sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
65d09a31d21a9e68b82aebf707a29fd99873fe5263fc806e25b7bf26a25a0f75 | R | 6,346 | 134 | # meta-stats for
## a) regional L-R hemisphere differences in T1w and diffusion metrics
## b) age sensitivity
# load packages
library(dplyr)
library(ggplot2)
library(ggpubr)
# load data
#t1_diff = read.csv("/home/max/Documents/Projects/Brain_Age/Hemispheric_Brain_Age/export7/Hemi_T1w_features_diff.csv")
#dmri_diff =... |
5c84921415530042abf05ce18fd3d99be6a4caa3f84a1fc3c7d8ff54953108ed | R | 6,355 | 124 | ontology.name.children <- function(ontology){
# INPUTS:
# ontology: nested list from ABA structure ontology
# OUTPUTS:
# ontology: input ontology list, with children recursively renamed for their acronyms
names(ontology$children) <- sapply(ontology$children, function(x) x$acronym)
ontology$children <... |
cf264b8b4b4fe91652dd00e93bf17502676882cae753d58714160c07d8930f4e | R | 6,360 | 121 | ### Hong X. et al 2023
### produce figure 2
### hongxiaoning@outlook.com
library("ggplot2")
library("ggpubr")
library("ggsci")
library(ggridges)
suppressPackageStartupMessages(library(circlize))
#Figure2a
A_bed = read.table("Aged_brain.bed",header=F)
C_bed = read.table("Young_brain.bed",header=F)
pdf("Figure2a_circo... |
ff453a43c840df087a3e6bfd6f304ae5656cccc20998cb8f57a065e49b387f6b | R | 6,372 | 140 | progress.bar <- function (x, max = 100, startTime, nowTime) {
timePerPercent = difftime(nowTime, startTime, units ="secs") / x
percent <- x / max * 100
cat(sprintf("\r[%-50s] %d%% Est time remaining: %d s",
paste(rep('=', percent / 2), collapse = ''),
floor(percent), floor((max - x) * ... |
60c224246a52f59a595ded8a0dd2df42ebfc4e858252abf20be519dc5cec2e84 | R | 6,382 | 172 | # How to establish the intended GRanges object for counting?
# The following methods are suggested:
# 1. Read MACS2 narrowPeaks or other BED-like files
# 2. Genomic features from packages, e.g., `GenomicFeatures::TxDb`.
# This package provides a set of helper functions to deal with #1.
# To process #2 dat... |
91c67532e1e9b7cf4c9dff924e46cfacf9b05910542203d80cd9f46a723c9bdc | R | 6,385 | 107 | #! /usr/bin/env Rscript
###############################################################################
#
# Except as indicated otherwise, this file is a 'United States Government
# Work', and is released in the public domain.
#
# File 'LICENSE' in this directory (xtr_search) of this distribution
# contains full c... |
f3652072a4fbb554ef81f16e9b476fa4f4eebd2353f46bc4aa4c7ce9088d9858 | R | 6,392 | 226 | ---
title: "Agreement analysis"
author: "Harry Clark and Matt Nolan"
date: "01/11/2023"
output: html_document
---
```{r}
library(tidyverse)
library(broom)
library(lme4)
library(Matrix)
library(lmerTest)
library(emmeans)
library(glmmTMB)
```
# subset the data acccording to trial type
```{r}
agreement_df <- read_rds(f... |
6e9288d8c2615a77aaf2167c7cc6326323c73f347b3e856294c63e347cc81057 | R | 6,398 | 148 | ---
title: "Using UCell with Seurat"
author:
- name: Massimo Andreatta
affiliation: Department of Pathology and Immunology, Faculty of Medicine, University of Geneva, 1206, Geneva, Switzerland; Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
- name: Santiago J. Carmona
affiliation: Department of Path... |
7e35f61af61d9ab4791f8eb590c5c967585ed4eabbc5ddba5da6a9976a12a722 | R | 6,425 | 160 | ---
title: "marker_genes_acroos_Parenchyma"
author: "T Attenborough"
date: "2023-10-17"
output: html_document
---
Load packages
```{r}
library(Seurat) #Seurat v4.0.2
library(dplyr)
library(patchwork)
library(here)
library(ggplot2)
library(clustree)
library(ggrepel)
library(SeuratDisk)
library(ggpubr)
library(RVenn)
li... |
fe4d2aa034ad2fe8abc0c5a681ca6ca1c85c19756d55c1539deda268160fb0e2 | R | 6,439 | 199 | ################ 5_cloneID_assignment.R ###################
## infer clone IDs from sparse matrix ##
library(readr)
library(data.table)
library(dplyr)
library(tibble)
library(igraph)
library(ggplot2)
library(ggrepel)
## function to create network from sparse matrix ##
build_graph_from_sparse_mtx <- function(mtx) {
... |
8cbf8b3a6582491d72cbf5592e3d5e2fb0c8d3fbc9837321eb287c85a99aeb36 | R | 6,486 | 185 | #!/usr/bin/env Rscript
#' Single-cell RNA-seq Processing Pipeline for COVID-19 PBMC Dataset
#' Dataset: Wilk et al., 2020 (PMID: 32514174)
#' Description: Process and analyze single-cell RNA-seq data from COVID-19 patients
#' https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE150728
# 1. Setup and Configuration --... |
9736648b821b70dc006b8d1a1efcf8c482d519fb1e298e25fd2fc3119b6acc81 | R | 6,497 | 154 | ############### SYNOPSIS ###################
