sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
0bccc4c9e0f4f5437ca84cbf32bc11d5cc7f3582e311e4696d045af3c79aa9cb | R | 5,247 | 125 | #!/usr/bin/env Rscript
# Removing reads from secondary ORs based on known similar ORs from multimappers and gapped reads
###
knownpairs = scan("empirical_OR_pairs_filtered",what = character())
#####################################################################
library(ggplot2)
library(dplyr)
args = commandArgs(traili... |
fe6955780c48ff58ba10bdb66c73644cdbfcf425396a2a6acc873bc572f382b6 | R | 5,254 | 137 | #20240822
#ZE and TBS - CV calculation for metabolites
# This script processes metabolite data to remove outliers and calculate the coefficient of variation (CV)
# for different combinations of experimental variables (metabolites, sequences, fields, and locations).
# The CV is a measure of relative variability, cal... |
b4acf5c0a09fd627acb7f48ce504e479a1b087e9553717ddeefaa210107c472b | R | 5,285 | 112 | ---
title: "UINMF integration of Dual-omics data"
author: "April Kriebel and Joshua Welch"
date: "04/25/2023"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
```
Here we integrate the scATAC and scRNA reads from the dual-omics dataset SNARE-s... |
64cdce6b1cc998c734c29f902fdeaeb1114915348df9bcc8c2057881b078ecc5 | R | 5,361 | 123 | library('tidyverse')
library('ggplot2')
library('Cairo')
library('RColorBrewer')
library('viridis')
library('signs', quietly = T)
library('ggpointdensity', quietly = T)
custom.breaks <- function(bmin, bmax, digits = 0, length.out = 8, zero = TRUE) {
bmin = floor(bmin * (10 ^ digits)) / (10 ^ digits)
bmax = ceiling(b... |
923cbf7eee29eacd8cea90439df096e021a73dc50d79912cfc0fdbae5ac2b14e | R | 5,366 | 207 | projDir <-""
setwd(projDir)
source("functions.R")
package.list <- c("psych","reshape2","rstatix","lmerTest","lme4","afex","car","dplyr","ggplot2","Hmisc", "purrr",
"broom","tidyr","corrplot","ggpubr","Matrix","tibble","ggeffects","effects","stringr","readxl","readr","purrr",
"patter... |
ee78f18865244a5e5a98536f21c65c2e89845859cf1b4dd6c1a93a2e0c1b639e | R | 5,441 | 141 | # Construct the txt annotation file for each comparison
# Written by Lucia Trastulla -- email: lucia_trastulla@psych.mpg.de
# note: output 1 column file, first element refers to pre reprogrammed, all the other elements to post reprogrammed
suppressPackageStartupMessages(library(argparse))
parser <- ArgumentParser(de... |
74ddd5374e83a25559b869248ffcda76adf3b1481b1890f6c3e4ff8575d4b08a | R | 5,447 | 125 | # trim alignment, realign, filter for conserved regions
library(tidyverse)
library(Biostrings)
setwd("")
count_start_gaps <- function(alignment){
split_align <- strsplit(as.character(alignment), "[A-Z]+[-]+")[[1]]
split_align_1 <- split_align[nzchar(split_align)]
length_first_gaps <- ifelse(length(split_align_1)... |
7065c3db7cf227bac0f74a050ed42ad2f07da6f8c8fec57845917264211bf707 | R | 5,449 | 143 | # execute as:
# Rscript annotate_m6anet_output.R high_confidence_modification_sites_from_m6anet.csv gencode.gtf output_prefix
suppressPackageStartupMessages({
library(tidyverse)
library(data.table)
library(reshape2)
library(purrr)
library(GenomicRanges)
library(GenomicFeatures)
})
options(dplyr.summarise.i... |
24bb30ec0ab326861cecff84985d96fba4f8eea91384f8bb4519d5b6828897e9 | R | 5,453 | 117 |
###this is for plotting Venn and Euler diagrams for gene_ids enriched in multiple comparisons
#load libraries
library(ggvenn)
library(tidyverse)
library(readr)
library(stringr)
#Human
#upload output tables from Deseq2
#this script is currently set up to be performed on the human comparisons
#replace h for m t... |
6e10bd20380eff3af2933b406faa0fe5df010114fe7c81a0c5d7f474c8e6cf71 | R | 5,465 | 130 | library(tidyverse)
library(devtools)
library(data.table)
library(GenomicRanges)
library(here)
load_all(here("code","rlucas"))
bins <- unique(GRanges(bins5mb %>% select(chr, start, end, arm, bin)))
#--------------------------------------------------------#
get_arm_ranges <- function(assembly){
mySession <- rtrackla... |
501369aa756e8fbb089f3ca9eb126b007406cb4e501721ec638c1597b193ccc4 | R | 5,485 | 142 | library(Seurat);library(tidyverse)
#Subset PC clusters and use scRNA data to transfer annotation
cdS <- readRDS("data/integrated_all.rds")
PC <- readRDS("data/PCi.rds")
DefaultAssay(PC) <- "RNA"
Idents(PC) <- "PC0.8"
PCsp <- subset(cdS,idents = c(paste("PC",1:11,sep = "")))
p <- DimPlot(PCsp, pt.size = 1, label = T)+N... |
199ea5d0c08e96dc458606c2754337babc68b939460ed327fe530d571eb66886 | R | 5,488 | 112 | #22/1/2024
#Zeinab Eftekhari and Dr. Thomas Shaw
