sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
67396ada14967b01d69ebc4cd9344b11f59f1e94f2d23fd05500d6c18adec6fb | R | 2,823 | 91 | # Build the par_snp txt file based on the manifest file
# Written by Lucia Trastulla -- email: lucia_trastulla@psych.mpg.de
suppressPackageStartupMessages(library(argparse))
parser <- ArgumentParser(description = "Write PAR file for each SNP in the manifest file" )
parser$add_argument("--manifest_file", type = "char... |
3ed70804abc39baffd65492f027119e374746ca37d8e5197fba9db479e9a7774 | R | 2,848 | 85 | #' @importFrom Rcpp evalCpp
#' @importFrom Matrix colSums rowSums t summary
#' @importFrom rlang .data %||%
#' @importFrom methods new show
#' @importFrom utils .DollarNames
#' @useDynLib rliger, .registration = TRUE
NULL
#' @importFrom magrittr %>%
#' @export
magrittr::`%>%`
#' @importFrom magrittr %<>%
#' @export
m... |
e7a8bb15340070cffe87dafd615c2287a11fff9935ec29a558119d3c4e726b30 | R | 2,862 | 108 | library(GenomicRanges)
library(ArchR)
library(stringr)
library(dplyr)
library(BSgenome.Hsapiens.UCSC.hg38)
addArchRThreads(threads = 60)
addArchRGenome("hg38") ## use hg38
### --------------------------
## 1. read in Arrow files
### --------------------------
setwd("./01_Fragments/all_arrow")
ArrowFiles <- list.... |
9bc701f3211d000837185402fb75635e2335afd3e0e96d1cdcd70415d626029f | R | 2,878 | 87 |
### AUROC Violin plot
AUROC <- read.table("AUROC_table.txt", header = T, sep = "\t")
dim(AUROC)
AUROC
colnames(AUROC) <- c("Organism", "Operon-mapper", "Rockhopper", "Operon Finder", "OperonSEQer", "OpDetect")
library(ggplot2)
AUROC_stack <- stack(AUROC[,2:6])
AUROC_stack[,"ind"] <- as.factor(AUROC_stack[,"ind"])... |
d79ad5d2941ecac1673bae5fc787e4690cc91b0d6ed1f5601cc83c0eb4fd15e7 | R | 2,881 | 88 | ---
title: "Relative fragment size plot and ROC for all 11 SCLC cases vs. all healthies"
site: workflowr::wflow_site
output:
workflowr::wflow_html:
code_folding: hide
toc: true
editor_options:
chunk_output_type: console
---
```{r caching, echo=FALSE}
knitr::opts_chunk$set(autodep = TRUE, echo=FALSE)
```
... |
7fd599fe6aa490554886db2c79d7bb9e6641bca3c80aeb69a871f951cc1f356c | R | 2,886 | 95 | ### Author : Marie-Michelle Simon
### Date : April 30th, 2023
### Principal component analysis (PCA) for WT and KO condition comparison in RNA-Seq libraries
### Goal : Generate PCA plot
### Type of samples : myofibers, muscle stem cells (musc) and myoblasts (myob)
#setwd("~/Library/Mobile Documents/com~apple~CloudD... |
519b7f2fd8b7269e1538805d57712ec5ef69bbe11e67b39c1fa53e6c6cf7044b | R | 2,911 | 85 | # Load required packages
library(nebula) # For differential expression analysis using Negative Binomial GLMM
library(Seurat) # For handling single-cell data
# Define input and output directories
indir <- "./indir/"
out_dir <- "./outdir/"
# Read command line arguments
argv <- commandArgs(T)
name <- argv[1] # Cell t... |
1152ddcb8018b8598f76068374b24d07376a50e7a8cc7e74712f6cf98442f355 | R | 2,982 | 57 | library('tidyverse')
source('helper.R')
library('matrixStats')
###----------------------------------------------------------------------------------------------------------
##-- violin plot for distributions of r
###--- Supplementary Fig. 1a ---###
info.r <- read.table('../Data/Linear_regression/Info_linear_regression... |
66f741d743a7968fbefb358fb319de594e0ae4e2464e453e4ea867adb7feb29d | R | 2,983 | 72 | library(Seurat)
library(harmony)
library(patchwork)
library(ggplot2)
library(viridis)
library(pheatmap)
library(reshape2)
MTC = readRDS("data/MTC.rds")
vizgen.obj = readRDS("data/Sample_0601_filtered_50_log.rds")
genes_sjp = c("Nrxn1","Nrxn2","Nrxn3",
"Nxph1","Nxph2","Nxph3","Nxph4",
"Nlgn1... |
f9bcc671218eb1909c2115882f05e060ae2aca3e29bbfe0e28a284d27e25495a | R | 3,038 | 55 | EMP_COR_FIT <- function(data,meta = NULL,var_select,formula = y~poly(x,1,raw = T),eq_size=3,se = F,group = F,width = 5, height = 5,palette=c("#E64B35FF","#4DBBD5FF","#00A087FF","#3C5488FF","#F39B7FFF","#8491B4FF",
... |
5bcf5401388deef67e6780d143c197c1bc43303a0f7f730f378801ab96a7e203 | R | 3,055 | 92 | ### This script creates an R function to generate raincloud plots, then simulates