### AIM: Generate hsapiens to mmusculus unique orthologs gene mapping (via biomaRt)
### DESCRIPTION
# ...
### OUTPUT:
# ....
### REMARKS:
# ....
### REFERENCE:
# Ensembl biomaRt guide: http://www.ensembl.org/info/data/biomart/biomart_r_package.html
### AUTHOR:
# Pascal... |
2ffadb85eb712752f9e205aa0cefb04b2b731a3ecfd6ec9158d7debf58c43d3c | R | 6,503 | 198 | #!/usr/bin/env Rscript
# This script takes the input reference fasta of the genome and the
# corresponding gtf file, identifies the regions of interest corresponding
# to spliced transcripts and introns, then subsets the genome and gtf for
# only those particular regions, and finally outputs a spliced_intron.txome.f... |
9a64646d2c5c59d3ff291cf6c338604fce68cb030e32a2016624b8beb8c81e6d | R | 6,518 | 132 | #! /usr/bin/env Rscript
rm(list=ls())
library(data.table)
library(ggplot2)
library(factoextra)
library(fastICA)
library(dplyr)
library(tidyr)
library(magick)
Path <- "/Users/Documents/"
setwd(Path)
### Read in Kang preproc files
ensgkey <- fread(paste0(Path,"ensg2symbol.txt"),data.table = F)
expr <- fread(paste0(Pat... |
d6a5c9020fafec71671475a4f319cd3b06e0ad13bce420e80e2a2ead8fe354d6 | R | 6,537 | 126 | library(data.table)
library(dplyr)
library(tidyr)
library(ggplot2)
###### LOAD DATA #######
### 1. Use Watanabe et al 2019 as reference lead SNPs of 480 traits
ref_data <- fread("/Users/elleke/SURFdrive/Projects/MOSTest/Results/Discovery/Pleiotropy/Loci/Watanabe_Table2.txt", header = T,dec = ",")[,1:3]
ref_data$LeadSN... |
1134e0813de0d6684a55e959e678bcabd27f09215b3fd669f3d3d1d302a2f03c | R | 6,551 | 188 |
# Go through all learning unit files and check whether they are ok.
library(tidyverse)
##################################
## EXTRACT RELEVANT INFORMATION ##
##################################
# Identify learning unit files
all_files <- list.files("./category_graph", full.names = TRUE)
learning_unit_files <- all_fil... |
5f19a70d3b3b5194d6fab798fb5ee49f738c37bc9ed2d5f23ea4a7deb29a5312 | R | 6,566 | 152 | ################################################################################
# prepareExplorAna.R 1.0.7 #
# Authors: Patrick Poullet, Stephane Liva, Guillaume Arras (Institut Curie) #
# Contact: myproms@curie.fr ... |
2cc281a365b4321d4adc5a2b8385da85b3c9c1e4da5229812a7e7747d6b1e936 | R | 6,589 | 216 | # perform the SIMLR clustering algorithm
"SIMLR" <- function( X, c, no.dim = NA, k = 10, if.impute = FALSE, normalize = FALSE, cores.ratio = 1 ) {
# set any required parameter to the defaults
if(is.na(no.dim)) {
no.dim = c
}
# check the if.impute parameter
if(if.impute == TRUE) {
... |
99d2f477b4915ab174e1e9a0387bebd2de2098959c3cb610cbda74efb05c7725 | R | 6,592 | 143 | #' Make metadata table with information about the run for each sample and tool combination.
#'
#' @param data_dir Path to the local directory where quants output files are stored.
#' @param tools Tools used to produce the quants output for each sample.
#' Each tool should have a subdirectory within the data_dir and... |
f8ee7925a7fcf58380667a641833eb70daf34fb1f37c8eb4247bd65a03462ed0 | R | 6,611 | 98 | # This script assesses specificity of model to the actual seed site
# accounting for the fact that we are using multiple seed sites
# and there may be a benefit of picking spatially contiguous seed sites
rm(list=setdiff(ls(),c('params','injection.site')))
basedir <- params$basedir
setwd(basedir)
savedir <- paste0(para... |
f00515642837debdcbfafb5343706f5dcb839a7ddeb4d33b3b85b453c09092ea | R | 6,613 | 160 | ---
title: "marker_genes_acroos_Muscle"
author: "T Attenborough"
date: "2023-10-17"
output: html_document
---
Load packages
```{r}
library(Seurat) #Seurat v4.0.2
library(dplyr)
library(patchwork)
library(here)
library(ggplot2)
library(clustree)
library(ggrepel)
library(SeuratDisk)
library(ggpubr)
library(RVenn)
librar... |
ea0997ebe48f3e320d434069f14b91ad352bae914f206db3affb5862d3e9899c | R | 6,647 | 182 | ---
title: "GRCh38 + SARS-CoV-2 + EBV1 genome generation"
output: html_notebook
---
# Setup
## Goals
Generate a hyrbid genome with the following components for scRNA-seq:
1. Human (10X genomics version of GRCh38)
2. SARS-CoV-2 (NC_45512.2)
3. EBV1 / Human gammaherpesvirus 4.1 (NC_007605.1)
##... |
301adf961ddf56d33dc0a1b1799da55fba8cb38686ea0e7985638de00a4be177 | R | 6,675 | 160 |
# Compute the path integration error
library(ggplot2)
library(lme4)
library(ggprism)
# log PI envrionment creation
PathCreation<-read.csv('/Volumes/ROBBOT/Projects/GridMagination/Tables/05_PathIntegration/PathIntegration_Environment.csv')
PathCreation<-PathCreation[,-c(2,5,6,7,8)]
colnames(PathCreation)<-c('Start... |