# This R script creates scatterplots to visualize metabolite concentrations across different MRI sequences
# (sLASER and STEAM) and field strengths (3T and 7T). It reads data from a CSV file, filters it, and generates
# four plots that compare concentrations betw... |
7dc1a4dcfe037a493e2b154a18e6a02473383d4c8d0225de63a63e30acbee7f5 | R | 5,499 | 162 | ### Author : Marie-Michelle Simon
### Date : April 30th, 2023
### Goal 1 : Generate a scatter plot showing the Pearson Correlation between the replicates (n=3) for condition WT, for H3K27me3 data on musc
### Goal 2 : Generate a scatter plot showing the Pearson Correlation between the replicates (n=3) for REST Cut and ... |
7940b72e6a1b65b91309c4f68a0a8bde6292631fbec1f6c37b0fc07ec15d2164 | R | 5,528 | 172 |
EMP_COR_SANKEY<- function(data_list,pvalue=0.05,rvalue=0,cor_method='spearman',sankey_ouput = F,file = 'Sankey.html' ,positive_col = 'darkred',negtive_col = 'steelblue',palette=c("#009E73","#F0E442","#E64B35FF","#CC79A7","#4DBBD5FF","#00A087FF","#3C5488FF","#F39B7FFF","#8491B4FF",
... |
eb59a1428ca31fe99dd1549662d66af8b87e44e97177295fa9698d500e23655e | R | 5,558 | 143 | #######################
# 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... |
6431fde688b6604ebef225117cf973a7d4fa936f9a690b03696006857cd6afd8 | R | 5,714 | 164 | # Matrix eQTL by Andrey A. Shabalin
# http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL/
#
# Be sure to use an up to date version of R and Matrix eQTL.
# source("Matrix_eQTL_R/Matrix_eQTL_engine.r");
library(MatrixEQTL)
library(tidyverse)
library(qvalue)
args = commandArgs(trailingOnly=TRUE)
snpPrefix <- ... |
2a70d4b85c62745e6fac14b31ee4b04d0f8c7c98eb39c04199d02c39dc06237f | R | 5,721 | 108 | #' @title get_sigcell_simple
#'
#' @description a function to determine statistically trait-enriched
#' cell by permutation test.
#'
#' @param knn_sparse_mat a sparse matrix used for network propagation,
#' which indicates the adjacent matrix (m x m, where m is the cell number)
#' of cell-to-cell network (M-kNN graph)... |
3692d3292f37f5c8c2d8b75e052f26260d8fcea2c057343779bece65dd4cbe18 | R | 5,727 | 168 | projDir <- "~/npj"
setwd(projDir)
source("../../script/functions_basic_stats.R")
package.list <- c("psych","reshape2","lmerTest","lme4","afex","car","QuantPsyc","dplyr","ggplot2","Hmisc",
"broom","tidyr","corrplot","ggpubr","Matrix","tibble","ggeffects","effects","stringr","ggpattern", "effsize","rsta... |
8846e1d4f2d3fdfae6edbc0fbca2a6b29914095fc83e27a788ed92e3ec5d0274 | R | 5,739 | 112 | #library(devtools, lib.loc="/net/bmc-lab6/data/lab/kellis/users/ssz/software/Miniconda3/envs/devtools/lib/R/library")
Run_SCAVENGE <- function(SE_Data, trait_file){
# trait_file: <chr> <snp_loc> <snp_loc> <snp_id> <posterior probability> <filename>
require(SCAVENGE)
require(chromVAR)
require(gchromVAR)
... |
316dcd51b9f76b793c4dcf2589002855e419854a57422d68988e5cc71fccde3c | R | 5,756 | 153 | # =======================================================================================
# Purpose: Cell clusering and domain segmentation based on imputed spatial transcriptome
# Author: James Li
# Date: Jan 05, 2025
#
library(Seurat)
library(BASS)
library(tidyverse)
obj = readRDS("data/merge_4x.rds")
obj.list = ... |
752e7677f009d9c75558c8d128b1e2257380338173c39b614d990cab47ce046a | R | 5,778 | 131 | ---
title: "S21a"
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
```{r}
library(here)
library(data.table)
library(tidyverse)
library(devtools)
library(ggplot2)
library(ggpubr)
library(cowplot)
library(caret)
library(recipes)
library(pROC)
library(gbm)
#These are the ... |
edfecc7151b062a937a32e2f8fc95943a41b0abd44a09cc6b92bb8fb5034a78f | R | 5,845 | 123 | library(tidyverse)
library(parallel)
library(RcppAlgos)
source('helper.R')
# get prediction results for all variants
input_folder <- '/lab/solexa_bartel/coffee/Sequencing/Gnomad/PolyA/Gnomad_v4_variants_TL_prediction_CV_MINN_XL_HS_MM_L_2000_CDS/'
pred_files <- list.files(path = input_folder, full.names = TRUE, pattern... |
2dd38918abca65520d2679728a027631fdf23acb8b646beb4af9dfc9a210fbcb | R | 5,875 | 157 | ---
title: "PSF toolkit: Getting Started"
author: "Siras Hakobyan"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{PSF toolkit: Getting Started}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(collapse =... |
37b2b0cb277dc8f541f2afc77d5cc47878a1f658acd2cfa8c49a862cc100d12b | R | 5,895 | 248 | ---
title: "Mice CSF-cN Intrinsic Properties along the rostro-caudal axis"
author: "Nicolas Wanaverbecq - Elysa Crozat - Edith Blasco"
date: "`r Sys.Date()`"
output:
pdf_document:
toc: yes
toc_depth: 5
params: null
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## OPEN REQUIRED PA... |
90d5d038c314bc868f9bbc18e28159dfaf960ddec2959e571d72d423f6588093 | R | 5,963 | 133 | #' Downsample datasets
#' @description This function mainly aims at downsampling datasets to a size
#' suitable for plotting or expensive in-memmory calculation.
