingOnly=FALSE)
script <- sub('^--file=', '', args[grepl('^--file=', args)])
root <- normalizePath(file.path(dirname(script), '..'), winslash='/')
res <- file.path(root, 'results')
cells <- read.csv(file.path(res, 'nativ... |
11e355357ab0995439aecf042151339c1ffdf6c4227df8b16f6db9d1588e52a5 | R | 3,865 | 120 | setwd('/work/AnneSophiaHartebrodt#6698/ms-project/')
require(rhdf5filters)
require(rhdf5)
require(Rhdf5lib)
renv::activate('/work/AnneSophiaHartebrodt#6698/ms-project/r-single-cell')
renv::repair()
set.seed(11)
library(harmony)
require(data.table)
require(SingleCellExperiment)
require(scDblFinder)
require(scds)
librar... |
99aeccd4cf7eb06f01799a97655a0548b504d5056dda7beb528aa43090007f14 | R | 3,866 | 92 | source ~/.bashrc
#! /usr/bin/env Rscript
#loadr4
rm(list=ls())
library(data.table)
Path <- "/cluster/projects/p33/groups/imaging/ukbio"
Batch="42k"
BatchDate="160320"
Date="051119"
#################
# Prep DM GWAS #
#################
############### Add pop comps and genetic batch to demog file
Demog <- fread(past... |
a5cda8c3ecf5284688d1b0528c3cab272dcac7e866e3f71d65fcf05c5ed4577d | R | 3,879 | 122 | ---
title: "Analysis of spatial information during common grid modes"
author: "Harry Clark and Matt Nolan"
date: "01/11/2023"
output: html_document
---
# Setup
```{r}
library(tidyverse)
library(lme4)
library(emmeans)
```
# load data and create R friendly data tables
```{r}
cells_data <- readRDS(file = "cells_df_singu... |
12cb80b6ec38ca6b95a4caacf43e56e9f3c09a7031e29c86a3cb7c7be9b22c5e | R | 3,894 | 139 | library(tidyverse)
library(DropletUtils)
library(Seurat)
dir.name <- list("/usr/users/mmnb1ngs/collab_work/workspace/ykbabal/Novaseq_Ferret/res_STAR/T1/Solo.out/Gene/raw",
"/usr/users/mmnb1ngs/collab_work/workspace/ykbabal/Novaseq_Ferret/res_STAR/T2/Solo.out/Gene/raw",
"/usr/users/mmn... |
859048c201e47e32e5b4b54753e7fbac0632853fb2e594d63d513d24741b955f | R | 3,898 | 176 | ---
title: "QC"
output:
html_document:
df_print: paged
---
```{r global_options, include=FALSE}
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE)
```
```{r, libraries}
library(Seurat)
library(cowplot)
library(SCFunctionsV3)
library(dplyr)
library(tidyr)
library(stringr)
library(tidyverse)
li... |
167d1fba30e354ad763e4bcdf54f500926dbd87ac9c41df470cbbfc1bc32d8cf | R | 3,901 | 111 | load('/data2/zj/atac/second.Rdata')
addArchRGenome("mm10")
set.seed(42)
addArchRThreads(threads = 30)
##
prog1wt <- proj1[proj1$Sample%in%('WT-pcdhaq'),]
prog1mut <- proj1[proj1$Sample%in%('MUT-pcdhaq'),]
mouwt <- subset(mou.combined.sct,orig.ident%in%'wt')
mouwt <- CreateSeuratObject(mouwt@assays$RNA@counts,'wt')
... |
d1c267c675c92025f4be9fb6138c18c4b53bdaf8b1d7f8aa855ce6de0c5ce6ed | R | 3,904 | 123 | modularity_matrix <- function(g, gamma = 1, weighted = TRUE){
#
# INPUT:
# g, igraph or matrix object (to work as adjacency matrix)
# memb, community membership
# gamma, (optional) resolution parameter. Default is 1, which gives equivalent to Newman (2004) modularity
# weighted, (optional)... |
85f1b127cc89e49ba87b9706c1d77b26444f994e15875c86fbfc698a71dcc44a | R | 3,913 | 105 | ---
title: "figure 1"
author: "Pavitra Roychoudhury"
date: "2023-07-15"
output: html_document
---
panels for Figure 1
Fig 1 shows effect of filtering
```{r}
rm(list=ls())
library(Seurat)
library(tidyverse)
options(dplyr.width=Inf)
library(cowplot); options(scipen = 999)
all3<-readRDS('./seurat_analysis_all3.rds')
pl... |
6793bd40b1f5ad2d8eefc9b1e296d004af636bbe6b8aa6af919dd3c5f7a0df21 | R | 3,917 | 71 | # This script assesses specificity of model to the actual seed site
rm(list=setdiff(ls(),c('params','injection.site')))
basedir <- params$basedir
setwd(basedir)
savedir <- paste0(params$opdir,'nullmodels/seedspec_multi/',paste0(injection.site,collapse='-'),'/')
opdir <- params$opdir
dir.create(savedir,recursive=T)
so... |
6545f9d6514127e131afe906f0a5eb241d60b1ddf063a3dc07a96c51d56b5270 | R | 3,932 | 69 | #################
### Load data ###
#################
rm(list=setdiff(ls(),c('params','grp','treatment','injection.site')))
print(grp)
basedir <- params$basedir
setwd(basedir)
savedir <- paste(params$opdir,'diffmodel/bidirectional/',paste0(injection.site,collapse='-'),'_independentfit/',sep='')
dir.create(savedir,recu... |
4b69f0815e2bb9bb971896be2ac72d6bf550a5ad45029ecf77468a763a129736 | R | 3,941 | 156 | ---
title: "Retina Samples"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE)
```
```{r, libraries}
library(Seurat)
library(cowplot)
library(SCFunctionsV3)
library(dplyr)
library(tidyr)
library(stringr)
library(tid... |
4fba6980b9c37a9bd3b0b882a94cc8c79b36fe39993baa816d43c67d8e1dba89 | R | 3,947 | 158 | #Analysis for "Turbidity drives plasticity in the eyes and brains of an African cichlid"
#Allometric size standardization for each morphological trait (following Hendry & Taylor, 2004)
#Analyzed by Jai Tiarks
#E: tiarks.2@osu.edu
##Pull necessary libraries
library('moments')
library('ggplot2')
library('gg... |
b89ee5e33b5654e21ab5270327d5e7dd623bfda7985b741b5788864cd338f2d0 | R | 3,956 | 108 | ---
