sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
b640fbca584ab21b0e76d68a56fc8eda72d8e1591213d56963a663bd548f1537 | R | 4,581 | 109 | #!/usr/bin/env Rscript
# Script to overlap public database file
#
# Copyright (c) 2015 Tobias Neumann, Philipp Rescheneder.
#
# This file is part of Slamdunk.
#
# Slamdunk is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free So... |
c424874021f85df0c477e5ff7d3b4781ca76587b01fd64243ac776329cac4e10 | R | 4,592 | 102 | #' @title One-liner wrapper to create a MOFA2 object from multiple input objects and ready for training
#' @name mofa2
#' @description
#' This is a one-line wrapper that combines the use of \code{\link{create_mofa}} followed by \code{\link{prepare_mofa}}.
#' Please read the documentation of the corresponding functions ... |
8c9a491b4a228b4e60ac026cfda1cf4afcb64b7c83e93bd3e0a3f50ebef26375 | R | 4,601 | 121 | setwd("/media/user/disk21/completeAnalysis/visium_2Jun/")
library(Seurat)
obj = readRDS("../visium_15Sept/visiumObj_20Sept.rds")
meta = read.csv("meta_12Mar2025.csv", row.names = 1)
identical(rownames(obj@meta.data), rownames(meta))
cpm = readRDS('../visium_15Sept/log2cpm.rds')
orig_cpm = cpm
identical(colnames(cpm... |
54940ed3f7d204fbf94eb69f9d05abe47d07ba8c8616bf652675a2841b7b20f7 | R | 4,608 | 134 | ########################### Generate ground truth outputs ###################
#
# Objective: Script to generate decision outputs for ground truth
# parameters; should be run after establishing the model sample size
# in analysis/01_load_calibration_params.R, which
########################### <<<<<>>>>> #########... |
5f37f903536f3ebb89ff53a751c4a297d98e1c58cc121b8d497d9e4bfcca7897 | R | 4,610 | 106 | # Need to export R_MAX_NUM_DLLS=1000 before sourcing this script.
library(sos)
library(htmlTable)
library(stringr)
library(dplyr)
# Get auc functions
auc.search <- findFn("auc", maxPages=1000)
auc.functions <- auc.search %>%
filter(Function == "auc", Package != "pROC") %>%
select(Package, Function, Description, Lin... |
ca801f3ae1d137949a194cb69921a3b9518ce0044efc359174523b089b9aa9f3 | R | 4,611 | 109 | #' @title Prepare Data for Volcano Plot
#' @description Formats differential analysis results for volcano plotting.
#' Handles numeric conversions, factor levels for 'State' (Up/Down/NS), and optional VIP scores.
#' @param data Data frame containing differential analysis results.
#' @return A list containing:
#' ... |
d9ae20a7595382e0f28d946811d9b378273502222830acd73c34f1b90b02466a | R | 4,619 | 136 | #!/usr/bin/env Rscript
suppressPackageStartupMessages({
library(WGCNA)
library(tidyverse)
})
# ---------- CLI 參數 ----------
args <- commandArgs(trailingOnly = TRUE)
# 預設參數（可在 CLI 覆寫）
opts <- list(
expr_csv = "RNAseq_pre.csv", # 樣本 x 基因
pheno_csv = "luad_clinical.csv", # 至少要含 sample rownames + 一個標籤欄位
label... |
f54cb42dca988d08be6df638233687a7e93d75cd4122ed90db7db09198f18947 | R | 4,619 | 100 |
# GenT vs MAGMA Type I error
# Steps:
## 1) (R) Generate simulated GWAS summary statistics under null hypothesis
## a) (R) Identify all SNP-gene pairs
## b) (R) Extract LD matrices for each gene
## c) (R) Draw effect sizes from multivariate normal distribution with known LD
## d) (R) Save file of GWAS summary stat... |
6efa3b20bba62258ecbe6e0f330c9163c61d2e90bebd21eaab050149f89d59f1 | R | 4,620 | 119 |
### PACKAGES TO LOAD
###------------------------------------------------------------------####
library(ggplot2)
library(colorspace)
library(tidyr)
library(dplyr)
library(ggthemes)
library(ggpubr)
library(ggrepel)
library(effectsize)
library(ggthemes)
library(scales)
library(forcats)
### FILE/FOLDER PATHS
###-----... |
2c681d543d25451b50554f7245c111ff334bd0d907d33a731e7bb47652282eaf | R | 4,623 | 114 | #' Perform a statistical test for differential prioritization
#'
#' Execute a permutation test to identify cell types with statistically
#' significant differences in AUC between two different rounds of cell type
#' prioritization (for instance, the response to drugs A and B, as compared
#' to a common untreated contro... |
e89ad98a7272789794ba30a624e2debfc678ef83f0dd3e837376f8a1cfe6f39a | R | 4,623 | 150 |
##-------------------------------------##
## Pseudobulk TAB ##
##-------------------------------------##
majority_vote <- function(x) {
ux <- unique(x)
ux[which.max(tabulate(match(x, ux)))]
}
aggregate_column <- function(x) {
if (is.numeric(x)) {
return(mean(x, na.rm = TRUE))
} else ... |
17bf19d569ad4f12fb7ef1286e3ce808f9407a6dcef84fc3d71339b3c9cd76b6 | R | 4,639 | 153 | library(lightgbm)
# We load in the agaricus dataset
# In this example, we are aiming to predict whether a mushroom is edible
data(agaricus.train, package = "lightgbm")