### data for plots. If using for your own data, you only need lines 1-80.
### It relies largely on code previously written by David Robinson
### (https://gist.github.com/dgrtwo/eb7750e74997891d7c20)
### and the package ggplot2 by Hadley W... |
2138c1dbf18fbe7c980217bb5d0b47178531ac8f657c4b71f7f4b04a114a3a11 | R | 3,062 | 60 | # Plot quality control for samples
# Written by Lucia Trastulla -- email: lucia_trastulla@psych.mpg.de
suppressPackageStartupMessages(library(argparse))
suppressPackageStartupMessages(library('ggplot2'))
suppressPackageStartupMessages(library('RColorBrewer'))
suppressPackageStartupMessages(library('ggrepel'))
parser... |
655e303c58a53d2696a91622eb40ecfed14b92e90411eb5253d82bcddddc74b0 | R | 3,072 | 107 | library(Seurat);library(ggplot2);
library(dplyr)
library(tidyverse)
library(SeuratWrappers)
library(uwot)
adPC <- readRDS("adPC.RDS")
DefaultAssay(adPC) <- "RNA"
ref <- adPC
ref$subcluster = gsub("Purkinje_","", ref$subcluster)
Idents(ref) <- ref$subcluster
ref <- RenameIdents(ref, "Aldoc_1"="Aldoc","Aldoc_2"="Aldoc... |
4adc4de6e2a5b53c66056f1b592666a9e5460ecb7e4c50fc96f0b0de202e57b9 | R | 3,089 | 110 | ---
title: "Report8"
author: "Jamie Medina"
date: "2/19/2021"
output: html_document
---
# Final Figure Requested for Reviewer Comment
```{r background, echo=FALSE, include=FALSE}
###################################
# Task: Predict
# Created: 012221
# Due date: Monday ... |
d07892772bfabe2247f865da4528396c979b5d55a4b1d359eca760c7403f88da | R | 3,108 | 61 | ---
title: "Data integration with LIGER"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Data integration with LIGER}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
## Introduction
LIGER... |
d297198fb7916f20d4018f4730ce2a45ab2b3b2d5eb905ca3c965131e108983d | R | 3,110 | 100 |
#' @export
get_cv_preds<-function(features,model) {
features <- features %>% dplyr::mutate(rowIndex = 1:n())
ids <- features %>% select(id, rowIndex,type)
preds <- model$pred
preds <- preds %>% dplyr::group_by(rowIndex) %>% dplyr::summarize(score = mean(cancer))
preds <- inner_join(ids, preds, by="rowIndex")
pre... |
664a05ee3f97c1ce6c1f4ef75d2cb7ac08c2272377181270abe406a373964080 | R | 3,136 | 64 | ### Author : Marie-Michelle Simon
### Date : October 3rd, 2024
### Using as input the list of genes not expressed in musc, myoblast and mf (i.e. non-muscle genes) and annotated to H4K3me3 peaks in musc, the objective is to extract the region + or - 1 KB of TSS
### of those genes
### Goal : generate an input file (con... |
697c5c05325e496212b0b041d024e6ceab271ffb0498d8a92a854401b56b7f08 | R | 3,140 | 89 | #' @importFrom corrplot corrplot
cor_plot_detail <-function(data,meta_data = NULL,method = 'spearman',width=10,height=10,cor_output=T,file_name='cor_plot'){
deposit <- list()
try(data <- subset(data,select = -c(Group)), silent = T)
if (!is.null(meta_data)) {
cor_data_combie <- dplyr::inner_join(data,meta_d... |
970fc0a84e8dc90533f08f88785d14b6ac92c0222450da94984b5877ff871025 | R | 3,142 | 106 | ---
title: "PCAWGROCs"
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
ROCs for PCAWG T vs N
```{r}
library(here)
library(data.table)
library(tidyverse)
library(devtools)
library(ggplot2)
library(ggpubr)
library(cowplot)
#load_all("~/Dropbox/useful.stuff.aa") ... |
d155fd79bd69047199dd4b48fc471ac0b3fbe7691eacdea3224973de9da9fd96 | R | 3,215 | 89 | # List of your required packages
required_packages <- c(
"lme4", # For fitting linear mixed-effects models
"pbkrtest", # For Kenward-Roger approximation for mixed models
"simpleboot", # For simple bootstrap methods
"lmeresampler",# For resampling methods for mixed models
"foreach", # For loopin... |
277c16894b0a5e9a8e75c50aeb753baafdeae3200fa86b118059fd6f71eee0be | R | 3,241 | 107 | #'\code{assignPROGENyScores}
#'
#'@details This function is used to assign the PROGENy weights to pathway
#'members for a selected set of samples.
#'
#'@param progeny contains the progeny scores as obtained from \code{runPROGENy}.
#'@param progenyMembers contains the list of members for each PROGENy pathway.
#'@param ... |
4d279acaeb957362fdf47b00f06ccbe41d1fdd9e3ca7eebfe70468d3e0c5f864 | R | 3,253 | 37 | # This function makes an expression figure showing the relative expression and standard deviation of cases and controls.
# Datasets is a vector of dataset names. CellTypeLevel indicates the cell type level resolution (e.g. "predicted.celltype.l1").
# CaseName is a string naming what cases are called in the dataset, su... |
c338d21209d8c0ed33f479ffc684f875ceb00b342e0adfc4520f8fe016b38657 | R | 3,253 | 101 | ---
title: "Kaplan-Meier curves by dichotomized DELFI score"
site: workflowr::wflow_site
output:
workflowr::wflow_html:
toc: true
code_folding: hide
editor_options:
chunk_output_type: console
---
Patients with stage IV adenocarcinoma of the lung.
```{r packages, message=FALSE}
library(SummarizedExperim... |
e18f2536e56e0272ee5dbfb40de43f415e30b1382e5c5627846e4f46b11ede44 | R | 3,257 | 90 | ### TODO:
#
## Read in data.table of fragments, return data.table of density
frag.density <- function(fragments, x="w", ..., groups=NULL) {
d.list <- function(x, ...) {
d <- density(x, ...)