#'
#' Users can balance the sample size of categories of interests with
#' \code{balance}. Multi-variable specification to \code{balance} is supported,
#' so ... |
2bf9639228dca17ad8c5dfef9c90e79d9cf73be34f9ee43f22b83d8614726696 | R | 5,965 | 102 | install.packages("openxlsx")
library(openxlsx)
library(tidyverse)
library(readxl)
library(dplyr)
library(knitr)
library(naniar)
library(plyr)
library(tibble)
library(writexl)
#read in unstained NM control data
df_2 <- readxl::read_excel("//shared.sydney.edu.au/research-data/PRJ-NeurodegenNewApp/Anastatia/Y... |
8f7367efbde79b65b64d7ae7a771119b980fc22614a3b8739c639dae80897c62 | R | 5,974 | 200 | library(lme4)
library(pbkrtest)
library(simpleboot)
library(lmeresampler)
library(foreach)
library(doParallel)
#define MLM w/ three-way interaction --------------------------------------------
# x = rv_list
lmer_three_way = function(x, data = df_lite) {
mod = lmer(paste(x, "~ p15_cno * sex * f.layer + (1 | subject_... |
922a784f1af0ccc0f0d4d8fb44c77371922ea12e22fc0b2c9c10be15f43a7ab0 | R | 5,993 | 202 | # ----------------------------
# Enhanced GRN Visualization with Split MoR_sum and Clean Layout
# ----------------------------
# ---- Load Required Packages ----
if (!require("igraph")) install.packages("igraph", dependencies = TRUE)
if (!require("visNetwork")) install.packages("visNetwork", dependencies = TRUE)
if (!... |
40fa5f79f3e8ac29dd49f5a3d667d6a88ed0b7274061e96b88f2ded6aa839745 | R | 6,034 | 127 | prepare_Carnival <- function(mapk_data,
filtered_expression_matrix,
USER_EM2,
results_dir,
inputs_dir,
disease_filename,
j,
... |
5851f717decd68c522fc3cf2fcb34597928f3fdb71959e712fe0e0390eaaaf06 | R | 6,065 | 124 | ---
title: "Install LIGER with R"
author: "Yichen Wang"
date: "2024-03-20"
output:
html_document:
toc: 3
toc_float: true
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, eval = FALSE)
```
Before setting up the "rliger" package, users should have [R](https://www.r-project.org/) version 3.6... |
5d51317b8414edbb666698226c899ec51fca16ea61f4c92c867613564e513ed6 | R | 6,106 | 184 | #' Liger cell/feature metadata
#' @rdname liger-metadata
#' @description
#' The rationale for defining these classes is to have a data frame extension
#' class holding the data, so that printed view looks tidy. \code{tibble} is
#' selected as the base class for metadata. However, \code{tibble} does not
#' allow \code{r... |
a1d2397421f4853c6b4f63b3cc6d22b029606c2bb83c1d86084033c7b9442bfd | R | 6,146 | 153 | ---
title: "Patients age 50-80 with 20+ pack years"
author: "Jamie Medina"
date: "2/9/2021"
output: html_document
---
```{r background, echo=FALSE, include=FALSE}
###################################
# Task: Predict
# Created: 012221
# Due date: Monday 25, 2021 or Tuesday 26, 2021
####################################... |
1a991c1a9ee49ea5d6d826a516d6f5843cb4d568dd8343a5f62b0c683d103c7b | R | 6,164 | 153 | library('signs')
library('Cairo')
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 - bmin) / (length.... |
dcc2910bf8a4458713890fcaba003b046c06f13521a70fb84686d3b50d78bb93 | R | 6,178 | 137 | ---
title: "Cross-Species Analysis with UINMF"
author: "April Kriebel and Joshua Welch"
date: "12/6/2021"
output: html_document
---
```{r setup, echo=FALSE}
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
```
In this vignette, we demonstrate how to integrate dataset from different species.
