sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
79c751103f78bbb169cc81d6b0647e3f5616c4db525ca6d41bb6fffbc9e8a421 | R | 7,044 | 161 | ## helpers.R — shared utilities for Transcriptome/r_notebooks
## Loaded with: source("helpers.R")
##
## Provides:
## - PROJ_ROOT, TX_ROOT, OUT_DIR, FIG_DIR, STRATA_DIR
## - CROSS_OMICS / RECURRING / PAPER_TOP / ECM_FAMILY (same panels as proteomics)
## - load_stratum(name) -> list(mat, groups, meta) for a stratum... |
9ccc00f58400eeea41703f32184338038e48cb05fcbcafe93765a23006c0c33c | R | 7,120 | 156 | #!/usr/bin/env Rscript
.libPaths(setdiff(.libPaths(), normalizePath(Sys.getenv("R_LIBS_USER"))))
################################################
################################################
## LOAD LIBRARIES ##
################################################
##########################... |
b9a1c306368eaa0cd7c0e9027cfb1fe4bd7ed69751236d0fc8e74f0d6d36f95a | R | 7,124 | 182 | library(dplyr)
library(tidyr)
library(Rmisc)
library(ggplot2)
library(ggpubr)
library(stringr)
library(ggforce)
library(paletteer)
library(ggsci)
epsilons <- c(5, 10, 25, 50, 100, 200, NA)
samples <- c(rep(100, length(epsilons)-length(which(is.na(epsilons)))), 18)
file_paths <- paste0("~/Python/WASP-DDLS/SE-benchmark... |
8142ee465d5a358238f11a94a4a320c34fa242aafd27dd8617c88c65830cb040 | R | 7,151 | 209 | ############################################################
# Canonical Correlation Analysis (CCA) - CMI Cohort (SES-Controlled)
# Author: Yuan Zhang
# Date: 2025-07-25
#
# Description:
# This script runs CCA analyses for both math and reading
# tasks on the CMI cohort, controlling for age and regressing
# out SES (... |
06d04548db5674e912b28911e030f05f158d406a3c62f453e56671185e6104e1 | R | 7,172 | 152 | # =============================================================================
# 01_tcga_dea.R
# TCGA bulk RNA-seq differential expression analysis (Methods 4.3)
#
# Produces:
# Supplementary Table S1 - DEA Normal Brain vs GBM
# Supplementary Table S2 - DEA LGG vs GBM
# Supplementary Table S3 - DEA Mesenchyma... |
78c9e625086e40ece8a0a8284bfa3bea1b80f7dfaea04ffb681411e468dde788 | R | 7,173 | 212 | ---
title: "R Notebook"
output: html_notebook
---
<!--
## What this file does
Reads three Fiji ROI-batch-measurement CSVs (`Myl_quant`, `Arp3_quant`,
`Actin_quant`) from PA-Rac1 + para-aminoblebbistatin photoactivation
movies. For each of the N=13 activation events, intensity at the growth
cone is sampled at three ti... |
b2f123e38f806f7b66922fcd824c1b8b0e4768653a4131ca3bdf1dfa072619a1 | R | 7,175 | 205 | #Fig. 3a and Fig.3b
# Co-expression of Lepr and Adrb2 in different cell clusters of murine SCG and stellate ganglia
library(tidyverse)
library(Seurat)
library(ggplot2)
##Load integrated data using relative path
data_path <- "data/ganglia_seurat_object.rds"
if (!file.exists(data_path)) {
stop("Seurat object not fou... |
61150909da8d0cfd5192a9fb48766cebafa460caef770c388f1d10a65aa3acad | R | 7,229 | 232 | ## module load conda_R/3.6.x
## ----Libraries ------------------
library(parallel)
library(SummarizedExperiment)
library(Matrix)
library(RColorBrewer)
library(jaffelab)
library(edgeR)
library('zinbwave')
library('SingleCellExperiment')
library('magrittr')
library('ggplot2')
library('Seurat')
## load rse list
load("... |
33b20f8f676cb835b1c392f18cc903cc0964f3877a5b76c58fb51a497b03ed62 | R | 7,249 | 133 | #!/usr/bin/env Rscript
