sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
1d9e5332de22242d37101e1c0056511bbbfd5591a440aa8c3fbc31bcc515ae66 | R | 5,992 | 163 | #' Update plot data with a column that list all the represented tips and duplicates sequences, so this can be linked to the metadata
#'
#' This function updates the plot data by creating a new column where all the represented tips are stored in a list.
#' It takes the original plot data, the column name for the new dat... |
30beb71d2026aacbb8263ab90f07e2e98e1175390b61bbd16cb41f81fc91d232 | R | 5,998 | 185 | #' (From Seurat) logs a command
#'
#' Logs a command run, storing the name, timestamp, and argument list. Stores in
#' the Seurat object
#' @param object Name of Seurat object
#'
#' @return returns the Seurat object with command stored
#'
#' @export
LogSeuratCommand <- function(object, return.command = FALSE) {
time... |
2f40922f73393f068da219ba71c91c7c2ff51cb1cee7571aa12c1422d7e569ef | R | 6,000 | 257 | # Summary -----------------------------------------------------------------
# Run parcel-wise ANOVAs to detect differences in ISC between movies
# Schaefer 2018 atlas: 300 parcels, 17 networks
# Packages ----------------------------------------------------------------
library(tidyverse)
library(ggplot2)
library(rst... |
6a9434c5e9e7c4474a1f31cde15d8c0d666899172555fcfb2213f08ded5e005a | R | 6,010 | 198 | #' @name DEPRECATED-spc.bin
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Wavelength binning
#'
#'
#' @description
#'
#' These \pkg{hyperSpec} functions are **deprecated** and not maintained any
#' more. You should not use these.
#' Currently they are present due to back-compatibility reasons and will be
#... |
fa6ad650d302bd409b1ee8b9088cfa282185b3c04fb965def0ac7969312c49b0 | R | 6,024 | 121 | library(ggplot2)
library(stringr)
library(ggridges)
## density basename and alternative base
densBase <- 'debug_est_alt'
altBase <- 'C'
## parse density data
densDat <- read.table(paste0(densBase, '.alternate_density.txt'), header=TRUE)
standardDensDat <- read.table(paste0(densBase, '.control_density.txt'), header=... |
f9ad7dc3b0507079d3a5347c801e9e795397f8789371cbb46b753bc758d380ec | R | 6,029 | 187 | #' @name DEPRECATED-spc.NA.approx
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Impute missing data points
#'
#' @description
#' These \pkg{hyperSpec} functions are **deprecated** and not maintained any
#' more. You should not use these.
#' Currently they are present due to back-compatibility reasons and w... |
516c7f1deafd3834225119f0945a2b8b66258d4f7c9d53a175a51a6d7a1bd653 | R | 6,030 | 149 |
library(tidyverse)
beh = 'Age'
f = 'ridgeCV_zscore_averaged-source_Schaefer400x17_WM+CSF+GS_hcpaging_695_zscored-beh_interview_age_wnoise_all.csv'
plt_out = '/home/mgell/Work/reliability/plots/simulation_res/'
d = read_csv(paste0('/home/mgell/Work/reliability/input/',f))
d$test_MAE = d$test_MAE*-1
empirical = d[1... |
363da956380cf5f4c76102d883c3f91f7f85d283ab7561ecdac2bf2e447206a7 | R | 6,043 | 176 | #!/usr/bin/Rscript
# Clear workspace
rm(list = ls())
# Load libraries
if(!require(emmeans)) install.packages("emmeans"); library(emmeans)
if(!require(ggplot2)) install.packages("ggplot2"); library(ggplot2)
if(!require(ggpubr)) install.packages("ggpubr"); library(ggpubr)
if(!require(gridExtra)) install.packages("gridE... |
bb8a47de6ce172832f26b85d9f0cdcb2fd3d62c4b27a08e656449aa9673f9079 | R | 6,049 | 172 | # File: hERG_fitting.R
# Author: Kelly Chang
# Date: Oct 2017
# Version: 1.0
#
# Description: R script to fit hERG drug binding model parameters to Milnes
# protocol data. The drug data to be fitted must be specified
# with "-d DRUG", where "DRUG" indicates the p... |
59362c250c613be70dee1808a6736a9c7351afd849eecfb4fff152c74dc338c5 | R | 6,068 | 275 | library(purrr)
library(dplyr)
library(vctrs)
library(tidyr)
library(ggplot2)
library(tibble)
library(data.table)
# Will be the dataset "wang" or "pbmc"
dataset <- commandArgs(trailingOnly = TRUE)[1]
output_path <- "output"
source("../utils/0-indices.R")
gene_indices <- read_gene_indices(sprintf("data/%s-gene-indices... |
5764d7873304b2d60e8da8a08a5d333c97df3f72d8c8fc07bb0d7ef4606e08cb | R | 6,102 | 170 | # Filename: iidp_regression.R
# Date: 08-11-2023
library("tidyverse")
library("gtsummary")
library("broom")
library("MASS")
# upload data:
iidp_mcaide <- read_csv("mcaide_v2_last.csv")
iidp_caide <- read_csv("caide_v2_last.csv")
# prepare data:
iidp_mcaide$diagnosis <- as.factor(iidp_mcaide$diagnosis)
iidp_mcaide$mca... |
e3fb11bcba18c63e0fdfe05ba49ac7e2c3c6629ca0d934d58ad6aba53684fa89 | R | 6,108 | 117 |
#This script analyzes fluorescence kymographs to measure
#and visualize the Contraction or diameter changes over time.
