sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
25ae4884f06de229db8a15812b6e4dc69fee49bc837ce779335845f11e2dd20b | R | 9,844 | 266 | #!/usr/bin/R
# -----------------------------------------------------------
# Use MAST + RE to run differential gene expression
# - Per region x per celltype subdivision
# Submit as chunks (2500 -> 8 chunks) per combination.
# TODO: Write separate script to batch and one to combine
# Updated: 01/21/2021
# --------------... |
7172608d7bff137dd03909a3212d0321fdc1403dc4806d33a80fa82442acd1ce | R | 9,878 | 310 |
# Analyse p-values of genes w/ wo correcting for common variants
to get the results for the condtional analysis for qunatiative traits run https://github.com/PMBio/deeprvat/blob/main/pipelines/association_testing_control_for_common_variants.snakefile
```{r}
library(dplyr)
library(tidyr)
library(ggplot2)
library(arrow... |
cffad8c9d9ed7f99acd396b2ccffaeef610fe49b8f65a3b7b2d771058db42d63 | R | 9,940 | 160 | ---
title:
output:
html_document:
toc: true
toc_float: true
---
```{=html}
<style>
.list-group-item.active, .list-group-item.active:focus, .list-group-item.active:hover {
background-color: #754E9B;
}
</style>
```
## Overview
<img src="updated_logo.png" align="right" width="142"/>
Welcome to the the *... |
af2df258b55d7aa1759b91fca18c1efb49236bc8d7372abb2e13162dbc114367 | R | 9,978 | 210 | # TODO: clean up the mess of the 'local' approach, but for now, prefer the glue expression
# consider whether we want a 'what' type argument, like subject directory, analysis directory,
# run directory, model-specific analysis directory, etc.
#' small helper function to return the location of an l1 directory based on
... |
b1ca525af5383858b3715c53ac9dc92e157cecfb4dd6ac9250cb54f3dd015eb2 | R | 10,049 | 241 |
##POE dominate expression identification
setwd("F:\\RNA_20221101\\02_ModifiedGenome\\C_POEgeneSelection\\result")
library(dplyr)
POE <- read.csv("ASE_N-1_filter_final.PO2.csv", header = T, row.names = 1)
Br_40 <- POE$Br_40[!is.na(POE$Br_40)]
Br_70 <- POE$Br_70[!is.na(POE$Br_70)]
Br_115 <- POE$Br_115[!is.na(... |
ba531acd1562ed7b46c1eee48147a8517e4c5c7a2c40415947f2a8deb5159d81 | R | 10,058 | 270 |
library(tidyr)
library(ggplot2)
library(glmnet)
library(ROCR)
library("optparse")
# Get the passed parameters
option_list = list(
make_option(c("-f", "--featureSelection"), type="character",
default="Binary_Combined_Motif_Counts", # "Binary_Combined_Motif_Counts", "Binary_PromOnly_Motif_Counts"... |
48a4a48c4b61b33a1e99df30b0bec0631778ee5bc9af42819d6256001086d509 | R | 10,091 | 176 | ```{r init}
library(Matrix)
library(tidyverse)
library(biomaRt)
library(monocle3)
library(rgl)
library(tricycle)
library(SignacX)
library(princurve)
library(ggplot2)
library(Hmisc)
library(pheatmap)
library("ComplexHeatmap")
library(circlize)
library(dplyr)
library(ggrepel)
library(latex2exp)
library(scales)
```
```{r... |
cafa8fb27f48cebe54b37b9abef705b2f9964e820265ccfa79370f3c4f226455 | R | 10,120 | 276 | #!/usr/bin/R
# --------------------------------------------------------------------------
# Calculate the fraction differences, by logistic regression + mixed effects
# Updated 10/23/2020
# --------------------------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ')
source(paste0(bin... |
92c99a8aa05c79150885f54e78c45f53881a3445af1db29631e4852e801eef22 | R | 10,136 | 257 | #' helper function to insert a keyed data.frame into the sqlite storage database
#'
#' @param gpa A \code{glm_pipeline_arguments} object.
#' @param id the id of the subject to whom these data belong
#' @param session the session of these data
#' @param run_number the run_number of these data
#' @param data A \code{data... |
7af0418fd07d46343451518e54ba50c7f6d51910c818c902a3fa4482c9bd48a0 | R | 10,155 | 250 | ---
output: pdf_document
params:
pmean: 0.5
pphi: 0
iter: 1000
---
```{r}
# confidence interval coverage simulation for binomial, beta-binomial
# with varying p, var(p), total UMI (sum(y)) = m, total observations n
library(dplyr)
library(ggplot2)
library(reshape2)
library(aod)
library(RCurl)
```
```{r}
# get re... |
884388433c165c5c76bc97b88b3eb7bbfd8dc8dd2d95df03805246bff124db5a | R | 10,167 | 250 |
##################### Plotting. #####################
colors <- c('#c6dbef', '#9ecae1', '#6baed6', '#3182bd', '#08519c', #'pink',
"aquamarine4", '#E6AB02',
'#7A2961')
methods <- c('Burden pLOF', 'Burden missense', 'SKAT pLOF', 'SKAT missense', 'Burden/SKAT combined', "Monti et al.", "STAAR",... |
baf2b3e3919903dc13d69e5377a8910a67c89357d746b22bee94046e6951a360 | R | 10,207 | 321 | ### -------------------------------------------------------------------------~
###
### plot methods
###
### -------------------------------------------------------------------------~
# Set generic ----------------------------------------------------------------
#' @noRd
#' @export
setGeneric("plot")
# Functions ---... |
45d147d7aaecc9acc5a1ae02619b74699a0a7ddbf19b49c14ed7b3e9a379e026 | R | 10,224 | 294 | #!/usr/bin/R
# ---------------------------------------------------------------------
# Compute and plot differences between subtypes for a certain celltype:
# Updated 12/02/21
# ---------------------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindi... |
a44171c5fc6ecbba819182a679dd8c5843b842844761203997f756b6b3ab7396 | R | 10,229 | 247 | #' Helper function to better summarize GPA object
#' @param object the \code{glm_pipeline_arguments} object
#' @param ... ignored.