data(agaricus.test, package = "lightgbm")
train <- agaricus.train
test <- agaricus.test
# The loaded data is stored in sparseMatrix, and label is a nu... |
1cdfd25132a19543d5cad5b620bd2e82c495afc76deaad1c9d8b6e1e669d4fe8 | R | 4,640 | 154 | # =============================================================================
# 01 --- example extraction from simulation data
# Author: Marc Grünig
#
# Description:
# This script extracts forest state transitions from a forest simulation
# database. The extracted transitions serve as training data for a deep neur... |
cbb2726a9269f7a7a777daeeeb7945a2d6e23d357ed4070bfdcddc5df79e0d00 | R | 4,640 | 118 | ## helper functions for constructing SPM calls used by targets ----
# only finds the existing smoothed data, doesn't rerun it
get_smoothed <- function (bold_path, kernel = 4) {
out_prefix <- glue("smoothed_{kernel}mm_")
out_path <- file.path(dirname(bold_path), paste0(out_prefix, basename(bold_path)))
return (ou... |
82c06abf65c0fdac21afa1157341e6099aaa871e6b345df776a8992a46f31504 | R | 4,642 | 157 | ---
title: "00_Preprocessed"
output: html_document
date: "2024-08-29"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library("Matrix")
library("readr")
library(Seurat)
library(tidyverse)
library(harmony)
```
```{r message=FALSE}
source("~/Rfunction/scTheme.R")
scThemes<-scThemes()
```
... |
12803ac61954c2bf922d43e05e6caff038b819c7781c3501dff9227e7db14831 | R | 4,643 | 123 | library(tidyverse)
library(optparse)
library(Biostrings)
library(DESeq2)
library(tximport)
# Also need to add the deseq parts
# To do/plan:
# 1. Need a csv file for metadata
# 2. Need a grouping/cofactors text file to match and extract group names and other metadata
# order of this text file needs to be... |
14a0f3d8720acc635c4b9d1b00b899e2d7c8754a1ebbf7bfebbbf2740fb8e9b9 | R | 4,652 | 121 | library(pROC)
data(aSAH)
test_that("cov with delong works", {
expect_equal(cov(r.wfns, r.ndka), -0.000532967856762438)
expect_equal(cov(r.ndka, r.s100b), -0.000756164938056579)
expect_equal(cov(r.s100b, r.wfns), 0.00119615567376754)
})
test_that("cov with obuchowski works", {
expect_equal(cov(r.wfns, r.ndka,... |
03ae2b7f8adce190a119772dd96a1420516d6ff9ff65f0abb1d3455c5fda8a55 | R | 4,659 | 132 | ---
title: "PCA"
author: "Tingting"
date: "2024-05-01"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## R Markdown
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(broom)
library(ggstance)
library(readxl)
library(ggpubr)
library(... |
a9e17e1102ce5f1fe436c2046fbf035dd358038abe52e19a0c5e1d19f9dccb5b | R | 4,665 | 77 | #' @description CNS tumors have subtypes as per the [WHO 2016 CNS subtypes](https://link.springer.com/content/pdf/10.1007/s00401-016-1545-1.pdf).
#' However, these are not captured in our molecular data so would need to be updated by
#' searching for terms in the reported pathology_free_text_diagnosis column in OpenPB... |
1399f3c383fafcaad299a8133696b502c5d8b657e81136a4ac929a7ed4f9e69d | R | 4,670 | 120 | runcount=function(data,sample){
count=as.data.frame(t(data@assays$Spatial$counts))
id<-colnames(count)
count$x_y=rownames(count)
samplename<-paste0("^",sample,":")
count$x_y <- gsub(samplename, "", count$x_y)#1_1
count %<>% separate(x_y, into = c("x", "y"),sep = "_",remove = FALSE)
count$x_y <- str_replac... |
14779f67f375808b18328ed1e3f1c166312a4752ada23f89088cf7e69dc7d3bb | R | 4,673 | 122 | # pROC: Tools Receiver operating characteristic (ROC curves) with
# (partial) area under the curve, confidence intervals and comparison.
# Copyright (C) 2010-2014 Xavier Robin, Alexandre Hainard, Natacha Turck,
# Natalia Tiberti, Frédérique Lisacek, Jean-Charles Sanchez
# and Markus Müller
#
# This program is free soft... |
8daf8d357644eecf75a7755d7417c4e1484565babb32fc4078a5e48ab4ed99a0 | R | 4,677 | 107 | library(data.table)
library(ggplot2)
library(ggplotify)
library(cowplot)
source("../Plot_theme.R")
# Load color scheme
colors <- fread("../Plotting/colors.csv", strip.white = F)
color_v <- colors$Color
names(color_v) <- colors$ID
# Function to color facet strip backgrounds
fill_title <- function(p, palette){
g <- g... |
9784d475269d2256fe3b285c126960bcb6d40fc0f0c632be7654dda58bcec8ec | R | 4,682 | 117 | ---
title: "Plotting #5: Spatial Plotting Functions"
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output: rmarkdown::html_vignette
theme: united
df_print: kable
vignette: >
%\VignetteIndexEntry{Plotting #5: Spatial Plotting Functions}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
***
... |
3dd714c3de9c0ecdd1373beae0de1e4ea849026e7ec82c336839753065f09cd6 | R | 4,685 | 102 | # Merges methylation beta-values, m-values, and cp-values matrices for
