sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
a02926b4d6354e322416cea9ef24f64761306918b75af935967bae5ad2e2fa31 | R | 16,195 | 444 |
#Conflict experiment
# 1. Transform and explore data
# 2. Models for the conflict experiment
# 3. Model simplification and comparison
# 4. Differences from 50% for conflict for each species
# 5. Differences in the number of feeding attempts
# 6. Plots
library(plyr)
library(dplyr)
library(sqldf)
libra... |
0fce86995db7e8bd105fc760ada01b0fbbb1eb879b2e2a8a27f6c042c88a2799 | R | 16,258 | 538 | # Created on Fri Jul 30 10:46:06 2021
# Name: glomerulus_ranking.R
# Purpose: Evaluates the distance matrix of the centroid evaluated in
# glomerulus.R evaluated according to the best separation plane
# calculated from the top anti-parallel pairs of posts of a given pre.
# The user can use... |
e1547302a07fc63168f6b32d83c80bc7df4145b43474d0a06ee1e5f695959db2 | R | 16,267 | 410 | ####This is a demo for using the vcf file from 1000G project to simulate individuals, and generate Z-scores used in the main manuscript in the scenarios: 1)functional annotations 2) complex meta-analysis.
###Create a directory
#git clone https://github.com/ZikunY/CARMA.git
#Load libraries
library(hapsim )
library(stri... |
481f47094b067f79f3436781223d7112cdcf50cca6b9f9e5aec096688dd37d5a | R | 16,366 | 560 | ---
title: "MDACC RNASeq DE for Anders lab MelBrM"
author: "Marissa Lee, DCI Bioinformatics"
date: '`r format(Sys.Date(), "%B %d, %Y")`'
output:
html_document:
toc: true
toc_depth: 2
toc_float: true
code_folding: hide
number_sections: true
editor_options:
chunk_output_type: console
---
```{r ... |
665c7bc87ab66250d0f4c11eff7327c40cdde4a2655a7e77fcc5290013577368 | R | 16,448 | 338 | # creates final plots for the PRS GXE project
# grab command line arguments
args = commandArgs(trailingOnly = TRUE)
print("Arguments received:")
print(args)
if(length(args) < 2) {stop("not enough Arguments received")}
baseLoc = args[1]
outputLoc= args[2]
plotName= args[3]
# Debug vars
# baseLoc = "C:/0LocalHPC/PR... |
a1714b42a71b4cf484c38ecef2796230c65a321c00244a92de6ca9d65f9a0b5a | R | 16,471 | 283 | #Exploring the distribution of gene expression values for the Gemma datasets:
#Megan Hagenauer
#7-10-2023
#This pulls up the log2 expression for the dataset (unfiltered):
GSE27532_Expression<-gemma.R::get_dataset_expression("GSE27532")
#Note that these values should probably range between -1 and 12 for RNA-Seq and 4-1... |
801faad5e32258b2a98acab14bd7078c69354b78c6f32b126a03cea8b0d4b458 | R | 16,472 | 301 | rm(list = ls())
library(openxlsx)
library(ggplot2)
library(pheatmap)
library(org.Mm.eg.db)
library(dplyr)
library(CRISPRcleanR)
setwd("\\\\fileserv1/groups/Neuro-Oncology/Clinical Neuro-Oncology/Daniel/SHH MB Screens/Dependency_screens/input")
reads_SMB21 = read.delim('counts_SMB21.txt')
reads_DAOY = read.... |
2e87df9da27082bcbe17a767ccf9333585ea74528666f3bf1af1de8c1b4d43a9 | R | 16,483 | 438 | ---
title: 'Assess BRAF fusion breakpoints in PBTA ancestry cohort'
output:
html_document:
toc: TRUE
toc_float: TRUE
author: Ryan Corbett, Jo Lynne Rokita
date: "2024"
---
This script calculates frequency of BRAF fusion breakpoint types by predicted ancestry in pLGG, BRAF fusion subtype patients in the PBTA ances... |
4870dddef445dcd6f62286affe608268a67e4453b1923f88d04189d5629e9f00 | R | 16,531 | 379 | ---
title: "Quality control for LCLs"
output: html_document
date: 2015-07-10
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
opts_chunk$set(fig.width = 8, fig.height = 8)
```
This is the qualit... |
9a0a1a776365581ac25fd3e360f68e0cf966297798471fbd1c20166b7d13f0f3 | R | 16,535 | 425 | #####################################ExPeCoN study#################################################
# generalized linear mixed models to estimate signal detection theory parameters
# including previous choice effects
# Paradigm 1: block design: stable environment
# 43 participants completed a behavioral and EEG s... |
3391ae75f1d6f48b8c3f19c341178e91733766e4ac64a51e66695fa3a93c91ee | R | 16,732 | 479 | # Add rotation matrices to movement data frame
#'
#' Helper function that takes a movement data frame and adds
#' a new column `R` with the rotation matrices.
#'
#' @param df Movement data frame. Expected columns: `rot_x`, `rot_y`, `rot_z`
#'
#' @return Same df with rotation matrices added
#' @export
#'
#' @examples
ad... |
509c9d131c1b7e0e756bb139ba10deb4d47b0263efa081cd3d35b49c3b514799 | R | 16,766 | 308 | if (!requireNamespace("ggpubr", quietly = TRUE)) install.packages("ggpubr")
if (!requireNamespace("rstatix", quietly = TRUE)) install.packages("rstatix")
if (!requireNamespace("tidyr", quietly = TRUE)) install.packages("tidyr")
if (!requireNamespace("ggprism", quietly = TRUE)) install.packages("ggprism")
if (!requireNa... |
4946c08be0e04b31338273f264d1280c7c66e440958e2f85bd7974c50eeb6685 | R | 17,182 | 531 | #' @export
#' @import methods
#' @import limma
#' @importClassesFrom SummarizedExperiment SummarizedExperiment
#' @import RColorBrewer
#' @import ggplot2
#' @import reshape2
#' @import matrixStats
#' @import dplyr
#' @import tidyr
#' @import RColorBrewer
#' @import factoextra
#' @title limma.diff.expr
#' @description... |
ea4d588773a2a3ef33494793fcb0b4622481432257a71a9f6dd556e77c84aa62 | R | 17,270 | 359 | # This is a code for simulating compartments in single-cell Hi-C data.