#' @return a readable summary of the given gpa object
#' @export
summary.glm_pipeline_arguments <- function(object, ...) {
gpa <- object
cat("\nSummary of GLM Pipeline Analysis:... |
bb8f358a0fddc91fd39f9dce9171ac8ad96ac5e515873f2842a638fbd82e496e | R | 10,229 | 352 |
#### Spatial shrunken centroids (classification) ####
## -------------------------------------------------
setMethod("spatialShrunkenCentroids", c(x = "ANY", y = "ANY"),
function(x, y, coord, r = 1, s = 0,
weights = c("gaussian", "adaptive"),
neighbors = findNeighbors(coord, r=r), bags = NULL,
priors = table(y... |
2a54a001d881faf25815f920d72701f216a1bbe726781c0388f9dc1581589271 | R | 10,251 | 270 |
library(tidyr)
library(ggplot2)
library(glmnet)
library(ROCR)
library(data.table)
library(viridis)
# Get shared functions between this and runRegressionModel.R
source("./utilityFunctions.R")
library("optparse")
# Get the passed parameters
option_list = list(
make_option(c("-p", "--pValFIMOcutoff"), t... |
c830bfe812355f09971b28ab341def948cb78b52c4ef9315816e8b8986e1ddfd | R | 10,280 | 364 | # ---- Utility functions for making nice file names ----
prefix_filename <- function(file.name, prefix=NULL) {
if (!is.null(prefix)) {
file.name <- paste(prefix, file.name)
}
return(file.name)
}
groupby_splitby_filename <- function(file.name, group.by=NULL, split.by=NULL) {
if (!is.null(group.by)) {
fi... |
aae233a92b0c8420f7eb40972174304d5223af03de3027ae42fc23b59bb16303 | R | 10,287 | 294 | #!/usr/bin/R
# -----------------------------------------
# Comparison of published with own DEGs:
# - major cell types, across pathology vars
# Updated: 07/27/23
# -----------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(bindir, 'multiRegion/load_metadata.R'))
library... |
aaf001c2f5bc61267fac802719be555e0b3666e3b8a93d211df9b02621950ac7 | R | 10,324 | 251 | library(testthat)
library(tibble)
library(dplyr)
library(tidyr)
library(rlang)
library(sccomp)
test_that("get_random_effect_design3 with new NA groups", {
skip_cmdstan()
# Create test input data
test_data <- tibble(
sample = c("sample1", "sample2", "sample3", "sample4"),
type = c("healthy", "healthy", "c... |
4ec898ecdcc7f18e2283efd4ce4d82155f59dfb057d23185729d1490284829ff | R | 10,327 | 328 | #!/usr/bin/R
# ------------------------------------------------------------------------
# Plot cross-module scores within the same celltype (at pseudobulk level):
# Updated 02/16/2021
# ------------------------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exis... |
24ce01267886b61a6f2c0b8bafe086566fbbff896cf2cb45891da5f76982f66d | R | 10,372 | 277 | rm(list = ls())
library(ggplot2)
library(stats)
library(plyr)
library(lsr)
library(ggridges)
library(matrixStats)
library(rapportools)
library(optparse)
library(dplyr)
#################
# Set themes and functions
#################
# -- Set ggplot basic theme
theme1<- theme(panel.border = element_rect(size = 2, colou... |
365f25ddbf5b05431d507fe5a6d6ad404f107c2584da81ca1a18ab9e7af80805 | R | 10,384 | 276 |
library(SingleCellExperiment);library(tidyverse);library(reticulate);library(tidyverse)
library(ggplot2);library(scmap);library(fmsb);library(ggsci);library(ROGUE)
library(Seurat);library(M3Drop);library(ROCR);library(cluster);library(parallel);library(scran)
# Load data
lung.sce <- readr::read_rds("/data1/pauling/04... |
6196722f78d71784a4940880c8bacd810b50f9a021fea1da2eba1a742c6e7dce | R | 10,395 | 213 |
#' ---
#' title: "Top 23 connectivity analysis output"
#' author: "Tomas Bjorklund"
#' output:
#' pdf_document:
#' highlight: tango
#' geometry: margin=0.7in
#' fontsize: 10pt
#' ---
#' This script presents top 23 candidates as connectivity graph.
suppressPackageStartupMessages(library(knitr))
#+ setup, inclu... |
249b8d87461b2715c8af8efc7e659a1985aa46238e79479158e00860f5bfe4c1 | R | 10,449 | 148 |
#' archstabms__plot_model_performance
#'
#' Plot model performance.
#'
#' @param input_dt data.table with model free energy estimates (required)
#' @param report_outpath output path for scatterplots (required)
#' @param trait trait name and suffix for output files (required)
#' @param highlight_colour colour for highl... |
8956d15e7d7c8934a4876879a43747550bf503cc2d192db149af228e418429ce | R | 10,462 | 233 | # allow a set of .feat folders as input?