list(x=d$x, y=d$y)
}
## Add grouping arguments (gc etc) to pass into keyby
dens <- fragments[,c... |
1e2b89fb009c7d562139e3d6b6d65751e155eaee590cfb6a3bf04e87ed7b5940 | R | 3,272 | 77 |
# Source: https://github.com/dynverse/dynplot/blob/master/R/plot_dimred.R
#' Project the waypoints
#' @inheritParams add_cell_coloring
#' @param waypoints The waypoints to use for projecting, as generated by [dynwrap::select_waypoints()]
#' @param trajectory_projection_sd The standard deviation of the gaussian kernel
... |
06684dd3a71ce844904a528f8312ccbeebd2249003a20ff6c29977879813bdc7 | R | 3,290 | 97 | #'\code{generateTFList}
#'
#'@details This function generates a list of data frames containing activity
#'values for each TF. These data-frames can then be used as inputs for the
#'CARNIVAL analysis (measObj). We can control the amount of TF's we include in
#'our data-frames based on their absolute activity val... |
3ec4f9e3ea5d15a943cc2fc0f55cd3f774ddec6ab9452e34de1e9aab15909464 | R | 3,339 | 74 | ---
title: "S15"
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
```{r TN, echo=FALSE, fig.width=6, fig.height=12, dev=c("png", "pdf")}
library(here)
library(data.table)
library(tidyverse)
library(devtools)
library(ggplot2)
library(ggpubr)
library(cowplot)
library(readx... |
285a399a659d99602de9bab715b4d2918c06a26e98e13fc0f260d3971bae8bad | R | 3,371 | 96 | rm(list=ls())
# load libraries
library(RCurl)
library(RJSONIO)
library(jsonlite)
library(dplyr)
library(foreach)
library(doParallel)
library(R.utils)
# load exp matrix, you can use any other initial feature selection other than DEGs
deseq2 <- read.csv("expression_deseq2_results.csv", row.names = 1)
#### ------------... |
bfa996939615dd2e1aa1bcf5a3a04d63b64750051a47d48555bd0984e9d0b4c9 | R | 3,371 | 96 | rm(list=ls())
# load libraries
library(RCurl)
library(RJSONIO)
library(jsonlite)
library(dplyr)
library(foreach)
library(doParallel)
library(R.utils)
# load exp matrix, you can use any other initial feature selection other than DEGs
deseq2 <- read.csv("expression_deseq2_results.csv", row.names = 1)
#### ------------... |
21ae713b1649c4871d5672e2fb856e4171cd8d3b6c08649ea4291c921a249419 | R | 3,377 | 90 | library(tidyverse)
library(caret)
library(recipes)
library(pROC)
library(devtools)
library(openxlsx)
load_all(here("code","rlucas")) ## -> library(rlucas)
features <- read_csv(here("data","training-set.csv"))
multinucs <- bins5mb %>% group_by(id) %>% summarize(multinucratio = sum(multinucs)/(sum(short+long)))
feature... |
41c107fdb6fdff404295f14e1091fc210aa5288d203efc4ef31cb06cef82f685 | R | 3,381 | 58 | # DISCLOSURE: This is a collection of palettes that includes some original
# palettes and some palettes originally implemented by others in other packages.
blackbright = c("#090214", "#300f51", "#842152", "#b43033", "#e3551e",
"#f5a31c", "#e1c53f", "#eef174")
black_yellow_red <- c("#393A3C", "#757374", ... |
fb925488cafd50a3d21b94114bb66f5a42a021535780a9f3666ad92aca30b3e7 | R | 3,387 | 143 | #!/usr/bin/env Rscript
library(here)
library(data.table)
library(stringr)
library(lubridate)
### INPUT
fpaths <- list(
RDS = "adnimerge_baseline" |>
sprintf(fmt = "data/rds/%s.rds") |> here(),
LST = c(
"adni_acquisition_failures.lst",
"qrater_malf_2022-12-20.csv",
"qrater_malf_reg_fails_2022-12-20.... |
5d0eb592682d482b792b99e6e52f579af2a78f376db111686fc3014afc3484e9 | R | 3,392 | 90 | #######################
# caret path vs neut #
#######################
# read and install libraries if necessary ####
if (!require("ggplot2")) install.packages("ggplot2")
if (!require("data.table")) install.packages("data.table")
if (!require("caret")) install.packages("caret")
if (!require("doParallel")) install.pac... |
908e1143db054fecc184d1b653b4e5f54e280d0ec5d28d2dffbf76cf59ca76da | R | 3,407 | 135 | lastC <-
function(x) {
y<-sub(" +$", "",x)
p1<-nchar(y)
cc<-substr(y,p1,p1)
return(cc)
}
orderPvalue <-
function (treatment, means, alpha, pvalue, console)
{
n <- length(means)
z <- data.frame(treatment, means)
letras<-c(letters[1:26],LETTERS[1:26],1:9,c(".","+","-","*","/","#","$"... |
506e5d18ebeb62b7003d1f7b39937abe8db5b9d769c6db2c3c1c978d439361c3 | R | 3,409 | 86 |
data_image<-readRDS(".../codes revised/reproducibility/output from the server/real_data/pseudo_data.rds")
fxca<-cor.test(data_image$X21003.2.0,data_image$FX,method = "kendall")
fx_ca<-ggplot(data_image, aes(x = X21003.2.0, y = FX)) +
geom_point(alpha = 0.5, color="lemonchiffon",size=3) +
stat_density2d(aes(fil... |
6974d72187ec2af3db25e0cb7d1bfc313f5918a26a051955d78053d398663842 | R | 3,424 | 115 | ---
title: "Distribution of CEA scores by diagnosis group: Extended data Figure 8"
site: workflowr::wflow_site
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
html_document:
code_folding: hide
chunk_output_type: console
---
```{r caching, echo=FALSE}
knitr::opts_chunk$set(autodep = TRUE)
```
```... |
52d7ec9f6f6e05c4a213b2b5adafac74c99ee6092d2f16842ff55aec6f6fe967 | R | 3,431 | 106 | rm(list=ls())
# load libraries
library(RCurl)
library(RJSONIO)
library(jsonlite)
library(dplyr)
library(foreach)
library(doParallel)
library(R.utils)
library(httr)
# load exp matrix, you can use any other initial feature selection other than DEGs
deseq2 <- read.csv("expression_deseq2_results.csv", row.names = 1)
###... |
d9c1ffa184d75ac2b01db6e2f7530ff47b81909da4a629a06fc309a999be7e65 | R | 3,434 | 106 | rm(list=ls())
# load libraries
library(RCurl)
library(RJSONIO)
library(jsonlite)
library(dplyr)
library(foreach)
library(doParallel)
library(R.utils)
library(httr)
# load exp matrix, you can use any other initial feature selection other than DEGs
deseq2 <- read.csv("expression_deseq2_results.csv", row.names = 1)
###... |
8563a7391ce3213888df1f04eaf1f4eb113cec6eb631fc7907d92d54ca616762 | R | 3,446 | 97 | ---
title: "PCAWGROCs"
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
ROCs for PCAWG T vs N
```{r}
library(here)
library(data.table)
library(tidyverse)
library(devtools)
library(ggplot2)
library(ggpubr)
library(cowplot)
load_all("~/Dropbox/useful.stuff.aa") ... |
677d2f52ac4d1fde35cdc35827efda16df55915d25098b534e1b5aa2aabd9e5f | R | 3,490 | 119 | # Refresh the R session to clear the environment