title: "Comparing task-anchored vs task-independent firing as a function of task performance on a single-trial level"
author: "Harry Clark"
date: "04/04/2023"
output: html_document
---
```{r}
library(sjPlot)
library(lme4)
```
# subset the data acccording to trial type
```{r}
spatial_firing <- readRDS(file="all_c... |
f66f580177ed6998c216f3c2ed82e9dc6f38c038d202488dbae8e9c658f6d248 | R | 3,970 | 71 | # KNN testing
library(class)
library(ggrepel)
library(patchwork)
# Fetch data and split it
data <- FetchData(all.sub.integrated, c("UMAP_1", "UMAP_2", "UMAP_3", "orig.ident", "Type_step2"))
data.vivo <- subset(data, subset = orig.ident == "briscoe")
data.vitro <- subset(data, subset = orig.ident != "briscoe")
... |
93731b7e083904959ddfee96601548bde5554575ccffd90de2f7565dc7f4ddf7 | R | 4,008 | 98 | library(base64enc)
library(cifti)
library(dplyr)
library(gifti)
library(magrittr)
library(rjson)
library(stringr)
print("This script 1) loads each subjects' timeseries, and 2) concatenates timeseries and makes connectivity matrices ")
##################
# Set Directories
##################
config_data <- fromJSON(... |
2edbbe71ff32336426f9a2ef7073e069ab3ae8c24fea55abd5a914e6032bd483 | R | 4,009 | 126 | library(dplyr)
library(Seurat)
library("ggplot2")
library(monocle3)
library(patchwork)
library(SeuratWrappers)
library(harmony)
library(ggplotify)
library(rmarkdown)
sobjToCds <- function(sObj){
harmonyLoadings <- sObj@reductions[["harmony"]]@feature.loadings.projected
sObj <- DietSeurat(sObj, graphs = "umap")
c... |
b2c220aa7e965effdc9a7866732d380648a8b13300081776db6bcd763233d9d7 | R | 4,014 | 27 | # Barnes-Hut implementation of t-Distributed Stochastic Neighbor Embedding
#
# Wrapper for the C++ implementation of Barnes-Hut t-Distributed Stochastic Neighbor Embedding. t-SNE is a method for constructing a low dimensional embedding of high-dimensional data, distances or similarities. Exact t-SNE can be computed b... |
a33e88ce67c309fc6e52e05bf4f21fe49918a16e9f51644a31331ca43388368e | R | 4,015 | 109 | ---
title: "Comparing task-anchored vs task-independent firing as a function of task performance on a single-trial level using the ensemble classification method"
author: "Harry Clark"
date: "04/04/2023"
output: html_document
---
```{r}
library(sjPlot)
library(lme4)
```
# subset the data acccording to trial type
```{... |
9201761d1320cc66518c6502e21634fba9647c424e559bcd004fe29d60e936f7 | R | 4,035 | 90 | rm(list=ls())
library(openxlsx)
library(tidyverse)
library(Hmisc)
library(corrplot)
library(pcaMethods)
library(RRPP)
library(dplyr)
library(scales)
options(scipen=999)
#Import the current new data set - this includes data up till 2018
data <- read.csv("Input_files/Phenotype_data_final_June2023.csv")
... |
1f0d48d23dd24a861bdb0edda19dc6ea935f651e69ecda0cfeb9ba6fd6798512 | R | 4,038 | 102 | # Correlates Six Fold symmetry with the accuracy in the Clock Navigaiton task for Both groups
# Investigate the possible correlation between accuracy and grid-like code:
library('ggplot2')
library("ggpubr")
library(ppcor)
library(rstatix)
library(ggprism)
library(car)
# Load the accuracy table ---
ClockNavExpAcc<-... |
4a342c2a392473211dfea5fab4170c8c429f9ea1ee33f73a27f0119bb757bf59 | R | 4,064 | 117 | ##### Human
### DATA
fix_name <- function(name){
tmp <- strsplit(name, "_")
return(tmp[[1]][1])
}
fix_type <- function(x) {as.numeric(as.character(x))}
x1 = read.delim("GSE76381_ESMoleculeCounts.cef.txt", sep="\t", header=F)
cellid = x1[2,]
celltype1=x1[3,]
timepoint1=x1[4,]
x1 <- x1[-c(1:5),]
rowname... |
c4b01004a34512e199dd93173fb6c5dcaa782944f7e0e317a6c56f17fccf99c1 | R | 4,068 | 130 | library("dorothea")
library("DESeq2")
library("limma")
setwd("C:/work/publications/technologyPaper/Artificial-Signaling-Network/TF activities Ligand Screen")
##
countData = read.table('filtered/counts.tsv', sep ='\t', stringsAsFactors = FALSE, header=TRUE, row.names=1)
metaData = read.table('filtered/metadata.tsv', se... |
2d410b4e1dc6dd77ce2166882ae18d1fce8451e2b6de96a315c2f2a244f95b7f | R | 4,079 | 123 | c25 <- c(
"dodgerblue2", "#E31A1C", # red
"green4",
"#6A3D9A", # purple
"#FF7F00", # orange
"black", "gold1",
"skyblue2", "#FB9A99", # lt pink
"palegreen2",
"#CAB2D6", # lt purple
"#FDBF6F", # lt orange
"gray70", "khaki2",
"maroon", "orchid1", "deeppink1", "blue1", "steelblue4",
"darkturquoise",... |
5f093427b860c5e119667dd2503eff00502c1c3f1cbc3fc8ffff272987959783 | R | 4,084 | 162 | ---
title: "220808_PRISM_data"
author: "Jennifer Fisher"
date: "8/8/2022"
output:
html_document:
toc: true
theme: united
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# START
Goal/Purpose of operations:
Organize and filter PRISM data for downstream analysis. This creates ... |
8a1491e0a11f52a82e24a66a9cfa5aaecc5162ae7cded7e1bf2da38df68d9a60 | R | 4,094 | 85 | #################
### Load data ###
#################
rm(list=setdiff(ls(),c('params', 'injection.site')))
basedir <- params$basedir
setwd(basedir)
savedir <- paste(params$opdir,'modelcomparison/traintest/',paste0(injection.site,collapse='-'),'/',sep='')