## dep_bh_equivalence_check.R
## =========================
## Shows that the complete-case limma path used for the reported CSF proteomics is equivalent to
## running DEP itself, without imputation and with Benjamini-Hochberg adjustment.
##
## WHY THIS EXISTS. Methods states that protein intensit... |
049f42d477268fd0fdba7183c51a38694df89e65535e1d1bcb310d9546f715e0 | R | 7,290 | 221 | #!/usr/bin/env Rscript
# Author_and_contribution: Niklas Mueller-Boetticher; created template
# Author_and_contribution: Søren Helweg Dam; implemented method
suppressPackageStartupMessages({
library(optparse)
library(jsonlite)
library(SingleCellExperiment)
library(Seurat)
library(PRECAST)
})
opti... |
a8cb03819bc99786b64b5c5ce47159f732da6206b6ed3edb7b0b7f620a13fc4f | R | 7,290 | 231 | ---
title: "R Notebook"
output: html_notebook
---
<!--
# Arp3 / F-actin growth-cone Airyscan line-profile colocalization
## What this file does
Reads paired Airyscan growth-cone line-profile CSVs (`profiles.csv` and
`profiles2.csv` per neuron, lines 53, 58, 70, 74) for the Arp3-A488 +
phalloidin-Rhodamine staining c... |
6cc157bbf3d678b7c7d603f990878988ec1cbe85263f027811a2e50c9cf7ca2b | R | 7,362 | 257 | ---
title: "Group Coupling"
output: html_document
date: "2024-11-29"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
This script preprocesses the spindle and slow oscillation data to feed it into the python scripts.
Because these data were analyzed by a team, there are different IDs ... |
5ac36d21e26e0850eaa2e770ffc83ec2cc3a2e2d6ae45c1d45b2f38ddb53f8de | R | 7,372 | 176 | #!/usr/bin/env Rscript
## SUPERSEDED / STILL IMPUTED. This cross-platform ComBat meta calls the MinProb helper on
## both platforms before concatenation, which is the procedure the revision withdrew: it is
## what manufactured the spurious MS-up ITGB2 CSF call. Its output
## CSF_combined_R_ComBat_DE.tsv is no longer re... |
aa560513281d992393ef1704c3b9eab2f7b18caa02b02f52b6ae4653e8b07912 | R | 7,382 | 196 | library('SingleCellExperiment')
library('here')
library('sessioninfo')
## Load data
load(here(
'Analysis',
'Human_DLPFC_Visium_processedData_sce_scran.Rdata'
))
## For building the checking function
path <- here('Analysis', 'Layer_Guesses', 'First_Round')
merged_name <- 'Merged'
path <- here('Analysis', 'Lay... |
56b3fe12dffb0413c4d0d697653dedf2769b7df6d94b37e3c69d6306cb7ccb1f | R | 7,385 | 138 | .munge_main <- function(i, utilfuncs, file, filename, trait.name, N, ref, hm3, info.filter, maf.filter, column.names, overwrite, log.file=NULL) {
if (is.null(log.file)) {
log.file <- file(paste0(trait.name, "_munge.log"),open="wt")
on.exit(flush(log.file))
on.exit(close(log.file))
} else {
.LOG("\n\... |
b90ed7ef26e6b8bad14c3b92cc6ab8c8c3a4daa5453044fe0192fda9eaaf3b48 | R | 7,425 | 237 | ---
title: "R Notebook"
output: html_notebook
---
<!--
# Rab11a / F-actin soma-patch line-profile correlation (alternative-cohort variant)
## What this file does
Alternative-cohort variant sourced from `D:\DVElite` rather than the
published G:\ cohort. Reads paired soma-patch line-profile CSVs `Rab11.csv`
and `Actin... |
33066da80e2da5ff940f5184523b9c3aee0cf7dc22522c4b743e807ea329ead2 | R | 7,435 | 228 | #' @title Proportion of features
#' @description Check the Proportion of positive cells (default: expression above 0)
#' in certain clusters
#' @param seu Seurat object
#' @param feature Features to plot (gene expression, metrics, PC scores, anything that can be
#' retreived by FetchData)
#' @param ident cluster name, ... |
47a86db9f1579d23b586fba19e09ced40b3bece064f24fd0f03232be7cb1345e | R | 7,471 | 265 | ---
title: "SpatialDE subsampling comparison"
author: "Lukas Weber"
date: "`r format(Sys.time(), '%Y-%m-%d')`"
output:
html_document:
toc: true
toc_depth: 2
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE)
