sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
a6d6e2292f3bc5b4aaabfcf9fdc31d074d6d06f824301c639b47e1bd61d23e7a | R | 4,508 | 165 | library(lme4)
library(dplyr)
library(brms)
library(foreach)
library(data.table)
#rds <- readRDS("/Users/michael/Downloads/meg_tf_rdf_wholebrain_rt_ri_single_sensor_RT1165")
rds <- readRDS("/Users/hallquist/OneDrive/collected_letters/papers/meg/plots/wholebrain/rt/output/meg_tf_rdf_wholebrain_rt_rs_single_sensor_RT1165... |
7eb3939afcae521fb0071a0d0d1022e4fdd1bb0e65f355dc4c4a311380dc3a8b | R | 4,509 | 158 |
library(ggplot2)
library(dplyr)
library("optparse")
library(stringr)
library(ggpubr)
print("Libraries loaded, starting now")
getFitResults <- function(opt){
rdsPath = "./rdsOutput/nebulaFits/"
# Loop through each of the splits
resultDF = data.frame()
covarDF = data.frame()
for (eachSplit in 1:(... |
2ffd09ed9331378b993fc5f1616a7413d30fc09eb3c8a377bc94e3ca53f4ce68 | R | 4,515 | 180 | options(bitmapType='cairo')
args <- commandArgs(TRUE)
dir<-args[1]
inputFile<-args[2]
headerSize<-as.numeric(args[3])
headerSpacing<-as.numeric(args[4])
imageWidth<-as.numeric(args[5])
imageHeight<-ceiling(imageWidth/8)
yBound<-as.numeric(args[6])
transparentBGFlag<-as.numeric(args[7])
setwd(dir)
dir... |
1b248b84fb9b8fc0ccebdd93e839f1bb23c676c7ae8d7324052d9988d232611f | R | 4,530 | 76 | dirnames$protein <- paste0("/lustre/projects/Research_Project-MRC148213/lsl693/rTg4510/G_Merged_Targeted/B_cupcake_pipeline/4_proteogenomics/")
protein = list(
cpat = read.table(paste0(dirnames$protein,"5_calledOrfs/all_iso_ont_best_orf.tsv"), sep ="\t", header = T),
t2p.collapse = read.table(paste0(dirnames$protei... |
bb3653818d82da11d549961bb25a5e1e3e79c4e4c351fcc14ae50d545ad70b90 | R | 4,532 | 136 |
#' archstabms_thermo_model_results
#'
#' Evaluate thermo model results.
#'
#' @param mochi_outpath path to MoCHI thermo model fit results (required)
#' @param temperature temperature in degrees celcuis (default:30)
#' @param literature_free_energies path to literature free energies (default:NA)
#' @param ddPCA_free_en... |
b4920e0cfe3f5dd3a6f6c85a87a070a577b6b3a0ba8004cad6b9d432c0870a36 | R | 4,533 | 123 | make_fake_l3_cope_feat <- function(feat_dir, contrast_names) {
cope_feat <- file.path(feat_dir, "cope1.feat")
stats_dir <- file.path(cope_feat, "stats")
dir.create(stats_dir, recursive = TRUE, showWarnings = FALSE)
for (ii in seq_along(contrast_names)) {
file.create(file.path(stats_dir, sprintf("cope%d.nii... |
6d577d7e74e75e049086b7878735627d799beb8f46c63416ced6209b200e3d84 | R | 4,537 | 144 |
library(ggplot2)
library(dplyr)
library("optparse")
library(stringr)
library(tidyr)
library(tidyverse)
print("Libraries loaded, starting now")
formatCellType <- function(inputColumn){
inputColumn = ifelse(inputColumn == "T_Cell", "T Cell", inputColumn)
inputColumn = ifelse(inputColumn == "VSM_and_P... |
1e4a23e4a2bc345684a9657ddf68a7a870f2732606ab1f78a6efa127240b8729 | R | 4,551 | 133 | #' @details
#' Nicolet uses some more keywords in their header file.
#' They are interpreted as follows:
#' \tabular{ll}{
#' description \tab giving the position of the first spectrum \cr
#' z plot titles \tab wavelength and intensity axis units, comma separated \cr
#' pixel size \tab interpreted as x and y step s... |
89c2a06805ae4c3279d14e3953935c3f409387b23451ab63135f21461afa1078 | R | 4,560 | 151 | # library(tidyverse)
library(dplyr)
library(tidyr)
library(ggplot2)
library(purrr)
library(Seurat)
library(tidyseurat)
library(tidysc)
library(sccomp)
library(job)
library(patchwork)
library(future)
options(future.globals.maxSize= 100000*1024^2)
job({
oligo_adjusted_cell_type <- readRDS("/stornext/Bioinf/data/... |
fe21d99ccdab2794ee3f5b9b57c5e271546d04c6e6f91854100ceae7b03743ee | R | 4,577 | 144 | #!/usr/bin/R
# ----------------------------------
# Prelim plots scDRS
# Updated 10/26/2022
# ----------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(paste0(sbindir, 'load_metadata.R'))
}
source(paste0(sbindir, 'auxiliary_pseudobulk_loading_fns.R'))
... |
7d0e99ef60150321ea78bdfe8fb39b5906979cebc303c1d593bcb9b92b9c45d9 | R | 4,581 | 103 |
library(dplyr)
library(pbapply)
library(data.table)
library(plyr)
library(openxlsx)
library(broom)
Mic9_AD_df <- read.table(file = "DA9_meta.csv", sep = ",", header = TRUE) #Data frame of Sample ID, DA9/CD83 status and comorbidity, demographic & clinicopathological covariates
Mic9_AD_df <- Mic9_df[which(Mic9_df$upda... |
e86abf19d3bdc285e639f86ab47694e9938dc441b35568efc58040b4a18faec2 | R | 4,584 | 135 | # Fig 4. Survey results 1: Model performance across different modeling targets
# Set up ----
# Load libraries
## uncomment the lines below to install the packages
# install.packages("dplyr")
# install.packages("ggplot2")
# install.packages("tidyquant")
library(readr)
library(dplyr)
library(ggplot2)
library(tidyquan... |
cbf886003733c54ad3c2c8ea6f8bf65f336395dd2e54b84e7f1b73d5a1bc1585 | R | 4,591 | 84 | #View(paclitaxel_uct_imputed_log)
names(paclitaxel_uct_imputed_log)
Lipids_Final <- subset(paclitaxel_uct_imputed_log, select = c("Patientennummer", "Probe.1.oder.2","Neuropathie",
"SA1P", "Sphingomyelin_331", "Sphingomyelin_431"))
PatNrNeuropathy <- na.o... |
b601904ab8f812ee2aa10095f928ff824e9b2a73625fa54f39a70bb156f4912f | R | 4,598 | 158 | library(testthat)
# Helper to create a minimal hi_model_spec for respecify_l3_model testing
make_l3_mobj <- function(formula_str = "~ 1", l3_input_mode = "per_session",
subject_data = NULL) {
if (is.null(subject_data)) {
subject_data <- data.frame(
id = rep(c("sub1", "sub2", "sub3"... |
932124eccf095bfb3cfe41bb2918b5cd812ac3009515c3e0054761e849de0afc | R | 4,602 | 124 | #!/usr/bin/R