#' Generate a set of diagnostic images and plots for brain masks relative to one another and a template
#' @param input Can be a character vector of mask files, a \code{gpa} object, or a \code{data.frame} containing
#' a \code{brain_mask} field, or at least a \code{$run_nifti... |
f0296e91e7f0fb3cb4bad5682c305253790758bc03af6e0dbc252a4f876058a4 | R | 10,462 | 261 | #!/usr/bin/R
# ----------------------------------------------------------------------------
# Investigate the co-variation of selective vulnerability in EC/HC and others:
# TODO: Neurons alone and neurons + glia
# Updated 12/21/2021 to perform beta regression
# ----------------------------------------------------------... |
7d73ce05cd7579c91c14a7ccf227ff8fe3e59102dbc866540e1ba92ff75bb7ee | R | 10,469 | 267 | matchpos <- function(tomatch, ref.df,
auto.detect.tomatch=T, auto.detect.ref=T,
chr="", ref.chr="",
pos="", ref.pos="",
snp="", ref.snp="",
ref="", ref.ref="",
alt="", ref.alt="",
... |
53ee78ee948e13b6896e90ff43e5ffd62d8f528e2fdddce66782b7a067c9226b | R | 10,511 | 272 | #!/usr/bin/R
# ----------------------------------------------------------
# Plot enrichments for the collated + aggregated DE results:
# Shared and specific pathways up and down.
# Updated: 03/22/22
# ----------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(p... |
892f2cfec99791a4b8579595b3e9f6d5c68276e1541fa12e7d9a645fb153179a | R | 10,511 | 256 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
### Analysis of interspecies deformation and aging utilising OPNMF structural
### covariance regions
### 5 INPUTS:
### ARG_1 - Working directory (wd) where relative paths can be used to find files
### ARG_2 - Cohort investigated
### ARG_3 - Parcellation Im... |
f69c52ad19907882c19342031b62db11d314b31b0df366b1221b115d65f18426 | R | 10,523 | 286 | library(dplyr)
library(fmri.pipeline)
# setwd(file.path(getMainDir(), "projects", "clock_analysis", "fmri", "hippo_voxelwise"))
setwd(file.path(getMainDir(), "users", "michael", "sceptic_decon"))
trial_df <- get_mmy3_trial_df(model="selective", groupfixed=TRUE) %>%
mutate(rt_time=clock_onset + rt_csv/1000, #should b... |
058b210ac3e93075bdc0c38e4e1a4a3329419dac0e2f28e490d3aa7d05d532e8 | R | 10,538 | 277 | #########################
########Licensing########
#########################
# Breathe Easy - an automated waveform analysis pipeline
# Copyright (C) 2022
# Savannah Lusk, Andersen Chang,
# Avery Twitchell-Heyne, Shaun Fattig,
# Christopher Scott Ward, Russell Ray.
#
# This program is free software: you can redis... |
41b1275fb2706c10b9da6749bd23a9835962ef0938002640a352533cc399e683 | R | 10,566 | 356 |
#### Read MS imaging data file(s) ####
## -----------------------------------
readMSIData <- function(file, ...)
{
ext <- tools::file_ext(file)
if ( tolower(ext) %in% c("ibd", "imzml") ) {
readImzML(file, ...)
} else if ( tolower(ext) %in% c("img", "hdr", "t2m") ) {
readAnalyze(file, ...)
} else {
stop("can... |
b1d2df546009b487235c6e8d01a481bea335d8c50b7a11817a5ede6eae69b5f0 | R | 10,605 | 272 | ---
title: "prep_all_mira_forscvelo"
author: "T Attenborough"
date: "11/01/2023"
output: html_document
---
```{r, message=FALSE}
library(remotes)
library(Seurat)
library(zeallot) # For %<-% that unpacks lists in the Python manner
library(DropletUtils)
library(tidyverse)
library(GGally) # For ggpairs
library(scales)
l... |
c59dc737deef3172f33241fb6bfbf76f2fa100970c44e17f44ff96e4f1c3e2cf | R | 10,611 | 233 | exptDir = '/path/to/along-tract-stats/example/between_group'
grpLabs = c('Control', 'FASD')
thresh = 0.05
nPerms = 100
library(nlme) # Mixed-effects models
library(ggplot2) # Plotting tools
library(plyr) # Data manipulation
library(RColorBrewer) # Color tables
##################################... |
fb5f139092697d3ebb1aee67aace203c7ce7ea7b4d4d509205e1828338305478 | R | 10,617 | 226 | #' helper class to generate 3dttest++ null datasets using -randomsign
#' @keywords internal
simulate_null_3dttest <- R6::R6Class("simulate_null_3dttest",
private = list(
residuals_file = NULL,
mask_file = NULL,
n_permutations = 20000,
njobs = 16,
job_output_files = NULL, # full path to permutation... |
c6da66058191319d21b2c1993c31a2509fb7a7c7154b1f27af970b6c48ebd5e4 | R | 10,622 | 303 | #' sccomp_scatterplot
#'
#' @description
#' Creates a scatterplot of cell-group proportions against a continuous covariate,
#' optionally overlaying the posterior predictive trend from the fitted model.
#' Use this visualisation when the factor of interest is numeric; for discrete
#' factors prefer [sccomp_boxplot()].