# This ensures that any previous objects, functions, or loaded packages are removed.
freshr::freshr()
# Verify the working directory
print(getwd()) # Should print the path to your working directory
# Load necessary libraries
# These libraries provide various functions ... |
272cad7ceec76b0247cd7d7d930048ff8ea36973235b84647734fb649b1724a2 | R | 3,505 | 42 | # This code permutes sex. It requires the following: DatasetName (a string with the name of the Dataset in PresenceofDataTable)
# PresenceofDataTable (a table indicating whether the dataset has cells for each cell type), CellTypeName (a string indicating what is the cell type label, such as "predicted.celltype.l1"),
# ... |
f3ad534ad23e8deadafea0576fda9c3173a440faa5124379456846056b922d5d | R | 3,540 | 96 | # Description ####
# Longitudinal plots to characterize our data
# Set environment ####
rm(list= ls())# ctrl + L to clear console
setwd("~/Documents/GitHub/Striatocortical-connectivity-FEPtrt/LongitudinalPLOT/")
#setwd("/Users/brainsur/Desktop/GitHub_repos/Striatocortical-connectivity-FEPtrt/LongitudinalPLOT")
librar... |
0859b6ac6b6c9072ed4fc2fca5868bcd010b8009555edbe2b6db0e6ad6c4ebdd | R | 3,576 | 44 | # This code permutes cases and controls. It requires the following: DatasetName (a string with the name of the Dataset in PresenceofDataTable)
# PresenceofDataTable (a table indicating whether the dataset has cells for each cell type), CellTypeLevel (a string indicating what is the cell type label, such as "predicted.c... |
f82f40f6e704c1e4a96e5a58a99a3b53d4a06102f404356788f22d0a80320447 | R | 3,591 | 100 |
#' @rdname peak
#' @export
setMethod("rawPeak", signature(x = "ligerATACDataset", dataset = "missing"),
function(x, dataset = NULL) {
x@rawPeak
})
#' @rdname peak
#' @export
setReplaceMethod(
"rawPeak",
signature(x = "ligerATACDataset", dataset = "missing"),
function(x, d... |
da2f98c1504ebba913c1fbad06929bc755c1d676b9ba9328ff48529afebbb1b2 | R | 3,602 | 67 | library(Seurat);library(tidyverse)
# merging all samples
cdS1 <- readRDS("Multiplexed/integration_P1cKO/data/integrate_celltype.rds")
cdS2 <- readRDS("Multiplexed/Integration_P2-deficient/data/P2-deficient_filtered.rds")
cdS <- merge(cdS1, cdS2)
#feature.common = Reduce(intersect, list(rownames(cdS1),rownames(cdS2)));l... |
d5c94f356fd19091dbee1664e88e7c8299ec0cce6f7a076003efb12baddafd88 | R | 3,631 | 84 |
# upsampling function to have same numbers of scn,cac,lof,gof
func = function (x, y) {
# which of the four class x scn combos is highest? -> upsample all data to that!
xup <- if (is.data.frame(x)) x else as.data.frame(x)
xup$Class <- y
frqtab <- data.frame(table(xup[,c("Class", "scn")])) # frquency table
frq... |
0b5b2f92f85762844c67c7723697252410e6f0572f3daf99e45b946de5ce6a54 | R | 3,659 | 94 | ---
title: "About"
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
```{r}
library(tidyverse)
library(here)
library(readxl)
library(data.table)
novel<-fread(here("data","NoveltyTables","Subfamily.csv"))
a<-fread(here("data","Supp_Tables","s6_All_features.csv"))
a<-a %>% ... |
49ffff6036399953b5a0a48490b9c34698111d90d8a1ac8419e4f2051a8deaea | R | 3,665 | 63 | library('tidyverse')
source('helper.R')
count_cutoff <- 50
# Nanopore data from Lee et al. (PMID: 38306272)
ont <- read_delim('../Data/Mouse_oocytes/MM_oocyte_tail_lengths_Nanopore_Lee_2024_PMID_38306272.txt', delim = '\t') %>%
rename_with(~ c('stage', 'gene_id', 'gene_name', 'count', 'tl_gmean', 'tl_mean', 'tl_med... |
4ca423523ccb280bc943de9dcfe90a796b7d03bcf8d18235e3d08cff63496f88 | R | 3,666 | 111 | # Scripts to quantify iNM and eNM from NM brightfield image analyses using 'TrueAI' as part of VS200 software
library(readxl)
library(ggplot2)
library(dplyr)
library(plyr)
library(ggpubr)
library(MASS)
theme_set(theme_minimal())
###############
### Inputs ###
###############
analysis_dir <- "/Users/zacc/USyd/NM_anal... |
37aed7b3023e2daed77e659884014bdfd0c03e25acef3cc4f48f42b7bf182ff4 | R | 3,709 | 81 |
library(Seurat)
library(dplyr)
library(Azimuth)
library(SeuratData)
library(presto)
library(car)
##### Step 1: Obtain cell types by mapping to Azimuth reference
# Download Azimuth human motor cortex reference from here: https://zenodo.org/records/4546932
# Load Azimuth human motor cortex
CortexRef = readRDS("ref.Rd... |
f88df51f97de2962a965409d41bb9a1b45b3d420483017f7c44aa21c92da8bd8 | R | 3,715 | 148 | #!/usr/bin/env Rscript
library(here)
library(data.table)
### INPUT
fpaths <- list(
RDS = c("adni-bl_volumes_hcvc", "adnimerge_baseline") |>
sprintf(fmt = "data/rds/%s.rds") |> here(),
CSV = c(
"UCSFFSX_11_02_15_20Nov2023",
"UCSFFSX51_11_08_19_20Nov2023",
"ADNI_FS_hc",
"ADNI_FS_hc_vc"
) |> spr... |
bfb8091378c8d1fac22b7ca3d6f1fa48f8186124f1c957e5e9578e978b29eda3 | R | 3,720 | 96 | library(BayesFactor)
# metaBF: combining t-values of pilot and full sample into a meta-analytical Bayes Factor
# t = (t-value pilot, t-value full sample)
# Unrounded t-values are found in the corresponding JASP and HTML files (pilot or full)
# n1 = (n_participants HS pilot, n_participants HS full)
# n2 = (n_... |
cbb8cbf1024969d092f94f7b75950305ebe001c21ea4cb0bec393e0fd209e5f5 | R | 3,767 | 104 | library('signs')
library('Cairo')
library('viridis')
library('RColorBrewer')