# ------------------------------------------------------------
# Make a figure to show our cohort along multimodal reference.
# Also plot the full ROSMAP cohort within that figure (alt)
# Updated 04/26/2021
# ----------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ')
so... |
de45daff7fbe03c0efd6c51baefd05af41b4b0b7c49fde6f6f02d982312824f1 | R | 4,603 | 152 | #!R
## Compare CNVs
#!/usr/bin/env Rscript
library("optparse")
option_list = list(
make_option(c("-i", "--inputbed"), type="character", default=NULL,
help="input bed from bedtools closest", metavar="character"),
make_option(c("-o", "--outputbed"), type="character", default=NULL,
... |
8c16da927e0987ca92018f8e1fbaa12b0c73e9e15b76d266d9c48cb23d068578 | R | 4,611 | 154 | #!/usr/bin/R
# ------------------------------------
# Plot specific pairs of correlations:
# - score using DEGs
# Updated 06/20/2023
# ------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(paste0(sbindir, 'load_metadata.R'))
}
# Settings for plots... |
3cecc7116b5f8a8cb91d36187d3b2916420c76068da3e167957184640af16381 | R | 4,614 | 149 |
# Need to use cluster installations of seruatDisk
.libPaths('2')
library(Seurat)
library(SeuratDisk)
library(monocle3)
library(SeuratWrappers)
getLitvAge = function(x){
ages = as.numeric(strsplit(x["age_group"], "-", fixed=TRUE)[[1]] )
ageMidpoint = (ages[1] + ages[2]) / 2.0
return(ageMidpoint)
}
# Get litvinuk... |
bb2b97e9afad181c1c171cb14f0daa2a84baa1342a06a99a9ec69bb055b6a6b4 | R | 4,626 | 121 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
#' Count number of lines in a text file
#'
#' @param fileName Name of file
#' @keywords internal
#'
countlines <- function(fileName) {
.Call('_lassosum_countlines', PACKAGE = 'lassosum', ... |
6af42bacd5fb8b8dc2dd6debabb5b7accba181fde89b12086236d1fea95e059d | R | 4,629 | 110 | #' helper function to locate nifti inputs for each run of data
#'
#' @param gpa a \code{glm_pipeline_arguments} object created by setup_glm_pipeline
#' @param add_run_volumes whether to lookup and add the number of volumes in each run_nifti in the run_volumes column
#' @param add_dim whether to lookup and add the dimen... |
768d0f67ee7b652cd19fed4eb67b4c77290e22cc32e17c735a3640a8722ee8da | R | 4,634 | 136 | library(testthat)
library(dplyr)
library(sccomp)
test_that("get_variability_to_composition_map matches by column name", {
X <- matrix(0, nrow = 2, ncol = 4)
colnames(X) <- c("(Intercept)", "typehealthy", "phenotypep2", "phenotypep3")
Xa <- matrix(0, nrow = 2, ncol = 4)
colnames(Xa) <- c("(Intercept)", "phenot... |
909275c2c26e254e547106b0b751131c1f3de73db8e959e79dd6b98184e8c3d3 | R | 4,643 | 105 | ## ---------- Script -----------------
##
## Script name:
##
## Purpose of script:
##
## Author: Szi Kay Leung
##
## Email: S.K.Leung@exeter.ac.uk
##
## ---------- Notes -----------------
##
##
##
##
##
## ---------- Source function and config files ---------------
# Specific analysis related
SC_ROOT <- "/gpfs/... |
e8d06e99c0c9c24b0a36abcd404a38f85dd88a7686f7488c7e9c48160840ad36 | R | 4,644 | 181 | options(bitmapType='cairo')
args <- commandArgs(TRUE)
dir<-args[1]
inputFile<-args[2]
inputName1<-args[3]
inputName2<-args[4]
headerSize<-as.numeric(args[5])
headerSpacing<-as.numeric(args[6])
imageWidth<-as.numeric(args[7])
imageHeight<-ceiling(imageWidth/8)
yBound<-as.numeric(args[8])
transparentBGFlag<-... |
a04f88f2c4a8c8500c870cf592682c4f758fb9b21b142ae7551251742f1d95e7 | R | 4,647 | 114 | #!/usr/bin/R
# --------------------------------------------------------------------------
# Calculate the fraction differences, by logistic regression + mixed effects
# Updated 10/23/2020
# --------------------------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ')
source(paste0(bin... |
53458c3b1742c05bce9347f820f57740a9c2dd7973121ff436a540cf6b40bf0e | R | 4,648 | 127 | library(dplyr)
library(Seurat)
library(ggplot2)
library(xlsx)
library(biomaRt)
library(Matrix)
library(progress)
future::plan("multicore", workers = 4)
##########################################USER INPUT##################################################################
#this can convert multiple rds file ... |
2b291cf3a885c2c03474059b4cfef96b31721b123c0338aa7137ff53067faa73 | R | 4,660 | 102 |
#' archstabms__get_model_results_ddPCA
#'
#' Get model results and coefficients.