45643ee8f62eaacbfefbfe66ba66a5275e0668c19ff989a281e2d1237567189b | R | 6,730 | 136 | # Plot summary of PSD features from FOOOF analysis
#
# Vinding, M. C., Eriksson, A., Low, C. M. T., Waldthaler, J., Ferreira, D., Ingvar, M.,
# Svenningsson, P., & Lundqvist, D. (2021). Different features of the cortical sensorimotor
# rhythms are uniquely linked to the severity of specific symptoms in Parkinson's ... |
caafec5af4910c6ea04d0fef740bac587736186db2f0498a1aab6c0f1cb2770f | R | 6,734 | 166 | #!/usr/bin/env Rscript
#
# plot-roc.R <stats TSV> <destination image file> [<comma-separated "aligner" names to include> [title]]
#
# plots a pseudo-ROC that allows the comparison of different alignment methods and their mapping quality calculations
# the format is clarified in the map-sim script, and should be a table... |
c0279e3a7d26c8fd8235ae853de79f7f64ab4c1e4362b2d0f2c970b0e12c9964 | R | 6,738 | 198 | #### code for FigureS6
library(Seurat)
library(ggplot2)
library(sctransform)
library(dplyr)
library(CellChat)
library(patchwork)
library(ComplexHeatmap)
### load the mouse object ###
setwd("~/Downloads/Yi Huang et al objects/")
fHFDcci <- readRDS(file = "fHFDcci.rds")
fLFDcci <- readRDS(file = "fLFDcci.rds")
mHFDcci... |
1a21f74df053a1053f988ffdff32110a31a59a2f138d2d7c5f0e779ba2811cc7 | R | 6,739 | 276 | ---
title: "Compare firing properties across session-level firing modes for stable cells"
author: "Matt Nolan"
date: "01/11/2023"
output: html_document
---
# Setup
```{r}
library(tidyverse)
library(lme4)
library(emmeans)
```
# load data
```{r}
cells_data <- readRDS(file = "cells_df_singular_values.Rda")
cells_data <... |
211cf7fb6aded9a94e9f25bf812d620f9197e9d88180ac6f4303867746bf312f | R | 6,747 | 216 | unit.test <- function(test,pass,fail){
# test: logical expression to evaluate
# pass: error to print if true
# fail: error to print if false
if(test){
print(pass)
} else{print(fail)}
}
fisher.r.to.z <- function(r){
r <- 0.5*(log(1+r) - log(1-r))
return(r)
}
colSD <- function(x){
return(apply(x,2,sd))
}
... |
dc68e100b9be096bb7a762de2b52ef126949c0cd39a8cfa14894bad1f65cb339 | R | 6,757 | 167 | ## This is the script used to make the figures that are found in scpca-docs comparing Alevin-fry to Cell Ranger
## The sces used as input were generated during benchmarking using
## quantifier-comparisons/benchmarking_generate_qc_df.R and saved in data/results
library(magrittr)
library(ggplot2)
library(SingleCellExp... |
ab8297dd9d217b5a0cb695321ea133eb2217d684c4ea4566370d8c3ab3f1555a | R | 6,775 | 190 | ################################################
##### T-Test analysis of Opto hot plate trylick#
################################################
rm(list=ls())
library(reshape2)
library(openxlsx)
library(plyr)
library(pastecs)
library(car)
library(ggplot2)
library(dplyr)
library(effsize)
#create outp... |
17e5effb71852a8100a820a1021932fb70debf9296b2212bc23800684a7e08fb | R | 6,792 | 191 | ###################################################
#T-Test analysis of Opto hot plate T to 1st shake##
###################################################
rm(list=ls())
library(reshape2)
library(openxlsx)
library(plyr)
library(pastecs)
library(car)
library(ggplot2)
library(dplyr)
library(effsize)
#cr... |
11d98bca69f6e958113b3c6ef228127036c7008ef8f7d0ae66104bbf29d3fb50 | R | 6,808 | 167 | library(sp)
library(rgdal)
data=read.csv("FFT_Cleaned_Final.csv")
data$timestamp<-as.POSIXct(as.character(data$timestamp), format="%Y-%m-%d %H:%M:%OS",origin="01-01-1900", tz="UTC")
days_2015=seq(from=as.POSIXct("2015-12-11 10:00:00", format="%Y-%m-%d %H:%M:%OS",origin="01-01-1900", tz="UTC"),
to... |
ee82d1663abcc421670bbd3af3b6a8ddc9666667791e2e7e8015fdf7766426bf | R | 6,809 | 188 | # This script estimates slopes for the (linear and non-linear) relationship between brain asymmetries and age
# created 05.09.2023, updated 1.11.2023, Max Korbmacher (max.korbmacher@gmail.com)
# load packages
library(dplyr)
library(ggplot2)
library(ggpubr)
# load data
L = read.csv("/cluster/projects/p33/users/maxk/UK... |
c5b17f19dd497eaf5e8cf2f13987bb65fa82b8978420bee8d4af5ff41b8f9185 | R | 6,833 | 176 | #!/usr/bin/env Rscript
options(width=200,digits=7)
########################################################
#
# Reusable evaluation containing typical columns
# used in experiments using reading latency as a
# dependent variable with memory and syntactic
# predictors.
#
###############################################... |
df3f1347832e746f4ca48e7fb7b5b9b7813978642ccf5dcda78bb0b917d72e40 | R | 6,837 | 176 | #!/usr/bin/env Rscript
options(width=200,digits=7)