# It takes an experimental compartment file and related parameters as input,
# and generates a file containing multiple sets of simulated data.
#
# The main steps of the simulation include:
# 1. Parameter estimation
# 2. Adding differences
# 3. Res... |
a33b64d67b297fd61cd4d04cfe6f74b24a064c62bae0e3f7ff35154a076cb383 | R | 17,360 | 412 | #################################################################################
#
# This is a function to run all SIAMCAT steps
# At the end, the scripts gives a list including SIAMCAT object + auroc
#
#
#################################################################################
siamcat.wf<-function(df, me... |
f44db6066c429a3e7fafaac5c88bd46806a82b8a37d170d1044e5e2199e84245 | R | 17,431 | 500 | ---
title: "DA figures"
author: "SRomano"
output:
html_document:
toc: yes
editor_options:
chunk_output_type: console
---
# Load packages
```{r setup}
library(dplyr)
library(RColorBrewer)
library(ggnewscale)
library(cowplot)
library(ggplot2)
set.seed(9821)
```
# Load the data and format
```{r}
meta<-read... |
5595269c0cfe70d477b76dd8400e0d6486ad14807f6671b77539a878bdd22be1 | R | 17,484 | 525 | ---
title: "Transform and then compute/plot PCA on imputed data"
output: html_notebook
---
```{r load packages}
library(here)
library(ggplot2)
library(ggeasy)
library(reshape2)
library(bestNormalize)
library(parallel)
library(fabricatr)
library(tidyverse)
library(scales)
library(ggrepel)
cl <- makeCluster(8)
library(... |
ebc78bb6224a935337021b46b419d4a7b118025e6e931168dc811ed4fed14ec3 | R | 17,506 | 442 | # T4 morphology and PDs
library(natverse)
library(tidyverse)
library(igraph)
library(RColorBrewer)
library(sf) #intersection
library(deldir)
library(np)
# clean everything up.
rm(list=ls())
source("eyemap_func.R")
# load data ---------------------------------------------------------------
load("data/eyemap.RData"... |
c5faaad9da84e72890d706e24a8edeac932e2408c16dea83e24c6c411c6767dc | R | 17,553 | 503 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
SetSampleIdx_forGenetest_vcfDosage <- function(sample_idx, Ntest) {
invisible(.Call('_SAIGE_SetSampleIdx_forGenetest_vcfDosage', PACKAGE = 'SAIGE', sample_idx, Ntest))
}
setMAFcutoffs <- f... |
389ce07e7cfaf34fb5a7b2e7af987b2ec87cbfaafffaf82512741a9dde918ff9 | R | 17,706 | 406 | #Code for MetaData Extraction for Gemma Datasets
#Topic: Antidepressant effects in the hippocampus
#Megan Hagenauer, June 23, 2023
#####################
#General notes:
#This meta-analysis topic was originally pursued by Erin Hernandez (summer 2022), but after exclusions there were very few datasets left
#I'm going ... |
005d297e2525947ec06a05f81d1774cd8316186b64e0d377b5c33da2e159b047 | R | 17,731 | 447 |
#!/usr/bin/env R
#
# Main code to run S factor optimization.
#
libv <- c("snow", "dplyr", "parallel", "doParallel", "lute", "dplyr")
sapply(libv, library, character.only = T)
#------
# setup
#------
# helper functions
# get series of s cell size factors (THIS SCRIPT, AND A FEW OTHERS)
dfs.series <- function(s.glial... |
c917ce91a64485695f03547cb57bae299385350d479f46fbbd4ab8a0b30f947f | R | 17,829 | 532 | #DEG ANALYSIS
# SETUP
library(Seurat)
library(dplyr)
library(tibble)
library(kableExtra)
library(openxlsx)
library(here)
Idents(SO) <- "class.TAL.l2"
levels(x = SO)
DefaultAssay(SO) <- "RNA"
# DEGs that Define Each Cluster
## Step 1: Set the active identity of the object
Idents(SO) <- "class.TAL.l2"
levels(x = SO)
... |
9e5a6eada06eb055fe97c52af415efce6fe00216473076e876f51dd35d86736f | R | 17,834 | 521 | rm(list = ls())
library(plotly)
library(tidyr)
library(ggrepel)
library(gtools)
library(data.table)
library(gplots)
library(useful)
library(locfit)
library(Matrix)
library(dplyr)
library(ggplot2)
library(Seurat)
library(Rmagic)
library(readr)
library(phateR)
library(viridis)
library(DoubletFinder)
library(harmony)
libr... |
f71bbb8b80239f0af94a4d51d8cb43342a103302e507a14d56eb10d20318e1d7 | R | 17,890 | 469 | ---
title: "Identification of noisy genes"
author: "Po-Yuan Tung"
date: 2015-06-03
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
opts_chunk$set(fig.width = 8, fig.height = 8)
```
## Input
``... |
b01e88c29d149bbe090672e07763a221671ffd6080763e3d49c71cda957cdbf7 | R | 18,028 | 410 | ---
title: "Harris scRNA-seq Ascl1 qNSCs mouse model trajectory analysis"
subtitle: "tradeSeq fitGAM nknots = 6"
author: "Rebecca Johnston and Lachlan Harris"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
html_document:
toc: true
editor_options:
markdown:
wrap: 72
---
```{r original-data-directory... |
3263de8e1ffe01e820ca16b123446ef2beff62d4dd7d325a0a59ce608242c55a | R | 18,053 | 492 | ---
title: "Compare read and molecule counts"
date: 2015-06-11