#It involves:
# 1. Reading and processing TIFF images.
# 2. Computing the diameter of features using autocorrelation.
# 3. Normalizing and plotting the results.
# 4. Saving the results to fil... |
2fe4a3fdeb64c3bde6581a098bd8887a1d24d04fdd91b90b258664d319433235 | R | 6,117 | 176 | #set working dir
setwd("E:\\platform-paper\\fig2modules_figs3neurites_1224\\\\")
print(getwd())
#import df
df = read.csv("middle_files\\fig2.csv")
#create corr
b_num = dim(df)[[1]]
r_num = dim(df)[[2]]-2
corr = data.frame(matrix(ncol = r_num, nrow = r_num))
colnames(corr) = colnames(df)[1:r_num+1]
rownam... |
21941624cf0557d1a2f17d28e5fcd20298b69818dffe9e9eab0bbdbcb4a36d7f | R | 6,139 | 211 | # Function -------------------------------------------------------------------
.sample <- function(x, size, replace = FALSE, prob = NULL) {
validObject(x)
if (missing(size)) size <- nrow(x) # normal default does not work!
s <- sample.int(nrow(x@data), size = size, replace = replace, prob = prob)
x[s]
}
# ... |
4a97900429e04bf9efdfd1384ebbc230234afe3b036199b0632037194ab345de | R | 6,152 | 169 | ---
title: "Comparing group onsets using change point estimation: simulation using EEG-like noise"
author: "Guillaume A. Rousselet"
date: "`r Sys.Date()`"
output:
# pdf_document:
# fig_caption: no
# number_sections: no
# toc: yes
# toc_depth: 2
github_document:
html_preview: yes
toc: yes
... |
77eee5cae57965a6af60c16d8f538aea4eef9aa20a3da4c784f216ec6e59b2d9 | R | 6,160 | 200 | ---
title: "Estimating onsets using cluster-depth statistics: simulation"
author: "Guillaume A. Rousselet"
date: "`r Sys.Date()`"
output:
pdf_document:
fig_caption: no
number_sections: no
toc: yes
toc_depth: 2
# github_document:
# html_preview: yes
# toc: yes
# toc_depth: 2
---
Same a... |
d1ed56b076c5bcea4d315dded35350372aac979296b3317a49f764381097c9c4 | R | 6,163 | 134 | setwd("/home/wangmg/Documents/deconvolution/Data_and_code_for_reproduction")
library(ggplot2)
library(reshape2)
#################### Run PANDA
### PANDA package can be installed by install.packages("Code/PANDA_1.1.0.tar.gz", repos = NULL, type = "source").
### PANDA package is also available at https://github.com/... |
d93e650863b2186f3bf6aa9e1d705c814ed38003a36fc06df45b87acdc4e8352 | R | 6,177 | 100 | ---
title: "Tutorial IV"
author: "Juan A. Botía"
date: "03/07/2018"
output:
pdf_document: default
html_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Introduction to randomization based enrichment
In this part of the co-exp distribution we will explain how to incorpor... |
9221dd660d62f475b88d40aa21d94d0e70aef6296f34ef77966fddf35f728d40 | R | 6,180 | 227 | ---
title: "Regression coefficients for interaction analyses"
output:
html_document:
df_print: paged
---
This notebook contains the regression coefficients for the interaction analyses (across all ancestries and all allele coding strategies).
The results of this notebook are also available in Supplementary Tabl... |
942791a673d1ba3090ffc403a5ab15c579a385af81f32c28d1e6505e0d9d63bf | R | 6,205 | 150 | ### R-4.3.1
# library
library(edgeR)
library(RColorBrewer)
library(pheatmap)
colors = brewer.pal(8, "Set2")
### HCC data
data = read.table('../data/pivot_160_FRepr.txt', sep = '\t', header = T)
rownames(data) = data$Gene
data = data[-c(1:13)]
group = factor(ifelse(grepl('T', colnames(data)), 'Tumor', 'N... |
5016b15c04eaa357844182967c50dfa2c26478db9edd154cecef219f5ff34cba | R | 6,208 | 184 | # load arguments
args=commandArgs(TRUE)
for(i in 1:length(args)){
eval(parse(text=args[i]))
}
source('loadFunctions.R')
source('doSnapDykstra_experimental.R')
libloc = '/home2/RAM_maint/R_packages'
radius = 20 # radius around electrodes to keep vertices
xtol_rel=0.001 # tolerance for optimization 0.01 - 0.000... |
0b73b8b130154450cf1f1371e4eb798a80604906eb0987d1a07a86588fd06914 | R | 6,233 | 146 | source("Code/utils/plot.R")
split_dataset <- function(sc_counts, sc_labels, min_cells_per_type = 30, prefix = "", save_dir = "."){
if (!dir.exists(save_dir)){
dir.create(save_dir, recursive = TRUE)
}
unique_labels <- unique(sc_labels)
keep_types <- unique_labels[table(sc_labels)[unique_la... |
854640453c1375c6dbc111e2bb32c03a4d81f50e9aa65859e316b207a9e53c57 | R | 6,235 | 195 | # Bulk RNA-Seq of Tlx3 WT vs KO samples
# Budhaditya Basu
library(clusterProfiler)
library(enrichplot)
library(tidyverse)
library(msigdbr)
library(fgsea)
library(org.Mm.eg.db)
library(paletteer)
library(ComplexHeatmap)
# Load diff expressed gene Anterior cerebellum
Ant <- read.csv("Ant_WT_vs_KO.csv",
... |
4931bbd6f6a961750c8b6012c3b034c5e730689213cc4141f8209dce65ffe73a | R | 6,242 | 144 | # FNIH Project 2
# make cross-sectional dataset
#### Coded: Kellen Petersen
#### Date as of commenting and documentation - 05/28/2024
# Load libraries
library(tidyverse)
library(here)
#here::i_am("make_cross_sectional_dataset_CLEAN.R")
out_project <- here() #Folder where you want all output written to, a results an... |
a801269449cedecf6516415ab3ed11172d7b84c6c6f7c0985976576228de1eac | R | 6,244 | 236 | #' Radial plot
#'
#' @description
#' Draw a radial plot (also called Galbraith plot) which can be used
#' to assess bias in meta-analysis.
