sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
a64a0bf270be24ca2d245eef79bc41e7aa7e07aa4932cbadd12394a224fb350c | R | 8,557 | 252 | #####cell lysates from my BV2 polarization experiment Data Analysis - Aditya Natu - Rangaraju Lab
##################Setting the work space##################
cat("\014")
rm(list = ls())
dev.off()
library(M3C)
setwd("/Set/Your/Working/Dir")
getwd()
options(stringsAsFactors = FALSE)
##################Loading | Inspec... |
3f494f3bc565dd5ab706e198ec0ed603efb3441db43156d8d7f58c81f76ece1c | R | 8,569 | 220 | #Quality control metrics per cluster
#supplementary figure
#run locally on Ayushi's laptop
#load required packages
library(Seurat)
library(dplyr)
library(ggplot2)
library(grid)
library(gridExtra)
library(cowplot)
#set all directory paths
basedir <- "~/Dropbox (Gladstone)/YH_MN02/"
indir <- paste0(basedir,
... |
ee24b8cf7d56e1b87661b1dffa68883a8e3fa6d7e33b1554cbe6994a297c70dd | R | 8,604 | 246 | #Load necessary packages
library(dplyr)
library(lme4)
library(lmerTest)
library(emmeans)
library(DHARMa)
library(tidyverse)
library(MCMCglmm)
# Read data and transform factor variables
dat<- read.delim("Sociability_heritability.txt")
dat[, c("animal", "sex", "rep", "line", "family", "testnr", "genera... |
a83f4741e9ea0fbb264c10587742f90803c17738c5910c6ff9e0f491f1e3fc7a | R | 8,638 | 310 | # This code is to plot the Fig.S6
# a. mean values of brain volume
# b. ranked brain regions according to the brain volume
# c. percentage changes along IQMs
# d. percentage changes along the daylight length
# WANG. 27-Mar-2024
# Remove all objects created before to prevent clash
rm(list = ls())
library(dplyr)
lib... |
0eeb3b92e9cf53f06451f8f917f4d7f683ef7bc60366b5057e469a8f0e58975b | R | 8,639 | 309 | # This code is to plot the Fig.S4
# a. mean values of brain area
# b. ranked brain regions according to the brain area
# c. percentage changes along IQMs
# d. percentage changes along the daylight length
# WANG. 27-Mar-2024
# Remove all objects created before to prevent clash
rm(list = ls())
library(dplyr)
library(... |
161a567ccd96621320daf88645693c350e339203813ab80bc8f9b5cd8346424b | R | 8,643 | 135 | source('scripts/config.R')
source('scripts/aux_functions.R')
source('scripts/plot_functions.R')
source(paste0(main_dir,'results/mm10/hic/config.R'))
source(paste0(main_f,'scripts/main_functions.R'))
source(paste0(main_f,'scripts/aux_functions.R'))
source(paste0(main_f,'scripts/plot_functions.R'))
source(paste0(main_f,... |
0ce99ce00ac83ce2cc03def4a098cb49b13303737d34694ed69d9a7f297c3aff | R | 8,683 | 254 | ---
title: "R Notebook"
output: html_notebook
---
# Set Up
```{r setup}
knitr::opts_knit$set(root.dir = normalizePath("../.."))
```
# Imports
```{r}
library(finalfit)
library(mice)
library(purrr)
library(dplyr)
library(lavaan)
library(ggplot2)
library(gplots)
source('functions/heatmaps.custom.R')
```
# Load Data
```... |
7f66515f0fc603544a76114e49d35f6962667fd6eae5b07563324c4cb84d67a6 | R | 8,688 | 272 | ################################################
# LC analyses: Mulvey et al. (2018) rodent genes
# Lukas Weber, Oct 2022
################################################
# module load conda_R/4.2
# Rscript filename.R
# file location:
# /dcs04/lieber/lcolladotor/pilotLC_LIBD001/locus-c/
library(SpatialExperiment)
l... |
f2a2f4abc1c778bcc80901dfb9e7e0ecc7a90c4af5821804a56624f7191fafb5 | R | 8,690 | 258 | redefineEnumeration <- function(enumerationName, the.function)
{
current.enum <- get(paste(".__E__", enumerationName, sep = ""))
the.names <- names(current.enum)
values <- sapply(the.names, the.function)
if (any(values== -99))
{
warning("Some enumeration names are not recognised\n")
}
... |
28dd434fad223d7899f9e250a0fdc4406aa64060e4fd78df1e67f55ef3b6b182 | R | 8,730 | 298 | # ============= #
# 8. Figure 3 #
# ============= #
## Set up ----
filepath <- ""
setwd(filepath)
## Load libraries ----
library(cowplot)
library(ggstance)
library(grid)
library(tidyverse)
## Format data ----
# List of antigens
antigens <- c(
"HSV1 (1gG)",
"HSV2 (2mgG)",
"VZV (gEgI)",
"EBV (VCA)*",
"CMV ... |
6a43baffbdb543bcfc274e5491e08dacd6e3429c35ec6a46e8a03f150858f2c6 | R | 8,730 | 303 | #############################################
# LC analyses: batch correction using Harmony
# Lukas Weber, Oct 2022
#############################################
# module load conda_R/4.2
# Rscript filename.R
# file location:
# /dcs04/lieber/lcolladotor/pilotLC_LIBD001/locus-c/
library(SpatialExperiment)
library(he... |
4a3eea6758bcf6351bd80585ebd88cefe91fe5bd0e631f791c09a18c6be81bc2 | R | 8,740 | 329 | # install.packages("ggplot2")
# install.packages("tidyverse")
# install.packages("ggsignif")
# install.packages("RVAideMemoire")
# install.packages("lawstat")
# install.packages("ARTool")
# install.packages("rcompanion")
library(ARTool)
library(lawstat)
library(ggplot2)
library(tidyverse)
library(ggsignif... |
4c255db7de261950e50e6b19718d1e820c17763d214ef798130cc8ef3d26a4e2 | R | 8,769 | 299 | LaminarDepth <- function(obj){
#compute laminar depth
obj[['laminar_depth']] <-NA
#compute laminar depth
#create intervals to prject depth values onto.
