sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
a7093bf1bb8625da09900b76c577bbaf40375392b2e67f1d8e5a757bbaafac16 | R | 5,767 | 156 | # ------------------------------------------------------------------------------
# Title: Construction of Co-Expression Network (SigNET)
# Author: Yiran Song
# Date: March 18, 2025
# Description:
# This script constructs a co-expression network using transcription factor (TF)
# modules derived from Commot gene regulato... |
d30cb87392e3c8ed761b98caf0454343a9a26e3dc9072557355de3a7857a677c | R | 5,774 | 183 | ---
title: "Using fgsea package"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Using fgsea package}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
`fgsea` is an R-package for fast preranked gene set enrichment analysis (GSEA).
This package allows to quickly and accurately c... |
792129b755a3d549a40f9eafe461fa69ea99d1da9b0abaef9bbfcbbcb774e421 | R | 5,795 | 109 | #' Performs Platform Effect Normalization:
#'
#' Estimates bulk cell type composition and uses this
#' to estimate platform effects and normalize cell type proportions
#'
#' @param RCTD an \code{\linkS4class{RCTD}} object after running the \code{\link{create.RCTD}} function.
#' @return Returns an \code{\linkS4class{RCT... |
2c4ec925c627ff2b5493565dd9a1fe5ca25c41914891b01278f4d2fcd5d3c166 | R | 5,796 | 149 | #' By Anoushka Joglekar 2019. Edited 04/2020
#' Added layer to regular exon level analysis script where hierarchical testing is allowed.
#' Input args: 1. file.tsv with Exon-Gene-Inclusion-Exclusion counts
#' args 2,3,4,5 (tsv from each line of config file):
#' --- Shorthand comparison1 of interest,
#' --- comma separa... |
35fa1f1a3af3f57fb6e432826f0c00a7d7a86f324ced2b62fe88b19182cfc1d1 | R | 5,798 | 148 | ---
title: "Statistical analysis (supporting information)"
output:
word_document:
reference_docx: template.docx
html_document:
df_print: paged
---
<font size="4"> Brent J. Raiteri <br>
2025-12-12 </font>
## Rate of agreement
Fixed effects were fitted using a linear mixed-effects model with and... |
51bd556637b2ca572cd6a41628e612d37d760b477bba8062d94590a0ff2a49c3 | R | 5,819 | 148 | ---
title: "Statistical analysis (supporting information)"
output:
word_document:
reference_docx: template.docx
html_document:
df_print: paged
---
<font size="4"> Brent J. Raiteri <br>
2025-12-12 </font>
## Rate of agreement
Fixed effects were fitted using a linear mixed-effects model with and... |
fe132ebaee23e17e6bf610f6956062151ff161c3816f3be32d98a7dd6c398233 | R | 5,857 | 147 | ---
title: "Geographic Components"
bibliography: "biblio.bib"
link-citations: true
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Geographic Components}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment ... |
50cabead42ea2c5bfbbdd29e8c32511b026c025d3df98ec57e4ea8a84785c11e | R | 5,977 | 201 | library(reactome.db)
library(org.Mm.eg.db)
library(data.table)
library(fgsea)
rnk.file <- "./inst/extdata/naive.vs.th1.rnk"
gmt.file <- "./inst/extdata/mouse.reactome.gmt"
##### Loading pathways from Reactome
mouse.universe <- keys(org.Mm.eg.db, "ENTREZID")
# Selecting reactome gene sets
pathways <- na.omit(select(... |
7627df24167d11dea8935f9fe5f81118e2fb657eb8d61d46ca83f5954e39976c | R | 6,095 | 146 | #' Simple overrepresentation analysis based on hypergeometric test
#' @param pathways List of gene sets to check.
#' @param genes Set of query genes
#' @param universe A universe from which `genes` were selected
#' @param minSize Minimal size of a gene set to test. All pathways below the threshold are excluded.
#' @par... |
0d56edae24a114db366b501e5b8806350ed68ef53d9fd41ae246968b96acb93f | R | 6,105 | 109 | # install_packages.R
# Auto-generated from r_packages.csv
options(repos = c(
CRAN = "https://cran.rstudio.com/",
BioCsoft = "https://bioconductor.org/packages/3.22/bioc",
BioCann = "https://bioconductor.org/packages/3.22/data/annotation",
BioCexp = "https://bioconductor.org/packages/3.22/data/experiment"
))
#... |
0d16eceb9476f4ecc61eeb4a204dda2926a3e8c7c96324970c80f4a09421afe9 | R | 6,197 | 191 | #!/usr/bin/Rscript --vanilla
##################################################################################
#
# MIT License
#
# Copyright (c) 2025 Kevin Rockenbach, Agnieszka Golicz
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation file... |
af9875a5631cbc6c90f4bb9a7e293e9505237514279175c216f01a7adc8bddb4 | R | 6,227 | 125 | #example:"c:\Program Files\Microsoft\R Open\R-4.0.2\bin\Rscript.exe" proteinGroupsPD.r "F:\promec\Animesh\Kathleen\PCA\Exosome_Shotgun_190118_KATHLENN_HCT_R5.xlsx"
Sys.setenv(TZ="GMT")
print("USAGE:Rscript proteinGroupsPD.r <complete path to proteinGroups.txt file>")
print("default values: Abundances.Scaled.")