#'
#' @param input_folder path to input folder with model results (required)
#' @param input_dt data.table with model free energy estimates (required)
#' @param n_bootstraps number of bootstraps (default:10)
#' @param bootstrap_prefix boo... |
56d9dfd1fbab3e46ea4fa77da2e9203e1d3beeab6fc13ee86ce0b0e634fc2bfe | R | 4,667 | 117 | #' @title Computes polygenic scores as a genotype matrix multiplied by a matrix of weights
#'
#' @param weights The weights for the SNPs (\eqn{\beta})
#' @param bfile A plink bfile stem
#' @param extract SNPs to extract (see \code{\link{parseselect}})
#' @param exclude SNPs to exclude (see \code{\link{parseselect}})
#'... |
29a01f13033926bed67f9e70be5c902f202d227808bd3c799f0b945924952af1 | R | 4,684 | 189 | ---
title: "ANOVA stats"
author: "Cazalé-Debat et al.2024"
date: "mars 2024"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
rm(list=ls())
data<-read.delim("/Users/cazaledl-admin/Library/CloudStorage/OneDrive-UniversityofBirmingham/Post_doc/XP/data/courtscape/screen... |
5930ef6f01abbdc95ff80d6fd63dae7cbba7bb7bf69081274b7b73f006af3783 | R | 4,688 | 95 | # load the reshape2 package
library(reshape2)
library(dplyr)
#Table interactions that are significant in at least one region
##########################################################################################
setwd("D:/home_wkstn/F/Brain_region_project/LIANA/Results_aggregate_rank_magnitude_all_projid")... |
2cdc3a7601bd96c13c29d17cca7f8f5459b6a4513a813c867908901c21ee9a78 | R | 4,693 | 164 | # Set generic ----------------------------------------------------------------
#' `as.hyperSpec`: convenience conversion functions
#'
#' These functions are shortcuts to convert other objects into `hypeSpec`
#' objects.
#'
#' @param X the object to convert. If `X` is:
#'
#' - a `matrix`, it is assumed to contain the s... |
ec06789a36af7497f1f7f1db98de5a7a773c1668b87b7fbf97636a7a420326b8 | R | 4,702 | 151 | ---
title: "CLA spots selection and DE analysis"
author: "Yuvarani Masarapu"
date: "2023-02-01"
output: html_document
---
```{r message=FALSE, verbose=FALSE, warning=FALSE, eval=FALSE}
### Loading package libraries
library(Seurat)
library(SeuratObject)
#hdfr to read 10x hdf5r files, won't work otherwise
library(hdf5r)... |
ecbb4cddea82e42c2be8e4d256e47d4dd4fc5c94fb2ffec4f2c025c9e1e91ad4 | R | 4,721 | 98 | ####################################################################################################
###
### File: 01_data_visualisation.R
### Purpose: Obtaining the features related to the coordinates.
### Authors: Gabriel Rodrigues Palma, Rafael Moral
### Date: 04/04/23
###
#####################################... |
354468d6b115de66a65fa3823023995e8927fdf6a61c483f597ac99eca2eb712 | R | 4,722 | 185 | options(bitmapType='cairo')
args <- commandArgs(TRUE)
dir<-args[1]
inputFile<-args[2]
headerSize<-as.numeric(args[3])
headerSpacing<-as.numeric(args[4])
binSize<-as.numeric(args[5])
binSizeDistance<-as.numeric(args[6])
imageWidth<-as.numeric(args[7])
imageHeight<-ceiling(imageWidth/8)
yBound<-as.numeric(arg... |
75bb2109b0f303451c142b144f50fce6ea036156d1a69e2fad2695c7e43f4e0b | R | 4,731 | 121 | #!/usr/bin/R
# ------------------------------------------------
# Explore astrocyte changes in larger data cohort:
# Updated 10/07/2021
# ------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ')
source(paste0(bindir, 'multiRegion/load_metadata.R'))
library(tidyr)
library(ggplot2)
library(... |
705850e5f2cbd807a6780af564c8b555814f90cb64e986d8d6fb677da051a4c4 | R | 4,740 | 157 | #' @useDynLib permFDP, .registration=TRUE
#' @importFrom Rcpp evalCpp
NULL
#' Permutation-Based FDP Method for Rejection Threshold Correction
#'
#' This function controls FDR using the permutation method described in our manuscript. Like the BH method above, it corrects the rejection threshold rather than the p-values... |
d3101c1696735dd2775ab69ad4e960bb67058ebe05701daebf3a0a9cf495e625 | R | 4,745 | 129 |
#' archstabms_fit_linear_model
#'
#' Fit linear model to combinatorial data.