r<-seq(0, sum(unique(obj$GeneralLayers) != ''),1)
intervals<-lapply(r,FUN=function(x){
if(x != sum(unique(obj$GeneralLayers) != '')){
... |
5a5c0915989357d2b91486ffc8effe6b0845fbf0a44384b5e790e9239200c075 | R | 8,775 | 201 | #' @rdname mapping
#' @title map the vertices between two graph
#' @name map_graph
#' @param cluster_distance a data.frame with unique cluster names as index and columns, which
#' contains a distance adjacent matrix for clusters, representing the dissimilarity between clusters
#' @param color_distance a data.frame with... |
e3d28da060c1c9b506149b2d0f09d0e2927e420e92f99a5359a869a81963936c | R | 8,802 | 182 | ---
title: "Model Evaluation for 410 N2a TRAP Data"
author: "Stephen P Plassmeyer"
date: "7/29/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
source('../../../code/mpra_tools/mpra_tools.r')
```
# Load and Format Data
```{r}
compiled_counts <- fread('../../../data/comp... |
9deeb4aaf2ee0255cfa07eebf681514042078f38f6b6393c6234a526fac1133a | R | 8,805 | 178 | #' Function for building a Symphony reference starting from expression matrix
#'
#' @param exp_ref Reference gene expression (genes by cells)
#' @param metadata_ref Reference cell metadata (cells by attributes)
#' @param vars Reference variables to Harmonize over e.g. c('donor', 'technology')
#' @param K Number of soft... |
32cd9c8a016351aa5e6a0848371d17a5f2d034968de12e8af9491abd3f86e829 | R | 8,825 | 277 |
#analysis of ABIDE QC metrics
library(plyr)
library(dplyr)
library(tidyverse)
library(tidyr)
library(ggpubr)
library(stringr)
library(purrr)
#read in and format FSQC ratings
abide_SB_fsqc <- read.csv('path/to/ratings') #if done in batches, read in multiple csvs and combine with rbind
#rename back from MD5 hash
n... |
9a97b54935ef2c0b9672ef4767737d0f2e8beb915fe07cecbe8e8593c27db296 | R | 8,830 | 187 | required_packages <- c("shiny", "ggplot2", "cowplot", "viridis",
"R6", "reshape2", "gifski", "av",
"RCurl", "httr", "remotes", "shinyWidgets", "gganimate", "expm")
# Install any missing packages
installed <- rownames(installed.packages())
to_install <- setdiff(required_pac... |
a758c9ad04e58a11250222e233d91684541607d26aae716367269354b6efdcb1 | R | 8,834 | 200 | ## 1. Install and load libraries
BiocManager::install("biomaRt")
install.packages("Hmisc")
BiocManager::install("DESeq2")
install.packages("NMF")
BiocManager::install("BiocParallel")
install.packages("knitr")
library("biomaRt")
library("Hmisc")
library("DESeq2")
library("BiocParallel")
register(MulticoreParam(20))
libr... |
332ba0e7e622de849b7eccfc8caf7c5e57a68c09aedc4f19ca57cc95c973193e | R | 8,838 | 248 | library(Seurat)
library(Signac)
library(cicero)
library(tidyverse)
library(data.table)
library(Matrix)
library(monocle3)
library(SeuratWrappers)
######## Create Seurat object for neuronal cells ########
load('obj_all_processed_v3.rda')
## Select only neuronal cells
# 22635 cells in total
neurons <- rownames(obj_al... |
5c00a8199f605ab4da7a85adf61b2be67812a8c7238cf8ba7e92b5d925bcba05 | R | 8,844 | 271 | library(patchwork)
library(tidyverse)
library(ggvenn)
library(data.table)
library(ggforce)
library(fastmatrix)
library(ComplexHeatmap)
library(Matrix)
library(future)
options(future.globals.maxSize = 5e9)
Sys.setenv("_R_USE_PIPEBIND_" = TRUE)
projdir <- here::here()
packdir <- file.path(projdir, "package/R")
import::... |
725ba1a198fc4c7c112e2933458d5ec0865963c6d53bf5c5378dec44e8824b68 | R | 8,863 | 134 | ---
title: "How to use the BIDSconvertR"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{How to use the BIDSconvertR}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
eval = FALSE
)
```
```{r ... |
94c465f35c556dc8f40fdbff2ab8570ca8666871a8a8a7c34b8327ca9cc38b31 | R | 8,882 | 272 | library(patchwork)
library(tidyverse)
library(ggvenn)
library(data.table)
library(ggforce)
library(fastmatrix)
library(ComplexHeatmap)
library(Matrix)
library(future)
options(future.globals.maxSize = 5e9)
Sys.setenv("_R_USE_PIPEBIND_" = TRUE)
projdir <- here::here()
packdir <- file.path(projdir, "package/R")
import::... |
20e6b10d7aca3adadc4950d950de94415ec54489b95d75ec10f6c5d78180762f | R | 8,903 | 159 |
# This code is to plot fig.3
# WANG. 26-Mar-2024
# Remove all objects created before to prevent clashing
rm(list=ls())
library(dplyr)
library(ggseg)
library(ggplot2)
library(tidyr)
library(patchwork)
# Set the working directory to the path where your files are located
setwd("/Users/wang/Desktop/Research_projects/... |
5b9304c4d16c3073f88fd88f7d8cc747ab83bbd287725c83b4c1263907964d02 | R | 8,905 | 255 | ###############################################
# LC snRNA-seq analyses: DE testing comparisons
# Lukas Weber, Jul 2023
###############################################
library(here)
library(dplyr)