... |
f10cc8719079e00e5c6f2a43de582e64bd01058d104a472a2ecae48e513cdbc3 | R | 10,624 | 412 | suppressPackageStartupMessages({
library(Seurat)
library(magrittr)
library(dplyr)
library(purrr)
library(stringr)
library(ggplot2)
library(patchwork)
library(forcats)
})
SAMPLE_DIRS <- list(
"AH59.5" = "202309211049_MsEmbryo-VS147-AH59-2-5-A_VMSC18002/region_1",
"AH59.6" = "202309151103_MsEmbryo-VS... |
29c04eda91d158d437beb301cfce4506cb1e868fc2e35ba07cc2d3b8d477513f | R | 10,627 | 260 | #!/usr/bin/env Rscript
#
# This is a modification of the original pTFCE shell script: https://spisakt.github.io/pTFCE/
# The goal is to apply pTFCE to a single zstat image based on relevant inputs
print_help <- function() {
cat(paste("This script runs pTFCE (probabilistic threshold-free cluster enhancement) on a sin... |
80295bb16631cfa6e81ef9fd0d4ff49fbe6534b82c08c3e912651c750593163f | R | 10,638 | 248 | # helper function to return a logical vector of trials included in subset
get_trial_set_from_signal <- function(ss, trial_data=NULL) {
checkmate::assert_data_frame(trial_data)
# if user has a cached trial expression, need to evaluate the trial set before getting value df
if (isTRUE(ss$trial_subset)) {
# calcu... |
3935cd36b6c9e8b2eae6521e9c71e0528178d3d8f19905c33aade82279153858 | R | 10,666 | 301 | #!/usr/bin/R
# ----------------------------------------
# Calculate relative changes in markers of
# vulnerable neurons w.r.t. APOE genotype
# Updated: 07/22/2022
# ----------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.R'))
library(tidyr)
li... |
6055bfa3bef088f882b64773f39a29cf56c02ddbc6172282743fd65b98a5761e | R | 10,675 | 401 | ---
title: "PPMI differential expression analysis with DESeq2"
output:
html_document:
toc: true
---
Subtype III vs healthy controls. Gender as covariate. Protein-coding genes only.
Code adapted largely from:
* http://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html
* https://bioco... |
e5f6ab73920f643ceab5493309a99212ec8865696c17fb2c9808093fca115253 | R | 10,678 | 267 | #' simulate_data
#'
#' @description This function simulates data from a fitted model.
#'
#' @import dplyr
#' @importFrom magrittr %$%
#' @importFrom magrittr divide_by
#' @importFrom magrittr multiply_by
#' @importFrom magrittr equals
#' @importFrom rlang quo_is_null
#' @importFrom SingleCellExperiment colData
#' @impo... |
2b8cd0141002c5becfc2dfbaf39c07a01656c8bfbc08a5d3db0a3b83d15543bf | R | 10,701 | 243 | ---
title: "Analysis of m/k = 1"
output:
html_document:
self_contained: false
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Enrichment analyses on variants with m/k = 1 from single-cell data.
#### Enrichment analysis
```{r, echo = FALSE, warning=FALSE, message = FALSE, dev=c('png', 'p... |
4a91eed420c86d27b979fd3463e502b208886b37c1bd30531312b3e5695fca80 | R | 10,727 | 402 | ---
title: "PPMI differential expression analysis with DESeq2"
output:
html_document:
toc: true
---
Subtype III vs healthy controls. Gender as covariate. Protein-coding genes only.
Code adapted largely from:
* http://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html
* https://bioco... |
906a0debf7b0132e5aeb82f82612b4b6c8394cac86f041029a592ad3856ee3c9 | R | 10,729 | 403 | ---
title: "PPMI differential expression analysis with DESeq2"
output:
html_document:
toc: true
---
Subtype III vs healthy controls. Gender as covariate. Protein-coding genes only.
Code adapted largely from:
* http://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html
* https://bioco... |
b0c05af4ae25c14b2b4f5a1cee7b6620fbba2ce21693bd2776b401be31ea3cac | R | 10,739 | 336 | # Function -------------------------------------------------------------------
#' Identifying spectra and spectral data points
#'
#' Function [identify_spc()] allows identifing the spectrum and the wavelength
#' of a point in a plot produced by [plot_spc()].
#'
#' @rdname identify_spc
#'
#' @aliases identify_spc
#' ... |
ec127d01e5084cb6c6a38ccbd24ec3141095f12ca2f4f1a76342356db67a3a84 | R | 10,749 | 306 | ---
title: "THRA Isoform 1 and Isoform 2 Relative Expression"
output:
html_document:
toc: True
toc_float: True
df_print: paged
editor_options:
chunk_output_type: inline
---
INTRODUCTION to the Experiment
Dataset from Testa et al 2020 (E-MTAB-8325 https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E... |
ac88a7c03dd5db62aa7a5d9a2df4e7bbc01666afaee03d5fa17257de02afe07c | R | 10,750 | 329 | #!/usr/bin/R
# --------------------------------------------------
# Ask whether modules predict AD and other variables
# at sample & cell type level
# Updated 01/26/2022
# --------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(paste0(... |
5c8904051a410308bb9165db71083723be5956fd19bddc230434bcba4840093e | R | 10,764 | 249 | ---
title: "Phosphoproteomics data set of multiple mouse tissues"
author: "Patrick Forny"
date: "2023-10-01"
---
Z-score based analysis.
Load packages.
```{r packages}
# Load packages
library(pacman)
p_load(data.table)
p_load(tidyverse)
p_load(readxl)
```
Load the data.