#'
#' @aliases radial radial.default radial.meta
#'
#' @param x An object of class \code{meta}, or estimated treatment
#' effect in individual studies.
#' @param y Standard err... |
0f37890444474320b349db12def658bfc5baa3e3899a32ec771c635da37a9d96 | R | 6,252 | 188 |
.options <- list(
debuglevel = 0L,
gc = FALSE,
file.remove.emptyspc = TRUE,
file.keep.name = TRUE,
tolerance = sqrt(.Machine$double.eps),
wl.tolerance = sqrt(.Machine$double.eps),
plot.spc.nmax = 25,
ggplot.spc.nmax = 10
)
#' Options for package \pkg{hyperSpec}
#'
#' Functions to access and set \pkg{... |
ee2ca541f406cdfa33ab9dd30d0603875e10b953746bbc6cb470fcbc942a52e6 | R | 6,259 | 130 |
# Get HCP A unrelated sample with all 4 REST scans
library(tidyverse)
# all data
all_data <- tibble(read_csv('/home/mgell/Work/Preprocess_HCP/text_files/HCA_LS_2.0_subject_completeness.csv'))
all_data <- all_data[-1,]
#all_data <- subset(all_data, select = c(participant_id, session, SUB_STUDY, VISIT, age, sex, isPa... |
8c50558c55fec65395cecae1acd402cc31a3421a47072bc49c6f3050bcd7ea3a | R | 6,262 | 175 | ---
title: "Population identification with microarray signatures matching"
output:
html_document:
df_print: paged
---
In this script, we load the population signatures defined using our microarrays experiments
We then select the top genes for each signature, and calculate a UCell score for each signature in each... |
7b73f9d2ef9919e6f87424fe5013e91b4f510ce6b27b10deae178c3033270efc | R | 6,264 | 211 | ciAgrestiCoull <- function(event, n, level) {
chknumeric(event, 0)
chknumeric(n, 0, zero = TRUE)
chklevel(level)
#
if (length(event) == 1 & length(n) > 1)
event <- rep(event, length(n))
else if (length(event) > 1 & length(n) == 1)
n <- rep(n, length(event))
else
if (length(event) != length(n))... |
37d3f322256c8428f12f253655b7b90d2a0336f7518faf0337a8abaa6e8f32f7 | R | 6,279 | 148 | #' assessRaster
#'
#' Assess semantic segmentation model using categorical raster grids (wall-to-wall
#' reference data and predictions)
#'
#' This function generates a set of summary assessment metrics when provided
#' reference and predicted classes. Results are returned as a list object. For
#' multiclass assessment... |
6941a818b5c9f518d5819738316e504788ff4fb61a791c09a9a6b8612edd9565 | R | 6,279 | 139 | # Load and install missing packages ---------------------------------------
packages <- c("ggplot2", "httr", "jsonlite", "RCurl")
package.check <- lapply(
packages,
FUN = function(x) {
if (!require(x, character.only = TRUE)) {
install.packages(x, dependencies = TRUE)
library(x, c... |
11fdd2d386f2cc8f30ae7fc83350e1008ad7de874fcbec72fd407a53254030d0 | R | 6,283 | 212 | #' Summary of meta-analysis results
#'
#' @description
#' Summary method for objects of class \code{meta}.
#'
#' @param object An object of class \code{meta}.
#' @param \dots Additional arguments (ignored).
#'
#' @details
#' Summary method for objects of class \code{meta}.
#'
#' @return
#' An object of classes \code... |
9d7bb0b080e040710e76df65276a8c9a1b8ab64cab756d003a3df82e935c009e | R | 6,304 | 174 | # Load necessary libraries
library(ggplot2)
library(clusterProfiler)
library(org.Mm.eg.db) # Mouse gene annotation
library(enrichplot)
library(DOSE)
# File path to RNA-seq data
file_path <- "E:/seq/OPC/OPC seq lipao.csv"
# Load the data
rna_data <- read.csv(file_path)
# Filter DEGs based on p-value thr... |
e141141ad5880236981573854e06d390fe3fd0883e8c26be3419d0b87c82f1a8 | R | 6,307 | 152 | ```{r load-config, incluse=FALSE}
source("config.R", local = knitr::knit_global())
```
```{r load-data}
if(!SENSITIVITY){
df_fmerge = readRDS(paste("parts/patient_grouped_wo", paste(idmt, collapse=""), ".Rda", sep=""))
}else{
df_fmerge = readRDS(paste("parts/patient_grouped_wo", paste(idmt, collapse=""), "_sens... |
35317a13787844df953689a94feae1d4b130ad91325e5e8d201f7739142295ac | R | 6,313 | 256 | #' Highlights and test the time-course of effects through crossvalidation (uses glmer)
#'
#' Same as 'decode_signal()' except it is powered by
#' 'glmer()' and thus performs generalized LMEMs.
#'
#' @param data A data.frame containing all the necessary variables.
#' @param formula A 'lme4'-style formula, passed as a st... |
77f3ba9ee7c7a0fa476842a2e527cd7268e31e9aec66c551a98048ecb0bcc12e | R | 6,317 | 137 | ---
title: "Simulations for Causal Group Effects with Conditional ATE"
author: "Eric W. Bridgeford"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{cb.simulations.cate}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::o... |
ea079db5d5d602884f9e27951a0ac3c9cb276f228459b4a07b1ee656c5f157b1 | R | 6,324 | 149 | setwd("...")