#'
#' @param mochi_outpath path to MoCHI thermo model fit results (required)
#' @param position_offset residue position offset (required)
#' @param load_mochi_data python (required)
#' @param outpath output path for plots and saved objects (r... |
084b8fc7431cf70dddcda71c498aae3c6c7986425e835a51f047d92dc2aeb01a | R | 4,750 | 134 | #!/usr/bin/R
# ----------------------------------------------------------
# Plot the full ROSMAP cohort + our data in the context of dim. red.
# Updated 02/18/2021
# ----------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ')
source(paste0(bindir, 'multiRegion/load_metadata.R'))
lib... |
5c15412954e01c47906a8eca47875d6a27c5a75a8739e3e7f264ff0719443530 | R | 4,761 | 127 | #!/usr/bin/R
# ----------------------
# Extract cell matrices:
# Updated: 01/20/2021
# ----------------------
library(cbrbase)
set_proj('DEVTRAJ')
source(paste0(bindir, 'multiRegion/load_metadata.R'))
library(tidyr)
library(rhdf5)
library(Matrix)
# Function to estimate end time of a loop:
est.endtime <- function(star... |
d94f23fc5ed21e40dc772571d7320d1ead59d19775883fbdc611c9e739be9547 | R | 4,774 | 96 | ## ----------Script-----------------
##
## Purpose: Generate plots for annotation of target genes after running FICLE
##
## Author: Szi Kay Leung (S.K.Leung@exeter.ac.uk)
##
## ---------------------------------
## ---------- Source function and config files -----------------
# source all general scripts related to l... |
c2b870ca3f3486dadb7d8686834bfcaa1a47d7c47ae0cc61d06abdd7708cfc9b | R | 4,786 | 175 | ### ---------------------------------------------------------------------------
###
### read.txt.long: import measurements from .txt file
###
### Format:
### (y x) wl int
###
#' @rdname DEPRECATED-textio
#' @concept moved to hySpc.read.txt
#'
#' @param file filename or connection
#' @param cols the column names spe... |
2a087d8af6fe8bdea34f49769c7a653724b23c4751f60ccfd059505de48b7c37 | R | 4,799 | 113 | #' Pipe operator
#'
#' See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details.
#'
#' @name %>%
#' @rdname pipe
#' @keywords internal
#' @export
#' @importFrom magrittr %>%
#' @usage lhs \%>\% rhs
#' @param lhs A value or the magrittr placeholder.
#' @param rhs A function call using the magrittr semantics.
#' @ret... |
6f3be0cee627ada876c12a3e46650bf0fdd24969c75693c223a80f84a901577e | R | 4,804 | 174 |
#### Spatially-aware k-means ####
## ------------------------------
setMethod("spatialKMeans", "ANY",
function(x, coord, r = 1, k = 2, ncomp = max(k),
weights = c("gaussian", "adaptive"),
neighbors = findNeighbors(coord, r=r),
transpose = TRUE, niter = 10L,
centers = TRUE, correlation = TRUE,
verbose = get... |
031fe6f3aa145082666b043cfbdabf072d388e2040b8e96729b3f3968eb446ca | R | 4,807 | 120 | # Load info table
label_df = read.csv('[your directory]/cluster_label.csv', check.names = FALSE)
dk_label = read.csv('[your directory]/dk.csv', check.names = FALSE) # brain region dictionary
dk_label = dk_label[complete.cases(dk_label), ]
dk_label$label = gsub('lh_', '', dk_label$label)
dk_label$label = gsub('rh_', ''... |
aa524d7d2d2f217919be9f5c97356351eedb9d63fcddc46b72a87c4af36eb75f | R | 4,807 | 128 | #!/usr/bin/R
# ------------------------------------------------------------------------
# Load the cross-module scores across all celltypes (at pseudobulk level):
# Updated 11/30/2021
# ------------------------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exis... |
83ed40e69fa622ab965a3e1223c6fbe7531cf9ae0ffd9185e2999f00fc9980bd | R | 4,810 | 151 | #!/usr/bin/R
# -----------------------------------------------------------------
# Plot module insets, highlighting a specific module in the network
# Updated 12/08/2022
# -----------------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
... |
e2c073963492d1649d0cc21e862f6fcc4fd064be789937e73a5e7680b3bd646c | R | 4,815 | 164 | #!/usr/bin/R
# -------------------------------------------------------
# Plots for the integrated neuronal trajectory from DEGs:
# Updated: 03/11/2022
# -------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.R'))
library(tidyr)
... |
a821728d8924a02a40cdd3a485efc246fc525eb87d564c837ba6383549147620 | R | 4,817 | 128 | # ********
# 3-de.r
# Code for conducting cellwise differential expression using the MAST model
source("Analysis/util.r")
source("Analysis/singlecell/differential-genes.R")
library(Seurat)
library(dplyr)
library(magrittr)
library(ggplot2)
library(MAST)
# -------- load data -----------
#x <- load_integrated()
DefaultA... |
7c7aa060a500a4aa9e89f97ef860f637e025f7acbeaa5dfc341312dc3175f7b9 | R | 4,818 | 93 | #
library(dplyr)
library(stringr)
library(magrittr)
library(ggplot2)
library(ggpubr)
library(reshape2)
setwd("F:\\Manuscript\\WGBS")
##peaks containing CpG
datBS <- read.csv("AllSamples.CpG.removeSNP.CovFilter.MethyLevel.csv", header = T, row.names = 1)
##peaks intensity (TPM)
datATAC <- read.table("F:... |
f5430a6c08dd8cda25b183529ae296746b474058aa08ba82d170a0ee9120e93b | R | 4,826 | 125 | if (!require("dplyr")){
install.packages('dplyr')
library(dplyr)
}
if (!require("lme4")){
install.packages('lme4')
library(lme4)
}
group_member=read.table("group_membership_3group.txt", header = TRUE)
colnames(group_member)[2]="membership"
tot_var=read.csv("./Integ_var.csv", header = TRUE)
... |
f188f889fdd5b28060d73c6ae22094c500c1fbbdb7689d49d9ab72a3b6514862 | R | 4,845 | 115 | options(bitmapType='cairo')
args <- commandArgs(TRUE)
dir<-args[1]
anchorPlotFile<-args[2]
expectedPlotFile<-args[3]
anchorName<-args[4]
anchorStart<-as.numeric(args[5])
anchorEnd<-as.numeric(args[6])
ymin<-as.numeric(args[7])
ymax<-as.numeric(args[8])
wd<-paste(dir,sep='')
setwd(wd)
expectedData<-rea... |
b312786e77f5c87a0863c39ffea08817ddeb505cdac079c9d0d47247973071a2 | R | 4,852 | 177 |
#### Spectral processing ####
## --------------------------
## Normalization
setMethod("normalize", "MSImagingExperiment_OR_Arrays",
function(object,
method = c("tic", "rms", "reference"),
scale = NA, ref = NULL, ...)