# all pre-processed array datasets.
# Eric Wafula for Pediatric OpenTargets
# 09/29/2022
# Load libraries
suppressPackageStartupMessages(library(tidyverse))
# Magrittr pipe
`%>%` <- dplyr::`%>%`
# establish base dir
root_dir <- rprojroot::find_... |
db11ae5854c5fb164200a0c15e7c08be945ca5022277ac507baa166e5a056d68 | R | 4,697 | 129 | ########################### Internal Validation #########################################
#
# Objective: Validate BayCANN posteriors by plotting fit of calibration outputs
########################### <<<<<>>>>> ##############################################
rm(list = ls()) # Clean environment
options(scipen = 999) ... |
aa1a506afe3f40612470461fa74d78dfe6484f503d3d39278084a2d6f2757c3f | R | 4,702 | 121 | # CREATE QQ PLOTS AND GET LAMBA VALUES
library(tidyverse)
library(normentR)
library(reporter)
library(dplyr)
# Set directories
DATA.DIR <- dirname(rstudioapi::getActiveDocumentContext()$path)
setwd(DATA.DIR)
iwrd <- read.table("iwrd_imputed_results.txt", header = T, sep = "\t")
pics <- read.table("pics_imp... |
ca0281b2a2130b3b4b5f9af736a6ab711f6902678372904e58dddb6b2729158d | R | 4,707 | 134 | # Load packages ----
library(shiny)
library(shinydashboard)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggprism)
library(shinythemes)
library(googlesheets4)
# Load datasets of AIRE dependant genes
AIREdep = read.csv2("data/TRA_AIRE_dependency.csv")
# Load datasets of gene expression in mouse and human
gene... |
74f0a853f419bfd02892a79d9b29e12919e0ed338cf9b715295894f983484da1 | R | 4,710 | 162 |
### This script plots global results across channel densities for connectivity
### Christina Stier, 2025
## R version 4.2.2 (2022-10-31)
## RStudio 2023.3.0.386 for macOS
rm(list = ls())
install.packages("corrplot")
install.packages("igraph")
install.packages("qgraph")
install.packages("car")
install.packages("comp... |
363925aef0c26acb2cf030ba0508126edab6c811f69d22b81a0575f8b54ad294 | R | 4,712 | 105 | library(Seurat)
library(ggplot2)
library(dplyr)
library(ggpubr)
library(tidyverse)
library(pheatmap)
library(reshape2)
library(tidyr)
library(ComplexHeatmap)
library(rstatix)
library(ggrepel)
source("~/PD_project_analysis/manuscript_scripts/MV_utils.R")
color_palette_cluster_DaN <- c("SOX6+/CALB1- Mature" = "#006400",... |
3f7d8955d5643295341271e7d57763eeb88666f80ab54b42ddf65cdf4203cdf7 | R | 4,713 | 97 | # pROC: Tools Receiver operating characteristic (ROC curves) with
# (partial) area under the curve, confidence intervals and comparison.
# Copyright (C) 2010-2014 Xavier Robin, Alexandre Hainard, Natacha Turck,
# Natalia Tiberti, Frédérique Lisacek, Jean-Charles Sanchez
# and Markus Müller
#
# This program is free soft... |
808d8cb6000a253a66b1aa5381d47099a7105e29ae638e173a8fea4aee45b61d | R | 4,731 | 102 | ##########################################################
#
# This function is used to extradt the within-network
# time delay estimation of visual and somatomotor,
# and the visual-somatomotor time delay
#
# Liang Qunjun 2023/10/10
#
ObtainBrainData_custom <- function(sbj_use, td_list, weight_list, net_annotation)... |
2099ecb11becae1b6f63e021ea96a2400211a0bad905c2b8cf308f327507294a | R | 4,744 | 116 | #' @title Updates shinycell config to recognize a metadata as a discrete one
#' @description Updates shinycell config to recognize a metadata as a discrete one. This
#' function is useful when a discrete metadata only contains integers, e.g.
#' unspervised cluster labels starting from 0 to (n-1) clusters. If these
#... |
5f7ce9ab65bf40acb8b710577779aa92aa538ea0d28f49f5cc2440f4eb885ca1 | R | 4,758 | 132 | #' @title Compute Pairwise Bivariate Moran's I
#' @description Calculates the bivariate Moran's I correlation matrix for all pairs of variables starting with "Metabolite" or "Gene".
#' Uses a k-nearest neighbor (k=5) spatial weight matrix.
#' Computations are parallelized.
#' @param df Data frame containing 'x', 'y... |
86e10166307c6e78e1e9ff0b02c32aae922665389d0ecca3bfaf0b3177b464b5 | R | 4,758 | 143 | #' A 'ggplot2' geom to draw genomic ranges as round rectangles.
#'
#' `geom_roundrect()` draws ranges defined by `xmin` and `xmax` coordinates with rounded edges.
#'
#' This geom draws rectangle with round or sharp edges between defined start and end coordinates.
#' Intended application of this geom is to visualize gen... |
545c5b7283ca16014cd1a8f9827bd526579d0c2a66f269df99150687b0d8bcb4 | R | 4,767 | 161 | # This script outputs the oncoprint N counts table
#### Set Up --------------------------------------------------------------------
# Load libraries
library(dplyr)
library(maftools)
#### Directories and Files -----------------------------------------------------
# Detect the ".git" folder -- this will in the proje... |
f8f40ef3eb095d4341f99c842c2d1dc2b6252788f28f1767bf10394188b42b94 | R | 4,776 | 134 | #' Aggregate and count values across logical membership columns
#'
#' For every logical membership column, aggregates a collapse column per key,
#' counts the members and binds the collapsed values and counts back to the
#' input. Extends \code{\link{col_agrecounter}} with an outer loop over multiple
#' logical columns... |
9dc5790a2b67da85371f1972f74ba97228057f883cfe2d9e9c30b24beaa35086 | R | 4,778 | 204 | ---
title: "richR: Functional Enrichment Analysis and Visualization"
author: "Kai Guo"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{richR: Functional Enrichment Analysis and Visualization}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, in... |
24a5cbb5384033700ad4237a50d69895d00d16742a69059bf5dd66d5c5c68949 | R | 4,792 | 117 | #' Fit an initial MRF model
#'
#' @param dat.list A list containing multi-omics datasets with samples in columns and features in rows. Samples should be matched across datasets.
#' @param ntree Number of trees for fitting MRF model. Default is 300.