library(ggplot2)
library(ggpubr)
library(dplyr)
library(multcomp)
data <- read.table("EM_gratios.txt",sep="\t", header=TRUE, fill=TRUE, quote = "")
head(data)
tail(data)
dim(data) #753 3
dat <- subset(data, axon_diameter < 2.7) #exclude 5 outliers to the largest axonal diameter
head(dat)
dim(... |
6a77d05fb1bf1ee66be153e5679742afb9f91ac50a991e588125a925cac6ddca | R | 6,337 | 193 | #==========================================
# Produce Table 2: GWAS results summary
#==========================================
library(data.table)
library(magrittr)
library(rtracklayer)
library(flextable)
source("code/functions/find_closest_gene.R")
source("code/functions/make_or_95_ci.R")
bim <- fread("data/adc/adc... |
d457bd41693ebefe736379ed665e78f5775e9b0c8bb83e970882c0e6465c2d02 | R | 6,356 | 241 | # Summary -----------------------------------------------------------------
# Parcel-wise ISC variability and ISC-level correlation,
# controlled for parcel-wise tSNR
# Packages ----------------------------------------------------------------
library(tidyverse)
library(ggplot2)
library(broom)
library(rstatix)
library... |
58c6784ab55e313e46aff7ee5beb0a4f5e69f2a318b1ea2748071dc92dbd584b | R | 6,360 | 189 |
library(tidyverse)
library(glue)
# get path to script to load custom functions
initial.options = commandArgs(trailingOnly = FALSE)
file.arg.name = "--file="
script.name = sub(file.arg.name, "", initial.options[grep(file.arg.name, initial.options)])
source(paste0(dirname(script.name), "/Functions/functions.R"))
... |
9970789693f60409fee5165d864d80c31ef46256bd62f5269a67c11cdea8df90 | R | 6,377 | 157 | # --------------------------------------------------------------------------------- #
# #
# Hippocampal volumes in UK Biobank are associated with APOE only in older adults #
# ... |
ba6ab706c4ebc6d9c5d056a1fa3434e91c8dd6536e2a4ee8e7f42a2fb7665164 | R | 6,387 | 151 | # Calcuate ICC for metrics.
# args[1]: The input matlab .mat file. Has: 'AllVolume','CovW','CovB','time','sID','nSubj','nSession','nVoxels'
# args[2]: The name for the output matlab .mat file.
# Called by y_ICC_Image_LMM_CallR.m (in DPABI), which handles the input brain imaging file and covariates, and write out .nii I... |
8d76f501906fa94ee21950784157e7bd22bbe5218cd24af8b77980b30683a555 | R | 6,393 | 200 | ---
title: "20240306_sim_data_plots"
output: html_document
date: "2024-03-06"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r load libraries}
library(tidyverse)
library(ggplot2)
library(agricolae)
library(ggsignif)
library(multcomp)
library(nlme)
```
```{r load data}
#manual_synbot_dat... |
55cd300d6194e49e9ddf87d6fb79747edf408dacd11d522f24c9b6c5fe96d1d6 | R | 6,429 | 267 | library(purrr)
library(dplyr)
library(vctrs)
library(tidyr)
library(ggplot2)
library(tibble)
library(data.table)
# Will be the dataset "wang" or "pbmc"
dataset <- commandArgs(trailingOnly = TRUE)[1]
output_path <- "output"
source("../utils/0-indices.R")
gene_indices <- read_gene_indices(sprintf("data/%s-gene-indices... |
c53319150aedd7950427b7ed6cf82eafd1b7fd871a4f18971eb0d94d467ed1d2 | R | 6,454 | 148 | ```{r setup_raincloud, include = TRUE, echo = TRUE}
# required packages
packages_to_install <- c("cowplot", "tidyverse","gridExtra", "readr", "ggplot2", "dplyr")
# install and load required packages
for (package in packages_to_install) {
if (!requireNamespace(package, quietly = TRUE)) {
install.packages(packag... |
292b0ef59fed45b544d31b6dd11e0a19490958daf69b491a9f3db631df8dd08a | R | 6,460 | 203 | ---
title: "Data input from website"
author: "Jeremy Miller"
date: "March 8, 2019"
output: html_notebook
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Overview
This script reads converts data downloaded from the Allen Institute Cell Types Database (http://celltypes.brain-map.o... |
67c2725678a58c1fd3dee4c9994445bbaadbc4f8a5585d891ad62d0395b30999 | R | 6,469 | 170 |
library(scater)
library(Seurat)
library(tidyverse)
library(cowplot)
library(Matrix.utils)
library(edgeR)
library(dplyr)
library(magrittr)
library(Matrix)
library(purrr)
library(reshape2)
library(S4Vectors)
library(tibble)
library(SingleCellExperiment)
library(pheatmap)
library(apeglm)
library(png)
li... |
e41181c10847a952f1d32856d2879d323cb3a46bc28a95a9874a77e0b003c344 | R | 6,473 | 324 | ---
title: "SEFL10K9_Analysis"
author: "Zach"
output:
html_document:
fig_height: 4.5
fig_width: 4.5
highlight: kate
number_sections: yes
theme: journal
toc: yes
toc_depth: 6
editor_options:
chunk_output_type: console
---
# Set Global Options
```{r global_options}
knitr::opts_chunk$se... |
17aa647370143c1473d617842b93b99c6f248c240ccb9def61eb344eb4cdc45b | R | 6,483 | 128 | library(dplyr)
library(ggplot2)
library(ggrastr)
#Load all RNAscope data
exp.dt <- lapply(Sys.glob("raw data/*"), function(i){return(read.csv(i, row.names = 1))})
exp.dt <- do.call(rbind, exp.dt)
exp.dt$final.celltype <- gsub("_", " ", exp.dt$celltype)
exp.dt[grep("GABA", exp.dt$celltype),]$final.celltype <... |
50b2f9895ff4f0e87cb1d9f506e26bc4d8a6917bef700a9b36578f83f4f29f9a | R | 6,501 | 204 | ---
title: "TWAS"
author: "Jack Humphrey"
date: "2024-03-21"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(ggrepel)
```
```{r}
# twas_files <- list.files( pattern = "*fusion_res.tsv", recursive = TRUE, full.names = TRUE)
# names(twas_files) <- base... |
b3f0c84a21af3bee2905fe9d6735c32fce37258c2521d3dc8d37b42fa9fab6c4 | R | 6,515 | 158 | #==================================