########################################################
#
# Reusable evaluation containing typical columns
# used in experiments using reading latency as a
# dependent variable with memory and syntactic
# predictors.
#
###############################################... |
f6bcd541bd39b5e178c055ab8d1feacc68c9f3323dbc3aaf1ca8bbed44d44f47 | R | 6,838 | 299 | ---
title: "Compare firing properties across session-level firing modes"
author: "Matt Nolan"
date: "01/11/2023"
output: html_document
---
# Setup
```{r}
library(tidyverse)
library(lme4)
library(emmeans)
```
# load data
```{r}
cells_data <- readRDS(file = "cells_df_singular_values.Rda")
cells_data <- filter(cells_dat... |
ae7a8a0527c128e0fc7b0bb2ffc78550f8832963165813e27c5cf7be55f98280 | R | 6,841 | 147 | # Hemispheric brain age plot: beta coefficients by p-values
# Max Korbmacher, 02.06.2022, R version 4.1.2 (2021-11-01)
####### PREP ####
# load library
library(ggrepel)
library(ggpubr)
library(stringr)
# load data
T1 = read.csv("/home/max/Documents/Projects/Brain_Age/Hemispheric_Brain_Age/export7/Hemi_T1w_LI_pvals.c... |
3005f2badb157c953ce8dec0afe2cf7b5f1b6b4fcee7af8b3038b6bc1a995ec2 | R | 6,882 | 163 | library(sp)
library(rgdal)
data=read.csv("FFT_Cleaned_Final.csv")
data$timestamp<-as.POSIXct(as.character(data$timestamp), format="%Y-%m-%d %H:%M:%OS",origin="01-01-1900", tz="UTC")
#Add study day labels
days_2015=seq(from=as.POSIXct("2015-12-11 10:00:00", format="%Y-%m-%d %H:%M:%OS",origin="01-01-1900", tz=... |
8fc86360e00a2d9031018369a2dfbb6b6d0dae070b6ab06094a56dd2b7af1b27 | R | 6,894 | 112 | ## Interpolate nightlights stack to produce 2000-2012 dataset
## https://doi.org/10.3390/rs13050922
library(rgdal)
library(parallel)
source('land1km_functions.R')
in.tifs = list.files("/mnt/lacus/raw/Nightlights_500m/v21", pattern=glob2rx("*.average_masked.dat.tif"), full.names = TRUE, recursive = TRUE)
#in.tifs = lis... |
1d6d9eba3715bf4ff8ea9856b3f06101a5b5fe2cbb16fcaf2c12ea5a7759dea2 | R | 6,914 | 186 | 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... |
83aaa9c971d3d41983e0225172360de0722b11cbfe825260ecc14bb76f501b18 | R | 6,919 | 211 | #!/usr/bin/env Rscript
#' COVID-19 Single-cell RNA-seq Processing Pipeline
#' Dataset: Combes et al., 2021 (PMID: 33494096)
#' Description: Analysis of COVID-19 patient immune responses
#' GEO: GSE163668
# 1. Setup and Configuration -------------------------------------------
suppressPackageMessages({
library(Seura... |
38ad1cd9dfea6a40c906bb93ca823018d427149af739ea83da923cf26096a3d7 | R | 6,975 | 129 | library(mclust)
args = commandArgs(TRUE)
#Prefix of current experiment
currexpt = args[1]
#cellfloor = No. reads to be considered a cell
#Either specify a number or "mclust" to calculate automatically
cellfloor = args[2]
report2 = read.table(paste0(currexpt,".report.txt"),header=T)
bkgd.ind = grep("bkgd",report2$Tag)... |
f18662a287e7d84613faccc2fc9d09a77d5f33d00d20d37caa2397453a17d14a | R | 6,984 | 131 | library(readr)
library(dplyr)
library(ggplot2)
library(stringr)
devtools::load_all('breaktools/')
source("00-utils.R")
promoter_enhancer_deletion = function()
{
dir.create("reports/06-promoter_enhancer_deletion", recursive=T, showWarnings=F)
#
# Read TLX files
#
samples_df = tlx_read_samples("data/htgts_sa... |
d8fe2c7fcef6498857b58027a69c5f42bbb70947ae01fbeda3b153e2a64b6db1 | R | 7,014 | 221 | setwd('/work/AnneSophiaHartebrodt#6698/ms-project/')
require(rhdf5filters)
require(rhdf5)
require(Rhdf5lib)
renv::activate('/work/AnneSophiaHartebrodt#6698/ms-project/r-single-cell')
renv::repair()
set.seed(11)
require(Seurat)
require(data.table)
require(ggplot2)
require(ggrepel)
require(dplyr)
require(tidyr)
require(c... |
16dfdbd2279ad65617aec9b1151f470f10a2e553bdfad82c880abb073311e117 | R | 7,019 | 168 | ---
title: "marker_genes_acroos_tegument"
author: "T Attenborough"
date: "2023-10-17"
output: html_document
---
Load packages
```{r}
library(Seurat) #Seurat v4.0.2
library(dplyr)
library(patchwork)
library(here)
library(ggplot2)
library(clustree)
library(ggrepel)
library(SeuratDisk)
library(ggpubr)
library(RVenn)
libr... |
dd88a8f93aa13d88677c9ae08345ec9f8d39d32480a45699c4ca78e1a186c199 | R | 7,030 | 164 | #############################################################################################
# These codes have been costum designed for brain network analysis of 1-2 time point data
# generally acquired by immunolabelling of activity-dependent proteins (i.e. c-fos, Arc)
#
# Many functions here have been largely based... |
dcf7c7c9af9294d072115e7c0ead74ee5d173a9bd78c56f042ca5eb9040e5226 | R | 7,032 | 185 | ---
title: "220614_LINCS_profile_candidates_all_cancers"
author: "Jennifer Fisher"
date: "6/14/2022"
output:
html_document:
toc: true
theme: united
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# START
Goal/Purpose of operations:
... |
e9318864f07201bff44a6838231e748396cf1195ed36c892c633ed3e8ac49ef0 | R | 7,041 | 144 | #!/usr/bin/env Rscript
options(future.globals.maxSize= 15*850*1024^2 )
suppressMessages(lapply(c( "tidyverse", "ggplot2", "umap", "cowplot", "patchwork"), library, character.only=TRUE))
library(BandNorm)
library(future.apply)
# cat /share/home/zhangjk/data/hsp_genome/genecode_v34_gene_coords.bed \
# | awk -F"\t" '... |
097e137aa0e3badbdb86dc1d1a6abb084514f544f70f8588a5ec1b4711ab4872 | R | 7,053 | 139 | setwd("~/Workspace/DSB_Paper")
library(readr)
library(dplyr)
library(ggplot2)
library(cowplot)
library(stringr)
# library(plyranges)
devtools::load_all('~/Workspace/breaktools/')
plot_libsizes_all = function()
{
samples_df = readr::read_tsv("~/Workspace/Datasets/HTGTS/samples/All_samples.tsv", comment="#") %>%
... |