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
# opts_chunk$set(cache = TRUE)
```
First, compared counts via three methods:
* *... |
34a0c26d8bf629c244ca22238616fcac3ba1ead0486a1c568b9642c01baadf0b | R | 18,120 | 451 | # re-run Figure_0 after this script
# set up Mollweide projection
ucl_rot_tp <- cart2sphZ(ucl_rot_2eye)[,2:3] %>%
as_tibble() %>%
mutate(theta = theta/pi*180, phi = phi/pi*180) %>%
mutate(phi = if_else(phi > 180, phi - 360, phi)) %>%
mutate(phi = - phi) %>% #inside out
as.matrix()
ucl_rot_Mo2 <- Mollweide(uc... |
dba154b8a93ec8fc85a9ca3afa619602ca50bc92500c82d7a97d581cb909bc06 | R | 18,149 | 383 | #Load in ASCL1 samples
#four iterations of normalisation and clutering
#1 - all data, all clusters
#2 - neurogenic lineage
#3 - NSCs
#4 - quiescent NSCs
library(Seurat)
library(tidyverse)
library(gdata)
library(slingshot)
library(RColorBrewer)
library(sctransform)
library(glmGamPoi)
set.seed(1000)
# Read in and creat... |
d9a6413d9516c184fb1515e9ce8e4ba0ced64b818e0a774916c9dd8f4c841df9 | R | 18,229 | 237 | #!/usr/bin/env Rscript
#options(stringsAsFactors=F, scipen = 999)
options(stringsAsFactors=F)
library(SAIGE)
#library(SAIGE, lib.loc="/net/hunt/zhowei/project/imbalancedCaseCtrlMixedModel/Rpackage_SPAGMMAT/installSAIGEFolder/0.44.6.5")
#library(SAIGE, lib.loc="../../install_dir/0.43.1")
#library(SAIGE, lib.loc="../../... |
41886ec1d0f95e6d051b498fcd5edb0020cbc8c1c1f175d8df3b3ef2726471db | R | 18,306 | 407 | ---
title: "PBTA Ancestry Principal Component Survival Analyses"
output:
html_notebook:
toc: TRUE
toc_float: TRUE
author: Ryan Corbett
date: 2024
---
**Purpose:**
Runs survival analysis models within tumor histologies and molecular subtypes by somalier-derived principal component (PC) values
## Setup
##... |
2094d5251d003c2c72c460b6b970093fa617984fdb8872122842512477524940 | R | 18,315 | 396 | # fits a regression based linear models for the PRS GXE project: Cov-only and PRS+Cov
#options(error=traceback)
# grab command line arguments
# options(error=traceback)
args = commandArgs(trailingOnly = TRUE)
print("Arguments received:")
print(args)
if(length(args) < 9) {stop("not enough Arguments received")}
train... |
ac914e774513e1b2e6c9ad6475b2541262a1353c87a9d495b44bf61a48b9bd97 | R | 18,373 | 509 | ---
title: "Cell-to-cell variation analysis: expressed cells"
author: "Joyce Hsiao"
date: 2016-07-01
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
library(knitr)
opts_chunk$set(message = FA... |
18ee5d752c2c88cb36c7303ca6e262a821e13009ae27348559bf10bd2455ca36 | R | 18,375 | 302 | # R script
#
# Generates PNG images for micapipe Quality Check
# micapipe v0.1.5
# Extra functions
#
# Created by RRC on Feb-Apr 2021 (the second year of the pademic)
#
## Collect arguments
args <- commandArgs(TRUE)
## Default setting when no arguments passed
if(length(args) < 1) {
args <- c("--help")
}
## Help sec... |
bce7aa9460aabe0cc30eb93e99349b3e6bc493a393423e959688c21f8450f833 | R | 18,418 | 613 | ---
title: "MDACC RNASeq GSEA for Anders lab MelBrM"
author: "Marissa Lee, DCI Bioinformatics"
date: '`r format(Sys.Date(), "%B %d, %Y")`'
output:
html_document:
toc: true
toc_depth: 2
toc_float: true
code_folding: hide
number_sections: true
editor_options:
chunk_output_type: console
---
```{... |
87912a0dada8ea60c454097e82e5a2b4ff76d49334dd757c5e8c5087804538aa | R | 18,499 | 391 | ---
title: "Tracking down bug in reads to molecules conversion - Attempt 03"
author: "John Blischak"
date: 2015-02-23
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
opts_chunk$set(cache = TRUE)... |
f23abc079daec3ee2d7e6a8eec43c02202f3d1c60a9c131b029723bf32294c79 | R | 18,550 | 417 | #####################################ExPeCoN study#################################################
# generalized linear mixed models to estimate signal detection theory parameters
# including previous choice effects: Brain-behavior modelling
# script produces figure 4 and 5 from Forster et al., 20205
# author: C... |
2e4a15f71be8187fe3ed5aa293d54433b33e77bb3e91f2e8fb462936328e1d0f | R | 18,561 | 466 | #'@export
#'@title PackageDEdata
#'@description Takes in input an mlist, a common_cond and
#'returns a dataframe with combined differential expression data.
#'@param mlist a list containing all MultipleExperiments
#'@param common_cond a list containing mapping between conditions
#'@param annotate a column from the a... |
15e363a3bcd470d3049aee11f7616318752fa8f9dc9c4744dd5d84928cc8cf21 | R | 18,844 | 471 | #'@export
#'@title CalculateDecoupling2
#'@description Takes in input two MultiExperiment, one as to be of type Transcriptome, and the
#'other of type proteomes. It creates a new MultiExperiment with the data coming from the decoupling.