#parse ... |
c802e170540376d977cb25359a31890e51bbfd12f2a06617daf2d63323dbc69a | R | 6,229 | 187 | getwd()
setwd("C:/Users/u0171728/Downloads/comparative")
suppressPackageStartupMessages({
library(data.table)
library(dplyr)
library(tidyr)
library(stringr)
})
genes_of_interest <- c("ATP13A2","ATP13A3","ATP13A4","ATP13A5")
drop_low_quality <- TRUE
species_files <- list(
human = list(genes="... |
0ac92dee403d17889483c9ffc7406a69003f3277993b0e9807c2443b6f3fbab1 | R | 6,236 | 208 | ---
title: "Reproduction of Figure 2"
output:
html_document:
df_print: paged
toc: true
toc_depth: 2
toc_float: yes
number_sections: yes
code_folding: show
---
# Setup and Libraries
```{r message=FALSE, warning=FALSE}
required_packages <- c("readr", "ggplot2", "dplyr", "stringr", "svglite", "... |
775e068720defd167d220013e6ba5f5e9f5f8e21867603d2c7c6861da31858a3 | R | 6,263 | 131 | remap_celltypes <- function(cell_dict_file, cell_ident) {
cell_type_dict <- read.csv(file=cell_dict_file, header=TRUE, sep=",")
cell_type_dict$Name <- factor(cell_type_dict$Name)
rownames(cell_type_dict) = cell_type_dict[,'Cluster']
true_type_names = lapply(cell_ident, function(x) cell_type_dict[as.character(x)... |
fc20eebe20b7a4fcea9cc5028e6f4dae8460ad41e90a9f65756cd40b7b0110b4 | R | 6,266 | 331 | #!/usr/bin/env Rscript
# ==============================================================================
# 10d. Selected TF expression on Seurat UMAP embeddings
#
# Description:
# This script visualizes expression of selected TF genes on existing Seurat UMAP
# embeddings. These panels display TF gene expression and mus... |
7246fb59d40d88a2c369a12e9063c1bab6b572a96b4ed9c039ecdddf94d53935 | R | 6,313 | 135 | #install.packages(c("readxl","writexl","svglite","ggplot2"),repos="http://cran.us.r-project.org",lib=.libPaths())
#..\R-4.5.0\bin\Rscript.exe .\proteinGroupsQC.r "L:\promec\TIMSTOF\LARS\2023\230310 Sonali\combined\txtNoDN\proteinGroups.txt" "L:\promec\TIMSTOF\LARS\2023\230310 Sonali\combined\txtNoDN\SurvivalUpdates_ot... |
3d20437e8999231d72d498b9a1e9d938dc4073b30a9c12e4d84865756766ed18 | R | 6,321 | 106 | #C:\Users\animeshs\R-4.4.0\bin\x64\Rscript diffExprPhos.r L:/promec/TIMSTOF/LARS/2024/241118_Deo/phos/combined/txt/ Bio
#BiocManager::install("PhosR")
args = commandArgs(trailingOnly=TRUE)
print(paste("supplied argument(s):", length(args)))
print(args)
inpD <- args[1]
#inpD <-"L:/promec/TIMSTOF/LARS/2024/241118_Deo/pho... |
3ccc613043eb5ba0b5b515bd66655f7abe598b5805dad4ddc839879a3468d4d0 | R | 6,343 | 188 | ---
output:
html_notebook:
fig_caption: yes
toc: yes
runtime: shiny
---
```{r msms}
#inpF <- "L:/Animesh/IdaJ/Bafh1resMQ/msmsScans.txt"
#data <- read.delim(inpF, sep = "\t", header = T)
#dataID = data[, grep("Identified", names(data))]
#summary(dataID)
```
```{r protein}
#inpF <- '../../comb... |
81f69532600565404955bb19801c6c55d42aae68e27a0b788ba7024fe4c34807 | R | 6,403 | 118 |
#' constructor of \code{\linkS4class{Reference}} object
#' @param counts A matrix (or dgCmatrix) representing Digital Gene Expression (DGE). Rownames should be genes
#' and colnames represent barcodes/cell names.
#' @param cell_types A named (by cell barcode) factor of cell type for each cell. The 'levels' of the fact... |
f4a92d6e5bc2c8cb8c3d730b0e512e6bbaa50a2a08cd7ca02bad2a153022f4cd | R | 6,463 | 178 | ## By Anoushka Joglekar 2019. Edited 04/2020, 07/2020
#' @import dplyr
#' @importFrom magrittr %>%
`%>%` <- magrittr::`%>%`
args <- commandArgs(trailingOnly=TRUE)
### Initial set-up
numIsoPerCluster <- data.table::fread(args[1]) ## Need to make the package seamless
# enough that it takes the correct
#file ... |
6913306dc16e62b29c07cb646d182c9ed8ac3cb1432239db178322e20b009bf7 | R | 6,526 | 158 | dtrain <- xgboost::xgb.DMatrix(
data.matrix(iris[, -1L]),
label = iris[, 1L], nthread = 1
)
fit <- xgboost::xgb.train(params = list(nthread = 1L), data = dtrain, nrounds = 50L)
x <- shapviz(fit, X_pred = dtrain, X = iris[, -1L])
x <- c(m1 = x, m2 = x)
test_that("plots work for basic example", {
expect_s3_class(s... |
894bfa4d5211c1351f95a955b006b6a8437b3deedf113c15cfd8e7458bab549e | R | 6,553 | 140 | #' Distribution map generation of proteins
#'
#' Distribution map generation of proteins
#'
#' @param df the quantitative protein result
#' @param location the original location information
#' @param target_name the protein in the map
#' @param limit a logical value, if setting the value limits of protein abundance/rat... |
9f6bea63553cab3b86f316bc51a3af6c396f23f137a6f4efc81fa15ad6f5b091 | R | 6,554 | 270 | # ==============================================================================
# 08d. Optional ClusterGVis pseudotime heatmap
#
# Description:
# This optional script reproduces the ClusterGVis-based visualization workflow.
# ClusterGVis performs a separate k-means clustering of the smoothed pseudotime
# expression ma... |
7bfa6fda956cfd74bccbd15f1fd2ed3214ccb4da7a4cc6fda92ab1511fa6fb15 | R | 6,600 | 213 | # ===============================
# Clinical plots + 3-cluster association tests
# ===============================
library(dplyr)
library(tidyr)
library(ggplot2)
library(purrr)
# -------------------------------
# 1) Input: metadata table
# -------------------------------
# Assumes you have a data.frame called `metada... |
c2a3b62221fa36a7c4d53fb104be725a295ae817a49722a57b793077c7f5be33 | R | 6,606 | 194 | #' SHAP Interaction Plot
#'
#' @description
#' Creates a beeswarm plot or a barplot of SHAP interaction values/main effects.
