sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
043d3ab00ab353088c9e26ad787bc5adb765de2f09e53f35b8b3eeee8e253826 | R | 14,452 | 315 | ---
output:
html_document:
keep_md: true
pdf_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = T, warning = F, message = F, cache = T, cache.lazy = FALSE, results = 'hide')
```
# CSIDE on the Slide-seq testes data
### Pre-process testes data
```{r preprocess}
library(spacexr)... |
71eca88b188509216b7e097ef9fb886fbbc500aaf594042cfad5893124e4b0ec | R | 14,500 | 284 | #Rscript diffExprPlots.r "L:/promec/Animesh/Kamila/Help_with_poster_for_Eadv_conference/BCC_PRM90_final for LARS.xlsx" 0.5 0.5 Gene
#setup####
#install.packages("readxl")
#install.packages("svglite")
#install.packages("ggplot2")
#install.packages("ggrepel")
args = commandArgs(trailingOnly=TRUE)
inpW <- args[1]
#inpW<-"... |
c844fc282635049445aac9cee77a52b0d083bf715a46c14e82a853fc4b5af79e | R | 14,538 | 322 | #!/usr/bin/Rscript --vanilla
##################################################################################
#
# MIT License
#
# Copyright (c) 2025 Kevin Rockenbach, Agnieszka Golicz
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation file... |
47b73affd604c9b5780ca86da4c497ea19348fb0ee8a9515a9c60d9f16a27dba | R | 14,543 | 392 | ---
title: "E16_clonal_information"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir = normalizePath(".."))
library(Seurat)
options(Seurat.object.assay.version = "v5")
library(ggplot2)
library(dplyr)
library(stringr)
library(patchwork)
library(scale... |
90520d6c34acfae9b5c8942e8b7aac6a85a00c1fd03b50dbec155be81830d169 | R | 14,720 | 346 | # ============================================================================
# Rebuild EEGestimates4stan.Rdata from dList
# Also export: thetapower.csv, conflicttrials.csv
# Additional step: generate conflict_raw.csv / conflict_z.csv from the M3c fit
# Adapted for: 37 subjects, 8 stimuli, 480 trials, conflict stimuli... |
6a8cda2afd8dd45d6ed26d830494ff05ee02328a83fe9c3b00ea3d366de1ba61 | R | 15,049 | 330 | ---
title: "Cell-Type Specific L1s Differential Expression Analysis"
author: "Raquel Garza"
output:
html_document:
df_print: paged
---
## Overview
This notebook performs differential expression analysis of human endogenous retroviruses (L1s) across cell-type clusters and brain regions from ASAP PMDBS snRNA-seq... |
c7de66d7e26aced6510cf97caab1b88515d3f4141c72c1393bb626f07b52b2fa | R | 15,168 | 277 | ---
output:
html_document:
keep_md: true
pdf_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = T, warning = F, message = F, cache = T, results = 'hide')
```
# RCTD on the Hippocampus and spatially localizing three interneuron subclasses
```{r libraries}
library(spacexr)
library... |
1befc592e62fa54509c3fa959e48ff1bb6558c7f5bdce576806c296404013841 | R | 15,297 | 381 | #' Runs multilevel Monte-Carlo variant for performing gene sets co-regulation analysis
#'
#' This function is based on the adaptive multilevel splitting Monte Carlo approach
#' and allows to estimate arbitrarily small P-values for the task of analyzing
#' variance along a set of genes.
#' @param pathways List of gene s... |
30d2b8707570131c6eeb2296c583697276cf91ac824b1e521ae6dc9cd5e0ba17 | R | 15,358 | 683 | # ==============================================================================
# 08c. Monocle 3 pseudotime-gene visualization
#
# Description:
# This script generates pseudotime expression curves and a continuous
# pseudotime heatmap for one or more Monocle 3 analyses. It uses the ordered CDS
# from 08a, graph-test a... |
0e33762a3d2348fcf9ccd94410661a7e11562c0de42b3112cc141278a39c04ad | R | 15,377 | 285 | ---
output:
html_document:
keep_md: true
pdf_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = T, warning = F, message = F, cache = T, results = 'hide')
```
# Validation of RCTD on decomposition of simulated doublets
```{r libraries}
library(spacexr)
library(Matrix)
library(ggp... |
14417a826fdcd1be1d90611ac4abe6f462269e0b7d63896b316b9bcbb2c3f151 | R | 15,451 | 301 | ---
title: "Microglia Subcluster Gene Expression Analysis"
author: "Raquel Garza"
output:
html_document:
df_print: paged
---
## Overview
This notebook analyzes gene expression in microglia subclusters across multiple brain regions (SN, PUT, PFC, AMY) in the ASAP-PMDBS snRNA-seq dataset. The workflow focuses on:... |
398fb53e2589091dea248a2871e10a65500e9797e13fa6def35c652ab7f13fab | R | 15,453 | 292 | ---
title: "Heatmaps of upregulated TEs in PD"
author: "Raquel Garza"
output:
html_document:
df_print: paged
---
## Setup
Load preprocessed TE counts, along with helper functions for heatmap plotting.