## Step 1: Load ... |
2c202fef113b15cd325d888ea90184758591cf3a3a299afbc9194d1331961005 | R | 6,179 | 181 | # Load required libraries
library(devtools)
library(data.table)
library(GenomicRanges)
library(HMMt)
library(stringr)
# HMM function to run Hidden Markov Model on genomic regions
HMM <- function(normalized, na_solution, file) {
# Validate the 'na_solution' input
if (!na_solution %in% c("NA", "keep", "-")) {
... |
75feaf972eff7cff58f2d26fb3d24f4a5538d69e3f75c745b7babf7109a083d1 | R | 6,202 | 148 | #20240822
#ZE and TBS - wide-to-long format transformation for metabolite data
# This script processes a series of wide format CSV files, transforms them into long format, and then
# combines these transformed files into a unified dataset. The script is organized into several key steps:
#
# 1. Load the necessary ... |
db1af1c95566077360b15dc6551308a78916359acc0d235d4cf224eedc9a0fd1 | R | 6,205 | 211 | ---
title: "SCopeLoomR Seurat tutorial"
author: "Katina"
author_gh_url: "https://github.com/tropfenameimer"
date: "20/02/2021"
output:
html_notebook:
toc: yes
html_document:
keep_md: true
df_print: paged
toc: yes
toc_float: yes
BiocStyle::html_document:
number_sections: no
pdf_document:
... |
c0409f49db15cd5e63d489f0335fb18b466cfc374d1265a41e9ff4ce1d3aead7 | R | 6,208 | 178 | # remove genes with 80% zeroes and na rows
remove_sparse_rows <- function(df, threshold = 0.8) {
df[rowMeans(df == 0, na.rm = TRUE) < threshold & !apply(df, 1, function(x) all(is.na(x))), , drop = FALSE]
}
# log2 and z-score normalization
norm_dat <- function(df, nor) {
df <- log2(df+1)
if (nor=="t... |
41c4dd827929783caf7d57284e9ad4987c84209846b27e02e0ac29db676d87ef | R | 6,231 | 123 | ---
title: "Unshared Features UINMF"
author: "Joshua Welch and April Kriebel"
date: "12/03/2021"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
```
Unshared integrative non-negative matrix factorization (UINMF) [[Kriebel and Welch, 2022](http... |
1f9f858be65f26b6951ad8e674c2a2893e500e7241aa9e1fdba979ae5cfa0394 | R | 6,241 | 89 | library(tidyverse)
library(Biostrings)
source('helper.R')
###----------------------------------------------------------------------------------------------------------
###--- Fig. 1e ---###
##-- prediction versus measured scatter plot
# Training data: Frog endogenous mRNA tail-length change during oocyte maturation
#... |
67d604efc404dfa120ad247f777cf2218507373f56394d0b7e38fad98ff65705 | R | 6,262 | 197 | # Load necessary libraries
library(lme4) # For fitting linear mixed-effects models
library(pbkrtest) # For Kenward-Roger approximation for mixed models
library(simpleboot) # For simple bootstrap methods
library(lmeresampler) # For resampling methods for mixed models
library(foreach) # For looping con... |
b42eaf007839ac9d42e2006c8aa48267313b71731fd260e5b1e2d08eb5c9d073 | R | 6,318 | 162 | # =======================================================================================
# Purpose: Quantification of eCN neurons among 4 different genotypes
# Author: James Li
# Date: December 31, 2024
#
library(Seurat);library(tidyverse)
setwd("/Users/jamesli/Documents/Manuscripts/PC_Manuscript/data_analysis/Multi... |
e6cd9175c3fba4a87839fad3486cde76c21d2d694a3ae1c233845dd962ef2070 | R | 6,370 | 207 | ---
title: "Fragmentation profiles"
site: workflowr::wflow_site
date: "`r format(Sys.time(), '%d %B, %Y')`"
output: html_document
chunk_output_type: console
---
```{r caching, echo=FALSE}
knitr::opts_chunk$set(autodep = TRUE)
```
```{r packages, echo=FALSE, message=FALSE, warning=FALSE}
library(ggplot2)
library(magri... |
bdcfb7713bd30e1e9258acd19adde7724dfd1d38356f638b39fed786f243645a | R | 6,412 | 143 | ## reading manually selected pathway list
selected_pathway_set <- read.delim(file = "/home/siras/PSFC/inst/extdata/selected_pathways.txt", sep = "\t", stringsAsFactors = F)
## building keggres links data frame for downloading
selected_pathway_set <- data.frame(pathway_code = selected_pathway_set$pathway_code,
... |
abddd27be7a4b5ba1d536588e99e281d18a6b868b952a6ee626532212e9e8d1b | R | 6,435 | 132 |
####### Dec 2023
# This script subtracts RRBS oxidative Bisulphite signal (oxBS, 5mC only) from RRBS Bisulphite signal (BS, 5mC & 5hmC)
# It subtracts percent methylated oxBS from BS to arrive at 5hmC percent methylated (vairable b)
# It subtracts count oxBS methylated from BS methylated to get 'count 5hmC' (variabl... |
3524b8719480fe5067b101b56bc3f5ff1af4d29989073ce9842f513f1e82d592 | R | 6,458 | 158 | #functions for loading packages, labeling boxplot outliers, and creating and saving pairwise and listwise deleted correlation tables, box plots, histograms, scatter plots, bar graphs, and multi-individual line graphs####
#package loading function####
load.packages <- function(package.list){
new.packages <- package.li... |
f4c555180f731a61eaa5910f5d7718d45ce1d6eddd3ee86cbf812f4a0bd5a5f2 | R | 6,527 | 135 | library(data.table)
library(DT)
library(miniUI)
library(shiny)
library(Seurat)
library(ggplot2)
library(psf)
library(magick)
library(shinyjs)
library(visNetwork)
library(plotly)
shinyUI(
navbarPage("PSF Spatial Browser",
tabPanel("App",
fluidPage(
tags$scrip... |
6483c01c96f5f03b35d28a28df7b342e15f6d7f7ace5610d24c7e8824464af2d | R | 6,555 | 127 | #' @import ggplot2
#' @importFrom reshape2 melt