# categorize the NACC NP variables
# by outcome type, coding, etc.
#==================================
library(data.table)
library(magrittr)
np <- readRDS("data/adc/np.Rds")
covar <- fread("data/adc/adc_np.covar")
np <- np[IID %in% covar$IID]
apply_exclusion_criteria <- function(d... |
3e09a8fd2414cd1f07003b2b4c86da5a675c890d4b3ede4963a578439aaab1e4 | R | 6,516 | 131 | library(rtracklayer)
library(parallel)
library(ggplot2)
library(reshape2)
options(mc.cores=8)
bed2gr <- function(x){
a=read.table(x,sep='\t',comment.char = '',quote="",header = F)
colnames(a)[1:6]=c('chr','start','end','width','strand','anno')
makeGRangesFromDataFrame(a,keep.extra.columns = T)
}
setwd... |
8c6801dc7be50f531f02e95c93a54d803104f66142965fea86db5001ee965e9b | R | 6,518 | 124 | run_SpatialDWLS <- function(st_counts_path, sc_counts_path, sc_labels_path, output_dir = ".", python_path = NULL){
library(Giotto)
library(peakRAM)
if (!dir.exists(output_dir)){
dir.create(output_dir, recursive = TRUE)
}
sc_counts <- read.csv(sc_counts_path, header = TRUE, row.names = 1)
... |
829f6050d4c7ce4bcf7aa35405d6fd03a87c029156d763d12c2cf4ab52f48e2b | R | 6,549 | 173 | # docker run -ti --entrypoint /bin/bash -v /cis/home/ebridge2/Documents/research/graphstats_repos/causal_batch/:/causal_batch neurodata/cdcorr:0.0.1
# cd causal_batch/docs/causal_cdcorr_paper/Figure_reproduction/
source("../helpers/test_statistics.R")
require(causalBatch)
require(tidyverse)
require(energy)
require(json... |
00426f69e7769ef1d9192c6b22a04c32098271ca61c58392d3985de7072224b3 | R | 6,552 | 175 | ### internal functions
source(codes.makepath("autofocus/Backend_Modules.R"))
#### Parse Input ####
#' input
#'
#' Parses input data
#'
#' @param mol_files List of excel file names containing molecular data
#' @param sample_sheet Excel file name containing sample data
#' @param sample_id string, name of column in samp... |
56090c686b9d1d46aa50d9a565c638e38e650092cbe898d939d44b892a60bcdc | R | 6,563 | 189 | library(ArchR)
library(Signac)
library(Seurat)
library(Matrix)
library(readr)
celltype_list = c('EX', 'IN', 'ASC', 'ODC', 'OPC', 'MG')
Human_Sample = c('HM2013017', 'HM20200905', 'HM20200927', 'HM20201129',
'HM20201213', 'HM20201222')
Monkey_Sample = c('RM06013', 'RM07071', 'RM07333', 'RM08403', 'RM110... |
c8d74e2e63b031899ca67dd6fc059293240fb72ce55c516cb5fed2e1d1871051 | R | 6,567 | 150 | ```{r setup_raincloud, include = TRUE, echo = TRUE}
# required packages
packages_to_install <- c("cowplot", "tidyverse","gridExtra", "readr", "ggplot2", "dplyr")
# install and load required packages
for (package in packages_to_install) {
if (!requireNamespace(package, quietly = TRUE)) {
install.packages(package... |
107b85b7aa9e9689f8369eb15f2e3dea47b6d8f9b76740d0663836e6d1244d1d | R | 6,573 | 79 | source('./scripts/01_Setup.R')
# A treemap R script produced by the Revigo server at http://revigo.irb.hr/
# If you found Revigo useful in your work, please cite the following reference:
# Supek F et al. "REVIGO summarizes and visualizes long lists of Gene Ontology
# terms" PLoS ONE 2011. doi:10.1371/journal.pone.0021... |
a9e5c28a2f9c31c8bf88886cedaf60a22dc0ad18c2f1c00a9920115aef348cca | R | 6,584 | 172 | #This script is a modified version of reticulate's install_python(), which installs pyenv in a user-specified directory instead of the "r-reticulate" root folder.
#As opposed to install_python() it does NOT check and handle a list of the different pyenv versions. It only installs the latest pyenv version at the user-s... |
1c2be3b593201ce6dfae02afe88ce890e567a881a5bf052e51176811d76682c3 | R | 6,587 | 293 | ---
title: "Markos et al Transitions graphs"
author: "Jan Kubovciak, Institute of Molecular Genetics of the Czech Academy of Sciences"
output:
pdf_document:
number_sections: false
toc: true
df_print: paged
---
last updated: `r Sys.time()`
```{r setup_glob, include=FALSE}
knitr::opts_chunk$set(
echo = ... |
2efca207e4519300d678839d3b374f104ff012cc31bc730b62a3ddd790de01b5 | R | 6,602 | 124 | library(GenomicRanges)
library(data.table)
library(stringr)
library(RColorBrewer)
library(ggplot2)
library(ggpointdensity)
library(viridis)
library(qqman)
library(readxl)
#Plot Fig2a, QQ plot
dat.results<-data.frame(read_excel("plot_table_fig2.xlsx", sheet=1), stringsAsFactors = F) #,header=TRUE)
dat.results$p_adj<-a... |
85fc9a3ec5bfe6dab8fd110aa770f5e948f42615129c4ff3cec449ab0023b6d1 | R | 6,609 | 185 | ---
title: "STADIA"
author: Yanfang Li
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{STADIA}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
out.width="60%",
fig.width=7, fig.heigh... |
81433f96767bca7fac23b8c9a15da3fff762b8e2fa9afaf8a7212a8ef2a0d289 | R | 6,611 | 157 | library(Seurat)
library(sctransform)
library(patchwork)
library(dplyr)
library(viridis)
library(ggplot2)
library(ComplexHeatmap)
library(enrichR)
library(RColorBrewer)
library(MAST)
library(ggrastr)
library(ggrepel)
library(fgsea)
md <- readRDS("datasets/scGSEA_neuron_subset_metadata.rds")
table(md$cell... |
307c7cff769dd0c2834e51bf99a462cf6a38f3d742953a4d8c003547c311947d | R | 6,636 | 203 | ---
title: "Roche_DiazAndYohimbie_Clustering"
author: "Lukas von Ziegler"
date: '2024-02-01'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Clustering of Yohimbine and Diazepam data from Roche together
```{r, message=FALSE, warning = FALSE}
source("DLCAna... |
34f75fc309f61c31c18180a3c2893edd07b494c131af41a72d7616a1c81b186d | R | 6,643 | 209 |
#' Convert between different wavelength units
#'
#' The following units can be converted into each other:
#' *nm*, \emph{\eqn{cm^{-1}}{inverse cm}}, *eV*, *THz* and *Raman shift*.