dir.create(savedir,recursive=T)
source('code/misc/modelcomparis... |
d7f389df424a6148547c209c2d0ce7627b4e68eb9f7dc92b4500590beaf5d3de | R | 4,134 | 134 | setwd('/work/AnneSophiaHartebrodt#6698/ms-project/')
require(rhdf5filters)
require(rhdf5)
require(Rhdf5lib)
renv::activate('/work/AnneSophiaHartebrodt#6698/ms-project/r-single-cell')
renv::repair()
set.seed(11)
require(data.table)
library(cowplot)
library(rsvd)
library(Rtsne)
library(Signac)
library(Seurat)
library(Ge... |
9c2ea86cdcca7bacb7ecab30fddeb6b41ae5cbd206864660c9214a5f11ca995e | R | 4,165 | 61 | # Figure 1B
rm(list = ls())
require("writexl")
require("readxl")
library("dplyr",quietly=T)
library("ggplot2",quietly = T)
library("ggtree",quietly = T)
library("ape")
library("aplot")
library("ggsci")
setwd("Source_Data/Figure_2/Figure_2A/")
ERVK_proportion<-read_excel("Figure_2A.xlsx")
Aves.Sp<-read.tree("Figure_2A... |
6f6cedb3cf871987d95577b1e54b145949297ca6ed6a11e9fe99e70db6864915 | R | 4,175 | 112 | #! /usr/bin/env Rscript
rm(list=ls())
library(data.table)
library(ggplot2)
library(VennDiagram)
library(DescTools)
library(dplyr)
library(eulerr)
options(bitmapType="cairo")
Path <- "/path/"
setwd(Path)
signif_thr = 0.05/18877
###########################################
# point towards loci txt and calc overlap #
#... |
ab7ae96cfb19125e39478b9ba827334be39fb9262e9b4c28f8dee22dfec6712d | R | 4,175 | 115 | # This script estimates slopes for the (linear) relationship between ABSOLUTE brain asymmetries and age
# 30.10.2023 Max Korbmacher (max.korbmacher@gmail.com)
# PREP ####
# load packages
if (!require("pacman")) install.packages("pacman")
pacman::p_load(dplyr, marginaleffects, ggpubr, ggplot2, Rmpfr, mgcv)
# load data
... |
30d8e7bcc5b7a88bbd9fe59d415bd97af6e75c38aba406ead7c9bedf5683015a | R | 4,181 | 121 | #Analysis for "Turbidity drives plasticity in the eyes and brains of an African cichlid"
#PCA
#Analyzed by Jai Tiarks
#E: tiarks.2@osu.edu
# data prep ---------------------------------------------------------------
##Pull necessary libraries
#library('moments')
library('ggplot2')
library('ggpubr')
libr... |
22ef2c51a5bb8c1c326a21c25a8ecb227d02654c4cc2a1944353932eeb5f2e43 | R | 4,186 | 138 | # perform network diffusion of K steps over the network A
"network.diffusion" <- function( A, K ) {
# set the values of the diagonal of A to 0
diag(A) = 0
# compute the sign matrix of A
sign_A = A
sign_A[which(A>0,arr.ind=TRUE)] = 1
sign_A[which(A<0,arr.ind=TRUE)] = -1
# compu... |
95b175a6a662f0fa5f5b1d18fdf54c4f94b4af629873335779a0661ca523e0f7 | R | 4,200 | 80 | # Load required libraries
library(Seurat)
library(Libra)
library(readr)
library(ggplot2)
library(forcats)
library(EnhancedVolcano)
library(grid)
# Load Seurat objects and perform cell type assignment
E16_CCA_inhib <- readRDS("/data/mayerlab/Github/Dvoretskova_et_al_2023-DATASETS/E16_CCA_inhib.rds")
E16_CCA_inhib@meta.... |
ddd602a07d2e9fa377e043c312458cef9482b0cce1ecb1d52c621254718a1f2f | R | 4,224 | 111 | ##' cca_main()
#'
#' @description Perform Sparse CCA using the optimal tuning parameters
#'
#'
#' @param x Expression-driven adjacency matrix
#' @param z Spatial-driven adjacency matrix
#' @param penaltyx The penalty to be applied to the matrix x, i.e. the penalty that results in the canonical vector u. If typex is "st... |
34b9b90be4457b1e983e624f4833ee7b5904239444842e290a54e12ed5c0673a | R | 4,243 | 104 | setwd('/work/AnneSophiaHartebrodt#6698/ms-project/')
require(rhdf5filters)
require(rhdf5)
require(Rhdf5lib)
renv::activate('/work/AnneSophiaHartebrodt#6698/ms-project/r-single-cell')
renv::repair()
set.seed(11)
require(data.table)
require(wesanderson)
library(ggplot2)
require(colorspace)
require(stringr)
requ... |
fbcdd30817ba3012b2970d8276aeed2c804ecfec59931a5bb0862e20c757ca84 | R | 4,250 | 110 | ######################################################################################################
# Create_the_gruffi_Package_OLD.R
######################################################################################################
# source("~/GitHub/Packages/gruffi/dev/Create_the_gruffi_Package_OLD.R")
# rm(li... |
c613d98b9091d6668c66cf53ec993ba98db7363ec1cf5694a6cbf51387ae52ba | R | 4,261 | 75 | ### Hong X. et al 2023
### produce figure 5
### hongxiaoning@outlook.com
library("ggplot2")
library("ggpubr")
library("ggsci")
library(lemon)
#Figure5 b
SEM_ebtop<-function(x){
return(mean(x)+(sd(x)/sqrt(length(x))))
}
SEM_ebbottom<-function(x){
#return(mean(x)-(sd(x)/sqrt(length(x))))
return(mean(x))
}
data <... |
f5cffdb7f3629acaf1b6d3a720e9c25088ca20fb54247b866ebc9398cf166bd6 | R | 4,286 | 103 | #Script for comparing random selection of proteins to organelle protein list of interest
library(ggplot2)
library(reshape2)
library(dplyr)
# Load data
# Input file must contain a column with the Gene names, a column with log2 fold-changes for each experiment and
# a column with the organelle/compartment annotation
fi... |
0158f2b69b03149dc31ded00f3bd13386cc046d0349611f89426b45953224aea | R | 4,293 | 108 | #!/usr/bin/env Rscript