```
# SpatialDE subsampling comparison
Comparison of ge... |
0a23d894f8a7353846162b2ea2fe3c4a18a6789ff1a3139ca006555317fcbfd5 | R | 7,507 | 167 | library(ggplot2)
library(patchwork)
library(reshape2)
library(dplyr)
library(RColorBrewer)
# Import data
Wt_E8_data <- as.data.frame(read_excel("Data_1.xlsx", sheet = "Wt_E8.5_data", col_names = TRUE))
hNMP_data <- as.data.frame(read_excel("Data_2.xlsx", sheet = "hNMP_Spatial_D3_Data", col_names = TRUE))
Gloid_data <-... |
d066cb5cd64dbb916871f5508c3fb2dbb0a4e4663d53039fd9cbe2867fd240c7 | R | 7,516 | 197 | color_palette_fig1A <- c(
"original_paper" = "black",
"benchmark_Hu2024" = "#E41A1C",
"scMEB" = "#377EB8",
"conST" = "#4DAF4A",
"BANKSY" = "#984EA3",
"SpaceFlow"= "#FF7F00",
"DeepST" = "#FFFF33",
"CellCharter" = "#A65628",
"STAGATE" = "#F781BF",
"SPICEMIX" = "#999999",
"DR-SC" = "#66C2A... |
7d197bb8824304a26fe0e38655a5ba531904228c9883477d705af463167a7630 | R | 7,553 | 240 | ---
title: "R Notebook"
output: html_notebook
---
<!--
# F-actin / Rab11a soma-patch line-profile colocalization
## What this file does
Reads paired soma-patch line-profile CSVs `Rab11.csv` and `Actin.csv` per
neuron (lines 52, 57, 67, 71), computes per-patch correlation between the two
intensity profiles, and pools... |
ff4b385ef12cd58b3e31c82b12a4b2e7ef8a5ed2916cf46c761e4ca407d9211a | R | 7,573 | 160 | # Vanni Bucci, Ph.D.
# Assistant Professor
# Department of Biology
# Room: 335A
# University of Massachusetts Dartmouth
# 285 Old Westport Road
# N. Dartmouth, MA 02747-2300
# Phone: (508)999-9219
# Email: vbucci@umassd.edu
# Web: www.vannibucci.org
#---------------------------------------------------------------------... |
296e5dffc5849728fe460b589a6c8e09f041097654d57f3393c0fead187cafb5 | R | 7,580 | 199 | #' @title Gene Set Enrichment Analysis
#' @description Calculate the GSEA score of gene sets at the single-cell level using the 'AUCell' package:
#'
#' Aibar et al. (2017) SCENIC: Single-cell regulatory network inference and clustering.
#' Nature Methods. doi: 10.1038/nmeth.4463
#'
#' Aibar et al. (2016) AUCell: Analys... |
8c460a91b9d5adab8678080169829e350be5668507afb1ec0deed1d1ffc2b6de | R | 7,602 | 257 | #!/usr/bin/env Rscript
# Author_and_contribution: Niklas Mueller-Boetticher; created template
# Author_and_contribution: Søren Helweg Dam; implemented method
suppressPackageStartupMessages({
library(optparse)
library(jsonlite)
library(SingleCellExperiment)
library(Matrix)
library(SpatialExperiment... |
8cd326eb3dde70730653df205096300f6a9c2b1d99f68fcb30af1c9ab9e39ccf | R | 7,602 | 187 | #!/usr/bin/env Rscript
## 05_t_lineage_meta.R — generated from notebook spec
## Run: Rscript 05_t_lineage_meta.R
## ============================================================
## # 05 — T-lineage microarray meta (GSE32915 + GSE78244)
##
## Cross-study T-lineage meta-analysis:
##
## | Series | Author | C... |
e2e1f7920f90636a80f2302871d5098c533b4796db10655c995590cfa18a6c07 | R | 7,603 | 180 | library(readxl)
library(ggplot2)
library(dplyr)
library(tidyr)
library(ggpubr)
#library(Microsoft365R)
########## Privacy ############
df <- read_excel("~/Python/WASP-DDLS/results.xlsx")
#df$samples <- c(NA, 18, rep(100, nrow(df)-2))
#df_srd <- df |> select("Epsilon", "Mean SRD", "...10", "samples") |>
# rename("A... |
e0597d5242a4061437063d4c15ba7e9a0da00d1b76aa1fb4746933f38850f340 | R | 7,730 | 292 | ############################################################
# Prediction Using CCA Models (CMI → Stanford)
# Author: Yuan Zhang
# Date: 2026-05-25
#
# Description:
# This script applies Canonical Correlation Analysis (CCA) models
# derived from the CMI cohort to predict brain–behavior scores
# in the Stanford cohort (... |
0a997de5a1be7c91d06476c6aeee10bf9fd3269c44f32f73a41588d0f9a47545 | R | 7,750 | 260 | #!/usr/bin/env Rscript