e6dafa93ba6dd15b5376a28fe81f534fddbe64b83623ccd1bc1bf3d2e0bd6ac8 | R | 7,056 | 187 | source('/home/cesar/Dropbox/R/Network sufficiency/Modular_codes/signed_louvain.R'); # for optimizer
source('/home/cesar/Dropbox/R/Network sufficiency/Modular_codes/modularity_functions.R'); # for modularity matrix func
source('/home/cesar/Dropbox/R/Network sufficiency/Modular_codes/gamma_range.R'); # to calculate g_min... |
f75db1ac37e5809b26aa0b6f3a54b0f9cc463593fafeeab2562c7b96ad598861 | R | 7,084 | 214 | #!/usr/bin/env Rscript
#' COVID-19 Single-cell RNA-seq Processing Pipeline
#' Dataset: Arunachalam et al., 2020 (PMID: 32788292)
#' Description: Systems-level immunological assessment of COVID-19 severity
#' GEO: GSE155673
# 1. Setup and Configuration -------------------------------------------
suppressPackageMessage... |
cf4bc64e5c6d20806d6a051d2f890bb18c5e2049ae318f091b97b274f4381225 | R | 7,102 | 131 | # File for loading and setting up r objects for separate analysis
# PACKAGE LOADING ----
library(Seurat)
library(dplyr)
library(ggplot2)
library(plotly)
library(reticulate)
library(gprofiler2)
# DATA LOADING AND PROCESSING ----
# Load in each object
D9RA <- Read10X("D9RA")
D9BMP <- Read10X("D9BMP")
# ... |
d7333407e2e991c02275d4ecc788ffe2b2256747bfb7cc9ba8ffe99d1c60167e | R | 7,127 | 223 | ---
title: "220608_gbm_CT_PRSIM_UPDATE"
author: "Jennifer Fisher"
date: "6/8/2022"
output:
html_document:
toc: true
theme: united
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# START
Goal/Purpose of operations:
Create comparison plots for disease signatures, enriched pat... |
5c653ddc959679a944ef5ca0566258dd8c114a9931707701a30c9e02255a2ffd | R | 7,129 | 207 |
suppressPackageStartupMessages({
library(Seurat)
library(patchwork)
library(Matrix)
library(ggplot2)
library(dplyr)
library(scales)
library(RColorBrewer)
})
### figure Supplement 8-1
# Mapping and annotating human query dataset with mouse as ref dataset
# ref: mouse intact-only mDA territories
# que... |
61c11b0f2f70223becb7b1655db1b0f5a2b1260ab12ecdb2ac882c24914939a6 | R | 7,208 | 140 | ## import package
.libPaths("~/R/x86_64-pc-linux-gnu-library/4.2")
library(Seurat)
library(ggplot2)
library(ggpubr)
library(sctransform)
library(harmony)
library(dplyr)
library(plyr)
library(viridis)
library(readxl)
library(stringr)
library(DAseq)
## load all the objects
Hypo_integrated <- readRDS("~/Yi_Huang/objects/... |
94e2e17f4da7eb83c620f1fd9bb899e249201509d921c06c2f28a17acacc8483 | R | 7,236 | 208 | #' Create Similarity Network based on Adjusted Weighted Jaccard similarity
#'
#' Determine which is the pairwise similarity between column profiles based on Adjusted Weighted Jaccard similarity.
#' Higher the variability of the same features between two profiles and lower is the similarity.
#' Smaller the variability o... |
0155b21379e5948aae7369eaaca87f1789f19f736e7fc1834b157af29fc37b68 | R | 7,247 | 225 | #!/usr/bin/env Rscript
#' SLE Single-cell RNA-seq Processing Pipeline
#' Dataset: Mistry et al., 2019 (PMID: 31754025)
#' Description: Analysis of SLE patient immune responses using scRNA-seq
#' GEO: GSE142016
# 1. Setup and Configuration -------------------------------------------
suppressPackageMessages({
library... |
2abdd158140fe08774a7afcc1f2445709d8d6f64aac7ada627d825b4f7c486d9 | R | 7,248 | 315 | ---
title: "Monocle pCM RPCs T1 T3 - organoids"
output:
html_document:
df_print: paged
---
```{r global_options, include=FALSE}
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE)
```
```{r, libraries}
library(tidyverse)
library(Signac)
library(Seurat)
library(JASPAR2020)
library(GenomeInfoDb)
li... |
83fa8503684bc7536b87385f10d8dfe8e9724f4e0a20b57ed227983411c8a4c2 | R | 7,266 | 180 | ---
title: "220706_drug_structures"
author: "Jennifer Fisher"
date: "`r Sys.Date()`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# START
Goal/Purpose of operations:
Investigating the similarity of drug candidates' structures. Wanting to address if the candidat... |
6be9c6cee34f2fa7d0cfedefbd8711a35e45841ea2f4999f5bf95e2cde59b8b0 | R | 7,279 | 277 | ---
title: "go_terms_Tegument"
author: "T Attenborough"
date: "2023-10-11"
output: html_document
---
Load packages
```{r}
library(Seurat) #Seurat v4.0.2
library(dplyr)
library(patchwork)
library(here)
library(ggplot2)
library(clustree)
library(ggrepel)
library(SeuratDisk)
library(ggpubr)
library(RVenn)
library(readxl)... |
593ca01202a41fa3fee1413cd44aec0004e95c65691a3499198e00cd5d41b8ea | R | 7,302 | 155 | ## POST-MITOTIC DIFFERENCES OF RGC OUTPUT ##
library(Seurat)
library(tidyverse)
library(patchwork)
library(UpSetR)
library(dplyr)
## read ferret seurat
ferret_seurat <- readRDS("/data/mayerlab/neuhaus/ferret_proj/final_results/03/ferret_seurat_clean_wLineage.rds")
## add broader clustering:
unique(ferret_seurat$pred... |
e92d0820feaae297edd3da5a9b5ccded7f41e3db42817cb4b17eb50da4808e1e | R | 7,344 | 134 | ### This code creates Fig. 2B and D
dev.off()
rm(list=ls())
library(ggplot2)
library(tidyr)
library(gridExtra)
library(ggthemes)
library(qqman)
library(dplyr)
library(stringr)
### FST outlier scans on WGS
Data <- read.table("FSTscan_GATK_10vs10_SA.weir.fst.averagesliding_100SNPs", header = F) #do this for both IT and... |
da957591c590234aa884d41d4525e0f9f4c8ce4307907b41098e355452ff2e7a | R | 7,438 | 242 | ---
title: "go_terms_parenchyma"
author: "T Attenborough"
date: "2023-10-11"
output: html_document
---
Load packages
```{r}
library(Seurat) #Seurat v4.0.2
library(dplyr)
library(patchwork)
library(here)
library(ggplot2)
library(clustree)
library(ggrepel)
library(SeuratDisk)
library(ggpubr)
library(RVenn)
library(readx... |
5999fb871358422aad689ea387a85bcfd56c46cf42bcfa44f7f8f4cc40de5f2e | R | 7,443 | 196 | # This script takes data from S3, imports it using the scpcaTools package, calculates QC metrics,