```{r load data and prep}
# vector of ti... |
9c59cbb9cec7382f85a3b24e55e7c167fbd5e5f4942963db23f7ebf09033fc46 | R | 10,791 | 288 | #!/usr/bin/R
# -----------------------------------------------
# Plot number of differential genes as a heatmap:
# Updated: 09/28/21
# -----------------------------------------------
# Aggregate number of sign. genes:
library(cbrbase)
set_proj('DEVTRAJ')
source(paste0(bindir, 'multiRegion/load_metadata.R'))
library(ti... |
32b94aaf16f39ce05c74a6b7809488f88453d967b12b30ccb967bc39341eee65 | R | 10,797 | 334 | ---
title: "DeepRVAT - Including random seed genes during training"
author: "Eva Holtkamp"
output:
pdf_document: default
html_notebook: default
---
```{r}
library(arrow)
library(tibble)
library(tidyr)
library(dplyr)
library(ggplot2)
library(cowplot)
library(ggpubr)
library(latex2exp)
theme_set(theme_cowplot())
`... |
1d4490cda6a62fc765326c4ad79a8e6c2521e806fa0fc43a0511b1906635cd24 | R | 10,807 | 412 | ---
title: "p_Contra (UBI, L, R; hand, machine)"
author: "Yaromir Kobikov <kobikov.yaromir@gmail.com>"
date: "10/7/2021"
output:
word_document: default
html_document:
toc: true
toc_float:
collapsed: false
editor_options:
chunk_output_type: inline
---
```{r setup, include=FALSE}
source("../BayesianPValue.R")
... |
3af3a7d6abe716e599d85d81558a6ac6cd33d882729e21c8e4bc9b17d141013f | R | 10,810 | 234 | library(tibble)
library(arrow)
library(groupdata2)
library(logger)
### name and input path settings ##########
genetic_pcs = paste0("genetic_PC_", 1:20)
prs = "prs"
age = "age"
sex = "genetic_sex"
covariates_dict <- list(c(prs, age, genetic_pcs), c(prs, genetic_pcs), c(genetic_pcs), c(prs, age, sex, genetic_pcs), c(... |
f150f918490cf14cd8949bc53ee417ded71bd4439fede1cc40f92725c4d8e915 | R | 10,877 | 301 | library(tidyverse)
library(broom)
library(quickpsy)
library(BayesFactor)
library(rstatix)
###########################################################################
# Load and preprocess data ------------------------------------------------
###########################################################################
... |
d3853858099c33ca521ceb7743f671c4df84a8da8802f8ac49332f4c454ad49b | R | 10,899 | 317 | ---
title: "Plotting for ZIV: simulation and application"
author: "Junting Ren"
date: "2023-07-08"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(ggplot2)
library(patchwork)
```
# Main simulation to evaluate algorithm validality
```{r}
sim_result... |
d6a3ff6d17aaa4a0718565c6a26a88745a6eba8d7f9f373c629a3e4eb63ec94c | R | 10,909 | 194 | Input="F:\\RNA_20221101\\02_ModifiedGenome\\A_GeneExpressionMatrix\\"
library(dplyr)
library(edgeR)
library(ggplot2)
library(stringr)
data_filter <- read.csv(paste0(Input,"AutosomeGeneCount_filtered2.csv"), header = T, row.names = 1)
POEgeneList <- read.csv("F:\\RNA_20221101\\02_ModifiedGenome\\C_POEgeneSel... |
26128db587f3de79768b084b110668484cb0d3f84fc469ee9bc93836daee3eeb | R | 10,915 | 295 | #!/usr/bin/R
# -----------------------------------------------------------
# Use the pathology to run some tests on linear mixed models:
# Using Liang's Nebula package to run the fast NBLMM
# Basic overall + per-region interactions, for relevant subtypes
# Updated: 10/14/2020
# -----------------------------------------... |
23a1b89f6c19451218e6db7b23f4643d56d298e21b41db0b22465c18f828351a | R | 10,959 | 331 | library(testthat)
library(dplyr)
test_that("build_3dlmer_command constructs valid strings", {
cmd <- fmri.pipeline:::build_3dlmer_command(
prefix = "myset",
model_formula = "Group*Session + (1|Subj)",
qVars = "Age",
glt_codes = list(con1 = "Group : 1*patient -1*control"),
data_table_file = "dt.tx... |
302e0554edd73085644bc4d37dccbf0b488e5c063e377b5b19e1a2c876482a7c | R | 10,990 | 197 | Input="F:\\RNA_20221101\\02_ModifiedGenome\\A_GeneExpressionMatrix\\"
library(dplyr)
library(edgeR)
library(ggplot2)
library(stringr)
data_filter <- read.csv(paste0(Input,"AutosomeGeneCount_filtered2.csv"), header = T, row.names = 1)
POEgeneList <- read.csv("F:\\RNA_20221101\\02_ModifiedGenome\\C_POEgeneSel... |
f36db6aefee05717b62fa3961df426be233b6a4898594202b4fd9ff1c20fa8c9 | R | 11,001 | 314 | #!/usr/bin/R
# -------------------------------------------------
# Make a heatmap across all regional shared/specific
# Updated: 11/06/23
# -------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(bindir, 'multiRegion/load_metadata.R'))
library(tidyr)
library(... |
d683ec5af6498bb338631f7cfd47d625418820a42664752f5ede64bd00eac17d | R | 11,018 | 322 | library(tidyverse)
library(broom)
library(quickpsy)
###########################################################################