library(tidyr)
library(readr)
library(ggplot2)
library(ggVennDiagram)
library(ggrepel)
dir_plots <- here("plots", "sing... |
3a73cd4e5802550ad23f333c4a2f1e2393270c6df3dca0f72510413bd3dfe744 | R | 8,925 | 243 | #Classify Cells:
#This script executes the following stages of analysis
# - Cluster cells iterativly, checking at each stage for DE genes and stopping if clustering criteria fail (see recursive clustering)
# - Validate that clustering with random forest classification, allowing some cells to be reclassificed from o... |
d26f3e08197a16c2f9b0def529706529fab86d7b72653b6964a25078c21ecb4d | R | 8,926 | 231 | library(Seurat)
setwd("/gpfs/data/awebb/dyu20/cell2location/MLAging/data/svz_10x/")
# train_y57o57 <- readRDS('../svz_processed/train_y57o57.RDS')
#####-----------preprocess--------
files <- list.files("./", recursive = FALSE)
object_list <- c()
for (i in (1:length(files))) {
print(files[i])
data <- Read10X(past... |
89c45bc9ce5e0cc3dab7276f437d30efeb05b299e54eab252987096eb465c505 | R | 8,940 | 225 | ---
title: "R Notebook"
output: html_notebook
---
---
```{r}
library(ggplot2)
library(pheatmap)
library(RColorBrewer)
library(tidyverse)
library(ggpubr)
library(ggsignif)
library(openxlsx)
library(viridis)
library(jcolors)
library(stringr)
```
```{r}
setwd("//atlas.uni.lux/users/isabel.rosety/GBA/GCase activity/Plots"... |
78d59c36c8dccd11dbcb36b82d99e7580a3fc8ba12ed640e0c0b5ba94c3ee4fe | R | 8,948 | 126 | #!/usr/bin/R
suppressPackageStartupMessages(library("stringr"))
suppressPackageStartupMessages(library("dplyr"))
suppressPackageStartupMessages(library("tidyverse"))
# * function
concatAllenAnnot1 <- function(allenAnnot, space2="_", slash2="-") {
r <- gsub("/", slash2, allenAnnot)
r <- gsub(" ", space2, r)
r <... |
87a823c321cc60b8607791df5cd0318df37ef91967e8125bd856fa2fa8f31ed6 | R | 8,964 | 286 | library(data.table)
library(Matrix)
library(ggplot2)
library(stringr)
library(ggpubr)
packdir <- file.path(here::here(), "package/R")
import::from(.from = "cembav2env.R", .directory = packdir,
cembav2env, cluSumBySa2,
loadL2Resos)
import::from(.from = "sa2Leiden.R", .directory = packdir,
LeidenSum,
newLeidenSu... |
5ccc61818e16376a117bfd1bfb3f3fd714ee2e042a5e0f168d10c138d5710438 | R | 8,978 | 159 |
# This code is to plot fig.4
# WANG. 26-Mar-2024
# Remove all objects created before to prevent clashing
rm(list=ls())
library(dplyr)
library(ggseg)
library(ggplot2)
library(tidyr)
library(patchwork)
# Set the working directory to the path where your files are located
setwd("/Users/wang/Desktop/Research_projects/... |
bb8880f4e8012528eb80a3011317d03392593200a4b987ec9374f8d3ab511ec8 | R | 8,980 | 171 | ## Author: Youtao Lu <luyoutao@sas.upenn.edu>
## Copyright (c) 2017-2023, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2017-2023, James Eberwine, Perelman School of Medicine, University of Pennsylvania
## All Rights Reserved.
## This Source Code Form is subject to th... |
3a794c371987483d2059bf3025eac1cc8eb251bceed8c1e6d5fb74f55490a311 | R | 8,995 | 153 | #' Converts ICGC Simple Somatic Mutation format file to MAF
#'
#' @description Converts ICGC Simple Somatic Mutation format file to Mutation Annotation Format. Basic fields are converted as per MAF specififcations, rest of the fields are retained as in the input file.
#' Ensemble gene IDs are converted to HGNC Symbols.... |
936d4d1f2af76a57e112e71cc2150ca7f6876d54e5ff943c9309126bdd43466a | R | 9,012 | 236 | require(R.matlab)
rm(list = ls())
ecgpath='/data/p_02186/TMS_ECG2/analyses/EKGdata/'
setwd(ecgpath)
fdipath='/data/p_02186/TMS_ECG2/analyses/EEGtms/FDIdata/'
bigdata=c()
subids=c('VP01', 'VP02', 'VP03', 'VP04', 'VP05','VP06', 'VP07', 'VP08', 'VP09', 'VP10', 'VP11',
'VP12', 'VP13', 'VP14', 'VP15', 'VP16', 'VP17... |
3487e36b555647c7f2a58f9f1adc52ae3dd1ddd3f5b6fe405fac33cb579149cd | R | 9,066 | 196 |
#' @title Volcano plot
#' @description Perform differential expression and create a volcano plot.
#' @param m A matrix of features (e.g. genes) by observations (e.g. cells). Default: NULL
#' @param group The group (of observations) to test. This should be a character vector or a two-level factor in which the first le... |
8c4a8aa8bca9df38030ff3313cc9a66e29e1ae8f9c8a25a95c3a116afed7dccb | R | 9,098 | 242 | library(Signac)
library(Seurat)
library(ggplot2)
library(harmony)
library(dplyr)
options(future.globals.maxSize = 50000 * 1024^2)
load('../data_processed/obj_all.rda')
######## Quality control to filter cells ########
## Filter cells
# RNA-seq: nCount_RNA, percent.mt
# ATAC-seq: nCount_ATAC, nucleosome_signal, TSS... |
8e01f523002f6344600384836210248f87aae7beed19e3d66708975d8a0a3679 | R | 9,102 | 256 | #' Deprecated function: Run EWCE with control for GC content and transcript
#' length.