```{r}
library(DESeq2)
library(data.table)
library(ggplot2)
library(tidyverse)
library(openxlsx)
library(RCo... |
f1e99288f131c2c08a1a339987715759fc43847c8e55de2fbff1187aabf09878 | R | 15,463 | 291 | #"C:\Program Files\R\R-4.4.0\bin\Rscript.exe" proteinGroups.r L:\promec\TIMSTOF\LARS\2025\250314_Maria\txtMQv2p6p7\proteinGroups.txt
print("USAGE:Rscript proteinGroups.r <complete path to proteinGroups.txt file> <LFQ or SILAC if performed else it defaults to raw Intensity columns>")
#supplying input file for testing
... |
d08bcec2a6e00fe42df87441334206923a83dc6a5accc2e9624363d2a975e74e | R | 15,472 | 420 | #!/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... |
7eeab6227a4ec262da4ac336c93858827597438a7463954f97149537e3a60b16 | R | 15,579 | 451 | #!/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... |
c1648f957bf037d4de40ef05af2620bd0ae601c300ad64b7a71fb09f12fcebe6 | R | 15,799 | 668 | # ==============================================================================
# 08a. Monocle 3 trajectory construction and pseudotime assignment
#
# Description:
# This script builds Monocle 3 cell_data_set objects for one or more Seurat
# objects defined in an external manifest. It imports the selected Seurat UMAP,... |
e514306b7f85d6573c17e47112cb1f53567797e7261d7c58742fb73a38ceb9b7 | R | 15,842 | 426 | #!/usr/bin/env Rscript
suppressPackageStartupMessages({
library(data.table)
})
# -----------------------------
# Utility functions
# -----------------------------
cohen_d <- function(x, y) {
x <- x[is.finite(x)]
y <- y[is.finite(y)]
n1 <- length(x)
n2 <- length(y)
if (n1 < 2 || n2 < 2) return(NA_real_)
... |
0f4f19e8d6f4764f7e67302eb95320fe13e41bf59fe59d044393976e7453a401 | R | 15,863 | 450 | ---
title: "E2 Processing"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir = normalizePath(".."))
library(Seurat)
options(Seurat.object.assay.version = "v5")
library(ggplot2)
library(scales)
library(sctransform)
library(dplyr)
library(patchwork)
li... |
b4352f646e7c069c4649ba9476bb7bee28278bccc21467c63b1a3a0ddd0d1605 | R | 15,880 | 286 | #..\R-4.4.0\bin\Rscript.exe diffExprTestTpair.r "L:\promec\TIMSTOF\LARS\2025\250404_Alessandro\combined\txt\proteinGroups.txt" "L:\promec\TIMSTOF\LARS\2025\250404_Alessandro\combined\txt\Groups.txt" "Bio" "Rem" "LFQ.intensity." "SA" "SB" 0.05 0.5 100
#setup####
#install.packages(c("readxl","writexl","svglite","ggplot2"... |
e6dab97f21bf5979cbae555c5b66505d0a9ed8ae947eff7b9db2db6b3cd9f5e1 | R | 16,253 | 498 | Protein Expression Analysis with Uniprot IDs
========================================================
```{r install libraries and setup}
#setwd('C:/Users/animeshs/SkyDrive')
#source("http://bioconductor.org/biocLite.R")
#biocLite("GSA")
#biocLite("BiocUpgrade")
#install.packages("pvclust")
#install.packages("kn... |
ee9c6ddc0ce62bd86e40d0ca4ad65c51c184f19679806b909f3c1d56ed63912c | R | 16,671 | 557 | ---
title: "Experiment 1 vs Experiment 2 — clonal comparison"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir = normalizePath(".."))
library(Seurat)
options(Seurat.object.assay.version = "v5")
library(ggplot2)
library(dplyr)
library(patchwork)
libr... |
6b4c1cbf0b3a38b3e9f9b1543a8eb040f2c1c953ddafd301bf7a79aaa553a5c3 | R | 16,705 | 303 | ---
title: "Cis-effect of upregulated TEs in PD microglia and MSN"
author: "Raquel Garza"
output:
html_document:
df_print: paged
---
## Cis-effects of TEs (HERVs and L1s) on gene expression across cell types (SN/PUT microglia, PUT MSN)
This R Notebook performs a cis-effect analysis, linking upregulated TEs (spec... |
fc87e55a0ec66f8f1743ea8a3184cd397e97c48b73fd2a409e6a0ec43fab8a5d | R | 16,797 | 516 | #Analysis of the patch clamp experiments involving astrocytic currents
#MFA + MFA(+BaCl2) experiments
#Packages
# Core Data Science, Plotting & Estimation Stats
library(tidyverse)
library(dabestr)
# Frequentist Modeling & Distribution Fitting
library(fitdistrplus)
library(car)
library(permuco)
library(... |
1839222fd9d8c7f5fe830358e0b0a62623751ba3c6dbf22d9d147529706150fb | R | 16,867 | 364 | ---
output:
pdf_document: default
html_document:
keep_md: true
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = T, warning = F, message = F, cache = T, cache.lazy = FALSE, results = 'hide')
```
# CSIDE on the Slide-seq testes data
### Pre-process testes data
```{r preprocess}
library(spacexr)
... |
ac3604e08a35a8dc1f29d7980f44f7edaf8ab304172256dd4bcfaa1e29019d02 | R | 17,289 | 389 | # ==========================================================