# Generate tAge parity fixtures from the authors' reference implementation
# (https://github.com/Gladyshev-Lab/tAge).
#
# Run from the repo root:
# Rscript clocks/generate_tage_fixtures.R <path-to-tAge-clone> tests/data/tage
#
# Missing R dependencies (edgeR, reticulate) are installed into a lo... |
fc3ab7c8d9c77f9ae8a11f6a537384823b8913efc754d91db3e945f48cfafe5b | R | 4,295 | 86 | # Export lossless linear compositions and independent original-author predictions.
# Only base R is needed. Original source files are checksum-verified by the builder.
args <- commandArgs(trailingOnly=TRUE)
source_dir <- args[[1]]
out_dir <- args[[2]]
options(digits=17)
make_inputs <- function(reference) {
n <- leng... |
9f2c159ce13a194dda40974f552c63894b9cb6c96729826deda9e2aada007c1c | R | 4,334 | 91 | st = ls())
options(stringsAsFactors = F)
library(fitdistrplus)
library(dplyr)
setwd('/work/cascades/bz10/VCU_human/data/HiC/new_chr/background')
chr1 = read.table('chr1.bed')
colnames(chr1) =c('chr_loci1','start_loci1','end_loci1','chr_loci2','start_loci2','end_loci2','read','num')
head(chr1)
f=list.dirs(path = '/wor... |
a27d5a43a71a6a367f0f3e71874218dc53c0305445f40229494253005f1ea320 | R | 4,337 | 105 | ---
title: "HBN Covbat"
author: "Audrey Luo"
output: html_document
---
# Perform CovBat Harmonization on GBC + network pair analysis
```{r setup_covbat, include=FALSE}
library(ComBatFamily)
library(dplyr)
library(mgcv)
atlases_GBC <- "schaefer200" # only include schaefer200 for sensitivity analysis in supplement
d... |
c71c9450e2564ed71d21c0ff549cd2562f231645372ef17daf658c46cb6a4e98 | R | 4,340 | 109 | ---
title: "HCPD Covbat"
author: "Audrey Luo"
output: html_document
---
# Perform CovBat Harmonization on GBC + network pair analysis
```{r setup_covbat, include=FALSE}
library(ComBatFamily)
library(dplyr)
library(mgcv)
atlases_GBC <- "schaefer200" # only include schaefer200 for sensitivity analysis in supplement
d... |
4509d4e2ce9963f3f1f9c2c239dc8f165f0f69c2e91772fe29641633b82b1a4e | R | 4,367 | 71 | # Independent base-R verification of the added sensitivity analyses.
args <- commandArgs(trailingOnly=TRUE)
root <- if(length(args)) args[1] else '.'
read <- function(f) read.csv(file.path(root,f),stringsAsFactors=FALSE,check.names=FALSE)
out <- list()
check <- function(label,got,expected) {
stopifnot(length(got)==len... |
3a6fca0902318d41ccc21002129a65cda4d44396eaf6117569ab0b66fb3d98b1 | R | 4,395 | 83 | ################################################################################
# cluster.R 2.0.3 #
# Authors: Patrick Poullet, Stephane Liva, Guillaume Arras (Institut Curie) #
# Contact: myproms@curie.fr ... |
b81bfccd01f9719759b28dc1dda6014c903eed7f8e86bec02785a0dbb8a537d0 | R | 4,475 | 144 | #!/usr/bin/env Rscript
#' Single-cell RNA-seq Processing Pipeline for COVID-19 PBMC Dataset
#' Dataset: Schulte-Schrepping et al., 2020 (PMID: 32810438)
#' Description: Process and analyze PBMC dynamics in COVID-19 patients
#' GEO: EGAS00001004571
# 1. Setup and Configuration -----------------------------------------... |
3d9de8037b8bc8d9e43dbbc5c2023954f93889658ebd55347515897b25e2a86b | R | 4,477 | 99 | library(future.apply)
options(future.seed = TRUE, future.globals.maxSize = 1024 * 1024 * 1024)
CCA.permute.both=
function(x,z,penaltyxs,penaltyzs,niter,v,trace,nperms,standardize,upos,uneg,vpos,vneg,outcome,y,cens, seed = 1234){
set.seed(seed)
call <- match.call()
if(standardize){
... |
c92a55f9e5c5bf78f7a1a1ee2cd9de820864f08fe89e66aaca8b7077b25b45a4 | R | 4,480 | 128 | #!/usr/bin/env Rscript
options(width=200)
#########################
#
# Loading Data and Libraries
#
#########################
args <- commandArgs(trailingOnly=TRUE)
cliargs <- args[-(1:2)]
options('warn'=1) #report non-convergences, etc
library(lme4)
# library(languageR)
library(optimx)
library(ggplot2)
library(mv... |
ec82cac14cc68c97525175b1faf85f2597fa0d7832f3ec89e3a0c8ecc3f82b82 | R | 4,507 | 168 | #!/usr/bin/env Rscript
#' Platelet Dataset Integration Pipeline Using Harmony
#' Description: Integrate multiple platelet datasets using Harmony batch correction
# 1. Setup and Configuration -------------------------------------------
suppressPackageMessages({
library(Seurat)
library(harmony)