#'
#' In the beeswarm plot (`kind = "beeswarm"`), diagonals represent the main effects,
#' while off-diagonals show SHAP interactions (multiplied by two due to symmetry).
#' The color axis repres... |
5cfb6cc8c4e0b6744ff577e09a24b0c5aef9e59d65315109b2b459b4f3461035 | R | 6,610 | 310 | #install.packages("iq")
suppressPackageStartupMessages({
library(data.table)
library(iq)
library(ggplot2)
})
############################################################
# Input files
############################################################
#wget https://ftp.pride.ebi.ac.uk/pride/data/archive/2014/09/PXD0002... |
8ff9bbb50288266cb31af82f17298e825a353484eb6d9d9a8f7eca454ce20c62 | R | 6,631 | 279 | ---
title: "Farmand et al 2026 - NeuroFit RCT Clinical and Cognitive Outcomes Analysis Examples"
output: html_document
---
```{r setup, message=FALSE, warning=FALSE}
library(readxl)
library(dplyr)
library(tidyr)
library(glmmTMB)
library(nlme)
library(ordinal)
library(DHARMa)
library(emmeans)
library(ggplot2)
```
## 1... |
1f5e9de32baf11ecfa876d9c4e578bf66a1fd6354af8191ec9d413fc693963cb | R | 6,649 | 200 | #
# Functions that prepare data for TU HMMs
#
# Details about data transformation:
#
# - per chromosome structure
# - step size
# - auxiliary position vector (in case of transformative HMM)
# - strand
# - chrom
# - optional mappability track
transform_data <- function(reads, reverse = FALSE, unmap = NULL) {
positio... |
8eb570acaf226b0f58cdf7cece808dd960897fdfef0a943d514c622b91f8d415 | R | 6,653 | 173 | library(tidyverse)
library(ggpubr)
library(grid)
# Define all the necessary variables
analysis_dir = "/Volumes/mahamid/Dorothy/Pipeline_after_CNN/20230906_test"
out_dir = "/Volumes/mahamid/Dorothy/Pipeline_after_CNN/20230906_test"
data_seg <- read.csv(file.path(analysis_dir, "bundle_analysis.csv"), sep = ',')
# filte... |
9511c50d6faf982bd760492772856e84ab085d3905bdaac10db5ae37a3c689c2 | R | 6,674 | 168 | #!/usr/bin/Rscript --vanilla
##################################################################################
#
# MIT License
#
# Copyright (c) 2025 Kevin Rockenbach, Agnieszka Golicz
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation file... |
b5ef856309d4bc341eeeb264bee57785012fc3327d8d62b0bdb7870b6623fb08 | R | 6,708 | 171 | library(tidyverse)
library(ggpubr)
library(grid)
# Define all the necessary variables
analysis_dir = "/Volumes/mahamid/Dorothy/Pipeline_after_CNN/20230906_test"
out_dir = "/Volumes/mahamid/Dorothy/Pipeline_after_CNN/20230906_test"
data_seg <- read.csv(file.path(analysis_dir, "branch_analysis.csv"), sep = ',')
# filte... |
b766e4428e4263d3a02532595f1cbd2082254324fba5581beaf809754dad0ec4 | R | 6,716 | 237 | # Load required package
#fit learning glm
library(lme4)
library(dplyr)
df <- df %>%
mutate(
group = case_when(
group_verbal %in% c("ASD", "ASD (low verbal)") ~ "ASD_all",
group_verbal == "TD" ~ "TD"
),
group = factor(group) # Make sure it's a factor
)
# Assuming your data frame is called `d... |
2b47c1fdea991f030e47c74ad8efffe52a14f1476fa2233e4d6445fa8bef9a49 | R | 6,782 | 202 | ---
title: "Lifestyle Factors and Whole-Brain Atrophy: Mixed-Effects Analysis"
output:
html_document:
toc: true
toc_float:
toc_collapsed: true
toc_depth: 3
theme: paper
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
```
# Overview
This scr... |
c6302404ebdcbbfd4a081db52f6d769f0211ee7cd2565713833dde0c9acab3f5 | R | 6,803 | 116 | #Rscript proteinGroupsQC.r "L:/promec/TIMSTOF/LARS/2023/230310 ChunMei/combined/txt/proteinGroups.txt" "Intensity." "L:/promec/Animesh/Mathilde/Groups.xlsx" "sample"
#mkdir -p /home/animeshs/promec/promec/TIMSTOF/LARS/2023/230310\ ChunMei/combined/txt
#rsync -Parv ash022@login.nird-lmd.sigma2.no:TIMSTOF/LARS/2023/2303... |
ab06a1f7606fa18ae149e2e1c8622c33f8eb68198521e0fe884f126eec2d9fed | R | 6,819 | 227 | # Bootstrapped learning curve fit (ASD vs TD) for hard phase
# Model: y = A0 + (P - A0) * (1 - exp(L * trial))
suppressPackageStartupMessages({
library(data.table)
library(dplyr)
library(tidyr)
library(purrr)
library(minpack.lm)
})
# Load data
load("/Users/doq/RCode/preprocessed_data.RData")
# Prepare data... |
11decb98b5954c78351f2ef0ffff86420614d0fe4cfbcc211e2e3f2529cc7f61 | R | 6,828 | 189 | library(tidyverse)
library(ggpubr)
library(ggplot2)
work_dir = "/Volumes/mahamid/Dorothy/Pipeline_after_CNN/20230527_new_analysis/analysis_output/"
setwd(work_dir)
data_read <- na.omit(read.csv(file = "tomogram_summary_averaged.csv", sep = ','))
data_read = data_read[-c(2),]
data_read <- data_read %>%
separate("to... |
85bd8433d22f681d4b8cdd2def7def33b0e1f2e20d9694a4e3900afad79c6fe1 | R | 6,888 | 195 | #!/usr/bin/Rscript --vanilla
##################################################################################
#
# MIT License
#
# Copyright (c) 2025 Kevin Rockenbach, Agnieszka Golicz
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation file... |
e3056a6a50871fbaf16d210e887cb230fe866621e12d91ea26ad79912a8985df | R | 6,924 | 163 | #!/usr/bin/Rscript --vanilla
##################################################################################
#
# MIT License
#
# Copyright (c) 2025 Kevin Rockenbach, Agnieszka Golicz
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation file... |
dfb0a9aaec66e4398f5cdb1075f21cc0a0a1ee6db14b9719cf048a74c0aa2e20 | R | 6,942 | 209 | #' Sets Precomputed Probabiliites as Global Variable
#'
#' Given a matrix, \code{Q_mat}, or log P(y|x), under the Poisson-Lognormal model.