{
method <- match.arg(method)
if ( is.na(scale) ) {
if ( method == "reference" ) {
if (... |
0251bfa3e387a5ab18b849db3db9fd8d0fc57483456c715a853d8b7591d8441d | R | 4,853 | 212 | # Function -------------------------------------------------------------------
#
.Summary <- function(x, ..., na.rm = FALSE) {
validObject(x)
if ((.Generic == "prod") || (.Generic == "sum")) {
warning(paste(
"Do you really want to use", .Generic, "on a 'hyperSpec' object?"
))
}
## dispatch also ... |
6b8e5bbba1c320c70875b39a1427a8a919ef1535496d1b7f27bf100521e0381d | R | 4,856 | 127 | #!/usr/bin/env Rscript
# Script to calculate median bin coverage per sample for all samples in a bincov
# matrix
# Note: loads entire coverage matrix into memory. This may pose a problem for
# large matrices or on small-memory machines. Other workarounds exist from the
# command line, but most are slower. One suggest... |
34ebac9549504dbcebf2e6d6eb10fb48df45c243b6a58c4f52cf36d4a0ca3b7d | R | 4,860 | 164 | test_that("setup_l3_models refreshes and enforces the resolved producer stage", {
refresh_calls <- list()
enforce_calls <- list()
l1_models <- structure(
list(models = list(model1 = list())),
class = c("l1_model_set", "list")
)
l2_models <- structure(
list(models = list(l2_model1 = list(l1_model_... |
c421980171e89022b2f7de5719532ab5ea4c6e98ba6865e168e6167db834f4af | R | 4,870 | 106 |
#' archstabms_library_design
#'
#' Library design.
#'
#' @param base_dir Base directory (required)
#' @param output_dir Output directory (required)
#' @param stagenum stage number (required)
#' @param colour_scheme colour scheme file (required)
#' @param execute whether or not to execute the analysis (default: TRUE)
#... |
a7ef8c66ade0ddc7af0c77b565167fe44bffa7fd70ab68241a305b2a8f48b18f | R | 4,874 | 128 | library(testthat)
make_signature_test_gpa <- function() {
gpa <- create_mock_gpa(
n_subjects = 3,
n_runs = 2,
include_l2_models = TRUE,
include_l3_models = TRUE
)
gpa$l2_models$models$l2_model2 <- gpa$l2_models$models$l2_model1
gpa$l2_models$models$l2_model2$name <- "l2_model2"
gpa$l3_model... |
ce15cf1e50fb8de8f2554b2207290a8f232299e7c0a297dd668df086ada1fac1 | R | 4,876 | 154 | #!/usr/bin/R
# -------------------------------------------------
# Are region scores different
# Updated: 06/23/23
# -------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(bindir, 'multiRegion/load_metadata.R'))
library(tidyr)
library(ComplexHeatmap)
library... |
d4c86649d5374fab17ae4db7d8690f00fe7250accca4da5c7d244cc193447334 | R | 4,882 | 176 | #!/usr/bin/R
# -------------------------------------------------------
# Plots on the ranked genes from the branched pseudotime:
# Updated: 03/06/2022
# -------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.R'))
library(tidyr)
... |
de2fea256a0ab216110d0aed8c20c517cd6246461f359bba77d48aa714b39050 | R | 4,886 | 109 | TimeSlots_Analyser = setRefClass(
"TimeSlots_Analyser",
contains = "Analyser",
methods = list(initialize = function()
{
callSuper(
description = "For each GroupingKeyWord found in the sample names, the input data is subseted.
Then, for each subset, the mean values for a given time window are cal... |
494b4bd407f6a7f1561d478a7e273c62a524781457307a8d74fa9ffa4a65662b | R | 4,889 | 169 |
# Use SymSim to generate simlated datasets for use in DE testing and power analysis
print("Starting Now")
library("SymSim")
source("./simFuncsDavidMod.R")
library(distr)
# Testing out, per vignette
ngenes <- 100
true_counts_res <- SimulateTrueCounts(ncells_total=300, ngenes=ngenes,
evf_type="one.population", ... |
64272a55d5fc23c49b7411c0a24be114ea19701cb11ea8d5ea9eae8ce9a4eb2a | R | 4,894 | 105 | options(bitmapType='cairo')
args <- commandArgs(TRUE)
dir<-args[1]
anchorPlotFile<-args[2]
expectedPlotFile<-args[3]
anchorName<-args[4]
anchorStart<-as.numeric(args[5])
anchorEnd<-as.numeric(args[6])
primerPerformanceFactor<-as.numeric(args[7])
wd<-paste(dir,sep='')
setwd(wd)
expectedData<-read.table(... |
462707719022499cc19ad882882c36cb799104565e4966dd92f2bcca61e2b64c | R | 4,896 | 114 |
#' archstabms_fitness_plots
#'
#' Plot fitness distributions and scatterplots.