library(tidyverse... |
046787c7cca89f6910c073770844bf997926b9000e543befb5ae2b43270c4ea6 | R | 4,515 | 98 | library(fgsea)
library(msigdbr)
library(ggrepel)
library(ggpointdensity)
library(tidyverse)
atac_de <- read_csv("data/005.1c_ATAC_de.csv")
dge <- readRDS("data/002_res_tbl.rds")
point_plot <- atac_de |>
inner_join(dge,
by = c("gene_symbol" = "symbol"), suffix = c("_atac", "_rna")) |>
m... |
5cdd3a26962f13cfa01edd3fc97d451b3ab280db81393dc918331aaa0d62e64a | R | 4,516 | 104 |
resamp_nodals <- function(df, to_sample = 'group', efficiency_loss = FALSE, local_eff = FALSE,
metrics = c('signed_strength'),
net_list_args = c(...), multi_net_measures_args = c(...)){
#shuffle the grouping labels
if(length(to_sample) > 1){
df$labels$in... |
3c62fa6797c1d4a275e55d93194c9d760586a9de946be7761ff262ad2553c497 | R | 4,518 | 135 | ---
title: "Modules"
output: html_notebook
---
```{r}
library(dplyr)
library(tibble)
library(stringr)
```
### Perturbation effect on module scores
Outputs **Fig.6i**
```{r}
P7GE_neurons <- readRDS(file = '/data/mayerlab/Github/Dvoretskova_et_al_2023-DATASETS/P7GE_neurons.rds')
```
```{r}
# initialize dataframe
qu... |
7dcfe28e89cfd3f7fc55ba5f516070954b09636a17fdfd9b77074b82e8ccd28e | R | 4,521 | 114 | # Rscript build_meco_sentitems.R /fs/project/schuler.77/meco/joint_fix_trimmed.rda ee
# Language codes (wave 1): "du" "ee" "en" "fi" "ge" "gr" "he" "it" "ko" "no" "ru" "sp" "tr"
# Language codes (wave 2): "ba" "bp" "da" "en_uk" "ge_po" "ge_zu" "hi_iiith" "hi_iitk" "ic" "no" "ru_mo" "se" "sp_ch" "tr"
args <- commandArgs... |
cdf501c5725e7d7d44bc03fbdee40150dfa8575262d158751bf7f470d61dd3ef | R | 4,521 | 85 | #################
### Load data ###
#################
rm(list=setdiff(ls(),c('params','injection.site')))
basedir <- params$basedir
setwd(basedir)
savedir <- paste(params$opdir,'modelcomparison/traintest/',paste0(injection.site,collapse='-'),'/',sep='')
dir.create(savedir,recursive=T)
source('code/misc/miscfxns.R')
s... |
72db58219d5edd2869d60e913777c29d3cef097c6416da991112dace04d81ef3 | R | 4,586 | 221 | ---
title: "Monocle ATAC all"
output:
html_document:
df_print: paged
---
```{r global_options, include=FALSE}
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE)
```
```{r, libraries}
library(tidyverse)
library(Signac)
library(Seurat)
library(JASPAR2020)
library(GenomeInfoDb)
library(EnsDb.Hsapie... |
48c40b92ba13697e77b191e51e203351853e265a94f8f5d1c4c800817b2404f0 | R | 4,596 | 105 | #=============================================================================
#coloc.R
#date: 2023-10-13
#by Judy Wang
#=============================================================================
library(data.table)
library(dplyr)
library(cowplot)
library(foreach)
library(Biobase)
library(readxl)
library(coloc)
lib... |
23149538d843151b92d73005f4c3256f29926d8d29c7d82c66ad036e2536f653 | R | 4,624 | 94 | rm(list=ls())
basedir <- '~/Box/PD_Project/Quant_pathology_dataset/'
setwd(basedir)
params <- list(basedir=basedir,
grps = c('NTG', 'G20'), # NTG are WT mice; G20 are mice with LRRK2 mutation
injection.site = c('iDG', 'iCA1', 'iCA3', 'iVISam', 'iRSPagl'), # injection sites (using ABA nomen... |
5e4ac3e4cafd8d629e3ca7d8547e125055905378b72f9c507ac65b9f45d200b7 | R | 4,629 | 174 | # compute and returns the multiple kernel
"multiple.kernel.cimlr" = function( x, cores.ratio = 1 ) {
# set the parameters
kernel.type = list()
kernel.type[1] = list("poly")
kernel.params = list()
kernel.params[1] = list(0)
# compute some parameters from the kernels
N = dim(x)[1]
... |
5d97cb18bfc60a5ba8a64411a9e4c0be77440d3ab23278011f9d72118a331040 | R | 4,637 | 162 | searchPair(signaling = 'CX3C', pairLR.use = moucellchat$mut@LR$LRsig, key = "pathway_name", matching.exact = T, pair.only = T)
cellchatgene <- function(data1,signal,data2){
print(signal)
ligand <- unique(str_match(str_split(netAnalysis_contribution(data1, signaling = signal,return.data =T)$LR.contribution$name,"-"... |
5abfb948be6af518862c72c202a780e24840d58fa464c433a0b47e0b4b9f0929 | R | 4,659 | 99 | suppressPackageStartupMessages({
library(Seurat)
library(stringr)
library(sctransform)
library(future)
require(scales)
library(RColorBrewer)
library("readxl")
library(dplyr)
library(dendextend)
})