#'
#' This function is deprecated on account of major changes to EWCE (see EWCE
#' news: \url{https://nathanskene.github.io/EWCE/news/index.html}), which also
#' include development of
#' [MultiEWCE](\url{https://github.com/neurogeno... |
e62d3b2e57adb300cd5659caf1c86d2b26919d8bc0f0a59f1ecdf4bcdcc63282 | R | 9,104 | 159 |
# This code is to plot Fig.2
# WANG. 26-Mar-2024
# Remove all objects created before to prevent clashing
rm(list=ls())
library(dplyr)
library(ggseg)
library(ggplot2)
library(tidyr)
library(patchwork)
# Set the working directory to the path where your files are located
setwd("/Users/wang/Desktop/Research_projects/... |
17c0129e662cee854b791fa14d4e19398c3f259f4c10b41c4c03e42a70ccde2b | R | 9,108 | 323 | # This code is to plot the Fig.S2
# a. mean values of brain thickness
# b. ranked brain regions according to the brain thickness
# c. percentage changes along IQMs
# d. percentage changes along the daylight length
# WANG. 27-Mar-2024
# Remove all objects created before to prevent clash
rm(list = ls())
library(dplyr... |
ffdbb051391969bb66cffe72574807e02dc6a29cf28e98c8a073971832d05efd | R | 9,132 | 288 |
#This script uses P10 scRNAseq data from Mayer et al. 2018. We use a random forest model to classify the data into
#cell types. Model 2 is the one we use for final labels.
#
# Mayer, C., Hafemeister, C., Bandler, R. et al. Developmental diversification of cortical inhibitory interneurons. Nature 555, 457–462 (2018).... |
67f6531c3d4c91840172c7362e0979609a8adab496faedd01eb51ab27a2eb690 | R | 9,143 | 164 | # Exercise input matching and volume calculations using synthetic data only.
# No Bayesian fitting is needed; temporary images and tables are removed on exit.
file_arg <- commandArgs(FALSE)[grepl("^--file=", commandArgs(FALSE))]
test_file <- gsub("~+~", " ", sub("^--file=", "", file_arg[1]), fixed = TRUE)
test_dir <- d... |
3e495432ea1f715e3c2318930a3306b26e2a1de26c702fe97e71202af2c5e9e0 | R | 9,171 | 347 | ---
title: "LC analyses: white matter"
author: "Lukas Weber"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
number_sections: true
toc_depth: 3
toc_float:
collapsed: false
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Introduction
Analyses for LC project: w... |
bdb2cb641d4cef8f9c8146450b0b0dc5cebd6101766605d912ac17868a46fa22 | R | 9,233 | 382 | ---
title: "LC analyses: features per sample"
author: "Lukas Weber"
date: "11/11/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Introduction
Analyses for LC project: features per sample.
```{r}
# to run on cluster
# module load conda_R/4.0
# R
```
## Load da... |
4a6250b0d9d5e5f27f085f3aec806c06d9bc98b122aa55d142736ee75dc024b9 | R | 9,290 | 149 | ## Author: Youtao Lu <luyoutao@sas.upenn.edu>
## Copyright (c) 2017-2023, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2017-2023, James Eberwine, Perelman School of Medicine, University of Pennsylvania
## All Rights Reserved.
## This Source Code Form is subject to th... |
8856a1471e2e5faea8596e84f330a341c645403fdb78608071bcc7fe5cc8e631 | R | 9,321 | 356 | set.seed(321)
# mock up some single-cell, mixture & marker data
sce <- mockSC(ng = 200, nc = 10, nt = 3)
spe <- mockSP(sce)
mgs <- getMGS(sce)
# Create SpatialExperiment
spe1 <- SpatialExperiment::SpatialExperiment(
assay = list(counts = SingleCellExperiment::counts(spe)),
colData = SummarizedExperiment::colDat... |
02ea2450c7f02c7def8a6444c2ba61a94997ea9630bc9f66cb8a8a8514b64194 | R | 9,325 | 196 | library(tidyverse)
# used to determine correlation between interchromosomal Hi-C contacts between OR genes and deposition of histone maks
# Supp Figure 2D
# input is dumped Hi-C contacts (50kb resolution) and file with quantification of ChIP deposition over OR cluster bins at 50kb resolution (using HOMER)
# each OR c... |
9e1f5a536464cae5dbe466d10fd8c186c5678b619e3c4aca4688e07156f9d6ec | R | 9,329 | 241 | ---
title: "R Notebook"
output: html_notebook
---
# This markdown will be used to compare S-A axis rank to prediction accuracy within each PFN
# We'll do this analysis in fsaverage5, since this downsampled version allows us to run spin tests
# Load in the libraries
```{r}
library(gifti)
library(R.matlab)
library(RNi... |
131fa3d6f87141cc1ff7d88a2ec7c23962fd2b1a03d96656be421d96ec4e72f5 | R | 9,331 | 203 | rm(list = ls())
##############################
# Load relevant Libraries ----
##############################
require(ggplot2)
require(stringr)
####################################################
## Read in demographics and data quality measures ##
####################################################
# harms_QA <- re... |
e59183b3a94aeae18ca3248dab5320b4d33b7c6a868b8c14ecce2419daf51b1c | R | 9,343 | 310 | library(tidyverse)
library(rlang)
library(R6)
Sys.setenv("_R_USE_PIPEBIND_" = TRUE)
library(data.table)
library(GenomicRanges)
library(S4Vectors)
library(BSgenome)
rdir <- file.path(here::here(), "package/R")
import::from(.from = "cembav2env.R", .directory = rdir,
cembav2env)
import::from(.from = "peak.R", .direct... |
7947e56c1fbdba5ce711f4ce775190e6c757e83b546690964f3bc705e7089d62 | R | 9,353 | 286 | ---
title: "Gut atlas MENS projection"
output: html_document
---
```{r check_interactive}
if(interactive()){
print(date())
print("This was run interactively. Please run with rmarkdown::render")
}else{
print(paste0("This notebook was compiled on ", date()))
}
```
```{r load, message = F, results= 'hide', warni... |
3d8ecb68bbb8e05673b15a0134bbe39fa6ba7612f3137f6296270ff1c91de28c | R | 9,373 | 379 | ############################################################################
# LC project
# Script for downstream analyses: pseudobulk differential expression testing
# Lukas Weber, May 2022
############################################################################
# adapting code by Leonardo Collado-Torres, Abby Sp... |
5ba45ecd9d5330c7c5a79e04c8c3de1a05801bcc899fb15d74e239d6ca119aa9 | R | 9,373 | 243 | # install.packages("ggplot2")
# install.packages("tidyverse")
# install.packages("ggsignif")
# install.packages("RVAideMemoire")
# install.packages("lawstat")
# install.packages("ARTool")
# install.packages("effsize")
library(ARTool)
library(lawstat)
library(ggplot2)
library(tidyverse)
library(ggsignif)
... |
dc4a08a6b58352cd41519e3e95b90c416591ff887e083610d9908317943aacfe | R | 9,378 | 149 | ## Author: Youtao Lu <luyoutao@sas.upenn.edu>
## Copyright (c) 2017-2023, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2017-2023, James Eberwine, Perelman School of Medicine, University of Pennsylvania