#'
#' @param x data for conversion
#' @param from source unit
#' @param to destination unit
#' @param ref_wl laser wavelength (required for... |
f309aabbf79936c372894def6e5f6cf744ebb0edf14e65022c3a4c04618adf36 | R | 6,649 | 197 | ---
title: "SOX2-VSX2_Boxplots"
author: "Abdullah Khan"
date: "2024-09-25"
output: html_document
editor_options:
chunk_output_type: console
---
```{r}
library(DiffBind)
library(rtracklayer)
library(pheatmap)
library(ggpubr)
library(ggsignif)
library(DESeq2)
library(RColorBrewer)
library(reshape2)
library(BiocParall... |
964e3c785f131033cba5e4e461f5e7fd9f67e3b1c9fcbdf388c06cc6ea23391f | R | 6,656 | 180 | ---
title: "Analysis_IFS_Shockbox"
author: "Lukas von Ziegler"
date: '2024-01-12'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, message=FALSE, warning = FALSE}
source("DLCAnalyzer.R")
source("BFA_BFF.R")
library(sp) #tested with v1.3-2
lib... |
05a1a99cd6951ebfacfc8e0c5146ff86595b3d5f328ab97140f995f3cc7df1b3 | R | 6,683 | 224 | # Functions ------------------------------------------------------------------
.na.if.different <- function(x) {
if (length(unique(x)) > 1) NA else x[1]
}
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.apply_workhorse <- function(data, MARGIN, FUN, ...) {
if (length(data$spc) == 0... |
c9270d570841d964f7e2babcd2e13d3de97da4613344a3f86d2a985e0d4f48a7 | R | 6,684 | 173 | library(scCustomize)
library(tidyr)
library(scater)
library(Seurat)
library(tidyverse)
library(cowplot)
library(Matrix.utils)
library(edgeR)
library(dplyr)
library(magrittr)
library(Matrix)
library(purrr)
library(reshape2)
library(S4Vectors)
library(tibble)
library(SingleCellExperiment)
library(pheatmap)
library(apeglm... |
c3465b98e97c256224034070c9d8e34e19b904898b009167e9fc95b18162b976 | R | 6,708 | 175 | ---
title: "Figure3_BFF"
author: "Fabienne Rössler"
date: '2023-03-23'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, message=FALSE, warning = FALSE}
source("../DLCAnalyzer_Functions_v4.R")
source("../UnsupervisedAnalysis_Functions.R")
library(sp) ... |
43c48b0c689d88d3924c1b4be447b94522f55fe3f39d894ad86dc5b621b08195 | R | 6,724 | 217 | ---
title: "hclust_clean"
author: "Kadi Vaher"
date: "09/08/2022"
output: html_document
---
---
title: "TEBC faecal samples: Clustering of the samples (hierarchical)"
author:
- "Kadi Vaher"
- "Hierarchical clustering with all samples together"
- "Based on Bray-Curtis dissimilarity matrix calculated on the TSS-no... |
7d684f2a7f48fb23ebe66da4dbac51e86fcef162965478441131f9bfdad6f1f3 | R | 6,742 | 264 | # Summary -----------------------------------------------------------------
# Run IS-RSA using linear mixed-effects models
# Schaefer 2018 atlas: 300 parcels, 17 networks
# Packages ----------------------------------------------------------------
library(tidyverse)
library(lme4)
library(broom.mixed)
# Prelude ---... |
dc9b385bce0eb8ff047cd323bac5017c069ed6d8ac3a9c90fcce4e43248f918d | R | 6,748 | 179 | library(readxl)
library(UCell)
library(tidyverse)
library(Seurat)
library(SeuratDisk)
library(tidyquant)
library(ggpointdensity)
library(viridis)
library(ggrastr)
library(extrafont)
library(scCustomize)
source(file.path("R","functions.R"))
#all <- LoadH5Seurat(file.path("data","v5_reference_mapped_all.H5Seurat"))
loa... |
5af1d62cde158ead2b2570cc217ab88f69334cb22f1232442cee691eedcaf77b | R | 6,772 | 200 | # Spatial Prediction Example ------------------------------------------------------
# Preparation -------------------------------------------------------------
#Load libraries
library(geodl)
library(dplyr)
library(torch)
library(luz)
library(ggplot2)
setwd("Your Working Directory")
#Get device
device ... |
4358daeb07174e92049af5b9adca253ec4b28a5e1b398342c7255f63bbcf726e | R | 6,787 | 252 | #' Conversion of a `hyperSpec` object into a data frame or matrix
#'
#' `as.data.frame()` returns `x@@data` (as data.frame), `as.matrix()`
#' returns the spectra matrix `x@@data$spc` as matrix.