### figure 3 + supplements
# import the Dopaminergic nuclei (mDA) dataset.
sobj <- readRDS("/path/to/d... |
fbd46f7def02d31f8c6e4993b4efb13d25d56bd809a7e23a6df46eaf96e92156 | R | 4,684 | 172 | #Only needs BioCParallel and BRISC
nnSVG <- function(input, spatial_coords = NULL, X = NULL,
assay_name = "logcounts",
n_neighbors = 10, order = "AMMD",
n_threads = 1, BPPARAM = NULL,
verbose = FALSE) {
if (is(input, "SpatialExperiment"))... |
8f40bfdc3e3fb56e31ac80da6ee986769cb1a3ff191e7a1414ee76652d7f4d17 | R | 4,715 | 204 | ---
title: "AD3 ATACseq - shared peaks"
output:
html_document:
df_print: paged
---
```{r global_options, include=FALSE}
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE)
```
```{r, libraries}
library(tidyverse)
library(Signac)
library(Seurat)
library(GenomeInfoDb)
library(EnsDb.Hsapiens.v86)
l... |
346070747485e2a740000338dea41de0722fbdb41398479f29ffcb20a04a4056 | R | 4,721 | 156 | ---
title: "220805_network_differences_methods"
author: "Jennifer Fisher"
date: "8/5/2022"
output:
html_document:
toc: true
theme: united
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# START
Goal/Purpose of operations:
To compare the centrality metrics for the disease ge... |
d17d7000030609d757b4f455d0ca39835c5fa9c1750e69d1f74f0c6064175f32 | R | 4,726 | 83 | rm(list = ls())
options(stringsAsFactors = F)
library(DiffBind)
setwd("~/Documents/Virginia Tech/Project_4_VCU_postmortem_SCZ/diffbind/input_sheet")
########################################################################################################
H3K4me3 <- dba(sampleSheet = "SCZ_Control_glia_K4.csv")
H3K4me3_bl... |
4e1abdb7e850944b6cb72bf19945bca1479fe1835886192853ed169b4d2ccbd5 | R | 4,754 | 153 | #!/usr/bin/env Rscript
#' Gene Name Processing Pipeline
#'
#' This script processes and standardizes gene names from a Seurat object using HGNChelper.
#' It handles special cases like AB- prefixes and dot notations, and resolves multiple symbols.
# 1. Setup and Configuration -----------------------------------------... |
21805afddb987ab8758335e4661cba9d9151cd9b92bb73e71de8cb88bad86f36 | R | 4,764 | 161 | ##################################################################################
# Statistical analysis of Sholl profiles based on mixed-effect models
#
# Adrian Gabriel Zucco
# Center for Translational Neuromedicine
# University of Copenhagen
#
# Adapted from:
# Wilson, M. D., Sethi, S., Lein, P. J. & Keil, K.... |
cf185951f154fe422bdfa47cebe46fa75df0349025791077f68e9c4df7ca6abf | R | 4,771 | 91 | library(readr)
library(dplyr)
library(ggplot2)
library(cowplot)
library(stringr)
devtools::load_all('~/Workspace/breaktools/')
main = function()
{
baits_df = readr::read_tsv("~/Workspace/Datasets/HTGTS/wei_pnas2018_baits.tsv")
#
# Read TLX files
#
samples_df = tlx_read_samples("~/Workspace/Datasets/HTGTS/sa... |
d4f4f84e6b971ef95b5abcae717e5722de4337506920ab11bb7e8d398817e6a0 | R | 4,809 | 154 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# STew <img width="28%" align = "right" src="https://github.com/fan... |
1a3d05cea1e703d3dc1ca17f46a089d8c7c7afbb6bf1868a100ad66f46252f4c | R | 4,816 | 72 | # Set directory to source file loaction
setwd(paste0(figureDirectory, "Figure3-Genomic-Regions" ) )
library(ggplot2)
library(cowplot)
load(file = "barplotOverviewDF.RData")
load(file = "pathwayGenePlotDf.RData")
load(file = "immunePlotDf.RData")
colorsExp <- c(rep("blue3",8),rep("forestgreen",6),rep("gold3",3),rep(... |
75aee188cbbf2d206eb251ca0a224a56b9834d2cf72a995573e36b0a35cb8b43 | R | 4,816 | 91 | ### Prepare structural connectome matrix for network analyses
rm(list=setdiff(ls(),'params'))
basedir <- params$basedir
setwd(basedir)
savedir <- paste(params$opdir,'processed/',sep='')
dir.create(savedir,recursive=T)
source('code/misc/fitfxns.R')
# read in raw pathology dataset to get region names
path.data <- read... |
a6c38de8945d13292d0debe449855187957382f2c7c6ee9d76f99c7219cdf885 | R | 4,820 | 122 | library(base64enc)
library(cifti)
library(dplyr)
library(gifti)
library(magrittr)
library(rjson)
library(stringr)
print("This script 1) loads a subject's cifti files, and 2) concatenates timeseries and makes thresholded / absvalue connectivity matrices")
##################
# Set Variables
##################
args ... |
36eaa7155c8e1984a1533f01f290dd656e374441582b60620e4bdf0d4e901e7a | R | 4,830 | 113 | #' Make a gene-wise analysis
#'
#' This function generates the gene-wise plot starting from the VCF input file
#'
#' @param VCF_FILE Path to the input VCF file
#' @param FASTA_FILE Path to the input FASTA file
#' @param ASSEMBLY (optional) which assembly your VCF is (hg38/hg19)
#' @param CENTILE percentile of genes to ... |
68719fe541e6815b22a9c60753b3b613b47d0811e6fc8b02188c15cf35fc84cb | R | 4,843 | 144 | setwd('/work/AnneSophiaHartebrodt#6698/ms-project/')
require(rhdf5filters)
require(rhdf5)
require(Rhdf5lib)
renv::activate('/work/AnneSophiaHartebrodt#6698/ms-project/r-single-cell')
renv::repair()
set.seed(11)
require(rlang)
require(Seurat)
require(data.table)
require(SingleCellExperiment)
require(scds)
require(ggpl... |
f8d1be4d581bf66727a5fb85f063b52de7bd17d1824bb0cf45b14ff45e843010 | R | 4,851 | 157 | library(Seurat)
library(cowplot)
library(SCFunctionsV3)
library(dplyr)
library(tidyr)
library(stringr)
library(tidyverse)
library(DoubletFinder)
library(pheatmap)
library(ggplot2)
sObj_Filtered <- readRDS("rObjects/sObj_Filtered_all.rds")
reference <- readRDS("/data/rachel/Linda_Lako/Retina/fetal_and_embryonic/rObjec... |