custom_breaks <- function(bmin, bmax, digits = 0, length.out = 8, zero = TRUE) {
bmin = floor(bmin * (10 ^ digits)) / (10 ^ digits)
bmax = ceiling(bmax * (10 ^ digits)) / (10 ^ digits)
if (bmin > 0 | bmax < 0) zero = FALSE
d = round((bmax... |
46c24c6d0eb90def4cb122316cfd114122e2b88c2b7030c86b17222544db3e8f | R | 3,774 | 93 | library('ggplot2')
library('Cairo')
library('RColorBrewer')
library('signs', quietly = T)
custom_breaks <- function(bmin, bmax, digits = 0, length.out = 8, zero = TRUE) {
bmin = floor(bmin * (10 ^ digits)) / (10 ^ digits)
bmax = ceiling(bmax * (10 ^ digits)) / (10 ^ digits)
if (bmin > 0 | bmax < 0) zero = FALSE
d ... |
684d722582330987d64f7aa220890bc6aa365f370e8722fddfa851734e866d26 | R | 3,818 | 94 | EMP_COR_RDA <- function(data, meta, seed =123,width = 15, height = 15, ellipse = NULL,zoom = c(1,1,1), arrow_col=c('#F0E442','#CC79A7'),palette=c("#E64B35FF","#4DBBD5FF","#00A087FF","#3C5488FF","#F39B7FFF","#8491B4FF","#B2182B","#E69F00","#56B4E9","#009E73","#F0E442","#0072B2","#D55E00","#CC79A7","#CC6666")){
deposit... |
168b6f669054e3f4bd14353d88033c01cc819b8475c7263fd3f7baf7f8a9c808 | R | 3,825 | 87 | # Load necessary libraries
library(nebula)
library(Seurat)
# Set the input directory and output directory
indir <- "./05_Nebula_DAR/Subtype/"
out_dir <- "./Subtype_output/"
# Read arguments from the command line, assuming the first argument is the cell type name
argv <- commandArgs(T)
name <- argv[1] # Example: "Exc... |
d45551131cb8d916eca2308e4f55cd4b5e9b157fc19b024d632753b70a905be8 | R | 3,840 | 78 | # ID conserved regions of trimmed alignment
library(Biostrings)
library(vegan)
library(ggplot2)
align_tfilt <- readAAStringSet("/path/to/folder/filtalign.fasta") # from the clean_align.R script
process_column <- function(i, align) {
aa <- as.character(subseq(align, start = i, end = i))
tbl <- as.data.frame(table(... |
8b4fc05adf1c92f076520a41ab1378996e45615fac7702b70178350316b5d5d2 | R | 3,848 | 119 | ---
title: "Sensitivity of DELFI, LDCT, and combined approaches in LUCAS"
site: workflowr::wflow_site
output:
workflowr::wflow_html:
code_folding: hide
toc: true
editor_options:
chunk_output_type: console
---
```{r caching, echo=FALSE}
knitr::opts_chunk$set(autodep = TRUE, echo=FALSE)
```
```{r packages,... |
60eb58f4e78ede1480211de8b07bf8282a359101afd2825ccbe6206c05a4efa2 | R | 3,896 | 120 | ---
title: "GC model"
site: workflowr::wflow_site
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
workflowr::wflow_html:
code_folding: hide
toc: true
toc_depth: 3
toc_float: true
editor_options:
chunk_output_type: console
---
```{r caching, echo=FALSE}
knitr::opts_chunk$set(autodep = TRUE, warn... |
56ef4fffce0135df82b434ca0a470632fc7a54baf07bee9fde5d03992c58f827 | R | 3,903 | 102 | library('tidyverse')
source('helper.R')
###----------------------------------------------------------------------------------------------------------
##--
tb <- read.csv('../Data/INN_train_test/INN_XL_L_2000_CDS_hyperparameter_serch.csv')
# Pearson R versus n_Conv1D_MaxPool_block
###--- Supplementary Fig. 2a ---###
... |
4ff711bc895bf54a01d0435c59793c452fc1f256159fa88a3a15420654c7ee55 | R | 3,946 | 104 | library(dplyr)
library(ggplot2)
set.seed(10)
# Fig 9A: Binomial test for the whole-brain model:
n_test<-5760
n_sample<-100
onesample_chance<-25
allsample_chance<-32
observed_acc<-42
binomial_data <- rbinom(n_test, n_sample, onesample_chance/100)
binomial_data <- as.data.frame(binomial_data)
names(binomial_data) <- ... |
5ec8960cc44442a62940da53edc699452ad49690800c6c6190f8393b0265780d | R | 3,981 | 149 | ---
title: "PCAWGROCs"
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
ROCs for PCAWG T vs N
```{r}
library(here)
library(data.table)
library(tidyverse)
library(devtools)
library(ggplot2)
library(ggpubr)
library(cowplot)
load_all("~/Dropbox/useful.stuff.aa") ... |
9951fe8314d1207edcbef678b956cb4585e415db9197247eacaa73db13538513 | R | 3,995 | 108 | library(tidyverse)
library(caret)
library(recipes)
library(pROC)
library(devtools)
library(here)
##
load_all(here("code","rlucas")) ## -> library(rlucas)
##
##features <- read_csv("../data/training-set.csv")
features <- read_csv(here("data", "training-set.csv"), show_col_types=FALSE)
multinucs <- bins5mb %>% group_by... |
2a50a16d65d4593420009dd2c69fbdd33cd15d9ad443aed00d34ba9799559d88 | R | 4,020 | 121 | psf.flow <- function(g, node.ordering, sink.nodes, split = TRUE, sum = FALSE) {
node.order <- node.ordering$node.order
node.rank <- node.ordering$node.rank
# sink.nodes <- NULL
nods = names(node.order)
symb.exprs = vector("list")
eval.exprs = vector("list")
#expressions
E = data.fram... |
b513f88df2ae5d44889b9983b558240755e41194b6f82f89750b7d482bc213b0 | R | 4,020 | 147 | ---
title: "Visualizng result: Dreadd"
output:
html_document:
toc: true
toc_float: true
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE, comment = "")
# Verify the working directory
print(getwd()) # Should print the path to your working directory
freshr:... |
bdb39a582053d9fc32b9e74eb925a3566021b49a57d34da2059c67911ad63417 | R | 4,067 | 130 | library(glmnet)
library(readxl)
source("align_hist.R")
library(zoo)
library(ggplot2)