#'
#' @param dataset_name character dataset name (required)
#' @param base_dir Base directory (required)
#' @param colour_scheme colour scheme file (required)
#' @param outpath output path for plots and saved objects (required)
#'
#' @retur... |
ed86efc86070035e8492244c5161b26f535551b7ada9de7d86f80e0464b7e875 | R | 4,899 | 163 | #!/usr/bin/R
# ---------------------------------------------------------------
# Plot module umaps and functional enrichment as indpt resources:
# Updated 02/16/2022
# ---------------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source... |
2f7902ead522b9d4dc5cca64f962f884274fecf350dd6616eec4165db4f2addf | R | 4,900 | 98 | ####################################################################################################
###
### File: 03_Merging_data.r
### Purpose: Merging the coordinate features with the EEG features.
### Authors: Gabriel Rodrigues Palma, Rafael Moral
### Date: 04/04/23
###
#######################################... |
706fbf4875b8dd84a6b049eeddfe8703dcfd2e717f1de677c20c6921be15f42d | R | 4,904 | 151 | require(testthat)
require(Cardinal)
context("SpectralImagingExperiment")
test_that("SpectralImagingExperiment accessors", {
se <- SpectralImagingExperiment()
expect_true(validObject(se))
se <- SpectralImagingExperiment(matrix(nrow=0, ncol=0))
expect_true(validObject(se))
set.seed(1)
nr <- 20
nc <- 10
a <... |
2818787533ee3137263a07666b5c34617699cf994ac9a4607c7c7441bd705b2d | R | 4,907 | 182 | ---
title: "`r params$target`-targeted GCN report "
subtitle: "`r params$tissue` expression data"
author: "Alicia Gómez"
date: "`r format(Sys.time(), '%d/%m/%y')`"
output:
html_document:
theme: spacelab
highlight: kate
df_print: paged
toc: yes
toc_float: yes
number_sections: yes
params:
targ... |
644c73ffc668af77193599db73b1b40b4e93d427a66840c5ce037f2a7dd09a84 | R | 4,921 | 115 | options(bitmapType='cairo')
args <- commandArgs(TRUE)
dir<-args[1]
anchorPlotFile<-args[2]
expectedPlotFile<-args[3]
anchorName<-args[4]
anchorStart<-as.numeric(args[5])
anchorEnd<-as.numeric(args[6])
primerPerformanceFactor<-as.numeric(args[7])
wd<-paste(dir,sep='')
setwd(wd)
expectedData<-read.table(... |
919453d69d3399f0dcc0598873636805b71afecaed16470e7ab197739599693a | R | 4,923 | 116 |
###########################################################################################
# Load relevant packages
source("/net/trapnell/vol1/home/readdf/trapLabDir/sharedProjectCode/utility/singleCellUtilFuncs.R")
modStatus <- loadMonoclePackages()
print(modStatus)
source("/net/trapnell/vol1/home/readdf/trapLabDir... |
dc952157d39608a7ee0f94bec1ed0985433825822b956c3292ca6bf2d373557c | R | 4,940 | 141 | #' This function generates syntax for FSL Feat .fsf files for the EVs tab of a higher-level fMRI analysis.
#' It accepts a numeric design matrix whose colum names correspond to individual EVs in the model
#'
#' @details This allows you to generate a design matrix dynamically based on a numeric matrix that has been
#' s... |
d571cee001f578b59a6ac560b99a8d29af5b4779d7a3621e7066a1473e7bf291 | R | 4,942 | 103 | #' Remove unwanted effects from a sccomp_tbl object
#'
#' @description This method removes unwanted effects from a dataset using the model estimates. For example, if you fit your data with the formula `~ factor_1 + factor_2` and use the formula `~ factor_1` to remove unwanted variation, the `factor_2` effect will be fa... |
e1e1eda15c3847c8559a9745ecadf0167aa0ed90ebcfca3f946805887d1e6641 | R | 4,962 | 189 | require(testthat)
require(Cardinal)
context("process-peaks")
test_that("process peaks - SpectralImagingArrays", {
path <- CardinalIO::exampleImzMLFile("processed")
s <- as(readImzML(path), "SpectralImagingArrays")
s2 <- peakPick(s, method="sd")
s2 <- process(s2)
n1 <- lengths(spectra(s, "index"))
n2 <- lengt... |
acd74bfac6dca610a32ffab832bb0b84d6f88209ecf67823318cebc22d159646 | R | 4,964 | 121 | #' Unit tests for restricted environment cache handling
#'
#' Tests both approaches for using custom Stan model cache in restricted environments:
#' 1. assignInNamespace - set global cache before any sccomp call
#' 2. cache_stan_model - pass explicitly to sccomp_estimate, sccomp_remove_outliers, sccomp_boxplot
#'
#' @s... |
37537c5dd74e4681ff431971d97e67e96fcb59800e882c3bace0c61419f9e51f | R | 4,966 | 169 | test_that("get_contrasts_from_spec warns and adds diagonal when no contrasts specified", {
data <- data.frame(
id = c("sub1", "sub2", "sub3"),
session = c("ses1", "ses1", "ses1"),
run_number = c(1, 2, 3),
stringsAsFactors = FALSE
)
mobj <- list(level = 2L)
class(mobj) <- c("list", "hi_model_spe... |
44fa9b28c2b9c179a204d2f6560f97df823a97eee6f2d0625f0cabae492d0327 | R | 4,967 | 129 | read.cross.msg <- function(file.csv,ancfile.par2, phenofile, ancfile.par1=NULL) {