# Load and preprocess data ------------------------------------------------
###########################################################################
data <- read_csv("../../data/data_exp1... |
ca4879ba9b6b47753bff50f7ecceb08fba294add10fab42351399b0918ceb9cb | R | 11,035 | 288 | #!/usr/bin/env Rscript
# Script to correct for multiple sequence features across a list of binCov files
# Note: Designed to be run on 24 chromosome-split files from the same library
#Requires tab-delimmed input file with the following 4 columns:
# 1: contig name
# 2: full paths to binCov files for each contig
# 3: fu... |
f512c5d0f645f0418bcc8deea2cc1454598fa5f3c369923cf96f4807bd439ff8 | R | 11,039 | 414 | ---
title: "Bilateral rhizotomy. Analyse pContra UBI"
author: "Yaromir Kobikov <kobikov.yaromir@gmail.com>"
date: "11/1/2021"
output:
word_document: default
html_document:
toc: true
toc_float:
collapsed: false
editor_options:
chunk_output_type: inline
---
```{r setup, include=FALSE}
source("../../BayesianPVa... |
eded2e30e319b2bf9bc64aae44421f65d7b53fdfa4434d0b7df2de8a3f7c34e2 | R | 11,090 | 300 | # devtools::install_github("mauritsunkel/EMC-SKlab-scRNAseq")
library(EMC.SKlab.scRNAseq)
##### USER INITIALIZATION #####
### USER CONFIG ###
# general
run_name <- "Bas_pipe_V8" # may be empty string: ""
project_dir <- "C:/SynologyDrive/Projects/scRNAseqR"
samples_dir <- file.path(project_dir, "data/samples/")
# sam... |
5db38b59388b3f8e23ce8b22ab3319511e79311fc08a0be514e6027a6ed00d91 | R | 11,104 | 267 |
#' ---
#' title: "Reverse mapping of CustumArray oligos to original proteins"
#' author: "Tomas Bjorklund"
#' output:
#' pdf_document:
#' highlight: tango
#' geometry: margin=0.7in
#' ---
#' This workflow aligns the short oligos from the CustomArray order to the full reference sequences using Bowtie2. This enabl... |
56b1212d1535573c40b34acd1f67a16ac2ab4afa52be3573aac72d969f0d994e | R | 11,165 | 323 | check_columns_exist = function(.data, ...){
tryCatch({
.data |> select(...) # Try selecting the column
TRUE # If successful, the column exists
}, error = function(e) {
# Print a warning with additional context
warning(
"sccomp says: please check typos in your formula_composition, formula_v... |
fc830fb4879fa49b932803e142a0bbbe0daec9673ff25f1871eacaffe49b085a | R | 11,183 | 289 |
basepath = "/net/trapnell/vol1/home/readdf/trapLabDir/hubmap/results/2022_04_03_redoATAC_proc_nobackup/"
dir.create(paste0(basepath, "archr/results/NB10/"))
out_dir = paste0(basepath, "archr/results/NB10/")
setwd(out_dir)
set.seed(7)
# load requirements
suppressPackageStartupMessages({
# library(ArchR)
library(mo... |
570bbeae357b9b1b0e31d1fff88aa657425ec53fcc9362d8d1f347ab01d01c86 | R | 11,200 | 162 |
process_cell_type_info <- function(reference, cell_type_names, CELL_MIN = 25) {
message("Begin: process_cell_type_info")
message(paste("process_cell_type_info: number of cells in reference:", dim(reference@counts)[2]))
message(paste("process_cell_type_info: number of genes in reference:", dim(reference@count... |
caa8a6f27a0650645416aba14fd040143b78bb1d28bd10e38e942d99b455990c | R | 11,202 | 123 | #modify color schemes for heatmap functions from NeuronChat
#################### For Heatmap plot
#' Heatmap plot with ligand abundance and target abundance for single ligand-target pair
#' @param object a NeuronChat object
#' @param interaction_name the ligand-target interaction used to plot
#' @param sender.names se... |
83ac286c2eac6c4cb704af2bd2a135b989c6856f73004ee7e58445663711f855 | R | 11,207 | 475 |
#### SpatialResults ####
## ---------------------
SpatialResults <- function(model, data,
featureData = if (!missing(data)) fData(data) else NULL,
pixelData = if (!missing(data)) pData(data) else NULL)
{
new("SpatialResults", model=model,
featureData=featureData, pixelData=pixelData)
}
setMethod("show", "Spatia... |
10bf2cf0089e3cdb96820bc7bcb9c9ea4357df7b95daf38889054b0497ae14f5 | R | 11,243 | 315 |
############################################### GOTERMS ################################################
table1<- read.csv("MSADPrkMsNew_allClusters.markers.csv")
library(org.Hs.eg.db)
hs <- org.Hs.eg.db
my.symbols <- as.character(table1 $gene)
myEntrez<- select(hs,
keys = my.symbols,
columns = c("EN... |
ff17c57c8ab1431f89345f9e3b163c01b2a1eba33e308076f5911b3bfd81931b | R | 11,252 | 336 | #' @name DEPRECATED-read.txt.Witec
#' @concept moved to hySpc.read.txt
#'
#' @title (DEPRECATED)
#' Import Raman spectra from Witec files
#'
#' @description
#'
#' These data input functions are **deprecated** and they will be removed in
#' the next release of \pkg{hyperspec} package.