top_abundance_caculate <- function(data,structure_method = 'mean',num = 10,estimate_group='default'){
if (estimate_group== 'default') {
estimate_group=unique(data$Group)
}
if (!all( estimate_group %in% data$Group )) {
warning('estimate_group is wrong, ... |
53290f67fb76283c5e30528deee8dfddf4e81124052a535345e86d5a8c39e985 | R | 6,560 | 203 |
projDir <- "~/Library/CloudStorage/Box-Box/YP Projects/MathFun/0_npj_revision2_md_td_tutoring/roi"
setwd(projDir)
source("../../script/functions_basic_stats.R")
package.list <- c("psych","reshape2","lmerTest","lme4","afex","car","QuantPsyc","dplyr","ggplot2","Hmisc",
"broom","tidyr","corrplot","ggpu... |
a1598e96a50148358d676dad3c33d85e68d2226b773c516c98643e330f836fa5 | R | 6,578 | 145 | # =======================================================================================
# Goal: Examine the robustness of PC subtype classification using publicly available data
# Author: Nagham Khouri-Farah1
# Date: Sep 1, 2023
#
library(Seurat);
library(tidyverse);
E1618a <- readRDS("/data/E16_18_All.rds")
DimPl... |
f845890eb251652d65c370261ee65c5f6d912d5e39babba2914402c2060b5a2b | R | 6,582 | 198 | ---
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... |
31979b96ef7cf681ecb8965cfb08f9f7e74fe8106c3002774727dde1673d8030 | R | 6,624 | 146 | ---
title: "2D_S10"
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
```{r composite, echo=FALSE, fig.width=16, fig.height=10, dev=c("png", "pdf")}
library(data.table)
library(gridExtra)
library(devtools)
library(here)
load_all(here("code","useful.stuff.aa")) #... |
36e4db0e319ff5af7e034e25a1477a86e02ab323dc8322ed8316ba2bac06686f | R | 6,692 | 162 | ---
title: "3A"
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
```{r}
library(here)
library(data.table)
library(tidyverse)
library(devtools)
library(ggplot2)
library(ggpubr)
library(cowplot)
features<-fread(here("data","Final_LUCAS_Ensemble","LUCAS_artemis.csv"))
feat... |
6e0acd5c089180ea3606bb975cb3bbb1b715266840014487d438d3b7c2512855 | R | 6,797 | 165 | ---
title: "S2_S3abc"
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
```{r}
library(here)
library(data.table)
library(tidyverse)
library(devtools)
library(ggplot2)
library(ggpubr)
library(cowplot)
library(RColorBrewer)
```
```{r}
data<-fread(here("data","Kmer_Distri... |
0ec6a07e1945140b77b8edab715c65b7fa647bca4e18da8ae906044f4ca6750f | R | 6,808 | 152 | ---
title: "S17"
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
```{r batch, echo=FALSE, fig.width=10, fig.height=6, dev=c("png", "pdf")}
library(here)
library(data.table)
library(tidyverse)
library(devtools)
library(ggplot2)
library(ggpubr)
library(cowplot)
library(r... |
e7accb04e99f05f620421b593aa01aa3c6de17c3d56fc815e0b118614a745d63 | R | 6,830 | 172 | ### Author : Marie-Michelle Simon
### Date : April 30th, 2023
### Goal : Generate a bargraph to compare the rate of complete RE-1 motifs found in H3K4me3 peaks (near non-muscle genes) VS the rate of complete RE-1 motifs found in the random genomic regions of the same length
### The matching random genomic regions of ... |
77660714767f43e247274df0fde55ed15214886c05436296f8d34e031e90b958 | R | 6,943 | 152 | # this script calculate translational efficiency changes for human and mouse mRNAs during oocyte maturation with re-precessed published datasets
library(tidyverse)
library(DESeq2)
size.factor = function(dframe) {
# a function for normalization using DESeq2
if (is.data.frame(dframe) == F) {
stop("Please use datafra... |
81c95de85d3d00c2f70924edb9028ca4f04391d7c0cc9266fdc8f16dba7d3780 | R | 6,955 | 154 |
# 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... |
677b10150b9c342a09253cd64232dd447c567670f5686f90e1708c1de44af2e9 | R | 7,015 | 165 | ---
title: "Visualizing Feature Distributions in a Heatmap"
site: workflowr::wflow_site
output: html_document
chunk_output_type: console
---
```{r packages, echo=FALSE, message=FALSE, warning=FALSE}
library(tidyverse)
library(openxlsx)
library(devtools)
library(ComplexHeatmap)
library(circlize)
library(data.table... |
f544de4a717c9d708f62226c5c66df54a7f34b18a7e84a222de17756fa6d3ef9 | R | 7,030 | 181 | library('signs')
library('Cairo')
library('RColorBrewer')
library('ggrepel')
library('ggseqlogo')
library('cowplot')
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 |... |
be337a34b5c75ff4c419e361ad46a1d1df2083004add072ab13a4f176ca0438a | R | 7,082 | 188 | ---
title: "Training Model Variation - Feature Importance"
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)
```
```{r packages, message = FALSE, ... |
632a9574a5294d0e9df532b701fb38cebd69b07c8aeb28538c7f083d84424ea4 | R | 7,086 | 229 | ---
title: "S3d_S4"
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
```{r}
library(dplyr)
library(devtools)
library(here)
load_all(here("code","useful.stuff.aa")) # Load
library(tidyverse)
library(data.table)
library(readxl)
library(ggplot2)
library(corrplot)
... |
5ecb1e2c6f1db9806df8f707720662da02a14d3514bb1eab15152c1b00624141 | R | 7,199 | 132 | ---
title: "Home"
site: workflowr::wflow_site
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
## Detection and characterization of lung cancer using cell-free DNA fragmentomes
Abstract:
Lung cancer remains the leading cause of cancer death world-wide, largely due to i... |
532b2f485aabe31d66c7508c976939715e166bc44d60cca7e4a66039edc06dd0 | R | 7,262 | 184 | library('tidyverse')