#'the expression matrix is given by the common sample, and it is calculated as ... |
5350ccb3aa158c6d0b2ad94a832876243e0cf96ec7cb6e7bbea45a388675fe79 | R | 18,913 | 498 | SetHighlight_man <- function(
cells.highlight,
cells.all,
sizes.highlight,
cols.highlight,
col.base = 'black',
pt.size = 1
) {
print("this ran")
if (is.character(x = cells.highlight)) {
cells.highlight <- list(cells.highlight)
} else if (is.data.frame(x = cells.highlight) || !is.list(x = cells.hig... |
f6831893ecd8964a4f4d70fedadcbaef54357f063512ef69c3b90c8e126f733f | R | 19,044 | 553 | #' Read EyeLink ASC Files
#'
#' Imports data from EyeLink ASC files into (relatively) tidy data frames for analysis and
#' visualization. Event data and/or raw sample data from the files can be imported, along with
#' information about the tracker hardware and configuration. All data is divided into numbered
#' blocks ... |
ce485be9de6b07b8940dcfd0be95bd42ee867fa20dfe8a3cce463598bc01ec8b | R | 19,194 | 548 | #' @export
#' @import methods
#' @import reshape2
#'
setClass("BundledExperiment",
representation(names="character",
organism="character",
keys = 'character',
exprs_data = "data.frame",
diff_exprs = "list",
... |
f7e47035cfa7e11e500af6f1e660bf5da19d0262781c5602861beb30e5c84c6b | R | 19,194 | 440 | #!/usr/bin/env Rscript
# Run -h for command-line options.
library("testit")
suppressPackageStartupMessages(library("docopt"))
suppressPackageStartupMessages(library("data.table"))
suppressPackageStartupMessages(library("edgeR"))
"Calculate statistics on the subsampled data.
Usage:
subsampler.R [options] <num_cells>... |
b6f3a838296f2300e57986ced632911c51a5158875bf093ea09b70ec24fd125a | R | 19,217 | 330 | ##############################################################
### Goal: Annotate CpGs, DMSs, and DMRs with genes parts
### Author: Janay Fox
### R script
#############################################################
## Set up ##
#install packages
#install.packages("S4Vectors")
#install.packages("IRanges")
#install.p... |
648f52c98a7d5bc1e22ac302b1cdf0dea4416f3a1f0b20cf1c3c3ade7b5ca580 | R | 19,226 | 465 | #'@desc workflow for pre-precess of scRNA-seq dataset from GSE129455
#'@author Yuan
#===============================================================================
# Part I, Basic Analysis with scRNA-seq data using Seurat #
#=========================================================================... |
43304d0d7f0a0421c0936b7c0804fccbc7146d12b4d3acb618d13aadc60163ea | R | 19,253 | 511 | rm(list = ls())
library(plotly)
library(tidyr)
library(ggrepel)
library(gtools)
library(data.table)
library(gplots)
library(useful)
library(locfit)
library(Matrix)
library(dplyr)
library(ggplot2)
library(Seurat)
library(Rmagic)
library(readr)
library(phateR)
library(viridis)
library(DoubletFinder)
library(harmony)
libr... |
c0c5b66d15a4c40e8ccef29e97382af353a86fa173c79419df46edf38bdeb82a | R | 19,338 | 537 | ---
title: "Pseudobulk k2 with and without cell size rescaling, cohort1"
author: "Sean Maden"
date: "2023-09-17"
output:
pdf_document: default
html_document: default
---
```{r setup, include=FALSE}
libv <- c("here", "nlme", "lute", "ggplot2", "gridExtra",
"dplyr", "ggforce", "MultiAssayExperiment",
... |
55a6ddf64d5e1bf805c4e0113456c760828e3bd68b89c6d0960362149858aac5 | R | 19,467 | 516 | rm(list = ls())
library(plotly)
library(tidyr)
library(ggrepel)
library(gtools)
library(data.table)
library(gplots)
library(useful)
library(locfit)
library(Matrix)
library(dplyr)
library(ggplot2)
library(Seurat)
library(Rmagic)
library(readr)
library(phateR)
library(viridis)
library(DoubletFinder)
library(harmony)
libr... |
7c69173b4b9a1d26b908ed0cfe163f5b61f2eff9da07385dbdb5e22d9a4ed429 | R | 19,483 | 394 | ---
title: "Cellular content"
author: "Po-Yuan Tung"
date: 2016-01-25
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
opts_chunk$set(fig.width = 12)
```
Recreate the [Sequencing depth and cellu... |
1daad8d587686e8b52f57d616c15ed1d37eef87354cc1f1c23fb4d05b36a03e8 | R | 19,491 | 515 | rm(list = ls())
library(plotly)
library(tidyr)
library(ggrepel)
library(gtools)
library(data.table)
library(gplots)
library(useful)
library(locfit)
library(Matrix)
library(dplyr)
library(ggplot2)
library(Seurat)
library(Rmagic)
library(readr)
library(phateR)
library(viridis)
library(DoubletFinder)
library(harmony)
libr... |
8bf74a47ea533ca17537e5da12e0b1a472e93f5d363b58172978394000a11300 | R | 19,498 | 438 | #####################################ExPeCoN study#################################################
# generalized linear mixed models to estimate signal detection theory parameters
# including previous choice effects: Brain-behavior modelling
# script produces figure 4 and figure 5 from Forster et al., 2025
# aut... |
f83932902ca04befb70dfdff6128f4e7b5a56148860a13e7fc01baa8bfa8c4fd | R | 19,724 | 676 | ---
title: "LOPIT"
output: html_document
date: '2022-04-06'
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(spatialR)
library(ggplot2)
library(cowplot)
library(figuR)
library(multiomics)
#data dir
dir <- "/wins/ori/Domenico/multiomics_notho/markdown_files/"
knitr::opts_knit$set(root.dir =di... |
9f3947a19134022904e93248d597bfb5c7eaa876bab09a55f4a75e24766430f7 | R | 19,770 | 547 | ---
title: "VarKoder and Skmer all SRA"
output: html_notebook
---
Here we use varKoder and [Skmer](https://github.com/shahab-sarmashghi/Skmer) on all families in SRA and check their accuracies
```{r}
library(tidyverse)
library(future)
library(ggthemes)
```
## Read data
```{r}
varkoder_summary = read_csv('varkoder_q... |
9f03643c19cc6314b9ef6d99b9424ca198d6432ee9fb9a94e24f010c0a641688 | R | 19,797 | 436 | ---
title: "Rootpoint Distribution"
author: "Filipa Torrão & Eugenia Chiappe"
date: "`r Sys.Date()`"
output:
html_document: default
editor_options:
chunk_output_type: console
---
## Load Libraries
```{r}
library(tibble)
library(fafbseg)
#if (!require("devtools")) install.packages("devtools")
#devtools::install_git... |
cf58c5c143f3c37ab55bc454caba44662ef85f851296fc5eb83e1c6ce486983d | R | 19,911 | 159 | ---
bibliography: refs.bib
---
```{r chunk-options, include=FALSE}
source("chunk-options-paper.R")
```
## Results
### Study design and quality control
We collected single cell RNA-seq (scRNA-seq) data from three YRI iPSC lines using the Fluidigm C1 microfluidic system followed by sequencing.