#'
#' @rdname asdataframe
#' @name as.data.frame
#' @aliases as.data.frame as.data.frame,hyperSpec-method as.data.frame.hyperS... |
d1f9f223c7ffdb79c49bd08f61991a2299493ef75f13a91a765a159831e92725 | R | 6,802 | 227 | ---
title: "Estimating onsets using FDR and Bonferroni corrections: simulation"
author: "Guillaume A. Rousselet"
date: "`r Sys.Date()`"
output:
pdf_document:
fig_caption: no
number_sections: no
toc: yes
toc_depth: 2
# github_document:
# html_preview: yes
# toc: yes
# toc_depth: 2
---
... |
5df622751a9d1d2fce97f4a7d0b86b2510ddfad75b17ce628157cab38472591d | R | 6,811 | 183 |
# Correlate HCP test retest data to get a sense of reliability
library(tidyverse)
library(corrplot)
library(psych)
#net_cols <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7") # original order
# Load
T1 <- read.csv('/home/mgell/Work/reliability/text_files/HCP_YA_retest_T1_beh.csv')
T... |
d12a0de3682dafe4d15ecdb3094205c9f08820ae55416f134ab1b1a92bc599cb | R | 6,816 | 197 | library(ggplot2)
library(lme4)
library(lmerTest)
library(effects)
library(broom.mixed)
library(patchwork)
#scripts_dir = 'Z:/Desktop/MINDLAB2020_MEG-AuditoryPatternRecognition/scripts/working_memory/'
scripts_dir = '/projects/MINDLAB2020_MEG-AuditoryPatternRecognition/scripts/working_memory/'
setwd(scripts_di... |
578567e922dedebc6140c0b77a7525f0a797ed5030e32a34145e6718d17e970a | R | 6,848 | 167 | #stats and figure for alpha diversity indices to compare timepoints
# IMPORTANT TO RUN THIS SCRIPT ON THE COMMAND LINE
# FOR SOME REASON FUNCTION lmer MAKES THE ENTIRE R SESSION TO CRASH SOMETIMES WHEN RUNNING IN RSTUDIO
# change directory names as needed
library(tidyverse)
library(here)
library(ggplot2)
library(ggpu... |
daa762391e71d123a1a8e705a09481955a2cde0bc0cffecc35b94695f72f1d4a | R | 6,875 | 222 | # Function -------------------------------------------------------------------
.aggregate <- function(x, by = stop("by is needed"), FUN = stop("FUN is needed."),
..., out.rows = NULL, append.rows = NULL,
by.isindex = FALSE) {
validObject(x)
if (!is.list(by) || !by.isi... |
16671317583b3326e414a7161b81c52b9fcfb29d8673a21c41d22ab3cb02de09 | R | 6,882 | 163 | library(Seurat)
library(sctransform)
library(patchwork)
library(dplyr)
library(viridis)
library(ggplot2)
library(ComplexHeatmap)
library(enrichR)
library(RColorBrewer)
library(MAST)
library(ggrastr)
library(ggrepel)
library(fgsea)
###add dataset
dt.sct <- readRDS("datasets/2_seurat_integrated_clustered.... |
a72d6d0c47d2ec11db4366e1c2fe39db825c1c2acce52db30cbeb32eedc754b5 | R | 6,889 | 145 | library(ggplot2)
library(tidyr)
library(dplyr)
library(cowplot)
library(magick)
theme_set(theme_bw())
# Parse the arguments
args <- commandArgs(trailing=T)
clusterProteinDomainPath <- args[1]
clusterFusedExonPath <- args[2]
circosPicturePath <- args[3]
fontSize <- as.numeric(args[4])
fusionLegendRows <- as.numeric(a... |
6b373a31f4236510791012b1e5a38fe7e8f4d1111dcae2f47635d3ba22f0fdd2 | R | 6,897 | 170 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# STADIA <img src="man/figures/logo.png" align="right" width="160" ... |
3807f303f7f2bdc671aabd372b227ed55f21418f2f5e8cf771328bc95c951fdc | R | 6,901 | 191 | ---
title: "Image_analysis"
output: html_document
---
```{r}
library(tidyverse)
library(ggforce)
library(ggsignif)
library(ComplexHeatmap)
library(ggpubr)
library(RColorBrewer)
```
```{r}
setwd("Z:/16-Our Papers/In Preparation/PD_Diabetes_Alise/Figures/Figure2/originals/B")
data <- readr::read_csv("TH... |
e58e34ff139f005419468a59ff19cf9ec75d76e83b856e6dd7e796e76361e615 | R | 6,922 | 229 | ---
title: "AB Plaque load"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Load packages
```{r}
require(Seurat)
require(data.table)
require(tidyverse)
require(ggpubr)
require(cowplot)
source("Helper_scripts/figure_themes.R")
```
The following code chunk was run on a GPU ... |
8de603e23612ad9fee7181899ea55ffac9041b492cf9c0e7dddd4bc0d713799a | R | 6,929 | 128 | ### generate plots for Figure 3 for both CO & H1
library(Seurat)
library(dplyr)
library(ggplot2)
library(cowplot)
### for Cortical Organoid
aei_co <- read.table("AEI_CO_v2/output_index_20230627/AEI_CO_v2_outputindex/EditingIndex.csv", header = 1, sep = ",")
cluster_levels <- c("Progenitor","IP","Glia","Glutamaterg... |
672d1ac54621c6aa09098d2522182bb93334ea31eda8243f8ba39dfed453cb77 | R | 6,931 | 105 | #----Figure_06/07_preparations--------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code is needed as preparation for Figure 06/7 as preparation to run
# the analysis for the connectivity analysis for Reinhard and Fukuda et ... |
9f6730fdde0884a158f0d31c2690927902a16c762407a39a6db734a02d389f7c | R | 6,933 | 192 | ---
title: "SupplFigure_BSOID_8Clusters"
author: "Fabienne Rössler"
date: '2023-02-23'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, message=FALSE, warning = FALSE}
source("../DLCAnalyzer_Functions_v4.R")
source("../UnsupervisedAnalysis_Functions.R")
... |
05b84312d49a7b18fcecf0df4a89020d6f8fa1ebcb188a188dd0a4813654b801 | R | 6,942 | 182 | #############################################################################
#
# Plot results
#
#
# Liang Qunjun 2024/06/13
library(tidyverse)
library(ggeasy)
library(ggsignif)
library(ggsci)
library(patchwork)
# load the data
dat_use <- rio::import("inputs/Analysis2_collect_fMRI.xlsx")
# define the cut-off
cut_ha... |
482f72d3d94865480ecf11e1d2c5a2b36b2d50ad5f65b4b04b04dc1937e4e7ae | R | 6,945 | 131 | #----Figure_05_A----------------------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code for Figure 05 Panel A for the connectivity analysis for
# Reinhard and Fukuda et al. 2024
# The first run might take long as all syna... |
cac042ed37f2b80dc4876f264a055b599d453f89cef83c7ec537664a9e2e30cd | R | 6,962 | 132 | library(neuprintr); library(hemibrainr); library(nat); library(natverse); library(nat.jrcbrains); library(fafbseg);
library(reticulate)
library(R.matlab)
library(progress);library(bit64);library(pracma);
# ### plotting packages
# library(pheatmap);library(ComplexHeatmap);library(zoo);library(lsa)
# library(RCol... |
430791987a9a99455f76f71d22b3bf4d62c468a6461795121ee38ee871e33c9e | R | 6,974 | 250 | ### -----------------------------------------------------------------------------