# Author_and_contribution: Niklas Mueller-Boetticher; created template
# Author_and_contribution: Søren Helweg Dam, implemented method.
suppressPackageStartupMessages({
library(optparse)
library(jsonlite)
library(SpatialExperiment)
library(Seurat)
library(stardust)
})
# Get... |
ac2a790b3b991107ac02a1412ad6f84385c52df47921e5185f12d7cccde23d71 | R | 7,810 | 257 | #!/usr/bin/env Rscript
# Plot transcript isoform structures for selected Meis1, Pax6, and Nfib transcripts
# using a SQANTI3-corrected GTF file and SQANTI3 classification table.
suppressPackageStartupMessages({
library(rtracklayer)
library(dplyr)
library(ggplot2)
library(ggtranscript)
library(magrittr)
li... |
64132acc2a6dff03960d787b28a82ed9e58940c57e9b5265b51e6343bf02bfcb | R | 7,847 | 128 |
---
title: "Comparison analysis of multiple datasets with different cell type compositions"
author: "Suoqin Jin"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
html_document:
toc: true
theme: united
mainfont: Arial
vignette: >
%\VignetteIndexEntry{Comparison analysis of multiple datasets using CellCha... |
05cb9c47de1333a58889c0924c17b4b037f6751e72f85afb2c585e1b406c97e6 | R | 7,849 | 162 | #!/usr/bin/env Rscript
.libPaths(setdiff(.libPaths(), normalizePath(Sys.getenv("R_LIBS_USER"))))
################################################
################################################
## LOAD LIBRARIES ##
################################################
##########################... |
625eaa90adda1088da90c67e9903db0ee1715826fffd301bdc9e0bd20a2f1d64 | R | 7,849 | 173 | #' @title Run Standard Seurat Pipeline
#' @description This function processes a Seurat object through various steps including normalization, PCA, and clustering based on specified parameters. It allows for conditional execution of each step based on prior executions and parameter changes.
#' @param seu A Seurat object... |
78c1ce0cedee54e6a51d3b50e2572196910b7909f7c5bec15376e6675d8b9020 | R | 7,923 | 237 | #!/usr/bin/env Rscript
# Written by Lorena Pantano and revised for flexibility in handling assays
# Released under the MIT license.
library(SummarizedExperiment)
#' Flexibly read CSV or TSV files
#'
#' @param file Input file
#' @param header Passed to read.delim()
#' @param row.names Passed to read.delim()
#'
#' @re... |
417c7afcebf47b2696a7dbb320bf0f373a85eec1a8572352c550d50dbe089f0c | R | 7,929 | 302 | ---
title: "K group fold ML winsorized w/grid tuning"
output: html_notebook
---
This script is for the ablation analysis removing AUC variables.
```{r, warning=FALSE, message=FALSE}
library(tidyverse)
library(caret)
library(randomForest)
library(beepr)
library(tictoc)
library(doParallel)
prl <- read.csv("winsorizedOu... |
9aaae2acd659b9c6c44f3335d48bd836f59012711e49eac769a0bd7d64337982 | R | 7,929 | 270 | ############################################################
# Partial correlation between original U and V controlling
# for global morphometric covariates
# 1) SST_GVOL
# 2) SST_BVOL
# + permutation test for significance
#
# Author: Yuan Zhang
# Date: 2026-03-30
###################################################... |
d32c3e8150495ca66b7745d7dfa2f4ad2ca45ccab48b8aacd01ea3c183eeaa8b | R | 7,948 | 304 | ---
title: "K group fold ML winsorized w/grid tuning"
output: html_notebook
---
This script is for the ablation analysis removing AUC and s variables.