## Read MSG ancestry probabilities from file and return an R/qtl
## "cross" object.
## MSG ancestry probabilities are contained in a simple tabular
## format with individual names in first column, and labels of the
## form... |
e9c3e9be062314be28e85a796c00dc9dbf8ea5fcbf212322b226493a0ac7714e | R | 4,982 | 120 | #' R6 class for a keyed data.table object that uses run-length encoding to reduce storage size
#'
#' @importFrom R6 R6Class
#' @importFrom data.table data.table
#' @importFrom checkmate assert_data_frame assert_logical
#' @export
rle_dt <- R6::R6Class("rle_dt",
private=list(
# data: keyed data.table object
d... |
a47bacab06d9f6ef169e22bbd71f48f92cf37015526a0b8629c5f6291f7d4224 | R | 4,984 | 135 | #!/usr/bin/R
# ----------------------------------------
# Plot neuron counts in entorhinal cortex:
# Updated: 12/06/2021
# ----------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.R'))
library(tidyr)
library(viridis)
library(ggplot2)
library(ggr... |
f903160d38fb02681fe8c9ce68cb6899aff1a8908c06d79bbdf9b615c93437f1 | R | 4,985 | 97 | ---
output: github_document
title: shinyÉPICo
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
<!-- badges: start -->
[![License: AGPL v3](http... |
991a7e1682ed8a2421898b6508fbcfd1eaa078c25bcac5f0054fe8e74ab2e027 | R | 4,986 | 114 | ---
title: "003_RA_gastruloid"
author: "Nobuhiko Hamazaki"
date: "7/15/2022"
output: html_document
---
```{r}
library(dplyr)
library(Seurat)
library(patchwork)
library(ggplot2)
library(stringr)
library(RColorBrewer)
library(tidyseurat)
library(cowplot)
library(Signac)
library(viridis)
library(ggthemes)
library(network... |
b1150a83c3c10b015dab40ceb9a8bb43b232549bf640f56dc95e4250c0fdf6f8 | R | 4,986 | 146 |
library(ggplot2)
library(dplyr)
library("optparse")
library(stringr)
library(tidyr)
library(tidyverse)
print("Libraries loaded, starting now")
formatCellType <- function(inputColumn){
inputColumn = ifelse(inputColumn == "T_Cell", "T Cell", inputColumn)
inputColumn = ifelse(inputColumn == "VSM_and_P... |
4e3945af8f5833bbdb0442bfd9bf6467613105891b080a488655b12889e3aa11 | R | 4,991 | 88 | read_stat <- read.table("/lustre/projects/Research_Project-MRC148213/lsl693/rTg4510/H_Sorted_Nuclei/6_merged/merged_collapse.read_stat.txt", header = T)
sampleID <- read.csv("/lustre/projects/Research_Project-MRC148213/lsl693/rTg4510/H_Sorted_Nuclei/6_merged/original_fasta/merged_sample_id.csv")
read_stat <- merge(rea... |
310b92d99d1bde9c4920387d0a6c91389b0fcc287247f7336c057dae6d7912ff | R | 4,994 | 122 | ##POE
stage <- c("40","70","115","168")
tissue <- c("Br", "Li", "Mu", "Pl")
library(customLayout)
library(dplyr)
lay <- lay_new(matrix(c(1,2,3,4,5,6,7,8,9,10,11,0,12,13,14,0), ncol=4, byrow = T), widths = c(1,1,1,1), heights = c(1,1,1,1))
lay_show(lay)
a <- c()
for(i in 1:4){
if(i == 3||i==4){
... |
2a287f8fa17fe5130185828f22762ae43acfc0c8ea09c60e493f27376801d880 | R | 4,996 | 131 | #' Build concatenated L1 confounds for multi-run analyses
#'
#' Internal helper that uses stored confound column metadata to align shared
#' columns and union spike regressors across runs with zero padding.
#' @keywords internal
#' @noRd
read_l1_confound_columns <- function(gpa, id, session, run_number, lg = NULL) {
... |
d6d40f70dfa229ea70805ae2dafa88008532443046449aa436d18d13edcd6ec0 | R | 4,997 | 154 | #!/usr/bin/R
# ---------------------------------------------------------
# Replot the DEG enrichment table from the modules package:
# Updated 11/27/2021
# ---------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(paste0(bindir, 'm... |
96016e7a3072e4339762d816909282fa5f4c528239f64feddf625828631d830d | R | 5,008 | 80 | #!/usr/bin/env Rscript
## ----------Script-----------------
##
## Purpose: Comparison of jones2024 dataset
##
## Author: Szi Kay Leung (S.K.Leung@exeter.ac.uk)
##
## ---------------------------
dirnames$jones2024 <- "/lustre/projects/Research_Project-MRC148213/lsl693/rTg4510/0_otherStudies/Jones2024/"
## ---------- c... |
5b10328702763c4bbdca4d5a0b66b1477394b7a4c09074a81fb707cd4940874f | R | 5,013 | 155 |
#-----------------------------Identification of tissue-specific and developmental stage-specific expressed genes---------------------------------------
#
#1. It was performed in separate periods to screen out tissue-specific expressed genes
setwd("F:\\ATAC\\processFiles\\RNA-tissue_specific0711")
library(dplyr)
... |
59ee26d3c61653f0661b4f2c33cf24ccb8456a4bb1ff3731e7f35b09fc2f342d | R | 5,015 | 141 | ---
title: "Updating a Systematic Search"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Updating a Systematic Search}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
## **Setup**
First,... |
5c78fa191525a8167dd184dbce7ab2b6472084cbe012a1ce94381e82fa50213a | R | 5,023 | 136 | ## HiC-Pro