# 15_sensitivity_analyses.R
#
# Sensitivity analyses requested by Reviewer 2 (comments 2, 3),
# plus a batch-effect investigation of the vascular longitudinal
# finding that neither reviewer raised but that determines how
# that finding should be framed.
#
# ... |
c33d6b1181364fca28d656d8641ba975c44fd7fd8f873cb77ccd0c27067053f2 | R | 17,397 | 404 | ```{r setup}
#install.packages("Peptides", dependencies=TRUE)
```
```{r dataG}
inpD<-"L:/promec/Animesh/Abba"
inpF<-paste0(inpD,"/SA24G.xlsx")
dataSA<-readxl::read_xlsx(inpF)
dataSA$File<-"SA24G"
dataSA$FileI<-1
inpF<-paste0(inpD,"/AP6G.xlsx")
dataAP<-readxl::read_xlsx(inpF)
dataAP$File<-"AP6G"
dataAP$FileI<-2
inpF<-p... |
ca13481def4f66bb823e2f5e2c4098c32549586ddf282f2afdd27bcaf7324fc7 | R | 17,625 | 349 | ---
output:
html_document:
keep_md: true
pdf_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = T, warning = F, message = F, cache = T, results = 'hide')
```
# Benchmarking CSIDE on a simulated spatial transcriptomics dataset
```{r libraries}
library(spacexr)
library(Matrix)
lib... |
142962f442efa4a4728f57cd12bd31d825fd27ef6d9cfc59d50a608442e2278e | R | 17,664 | 1,001 | # ==============================================================================
# 11. Cross-dataset label transfer, RPCA integration, and Figure I-J generation
#
# Description:
# This script performs the complete cross-dataset microglia analysis:
#
# 1. loads the physiological reference microglia and Hammond dataset... |
169608ef304789647de9c574afbed1b918599796d9ae681237388af2413a26a7 | R | 17,684 | 425 |
options(shiny.maxRequestSize=2000*1024^2)
######## 3D printing app #########
### Define UI ----
ui <- dashboardPage(
dashboardHeader(title = "MAsP"),
dashboardSidebar(
sidebarMenu(
menuItem("Homepage", tabName = "Homepage", icon = icon("home")),
menuItem("Distribution Map", tabName = "Distribut... |
15558e8e659cf24d78755fc9e7f98f652d9b62ef7cf75297d3e25f510b5e48ed | R | 17,960 | 287 | ---
output:
pdf_document: default
word_document: default
html_document:
keep_md: yes
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = T, warning = F, message = F, cache = T, results = 'hide')
```
# CSIDE on the MERFISH Hypothalamus
### Load in CSIDE results and calculate significant genes
```{... |
d6f5174f1e5b0af555d38d840b7c6b54e1cce7ddd0efcea9f632e9bce8b987c2 | R | 18,109 | 431 | setwd("D:/A.clarkii/anemone_DE/RSEM")
#Read gene.results from RSEM for each sample and merge to get a dataframe of combined cts
NFA1 <- read.table("NFA1RNA.genes.results",header=T)
NFA1_1 <- NFA1[,c(1,5)]
NFA2 <- read.table("NFA2RNA.genes.results",header=T)
NFA2_1 <- NFA2[,c(1,5)]
NFA3 <- read.table("NFA3RN... |
4437d51f5262921db8625d8c77db69d04d9b56a10b7d08e0cc85075b9ac4db53 | R | 18,243 | 364 | #dataQE####
inpF<-"L:/promec/Qexactive/LARS/2021/november/subcellular/combined/txt/proteinGroups.txt"
data<-read.table(inpF,header=T,sep="\t",row.names=1)
summary(data)
colnames(data)
dataLog2LFQ<-log2(as.matrix(data[65:70]))
row.names(dataLog2LFQ)<-row.names.data.frame(data)
dataLog2LFQ[is.na(dataLog2LFQ)]<-0
dataLog2... |
5920c17d19424bd12dda617af4ac7595335b9eb1b699dca3a4f9c3edf6444c7e | R | 18,245 | 459 | # This script runs group-wise comparison between a subset of VABS-3 matched TD and ASD (high adaptive)
# produce figure corresponding to supplementary figure 9
rm(list=ls())
# Load the required libraries
library(data.table)
library(ggplot2)
library(ggbeeswarm)
library(effectsize)
library(patchwork)
library(dplyr)
lib... |
700173be53e13d66d26b652edadb1571f57565ea03e09e2cd5ca92cb503ab232 | R | 18,275 | 454 | # ------------------------------------------------------------------------------
# Title: Preparation of Reference Seurat Datasets for Cytospace
# Author: Haofei Wang
# Date: March 24, 2025
# Description:
# This script prepares Seurat objects for reference datasets across timepoints
# (p0, p7, p14, p21). It reads 10X ... |
2691bfb7e5cc48a957707decb851d6af4db59872bac0cbcc0e4ed215aa3f770d | R | 18,316 | 320 | ---
title: "Microglia ERV Differential Expression Analysis: batch and sex covariates"
author: "Raquel Garza"
output:
html_document:
df_print: paged
---
## Overview
This notebook is a repetition of HERV_expression_PDvsCtl.Rmd to verify that the results hold when including genes and TEs into the same data frame ... |
cd3356702310fd590b99e78dd030d0fd047a0bdfb6b89cb1a84fc748a40eccb0 | R | 18,634 | 998 | # ==============================================================================
# 03. Dimensionality reduction, batch integration, and clustering
#
# Description:
# This script loads the QC-filtered Seurat object generated by
# 02_quality_control.R, performs SCTransform normalization and principal-
# component analysi... |
ff689eefd77fbc5cd8182486b0db1f2ea1853815cbb1ebcacf913f7697b636e9 | R | 18,687 | 375 | # Preprocessing Experiment 3: SoupX correction, QC, doublet removal, merge, SCTransform, Harmony, label transfer
# Run this script with working directory set to the project root
library(Seurat)
options(Seurat.object.assay.version = "v5")
library(DoubletFinder)
library(sctransform)
library(dplyr)
library(stringr)
libra... |
c78b9d4884b841daf47404b1e1183c6d6f6f6ec9ae49f6e42ed491f8b1076b4e | R | 18,873 | 419 | ```{r setup, include=FALSE}
#plot
#renv::deactivate()
par(mfrow=c(1,2))
#library/packages
inpD <-"L:/promec/HF/Lars/2021/mai/MortenH/"
fName<-"210520_MORTEN_G1-(1).xlsx"
inpF<-paste0(inpD,fName)
options(nwarnings = 1000000)
compName<-gsub("[^[:alnum:] ]", "",inpD)
.libPaths( c( .libPaths(), inpD) )
.libPaths()
#install... |
f21b36a3b0cedd5b9f00bd3dcd3f677e3f9a40033fbb7f0bb9a3bbea8f7a7632 | R | 19,158 | 1,106 | # ==============================================================================
# 02. Quality control of single-cell RNA-sequencing data
#
# Description:
# This script loads the merged raw Seurat object generated by
# 01_load_and_merge_scRNA_data.R, reconstructs a clean RNA assay from raw counts,
# calculates mitochon... |
8efbfd115d4cc6509e86ac719d8e4b64aae03ec38faa9673ab402100cfccbaf9 | R | 19,281 | 536 | ---
title: "Gene set co-regulation analysis tutorial"
output:
rmarkdown::html_vignette:
toc: true
vignette: >
%\VignetteIndexEntry{Gene set co-regulation analysis tutorial}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TR... |
0a03a497363aacb09e10aa8b6c6537f4207c8197c2f88f5cffd8cd81b7033462 | R | 19,731 | 315 | ---
title: "TE Differential Expression Analysis"
author: "Raquel Garza"
output:
html_document:
df_print: paged
---
## Overview
This notebook analyzes **differentially expressed transposable elements (specifically, >6kbp L1HS-L1PA3)** and **human endogenous retroviruses (HERVs)** across brain regions and cell typ... |
a7602b03afeebdae1f673d233647f25d2ee8ff03d93803ea2a892c6acaa4a48c | R | 19,782 | 451 | Analyses for "CELER: A 365-Participant Corpus of Eye Movements in L1 and L2 English Reading", Berzak et al., Open Mind (2022).