## All Rights Reserved.
## This Source Code Form is subject to th... |
4adeed1caa2d660a4b883eceaaad23e8127812aedbe1084914d9b3e2b2e0caac | R | 9,399 | 275 | ---
title: "Copy number analysis"
author: "Anand Mayakonda"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_depth: 3
toc_float: true
number_sections: true
self_contained: yes
css: corp-styles.css
highlight: pygments
vignette: >
%\VignetteIndexEntry{04: Copy number analysis}
... |
93179e91b7d14942d478af92f8178b393673a57ca045246ad9987fdef31c5da9 | R | 9,410 | 220 | #!/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
main_f <- as.character(args[5])
source(paste0(main_f,'config.R'))
ins_window <- as.numeric(args[1])
k=as.numeric(args[2])
cell=args[3]
near_cis <- as.numeric(args[4])
eigenBin <- as.numeric(args[6])
expand_cis <- as.numeric(args[7])
work_dir <- paste0(main_f,... |
d7c29fba86e098c74a5f9c904945c0d0a6817f932f0491338b69f853fefc4b5c | R | 9,410 | 220 | #!/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
main_f <- as.character(args[5])
source(paste0(main_f,'config.R'))
ins_window <- as.numeric(args[1])
k=as.numeric(args[2])
cell=args[3]
near_cis <- as.numeric(args[4])
eigenBin <- as.numeric(args[6])
expand_cis <- as.numeric(args[7])
work_dir <- paste0(main_f,... |
3ef7151500be90f50d1974fbffb2f24e17fe96c0f16255b1c99dab1f2db67aed | R | 9,414 | 316 | # this script will analyze degs for pathway enrichment
# to run locally:
# SCRATCH1=/mnt/g/scratch
# docker run -it --rm \
# --workdir $HOME \
# -v /mnt/g/reference:$HOME/reference \
# -v /mnt/g/cellranger_atac_counts:$HOME/cellranger_atac_counts \
# -v /mnt/g/cellranger_multi_counts:$HOME/cellranger_multi_counts \
# -... |
089aa6ecdc69ebf017293b6b39f9ad884618187824d1505c53b71daf4a5c8c81 | R | 9,416 | 228 | ---
title: "R Notebook"
output: html_notebook
---
```{r}
library(ggplot2)
library(pheatmap)
library(RColorBrewer)
#display.brewer.all()
library(tidyverse)
library(ggpubr)
library(stringr)
library(ggsignif)
library(openxlsx)
library(viridis)
library(xgboost)
library(data.table)
library(readxl)
library(janitor)
library(... |
72cf805be61bdf88a9f3477b887f26f5563b5aa6cc89c93fc7bb1607211ce564 | R | 9,417 | 208 | library(stats)
library(purrr)
library(dplyr)
load("bulk_dev_RNAseq.RData")
load("Cel_dup_N4Cel.RData")
load("Single_copy_genes.RData")
load("genes_dNdS.RData")
load("genes_Cel_Cinop_dNdS.RData")
load("cluster_1_2_3.RData")
load("PSG.RData")
load("pi_H_pNpS.RData")
load("divergent_genes.RData")
load("Genes19... |
26be5a8fb8fc9973a34ea8193f6a76cb238c392a803319704f8264cc8bf03a52 | R | 9,429 | 457 | # ================== #
# 2. Meta-Analyses #
# ================== #
## Set up ----
filepath <- ""
setwd(filepath)
## Load libraries ----
library(data.table)
library(dplyr)
library(magrittr)
library(metafor)
library(purrr)
## Read in data (serostatus & PBI) ----
nshd <- read.csv("results/nshd_serostatus.csv")
nshd_p... |
d018b576d6ffcf6a518a38969b8fb50af024cf8bf052b347d8ac865ced9dc4bb | R | 9,432 | 376 | ###################################################################
# LC snRNA-seq analyses: secondary clustering of inhibitory neurons
# Lukas Weber, Jul 2023
###################################################################
library(here)
library(SingleCellExperiment)
library(scater)
library(scran)
library(bluster... |
bc0048025baa337ee85d4b3aad6d40ec2ad0e85e7b2057b0f054e86e26cc7f7e | R | 9,434 | 194 | ---
title: "SeparateSamplesCognitionPCA"
output: html_document
date: '2023-02-02'
---
# Load in libraries
```{r}
library(mvtnorm)
library(tableone)
library(parallel)
library(rstan)
library(loo)
library(gamm4)
library(Hmisc)
library(FactoMineR)
library(nFactors)
library(reshape2)
library(psych)
library(data.table)
libr... |
bf55c658c1e8cb0ea4e211e643c24631778910fd20f9a8194b82db4a644157ee | R | 9,436 | 204 | # cortical depth
library(Seurat)
#
merFISH_integrated = readRDS(file = "./RDS/merFISH_final.0827.RDS")
corticalDepth = merFISH_integrated@meta.data
corticalDepth = corticalDepth[!is.na(corticalDepth$ABA_PFC),]
corticalDepth = corticalDepth[corticalDepth$ABA_PFC == "In", ]
ggplot(na.omit(corticalDepth[corticalDepth$Ce... |
0696270c3a1da49131811792445444b2ad8e9e94e2bc5c2053a7a92755ab2856 | R | 9,452 | 247 | #!/usr/bin/env Rscript
# this script will preprocess aggregated snATACseq data
# counted and aggregated by cellranger-atac v2.1 without library normalization
#
# to run locally
# counts=/mnt/g/cellranger_atac_counts
# project=/mnt/g/ckd
# SCRATCH1=/mnt/g/scratch
# docker run -it \
# --memory 100g \
# --cpus 4 \
# --wor... |
6b36c2f9743627d5e0e65ee2b9b462cc32766fba9e61707978738303459da753 | R | 9,456 | 206 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
library(RColorBrewer)
library(rcartocolor)
## main Seurat package snRNA-seq pacakges
library(Seurat)
library(SeuratDisk)
library(future)