# Define functions
# This function is used to calculate the moving average of a vector in a
# circular way
circular_ma <- function(k, window) {
k <- as.matrix(k)
n <- nrow(k)
out <- matrix(nrow = n, ncol = ncol(k)) # Init... |
7eea8f0d7016a581b05841998ba0e126e62d0dca6b5a07978d81e37f4260f0aa | R | 4,079 | 102 |
# -----------------------------
# this script creates a SAF of intronic regions for featureCounts to assign mapped reads to.
# -----------------------------
library(GenomicFeatures)
library(GenomicRanges)
# Load txdb and get gene/exon ranges
txdb <- makeTxDbFromGFF("PATH TO GTF/gencode.v40.chr_patch_hapl_scaff.a... |
21dc0f03ed5e89733fd560270f1761f53807817eab8f1a35d95785ff13312579 | R | 4,081 | 78 | #### Please download data file from Zenodo (https://zenodo.org/record/7396399/files/hsaSPIA.RData?download=1) and put it in R working directory
load(system.file("extdata", "melanoma_exp.RData", package="psf"))
load(system.file("extdata", "edited_pathways_new.RData", package="psf"))
### loading required libraries
libra... |
ddc6a7c005cc124678974a3a9a0afc7fd5f29f89d7e43b7ca5f110b1c58b1514 | R | 4,110 | 131 | # Refresh the R session to clear the environment
# This ensures that any previous objects, functions, or loaded packages are removed.
freshr::freshr()
# Set the working directory to the desired folder
# This is the folder where your project files are located.
# Verify the working directory
getwd() # Should print the ... |
7ec2973eff494c02d634d5921bc8276d8879b8bf1ca15148c296154c11f848e2 | R | 4,125 | 92 |
data_all <- readRDS(".../codes revised/reproducibility/output from the server/real_data/covariate_balance.rds") #revise as needed
data_used_plot_ctltrt1_filter<-data_all$data_used_plot_ctltrt1_filter
data_used_plot_ctltrt2_filter<-data_all$data_used_plot_ctltrt2_filter
data_used_plot_trt1trt2_filter<-data_all$data_u... |
a5fbd158de9040e74b31923f6ca423ca5a137c63db9761a0ddd8d8366a71b70c | R | 4,158 | 107 | library(Seurat);library(tidyverse)
#Subset PC clusters and use scRNA data to transfer annotation
cdS <- readRDS("data/integrated_all.rds")
Idents(cdS) <- "cellTypeAllM"
obj.list = SplitObject(cdS, split.by = "sample")
# print CTRL sections
for (n in levels(Idents(cdS))){
temp = obj.list[["X43430"]]
cell_id <- Whi... |
b38d88cfd87f9181c4f13b615a4381b444e38f51f8c2925b9c34cd37b2e15e18 | R | 4,167 | 127 | ---
title: "for Figure 4C-D"
output: html_notebook
R Script for Quantitative Analysis of Ultrasonic Vocalizations (USVs) in Mouse Pups
Author: James Li
Purpose: This script performs data processing, statistical analysis, and visualization
of USV data collected from mouse pups separated from their mother and littermates... |
a6f2004238d5dfa3e998a25ec0b3f3837212f18ca7479cc35701fe5768010df5 | R | 4,194 | 172 | #!/usr/bin/env Rscript
library(here)
library(data.table)
library(lubridate)
### INPUT
fpaths <- list(
CSV = here("data/derivatives/adni_icc_scale.csv"),
RDS = c(
"adni-bl_volumes_hcvc", "adni-bl_volumes_freesurfer", "adnimerge_baseline"
) |> sprintf(fmt = "data/rds/%s.rds") |> here(),
SRC = c(
"qc_se... |
d7dc870ce28a3359b551d45217a90a02ff761bd7e5b14a0807bcc797823f9cd1 | R | 4,298 | 136 | library(GenomicRanges)
library(ArchR)
library(stringr)
library(dplyr)
library(BSgenome.Hsapiens.UCSC.hg38)
addArchRThreads(threads = 48)
addArchRGenome("hg38") ## use hg38
### --------------------------
## 1. Loading an ArchRProject
### --------------------------
outd<-"./03_ArchR"
workdir<-paste0(outd,"/02_filterD... |
6ce64a6ea859769fec380c6a975c96ac9d06bad36a67c6650183f46c94c99e4d | R | 4,317 | 141 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
RunModularityClusteringCpp <- function(SNN, modularityFunction, resolution, algorithm, nRandomStarts, nIterations, randomSeed, printOutput, edgefilename) {
.Call(`_rliger_RunModularityClust... |
01e889adbf7fcb8f8012ef509508f65b6d746d14abb09255b2c8a2a11dc94ee2 | R | 4,351 | 138 | # --------------------------------------------------------
# Calibration + Histogram (Clean Version)
# X-axis limits: 0 to 1
# Right Y-axis: Labels and title removed
# --------------------------------------------------------
suppressPackageStartupMessages({
library(ggplot2)
library(caret)
library(Hmisc)
... |
d301fb1e3eee6bc4aa360adca421b97ed3fa6e2a58d373fbf4956655bda42bd8 | R | 4,369 | 114 | #'* SCRIPT: Mic/NM individual metabolites plots (Fig S1)*
# Libraries:
library(ggplot2)
library(ggprism)
library(rstatix)
library(ggpubr)
library(Hmisc)
library(ungeviz)
library(dplyr)
library(scales)
# Functions:
obtain_metabolite_info <- function(raw_data, proc_data, KEGGMOLID, metabolite){
# Merge original data... |
be3239340aad8fd70af7e9394811675d38eab17767eba88feb942440895d54bb | R | 4,408 | 58 | source('./helper.R')
library('tidyverse')
###------------------------------------------------------------------------------------------------------------------------------------------------------
# scatter plot for tail change associated with position-less 3-mers when one UUUUA is present
###--- Supplementary Fig. 3d ... |
27c8a49850fa9783915a3480614ad6098bd7cdc0982e748f6545a75e34fe7464 | R | 4,442 | 111 | #' liger object of PBMC subsample data with Control and Stimulated datasets
#' @format \linkS4class{liger} object with two datasets named by "ctrl" and
#' "stim".