We added ERCC spike-in co... |
52b803e5a462b2b3b9f0301e38c675c231beb17ae3d2b06ea1130c01b80921a2 | R | 20,045 | 531 | #modified igraph plotting code to allow for changes in edge.arrow.width. Edge.arrow.size still not supported
plot.igraph2=function (x, axes = FALSE, add = FALSE, xlim = c(-1, 1), ylim = c(-1,
1), mark.groups = list(), mark.shape = 1/2, mark.col = rainbow(length(mark.groups),
alpha = 0.3), mark.border = r... |
870fa0e52cdf34529668633a13e407fd81e31c66576818c2587e2445df4964a2 | R | 20,081 | 436 | #Sample code for MetaData extraction for Gemma datasets
#Megan Hagenauer, Apr 23 2024
#####################
#General notes: The Gemma Database
#Gemma is a database of curated and re-analyzed gene expression studies
#It currently includes >18,000 re-analyzed gene expression studies (mostly from the Gene Expression Om... |
4e86fce83d615cfd251c7ef7e641e50464e5942956bc8c9143a08e10933238ec | R | 20,236 | 542 | ---
title: "dOccLS GCOR and Occupancy Statistics"
output: html_notebook
editor_options:
chunk_output_type: console
---
This notebook reproduces the statistical analyses and figures from the dOccLS manuscript:
**Section 1 — 5-HT2A receptor occupancy curves**
- Plasma LSD level vs neocortex occupancy: NLS (Michaeli... |
55a6508ad9f767870d8738373327adccd85d547ec4994dc5300eb21018738a4e | R | 20,437 | 497 | rm(list = ls())
library(plotly)
library(tidyr)
library(ggrepel)
library(gtools)
library(data.table)
library(gplots)
library(useful)
library(locfit)
library(Matrix)
library(dplyr)
library(ggplot2)
library(Seurat)
library(Rmagic)
library(readr)
library(phateR)
library(viridis)
library(magrittr)
library(harmony)
library(R... |
916ec177ed65dac79b105c22b549764c8424fd79b05456043466e4e842f8b0bb | R | 20,502 | 432 | diff_node_clust <- function(diff_clus_elem, stats_sum, meas, hem, trk, dir_in, beh_names){
# Graph labels and colors
labels_colors <- graph_labels_colors(meas)
y_label <- labels_colors$y_label
y_ax <- labels_colors$y_ax
y_scal <- labels_colors$y_scal
y_lims <- labels_colors$y_lims
meas_color <- l... |
abf0acd38353ae733e19735f83904cefd652a3e58f51b4231788d9fe0e869cd7 | R | 20,617 | 667 | ---
title: "Network fig"
author: "SRomano"
output:
html_document:
toc: yes
editor_options:
chunk_output_type: console
---
# Load packages
```{r setup}
library(tidyverse)
library(igraph)
```
# Load the relevant rds objects
```{r}
df.sign<-readRDS( "/metaG/R_analyses/Functions/AA_KO/RDS/df.sign.KO.rds")
... |
12624d36cfe884176ed5f44112ca119129859bb5fc42788e255800229d648f4b | R | 20,695 | 658 | # Created on Sun Jul 20 10:31:02 2021