#' @param scale Whether to z-standardize each feature. Default is TRUE.... |
0b63fd0c7f3fe3b342136223f145d18d700d49080e75123e392200f411558b61 | R | 4,803 | 161 | ---
title: "Demographics"
author: "HannahSavage"
date: "2023-04-17"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## SET ENV
```{r, include = FALSE}
library(readxl)
library(dplyr)
library(tidyverse)
library(ggplot2)
library(grid)
library(reshape)
library(scales)
library(... |
8073cb669ee261d8f48ca7fe7b3480489cd7e6736457746f30a2a960809c7374 | R | 4,806 | 122 | rm(list=ls())
## COMMON LIBRARIES AND FUNCTIONS
source("100.common-variables.r")
source("101.common-functions.r")
source("300.variables.r")
source("301.functions.r")
## SCRIPT SPECIFIC LIBRARIES
## SCRIPT SPECIFIC FUNCTIONS
## SCRIPT CODE
##
##
if( 1 ){
Print.Disclaimer( )
for( lset in NOVEL.SET ) {
... |
5f649c6ba9ae204c1b1c706a89f38e537153b2cbd8bfa328c86b2ab75c907297 | R | 4,821 | 135 | # Code to generate Figure 2 Supplement 1 of the Jokura et al 2024 Ctenophore apical organ connectome paper
# source packages and functions ------------------------------------------------
source("analysis/scripts/packages_and_functions.R")
# load Subepithelial nerve net ----------------------------------------------... |
19b61b6b4ae10cb68b394a3b1ea48b71d8a7ba547ed202499e8da0feadfe9487 | R | 4,825 | 172 | ### analysis single channel nuclear intensities by cell
# go to main directory (parent directory of scripts)
if (basename(getwd())== "00_scripts"){setwd("../.")}
#load packages
library("tidyverse")
library(colorRamps)
#define working directories
in_dir = "./03_R input/"
out_dir = "./04_intens_distrib... |
a781c07be6dc112cdfe03ba59915fe26a2dbd22a9eca6d7d5e86c4c1bcb56f77 | R | 4,826 | 173 | set.seed(11235)
library(tidyverse)
data <- read.csv("D:/Program Files/MATLAB/Joint_Perception_Project_Final/EEG/CPP_amplitude_dataset.csv")
library(ggplot2)
library(ggprism)
library(gridExtra)
library(patchwork)
library(lme4)
library(ggpubr)
library(dplyr)
library(lmerTest)
library(sjPlot)
l... |
b0574eb8923fc6ee96e6769743f1e5bc6209905443340f3cfb72dfda63cdf468 | R | 4,833 | 137 | library(tidyr)
library(ggplot2)
library(dplyr)
library(aplot)
library(scales)
library(patchwork)
# ---- Define manual colors for the 5 combos ----
custom_colors <- c(
"FL_vs_VGC only" = "#fdae61",
"FL_vs_VGC & FL_vs_HGC" = "#fb6a4a",
"FL_vs_HGC only" = "#d53e4f",
"FL_vs_VGC & HGC_vs_VGC"... |
5a194e3341ee0b4f488823f21c3b3c51e2bdd5355c92776c5c3cf2abf3a905f6 | R | 4,850 | 166 | #' @name lgb.plot.interpretation
#' @title Plot feature contribution as a bar graph
#' @description Plot previously calculated feature contribution as a bar graph.
#' @param tree_interpretation_dt a \code{data.table} returned by \code{\link{lgb.interpret}}.
#' @param top_n maximal number of top features to include into... |
4162a70cc88cd61cf5f3498d0ab6e8c786d54afa587c66a44241a78273fa06e4 | R | 4,858 | 131 | #' A 'ggplot2' geom to draw arcs between genomic alignments.
#'
#' `geom_wide_arc()` draws wide polygons between two sets of start and end coordinates.
#'
#' This geom is intended to draws wide arc polygons between self-alignments defined in PAF format.
#' Such alignments can be directly visualized using a wrapper func... |
4d283c743874917693c393db303ea8c49eeffa027e0d943e8a48c3f53f9b2fa3 | R | 4,860 | 116 | #######################################
## Functions to train a MOFA model ##
#######################################
#' @title Train a MOFA model
#' @name run_mofa
#' @description Function to train an untrained \code{\link{MOFA}} object.
#' @details In this step the R package is calling the \code{mofapy2} Python pack... |
6a2ba84e9fef03f345b73e7dae54b66ea6b9602f38a297a63796ac0f40dec11a | R | 4,860 | 134 | library(oro.nifti)
source('~/src/R.lib/custom.ggplot.r')
cos.sim=function(A, B)
{
return( sum(A*B)/sqrt(sum(A^2)*sum(B^2)) )
}
recalculate=T
RSN=T
if (recalculate)
{
if (RSN==T)
atlas=readNIfTI("data/atlas_modules.nii.gz")
else
atlas=readNIfTI("data/atlas_relabeled.nii.gz")
atlas_labels=read.table... |
e3bf1d2188a8077ba40d44e374ab0e9af61ab4a58cb11f7108228083c9e58497 | R | 4,864 | 99 | ############################################################################################
# Functions for performing ANOVA and Tukey HSD tests on hallmark pathway GSVA scores #
# #
# Stephanie J. Spielman, 2020
# Jo ... |
9c7c65ef6a6ce376c32ea036a6d15fcb908388c2836b02654eeff03259db0c35 | R | 4,870 | 172 | ### analysis single channel nuclear intensities by cell
# go to main directory (parent directory of scripts)
if (basename(getwd())== "00_scripts"){setwd("../.")}
#load packages
library("tidyverse")
library(colorRamps)
#define working directories
in_dir = "./03_R input/"
out_dir = "./04_intens_distrib... |
dbb1ee0bcc70f9ed4082629b990c813056aac0fa5f10aea9df0f70759776ce81 | R | 4,872 | 112 | #' Generate code files required for shiny app (one dataset)
#'
#' Generate code files required for shiny app containing only one dataset. In
#' particular, two R scripts will be generated, namely \code{server.R} and
#' \code{ui.R}. If users want to include multiple dataset in one shiny app,
#' please use \code{makeS... |
50bee94dcc754f391f89cc32cb1073a1b8c85a04d145b43f7cb94e3840172f5a | R | 4,881 | 156 | ---
title: "Add molecular subtype for samples that have hallmark Ewings Sarcoma fusions"
output: html_notebook
author: K S Gaonkar for D3b
date: January 2020
---
Identify sample IDs with hallmark _EWSR1_ fusions and subtype as `EWS`
```{r}
library("tidyverse")
```
### Set directories and file paths
```{r}
# to get... |
699e857bcfa7a0ade802c6004d99cf373459d6d574f98d0690fda7d7675fee39 | R | 4,905 | 139 | # Script by J. Taroni for ALSF CCDL