###
### .getindex
###
###
## does the actual work of looking up the index for wl2i, .extract and .replace
## extrapolate = TRUE returns first resp. last index for wavelength outside
## hyperSpec@wavelength.
## extrapolate = FALSE returns ... |
a5f24382a184526ecfd9a573b48ec6ab732c82ac0c3c59201f9ae31c4ae574d7 | R | 6,977 | 199 |
pcaUI <- function(id) {
ns <- NS(id)
fluidRow(
column(width = 3,
shinydashboard::box(title = "PCA settings",
status = "warning",
width = NULL,
shiny::sliderInput(ns("pcaDims"), "Dimensions", min = 1, max = 10, value = c(1, 2)),
checkboxInput(ns("showPolygons"), "... |
7055e5205f04b3c25349d777959af9abab512f80d63f0d80c20d9a7e978a7ba7 | R | 6,989 | 116 | source('./scripts/01_Setup.R')
SampleInfo2= readRDS('./data/processed/rds/SampleInfo.rds')
SampleInfo2$Amplification.Method= gsub("PicoPlex v2|PicoPlex v3", 'PicoPlex', SampleInfo2$Amplification.Method)
delimiters = ".bowtie|.bt2"
aa2= strsplit(SampleInfo2$File_name, delimiters, perl = TRUE)
aa3= sappl... |
77f7aeeb626651c977dc1661cee38e167a207f34f55cb2fde1697a84a3983c98 | R | 6,989 | 193 | ---
title: "plot_lookups"
output: html_document
---
```{r setup, include=FALSE}
library("scatterplot3d")
library(ggplot2)
MA = read.table("/data/clusterfs/lag/users/gokala/beat-dyslexiaevol/results/FUMA/FUMA_job178902_dys_rhyimp_MA/IndSigSNPs.txt", header = T)
dys = read.table("/data/clusterfs/lag/users/gokala/beat-d... |
fd34702321a6951e1001d77183f28b6fa4061a829ba26e92cd587b226fea0824 | R | 6,992 | 158 | #making a panelled plots using the gg-objects saved in some previous scripts
# In this script it is very important that all figures have been made with the same version of packages (e.g. ggplot, ggpubr etc) that is in the use
# otherwise the plots will not display and you can't merge them together in panels
# if you g... |
afb041a72c503108580a6a25da0d71730bf8c0af31df3718a66cea8caa318d5f | R | 7,001 | 152 | library(reshape2)
library(ComplexHeatmap)
library(RColorBrewer)
library(circlize)
# for enhancers regulating genes and isoforms, respectively
# for 5' and non 5' promoter isoforms
# LDSC enrichment analysis for enhancers with ABC activity
mytraits=list(`Neuropsychiatric and behavioral`=c("sz3",'asd', "bip2", #"bdAndSc... |
953c21e3891a0ce1a31406b88f7edbed76d90f97e9cd91946bfe3826683cca31 | R | 7,009 | 212 | # Bulk RNA-Seq of Tlx3 WT vs KO samples
# Budhaditya Basu
library(clusterProfiler)
library(enrichplot)
library(tidyverse)
library(msigdbr)
library(fgsea)
library(org.Mm.eg.db)
library(paletteer)
library(ComplexHeatmap)
# Load diff expressed gene Posterior cerebellum
Post <- read.csv("Post_WT_vs_KO.csv",
... |
6c489e83142edef8010ae22f10744426b036d7755d90f95e99af2ff21437f77d | R | 7,031 | 305 | library(purrr)
library(dplyr)
library(vctrs)
library(tidyr)
library(ggplot2)
library(tibble)
library(data.table)
# Will be the dataset "wang" or "pbmc"
dataset <- commandArgs(trailingOnly = TRUE)[1]
output_path <- "output"
source("../utils/0-indices.R")
gene_indices <- read_gene_indices(sprintf("data/%s-gene-indices... |
9563009c520f38ad3842697e286356e8f54084d9496a0222272448c559a90387 | R | 7,042 | 153 | numModelVals <- 20
pseudoQuants <- seq(1/numModelVals,1-(1/numModelVals),
1/numModelVals)
USE_COWPLOT <- FALSE
plotMotifStats <- function(PlotDat, BaseDat, StatsDat,
ModelDat, AltModelDat=NULL){
ylim <- 4
regions <- unique(PlotDat$Region)
midReg <- regions[(le... |
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