#' Now functions in package ... |
fcb596854e98b929519e0aa1dd3bd975dd7fb7ad4d438a07c20c40d9eee379e4 | R | 11,260 | 285 | choose_sigma_gene <- function(sigma_init, Y, X1, X2, my_beta, nUMI,test_mode,
verbose = F, n.iter = 100, MIN_CHANGE = 0.001,
MAX_ITER_SIGMA = 10, PRECISION.THRESHOLD = .01,
spase = F) {
sigma_s_best <- sigma_init
if(spase){
... |
c33e4664310c6b7080fd4d6c16beddec0136c1cf7ccc73380d60c3066f0bbed1 | R | 11,277 | 344 | #!/usr/bin/R
# --------------------------------------------------------
# Plot the aggregated DE results across different methods:
# Raw counts of degs
# Number of overlaps across region/condition
# Updated: 03/22/22
# --------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiR... |
0098642ba808ee75fd31ba9b39a9f14945c3bf07abec0a3c0446ffc37ad94161 | R | 11,278 | 290 |
basepath = "/net/trapnell/vol1/home/readdf/trapLabDir/hubmap/results/2021_07_15_Greg_ATAC_Code_nobackup/"
dir.create(paste0(basepath, "archr/results/NB10/"))
out_dir = paste0(basepath, "archr/results/NB10/")
setwd(out_dir)
set.seed(7)
# load requirements
suppressPackageStartupMessages({
# library(ArchR)
library(m... |
ab49035a382dbb832f8995e4220f0027dde1048ba8256dc96bfd46c0f385c891 | R | 11,320 | 416 | ---
title: "Bilateral adrenalectomy: p_Contra (Pre, after adx, ADX+UBI, hand, machine) "
author: "Yaromir Kobikov <kobikov.yaromir@gmail.com>"
date: "10/7/2021"
output:
word_document: default
html_document:
toc: true
toc_float:
collapsed: false
editor_options:
chunk_output_type: inline
---
```{r setup, inclu... |
7101b72830ca7616a5c91e1cf04af9ed6996950004186fa9d859175c849f2eb2 | R | 11,380 | 294 |
library(tidyr)
library(ggplot2)
library(glmnet)
library(ROCR)
library(data.table)
library(viridis)
# Get shared functions between this and runRegressionModel.R
source("./utilityFunctions.R")
library("optparse")
# Get the passed parameters
option_list = list(
make_option(c("-p", "--pValFIMOcutoff"), t... |
7d6b0671bca420296c7f031926d451d815a4db8f41033850e6b9098c5302e95d | R | 11,410 | 320 | ---
title: "THRA Isoform 1 and Isoform 2 Expression pattern and exploration of dataset"
output:
html_document:
toc: True
toc_float: True
df_print: paged
---
INTRODUCTION to the Experiment
GEO_GSE153076. Organoids generated from 3 replicate batches of H9 and G1 cell lines were collected at 7 time points ... |
5782a5fa7f7553cf192f8b3cf6aac330b66e9f97e7742ea673fd8e9ae7c4aaf5 | R | 11,438 | 355 | # load the reshape2 package
library(reshape2)
library(dplyr)
library(lme4)
library(lmerTest)
######################################################
#MT
#metadata
setwd("D:/PFC_427_WS/Vanshika")
meta=read.csv("dataset_652_basic_04-23-2020.csv")
meta=meta[,c("projid","age_death","pmi","msex","gpath")]
setw... |
b461a71c69946a8991d16ad3a84126b46279887ef2604606c66151a27968288f | R | 11,544 | 307 | init_directories <- function() {
cat("Initializing directories...\n")
directories <- c("bin",
"data/facebase/counts",
"docs",
"logs/facebase",
"logs/neuroblastoma",
"output/facebase",
"output/neuroblast... |
ee1b064302994c1143dd984d9b64695d127c39822ef372879b467225c67d9241 | R | 11,557 | 338 | #!/usr/bin/R
# ----------------------------------
# Put together the full GWAS figure:
# Updated 02/21/2022
# ----------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(paste0(sbindir, 'load_metadata.R'))
}
source(paste0(sbindir, 'auxiliary_pseudobulk_lo... |
20cad16ba24340afb49f8ba02267b5cb02b3d32fda680330bd1e25ec67f6d258 | R | 11,564 | 406 | #' Polynomial baseline fitting
#'
#' These functions fit polynomial baselines.
#'
#' @details
#' Both functions fit polynomials to be used as baselines. If `apply.to`
#' is `NULL`, a `hyperSpec` object with the polynomial coefficients
#' is returned, otherwise the polynomials are evaluated on the spectral range
#' of `... |
0d54c249d6475ee7c61aa96e1e228ca4e54d255b9126d6d690388c4fd3109aef | R | 11,571 | 351 | #!/usr/bin/R
# ---------------------------------------------------------------------
# Compute and plot differences between subtypes for a certain celltype:
# Updated 12/02/21
# ---------------------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindi... |
a3d7813fddd54983cf78d8e52b74f438d65d1170e8bedb789a4ef1c5aa997b41 | R | 11,579 | 293 |
# Run to set up modules:
#########
# module unload R/4.0.0
# module unload pcre2/10.35
# module unload proj/4.9.3
# module unload gdal/2.4.1
# module unload hdf5/1.10.1
# module add sqlite/3.32.3 proj/7.1.0 gdal/3.5.2 hdf5/1.10.5 pcre2/10.39 R/4.1.2
##########
# library(nebula)
# #> Warning: package... |
0296ebaccc1f1694e1716d0bf2d081c11deb175226924bd84d86806cd0ee2cd2 | R | 11,609 | 315 | #' This function pauses execution of an R script while a scheduled qsub job is not yet complete.
#'
#' It is intended to give you control over job dependencies within R when the formal PBS
#' depend approach is insufficient, especially in the case of a script that spawns child jobs that
#' need to be scheduled or compl... |
30b7dc86b68005df58956c259cf2d0f85c55ceb9f77def0894c49783c7639a8a | R | 11,625 | 437 | # Function -------------------------------------------------------------------
#' Bind `hyperSpec` objects
#'
#' Functions to bind `hyperSpec` objects.