# and then creates two data frames with per cell and per gene metrics for all samples of interest.
# import libraries
library(magrittr)
library(scater)
library(optparse)
# import aws_copy_samples, make_sce_list, and quan... |
1b236eaf9ba48128ca4fc9dceb2d7a1655ecf32c8ad2dfeaa498a93ad285f991 | R | 7,473 | 190 | #! /usr/bin/env Rscript
###############################################################################
#
# Except as indicated otherwise, this file is a 'United States Government
# Work', and is released in the public domain.
#
# File 'LICENSE' in this directory (xtr_search) of this distribution
# contains full c... |
54d8d4bdd9511b3445dc50bc560284c568d1b50dfdfb756db697c8d8ad355a08 | R | 7,482 | 333 | ---
title: "Monocle T1 T3 cones rods BPs"
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(... |
b82b7def74a2c0b8180fd0a2634be96e3ca0c1cfc96c7843854b72678b61351a | R | 7,483 | 190 | ---
title: "Proportion"
---
## Proportion Change
# Load necessary libraries
library(data.table)
library(dplyr)
library(ggplot2)
library(scales)
# Set working directory
setwd("/data/mayerlab/cmayer/meis2_paper/results")
# Read Seurat objects
E16 <- readRDS("/data/mayerlab/Github/Dvoretskova_et_al_2023-DATASETS/E16.... |
6aa67899005b045ca3ee566d5b5840fa9028dba71925a31d6e7c383b5d49d7c2 | R | 7,498 | 234 | unit.test <- function(test,pass,fail){
# test: logical expression to evaluate
# pass: error to print if true
# fail: error to print if false
if(test){
print(pass)
} else{print(fail)}
}
fisher.r.to.z <- function(r){
r <- 0.5*(log(1+r) - log(1-r))
return(r)
}
colSD <- function(x){
return(apply(x,... |
0a78f3694dad13a8bb6497eb1d7dfc240d30567c479c90c4e9d8b47c912d6000 | R | 7,501 | 248 | ---
title: "go_terms_Muscle"
author: "T Attenborough"
date: "2023-10-11"
output: html_document
---
Load packages
```{r}
library(Seurat) #Seurat v4.0.2
library(dplyr)
library(patchwork)
library(here)
library(ggplot2)
library(clustree)
library(ggrepel)
library(SeuratDisk)
library(ggpubr)
library(RVenn)
library(readxl)
l... |
713f94496fbb1ea569fece2b3d03a2cf4182db042d9bd6a847532da0adb66eed | R | 7,520 | 130 | #!/usr/bin/env Rscript
library(DropletUtils)
library(Seurat)
library(ggplot2)
library(cowplot)
if(!require("cellhashR")){
devtools::install_github(repo = 'bimberlab/cellhashR', ref = 'master', dependencies = TRUE, upgrade = 'always')
library("cellhashR")
}
library(cellhashR)
library(here)
library(dplyr)
libra... |
31a5b1c1eb2e908396bc46e9a422382ee325b931701237600868399dba315dbd | R | 7,522 | 172 | library(base64enc)
library(cifti)
library(dplyr)
library(gifti)
library(magrittr)
library(rjson)
library(stringr)
print("This script 1) creates a list of ciftis to extract timeseries data from, and 2) concatenates timeseries and makes connectivity matrices ")
##################
# Set Directories
##################... |
a3165d629f98df92db3b536a7e1403b0a98aee08402e976432d576125feb2224 | R | 7,522 | 186 | ---
title: "Extract metadata from Seurat to integrate with Loom file for scVelo RNA velocity"
output: html_document
date: "2023-06-12"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
# main
library(tidyverse)
library(Seurat)
library(SeuratDisk)
library(viridis)
library(ggplot2)
library(scales)
libra... |
7f8da421f9fc8aba36aea464743d8342708564416d9f79a2fc2f770be9478f3c | R | 7,534 | 204 | ################################################
##### T-Test analysis of Opto hot plate shakes##
################################################
rm(list=ls())
library(reshape2)
library(openxlsx)
library(plyr)
library(pastecs)
library(car)
library(ggplot2)
library(dplyr)
#create outputdir
output_dir=... |
b4f673c9f83191b56516952bea82812ff65a7d9bea85f930aaa1bf6598f18b85 | R | 7,577 | 247 |
multi_net_measures <- function(G_list, sec_param, FUN = cluster_louvain, list_levels = 1, add_param = NULL,