#' Sets this as a global variable for fast computations in the future.
#'
#' @param Q_mat_loc Matrix of precomputed probabiliites, as previously computed by \code{\link{get_Q_mat}}
... |
194d6d8e41246666cedb73f5496575b705205ed6a8f484349f002d9197c522a0 | R | 6,946 | 278 | # ==========================================================
# 04_build_clinical_core_dataset.R
# Purpose: Build clean longitudinal clinical core from ADNIMERGE2 R data files
# ==========================================================
library(tidyverse)
library(janitor)
library(lubridate)
library(openxlsx)
... |
d43746b4ee698c6a549f5d03bb8025e5ba5ccb5189f1fcbe0c474ea174c316a8 | R | 6,964 | 244 | #' 2D SHAP Dependence Plot
#'
#' @description
#' Scatterplot of two features, showing the sum of their SHAP values on the color scale.
#' This allows to visualize the combined effect of two features, including interactions.
#' A typical application are models with latitude and longitude as features (plus
#' maybe other... |
b22ed0fea9d1a8ff1b6292d31823db62a73e105b9d63ef795b3cde476973b987 | R | 7,038 | 155 | #git checkout fe5f9e92f3fe5ca0c75e9d8acfd211feb7358066 diffExprProlfqua.r
#Rscript diffExprProlfqua.r
#install.packages("BiocManager")
#BiocManager::install("SummarizedExperiment")
#install.packages("devtools")
#remotes::install_github('fgcz/prolfqua', dependencies = TRUE,force = TRUE)
#https://fgcz.github.io/prolfqua/... |
e5b963398dc67324762c54b3f84caab565103863f40a9b89da9a3bb4e3f67fe7 | R | 7,121 | 138 | library(Matrix)
library(spacexr)
library(doParallel)
library(ggplot2)
library(xlsx)
library(dplyr)
library(fields)
library(stringr)
library(GSA)
# Load in spatialRNA data and Reference data
pwd = getwd()
datadir <- paste0(pwd,'/data/tumor','/')
resultsdir <- paste0(pwd,'/results/ResultsTumor','/')
# Load RCTD which c... |
e3c1f817a24508df3a54ca546ce9dbbaebe14d6b7946051bcbb4abf9bf8bad5f | R | 7,172 | 216 | #Clear working space
rm(list = ls(all.names = T))
# Load packages
if(!require(rstudioapi)){
install.packages("rstudioapi")
library(rstudioapi)}
if(!require(ggplot2)){
install.packages("ggplot2")
library(ggplot2)}
if(!require(ggsci)){
install.packages("ggsci")
library(ggsci)}
if(!require(qvalue)){
install... |
d7a2140268a3bd0ce45a470b20d63c58d011efb158a37d47bee1267d50f3eb7a | R | 7,195 | 227 | dtrain <- xgboost::xgb.DMatrix(
data.matrix(iris[, -1L]),
label = iris[, 1L], nthread = 1
)
fit <- xgboost::xgb.train(params = list(nthread = 1L), data = dtrain, nrounds = 50L)
x <- shapviz(fit, X_pred = dtrain, X = iris[, -1L])
test_that("plots work for basic example", {
expect_s3_class(sv_waterfall(x, 2), "ggp... |
7f6424907ed1ab10996c3ade1ea4a91c6af490867dba36bfd847fecf8141d7af | R | 7,208 | 221 | # Figure 1: Single-cell Analysis of Integrated Ventricles -----------------
# 1. Setup ----------------------------------------------------------------
# Load required libraries
library(Seurat)
library(dplyr)
library(ggplot2)
library(clustree)
library(clusterProfiler)
library(enrichplot)
library(org.Hs.eg.db)
library(... |
8f1a99b95c4f4d78b2f1c31110e04cc29872d5a45d5ca1ada6457a131c7b89ec | R | 7,271 | 360 | #!/usr/bin/env Rscript
# ==============================================================================
# 10e. Package TF-analysis outputs into verified RDS files
#
# Description:
# This script creates a compact TF-analysis result bundle and optionally stores
# TF-analysis references in the misc slot of each input Seu... |
7492049ba0d54ce5144cfb04de93e37d8665c232a8aeca4e8ce1ca1f5835c6ea | R | 7,283 | 135 | sim_CSIDE <- function(de_ground_truth, REPLICATES, de_gene, ref, N_samples, nUMI, common_cell_types,
UMI1, UMI_tot, sigma_init, puck, cell_type_info, beta, UMI_vect,
other_methods = F,region_orig = NULL, subset_cells = NULL, regularize_expr = F) {
puck_mod <- puck
p_res... |
d0d5e98f82c20bd3a8c04584f8474ad55894837bb2e99a58c285ce46e9b06db7 | R | 7,289 | 187 | #!/usr/bin/Rscript --vanilla
##################################################################################