# Name: labula_ranking.R
# Purpose: Evaluates the distance matrix of the weighted centroid evaluated in
# lobula.R evaluated according to the best separation line calculated
# from the top anti-parallel pairs of posts of a given pre.
# Author: Andrea Vaccari (... |
d32dc61785decf297291b512edccdd51d8f58ed0e5d0067fcd1cb994f0adc650 | R | 20,703 | 414 | ---
title: "Sequencing depth and cellular RNA content"
date: 2015-09-30
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
opts_chunk$set(fig.width = 10)
```
## Setup
```{r packages, message=FALS... |
96ac2cf391098bc6c6839e63fe80c9719de066806b7fb809f0488782707a3864 | R | 20,771 | 597 | # ED Fig.1A, ideal optic flows --------------------------------------------------
pt0 <- matrix(ncol = 3) #pts on screen
for (j in seq(0,90,by = 10)) {
for (k in seq(0,180*1,length.out = j %/% 10 *1 + 1)) {
pt0 <- rbind(pt0, c(sin(j/180*pi)*cos((k-90)/180*pi), sin(j/180*pi)*sin((k-90)/180*pi), cos(j/180*pi)))
... |
2432e71e8e57c796dfa4cf43aa1f71ec580ecc2ef03ee308bb85635247059e1a | R | 20,807 | 398 | ##################################################
#For Natalie
#produce the initial tables to upload for SQL
library(stats)
library(WGCNA)
library(reshape2)
allowWGCNAThreads()
setwd('C:/Users/zming/Desktop/GTEx')
load('working_dataset.RData')
GTEx_full=NULL
working_dataset=both
row.names(working_dataset) =... |
ec2d5cf64985842137749355a6cd7e5c25270fa1aee7ba02094e98da963bc97a | R | 20,926 | 452 | ---
title: "PBTA Ancestry Survival Analyses"
output:
html_notebook:
toc: TRUE
toc_float: TRUE
author: Ryan Corbett
date: 2023
---
**Purpose:**
Runs survival analysis models within tumor histologies and molecular subtypes by predicted ancestry, self-reported race, and self-reported ethnicity for PBTA-ances... |
3049e4d379c5b212287894e015efb0b32f3ab8024a0df03822d001744e9e7997 | R | 20,999 | 448 | #code for sup fig 3, panels b-e
#load useful libraries
library(nlme)
library(multcomp)
library(ggplot2)
library(tidyverse)
library(cowplot)
library(patchwork)
library(rstatix)
#stickiness index
SI <- read.csv("C:/Users/mbasn/Dropbox/Manuscript-Basnak/Nat Neurosci Aug 2024 revision/zenodo/Ext_data_fig_3_an... |
320fe357f3bca49fe8bca12962fdcd5d80c9984c97a43a5c1b9ee6a74af2b6d4 | R | 21,081 | 529 | ---
title: "Quality control for iPS cells"
output: html_document
date: '2015-05-27'
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
opts_chunk$set(fig.width = 8, fig.height = 8)
```
## Input
`... |
6e737079a7b88122b53be225f64d3cf1d961dc0dd0e3ffce2e8e480565ffab62 | R | 21,093 | 450 | ## Constructor for mouse object
#_________________________________________________________________________
## Hidden constructor for mouse
new_mouse <- function(data = list(slices = list(),
cell_table = NULL,
normalized_counts = NULL),
... |
486d97c660f8bfaa1eb53d34e4170e2f9f0274c9a763225f2390de9e496c52cf | R | 21,150 | 634 | ---
title: "Cell-to-cell variation analysis: noisy genes among all cells"
author: "Joyce Hsiao"
date: 2016-07-08
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
library(knitr)
opts_chunk$set(me... |
f647735b0e82e8ae81a0b4ae30ef1b40f24c8ba6c83985517787a74dbf952a2d | R | 21,151 | 542 | ## Quality control and pre-processing ##
R # R version 4.4.0
library(Seurat) # Seurat_5.1.0
library(glmGamPoi) # glmGamPoi_1.16.0
library(cluster) # cluster_2.1.6
library(ggplot2) # ggplot2_3.5.1
rm(list = ls())
options(stringsAsFactors = FALSE)
set.seed(1)
options(width=180)
load("./data/thy21_tau_mouse_cortex_scr... |
2d603519e3adf1c1cd995d4de4cd5086a7737f06eb6a2c1b114cd0d5fd7af9be | R | 21,237 | 619 | #' Extract direction of the orientation of a reference object relative to a
#' stationary target (in 2D, XZ) between a `min_time` and `max_time`.
#'
#' You can supply timestamps to `min_time` and `max_time` (e.g timestamp of
#' begin escape) to average over only a period.
#'
#' @param ref_movement A reference movement ... |
fedb7290f4ed7bdb0d9d484982f87d640df4e5bd06fda94698e5daabb9607c16 | R | 21,260 | 700 | ---
title: "MDACC: Investigate autophagy and BRAF relationship"
author: "Marissa Lee, DCI Bioinformatics"
date: '`r format(Sys.Date(), "%B %d, %Y")`'
output:
html_document:
toc: true
toc_depth: 2
toc_float: true
code_folding: hide
number_sections: true
editor_options:
chunk_output_type: consol... |
2a89b880d6cc37cf3a84578d44841f236fbff87984b9e75f97d8dee92060b815 | R | 21,593 | 461 | # This R script runs mass-univariate analysis by parallely fitting
# a given model (in text strings) on multiple outcome variables (Ys) based on the same predictors (Xs),
# and save the statistical outputs (TIDY) as well as model estimations (GLANCE) following Broom style.
# This script could be run using terminal co... |
6dae80df5f390655c7102cb25fcca315bad9681edac940d2d6916f52cc801fdd | R | 21,595 | 561 |
GeneModules <- setRefClass(
Class = "GeneModules",
fields = list(
antler_env = "environment",
lists = "list"
),
methods = list(
initialize = function(
...,
antler_env = new.env(),
lists = list()) {
callSuper(
...,
antler_env = antler_env,
... |
122166ab7130b698219cf16daed821363a5a8b4fa8f28b20818ec388d83d6581 | R | 21,992 | 572 | # matching IMC clusters to snRNASeq clusters + markers weighted (by intensity of marker expression)