## gene expression heatmap plots
library(Nebulosa)
ss <- function(x, pattern, slot = 1,... |
edc88d441f2b252ce91305216d0e1015b3fa44943ec0ac6be339d7e8f9c7e3c7 | R | 9,468 | 341 | ---
title: "Data_prep_for_fastcormics2"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
---
title: "Gene_activity"
author: "Alise"
date: "2019 M09 16"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
#BiocManager::insta... |
fd211efb58b74860ab223e3dd2b2e2974300cb39fb2d925c834ad1e8ea690631 | R | 9,476 | 238 | #############################################################
# Generate Figure 5C,5D,5E and Supplementary Figure 25
# Plot 3D plots of genes in RA
#############################################################
library(plot3D)
library(plotly)
setwd("../")
InputFile <- ".\\Data\\Manuscript_3D_RealData_Pattern\\"... |
78826f91c705d7c17e1abd201a18d2ec75124dc8bad90d124db6c8ada63e1d3d | R | 9,498 | 184 | #' Transform 'HUMAnN' metagenomic results to 'microtable' object.
#'
#' @description
#' Transform 'HUMAnN' metagenomic results to microtable object, reference: Franzosa et al. (2018) <doi:10.1038/s41592-018-0176-y>.
#' @param feature_table file path of 'HUMAnN' output abundance table; Please see the example.
#' @param ... |
dad260cadd406d17ac5ebf1d12eb96b02cac13e32a87d104af3a82e9326ae0be | R | 9,503 | 181 | library(tidyverse)
library(ggplot2)
library(ggpubr)
library(ggridges)
library(patchwork)
### Load performance results for different prior regularization--------------
results_stateLoss_normal <- data.table::fread('../results/PriorLossAnalysis/translation_results_normal_mselike_U2OS.csv')
colnames(results_stateLoss_nor... |
1ea947cdaa1ac1727ab6284d32e3dcf7137ace999bad5042580e2cf35936c9c5 | R | 9,536 | 216 | ## conda activate r4
## packages for data table processing
suppressPackageStartupMessages(library(here))
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(rlang))
suppressPackageStartupMessages(library(optparse))
ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x ... |
303a792f972e6c2fd2cd269e2d818bcfc72b47b94a44e3103c19188a8a7c782e | R | 9,563 | 238 | ### Between -cluster comparison
#For this analyses, we will use the **lme4** package in R to fit generalized
#linear mixed effects models. We are going the model the change in the chance
#(or more formally the odds) of cells from a given mouse belonging to a given
#cluster from the 3 genotypes to the fE4 genotype. T... |
52916c2a063d65d9024acb1c22baa105acc066343bc93bebd42337d96c559fb2 | R | 9,568 | 219 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
## for plot
library(RColorBrewer)
library(rcartocolor)
library(ggpubr)
## statistical analyses
library(tidymodels)
library(lme4)
library(broom.mixed)
library(lmerTest)
ss <- function(x, pattern, slot = 1, ...) {
sapply(st... |
696e11ea7843376d9561fb05ca17b62c7566a20f4dcc4862e4e5808d8abadaf6 | R | 9,576 | 340 | ---
title: "LC analysis pipeline: preprocessing"
author: "Lukas Weber"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
number_sections: true
toc_depth: 2
toc_float:
collapsed: false
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Introduction
Computational an... |
565af4b6244934514b903cfcc253eaeb46de9f3efc902162412203bd8aec7612 | R | 9,577 | 236 | library(stats)
library(purrr)
library(dplyr)
load("bulk_dev_RNAseq.RData")
load("Cel_dup_N4Cel.RData")
load("Single_copy_genes.RData")
load("genes_dNdS.RData")
load("cluster_1_2_3.RData")
load("PSG.RData")
load("pi_H_pNpS.RData")
zscore = function(x){
x = (x - mean(x))/sd(x)
}
mean2 = function(x){
... |
fbc0eccd4282814cc3c114dc148ec3ebb2be1a1957e764f75a931081c9ec6c65 | R | 9,601 | 206 | # This code is to
# 1. calculate the CV of different brain apartments
# 2. add NA to even the session numbers of all subjects
# 3. plot the percentage changes of brain apartments derived from long and normal pipelines
# WANG. 20-Feb-2024
# Remove all objects created before to prevent clustering
rm(list=ls())
# Load... |
b4176298a5fb1284022e1729051efc431907987ef05946cdbf2fd694c443e299 | R | 9,602 | 288 | library(dplyr)
library(stringr)
load("sc_exp.RData")
load("celltype.RData")
load("cluster_1_2_3.RData")
load("cell_type_num.RData")
### all lineage cells
tissue_lin = list()
tissue = c("AB","MS","D","C","E","P4")
for(i in 1:length(tissue)){
lin_2 = filter(cell_num_lineage, lin2 == tissue[i])
lin_type =... |
f4543dd035b4b90ba544eeecb8c5ef4a129902a96fa88cdbbf93d6130487a343 | R | 9,634 | 212 | ### ---------------------------------- ###
# Two important additional data checks
# N. Magielse, 2022
### ---------------------------------- ###
# We are using R version 4.1.0 (2021-05-18)
### ---------------------------------------------------------------------------- ###
# PART I:
# Testing parametric assumpti... |
fad3181b4c33f78067848608b6ce66499f77d742e139fd37ff23c8ba784febf3 | R | 9,634 | 207 | library(tidyverse)