different_sizes = F, ...){
#
# INPUT:
# G_list A list, or a list of lists, of igraph objects.
# sec_param Optional (NULL, default). Some functions will re... |
f4a992fa52bea7d1e02150159c056e48fc460d9f9c0a3707a28a8b01ee1b23bd | R | 7,584 | 205 | ###################################################
#T-Test analysis of Opto hot plate T to 1st shake##
###################################################
rm(list=ls())
library(reshape2)
library(openxlsx)
library(plyr)
library(pastecs)
library(car)
library(ggplot2)
library(dplyr)
library(effsize)
#cr... |
4899a87522d12e423629a33931cab23f0824ab01e7548dc1e94723ae3c79f2f3 | R | 7,614 | 190 | 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)
library(cowplot)
library(rsvd)
library(Rt... |
ee8ceacbc2e5387e8f8a0356bda7d9f5a3fb9aeba42bc356cbb2cefd46e028f0 | R | 7,615 | 205 | ###################################################
#T-Test analysis of Opto hot plate JumpOrCorrecLick##
###################################################
rm(list=ls())
library(reshape2)
library(openxlsx)
library(plyr)
library(pastecs)
library(car)
library(ggplot2)
library(dplyr)
library(effsize)
#... |
045521e52d9b73efa492777bd4446464487ee4bdaac543d850088c8e13b9364d | R | 7,620 | 164 | ---
title: "Gene signature scoring with UCell"
author:
- name: Massimo Andreatta
affiliation: Department of Pathology and Immunology, Faculty of Medicine, University of Geneva, 1206, Geneva, Switzerland; Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
- name: Santiago J. Carmona
affiliation: Departme... |
00740cbf37a23fd141066a412db4e575d6d27413c9f5613ab5dce0394a457863 | R | 7,647 | 321 | ---
title: "RPCs_T1_T2_AC_HCs_RGCs"
output:
html_document:
df_print: paged
---
```{r}
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE)
```
```{r libaries}
library(dplyr)
library(Seurat)
library("ggplot2")
library(monocle3)
library(patchwork)
library(SeuratWrappers)
library(harmony)
library(ggplot... |
a7f1f47c52f19448c50f7c2fe4d3d36197a8ab54993d6cd6c151e20f100995ea | R | 7,667 | 187 | #!/usr/bin/env Rscript
# plot-pr.R <stats TSV> <destination image file> [<comma-separated "aligner" names to include> [title]]
filename <- commandArgs(TRUE)[2]
# Install required packages
list.of.packages <- c("tidyverse")
if (endsWith(tolower(filename), ".svg")) {
list.of.packages <- append(list.of.packages, "s... |
ed1f6da15c2b22296c0121f13fd598473bcd8f66359141374c77f690606f0048 | R | 7,670 | 183 | ---
title: "221219_recount3_fix"
author: "Jennifer Fisher"
date: '2022-12-19'
output:
html_document:
toc: true
theme: united
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# START
Goal/Purpose of operations:
Due to changes to recount3 server in Dec. 2022, the recount3 data ... |
57bfe2e84f5de089951abf0e3c55b00c466d678e133de3dbb9f23fc8e62799a6 | R | 7,678 | 212 |
# Quadrature fiter analysis EB 4-Fold
library(ggplot2)
library(gridExtra)
library(ggprism)
library(ggpubr)
library(lsr)
# Combined (Left + Right)----
# Load the DF containing the data
EC_Subj<-read.csv('/Volumes/ROBBOT/Projects/GridMagination/Tables/02_QuadratureFilter/EB_CombinedROI_Subj.csv')
EC_Subj$SubjectID<... |
7bc82f5dfcd130f6810f843198523783de53ec4f6b61d4f8ecf79306bcd91ae4 | R | 7,708 | 287 | ---
title: "go_terms_stem"
author: "T Attenborough"
date: "2023-10-11"
output: html_document
---
Load packages
```{r}
library(Seurat) #Seurat v4.0.2
library(dplyr)
library(patchwork)
library(here)
library(ggplot2)
library(clustree)
library(ggrepel)
library(SeuratDisk)
library(ggpubr)
library(RVenn)
library(readxl)
lib... |
2bc8be55f4436e320027d392078591a3effe9b8600334c4373ea6ae6b2753c84 | R | 7,718 | 191 | #' Make a Manhattan plot from a VCF file
#'
#' This function generates the plot starting from the VCF input file
#'
#' @param VCF_FILE Path to the input VCF file
#' @param FASTA_FILE Path to the input FASTA file
#' @param ASSEMBLY (optional) which assembly your VCF is (hg38/hg19)
#' @param GENE (optional) genes to focu... |
7435068600400520695712d70bad89f2d801f76ebedede34eedfffbbab630538 | R | 7,750 | 156 | # Load required libraries
library(dplyr)
library(ggplot2)
library(readr)
library(philentropy)
library(dendextend)
library(UpSetR)
library(ComplexUpset)