#' @source https://www.nature.com/articles/nbt.4042
#' @references Hyun Min Kang and et. al., Nature Biotechnology, 2018
"pbmc"
#' liger object of PBMC subs... |
2244635a3f0e4016a374b779106e7e11b706ca14792940ffe95444a630b4ac5c | R | 4,462 | 101 |
data_all <- readRDS(".../codes revised/reproducibility/output from the server/real_data/assumption_check.rds") #revise as needed
positivity_check_lasso<-data_all$positivity_check_lasso
positivity_check_rf<-data_all$positivity_check_rf
positivity_check_gb<-data_all$positivity_check_gb
#Reshape data for ggplot
gps_lo... |
240e70b90ea5284a4a6340e7794e59cdb5f4fd9df265f9f20e4365009dc9f0c3 | R | 4,477 | 127 | # Description ####
# Longitudinal plots to characterize our data
# Set environment ####
rm(list= ls())# ctrl + L to clear console
#setwd("~/Documents/GitHub/Striatocortical-connectivity-FEPtrt/LongitudinalPLOT/")
setwd("/Users/brainsur/Desktop/GitHub_repos/Striatocortical-connectivity-FEPtrt/LongitudinalPLOT")
librar... |
6b1e8218e1b8727efad5a87cc0342f8c0d51eb873344e636eb49641c9ba7f728 | R | 4,544 | 112 | # integrate embryonic and adult PC scRNA-seq data
library(Seurat);library(tidyverse)
adPC <- readRDS("/Users/jamesli/Documents/Nagham/SynologyDrive/data_analysis/data/adPC.RDS")
PCin <- readRDS("/Users/jamesli/Documents/Nagham/SynologyDrive/data_analysis/data/PCin_new.rds")
DefaultAssay(adPC) <- "RNA"
ref <- adPC
r... |
1130a14bde55f8cae3f298ec97f966ba6f5635507c652f05fd7ca69ba82a08a2 | R | 4,550 | 101 | library(tidyverse)
library(GenomicRanges)
library(rtracklayer)
library(rhdf5)
library(parallel)
library(pbmcapply)
library(Biostrings)
source('helper.R')
# get the bed file
utr_bed <- GRanges(import('../Data/Annotation/HS_3UTR_annotation_PMID_38460509_Table_S3.bed', format = "BED"))
utr_bed$pa_id <- str_split_i(utr_be... |
c5b6ae099393d4166a81fc1a16510a9f006ac57ded5bb4dd6fc46a1d0b6c0549 | R | 4,653 | 154 | rm(list = ls())
library(psych)
library(tidyverse)
library(forcats)
df <- read.csv("/Users/angeles/Documents/Research_data/Striatocortical_FEPtrs/cleansample_covars_R2_Med.csv")
#Total obs per group
table(df$group)
#Clozapina ####
table(df$Clozapina)
table(df$Clozapina,df$group)
# Convert to YES or NO
df$Clozapina... |
48ad4fd610b51f5e0f4eeadc1c175a31204a478d45b2d29742746b026af4f408 | R | 4,664 | 112 | # Description ####
# Longitudinal plots to characterize our data
# Set environment ####
rm(list= ls())# ctrl + L to clear console
setwd("~/Documents/GitHub/Striatocortical-connectivity-FEPtrt/LongitudinalPLOT/")
#setwd("/Users/brainsur/Desktop/striatconnTRT")
library(ggplot2)
library(geomtextpath)
library(ggsci)
libra... |
70ac9f6f4c5003fd5d24d40070bb86043756308081208c055221e78ce1307d0e | R | 4,676 | 76 | ---
title: ""
author: "`r rworkflows::use_badges()`"
date: "<h4>README updated: <i>`r format( Sys.Date(), '%b-%d-%Y')`</i></h4>"
output:
github_document
---
<!-- To modify Package/Title/Description/Authors fields, edit the DESCRIPTION file -->
```{r, echo=FALSE, include=FALSE}
pkg <- read.dcf("DESCRIPTION", fiel... |
63b42e2058cd20a1303ffd093b60d93ca0b9843bf296bf1010083f194fe3fd46 | R | 4,699 | 135 | # ============================================================
# Unified model performance summary (LATEST logic)
# - One final object: results_final
# - Youden_J computed directly from each confusion matrix
# - AUC/CI computed once per ROC, then reused
# - Print and CSV export use the same object
# =============... |
d2ec2048b5fadb615918662d5dd12bb1e2b09db19c18dc528b5aee2b9989ebb4 | R | 4,754 | 111 |
library(Seurat);library(tidyverse)
# reload data
# cdS <- readRDS("data/integrated_all.rds")
cdS = readRDS("/Volumes/SSD_JamesLi/Experiments1/Multiplexed/Integrated_all/data/integrated_all.rds")
# == repeat following the multiome data
meta <- cdS@meta.data
# remove the posterior-most sections and cell clusters of i... |
f194f48b83c8d364eeb302ec3adb4b2a690422b2441966f27a10842624eccea0 | R | 4,776 | 128 | #' Counts number of reads overlapping bins and performs GC-correction
#'
#' Uses LOESS to return GC-adjusted read-depth for desired bins
#'
#' @param reads a \code{GRanges} object, obtained from \function{filterReads}
#' @param bins a \code{GRanges} object, obtained from \function{makeBins}
#'
#' @return a \code{GRange... |
43a0827081a4b636e4f70f4522a3790e18af9eb8e6277c1949a9a8fdb21371a3 | R | 4,799 | 158 | ---
title: "Fragment lengths"
site: workflowr::wflow_site
output:
workflowr::wflow_html:
code_folding: hide
toc: true
editor_options:
chunk_output_type: console
---
```{r caching, echo=FALSE}
knitr::opts_chunk$set(autodep = TRUE, echo=FALSE)
```
```{r packages, message=FALSE}
library(RColorBrewer)
libra... |
f2333fae1ab4634eddd0de5b38c9e23ef8b24f6044dc1b53ca9cdf19335ca4c1 | R | 4,806 | 133 | # Scripts to determine the modalities of NM Area distribution.