Before running this script please obtain the data as follows:
CELER:
1. Obtain the [PTB-WSJ](https://catalog.ldc.upenn.edu/LDC95T7) and [BLLIP](https://catalog.ldc.upenn.edu/LDC2000T43) corpo... |
b849d0cfe545863242f5671e82dde44c9418a0ec13f1c09ad52dddccfffbd24a | R | 19,898 | 1,078 | rm(list = ls())
clc <- function() cat("\014")
clc()
rootdir <- "H:/mt_behavalluse"
setwd(rootdir)
suppressPackageStartupMessages({
library(lme4)
library(lmerTest)
library(car)
library(emmeans)
library(dplyr)
library(ggplot2)
library(writexl)
})
outdir <- file.path(rootdir, "behavmixmodel")
figdir <- fi... |
275e16c8617ea00b0b534aead8cbaa2a1bee2b34fa01a63a29a0e2e2fa40ef59 | R | 20,106 | 493 | # ==========================================================
# 14_corrected_primary_analysis.R
#
# Produces the corrected versions of Tables 1, 2 and 3 plus the
# new supplementary analyses requested by the reviewers.
#
# Two corrections are applied relative to the published models:
# 1. VEGF / sICAM-1 / sVCAM-1 are ... |
5bf43a8a893b31f81230c4cec348a49d2c4f89de31ebf19cf4e30f2ea4682b12 | R | 20,420 | 401 | # ============================================================
# Purpose:
# 1) Run LIMPA once through peptide -> protein quantification
# 2) Fit TWO equivalent DE parameterizations on the SAME y.protein object:
# A. Six-group model: ~ 0 + Group6 (Group6 = Cell_Rep)
# B. Interaction model: ~ Cell *... |
aef6b95126a2624447734bd0de93b6b159d615813cb5f3ebee7c54d9115ae27b | R | 20,522 | 465 | ---
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>
## Motor unit discharge rate
Fixed effects were fitted usin... |
05eb9343018053aa116bfeec14aadc42df3d8f0cd7732207beb7c6378a4b872c | R | 20,555 | 400 | ---
title: "ASAP invitro systems differential expression analysis of TEs"
author: "Raquel Garza"
output:
html_document:
df_print: paged
---
Here we perform differential expression analysis (DEA) of ERVs for our in vitro experiments using microglia or dopaminergic neurons and different treatments (see metadata).
... |
46e007e14b271291b340ac4a044d3f76ef656076b336d2582247863fab3d71bc | R | 20,560 | 645 | ---
title: "Figure 5 — Regional and integrated clonal analysis"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir = normalizePath(".."))
library(dplyr)
library(Seurat)
options(Seurat.object.assay.version = "v5")
library(ggplot2)
library(stringr)
libr... |
b2265da5af69d2a18de161b1b4c981c442b055c26e1d60f34221ace696376013 | R | 20,634 | 457 | # ==========================================================
# 18_risk_adjustment_repaired.R
#
# Replaces Part 5 of script 17, which failed three ways:
# - BMI unit conversion produced impossible values (mean 37,
# 75th percentile 52) because the ADNIMERGE2 unit fields are
# not the numeric 1/2 codes the CSV ... |
79a2c91f1a9013b14f120c852ec8ee99d74c6ef48b655fbfe41a8c0374726177 | R | 20,682 | 353 | #F:\R-4.6.1\bin\Rscript.exe diffExprTestT.r "L:\promec\Animesh\Nicolas\proteinGroups.txt" "L:\promec\Animesh\Nicolas\Groups.txt" "Bio" "Remove" "LFQ.intensity." "AD" "control" 0.1 0.5 0.1
#setup####
#install.packages(c("readxl","writexl","svglite","ggplot2","BiocManager"),repos="http://cran.us.r-project.org",lib=.lib... |
39547f9e27d04ebddfd86a471cd733a895a443d18131ffefba62b7bcb541191e | R | 20,773 | 594 | #' Initialize "shapviz" Object
#'
#' @description
#' This function creates an object of class "shapviz" from a matrix of SHAP values, or
#' from a fitted model of type
#' - XGBoost,
#' - LightGBM, or
#' - H2O.