# Adapted from code written by Anna R. Poetsch and Candace L. Savonen
#
# Given a MAF file, extract de novo mutational signatures for a range of k as
# specified by the nsignatures_floor and nsignatures_ceiling arguments using
# the sigfit package.
#
# This script is essentially a... |
453bb38d372c8aa3c97d437a00b0d916ec15760c8922a254e495ce716f2c7c5f | R | 4,907 | 119 | options(Seurat.object.assay.version = "v3") # use old Seurat object version
library(Seurat)
library(ggplot2)
library(dplyr)
source("~/PD_project_analysis/manuscript_scripts/MV_utils.R")
setwd("/home/ubuntu/PDSCRBNG/03_04_24_Figure_4")
MLO_DA.big <- readRDS("MLO_DA") # from script Figure 2.R
MLO.big_batch_2_DA <- MLO_... |
35484aa8c5b0a33d0e866eff15eb43452907a23b898d29a6f3a3aa62e7d5ecdd | R | 4,918 | 186 | library(truncnorm)
get_param <- function(x){
x0 <- x[x == 0]
x1 <- x[x > 0]
cut <- mean(x1)
p0 <- length(x0)/length(x)
m1 <- mean(x1[x1 < cut])
sd1 <- sqrt(var(x1[x1 < cut]))
m2 <- mean(x1[x1 > cut])
sd2 <- sqrt(var(x1[x1 > cut]))
list(
mu = c(m1, m2),
sigma = c(sd1, sd2),
p = length(x1[... |
b18897a575a84dd06b90ddb6b0331f4a2b38bc0f39577700b3561dd6851b9e25 | R | 4,932 | 128 | #!/usr/env Rscript
#
# Rscript to test that we can recall all the known outliers and events
# in the Kremer et. al. dataset after a successful DROP run.
#
## Parameters to change
suppressPackageStartupMessages({
library(data.table)
library(OUTRIDER)
library(FRASER)
library(yaml)
})
# default groups and folder... |
686f7406eba541cbc2c5ae83e60beb4bc7a66de46f1b4044e451c4e83a3cfedb | R | 4,950 | 137 | source("Figure_3/util.R")
library(org.Hs.eg.db)
library(org.Mm.eg.db)
library(RColorBrewer)
library(enrichplot)
library(clusterProfiler)
library(msigdbr)
# ==== iN =====
## ==== GO GSEA =====
res_Wbo2 <- read.csv("Figure_3/results/iN1_neuron.csv", row.names = 1)
res_I27 <- read.csv("Figure_3/results/iN2_neuron.csv",... |
c1a7ce6f1685df67ef730acb57a13713ef4850e84f8f6b8021e0462a8848245f | R | 4,950 | 105 | mQTL_tables <- function(){
# Extract mQTLs for the red/blue significant sets to test for genetic colocalization
table_mqtls_EPICredC = data.frame()
table_mqtls_450kredC = data.frame()
table_mqtls_EPICblueC = data.frame()
table_mqtls_450kblueC = data.frame()
chr = dir("./mQTLdb/",full.names=TRUE)
... |
0899815cfbef0a3713ea18cdf3d69aa705fbda04b99fc7356b08e9f01a989fdf | R | 4,951 | 124 | library(ComBatFamily)
library(data.table)
library(dplyr)
library(mgcv)
library(rjson)
library(stringr)
library(tidyr)
##################
# Set Variables
##################
args <- commandArgs(trailingOnly = TRUE)
dataset = args[1]
print(paste("Processing", dataset))
##################
# Set Directories
########... |
544a563b4456cfcee559975dd7705e345d6de498efbc291127894322ae63866e | R | 4,951 | 153 | #' Read Olink data in R.
#'
#' @description
#' Imports a file exported from Olink software that quantifies protein levels in
#' NPX, Ct or absolute quantification.
#'
#' \strong{Note:} Do not modify the Olink software output file prior to
#' importing it with \code{\link{read_npx}} as it might fail.
#'
#' @details
#' O... |
f570a66e6e5f26e42443e725c8e6741f3ac97d44cfd12dc20d6d69ce36629153 | R | 4,955 | 126 | #' Convert counts or proportions matrix to list object for propeller
#'
#' This function takes a matrix of counts or proportions, and returns a list
#' object that is expected from the \code{propeller.ttest} and
#' \code{propeller.anova} functions. This allows the \code{propeller} framework
#' to be applied to any pro... |
e93c8fb47bbcc27b088fbc7ffeddb8a40e820ca82598a3bf64236156a73d79c0 | R | 4,956 | 166 | ---
title: "ChIP-Seq Heatmaps"
author: "AF"
date: "`r Sys.Date()`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Making heatmaps for MSL1, MSL2, H3K4me1, H3K4me3, H3K27ac, H3K27me3, H3K9me3 on the MSL1-V5 control peaks which we annotated as well in a separate script.