#
# MIT License
#
# Copyright (c) 2025 Kevin Rockenbach, Agnieszka Golicz
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation file... |
11ca1c751c3ffa7c4c1d025024c4e7cc1e9f6c0cfd1381f4608863bbc2b83f88 | R | 7,416 | 152 | library(Seurat)
library(dplyr)
library(ggplot2)
library(ggsci)
library(ggpubr)
library(patchwork)
library(umap)
library(pheatmap)
setwd("/mnt/alamo01/users/gbb/NDMG_snRNA/seurat/metacluster/downsample/")
snRNA <- readRDS("snRNA_5k.rds")
df <- snRNA@assays$RNA@scale.data
cor_df <- cor(df)
Anno <- as.data.frame(cbind(s... |
3f53fe6ee9e7ff1225db9e0af65591cd242312427bb5b609cf54ee712ddeb437 | R | 7,458 | 166 | ---
output:
pdf_document: default
html_document:
keep_md: yes
word_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = T, warning = F, message = F, cache = T, results = 'hide')
library(spacexr)
library(Matrix)
library(devtools)
library(ggplot2)
library(ggpubr)
library(reshape2)
lib... |
02acf39d0f097c3bef2a9aa6f9d532912379f06d59524176ba1d8921afb228f0 | R | 7,636 | 180 | ---
output:
pdf_document: default
word_document: default
html_document:
keep_md: yes
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = T, warning = F, message = F, cache = T, results = 'hide')
```
# CSIDE on the Slide-seq J20 Hippocampus
### Load in CSIDE Results and calculate significant genes
... |
3bb29c52f6fd6529bb70cecef30ad17f877fad74b4d8d0738148a8b4d8cac369 | R | 7,789 | 257 | ```{r data}
data<-readxl::read_excel("C:/Users/animeshs/GD/Prediction analysis.xlsx", sheet=1)
data$Class<-"Remission"
data<-subset(as.data.frame(data), select = -c(1:7) )
summary(data)
```
```{r data2}
data2<-readxl::read_excel("C:/Users/animeshs/GD/Prediction analysis.xlsx", sheet=2)
data2$Class<-"Relapse"
... |
11455b0c5b515629ea23c4d07426e732b9fb1c41f345148f726836e266095fe3 | R | 7,800 | 202 | context("GSEA stat calculation")
test_that("calcGseaStats works", {
stats <- 10:-10
sample <- 1:5
expect_equal(calcGseaStat(stats, selectedStats = sample), 1)
sample <- 16:21
expect_equal(calcGseaStat(stats, selectedStats = sample), -1)
sample <- (1:5)*2
expect_equal(calcGseaStat(stats, s... |
7a075d5866fc01d3e21ade0e1320458fddd5c50e05730d59a1bb5c5e6ac642b8 | R | 7,845 | 143 | ---
title: "Applying CSIDE to Spatial Transcriptomics Data"
author: "Dylan Cable"
date: "December 15th, 2021"
output:
rmarkdown::html_vignette:
keep_md: yes
pdf_document: default
html_document:
keep_md: yes
vignette: |
%\VignetteIndexEntry{differential-expression}
%\VignetteEncoding{UTF-8}
%\Vignett... |
5b2dc08e05ee5d226aa0d0b5f3a046fb45d75a21e9a1a21ffb492f0c2878207b | R | 7,856 | 144 | #Rscript proteins.r "C:/Users/animeshs/GD/Pseudomonas/140605_Pseudomonas_O1_K0K6-(2)_Proteins.txt" SILAC
#parse argument(s)
print("USAGE:Rscript proteins.r \"<complete path to *_Proteins.txt file>\" <SILAC if channel ratios available>")
print("default LFQ/abundance(scaled)")
args = commandArgs(trailingOnly=TRUE)
print(... |
d98284357d8d5fafed276260ff4c487aef3d63c3fa88cb0e0955cdcef24477b2 | R | 7,886 | 187 | # ------------------------------------------------------------------------------
# Title: Figure 6 - Summary of Perturbation Results Across Modules
# Author: Haofei Wang
# Date: March 24, 2025
# Description:
# This script visualizes the effect of perturbations using dot plots and module
# summary scores. It uses fold-c... |
0ee4a1350a25d6598dd5d7448b863c02dd9a6d2009f287188bb76950f2ef93c2 | R | 7,923 | 137 | #functions for processing data after RCTD is fit to the data
# Collects RCTD results
gather_results <- function(RCTD, results) {
cell_type_names = RCTD@cell_type_info$renorm[[2]]
barcodes <- colnames(RCTD@spatialRNA@counts)
N <- length(results)
weights = Matrix(0, nrow = N, ncol = length(cell_type_names))
we... |
e9279ff79cb3d44f43ac122e56d763c4b338d5a0fa316a6a9eb5a46f9db2b55f | R | 7,923 | 172 | #' spacexr: an R package for assigning cell types and cell type specific differential expression to spatial transcriptomics data.