#'
#' Furthermore, [shapviz()] can digest the results of
#' - `fastshap::explain()`,
#' - `shapr::explain()`,
... |
e679f66d3a0eed138c63f2ba08c285cf6f4a6b203356e1213cd67160aac38e0c | R | 20,905 | 243 | ---
title: "RCTD and C-SIDE Documentation"
author: "Dylan Cable"
date: "12/22/2021"
output: github_document
---
```{r setup, include=FALSE}
library(spacexr)
knitr::opts_chunk$set(echo = TRUE, eval = FALSE)
```
## Introduction
The *spacexr* manual can be found [here](https://github.com/dmcable/spacexr/tree/master/spa... |
11c5610640f45ddb2c301b0cf82b30283b9239553d00c9c7b8fe179bec00f079 | R | 21,031 | 640 | ---
title: "Experimentally observed features"
output: html_notebook
---
```{r}
library(ggplot2)
library(ggbeeswarm)
library(tidyverse)
library(dplyr)
library(tidyr)
library(svglite)
library(ggtree)
direction_choice = 'inhibited'
perspective_choice = 'other_MUs_as_ref' #'MU_as_ref'
# 1. Load experimental data
path = ... |
c083198d11006e97d1c647cac4a8624a7ed9e5d9f4d42a120dd28a9ec5b24d77 | R | 21,321 | 449 | ---
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>
## Normalised global EMG amplitude
Fixed effects were fitte... |
b0fc5a2ee965d355a476b6ac7f8f371e52f4512a25c46959c196adc82bb9af9c | R | 21,385 | 711 | ```{r funscoR, echo = FALSE}
#https://evocellnet.github.io/funscoR/
#devtools::install_github("evocellnet/funscoR")
library(funscoR)
library(knitr)
library(dplyr)
library(stringr)
```
```{r std, echo = FALSE}
phosphoproteome %>%
head() %>%
kable()
psp %>%
head() %>%
kable()
```
```{r annotation, echo = FALSE}... |
78b1a0e76362dd47e82d9a62657f6407a356f9564ce311193cd5a89b01c330e9 | R | 21,399 | 430 | #Coded By: Benjamin A Saef
#For FNIH project - Shiny app project data creation
ADNI_amytime <- read.csv(PET_Merged)
#dry run for function
###read in dataset
ADNI_Longitudinal <- read.csv(data_file)
ADNI_Centiloid <- read.csv(AmyloidPet_File)
ADNI_Centiloid_ForUse <- ADNI_Centiloid[,c("RID","SCANDATE","CENTILOI... |
7b5d90f3017ea5915ce7aafcdd063314770a123d38d10493c7b05141194bbb09 | R | 21,506 | 360 | #..\R-4.5.0\bin\Rscript.exe geneExprTest.r "L:/promec/TIMSTOF/LARS/2025/250902_Alessandro/DIANNv2p2/report.gg_matrix.tsv" "L:/promec/TIMSTOF/LARS/2025/250902_Alessandro/DIANNv2p2/GroupsOverallSurvivalgnoreGy.txt" "Groups" "Rem" 1 4 4 "Good prognosis" "Bad prognosis" 0.1 0.5 0.1
#..\R-4.5.0\bin\Rscript.exe geneExprTest.... |
5cbc48871014ea2e9472b8b20598024e8b6ba5e4fa8530738fb0841d4029bba6 | R | 21,605 | 540 | ```{r setup, echo = FALSE}
.libPaths( c("L:/promec/Animesh/R/win-library/4.0", .libPaths()) )
.libPaths()
#plot
par(mfrow=c(1,2))
#data
inpD<-"l:/promec/Animesh/Maria/"
```
```{r getRawRead}
download.file("https://github.com/animesh/RawRead/archive/refs/heads/blazor.zip",paste0(inpD,"RawRead.zip"))
unzip(paste0(inpD,"... |
a8c337d6ab6eb483c66ae2f863a80c16c4242cc7e92ad1143a300d2069e1e238 | R | 21,623 | 356 | #F:\R-4.6.1\bin\Rscript.exe proteinExprTest.r "L:\promec\TIMSTOF\LARS\2026\260902_AndrewS\DIANNv2P2.12.260909_090557.64.highacc\report.pg_matrix.tsv" "L:\promec\TIMSTOF\LARS\2026\260902_AndrewS\DIANNv2P2.12.260909_090557.64.highacc\Groups.txt" "Bio" "Inc" 1 7 "Tumor_Igg" "Tumor_LSD1" 0.1 0.5 0.1
#F:\R-4.6.1\bin\Rscri... |
6dadefedbfccc05cd33963941d4ca85be6ca56ffbbc4cd2dc1eefcae24489972 | R | 21,797 | 449 | #' Plots all doublets in space
#'
#' saves as 'all_doublets.pdf'
#'
#' @param puck an object of type \linkS4class{SpatialRNA}
#' @param doublets a dataframe of RCTD results restricted to doublets
#' @param resultsdir output directory
#' @param cell_type_names list of cell type names
#' @return returns \code{\link{ggplo... |
a2d35bdcefff81c22ef0fb4c71b16b02decdf74186347d0c5ddb36e832285abe | R | 22,118 | 623 | ## =====================================================================
## SINGLE-TRIAL MIDFRONTAL THETA / ICPC · OCD group
## - Based on the Swart (2017) framework + simple effects + Excel export
## - Exclude subjects in the OCD group: sID = 201, 218, 232, 238, 243, 256
## ============================================... |
ab3f4ea56e18626be7246f314274a376eca61b30bb2aeb7e69c82aa25a190003 | R | 22,475 | 446 | # ------------------------------------------------------------------------------
# Title: Figure 1 - Cell Count and Cell Composition Visualizations
# Author: Haofei Wang
# Date: March 24, 2025
# Description:
# This script generates plots for Figure 1 of the perturb-seq study, including:
# - Cell count barplots across t... |
0b099f3f07ba83fa9e11793d2ff4b2e4a6f2426a1a4a7982ab13949277d963f0 | R | 22,669 | 436 | ---
title: "Cluster Gene Expression Analysis"
author: "Raquel Garza"
output:
html_document:
df_print: paged
---
## Overview
This notebook analyzes gene expression in each cluster across multiple brain regions (SN, PUT, PFC, AMY) in the ASAP-PMDBS snRNA-seq dataset. The workflow focuses on:
1. **Data preparatio... |
9dffe6eb2a0cc312c44c944a0adde494884534c817b22987c827b14d03a65e4a | R | 23,052 | 612 | # ==========================================================