``... |
7e9c21f51ac68aa77bf09d676d915f494a853ec087fc2bb14f307e01a589dd2f | R | 4,958 | 123 | ---
title: "Using WHO 2016 CNS subtypes to improve meningioma harmonized diagnosis"
output:
html_notebook:
toc: true
toc_float: true
author: JN Taroni for ALSF CCDL (code)
date: 2021
---
Meningiomas have subtypes per the [WHO 2016 CNS subtypes](https://link.springer.com/content/pdf/10.1007/s00401-016-1545-1... |
91cbbccdd5bc5a2ab5444b4a0c1fd1a2154446efc897ceefc27226e9661851e8 | R | 4,960 | 133 | splicetype="SE" #type of alternative splicing, e.g., SE, A3SS, A5SS, MXE, IR
counttype="JCEC" #JCEC (junction count + exon body count) or JC (junction count only)
##################
#Input parameters#
##################
# inputpath="./02_PSI_value_quantification/01_Get_PSI_from_rMATS_output/example_input" ... |
df7bcd72fa18dca5e2e3ea7284c4dd69cd8316309eecdc5a9d6848c320945074 | R | 4,966 | 122 | # pROC: Tools Receiver operating characteristic (ROC curves) with
# (partial) area under the curve, confidence intervals and comparison.
# Copyright (C) 2010-2014 Xavier Robin, Alexandre Hainard, Natacha Turck,
# Natalia Tiberti, Frédérique Lisacek, Jean-Charles Sanchez
# and Markus Müller
#
# This program is free soft... |
e9e89ebe967de69dc6d2d799dd52ee7c72c812e582f9bf264bc6bbdf5ffa1726 | R | 4,967 | 162 | ##-------------------------------------##
## QC - GENES ##
##-------------------------------------##
calculate_gene_qc <- function(countMatrix){
print("Calculating GENE QC")
average_exp <- rowMeans(countMatrix)
total_exp <- rowSums(countMatrix)
expr_cells <- countMatrix > 0
expr... |
f5f656a1de32a2edd85fe70812f263dcf0f4502597cb300d02538fcd56c8ac02 | R | 4,973 | 119 | ###### Function to run LDSC ######
# #' Run LDSC
# #'
# #' Use GenomicSEM to perform cross-trait LDSC analysis and returns
# #' the heritability of the exposure (SE) and the cross-trait intercept (SE)
# #'
# #' @param exposure_data xx
# #' @param outcome_data xx
# #'
# #' @inheritParams MRlap
# # #' @export
# NOT EX... |
b6e3dfad0eb95f8098db0939422db613c6e2865e8989fc42773debdead897def | R | 4,978 | 126 | #' Read PAF from an input file
#'
#' This function takes an PAF output file from minimap2 and loads the file along
#' with user defined set of additional alignment tags (see PAF specification).
#'
#' @param paf.file A path to a PAF file containing alignments to be loaded.
#' @param include.paf.tags Set to \code{TRUE} i... |
24c2dafbb2e90dd608662676e6d8bd87173c7d66479d156d60e6e04f49d545d7 | R | 4,997 | 135 | ---
title: "High-Grade Glioma Molecular Subtyping - Fusions"
output:
html_notebook:
toc: TRUE
toc_float: TRUE
author: Chante Bethell and Jaclyn Taroni for ALSF CCDL
date: 2020
---
This notebook prepares putative oncogenic data for the purpose of subtyping HGG samples
([`AlexsLemonade/OpenPBTA-analysis#249`... |
f613169999bd44f5ab9451544a2a2232ea1c1e83442475c61c690007e544a031 | R | 5,000 | 134 | #rm(list=ls(all=TRUE))
library(data.table);library(magrittr);library(tidyr);library(dplyr);library(ggplot2)
library(mvnfast,lib='/home/lorincn/Rpkgs')
library(mvsusieR,lib='/home/lorincn/Rpkgs')
# library(snpsettest,lib='/home/lorincn/Rpkgs')
# source('/home/lorincn/Rpkgs/manual_snpsettestcode.R')
library(ACAT,lib='/ho... |
b2771bff546cd8d7e6696a62ea25679b74844244a8331405400944e891a69018 | R | 5,002 | 129 | # Author: Komal S. Rathi
# Date: 11/26/2019
# Function:
# 1. summarize RNA-seq to HUGO symbol x Sample matrix
# 2. tabulate corresponding gene annotations
# Example run: PolyA RNA-seq
# Rscript analyses/collapse-rnaseq/01-summarize_matrices.R \
# -i data/pbta-gene-expression-rsem-fpkm.polya.rds \
# -g data/gencode.v2... |
7a743c4662d936bb8a4f61d7e778b331bf28dd1c5f745069bbe0b5b0224ed9a8 | R | 5,003 | 118 | library(optparse)
library(tidyverse)
arguments <- parse_args(OptionParser(), positional_arguments = 3)
sj_dir_path <- arguments$args[1] %>% str_split(",") %>% unlist()
output_path <- arguments$args[2]
blacklist_path <- arguments$args[3]
# sj_dir_path <- "/home/jbrenton/nextflow_pd/output/STAR/align"
# base_dir<- "/h... |
0d0bbabb5d5d6a882e184c2341667381bfbb830a822966b46060e7ac9924aa3b | R | 5,007 | 106 | library(data.table)
library(stringr)
library(ggplot2)
library(ggplotify)
library(cowplot)
source("../Plot_theme.R")
# Load color scheme
colors <- fread("../Plotting/colors.csv", strip.white = F)
color_v <- colors$Color
names(color_v) <- colors$ID
# Function to color facet strip backgrounds
fill_title <- function(p, p... |
bb7e9f4764be99e68c422045a68777b0767b5876a6007ebc351ebcfc5dbd55c8 | R | 5,008 | 151 | ########################### Coverage Analysis ##########################
#
# Objective: Program to check coverage of targets
#
########################### <<<<<>>>>> #########################################
rm(list = ls()) # Clean environment
options(scipen = 999) # View data without scientific notation
#### 1.L... |
a22f5cb1993ec9bb807d67aad4c41dba448df1f67ebe9a89612ed40f24b8b041 | R | 5,019 | 107 | library('ggpubr')
stringsAsFactors=FALSE
library(grid)
library(optparse)
############################################### Comparing TERT and TERC expression with EXTEND Scores (Figure 2) ################################################################################################
root_dir <- rprojroot::find_ro... |
f8226cdeda4ad186a9286157a3564a0848a7a806a08365a7fd940dc75aac6e70 | R | 5,020 | 151 | # script for finding mistakes in the catmaid database, such as skeletons without