# input
# - IMC_markers_expression_in_RNAseq.csv (from snRNAseq)
# - clustered_cells.csv (input files)
# - clusters_labels.csv (metadata)
# - clusters_quartile_matching_score.csv (generated in 8.)
##### 0a. Load libr... |
7a8fbb9565685fefed016026a993ec48adb6479f3e465cd76b0df166b485227b | R | 22,001 | 481 | diff_node_clust <- function(diff_clus_elem, stats_sum, meas, hem, trk, dir_in, beh_names){
# Graph labels and colors
labels_colors <- graph_labels_colors(meas)
y_label <- labels_colors$y_label
y_ax <- labels_colors$y_ax
y_scal <- labels_colors$y_scal
y_lims <- labels_colors$y_lims
meas_color <- l... |
02473c394572e00d50de3613acf9703593cba70a24049079ffabe83fb6dadce5 | R | 22,150 | 878 | ---
title: "Load Data"
output:
pdf_document: default
html_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,message = FALSE,eval = FALSE)
```
## Install package
```{r}
install.packages("",repos = NULL,type = 'source')
```
## Load multiomics package
```{r load package}
librar... |
ed0d34a56e8bd97747a6e33b44673cd5c67ef5979f01bcf2df7f2d3a7a21ceb0 | R | 22,241 | 719 | #' @export
#' @import methods
#' @import reshape2
#' @import ComplexHeatmap
#' @import ggplot2
#' @include Class_MultiExperiment.R
#'@title Volcano
#'@description a function for plotting a simple volcano from a MultiExperiment
#'@param X an object of class MultiExperiment
#'@param cond if you want to select a specif... |
90d48c996047413977b532a0c2d7c258b3e740c3ddca9a9379e38b042a898e0d | R | 22,404 | 495 | ###Here is the R code for processing the temporal transcriptomics of Dehnel's phenomenon
#Reads were preprocessed on Noctillio server, where they were trimmed using fastp and aligned/quantified using Kalisto
#First I want to get the quantification files onto R and convert transcript abundance to gene abundance
#Then I ... |
e517326dbb197ec7098993eff441f992a0c1819f33a8ece533b5838b425d6c2b | R | 22,855 | 486 | ---
title: 'snRNA-seq analysis'
author: "Constance Merdrignac"
output:
BiocStyle::html_document:
highlight: tango
code_folding: hide
toc_float: TRUE
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
---
```{r setup, include=FALSE}
all_times <- list() # store the time for each chunk
knit... |
2eea5858f5098f57cc78b5454beacc7aedae7d78ffed1fc3458bae9fdcebb25b | R | 22,918 | 632 | #Determining which mouse genes align to which rat genes
#This code was updated May 11, 2026
#Gene Ortholog Database:
#Downloaded from Jackson Labs May 11, 2026
#https://www.informatics.jax.org/downloads/reports/HOM_AllOrganism.rpt
#https://www.informatics.jax.org/downloads/reports/MRK_ENSEMBL.rpt
#Another good sourc... |
d6ce14fef8b0db0786b25d1f5494744088ba6e7f4d3dd2f3111eafd4b10dd624 | R | 23,066 | 595 | #Annaka Saffron, summer 2022
#Code for identifying useful datasets:
# Search Terms
# LPS
#
# Bacterial infection
#
# LPS hippocampus
# R code
if(!requireNamespace("devtools", quietly=T)){
install.packages("devtools")
}
devtools:: install_github("PavlidisLab/gemma.R", force=T)
setwd("C:/Users/annak/Documents... |
986c7456dcb61f923de0153c86d14bfe1963ae0701a6a8e568c0541feaf0f67f | R | 23,262 | 646 | # Functions for conducting survival analyses
#
# C. Savonen and SJ Spielman for ALSF - CCDL
#
# 2019, 2022
#
# Attach this package
library(survminer)
# set locale to system default UTF-8 to print "±" in plots
Sys.setlocale("LC_ALL","en_US.UTF-8")
# Magrittr pipe
`%>%` <- dplyr::`%>%`
efs_palette <- c("#E69F01", "#5... |
39cda30dec6b07ae53b0f33d33008d3805023f60f119d47430c6a965e48412c6 | R | 23,344 | 522 | #Questions Olfactory data - each should be repeated for the filtered data at the different cut offs
#1. Is there a difference in the number of choices for lemongrass vs orange scent in the naïve test
#2. Is the choice for the odours different to 50% in the naïve test
#3. Is sex a significant predictor in... |
caa93922f54d0b57abcaad87e22863cc19d1aca8f6076e8cac4ab10ef56d75c3 | R | 23,393 | 704 | # Siwei 30 May 2023
# Siwei 07 Jul 2023
# Siwei 07 Aug 2023
# Yan modified 10 April 2024
# read in Jeff's raw dataset and make baseline correction
# use the 3rd batch data
# write out baseline-corrected data matrix
# init #####
require(zoo)
library(ggplot2)
library(gridExtra)
library(gplots)
library(RColorBrewer)
l... |
283b4f3fa8c2936f2871604b3cd661ee0f9428b47115db9961fb01f55be2e0b7 | R | 23,395 | 440 | #!/usr/bin/env R
# Author: Sean Maden
#
# Main parameters, or dependency objects, for deconvolution framework trials.
# dependencies
libv <- c("lute", "dplyr", "ggplot2", "gridExtra", "SingleCellExperiment",
"SummarizedExperiment", "scran")
sapply(libv, library, character.only = TRUE)
# save path
save.pat... |
4dc78acc5b9bd844ed69284a810de5095f2418a34bab6f2dd556f2b5ee15088b | R | 23,406 | 546 |
library(ggplot2)
library(ggsegDesterieux)
library(ggseg)
library(dplyr)
library(colorspace)
library(ggsegSchaefer)
library(ggpubr)
########################## Schaefer atlas PSD #######################################
library(ggsegSchaefer)
atlas= read.csv('~/Desktop/Schaefer_neuromaps/Schaefer2018_200Parcels_7Netwo... |
5ea120b54ae0be4110a937c0106a9f65aa0ddbdc373cbb853ce1d5cee4c63346 | R | 23,451 | 653 | # Functions for conducting survival analyses
#
# C. Savonen and SJ Spielman for ALSF - CCDL
#
# 2019, 2022
#
# Attach this package
library(survminer)
# set locale to system default UTF-8 to print "±" in plots
Sys.setlocale("LC_ALL","en_US.UTF-8")
# Magrittr pipe
`%>%` <- dplyr::`%>%`
efs_palette <- c("#E69F01", "#5... |
98e05682132ac431fe9533b2b6298575075c9060279eb7e431aac463f98dfbae | R | 23,520 | 661 | #!/usr/bin/env R
# Author: Sean Maden
#
# Experiment functions. To be ported to object classes, eventually.
#
#
#' getRandomExperimentResults
#'
#' Get experiment results from using randomized experiment data.