# Lineage Barcode distribution between classes.
# Author: May Ho
# Load Seurat object
E16_lin_subset <- readRDS("/data/mayerlab/Github/Dvoretskova_et_al_2023-DATAS... |
3d07da37bb78fb8d1777605d7bf2acb94839769cdbf1473754622fc40db9e151 | R | 7,764 | 232 |
# Quadrature Filter Analysis SC
library(ggplot2)
library(gridExtra)
library(ggprism)
library(ggpubr)
library(lsr)
# Bilateral EC (Left + Right)---
EC_Subj<-read.csv('/Volumes/ROBBOT/Projects/GridMagination/Tables/02_QuadratureFilter/SC_CombinedROI_Subj.csv')
EC_Subj$SubjectID<-factor(EC_Subj$SubjectID)
EC_Subj$Fold... |
f98f2cc844890bc1bab0a531d2bb9636582c38e7284bf4db830b35e4ae0aa5cf | R | 7,766 | 223 | ---
title: "Storing Spatial Transcriptomics Results"
author: "Ally Hawkins for CCDL"
output:
html_notebook:
toc: true
toc_float: true
---
In this notebook we are doing a quick exploration of the use of storing spatial data in a [`SpatialExperiment` object](https://bioconductor.org/packages/release/bioc/vign... |
eec9a6ae77b0988ce35d3a17b5bf5d7bc87585862f703f348dd4d2ad3ef3f695 | R | 7,798 | 274 | ---
title: "AD3 all timepoints, reclustering"
output:
html_document:
df_print: paged
---
```{r global_options, include=FALSE}
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE)
```
```{r libaries}
library(dplyr)
library(Seurat)
library("ggplot2")
library(monocle3)
library(patchwork)
library(SeuratW... |
0d7782905a04bf8fbc8a5fb936245a24e18805821e30ee7aa10d086735e7e13b | R | 7,843 | 283 | #' Save plot
save_my_plot <- function(name,
plt,
type,
h = 12,
asp = 1.618,
path = plots_dir,
format = ".pdf") {
cowplot::save_plot(
filename = here::here(
pa... |
685daf7c707538a22af331e41b95f1518e8a5e1184b96a9ad462aaec0b9a9d77 | R | 7,877 | 167 |
library(dplyr)
library(magrittr)
library(rjson)
source("/cbica/projects/network_replication/manuscript/code/scripts/main_analyses/GAM_functions.R")
##################
# Set Variables
##################
args <- commandArgs(trailingOnly=TRUE)
config_file <- args[1]
##################
# Set Directories
########... |
a3c4ca1a77cebf3f0c97d157ad8dd0a84edb80c6059f38cb12203c9714e43c50 | R | 7,895 | 185 | #
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
# To do ----
# Add orig.ident to the metadata (this will require me to change the indexing on the gene ... |
090f40215bf858643e6deb2e0674d9f25cb13470f1b04a2d8d810d2177778ea5 | R | 7,942 | 241 | ---
title: "Reanalysis of Wang et al"
author: "Pavitra Roychoudhury"
date: "2023-07-11"
output: html_document
editor_options:
chunk_output_type: console
---
Scripts for reanalysis of Wang et al matrices including import, QC, filtering, PCA, TSNE, clustering, and differential expression.
Matrices were dowloaded fro... |
f7d27e594e2bce5cf0c585c5f7e7d39ea1034e7f508b6b4d24c6b021d50d18b2 | R | 7,942 | 170 | #
# Linux packages dependencies
# sudo add-apt-repository ppa:ubuntugis/ppa
# sudo apt-get update
# sudo apt install -y libfontconfig1-dev build-essential checkinstall gdal-bin libgdal-dev
# sudo apt install -y libssl-dev zlib1g-dev libxml2-dev curl libcurl4-openssl-dev libfribidi-dev libharfbuzz-dev libjpeg8-dev libt... |
0544de04743cedcac069bf7d24fe76fcc653f01944d627aa18a4c87a47b91d72 | R | 8,023 | 161 | ---
title: "marker_genes_acroos_Neuron"
author: "T Attenborough"
date: "2023-10-17"
output: html_document
---
Load packages
```{r}
library(Seurat) #Seurat v4.0.2
library(dplyr)
library(patchwork)
library(here)
library(ggplot2)
library(clustree)
library(ggrepel)
library(SeuratDisk)
library(ggpubr)
library(RVenn)
librar... |
3a8951517debdd69ee46706d5f055ed8affda550da2cf4a20bda3e421d64fadb | R | 8,024 | 217 | ## import package
.libPaths("~/R/x86_64-pc-linux-gnu-library/4.2")
library(Seurat)
library(ggplot2)
library(ggpubr)
library(sctransform)
library(harmony)
library(dplyr)
library(plyr)
## load all the objects
Hypo_integrated <- readRDS("~/Yi_Huang/objects/Hypo_integrated.rds")
AgRP_subclusters <- readRDS("~/Yi_Huang/obj... |
7b4ef9c8a55f426e7ce8fb5f6eaf6fe45fa160438efa73811f5ccea2b0c37061 | R | 8,024 | 247 | #!/usr/bin/env Rscript
#' Single-cell RNA-seq Processing Pipeline for COVID-19 and Influenza Dataset
#' Dataset: Lee et al., 2020 (PMID: 32651212)
#' Description: Process and analyze single-cell RNA-seq data comparing COVID-19 and influenza patients
#' GSE149689: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1... |
71f90c93c31f32b54330cd9b747d8cd7a03a279ff6c7d6fd8af46fddbb012e58 | R | 8,030 | 200 | #' Harmonizes the matrix of sample's profiles with respect the network
#'
#' Remove all the features/rows that don't have their name matching with a node inside the network
#'
#' @param counts Numeric matrix with rows matching nodes of the network
#' @param net Adjacency matrix of a network s.t. nodes have same names o... |
028b9d7b1d2129934c9fdafcbeeb353118527e81fe8abe974299deb5cc47ec6d | R | 8,051 | 208 | source('/home/cesar/Dropbox/R/Network sufficiency/Modular_codes/signed_louvain.R')
source('/home/cesar/Dropbox/R/Network sufficiency/Modular_codes/modularity_functions.R');
if (!('doParallel' %in% installed.packages()[,'Package'])){install.packages("doParallel")}; require(doParallel)
#A <- as.matrix(read.csv('/home/ce... |
29663e15c2d4a3a7f1a2e96fb31ade525c1f79f342c45310f409cacfbd4f7ce0 | R | 8,102 | 171 | ### This code also generates fig. S1 and S2
dev.off()
rm(list=ls())
library(ggplot2)
library(RColorBrewer)
library(tidyr)
library(dplyr)
library(openxlsx)
library("rnaturalearth")
library("rnaturalearthdata")
library(ggrepel)
options(scipen=999)
library(gridExtra)
library(conStruct)
#load all necessary input data fi... |
90c30f31caa05400bfa82d508aea2b93ea86610f82de95abb3c0e29903474b5a | R | 8,103 | 230 | if (!('igraph' %in% installed.packages()[,'Package'])){install.packages("igraph")}; require(igraph)
if (!('psych' %in% installed.packages()[,'Package'])){install.packages("psych")}; require(psych)
signed_louvain <- function(g, gamma = NULL, weighted = T, comm = NULL, B = NULL, seed = NULL,
mod ... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.