# 13_revision_diagnostics.R
#
# Revision step 1: diagnostics only. Nothing here changes the
# published models. It establishes the facts needed to decide
# what the corrected models should look like.
#
# Sections
# A. Vascular biomarker scale verification ... |
c99ad5f3a753f4f0f2846d58910d844884fcbcb9d53b94f54374d5c4d9142ed5 | R | 23,182 | 1,386 | # ==============================================================================
# 04. Marker-gene identification, differential expression, and annotation
# across multiple Seurat objects
#
# Description:
# This script processes one or more Seurat objects defined in an external
# analysis manifest. For each object,... |
b463e69a0fdf55666b1feeb2201e1bc0a2e651386b1f57fbd0175ec529a4e11e | R | 23,271 | 568 | #raindrops actual
# tidyverse in particular is incredibly important for graphing and filtering.
list.of.packages <- c("tidyverse", "data.table","shiny","RColorBrewer","ggforce","ggbreak","av","polished","shinyjs")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.... |
a07bea9bb6700beaa57a699858af396164df4ba026c534e5afb30daff48d75bc | R | 24,485 | 702 | ---
title: "Analyzing simulations - figure and results output (results FIG2 of paper)"
output: html_notebook
---
```{r}
library(ggplot2)
library(ggbeeswarm)
library(tidyverse)
library(dplyr)
library(tidyr)
library(purrr)
library(ggnewscale)
library(svglite)
library(ggtree)
# Load data
# # RI connectivity
path_to_... |
5ee13a7cc55f46e269cd9ce0e999116f69a325aab05e612697f5a666613407ef | R | 24,504 | 605 | setwd("D:/A.clarkii/clarkii_skin_featureCounts")
# read and merge featurecounts output
f2 <- read.csv("F2_skin_ctrl.csv",header=T)
f3 <- read.csv("F3_skin_ctrl.csv",header = T)
f4 <- read.csv("F4_skin_ctrl.csv", header = T)
f5 <- read.csv("F5_skin_interaction.csv", header = T)
f6 <- read.csv("F6_skin_interactio... |
0fd838d4c2097eb56b96d092082ee22e418918c891b74e33867013b7e98c8711 | R | 24,549 | 467 | filter_genes <- function(puck, threshold = 5e-5, batch_size = 1000) {
message(c('filter_genes: filtering genes based on threshold = ', threshold))
gene_means <- numeric(length(rownames(puck@counts))); names(gene_means) <- rownames(puck@counts)
n_batches <- ceiling(length(gene_means) / batch_size)
for(j in 1:n_b... |
e593c44d51df52e9c9d5877923d76924e7e66e4a55f86d17f57c8c996d4b55e2 | R | 24,625 | 1,430 | # ==============================================================================
# 06. Gene Ontology enrichment analysis and bubble-plot visualization
# across multiple DEG tables
#
# Description:
# This script performs Gene Ontology enrichment for one or more differential-
# expression tables defined in an externa... |
7715e5ad9a6b9b46ebf9f2140e97c73e10a72172ca1ebb9acf74be7ac97b4eb8 | R | 24,852 | 308 | #' Creates an \code{\linkS4class{RCTD.replicates}} object across multiple \code{\linkS4class{SpatialRNA}} replicates
#'
#' Applies the \code{\link{create.RCTD}} function for each \code{\linkS4class{SpatialRNA}} replicate inputted using a
#' scRNA-seq reference \code{Reference} object.
#'
#' @param spatialRNA.replicates... |
4515e6b1ed950e8024f4039168084d319279e7ba86147da46001dd0fbff01b0d | R | 24,934 | 478 | ---
output:
pdf_document: default
html_document:
keep_md: true
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = T, warning = F, message = F, cache = T, results = 'hide')
```
# Benchmarking CSIDE on a simulated spatial transcriptomics dataset
```{r libraries}
library(spacexr)
library(Matrix)
lib... |
44388c0ec760359e75c040602e53ef011472f9795c8af48a20db8de22b8bcf6f | R | 24,997 | 555 | rdbuColors <- c("#053061", "#2166AC", "#4393C3", "#92C5DE", "#D1E5F0",
"#FDDBC7", "#F4A582", "#D6604D", "#B2182B", "#67001F")
#' Plots expression profile of a gene set
#' @param pathway Gene set to plot.
#' @param E matrix with gene expression values
#' @param center a logical value indicating wheth... |
b5c1185bec8baa892d2676c06f64c8e180f4bca2169ff56ed1bd9bd4cef280a5 | R | 25,629 | 456 | library(Seurat)
library(dplyr)
library(patchwork)
library(infercnv)
setwd("G:/Project4/snRNA/inferCNV/")
######################## 1428 Pons #############################################
snRNA <- readRDS("1428_pons.rds")
mtx <- GetAssayData(object = snRNA, slot = "counts")
cty <- cbind(colnames(snRNA), snRNA$c... |
f8a5ced98b142db7a4404b5f21a6fbd52084c1f0444cff2b020556d604399ef7 | R | 26,014 | 546 | ---
title: "Cell-Type Specific HERV Differential Expression Analysis"
author: "Raquel Garza"
output:
html_document:
df_print: paged
---
## Overview
This notebook performs differential expression analysis of human endogenous retroviruses (HERVs) across cell-type clusters and brain regions from ASAP PMDBS snRNA-... |
c7164583dc0cb4c6c32a69b49ea62608aa4448f82f6a84c87f17c7002bf2cbe0 | R | 26,195 | 524 | #' Makes all CSIDE plots on RCTD object, after running CSIDE
#'
#' @param myRCTD \code{\linkS4class{RCTD}} object containing \code{de_results}, after running CSIDE
#' @param datadir output directory
#' @export
make_all_de_plots <- function(myRCTD, datadir) {
if(!dir.exists(datadir))
dir.create(datadir)
make_de_... |
5c256874af3c3807f05a649be3527dd7b3735e205f7e78611a6ad0b85eef69cc | R | 26,412 | 747 | ---
title: "E1 Processing"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir = normalizePath(".."))