library(gridExtra)
# input is file with quantification of ORs within a specific distance in particle radii for every OR locus (produced by pairwise_distances.sh)
cells = read_tsv(file="../good_cells.txt", col_names=c("cell_name", "sex"))
######## LOAD IN ZONAL ANNOTATION
get_noGI_ORs<- function(fn... |
339cb0f98d5e8ee03a5ab6548a82eed764088e410c4ebee25034887e4f7d00e9 | R | 9,649 | 191 | source('scripts/config.R')
source('scripts/aux_functions.R')
source('scripts/plot_functions.R')
source(paste0(main_dir,'results/hg38/hic/config.R'))
source(paste0(main_f,'scripts/main_functions.R'))
source(paste0(main_f,'scripts/aux_functions.R'))
source(paste0(main_f,'scripts/plot_functions.R'))
source(paste0(main_f,... |
c502495a73506223d21b7da01be1cdff52ad989f1b04197c5e59de83141c0a2b | R | 9,651 | 309 | # Figure 1 neuroPN
# SP | 2022 | sebastian@pechmannlab.net
library(ggplot2)
library(reshape2)
library(ggdendro)
library(cowplot)
setwd("M2/neuro/")
# Figure 1A ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
umap_data <- as.data.frame(read.table("data/processed/umap.txt", header=T))
colnames(umap_data)... |
ed2d5aae28e8123342ae6a134a07717d3343d323b676e91b3304ca1a53e50a8d | R | 9,679 | 286 | ---
title: "Setting3_balloonplot"
author: "Hao Huang"
date: "03/11/2022"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## load packages
```{r}
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(caret)
library(kknn)
library... |
37f4d94724664f85b30a0f43da72bad967d6e9b0e94f2dd624f29ddc36b76795 | R | 9,682 | 245 | # This code is to do analysis on surface area
# 1. calculate, plot, save the CV with ggseg
# 2. calculate, plot, save the percentage change of each region with box plot
# WANG. 26-Feb-2024; updated 01-OCT-2024
# Remove all objects created before to prevent clash
rm(list=ls())
library(dplyr)
library(ggseg)
library(g... |
70082ae5ab3662e7a686c01ea3ea81034e4c0235b74cb06bc4182678b369456e | R | 9,692 | 140 | library(Seurat); library(dplyr); library(ggplot2); library(patchwork);library(RColorBrewer)
library(ktplots); library(pheatmap); library(viridis); library(tidyverse)
library(CellChat)
##################################
### CellChat for 10 cell types ###
##################################
pk <- readRDS("pk_all... |
01d8fedfd9b01000aa4d5afb6e77809eb7ec97a6d85818dbc8b4d107ac5903d0 | R | 9,713 | 242 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
## for maddie
## main Seurat package snRNA-seq pacakges
library(Seurat)
library(SeuratDisk)
library(SeuratWrappers)
library(future)
## packages for QC metric computation
library(SingleCellExperiment)
library(scds)
library(fl... |
b370417c52c39db1cf198f48d60ac32fa89650dd2413847532c71d180f2239ba | R | 9,714 | 317 | ---
title: "Customizing oncoplots"
author: "Anand Mayakonda"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_depth: 3
toc_float: true
number_sections: true
self_contained: yes
css: corp-styles.css
highlight: pygments
vignette: >
%\VignetteIndexEntry{02: Customizing oncoplots... |
548d703313069171da21100b0d467c57b05bd83bd7c37acf23ce41dc64068e7d | R | 9,741 | 257 | library(Seurat)
flexsplit <- function(dat,char){
test=strsplit(as.character(dat),char,fixed=TRUE)
n.obs <- sapply(test, length)
seq.max <- seq_len(max(n.obs))
mat <- t(sapply(test, "[", i = seq.max))
mat
}
subplots <- function(sobj,colName,filename,raster=FALSE){
classes = unique(sobj@meta.data[colName])[,1]
pdf(file... |
3055c69b70097879d5b61753de6fec19fddeddf1d585b5332d4001795208059d | R | 9,749 | 214 | ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = pattern, ...), '[', slot) }
options(stringsAsFactors = F)
## packages for data table processing/plotting
library(tidyverse)
library(rcartocolor)
library(data.table)
library(ggsci)
library(here)
library(readxl)
library(Matrix.utils)
DATADIR=... |
db4d8a17c9d0c196dd53cddb0c7cbd87f1a4eecc98dfd661f5f0f52ebdf1096c | R | 9,753 | 290 | ###############################################
# Setup for MAGMA with LC snRNA-seq populations
# code from Matthew N Tran
# adapted by Lukas Weber, Sep 2022
###############################################
# module load conda_R/4.2
library(rtracklayer)
library(GenomicRanges)
library(liftOver)
library(jaffelab)
libra... |
bb6d7f548d5d54b93ed9112e62c795ba619d0d2c95a2603363938457d4076b2c | R | 9,787 | 298 |
## ----receive input from nextflow----------------------------------------------
#Get parameters
args <- commandArgs(TRUE)
#If running locally, redefine
path<-args[1]
markers<-args[2]
print(markers)
print(class(markers))
#Load standard package list
source(paste0(path,'/bin/auxiliary/DirectoryChecker.R'))
source(pa... |
b720c52e31f9d0768e7d6e46050aa154d42954d9e4b0c7708a22ef319fab1471 | R | 9,788 | 229 | ####Required packages in the following. Please uncomment if you need to install them.