```{r, warning=FALSE, message=FALSE}
library(tidyverse)
library(caret)
library(randomForest)
library(beepr)
library(tictoc)
library(doParallel)
prl <- read.csv("winsor... |
21a11edf1fcaf368b25217a46ed584e928dfa5643cea919c2d81dc2576e223b5 | R | 7,975 | 223 | #!/usr/bin/env Rscript
setwd('/Users/guofanhua/Desktop/gfh/work/experiment/ASL_Mesoscopic2025/reference/2022Nature_VascularAtlas/')
##########################
library(scales)
library(plyr)
library(Seurat)
library(dplyr)
library(patchwork)
##################################
df=read.table('..//data/sampleinfo.txt',heade... |
150508475013e33c598bed1bb42d112f086cb7a15af21810dd2483f1afe7a650 | R | 7,976 | 221 | ---
title: "R Notebook"
output: html_notebook
---
<!--
# Western-blot densitometry quantification (Arp3, MRLC, pMRLC vs GAPDH)
## What this file does
Reads per-lane band-densitometry tables (`read_*` calls open the per-blot
intensity tables; the values are pasted in-line in this Rmd) for the Arp3,
total MRLC and pho... |
fb5964aa05a7c8ecb88a442b75f71f3fb44e669264a6d9d1f9e3a8b764b53b3d | R | 7,981 | 249 | # annotate modules with ClusterProfiler
# Adapted from:
# https://yulab-smu.top/biomedical-knowledge-mining-book/enrichment-overview.html
load("Consensus_neural_meta_hdWGCNA_object.RData")
# Load packages and data
library(clusterProfiler)
library(org.Hs.eg.db)
library(enrichplot)
modules = modules
background = modu... |
7bc15758fa74fade21e9a2341afb45f952e359ef793497b7f1fdfea584448671 | R | 8,016 | 181 | suppressPackageStartupMessages(library(monocle, warn.conflicts = FALSE , quietly = TRUE))
suppressPackageStartupMessages(library(Scribe, warn.conflicts = FALSE, quietly = TRUE))
suppressPackageStartupMessages(library (optparse, warn.conflicts = FALSE, quietly = TRUE))
suppressPackageStartupMessages(library (igraph, war... |
8ce5c21f32e362e481d4dc2115e9657b9caa82e37fce17c87bab52f76ce411c3 | R | 8,018 | 242 | library(arrow)
library(readr)
library(ggplot2)
library(rtracklayer)
library(ggpubr)
library(tidyr)
library(dplyr)
#---------------------------Read in datasets---------------------------------#
protein_class <- read_tsv("nextflow_results/V47/orfanage/SFARI.protein_classification.tsv")
expression <- read_parquet(("nex... |
3b98946088982134108d53b488df3360d2a9c000088683b793bc795ed8558294 | R | 8,038 | 140 | #' @title Compute and Visualize Cell Trajectories Using CellRank
#' @description `Cellrank.Compute()` calculates cell trajectories using pre-existing pseudotime data in an AnnData object, providing an alternative to scVelo when it produces trajectories that may not align with established biological knowledge. `Cellrank... |
5ebd53049c8e46a12b56fc17d199af672e9390b4f7291734ced15fdc305053d2 | R | 8,048 | 216 | # hdWGCNA: https://smorabit.github.io/hdWGCNA/
# load packages
library(Seurat)
library(reticulate)
library(Matrix)
# plotting and data science packages
library(tidyverse)
library(cowplot)
library(patchwork)
# co-expression network analysis packages:
library(WGCNA)
library(hdWGCNA)
load("day4_integrated_SMD.RData")
Hu... |
fc333daf426f1a13314f6d959e1f67fb16b4eb9933869b60344db9fdbc257f2b | R | 8,059 | 186 | #' @title Search Pathways in GO/Reactome Database
#' @description Search for pathways in the GO or Reactome database using a gene name, pathway ID (SetID), or pathway name (SetName).
#' @param item A gene name, pathway ID, or pathway name.