library(Seurat)
options(Seurat.object.assay.version = "v5")
library(ggplot2)
library(scales)
library(sctransform)
library(dplyr)
library(patchwork)
li... |
5795bb6d6323c17dbe299e6c2aa70b6ba07680dc7fc753b1f343dd330742363d | R | 27,793 | 745 | # Time Trails Shiny App
# Dependencies: tidyverse, data.table, shiny, and related visualization packages
# Use ragg for high-quality, fast PNG graphics in Shiny
options(shiny.useragg = TRUE)
# Example: Enable persistent on-disk cache for shared use across sessions
# shinyOptions(cache = cachem::cache_disk("./myapp-ca... |
f4820cfda6fcfa52802dd3ada27d5663fb48f477935403be2932affe4c9b6b21 | R | 27,929 | 593 | # ==========================================================
# 17_fixes_and_risk_covariates.R
#
# Repairs three problems in the previous run and resolves the
# sICAM-1 discrepancy.
#
# Part 1. Minimum detectable effect - formula corrected
# Part 2. Random slopes - tested where they are identifiable
# Part 3. Vasc... |
16604dd54ffb99c7f110de0a0d843a7aaced8a937c4f687eadf80a6dacee1aee | R | 28,096 | 736 | # Raindrops: Baseline/Rank Raindrop Viewer
# Tidyverse is critical for plotting and data filtering
# Use ragg for high-quality, fast PNG rendering in Shiny
options(shiny.useragg = TRUE)
# Install/load required packages
list.of.packages <- c(
"tidyverse", "data.table", "shiny", "RColorBrewer", "ggforce",
"ggbreak"... |
d924ca5de366df06c4e99542553ff9897abb7329848ee3da97e7c6d6bc8318eb | R | 29,439 | 547 | ---
title: ASAP invitro cell cultures + treatments. Differential expression analysis of
genes.
author: "Raquel Garza"
output:
html_document:
df_print: paged
---
Here we perform differential expression analysis (DEA) of genes for our in vitro experiments using microglia, astrocytes or dopaminergic neurons and d... |
8384df1432ed9abb6a0edb4385ad997bec6d3eb1fbafd924157c8d612683eb44 | R | 30,379 | 575 | #Useful Functions
#Ben Saef
#This will be added to whenever needed
library(tidyverse)
library(ggplot2)
library(data.table)
library(khroma)
library(cli)
####Raindrop Plot - Testing ####
##Replace APS with Tipping Point Probability ## Not
##
scramble_key <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20)... |
858109bbc1cb5048145623e3dc08f9fef567cb19fe62b082fcedce965f37930b | R | 30,736 | 591 | #Useful Functions
#Ben Saef
#This will be added to whenever needed
library(tidyverse)
library(ggplot2)
library(data.table)
library(khroma)
library(cli)
####Raindrop Plot - Testing ####
##Replace APS with Tipping Point Probability ## Not
##
scramble_key <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20)... |
8c1e38596ab80e6a79f34f92f0fb336251a2b625c2f11ad0b9cea433eb0508be | R | 30,745 | 743 | #' Wrapper to run methods for preranked gene set enrichment analysis.
#'
#' This function provide an interface to two existing functions:
#' \link[fgsea]{fgseaSimple}, \link[fgsea]{fgseaMultilevel}.
#' By default, the \link[fgsea]{fgseaMultilevel} function is used for analysis.
#' For compatibility with the previous im... |
18631c83133aecb7e06f3643485b5d86aca9ad1471f7a0e539605069017d1d85 | R | 31,249 | 1,841 | # ==============================================================================
# 07. UCell pathway scoring and visualization across multiple analyses
#
# Description:
# This script calculates UCell pathway scores for one or more annotated Seurat
# objects defined in an external manifest. Each analysis may use a diffe... |
2fca73285f35c6eb2167486763124d4bc2303826d8f758e36de6d82ba82ed8e6 | R | 31,311 | 844 |
KS.prelim<-function(Y, X=NULL, id=NULL, out_type="C"){
##Preliminary
Y<-as.matrix(Y);n<-nrow(Y)
if(length(X)!=0){X0<-svd(as.matrix(X))$u}else{X0<-NULL}
X0<-cbind(rep(1,n),X0)
if(out_type=="C"){nullglm<-glm(Y~0+X0,family=gaussian)}
if(out_type=="D"){nullglm<-glm(Y~0+X0,family=binomial)}
if (lengt... |
fbf8f4aecde85a4c832b799cd701fe7d2b7bd2ef79432b74b439c6a6d671262a | R | 31,668 | 768 | ```{r data, echo = FALSE}
inpD <-"F:\\promec\\Animesh\\HUNT\\txt106dpMBR\\"
data <- read.delim(paste0(inpD,"proteinGroups.txt"),row.names=1,sep="\t",header = T)
summary(data)
decoyPrefix="REV"
contaminantPrefix="CON"
dataC="LFQ.intensity."