3a06fc69eee0f6a0c567cce209f7a38b0e327bef5f809c0fbe4ce415173e755a | R | 4,856 | 195 | ---
title: "`r thetitle`"
output:
html_document:
df_print: paged
---
```{r}
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE)
```
```{r libaries}
library(dplyr)
library(Seurat)
library("ggplot2")
library(monocle3)
library(patchwork)
library(SeuratWrappers)
library(harmony)
library(ggplotify)
sobjT... |
bdd9285d99b6ee0c833954814cfb1795bab68b848d4149b30666a1fd4e6387a0 | R | 4,860 | 132 | #!/usr/bin/env Rscript
options(width=200)
#########################
#
# Loading Data and Libraries
#
#########################
library(optparse)
opt_list <- list(
make_option(c('-t', '--title'), type='character', default='title', help='Title of "diamond" ANOVA (e.g. "X vs. Y").'),
make_option(c('-l', '--left... |
dca3de080a8403cdc85d442c28fd2f36a1244981f9356b9d5e22571287ef25f9 | R | 4,897 | 135 | #!/usr/bin/env Rscript
options(width=200)
#########################
#
# Loading Data and Libraries
#
#########################
args <- commandArgs(trailingOnly=TRUE)
cliargs <- args[-(1:2)]
options('warn'=1) #report non-convergences, etc
library(lme4)
library(languageR)
library(optimx)
library(ggplot2)
#The below s... |
96e9ab4ed3d5edaf6695b664cf017a24bec34d99c55b51866e073fad985b296a | R | 4,902 | 113 | #function to determine the true number of days on a ventilator
#counts only days when intubated
correct_vent_days = function(endpoints, bal_data)
{
require(tidyverse)
#get colnames into standard form
colnames(bal_data) = gsub("\\.", "_", colnames(bal_data)) #I like underscores
colnames(bal_data) = gsub("_+$... |
e6d4520a094c99ddbbf7d7b9cc3d9362c2ebbcd626acf4163f5353125018592c | R | 4,920 | 141 | # Plot the Accuracy and RTS of the Clock Navigation Experiment
library(ggplot2)
library(ggforce)
library(ggdist)
library(gghalves)
library(ggprism)
library(dplyr)
library(rstatix)
library (lsr)
library(lme4)
library(effectsize)
# Clock Navigation Experimet
## Accuarcy -----
ClockNavExp<-read.csv('/Volumes/ROBBOT/Pro... |
19b28e216342421f474937ec81aac724e1b09373832ffb0bf0354dd4c20ab4a3 | R | 4,930 | 186 | ---
title: "`r thetitle`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE)
```
```{r}
library(Spaniel)
library(scran)
library(scater)
library(batchelor)
library(dplyr)
library(Seurat)
library(SCFunctionsV3)
library(cowplot)
library(RColorBrewer)
``... |
1040e79f91ea8b431fe07c85e9ffbbb052828b23724d4954e1cb6bccee4ac650 | R | 4,933 | 183 | ---
title: "Kallisto bustools Processing"
output: html_notebook
---
This notebook is an example of processing a kallsto|bustools run as implemented in the `run-kallisto.nf` workflow.
```{r setup}
library(magrittr)
library(ggplot2)
library(SingleCellExperiment)
```
First we will download a data set to work with from... |
e621752a042317170dea93ff479e195c7175828fd42841dc918ade6d37e79651 | R | 4,937 | 173 | ---
title: "Bap1 Glia Differential Gene Expression"
author: "Kat Beigel (beigelk@chop.edu)"
date: "`r Sys.Date()`"
output: html_document
---
# Glia KO. v. WT by cluster, using Seurat v3.1.2
```{bash, include=FALSE}
wget https://github.com/satijalab/seurat/archive/refs/tags/v3.1.2.tar.gz
```
```{r setup, include=TR... |
ada93ae3df7dfc3bad7b3d4262dae6df1fc2cc9d875ba29d777ee675d136ed68 | R | 4,947 | 144 | library(DESeq2)
library(corto)
library(babelgene)
library(tibble)
library(dplyr)
brainexpmat <- readRDS("data/gtex_Brain_expmat.rds")
descmatchr <- readRDS("data/002_res.rds") |>
as.data.frame() |>
filter(!duplicated(symbol))
rld <- readRDS("data/002_dds_rlog_normalized_agc1v2.rds")
agc1expmat <- ... |
0212fb1b250d655f58cb3e32648293d1784b86410b6996e1d9cdfd3912682374 | R | 4,955 | 88 | # Sncg analysis
# isolate sncg positive cells then do marker gene analysis
library(gprofiler2)
library(purrr)
library(ggrepel)
VlnPlot(data.combined, "Sncg")
sncg <- subset(data.combined, subset = Sncg > 1) %>% subset(subset = seurat_clusters %in% c(16, 7)) %>% subset(subset = Pseudotime > 0.3) # the pseudo... |
f544661c5bba481247699c2b498ffaa35a1fa6525b42ac7b2ccd167e346cc929 | R | 4,957 | 94 | #!/usr/bin/env Rscript
# Libraries
library(Seurat)
library(argparse)
library(ggplot2)
# Create a parser
parser <- ArgumentParser("Parameters for HTODemux Visualisation")
parser$add_argument("--hashtagPath", help = "folder where rds object was saved from the first part of HTODemux")
parser$add_argument("--assay", help ... |
43fbc8ffdbab6dcec91cc87735c441e4ba0eda2905a3f5a0b427a7082d069183 | R | 4,971 | 124 | #########################################@
## Assess conservation of modules - FCD 2b
rm(list=ls())
gc()
library(CoReMo)
library(randomcoloR)
library(TBTools)
library(RTBKM)
library(here)
library(parallel)
library(tidyverse)
library(DT)
#*******************************************************************************... |
a67223add7017781edd3bdd0d8d52fbd3a217c17f45e2efae03b33684d1afb8a | R | 4,975 | 88 | #################
### Load data ###
#################
rm(list=setdiff(ls(),c('params','grp','treatment','injection.site','goi')))
print(grp)
print(treatment)
basedir <- params$basedir
setwd(basedir)
savedir <- paste(params$opdir,'diffmodel/bidirectional_onelm/',paste0(injection.site,collapse='-'),'/',sep='')
dir.creat... |
cda3db3ce265661f23ca478354c17eba058d1b6146b9d45311c69c5936e549aa | R | 4,986 | 128 | ---
title: "HCPD Covbat"
author: "Audrey Luo"
output: html_document
---
# Perform CovBat Harmonization on Connectivity Metrics (GBC, BNC, WNC)
Covbat for GBC, BNC, and WNC can be performed locally using this Rmd file.