library('ggplot2')
library('Cairo')
library('RcppAlgos')
library('RColorBrewer')
library('viridis')
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... |
997b3823457f4b86bc9daeb0ae0a79f671eb27c98efb5ef64de610f687bff45c | R | 7,382 | 182 | #Sorts kraken output into 3 streams: unclassified, prokaryotic (bact + arch), and eukaryotic
library(Biostrings)
# Set the working directory
args <- commandArgs(trailingOnly = TRUE)
# Define the file path
file_id <- commandArgs(TRUE)[1] # The first argument will be the file path
sample_directory <- commandArgs(TRUE)[... |
2c7cbdc842f2f9db6d2922de12f2e8c10358297e4f712925c80858c0872949fe | R | 7,387 | 239 | #### General functions for operations on the genome
#### Functions in here likely can be sped up with compiled code
#input a bed file, get out a wig file
bedtowig <- function(pathToBed){
input <- pathToBed
out <- gsub(".bed", ".wig", input)
library(data.table)
library(rtracklayer)
tilesdt... |
8cb86bddd500d8e2376f1bea1af0ba4f4701745032ccc1feb6a958cec5706134 | R | 7,484 | 208 | #'*PLS-DA Plots of Metabolomics Datasets*
# LIBRARIES:
library(ggforce)
library(scales)
library(mixOmics)
library(ggrepel)
library(uwot)
library(tidyverse)
# Functions ------
metabolite_clean1 <- function(metabolite_list){
metabolite_list <- gsub(".TMS","",metabolite_list)
metabolite_list <- gsub(".2TMS","",metab... |
19eadfe4c81448422e3aaa333e925616bd776c5895ed60a7f217c4e88bb9f615 | R | 7,496 | 194 | ---
title: "2c_S8S"
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
```{r}
library(dplyr)
library(devtools)
library(tidyverse)
library(data.table)
library(here)
library(readxl)
library(ggpubr)
library(cowplot)
```
```{r MCCC1, echo=FALSE, fig.width=6, fig.height=6, d... |
f2df5c9cfb7e52579da547095a04ad3a7623d2493d6a4d720853111a7599416e | R | 7,548 | 209 | library(tidyverse)
library(readxl)
library(magrittr)
library(stringr)
library(purrr)
library(kableExtra)
library(SummarizedExperiment)
library(here)
library(devtools)
devtools::load_all("../../rlucas")
load(here("data", "prediction_lucas.rda"))
load(here("data", "metadata.rda"))
data(bins1kb, package="svfilters.hg19")
... |
582bd52898db1669b42b724a91819d5b9782a22b63fdd4eea1f78c63a26882f2 | R | 7,786 | 170 | tax_plot_ttest <- function(data,tax_select=NULL,width_total=20,height_total=20,width=10,height=8,seed=123,row_panel=NULL,mytheme=theme(),palette=c("#E64B35FF","#4DBBD5FF","#00A087FF","#3C5488FF","#F39B7FFF","#8491B4FF",
"#B2182B","#E69F00","#56B4E9","#009E73","#F0E442","#0072B2","#D55E00","#... |
d146e2e690c479205afbe0af02e78d792519a99f3e2796c020af559bc00ed679 | R | 7,849 | 168 | # Plot CNV for a single line in the entire genome, use QC summary files
# Written by Lucia Trastulla -- email: lucia_trastulla@psych.mpg.de
suppressPackageStartupMessages(library(argparse))
##############################
suppressPackageStartupMessages(library('ggplot2'))
suppressPackageStartupMessages(library('gridEx... |
051eec7a7b34dcdbe7b41423e8b4c5e8093ebe2f00e1bf825c20fcd71cba9a7c | R | 7,892 | 205 | ---
title: "2B_S7"
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
```{r}
library(here)
library(data.table)
library(tidyverse)
library(devtools)
library(ggplot2)
library(ggpubr)
library(cowplot)
library(RColorBrewer)
```
```{r}
kmer<-fread(here("data","CN_Analysis","... |
78fa0054d8cfae971284733aa6fc66d3e185f4372dc4767c00518fa2f8dde163 | R | 7,900 | 111 | library(unifed)
# This code requires the user to have files named DatasetName_CellType_DifferentialExpression_UpReg.txt in the directory
# DatasetNames is a vector of strings of dataset names. BroadClusterTypes is a vector of cell types. PresenceofDataTable is a table detailing whether a cell type has data for each ce... |
dcc3440b8c0f75bf87b540a12fa08f04c257189abf39fd1d30cf6444d6ae9de0 | R | 7,949 | 184 | ### Author : Marie-Michelle Simon
### Date : April 30th, 2023
### Differential accessible region analysis for REST WT and KO condition comparison in ATAC-Seq libraries
### GOAL : Generate list of differentially accessible region (more accessible in KO and less accessible in KO compared to WT)
### EXAMPLE CODE ONLY : ... |
2e0587a5dc7d08fc68ff63cbea792670d5df773ebb6c2922348d75bf181c8b0d | R | 7,969 | 236 | ---
title: "Effect of PCR on S/L ratios without GC correction"
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 packages, message=FALS... |
b0449f00a6228ca28a5b7c4f804b70e160c7627251101f3095c8f786cb851c16 | R | 7,971 | 197 | #-----------------------------
#-----------------------------
# standard DESeq2 and PCA analyzing new sequencing
#-----------------------------
#-----------------------------
#-----------------------------
# Load required libraries
#-----------------------------
library(readr)
library(DESeq2)
library(tidyverse)
lib... |
8d1d857fc0d7c7bd86640466a44b576b895d13e0102617a625733f2296961012 | R | 8,033 | 213 | #' `r lifecycle::badge("experimental")` Suggest optimal K value for the factorization
#' @export
#' @description
#' This function sweeps through a series of k values (number of ranks the
#' datasets are factorized into). For each k value, it repeats the factorization
#' for a number of random starts and obtains the obj... |
1dade06f0735bc693777bb9fa837500f6ce476e0e0b24359d3f53a3f807e5aed | R | 8,062 | 209 | # Call in a way like:
# object <- recordCommand(object, dependencies = ...)
# Conditionally, should be placed after input checks
# like `match.arg()` or `.checkUseDatasets()`
# `...` is for the ... arguments in real function call, so S3 arguments passed
# to downstream can be also captured
recordCommand <- function(
... |
4bef9f7a4f9122771fea2591e4db590c62d20695a4749f4858350cca73246bae | R | 8,213 | 170 | #!/usr/bin/Rscript
#=####################################################################O
# R Script with helper functions for displaying LMEM model details
#
# Author: R.Muralikrishnan
# Script Version: 2024-11-30
#=#####################################################################m
library(tidyverse)
library(mag... |
50c9ce9a9027c865d5eb6559081557e7bbccf64e033893de8467b29464d92708 | R | 8,281 | 196 | ---
title: "consensus HLA Report"
output:
pdf_document:
highlight: tango
number_sections: TRUE
params:
clin_hla_json: NAMESPACE
germline_hla_json: NAMESPACE
tumour_hla_json: NAMESPACE
rna_hla_json: NULL
pid: NAMESPACE
date: "`r format(Sys.time(), '%d %B, %Y')`"
subtitle: "`r paste('Patient ', params$pid... |
7672af64a9d9ee82223acf64a5d6227aa8c8c67d5f1712130e84f14f0d406aea | R | 8,301 | 229 | ---
title: "S18"
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
```{r}
library(tidyverse)
library(data.table)
library(ggplot2)
library(here)
library(devtools)
load_all(here("code","useful.stuff.aa")) # Load
library(gridExtra)
library(cowplot)
library(readxl)... |
00dd6e9d7367cc470fd6b7d361cd2354e003f11fe3d3d1b6dad5773bde4d58ee | R | 8,305 | 183 | library(tidyverse)
library(Biostrings)
source('helper.R')
#------------------------------------------------------------------------------------------------------------------------------------
# density plot for CPE and PAS in Xenopus
###--- Supplementary Fig. 3e ---###
l_max <- 1000 # max length of 3' UTR to analyze ... |
1984c13b69d807a8f636b1209ab5b4b0f38e190369bb86738de89fb2ad9c774f | R | 8,305 | 205 | #' ---
#' title: Experiencer Verbs in Malayalam -- Light Verb Constructions
#' subtitle: ERP Data analyses using Linear Mixed-effects Models - NP
#' author: " "
#' output:
#' html_document:
#' code_folding: show
#' theme: flatly
#' highlight: kate
#' ---
#'
#' <style>
#' pre {
#' overflow-x: auto;
#' ... |
1cef0237101e35369f9016d508365a50de725cb7cd2ed774597acf5134561879 | R | 8,318 | 182 | # Plot LRR and BAF for a pairwise compasion, 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'))
suppressPackageStartu... |
151c1a6aa4a4de064b7884adec23bcb88cdeb89bedf4feb3f86da8b284f8d1f5 | R | 8,321 | 228 | library(ggplot2)
library(dplyr)
#######################################################################################
tt=1 #### tt=1 to reproduce case 1 (linear) and tt=2 to reproduce case3 (nonlinear)
#######################################################################################
case_all<-c("lin... |
239dba0de5dba49701c7fcdc3e54a4a25bff8d72dc01064b3a416157f474bf70 | R | 8,321 | 228 | library(ggplot2)
library(dplyr)
#######################################################################################
tt=2 #### tt=1 to reproduce case 1 (linear) and tt=2 to reproduce case3 (nonlinear)
#######################################################################################
case_all<-c("lin... |
c909e057f25e8fe0de6342916c61d7fca9cb382a8e14dca3bf5954e95b24aa90 | R | 8,354 | 215 | # Load test data from STARsolo run, process with Seurat
# AUTHOR: Adam Reid
# Copyright (C) 2023 University of Cambridge
# This program is distributed under the terms of the GNU General Public License
library(Seurat)
library(SeuratDisk)
library(SeuratWrappers)
library(stringr)
library(dplyr)
library(ggplot2)
library(... |
817d625901c75ca72786569c47432283c2fb833e2eaf17e8c9efc9a8884c616f | R | 8,433 | 90 | ---
title: "Home"
site: workflowr::wflow_site
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
# Citation
Annapragada, A.V. Niknafs, N. White, J.R. Bruhm, D.C., Cherry, C., Medina, J.E., Adleff, V., Hruban C., Mathios, D., Foda, Z.H., Phallen, J., Scharpf, R.B., Velcules... |
06e58597ff0d688c67e94dd882254f00836ada358d277f6687da6ec2f82443bf | R | 8,457 | 147 | library(data.table)
signor_all_pathways <- fread("~/Signor_database/all_signor_pathways.tsv", sep = "\t", stringsAsFactors = F, data.table = F)
signor_all_pathways$pathway_name <- gsub(" ", "_", signor_all_pathways$pathway_name)