```
```{r dataNorm, echo = FALSE}
dataNorm=log2(data[,grep(dataC, names(data))... |
74f9092f28f7323a2881bb82b020598b90394240b9f447eb2d5aff55b219c8f7 | R | 31,926 | 600 | #Useful Functions
#Ben Saef
#This will be added to whenever needed
library(tidyverse)
library(ggplot2)
library(data.table)
library(khroma)
library(cli)
library(conflicted)
conflicts_prefer(dplyr::filter)
####Raindrop Plot - Testing ####
##Replace APS with Tipping Point Probability ## Not
##
scramble_key ... |
48e3282727aa06a4623dc2a5e9c904925d6381974601ec9cbe2909fd5021e263 | R | 32,681 | 504 | ---
output: pdf_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = F, warning = F, message = F, cache = T)
```
```{r libraries}
library(RCTD)
library(Matrix)
library(ggplot2)
library(ggpubr)
library(gridExtra)
library(reshape2)
```
```{r fig.height = 16, fig.width = 12, fig.align = 'center', eval... |
d2a4b6fe8a7989fea131603f21fbb5191961ff13d0f9a421a850873d994dbfa4 | R | 32,913 | 803 | setwd("D:/A.clarkii/clarkii_brain_DE/featurects_output")
#read featurecounts output
F1_BS <- read.table("F1-BSfeaturecounts_output.txt",header=T) # Read the counts file obtained from featurecounts
F1_BS_1 <- F1_BS[,c(1,7)] # Select the columns of interest - for me it was the 1st and 7th column which is gene ID and... |
4af18f2812ec17446f2ef3d905c54d4797c4f951d9c2fdf66e392be4883240ec | R | 32,988 | 1,907 | # ==============================================================================
# 05. Visualization of clustering, cell states, and marker-gene expression
# across multiple Seurat objects
#
# Description:
# This script generates manuscript-oriented visualizations for one or more
# annotated Seurat objects defined ... |
a092dc07fa0ec8024aa442315e016e4f5a67e793770b7a562d2a3504b89010e6 | R | 33,541 | 649 | # ------------------------------------------------------------------------------
# Title: Figure 2 - Spatial Niche Composition and Cellchat Analysis
# Author: Haofei Wang
# Date: March 24, 2025
# Description:
# This script generates visualizations for spatial clustering results across timepoints
# using Seurat and Cel... |
774d7c21af22fd5b4481d284e1a012a83e1f8e6ff430f3859d1f6c67f6673db4 | R | 34,477 | 978 | ```{r MGA}
#tar cvzf rep.tgz genomes/*/*.tsv stats/*.tsv genomes/*/*.nwk genomes/annotations/dram/*.tsv
#followed by https://metagenome-atlas.shinyapps.io
```
```{r libraries,inlclude=FALSE}
#install.packages("plotly")
#install.packages("heatmaply")
#install.packages("dplyr")
#install.packages("readr")
#install.pac... |
d062512771b57a7298801a4ab7e2cbd1a2a78bc9a80e10b1a38dd2b9385752f9 | R | 35,539 | 888 | ```{r data, echo = FALSE}
inpD <-"L:/promec/Animesh/Kristian/"
inpF<-paste0(inpD,"2019-Olivieiri-Supplementary Table 2.xlsx")
data <- readxl::read_xlsx(inpF)
warnings()
rowNames<-data[,1]
colNames<-colnames(data)
data<-apply(data,2,as.numeric,na.rm=TRUE)
warnings()
row.names(data)<-toupper(rowNames$Gene)
colnames(data)... |
73f3f23bf875bf54b25585648e6d915a717028a634658fab0657b357dc650ae5 | R | 35,822 | 1,137 | ---
title: "E16_clonal_information"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(Seurat)
options(Seurat.object.assay.version = "v5")
library(ggplot2)
library(dplyr)
library(stringr)
library(patchwork)
library(scales)
library(circlize)
library(UpSetR)
library(tidyr)
l... |
0508ab717b238ec92ac733a5db85a96f87e4c319953b54c3fefefa7e6bb03a61 | R | 37,358 | 915 | #data####
inpD <-"C:/Users/animeshs/OneDrive - NTNU/Pool/"
inpF <- paste0(inpD,"proteinGroups.txt")
data <- read.delim(inpF,sep="\t",header = T)
summary(data)
#clean####
data = data[!data$Reverse=="+",]
data = data[!data$Potential.contaminant=="+",]
data = data[!data$Only.identified.by.site=="+",]
row.names(data)<-da... |
a4e685c64847afdd0f7150d4eda9bce066f25b7c3aee3aaf81137488ef867ad3 | R | 37,754 | 944 | # ------------------------------------------------------------------------------
# Title: Manual Cell Type Annotation Based on Seurat Clusters
# Author: Haofei Wang
# Date: March 24, 2025
# Description:
# This script assigns manual annotations to snRNA cell clusters based on known marker
# genes and cluster relationshi... |
36af84d4e82943da1b17101e8629819383559246b6cf03f4b74f81cd10dc60bc | R | 38,544 | 2,199 | # ==============================================================================
# 09. scFEA flux post-processing and visualization across multiple analyses
#
# Description:
# This script imports per-cell metabolic-flux matrices generated by scFEA and
# processes one or more analyses defined in an external manifest. Fo... |
ad17689dc13ef6dce75b1f5bed883e931f239bacd71e84ef57ede143ff6476cf | R | 38,662 | 1,403 |
local({
# the requested version of renv
version <- "1.1.6"
attr(version, "md5") <- "3036c4b273d882c56e8cdd660ebaf6f0"
attr(version, "sha") <- NULL
# the project directory
project <- Sys.getenv("RENV_PROJECT")
if (!nzchar(project))
project <- getwd()
# use start-up diagnostics if enabled
diagno... |
61bdc2195e082b8d7655ad8520ebd82c5045f01b8d699feee3aff620fbcdd90a | R | 39,509 | 1,244 | ---
title: "E16_clonal_information for experiment 1"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(Seurat)
options(Seurat.object.assay.version = "v5")
library(ggplot2)
library(dplyr)
library(stringr)
library(patchwork)
library(ComplexHeatmap)
library(tidyr)
library(pu... |
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