#' @param type Types of search criteria. Can be one or a combination of "gene", ... |
ab3f4a6076c9b45d7592794075fe357a3373b5db3e8cdf637b9a624dbd88c9c7 | R | 8,106 | 231 | ---
title: "Applying spatialDE to identify spatially DE genes"
author: Stephanie Hicks
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Load libraries
```{r}
suppressMessages({
library(here)
library(SingleCellExperiment)
})
```
Copy data (if needed) to be able to w... |
b57363774cf180e41dc0e9fa3208ad2cd1493ed0dce24d9fe629b9e2fe547210 | R | 8,127 | 306 | ---
title: "K group fold ML winsorized w/grid tuning"
output: html_notebook
---
This script is for the ablation analysis removing s variables.
```{r, warning=FALSE, message=FALSE}
library(tidyverse)
library(caret)
library(randomForest)
library(beepr)
library(tictoc)
library(doParallel)
prl <- read.csv("winsorizedOut... |
7a03a3652882bd10a2429da979d7309ded9a3e95734f7dbf3ac9dd1bb0db07cb | R | 8,138 | 236 | #' Plot Running Enrichment Score
#'
#' Creates a two-panel plot showing the running enrichment score and
#' ranked statistic for a specific region set.
#'
#' @param ranking Named and sorted vector of genomic regions and their ranking score
#' @param region_sets List of region sets (from mapGRangesToRegionSets)
#' @p... |
d620493c26df5287fb59517ae03579f83fada76dd28b24c8ca05cc3de6d5fe0d | R | 8,146 | 173 | # =============================================================================
# 00_setup.R
# Shared configuration, gene panels, signatures, and helper functions
# for the multi-scale NPY / hypoxia transcriptomic analysis of IDH-wildtype GBM.
#
# Manuscript: "Loss of Neuropeptide Y Signaling Accompanies the Neural-to-... |
78e30c38bacd027d714129b308a70825c58b89a8b2361199f28c81c003b9bd17 | R | 8,206 | 207 |
# note: need to add cutoff option as well
# Usage:
# heatmap: function name
# log2fc: path to log2fc file
################ heatmap function ################
heatmap_ <- function(plot_type,heatmap,dend_labs,reordercols,legendtitle,squish_bounds) {
# read in logfc table (must be a tsv with columns: label, log2fc)... |
ea3f51bda13c88ce8eb73a1c860cfb96289fd125a367bb6a0eddf2509f75cc14 | R | 8,222 | 318 | #####################################
# Estimate correlation coefficient between metacognitive effiency
# estimate between two, three, or four domains.
#
# Adaptation in R of matlab function 'fit_meta_d_mcmc_groupCorr.m'
# by Steve Fleming
# for more details see Fleming (2017). HMeta-d: hierarchical Bayesian
# estim... |
dc65c2a33a985de0c3e31c8efce294178898e2623c3de12aff57c0095c34b833 | R | 8,250 | 304 | ############################################################
# NMDA scatter plots for CCA-derived GMV maps
# Author: Yuan Zhang
# Date: 2026-05-29
#
# Description:
# This script plots associations between CCA-derived GMV
# structural phenotypes and the NMDA receptor map for:
# 1. CMI math
# 2. CMI reading
# 3. St... |
c85d7ff5f18a3cdfee0ccebfada7f632ec73c47429f4c556c115acd69b19d4a4 | R | 8,256 | 306 | ---
title: "K group fold ML winsorized w/grid tuning"
output: html_notebook
---
This script is for the ablation analysis removing AUC and s variables.
```{r, warning=FALSE, message=FALSE}
library(tidyverse)
library(caret)
library(randomForest)
library(beepr)
library(tictoc)
library(doParallel)
prl <- read.csv("social... |
3060434f44cfb7ce65390db74602cd80e5a7fce8f131c5f4576fb996a278c360 | R | 8,305 | 288 | ---
title: "HumanPilot: Clustering using PCA, UMAP, marker genes, spatial coordinates"
author: "Lukas Weber"
date: "`r format(Sys.time(), '%Y-%m-%d')`"
output:
html_document:
toc: true
toc_depth: 2
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE)
```
# Introductio... |
533ac44c2b81b94005fa7c7ea7b3a3dc2be40b816f840f05b05201a40d407fe4 | R | 8,342 | 293 | ---
title: "Maze_Statistics"
output: html_document
date: "2025-01-23"
---
```{r setup, include=FALSE}
rm(list = ls())
knitr::opts_chunk$set(echo = TRUE)
library(ggplot2)
library(tidyverse)
library(dplyr)
library(ggpubr)
library(rstatix)
```
```{r}
library(emmeans)
data_maze_raw <- read.csv2("./dat... |
99893c713ed1ae83cf138a47cb0dfeadb6c0e721b6a71bf75492e7f532a143ab | R | 8,358 | 270 | ---
title: "Survey the expression of beat/side othorlogs in mosquitos Aedes aegypti"