signor_pathwa_names <- unique(signor_all_pathways$pathway_name)
# library(biomaRt)
# en... |
7d076ec609f3764c7e980d90c845beb8dd5c382fcbc5a9f6776839ca625e3b8a | R | 8,552 | 303 | # auxiliary functions for the QC of CNV and the plots
# Written by Lucia Trastulla -- email: lucia_trastulla@psych.mpg.de
find_centromere <- function(df_ch, initial = FALSE){
len <- length(df_ch)
diff_pos <- df_ch[-1] - df_ch[-len]
id_max_gap <- which.max(diff_pos)
centr_coord <- df_ch[(id_max_gap):(id_... |
e6f7e8030850a2c038210b79d6697541e03e09f017182582db8a47385ce4fc36 | R | 8,603 | 260 | #' @import ggplot2
#' @importFrom randomForest randomForest
#' @importFrom randomForest varImpPlot
#' @importFrom randomForest importance
#' @importFrom pROC roc
RFCVSEED <- function(RF,seed_start=123,ntree=1000,core=1,kfold=5,rep=10,RF_importance=1,step=1,each_ouput=F){
value=c("MeanDecreaseAccuracy","MeanDecrea... |
98026a7990638a3c44dfc7d8182628b57c46025e678c460c344e3546197d8c11 | R | 8,633 | 178 | ---
title: "S5_S6"
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
```{r}
library(here)
library(data.table)
library(tidyverse)
library(devtools)
library(ggplot2)
library(ggpubr)
library(cowplot)
library(RColorBrewer)
```
Organize the data for the matrix
```{r}
feature... |
efaeb95682a104fa8d0bd0d56d932f4f4b6d4bff1e2d61e6eafa384b2c267899 | R | 8,656 | 179 | library(data.table)
library(dplyr)
library(corrplot)
library(Hmisc)
library(ggpmisc)
# Correlation analysis
dat_corr<-read.csv("df_behavioural_ques_wide_subj25_2311_selected_v2.csv")
df<-dat_corr[1:25,-c(1:2)]
## Fig. C2:
c_df <- rcorr(as.matrix(df), type='pearson')
corrplot(corr=c_df$r, p.mat=c_df$P, sig.level=0.0... |
e986e1ac22c265667202470043b811fc9b2b85cbcd7058e6c8bea053c4a967be | R | 8,670 | 224 | # Compute the heatmap for the GT amtch and update the annotation file with the GT match
# Written by Lucia Trastulla -- email: lucia_trastulla@psych.mpg.de
suppressPackageStartupMessages(library(argparse))
suppressPackageStartupMessages(library('ComplexHeatmap'))
suppressPackageStartupMessages(library('RColorBrewer'))... |
5ab570e2a7c45ec767a29cc1120e52a5c66bd78a353c73b82ebb03d70fe4b5b1 | R | 8,747 | 321 | #!/usr/bin/env Rscript
library(here)
library(data.table)
library(gt)
library(dunn.test)
### INPUT
fpaths <- list(
RDS = c("adnimerge_baseline", "adni-bl_volumes_hcvc") |>
sprintf(fmt = "data/rds/%s.rds") |> here(),
SRC = c("parse_adnimerge-bl", "qc_segmentations_adni-bl") |>
sprintf(fmt = "code/data_parsi... |
f8df4529f9f84d3ffa8fa8366c924e39c058371d614b87cc4f6824c59e2412e7 | R | 8,775 | 251 | ---
title: "Relative coverage plot and ROC"
site: workflowr::wflow_site
output:
workflowr::wflow_html:
code_folding: hide
toc: true
editor_options:
chunk_output_type: console
---
```{r caching, echo=FALSE}
knitr::opts_chunk$set(autodep = TRUE, echo=FALSE)
```
```{r load_data, echo=FALSE, include=FALSE}
... |
94dedbec145e594a278b70e00cd043e248343e9d14d586603d54c4bd6f1536e2 | R | 8,865 | 156 | library(tidyverse)
library(Biostrings)
library(rhdf5)
source('helper.R')
#------------------------------------------------------------------------------------------------------------------------------------
#--- examine predicted tail-length changes in ISM results (not iterative exclusion)
kmer.gain <- read_delim('../... |
d838086c8c26217a3c2d75b4667e03877fe1b52d6922677827c4f442b19f7e04 | R | 9,187 | 208 |
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")
metaDD <- cdS@meta.data
# remove the posterior-most sections and cell clusters of imaging artifacts
# we also removed mi... |
695c9c3f17f6f30c01ddc19d2fefa80edeb1cb3faa7ab125f59ec8253edd0a02 | R | 9,212 | 205 | library(Seurat);library(tidyverse)
cdS <- readRDS("data/integrated_all.rds")
# reload data
metaDD <- cdS@meta.data
# remove the posterior-most sections and non-specific cells
#(similar results obtained without filtering)
meta_filtered = metaDD %>%
filter(!ID1 %in% c("P1_X44675","P2_X44675","P1_X44677","P1_X49530","... |
1389e871db2b06a7e9ebb03e8b81f1c97c19b846c002578b72addcea8e064de5 | R | 9,237 | 205 | library('Cairo')
library('ggrepel')
library('RColorBrewer')
library('viridis', quietly = T)
library('ggpointdensity', quietly = T)
library('signs', quietly = T)
library('circlize')
custom_breaks <- function(bmin, bmax, digits = 0, length.out = 8, zero = TRUE) {
bmin = floor(bmin * (10 ^ digits)) / (10 ^ digits)
bmax... |
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