#'
#'
getRandomExperimentResults <- function(
sampleCount=10, typeCount=3, expressionMean=4,
marker... |
5819ce41c58c29afc259e1b542ce1402bcc7b6492d960954836daeb1b65d7a80 | R | 23,662 | 768 | ---
title: "`dndscv` investigation"
author: "DCI Bioinformatics"
date: '`r format(Sys.Date(), "%B %d, %Y")`'
output:
html_document:
highlight: pygments
code_folding: hide
toc: true
toc_float:
collapsed: true
toc_depth: 4
number_sections: true
---
```{css, echo=FALSE}
.watch-out {
back... |
a84cd593b1fd9524fa4bb3078c8bcb19c1c1acbe7dffed520643e10dc3a770c2 | R | 23,716 | 514 | #Questions Olfactory data - each should be repeated for the filtered data at the different cut offs
#1. Is there a difference in the number of choices for lemongrass vs orange scent in the naïve test
#2. Is the choice for the odours different to 50% in the naïve test
#3. Is sex a significant predictor in... |
77f0bcde25b3480e496c56955238082999443082e3cc63a60425bcf80eafbd83 | R | 23,752 | 626 |
# Allow igraph S3 class to be used as field of CellStateGraph
setOldClass("igraph")
CellStateGraph <- setRefClass(
Class = "CellStateGraph",
fields = list(
antler_env = "environment",
dist_matrix = "matrix",
graph = "igraph",
graph_adjacency_mat... |
3023e723f7056278cdba259087ceebf06d6ffd8d3ff45a773cefb1a1cdf8aab9 | R | 23,759 | 702 | #'@export
#'@title BiophysicalProperties
#'@description Takes in input a protein name, or a list of protein.
#'and a FASTA file. It returns the biophysical properties for that
#'specific set of proteins as a dataframe. ONLY WORKS WITH FASTA
#'FROM SINGLE ORGANISM!! It uses seqinr, to read
#'the fasta file, and Peptid... |
c20ad2c523d7d42be87b092904b08d3fbd5ac290074f63672e2a0084e9ceccce | R | 23,874 | 718 | rm(list = ls())
library(plotly)
library(tidyr)
library(ggrepel)
library(gtools)
library(data.table)
library(gplots)
library(useful)
library(locfit)
library(Matrix)
library(dplyr)
library(ggplot2)
library(Seurat)
library(Rmagic)
library(readr)
library(phateR)
library(viridis)
library(DoubletFinder)
library(harmony)
libr... |
94335edc0108dcf2e69601736b6e07bcb7721a7ec5da4317fe37bbd814768939 | R | 24,162 | 724 | #' @export
#A bunch of useful function for classification purposes, example
#lda, and so on....
#'@title BinaryClassif
#'@description takes in input a dataframe, a the name of the column that
#'contains the measure that you want to use, es "Log2FC" etc etc..
#'and the column in the data.frame that indicates the tra... |
457e05fd1c06ce1651eebccd428623ab20d0917d5f9b511fba0a80777f806df8 | R | 24,314 | 573 | ---
title: "Figure 4"
author: "Sang LIU"
date: "`r Sys.Date()`"
output:
html_document:
code_download: TRUE
theme: united
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)
```
## Load packages
```{r}
library(nat)
library(ggplot2)
library(ggridges)
library(ggpubr)... |
ffd2b4d5e84623eaa7dcd233ab5fd0a3a16e343f3e45a5f35d12142b724dbe8f | R | 24,451 | 414 | #load the dataset
Stim_eval_clean <- read.csv("~/switchdrive/Boris/Recherche/Ambizione/Materials/Stimuli_PA_SB/pilot study/Stim_eval_clean.csv", sep=";")
# load the correspondance table
FeuilleCorrespondance <- read.csv("~/switchdrive/Boris/Recherche/Ambizione/Materials/Stimuli_PA_SB/pilot study/FeuilleCorrespondance.... |
32123d8a5da178d822388117e7df899663dd4538d4b7e2ff31dc139b004a4c5c | R | 24,555 | 479 | ## Global male-specific DEGs - GRN analysis ##
rm(list = ls())
set.seed(1)
load("./results/files/Global_DEGs.RData")
dim(global_gender_spec_genes) # 107 6
table(global_gender_spec_genes$DEG.type)
# female-specific gender-dimorphic gender-shared male-specific
# 1 34 1... |
ae00a225ec5a639df84a9859d6903adedecd6290142c2b85dab047f70ae33b7b | R | 24,667 | 650 | ---
title: "ERCC normalization"
date: 2015-06-15
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
```
Testing different approaches to normalize the data.
For the bulk samples, the data used is ... |
c8d04fcae1260ee5d56c4abd553f4472e6723adfaa609985aee2753c350f365b | R | 24,710 | 789 | ---
title: "PD_drugs"
output: html_document
editor_options:
chunk_output_type: console
---
download metadata from here https://zenodo.org/records/7183678
# Load packages
```{r}
library("readxl")
library("tidyverse")
set.seed(987)
m.w <- read_excel("Supplementary-Tables_Boktor/Supplementary-Tables/Table_S10 Sample-... |
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