## Copyleft 2015 Institut Curie
## Author(s): Nicolas Servant
## Contact: nicolas.servant@curie.fr
## This software is distributed without any guarantee under the terms of the GNU General
## Public License, either Version 2, June 1991 or Version 3, June 2007.
##
## Plot 3C filtering results
##
rm(list=ls()... |
b5e9c5b6c7a9cc3fdc90bcf167906263e43e2afea4ee1cfb3815f0d29d2b538c | R | 5,046 | 162 | #!/usr/bin/R
# --------------------------------------------------
# Part of metabolic modules figures
# - Systematic scan for e4-associated modules
# Updated 06/20/2023
# --------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(paste0(sb... |
bb60590a221ddaecd5015c44572b4d44fc06352bbac9ff4e56a883805d069ec5 | R | 5,054 | 97 |
library(GSVA)
library(Seurat)
library(magrittr)
library(stringr)
library(ggplot2)
source("/functions//pseudo_gsva_general.R")
#Loading object
comb_hd_cd44_pos <- readRDS("comb_hd_cd44_positive.rds")
comb_hd_cd44_neg <- readRDS("comb_hd_cd44_negative.rds")
#Gene set loading
astro_genemark <- read.csv("/data/astro_ge... |
25990e21ed7b9052ce5e8ff30767b7756116ae4b32041574564daf976d23d7d0 | R | 5,072 | 169 | #!/usr/bin/R
# ------------------------------------
# Plot specific pairs of correlations:
# Updated 02/09/2022
# ------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(paste0(sbindir, 'load_metadata.R'))
}
source(paste0(sbindir, 'auxiliary_pseudob... |
0dd68f66de94d997185757cd91ca5096d051c82839ccb0238b7716f48876b421 | R | 5,075 | 182 | test_that("function returns numeric threshold", {
# Create valid test data
set.seed(123)
controlVals <- matrix(rnorm(30), ncol = 3, nrow = 10)
testVals <- matrix(rnorm(30, mean = 1), ncol = 3, nrow = 10)
intOnly <- data.frame(cbind(controlVals, testVals))
myDesign <- c(1, 1, 1, 2, 2, 2)
pVals <- runif(10,... |
62de5b9b2058e40478ac32beb40578e3b490db59cb8682f6b7ae48cf274cd3a3 | R | 5,076 | 147 |
library(monocle3)
source("/net/trapnell/vol1/home/readdf/trapLabDir/sharedProjectCode/utility/singleCellUtilFuncs.R")
source("./formattingFunctions.R")
library("optparse")
# Get the passed parameters
option_list = list(
make_option(c("-c", "--cdsRNA"), type="character",
default="allCells_HM10UMI=100_mit... |
266f21a32528e8644c1453596a2669a6ec4a53e1cff56c3c6862d4b3d9133648 | R | 5,080 | 138 | #!/usr/bin/R
# ----------------------------------------------------------
# Plot enrichments for the collated + aggregated DE results:
# - Compare enrichments in same celltype, across regions.
# Updated: 01/30/23
# ----------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiReg... |
ec532d5bd285bc0455f2698338492bc8b9bade16f134fa4c5f13f2b0131c2708 | R | 5,089 | 89 | load('PDGC_CPM_exp.RData')
source('~/Workshop/18-09-12_Multicentric/codes/rfunc/ssgseaMOD.r')
tableS1 = read.table('supplementary_table1.txt',header=T,sep='\t',row.names=1)
PDGC_CPM_exp.f = PDGC_CPM_exp[,which(tableS1$In.house.subtype!='other')]
rs = mod.analyze2(PDGC_CPM_exp.f,c('OXPHOS','PN','MES','Proneural','Class... |
b6c3489eb7621ae7c776c95e7a30be346400a97ed41cb881b2ee8c11fc13a644 | R | 5,090 | 179 |
#### Spatially-aware Gaussian mixture models ####
## ---------------------------------------------
setMethod("spatialDGMM", "ANY",
function(x, coord, i, r = 1, k = 2, groups = NULL,
weights = c("gaussian", "adaptive"),
neighbors = findNeighbors(coord, r=r, groups=groups),
annealing = TRUE, compress = TRUE, byr... |
66dee3b4f53483382672e44cd379f50f1906cc07d16ae1f82b0d9296f7995a99 | R | 5,107 | 168 | #!/usr/bin/R
# ---------------------------------------------------------
# Plots on the ranked genes from the horizontal pseudotime:
# Updated: 03/09/2022
# ---------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.R'))
library(t... |
d22b1805669a3cce2103089f8025287b5894cf43c4b1d9023e603621db2d0146 | R | 5,109 | 116 | splitvalidate.lassosum.pipeline <- function(ls.pipeline, test.bfile=NULL,
keep=NULL, remove=NULL,
pheno=NULL, covar=NULL,
trace=1, split=NULL,
rematch=!is.null(... |
c8d41be26021a0d03d587aafa91b026c85541477f3353b4e88bfd20fe88a3d2f | R | 5,110 | 132 | #!/usr/bin/R
# ----------------------------------------------------------
# Plot DEGs with highlighted enrichments
# for the collated + aggregated DE results:
# - Compare enrichments in same celltype, across regions.
# Updated: 01/30/23
# ----------------------------------------------------------
library(cbrbase)
set_... |
929223b853651959823718aa13c9f1ea26ba26906a4737bae994712bfab6a965 | R | 5,111 | 158 | test_that("extract_fsl_betas resolves per-cope L2 cope-file outputs", {
root <- tempfile("extract_l2_cope_files_")
dir.create(root, recursive = TRUE)
mask_file <- file.path(root, "mask.nii.gz")
RNifti::writeNifti(array(1, dim = c(2, 2, 2)), mask_file)
make_l2_row <- function(cope_number, cope_name, value) {... |
e79487c44770415da17539c6c7a2e18c26c97a0a544cc04f4f3a8d5f278f82cd | R | 5,115 | 159 | #!/usr/bin/R
# --------------------------------------------
# Plot the expression patterns for GWAS genes:
# Updated 02/21/2022
# --------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(paste0(sbindir, 'load_metadata.R'))
}
source(paste0(sbi... |
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