```{r setup_covbat, include=FALSE}
library(ComBatFamily)
library(dplyr)
library(mgcv)
atlases_GB... |
b08f76fec3c45732d1276fc23474ab72a10daa944ddb33d9d29a548d7ca7fc30 | R | 4,995 | 203 | ---
title: "QC - ATACseq"
output:
html_document:
df_print: paged
---
```{r global_options, include=FALSE}
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE)
```
```{r, libraries}
library(tidyverse)
library(Signac)
library(Seurat)
library(GenomeInfoDb)
library(EnsDb.Hsapiens.v86)
library(ggplot2... |
b657dc61a8c49137271f42608a3f48febf9acaff1eb8e9c0b8004398cd3160a7 | R | 5,011 | 141 | #!/usr/bin/env Rscript
argoffset = 5 #this is the offset for sys.argv for running an rscript from cli
#########################
#
# Returns only the complexity lines that are shared between input files
# Made for intersecting %.eyemodels
#
#########################
args <- commandArgs(trailingOnly=TRUE)
write(pas... |
58e21791bc415b32839e09cd3706fa087088fe95105d15f6057ddb4c5592775b | R | 5,025 | 96 |
suppressPackageStartupMessages({
library(Seurat)
library(stringr)
library(sctransform)
library(future)
require(scales)
library(RColorBrewer)
library("readxl")
library(dplyr)
library(dendextend)
})
### figure 2 + supplements
# import the Dopaminergic nuclei (mDA) dataset.
sobj <- readRDS("/path/to/... |
46eb4f2569520eca5e56e95ca527f644b3916bc7a8f6676b781e0d2e147e208e | R | 5,028 | 162 | library(beeswarm)
library(BiocParallel)
library(DT)
library(edgeR)
library(ggplot2)
library(here)
library(limma)
library(MASS)
library(openxlsx)
library(plotly)
library(RColorBrewer)
library(readxl)
library(scales)
library(tibble)
library(tximport)
library(reshape2)
library(WGCNA)
library(CoReMo)
library(lme4)
library(... |
668b367f37775f43dd9150bf30445b3f01754b13bac1dad73d208de92812ba00 | R | 5,029 | 143 | # Install tidyverse if you haven't
install.packages("tidyverse")
# Load the tidyverse package
library(tidyverse)
# Load your data
spheredata2 <- read.csv("Figure1-Figuresupplement1_SourceData1.csv")
# Scatter plot between Migration and ISS, colored by Diagnosis
ggplot(spheredata2, aes(x = `ISS`, y = Migrat... |
4dc989688fb6bf55a9a92930fc37ca62c797ded03a9679cccdc24bc57afe577d | R | 5,032 | 117 |
if (!('pracma' %in% installed.packages()[,'Package'])){install.packages("pracma")}; require(pracma)
if (!('psych' %in% installed.packages()[,'Package'])){install.packages("psych")}; require(psych)
#trocar esse nome por matrix_difference
functional_effect <- function(df, to_sample = c('signal', 'group'), resamp = FALS... |
0663d540610be33d3ad69b6825f4152b0e292d6c2cf9c8429f0dc67294b2bc58 | R | 5,047 | 163 | library(beeswarm)
library(BiocParallel)
library(DT)
library(edgeR)
library(ggplot2)
library(here)
library(limma)
library(MASS)
library(openxlsx)
library(plotly)
library(RColorBrewer)
library(readxl)
library(scales)
library(tibble)
library(tximport)
library(reshape2)
library(WGCNA)
library(CoReMo)
library(lme4)
library(... |
31660d01586536d99c89287b331d9d1620b24249fdc0c3238f9bd34c6472135a | R | 5,067 | 129 | ---
title: "HBN Covbat"
author: "Audrey Luo"
output: html_document
---
# Perform CovBat Harmonization on Connectivity Metrics (GBC, BNC, WNC)
Covbat for GBC, BNC, and WNC can be performed locally using this Rmd file.
```{r setup, include=FALSE}
library(ComBatFamily)
library(dplyr)
library(mgcv)
atlases_GBC <- c("s... |
bfdc0575aa5585a15679b2681f709f5cd4ca6b66106f27871dd59328a8b7e10c | R | 5,080 | 117 | #' Make a chromosome-wise analysis
#'
#' This function generates the chromosome-wise plot starting from the VCF input file
#'
#' @param VCF_FILE Path to the input VCF file
#' @param METHOD how to count (and plot) variants for each chromosome ("RAW"=raw number, "LEN"=n/chr-length(ratio), "COD"=n/chr-coding-length(ratio)... |
2ee511382231758d33232b076c631e0db1f96620f4c367064f62dd236ac064a2 | R | 5,081 | 101 | library(msigdbr)
library(fgsea)
library(ggpattern)
library(tidyverse)
# Import metabolite table
metab_orig <- read_delim("data/metabolites_quantitative_values_siagc1_vs_control.csv", delim = ";")
metab <- metab_orig |>
rename_with(~gsub("Err_", "Err.", .x), .cols = starts_with("Err")) |>
rename_with(~pa... |
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