#install.packages('xml2')
#install.packages('neat')
#if (!requireNamespace("BiocManager", quietly = TRUE))
#install.packages("BiocManager")
#BiocManager::install("biomaRt")
#####Description######
#This script runs a diagnostic and ... |
0ee70a1b69bc5beed8d808cddc3d99d4ffcb9799c310936817f5fb8cba81112f | R | 9,803 | 241 | # This code is to do analysis on cortical volume
# 1. calculate, plot, save the CV with ggseg
# 2. calculate, plot, save the percentage change of each region with box plot
# WANG. 26-Feb-2024; updated 01-OCT-2024
# Remove all objects created before to prevent clash
rm(list=ls())
library(dplyr)
library(ggseg)
librar... |
ce95c427a63672ef96d50966ede4b88fc4f46ace7c5b5c268c0ee8f0eca163d5 | R | 9,810 | 181 | # !/usr/bin/env Rscript
## created by Yun Hao @MooreLab 2021
## This script uses visNetwork package to visualize the flow of relevance along VNN paths of interest between query compound, hidden pathway modules, and the outcome of interest
## functions
library(visNetwork);
library(RColorBrewer);
## 0. Input argume... |
01a92f68f647cff654b235fa3f1cd366d3855fb8a638897f0638e65ade33c850 | R | 9,815 | 267 | #' Merge multiple microtable objects into one.
#'
#' @description
#' Merge multiple \code{microtable} objects into one unified \code{microtable} object (e.g. combining 16S bacteria data with ITS fungi data).
#' This is especially useful when performing cross-domain analysis.
#'
#' @param ... two or more \code{microtab... |
5a85f04a9d23dffd38eb0b0b57402d24680b94fe4be785e7ef18e6d5f98575ee | R | 9,816 | 241 | #' Compare two lollipop plots
#' @details Draws lollipop plot for a gene from two cohorts
#' @param m1 first \code{\link{MAF}} object
#' @param m2 second \code{\link{MAF}} object
#' @param gene HGNC symbol for which protein structure to be drawn.
#' @param AACol1 manually specify column name for amino acid changes in m... |
4dc7c6c1e48fdc6ebddd92652283c20ec196f409249965729d59ea06994fd2d8 | R | 9,820 | 207 | dashboard = function(maf, color = NULL, rmOutlier = TRUE, log_conv = FALSE, titv.color = NULL, sfs = statFontSize, fontSize = fs, n = 10, donut = pie, rawcount = TRUE, stat = NULL, titleSize = NULL, barcodes = NULL, barcodeSize = NULL){
if(is.null(color)){
#hard coded color scheme if user doesnt provide any
... |
9bc2c19a7efc7df449b49f5c3ff93f048ede98452af2f08091a2e610b5935fe1 | R | 9,830 | 222 | ########### PLOTTING FUNCTIONS FOR FIXEL PAPER ###############
# This script contains plotting functions to generate scatterplots and age related changes for figures 4 and 5.
#### Load packages ####
library(gratia)
library(dplyr)
library(svglite)
library(cowplot)
library(scales)
library(grr)
#### Define network col... |
c908d5dd91a0d3d55cda79e90e8afd66b5f8ff98c6ad010d67f1ed970b452cd6 | R | 9,835 | 322 | ############################
# LC analyses: nnSVG results
# Lukas Weber, Oct 2022
############################
# module load conda_R/4.2
# Rscript filename.R
# file location:
# /dcs04/lieber/lcolladotor/pilotLC_LIBD001/locus-c/
library(here)
library(SpatialExperiment)
library(dplyr)
library(tidyr)
library(ggplot2)
... |
144dea5bb664819fd178db7bfc53c5ec3e0be63d5075e0da25884246daea30dc | R | 9,839 | 242 | library("readr")
library("ggplot2")
library("modelr")
library("parallel")
library("RColorBrewer")
library("tidyverse")
library("fuzzySim")
library("smplot2")
library("ggrepel")
library("scales")
library("colorspace")
library("ape")
library("geiger")
library("caper")
setwd("your directory")
##########... |
a295322fffadf859a58a573588b85b71e566bd53c720075b3e8e516bd1aaa62e | R | 9,851 | 236 | ### Between -cluster comparison
#For this analyses, we will use the **lme4** package in R to fit generalized
#linear mixed effects models. We are going the model the change in the chance
#(or more formally the odds) of cells from a given mouse belonging to a given
#cluster from the 3 genotypes to the PS19-fE4_Saline... |
26b3ec450396761adcd85a56d6cb90b839b7b2225a60f441475ef4b25f5a907d | R | 9,857 | 234 | #' Subset MAF objects
#'
#' @description Subsets MAF based on given conditions.
#' @param maf an MAF object generated by \code{\link{read.maf}}
#' @param tsb subset by these samples (Tumor Sample Barcodes)
#' @param genes subset by these genes
#' @param fields include only these fields along with necessary fields in th... |
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