output: html_notebook
---
# Load the packages.
```{r}
library(tidyverse)
library(magrittr)
library(ggridges)
library(RColorBrewer)
library(Seurat)
```
# Prepare the dataset.
```{r}
# The following datasets could be found in https... |
fbdabe0c26d1708669dd64c0c6ca48832f05a67cb0aae1a198f02398a020b9e4 | R | 8,369 | 225 | library(deSolve)
library(tidyverse)
GLV <- function(t, x, parameters) {
with(as.list(c(x, parameters)), {
x[x < 10^-5] <- 0
dxdt <- x * (r + A %*% x)
list(dxdt)
})
}
sigma=0.1
sparsity=0 ## 0.2;0.5;0.8
generate_glv_parameters <- function(p, seed) {
set.seed(seed)
A <- {
A_temp <- matrix(rnor... |
cd6cdb6142bc7ee4839f2e49c8fbf4960c3b0606f9c23515ef944273e6dd1322 | R | 8,379 | 310 | ---
title: "K group fold ML winsorized w/grid tuning"
output: html_notebook
---
This script is for the ablation analysis removing s variables.
```{r, warning=FALSE, message=FALSE}
library(tidyverse)
library(caret)
library(randomForest)
library(beepr)
library(tictoc)
library(doParallel)
prl <- read.csv("socialBehavior... |
4dab2eee27f3cb0eadc63120e00ddb43a27c846b2970b87311b879d38b9a5977 | R | 8,398 | 236 | ---
title: "Literature proportion analysis"
output:
BiocStyle::html_document:
toc: true
date: "2023-07-28"
---
```{r setup, include=FALSE, message=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source("../../utils/dario_functions.R")
LoadLibraries()
SourceFiles()
```
# Read in data
```{r, fig.height=4, fig.width=8... |
3042a27231ad48f5f87bc383b28e763dabcf4b56e3427f12181593b8f3fc3118 | R | 8,436 | 220 |
#' Smooth data by PCA
#'
#' Perform PCA, identify significant dimensions, and reverse the rotation using only significant dimensions.
#'
#' @param x A data matrix with genes as rows and cells as columns
#' @param elbow_th The fraction of PC sdev drop that is considered significant; low values will lead to more PCs bei... |
f9e9a50d7e5743e53d877c2814bf3e7d5c5b86e60417ff27c17e6d1df9a1d8f6 | R | 8,456 | 308 | ---
title: "K group fold ML winsorized w/grid tuning"
output: html_notebook
---
This script predicts Savg from the genotype and neural network metrics.
```{r, warning=FALSE, message=FALSE}
library(tidyverse)
library(caret)
library(randomForest)
library(beepr)
library(tictoc)
prl <- read.csv("socialBehaviorPrLdataPIi... |
72ffbcfb40eac291a26e67a9e6723d8b345343a5b312eca0dd48ca88baea612d | R | 8,464 | 207 | library(tidyverse)
my_theme <- theme_bw() +
theme(
plot.title = element_text(size = 15, hjust = 0.5),
axis.text.x = element_text(size = 14, vjust = 0.5, angle = 90, hjust = 1),
axis.text.y = element_text(size = 16),
strip.text = element_text(size = 18),
strip.placement = "ou... |
1045424585d874a8d369db2117d1827b192b4a5821e74b05b9bc469d4d4c9293 | R | 8,466 | 253 | ## From https://gist.githubusercontent.com/mages/5339689/raw/2aaa482dfbbecbfcb726525a3d81661f9d802a8e/add.alpha.R
add.alpha <- function(col, alpha = 1) {
if (missing(col))
stop("Please provide a vector of colours.")