#'
#' @section Running RCTD:
#'
#' To get started, create a \code{\linkS4class{SpatialRNA}} object (called \code{puck} here) for the
#' spatial transcriptomics data and a \code{\linkS4class{... |
c63c05ff1715cc4d6a0b8c692e2c3ec6b7354427b4faeed2314a810db45850f5 | R | 7,969 | 46 | library(clusterProfiler)
library(org.Mm.eg.db)
# Define the list of genes
# ECCITE
#gene_list <- c('KLRD1', 'TERT', 'AC098973.2', 'FABP6', 'TFPI2', 'KLHL31', 'SCGB1B2P', 'NXPH3', 'CCDC160', 'LRCOL1', 'CD207', 'AL590708.2', 'CALML5', 'CD1C', 'NUTM2B', 'C21orf62', 'LINC00346', 'C22orf23', 'KRT19', 'BTNL2', 'RP11-159H22.... |
6969e24844b2e8186f5891a914505293f61ff3f4ad1cbc9f0279be2a018f54d6 | R | 7,975 | 178 | #data####
#c:/Users/animeshs/R/bin/Rscript.exe diffExprTestT.r L:/promec/TIMSTOF/LARS/2021/November/SIGRID/combined/txtNoNQd/ group
inpD<-"L:/promec/TIMSTOF/LARS/2021/November/SIGRID/combined/txtNoNQd/"
ttDF<-read.csv(paste0(inpD,"proteinGroups.txtLFQ.intensity.16WT8hWTctrl0.050.50.05grouptTestBH.csv.sort.combined.csv"... |
7cb91afc3415aa2a2e609cd897771ff3d70b2ec8870b2cc858afb7fb930aaadd | R | 7,979 | 167 | #=============================================================================#
# ASEsampleDataReadCounter_OutputProcessing.R #
# #
# Version: 1.3 #
... |
88331cf7741f88afa3d406c1757ff93f2521ad0073e5c87283f3bcc53d21349f | R | 8,044 | 131 | #C:\users\animeshs\R-4.4.0\bin\Rscript.exe diffExprTestTmatrixXL.r "L:\promec\TIMSTOF\LARS\2022\februar\Sigrid\combined\txt\Copy of Copy of combined with explanatory headings_2 (version 1).xlsx" "EQ:ES" "CA:CC"
#C:\users\animeshs\R-4.4.0\bin\Rscript.exe diffExprTestTmatrixXL.r "L:\promec\TIMSTOF\LARS\2022\februar\Sigr... |
aec239458d8e5b040ace1fd11ffa637c8211c81a54ac8c6b764be53f2d0cb992 | R | 8,061 | 306 | ---
title: "Using 'shapviz'"
bibliography: "biblio.bib"
link-citations: true
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Using 'shapviz'}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
wa... |
624abf9d9ecaa102b209abb448fb36a0e639147bc70a28434504dc8d63f03eca | R | 8,126 | 164 | #..\R\bin\Rscript.exe diffExprANOVA.r "L:\promec\TIMSTOF\LARS\2022\august\220819 Toktam\combined\txt\proteinGroups.txt" "L:\promec\TIMSTOF\LARS\2022\august\220819 Toktam\combined\txt\Groups.txt" Sample
options(nwarnings = 1000000)
summary(warnings())
print("USAGE:<path to>Rscript diffExprANOVA.r <complete path to prote... |
c370f10f0264d4c754d6db384ba9cde16c7c63c033d1830c1930fa7d7a1bd32b | R | 8,190 | 267 |
##import packages
library(readxl)
library(tidyverse)
library(ggfortify)
library(ggplot2)
library(factoextra)
library(cluster)
library(DESeq2)
#install.packages("ashr")
library(ashr)
#BiocManager::install("EnhancedVolcano")
library(EnhancedVolcano)
#BiocManager::install("clusterProfiler")
library(clusterProfiler)
#Bi... |
9bb3401fe4614ad4211731799e79c8c9c3e1664e048812650d5ba2ad41ff21b6 | R | 8,202 | 140 | ---
title: "Applying CSIDE to Spatial Transcriptomics Data: Smooth spatial patterns in MERFISH"
author: "Dylan Cable"
date: "December 21st, 2021"
output:
html_document:
keep_md: yes
pdf_document: default
rmarkdown::html_vignette:
keep_md: yes
vignette: |
%\VignetteIndexEntry{merfish-nonparametric}
%\... |
57e8aa849144e9c1529cb062317d2964b1762f818db25d19e7104443edb1d7f8 | R | 8,203 | 147 | #curl -O "proteinGroups.txt" "https://zenodo.org/records/14557756/files/proteinGroups.txt?download=1"
#Rscript mapID.r "proteinGroups.txt" 1 76 81 uniprot ensembl max org.Hs.eg.db
suppressPackageStartupMessages({
if (!requireNamespace("AnnotationDbi", quietly=TRUE)) stop("AnnotationDbi required")
})
library(Annotat... |
e6076f1a010c9579677d7ae1fdb5cc9716b71cec280695417a544b8aed201b47 | R | 8,298 | 188 | ---
title: "plot_fig5"
author: "LGrabot"
date: "16/10/2024"
output:
pdf_document: default
html_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(lme4)
library(ggplot2)
library(reshape2)
library(nlme)
# function sem
sem <- function(x) sdt=sd(x)/sqrt(len... |
4dbe6c0e7d2a771414e33c365d823bb854e09cca639cc1c85fae3218686b9ba1 | R | 8,358 | 216 | align_images_now <- function(thresh_im_name, puck_im_name, use_cropped = F, pre_cropped = F, thresh = 150, thresh2 = 150) {
samples <- list.files(pattern = "^Hippo[1-6].tsv.gz", path = system.file("extdata/counts", package = "STutility"), full.names = TRUE)
spotfiles <- list.files(pattern = "^alignment_table_Hippo[... |
8ae5640b349df1e00c55fb4634d2fd58bafab0741a8efe920efe7a3eaa2620ef | R | 8,381 | 155 | ---
title: "Applying CSIDE to Spatial Transcriptomics Data: Cell-to-cell interactions"
author: "Dylan Cable"
date: "December 19th, 2021"
output:
html_document:
keep_md: yes
pdf_document: default
rmarkdown::html_vignette:
keep_md: yes
vignette: |
%\VignetteIndexEntry{CSIDE-celltocell-interactions}
%\V... |
8ebbd99ec51233376d54dafd805c72afadf1d7f0c297fd517ac9d92a6d7fc5da | R | 8,381 | 133 | #F:\R-4.3.1\bin\Rscript.exe diffExprRNAseqPlot.r "F:\seqRNA\count.thread6.txt"
#setup####
args = commandArgs(trailingOnly=TRUE)
if (length(args) != 1) {stop("USAGE:<path to Rscript.exe> diffExprRNAseqPlot.r <complete path to counts file>
example:
F:\\R-4.3.1\\bi... |
673da9c57e13ca8b54d2e8b7e6d73b47107ac14e55fa2a1fd3f34e9a5e60fadd | R | 8,450 | 190 | library(Matrix)
library(doParallel)
library(ggplot2)
library(dplyr)
library(spacexr)
library(GSA)
library(xlsx)
library(readr)