# annotations, cilia which don't have both tip and centriole tagged, etc.
source("analysis/scripts/packages_and_functions.R")
# find skeletons without annotations -------------------------------------------
skids <- unlist(
catmaid_fet... |
91204c3697a844e471a3689bc0920a5a0c0e31a3550f47ee7c8b2fe49943869a | R | 5,022 | 117 | # plot/table of each cohort + cancer_group or cancer_group
suppressPackageStartupMessages(library(tidyr))
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(ggplot2))
pan_cancer_plot <- function(expr_mat_gene, hist_file, map_file,
analysis_type = c("cohort... |
259111982113b2947e3402de65a53ab384f94833f95ceacb9614d3bd784d0b48 | R | 5,025 | 105 | args <- commandArgs(TRUE)
run_scmapcell <- function(DataPath,LabelsPath,CV_RDataPath,OutputDir,GeneOrderPath = NULL,NumGenes = NULL){
"
run scmapcell
Wrapper script to run scmap on a benchmark dataset with 5-fold cross validation,
outputs lists of true and predicted cell labels as csv files, as well as computa... |
c28c81b236c968d0ea85c447923ad065b1b9c86fedba923032e732d105f0cd4e | R | 5,028 | 142 | #' Performs F-tests for transformed cell type proportions
#'
#' This function is called by \code{propeller} and performs F-tests between
#' multiple experimental groups or conditions (> 2) on transformed cell type
#' proportions.
#'
#' In order to run this function, the user needs to run the
#' \code{getTransformedPro... |
5e401e706fc273531e77dff986509783b109b2296a2a6abebfc4a84a2110e6da | R | 5,029 | 176 | ---
title: "Fried_task_overview"
author: "HannahSavage"
date: "2023-04-28"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
#Set env
```{r, include = FALSE}
library(readxl)
library(dplyr)
library(tidyverse)
library(ggplot2)
library(grid)
library(reshape)
library(scales)
lib... |
4c6861fbd4b26b4d1598002c5338757dbc579823472c67f5899ea891289bed1a | R | 5,032 | 104 | args <- commandArgs(TRUE)
run_scmapcluster <- function(DataPath,LabelsPath,CV_RDataPath,OutputDir,GeneOrderPath = NULL,NumGenes = NULL){
"
run scmapcluster
Wrapper script to run scmap on a benchmark dataset with 5-fold cross validation,
outputs lists of true and predicted cell labels as csv files, as well as c... |
8f30a444bd21fa1dc3af2b9c7fdea370ac37ef91e8e9bd4a721f6ae5f9bc7b75 | R | 5,046 | 118 | library(optparse)
library(tidyverse)
arguments <- parse_args(OptionParser(), positional_arguments = 3)
sj_dir_path <- arguments$args[1] %>% str_split(",") %>% unlist()
output_path <- arguments$args[2]
blacklist_path <- arguments$args[3]
# sj_dir_path <- "/home/jbrenton/nextflow_pd/output/STAR/align"
# base_dir<- "/h... |
66c6e53f887a8c5a29cf349f86628a7910b2d95ec16abb73f1050fc940b01070 | R | 5,053 | 149 | ########## STEP 1.0
########## SETUP NECESSARY FILES FOR GWAS
# Clear working space
rm(list = ls(all.names = T))
# Install packages
if(!require(plyr)){
install.packages("plyr")
library(plyr)}
if(!require(dplyr)){
install.packages("dplyr")
library(dplyr)}
if(!require(tidyr)){
install.packa... |
092d20d2ce94dcdd057b5a76ecd6463296c63919de430c0f67b128d22d85159c | R | 5,055 | 127 | ---
output: github_document
# output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
# install.packages('pkgnet');
packagename <- 'psychtoolbox'
packageSubtitle <- "Tools for psychology research a... |
e3b5885b194d08b96d7d8903b3f0e3cf3c648c31dba8deb655001c4b979b0ec5 | R | 5,057 | 138 | # -------------------------------------------------------------------------
# Unit Test 03: Differential Analysis Logic
# -------------------------------------------------------------------------
# Purpose:
# Verify that differential expression analysis correctly identifies markers
# between defined groups (Treatme... |
1ab7cf578d71e81b44732c2adc798ea3834339fae1653fbc41e52dd54be2e3a9 | R | 5,058 | 177 | #' Function to set plot theme
#'
#' @description
#' This function sets a coherent plot theme for functions.
#'
#' @param font Font family to use for text elements. Default: "Arial".