#'
#'
#' The former difficulties with binding S4 objects are resolved since R version
#' 3.2.0 and `cbind()` and `rbind()` now work as intended and expected for
#' `hy... |
302f128ed030aeb7b7703d320ab378cb947ec49cc68901236f77a1bcca7ff011 | R | 11,655 | 269 | library(dplyr)
library(sccomp)
library(tidyverse)
data("seurat_obj")
data("sce_obj")
data("counts_obj")
my_estimate_0_1_a =
seurat_obj |>
sccomp_estimate(
formula_composition = ~ type + continuous_covariate,
formula_variability = ~ 1,
sample, cell_group, prior_overdispersion_mean_association = list(... |
adeff667b35780b5c38fb623581c520658f5e63ccc7dbb44f75b0d7c2f421fbb | R | 11,670 | 320 | #!/usr/bin/R
# -------------------------------------------
# Plots of top markers of vulnerable neurons:
# Updated: 12/22/2021
# -------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.R'))
library(tidyr)
library(viridis)
library(ggplot2)
li... |
8dc6891829878799e2cecdc75d54fa8bf4626155ac99bf168104c07efefb859d | R | 11,714 | 299 | #' R6 class for running 3dFWHMx on a single input file based on user specification
#'
#' @importFrom R6 R6Class
#' @details N.B. This class doesn't even expose the Gaussian ACF options given false positive problems
#' @export
afni_3dfwhmx <- R6::R6Class("afni_3dfwhmx",
private = list(
input_file = NULL,
mask_... |
bcc663a8b659c28b4d5369479f89667e976067a9f0477d1ed539cb91281e5ddd | R | 11,716 | 229 | #decompose with just two cell types
#if score_mode, then returns the objective function score
#if denoise, then it fits a "noise" dimension as the mean of all the data
decompose_sparse <- function(cell_type_profiles, nUMI, bead, type1=NULL, type2=NULL, score_mode = FALSE, plot = F, custom_list = NULL, verbose=F, constr... |
5426a17c110d2d55ab6d3fb19ed6e700e531c85a3aa8e93d0ac2bfb21aec5d90 | R | 11,757 | 286 | library("Seurat")
library("tidyverse")
setwd("./Step5.FinalClustering")
Seurat.SNC.final <- readRDS("Seurat.SNC.final.rds")
## DimPlot of the data
Idents(Seurat.SNC.final) <- Seurat.SNC.final$seurat_clusters
jpeg("Clusters.jpeg", width = 6.0, height = 6.0, unit = "in", res = 600)
DimPlot(Seurat.SNC.final, label = T,... |
2ebc6af9253012b8df3e2bde2d6fab4f780c46d90093d671f37ff15cefa01ef5 | R | 11,771 | 242 | ---
title: "figs_for_update"
author: "T Attenborough"
date: "2024-05-07"
output: html_document
---
```{r}
library(Seurat) #Seurat v4.0.2
library(dplyr)
library(patchwork)
library(here)
library(ggplot2)
library(clustree)
library(SingleCellExperiment)
library(scater)
library(DoubletFinder)
library(zellkonverter)
library... |
7a8d274fe8b0ccf0ded0a1649e505b3b002daebf4eb4edeb93afe32ac0db15f8 | R | 11,771 | 217 |
#' ---
#' title: "Heatmap analysis output"
#' author: "Tomas Bjorklund"
#' output:
#' pdf_document:
#' highlight: tango
#' geometry: margin=0.7in
#' fontsize: 10pt
#' ---
#' A secondary approach to visualizing the top candidates and their relative abundance.
suppressPackageStartupMessages(library(knitr))
#+ s... |
95c5a08b250aabb7b58e7240b82c60b3609b119a508281aeab5998984cb5b953 | R | 11,781 | 232 |
#' ---
#' title: "Clustering Polypeptide motifs using the Hammock hidden Markov model peptide clustering"
#' author: "Tomas Bjorklund"
#' header-includes: \usepackage{longtable}
#' output:
#' pdf_document:
#' highlight: tango
#' geometry: margin=0.6in
#' fontsize: 9.5pt
#' ---
#' This script clusters Polypeptide... |
00c0df71968599609cf79ee9bd7221cd447bb95042950f03794555bb8cc6fa29 | R | 11,805 | 249 | #!/usr/bin/env Rscript
# Parse KING output to assign relatedness
###Set master parameters & load libraries
options(stringsAsFactors=F,scipen=1000)
require(e1071)
###################
###HELPER FUNCTIONS
###################
#Import fam file and split into relative classes
#Note: assumes parents of trios are not dire... |
aed8b4e77f695e12ac3a05243c28dc0f681dd18c9a23b40514e663915c018d09 | R | 11,849 | 434 | ---
title: "Bilateral rhizotomy. Analyse Pa UBI"
author: "Yaromir Kobikov <kobikov.yaromir@gmail.com>"
date: "10/27/2021"
output:
word_document: default
html_document:
toc: true
toc_float:
collapsed: false
editor_options:
chunk_output_type: inline
---
```{r setup, include=FALSE}
source("../../BayesianPValue.... |
52b50110d485e584ec2208492a690927bb1ac51355c49524ba5b144eec82a31f | R | 11,852 | 363 | #!/usr/bin/R
# --------------------------------------------------
# Part of metabolic modules figures
# - GO enrichments
# - Distr vs. regions/path
# Updated 06/19/2023
# --------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(paste0(sb... |
373030935e397bb6d0e75219fe1c17dbd99942b37d8f48998ba072def8be83c1 | R | 11,867 | 339 | ---
title: "Rare cell type detection"
output: html_notebook
---
```{r}
#Generate simulated data using RaceID intestinal data
library(RaceID) #RaceID3
library(tidyverse)
library(ggsci)
library(ROGUE)
library(Seurat)
## input data
x <- read.csv("/home/pauling/projects/04_SEmodel/07_NC_revision/03.data/02.Rare.cell.t... |
240ff8bee1c594879dd3e230e3486eb723f032f2f593dccef131adb5d083cef9 | R | 11,892 | 204 | test_single_beads <- function(puck, gene_list, cell_type_info, trust_model = FALSE, constrain = T, OLS = F) {
cell_type_names = cell_type_info[[2]]; n_cell_types = cell_type_info[[3]]
beads = t(as.matrix(puck@counts[gene_list,]))
weights = decompose_batch(puck@nUMI, cell_type_info[[1]], beads, gene_list, constrai... |
f537db8ee4bae13cdd1d909de82a5386af0baeb3bc8a4a54629adf6d03737336 | R | 11,896 | 206 | #This script import and process scRNA-seq from 24,48,72 and 96 hours of orginal human gastruloids (Moris et al., 2021)
#The processed .RDS files from this script are provided at National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) under accession numbers GSE208369
suppressPackageStartupMe... |
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