library(readxl)
library(ggplot2)
library(dplyr)
library(plyr)
library(ggpubr)
theme_set(theme_minimal())
###############
### Inputs ###
###############
analysis_dir <- "/Users/zacc/USyd/NM_analysis/"
NM_data_dir = "/Users/zacc/USyd/NM_analysis/NM_data_ne... |
eaa0f34b80eb78fb129403adc27dc7ddff4a451ebe18b343dbf3dbfff2b39916 | R | 4,834 | 118 | # plot LRR and BAF for a single line, add the info on CN
# code similar to the python automatic code produced by bcftools
# Written by Lucia Trastulla -- email: lucia_trastulla@psych.mpg.de
suppressPackageStartupMessages(library(argparse))
suppressPackageStartupMessages(library('ggplot2'))
suppressPackageStartupMessa... |
66f7641adaf48fb9219d90b5b5e30298b787900f1c26864861ba1101f6dbf5b8 | R | 4,857 | 305 | ---
title: "SessionInfo"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r packages}
#2
library(ggplot2)
library(magrittr)
library(tidyverse)
library(fs)
library(grid)
library(data.table)
library(cowplot)
library(devtools)
library(here)
library(tidyverse)
library(open... |
617fd8e678b1837423cca0bf1de0a639bd3c0a9ccad888ffb05b854cd1012ff5 | R | 4,864 | 128 | library(Seurat);library(COSG);library(clusterProfiler);library(org.Mm.eg.db)
library(tidyverse)
# PCin <- readRDS("/Users/jamesli/Manuscripts/PC_Manuscript/data/PCin_new.rds")
PCin <- readRDS("/Users/jamesli/Manuscripts/PC_Manuscript/data_analysis/data/PCin_new.rds")
Idents(PCin)=PCin$PC0.8
mGenes = readRDS("~/Deskto... |
37eed03c9b4d7bc4f539760a3f95ff40bbe64b158abf86d03d378751d07dd620 | R | 4,878 | 105 | class_tpm <- function(cds, class_vector){
cds.class.mean <- class_means(cds, class_vector)
col.sum = Matrix::colSums(cds.class.mean)
return(t((t(cds.class.mean)/col.sum) * 1e+06))
}
find_DE_cluster_batch_regressed_out <- function (cds,
DE_conditions,
... |
639f6b0f80572856843b2afeffcc251e3b815ee5e168a4c45978ba37c6a72dd7 | R | 4,955 | 138 | ---
title: "Relative fragment size plot and ROC for SCLC vs. other comparison"
site: workflowr::wflow_site
output:
workflowr::wflow_html:
code_folding: hide
toc: true
editor_options:
chunk_output_type: console
---
```{r caching, echo=FALSE}
knitr::opts_chunk$set(autodep = TRUE, echo=FALSE)
```
```{r loa... |
f16689dce02d651e02787662b6d2e1afd388c790624c60fcafef1b6ac0bcfcb4 | R | 4,955 | 173 | ---
title: "Visualizing result"
output:
html_document:
toc: true
toc_float: true
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE, comment = "")
freshr::freshr()
# Verify the working directory
getwd() # Should print the path to your working directory
lib... |
0ae429a0e70b9e205b3021791d2ee1ff5385f4e88b9caff436d15603f8aea8a0 | R | 5,023 | 165 | ## C. Vriend - Amsterdam UMC - Aug '24
## Post-hoc Bayesian regression analyses of global graph measures using brms package
# clear variables
rm(list = ls())
#library(tidyverse)
library(dplyr)
library(readr)
library(moderndive)
library(reshape2)
library(brms)
library(readxl)
library(tidyr)
library(openxlsx)
library(g... |
ec176f06ac7c0ac66d2f5784e23fd3cd85264b3f77a14b8926d0a8d59924ea87 | R | 5,171 | 130 | data("pbmc", package = "rliger")
withNewH5Copy <- function(fun) {
ctrlpath.orig <- system.file("extdata/ctrl.h5", package = "rliger")
stimpath.orig <- system.file("extdata/stim.h5", package = "rliger")
if (!file.exists(ctrlpath.orig))
stop("Cannot find original h5 file at: ", ctrlpath.orig)
ctr... |
759422c00b4dcfdefe194b512805bc24a874532685c109d90baa5d6e3ff7b030 | R | 5,230 | 97 | #!/usr/bin/Rscript
#=####################################################################O
# R Script specific to include conditions, factors and levels
# Experiment: E1 (Malayalam)
#
# Author: R.Muralikrishnan
# 2024-06-11: V 1.0
#=####################################################################m
# Requir... |
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