#'
#' @return No return value, used as theme for ggplots
#'
#' @export
#'
#' @examples
#' \donttest{
#' if (rlang::is_installed(pkg = c("s... |
09a939771b47901bbf474444f74e22228d6d986bcd2d7810daf743911a9655f2 | R | 5,062 | 154 | ---
title: "I want to plot the MSLc primed genes showing the upregulation from NPC to Day14 Neurons"
output: html_document
date: "2024-06-02"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r plot saving function}
plot_save_as_svg <- function(plot, file_name) {
dir.create(paste0(dirname... |
19c9dd9c30d51522d39563695d01c55b25cb3c868939a2372da0d378331c7664 | R | 5,065 | 204 | #' Creates bargraph of top/selected enrichment terms from GSEA or ORA results
#' from `olink_pathway_enrichment`
#'
#' @description
#' Pathways are ordered by increasing p-value (unadjusted)
#'
#' @inherit olink_pathway_enrichment params
#' @param enrich_results data frame of enrichment results from
#' `olink_pathway_e... |
3cab571912b49ec7a028f9b565ae631abf2dd4b82fb571ab76c53a17166a7782 | R | 5,071 | 170 | ---
title: "High-Grade Glioma Molecular Subtyping - Gene Expression"
output:
html_notebook:
toc: TRUE
toc_float: TRUE
author: Chante Bethell and Jaclyn Taroni for ALSF CCDL, Jo Lynne Rokita for D3b
date: 2020
---
This notebook prepares gene expression data for the purpose of subtyping HGG
samples ([`AlexsLe... |
575c63fe8482ec085a09d3e52b36340496d6ad1953c1c7522d3daef672ab3ae5 | R | 5,073 | 205 | source("analysis/scripts/packages_and_functions.R")
stats_synapse <- read.csv("analysis/data/stats_synapse.csv")
SSN_Q1Q2 <- read_smooth_neuron("SSN_Q1Q2")[[1]]
skid_Q1Q2 <- SSN_Q1Q2$skid
SSN_Q3Q4 <- read_smooth_neuron("SSN_Q3Q4")[[1]]
skid_Q3Q4 <- SSN_Q3Q4$skid
SSN_Q1Q2Q3Q4 <- read_smooth_neuron("SSN_Q1Q2Q3Q4")[[1]]... |
6561bc507a7723e3ba3d85c63fddbcb1de8f4d408fbe80feeaae970c99be938d | R | 5,079 | 116 | #'---
#' title: "Count Summary: `r gsub('_', ' ', snakemake@wildcards$dataset)`"
#' author: Christian Mertes
#' wb:
#' log:
#' - snakemake: '`sm str(tmp_dir / "AS" / "{dataset}" / "CountSummary.Rds")`'
#' params:
#' - setup: '`sm cfg.AS.getWorkdir() + "/config.R"`'
#' - workingDir: '`sm cfg.getProcessedDataDir... |
9b098258d820ebaf4eda689915cdf8d089798eb5388559374d1bc2eba31e5edb | R | 5,083 | 112 | #' @title Generate code files required for shiny app (multi datasets)
#' @description Generate code files required for shiny app containing multiple datasets. In
#' particular, two R scripts will be generated, namely \code{server.R} and
#' \code{ui.R}. Note that \code{make_file} has to be ran prior to
#' generate the n... |
a4ddf34dbb93abf182c5dbb039bdf5a46a60d71ce1c44f092ef5618fd14cc23a | R | 5,083 | 185 | # Hua Sun
library(dplyr)
library(stringr)
library(ggplot2)
library(tidyr)
fmd <- ''
control <- ''
cutoff_region_size <- 5
min_ratio <- 0.5
min_cnv <- 100
min_gene <- 20
cutoff_gain <- 1.2
cutoff_loss <- 0.8
rm_clu <- ''
rm_chr <- ''
icnv_exp <- ''
hmm_gene <- ''
hmm_region <- ''
outdir <- 'out_malignant_cells'... |
d9aa99197c42b41a1fec0c09b1db2d9a26758fa23174bc6156583a2ff6b03e9c | R | 5,086 | 155 | #' Example: Progressive Methylation Analysis Report
#'
#' This script demonstrates how to use methylkey's Phase 6 report builder
#' to construct a complete methylation analysis report step-by-step.
#'
#' The approach is progressive: you can add steps incrementally, modify
#' parameters, and rebuild specific steps witho... |
3ec379f1933cf5e2e10d41498df1b1b98ae324cd8a4d9eb185fd42e785621f25 | R | 5,093 | 103 | library(Seurat)
library(ggplot2)
library(optparse)
library(dplyr)
library(stringr)
option_list <- list(
make_option(c("-w", "--workdir"), type='character', action='store', default=NA,
help="Path to the working directory"),
make_option(c("-r", "--rdsfiles"), type='character', action='store', default=N... |
415e4c279e20ce988858dd6d14a0341253fb69137d2f345fbe7af2c50cad51a4 | R | 5,093 | 138 | # Load libraries -------------------------------------------
library(optparse)
library(tidyverse)
library(signature.tools.lib) # contains the signal signatures
`%>%` <- dplyr::`%>%`
# Set up command line options -------------------------------
option_list <- list(
make_option(c("--abbreviated"),
type ... |
11695e1d7b50f15fc88ea5547e31e4f42c6265e60ea5ca49d8840eff2e7be929 | R | 5,102 | 167 | ############################
## iTReX helper functions ##
## Author: Dina ElHarouni ##
############################
## Generate sequence of row IDs "A", "B", ...
EXCEL_COLUMN_LETTERS <- paste0(
rep(c("", LETTERS), each = length(LETTERS)),
rep(LETTERS, times = 1 + length(LETTERS))
)
row_sequence <- function(n) {
... |
02d63774f3f039e038546db1ecad4295d00ad75e163ca0c27586e4e2f0748536 | R | 5,105 | 172 | ---
title: "Comparing foldchanges of 1xDensity GO terms"
author: "AF"
date: "`r Sys.Date()`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
suppressPackageStartupMessages({
library(tidyverse)
library(dplyr)
library(tidyr)
library(reshape2)
library(ggplot2)
library(rstudioapi)... |
aaf21135b05429c10716bebafe4d0e2886f34f94709586e66580cc79101be880 | R | 5,115 | 128 | # SMIntegration: Spatial Multi-omics Integration Platform
# ==============================================================================
#
# Purpose:
# Converts Seurat RDS objects containing spatial transcriptomics and
# metabolomics data into text-based formats (long format) for compatibility
# with d... |
b1e2c9bb3f709bf25dedff983ab417df9658c22aba8683714a870ba983135144 | R | 5,116 | 199 | test_that(
"olink_ttest - works - non-paired t-test",
{
# Load reference results
# tests are skipped if files are absent
reference_results <- get_example_data(filename = "reference_results.rds")
skip_if_not_installed(pkg = "broom")
skip_on_cran()
# tibble ----
check_log <- check_npx(d... |
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