# Data is obtained from the following article.
# https://science.sciencemag.org/content/362/6416/eaau5324/tab-figures-data
# https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248 merfis... |
900cc0d91780ed831b7d5d0bd79de413e3dea9c9fe8de9c143ebccb2f7e9d5bf | R | 8,489 | 155 | ---
output:
pdf_document: default
html_document:
keep_md: true
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = T, warning = F, message = F, cache = T, results = 'hide')
```
# Unsupervised clustering on the Slide-seq cerebellum
```{r libraries}
library(spacexr)
library(Matrix)
library(ggplot2)
... |
bdf3ea7c2342668415bebae210a6a82c208751da1a07a94e8b68fb78cba0da2f | R | 8,571 | 153 | check_designmatrix <- function(X, f_name, require_2d = FALSE) {
tryCatch({
X <- as(X,'matrix')
}, error = function(e) {
stop(paste0(f_name,': could not convert X to matrix using as(X,\'matrix\'). Please check that X is coercible to matrix, such as a matrix, dgCmatrix, or data.frame.'))
})
if(dim(X)[1] =... |
e51fb6b1de696a240eff732be1766ee918cc3276ce0f86f565232c9b09ca9172 | R | 8,581 | 157 | ---
title: "Applying CSIDE to Spatial Transcriptomics Data: Two Regions"
author: "Dylan Cable"
date: "December 19th, 2021"
output:
html_document:
keep_md: yes
pdf_document: default
rmarkdown::html_vignette:
keep_md: yes
vignette: |
%\VignetteIndexEntry{CSIDE-two-regions}
%\VignetteEncoding{UTF-8}
... |
75d1fc36262ae1ecc01f11817a2d819b0b2e65ee60aa99574929db11c9d71ba0 | R | 8,582 | 259 | #
# TUnit HMM Decoding (generic version)
#
blocks.to.coords <- function(blocks, step) {
apply(blocks, 2, function(col) {
# col = [start, end]
c((col[1] - 1) * step, col[2] * step) # [bed start, bed end)
})
}
coords.to.bed6 <- function(chrom, strand, coords) {
return(data.frame(chrom = chrom, start = coo... |
44a5109b996ddcfcb6569a5ec37878e5c9d861b5ffc4320c54a9a419f9456a46 | R | 8,596 | 226 | library(data.table)
library(NbClust)
library(mclust)
ari_calcul_sampled <- function(myData, cpcs, isOptimal=FALSE,
method_use='resnet',
base_name='', maxiter=30,
celltypelb='celltype', batchlb='batchlb' )
{
if... |
af32061e57f8000f0f4302509a05f95dbb435aebd37cf57bd72c8c2999c30027 | R | 8,597 | 154 | ---
title: "Applying CSIDE to Spatial Transcriptomics Data: Pathology interactions in Slide-seq"
author: "Dylan Cable"
date: "December 20th, 2021"
output:
html_document:
keep_md: yes
pdf_document: default
rmarkdown::html_vignette:
keep_md: yes
vignette: |
%\VignetteIndexEntry{CSIDE-pathology-interaction... |
4f27105dc66559ae5e62cc3b9b2ff47b56f6352adce1950826b7b153fa64ad4e | R | 8,628 | 153 | ---
title: "ASAP deep bulk RNAseq: Expressed ERVs"
author: "Raquel Garza"
output:
html_document:
df_print: paged
---
In this notebook we define the ERV predictions (retrotector) that are expressed in our postmortem cohort of PD and control individuals.
## Load libraries and count matrices
```{r}
library(data.... |
466932278d83d7579f5ee3882dc2b28a8f0f95a200c66c11d1414682824fbf45 | R | 8,715 | 207 | #' Weighted Removal Index (WRI) functions
#'
#' Standalone implementation of the demographic weighting scheme described in:
#' Romer, A. S., et al. 2026. Prioritizing removals of a highly cryptic invasive
#' species using machine learning and demographic weighting. Ecological Applications.
#'
#' The logic in this file ... |
8d103164a7da4c97c74dcec46b688af4ff19551c4057c97b8f05a51452a8f35c | R | 8,720 | 242 | #setup####
libLocal <- "/home/ash022/scripts/MM/rLib"
library("infotheo", lib = libLocal)
library("matrixStats", lib = libLocal)
library("GenomicRanges", lib = libLocal)
library("MatrixGenerics", lib = libLocal)
library("SummarizedExperiment", lib = libLocal)
library("lionessR", lib = libLocal)
library("igraph", lib = ... |
25687a595cbd1c1c508b7f75bc1bf07ec275888fbea70c89426a40d6d62ab56b | R | 8,730 | 329 | # ==========================================================
# 11_repair_age_from_ptdemog.R
# Purpose: Repair age using PTDEMOG.rda VISDATE + PTDOBYY
# ==========================================================
library(tidyverse)
library(janitor)
library(lubridate)
library(readr)
library(openxlsx)
project_... |
5b7f65f27b675b76e3f769bb83a54b524982208d7f5b5e1861eeb3ee9d732842 | R | 8,774 | 283 | #Undershoot Analysis R file
# Packages
# Core Data Science & Plotting
library(tidyverse)
# Frequentist Modeling & Distribution Fitting
library(fitdistrplus)
library(car)
library(permuco)
# Bayesian Modeling & Post-Hocs
library(brms)
library(emmeans)
##########################Loading################... |
477e0b8440e8f5ba5b06c51dae50335ed866122708504a632ec0de215a69c6f7 | R | 8,788 | 272 | #Rise time assessment Script################################################
#Packages
# Core Data Science & Plotting
library(tidyverse)
# Frequentist Modeling & Distribution Fitting
library(fitdistrplus)
library(car)
library(permuco)
# Bayesian Modeling & Post-Hocs
library(brms)
library(emmeans)
#... |
4588da72a5a9623dddbef19723419db73b2df4d942823dce613dfa14a511272a | R | 8,887 | 294 | #Dye-coupling analysis
#Packages
# Core Data Science, Dates & Plotting
library(tidyverse)
library(lubridate)
# Frequentist Modeling & Distribution Fitting
library(fitdistrplus)
library(car)
library(permuco)
# Bayesian Modeling & Post-Hocs
library(brms)
library(emmeans)
##########################Lo... |
c473770aac083c9adec41c59d229463d751e8bccdbaec8239c6f0d1783c5da85 | R | 8,890 | 110 | ---
output: github_document
---
<!-- 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%"
)
```
# Cell type-specific inference of differential expression for spati... |
d93ff8313343170674e7feff82f4292e738ee3a30b09b8adf8b3ba6593416400 | R | 8,942 | 225 | ---
title: "Statistical analysis (main dependent variable)"
output:
word_document:
reference_docx: template.docx
html_document:
df_print: paged
---
<font size="4"> Brent J. Raiteri (assisted by Alain C. Vandal) <br>
2025-12-12 </font>
## Coefficient of variation in active torque (torque steadin... |
bad24218b2bdb5b767267903a0382698275533fd5b7595b1f4e22e7112b57274 | R | 8,952 | 166 | ---
title: "ASAP deep bulk RNAseq: Expressed FL L1HS-PA3"
author: "Raquel Garza"
output:
html_document:
df_print: paged
---
In this notebook we define the >6kbp L1HS-PA3 elements that are expressed in our postmortem cohort of PD and control individuals.
## Load libraries and count matrices
```{r}
library(data... |
82e1e4b930dbbfe26b8cce99ecb49b861da7b45a2c6dfe46fe927a35805491b4 | R | 8,956 | 223 |
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) {
sigma_s_best <- sigma_init
sigma_vals <- names(Q_mat_all)
alpha1 <- NULL; alpha2 <- NULL;
MIN_ITERATIONS <- 15
n.iter.tot <- 0
for(i... |
fe61667207824ded9d27c5eef148bc7b3f77a3ab73483920e0c1cdc00d62adea | R | 8,958 | 172 | ---
title: "Upregulated HERV visualization"
author: "Raquel Garza"
output:
html_document:
df_print: paged
---
## Overview
This document explores the upregulation of HERVs in different brain regions, focusing on microglial cells.
We identify significantly upregulated HERVs and analyze their genomic features, p... |
06bc66e35282a403042b02f2ee508c749f9eab45ed830c4cbc69706e2b410dfd | R | 8,989 | 227 | ---
title: "Statistical analysis (main independent variable)"
output:
word_document:
reference_docx: template.docx
html_document:
df_print: paged
---
<font size="4"> Brent J. Raiteri (assisted by Alain C. Vandal) <br>
2025-12-12 </font>
## Active torque
Fixed effects were fitted using a linear... |
90e7c3645696e789b00bc25a460daa1c5055b5ce23965cc89ae178359deb39b0 | R | 9,035 | 291 | # ==========================================================
# 05_biomarker_candidate_QA.R
# Purpose: Inspect candidate biomarker files before merging
# ==========================================================
library(tidyverse)
library(readxl)
library(janitor)
library(openxlsx)
library(stringr)
project_... |
05aec44aea4e06cec54ae006e61c8324258bca514bdd29b91508b3bae2dbaef7 | R | 9,036 | 317 | # ==========================================================
# 08_model_readiness_QA.R
# Purpose: Diagnose model readiness and fix baseline_dx / age problems
# ==========================================================
library(tidyverse)
library(janitor)
library(openxlsx)
library(lubridate)
library(readr)
... |
4df115a96cde2c5e31677bee2e1c90873630626e1a539b098b5f6fa1ae3621c2 | R | 9,046 | 182 |
# R/4.1.0
library(DESeq2)
library(ggplot2)
library(plyr)
library(reshape2)
library(readr)
##===============All data===================
### Load STAR count data and generate DeSeq2 data object
### Histone genes and small RNAs are filtered out. Only Genes with > 1 count in any condition are kept.
ctr.r... |
bd1bce0974a0df1f3f31de815a4dfd3e546823cd905fd6ea342d9a3416172832 | R | 9,186 | 270 | #' SHAP Waterfall Plot
#'
#' Creates a waterfall plot of SHAP values of one observation. If multiple
#' observations are selected, their SHAP values and predictions are averaged.
#'
#' f(x) denotes the prediction on the SHAP scale, while E(f(x)) refers to the
#' baseline SHAP value.
#'
#' @param object An object of cla... |
f29d30084e1b23dae6909dee46cad97e2afe55c2dd92e12a01fb358f3d6096ec | R | 9,205 | 186 | #"c:\Program Files\R\R-4.5.2\bin\Rscript.exe" diffExprTestCor.r "L:\promec\TIMSTOF\LARS\2026\260518_Sonali\DIANNv2P2.63.260612_140833.64.highacc\report.pg_matrix.tsv" "L:\promec\TIMSTOF\LARS\2026\260518_Sonali\DIANNv2P2.63.260612_140833.64.highacc\Groups.txt" "F..promec.TIMSTOF.LARS.2026.260518_Sonali.260518_Sonali_" "... |
aeaf2ad12f6d9ede56dd01683a1ef718825f19be2e199ff1de10d4fa87d6f720 | R | 9,227 | 261 | context("GSEA multilevel analysis")
test_that("fgseaMultilevel works", {
data(examplePathways)
data(exampleRanks)
set.seed(42)
sampleSize <- 50
fgseaMultilevelRes <- fgseaMultilevel(examplePathways, exampleRanks,
sampleSize=sampleSize,
... |
8209f22b8d37cbe251a9d3311aaeb817aae411f27256691f6596d0dd5816617b | R | 9,456 | 177 | #Rscript.exe diffExprTestTphosphoSites.r L:/promec/TIMSTOF/LARS/2024/241118_Deo/phos/combined/txt/ Bio 1
#install.packages("ggplot2")
#install.packages("svglite")
#install.packages("limma")
#install.packages("writexl")
#install.packages("pheatmap")
args = commandArgs(trailingOnly=TRUE)
print(paste("supplied argument(s)... |
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