apply(sapply(col, col2rgb) / 255, 2,
function(x)
rgb(x[1], x[2], x[3... |
ab1ff0bae7011f1e959e99d5043f53b7a0cce065ea14eec0205adc8e33680c02 | R | 8,483 | 210 | # Fit NB regression models using different approaches
fit_poisson <- function(umi, model_str, data, theta_estimation_fun) {
regressor_data <- model.matrix(as.formula(gsub('^y', '', model_str)), data)
dfr <- ncol(umi) - ncol(regressor_data)
par_mat <- t(apply(umi, 1, function(y) {
fit <- qpois_reg(regressor_d... |
36cb6a14fed5793c1893aabfbf7cd5f3ae1192aa8855a2d8928e343cc4f55915 | R | 8,510 | 255 | calc_entropy <- function(u) {
p <- u[u>0]
p <- p/sum(p)
-sum(p*log(p))
}
# given a matrix of labels, calculate all pairwise ARIs
calc_aris <- function(m, flavour="ARI") {
a <- diag(ncol(m))
for(i in 1:(ncol(m)-1))
for(j in 2:ncol(m)) {
if(flavour=="ARI") {
require(mclust)
a[i,j] <-... |
b2e8eaabb91f4a458fe0b725b0d0478457a3dd9874b3697ab42543abcc148ed6 | R | 8,529 | 316 | ---
title: "K group fold ML winsorized w/grid tuning"
output: html_notebook
---
This script is for the ablation analysis removing s variables for M3.
```{r, warning=FALSE, message=FALSE}
library(tidyverse)
library(caret)
library(randomForest)
library(beepr)
library(tictoc)
library(doParallel)
prl <- read.csv("socialB... |
a40ca4af24d7b577b69b387d1b98905a85f26b44606a7a077bfda23dcdff7122 | R | 8,535 | 317 | ---
title: "K group fold ML winsorized w/grid tuning"
output: html_notebook
---
This script is for the ablation analysis removing AUC variables.
```{r, warning=FALSE, message=FALSE}
library(tidyverse)
library(caret)
library(randomForest)
library(beepr)
library(tictoc)
library(doParallel)
prl <- read.csv("socialBehavi... |
ed646f76a83e94cbaf8674d74a8b93f4a19f04730f0203a4b8ea1041f1eb32df | R | 8,547 | 317 | ---
title: "K group fold ML winsorized w/grid tuning"
output: html_notebook
---
This script is for the ablation analysis removing AUC variables.
```{r, warning=FALSE, message=FALSE}
library(tidyverse)
library(caret)
library(randomForest)
library(beepr)
library(tictoc)
library(doParallel)
prl <- read.csv("socialBehavi... |
f46397e265506a6b0bf76d8298fa1d8b342e2a807e61a09d17c91bb4057181b1 | R | 8,575 | 323 | ---
title: "K group fold ML winsorized w/grid tuning"
output: html_notebook
---
This script is for the ablation analysis removing AUC and s variables.
```{r, warning=FALSE, message=FALSE}
library(tidyverse)
library(caret)
library(randomForest)
library(beepr)
library(tictoc)
library(doParallel)
prl <- read.csv("social... |
40addd3af70f696833a39e16fa2de7d638c2f7256323f91c7647c217492f9906 | R | 8,579 | 209 | # cfDNA_enrichr_analysis.R
# this file is meant to be used inside Rstudio
# this script takes in gene lists, connects to the Enrichr databases and output graphs of enriched pathways and proteins
library(enrichR)
library(ggplot2)
# read in significant gene lists (q<0.01)
# intragenic 5hmCG
gene_5hmc <- fread('/mnt/i... |
a3e5a801b33410e3ba2d228bd6a763c0935e723821b237819934c89a83b269ec | R | 8,644 | 301 | #!/usr/bin/env Rscript
library(rmarkdown)
library(optparse)
option_list = list(
make_option(
c("-r", "--report"),
type = "character",
default = NULL,
help = "Report template file",
metavar = "character"
),
make_option(
c("-o", "--output"),
type = "ch... |
31e42ab0c7f61498b11a6b756bea7a4e282723a9d880135bdc7eb7788c9f3218 | R | 8,655 | 271 | rm(list = ls())
library(pbapply)
library(parallel)
library(mgcv)
library(emmeans)
df_combined <- readRDS("C:/df_combined_delta.rds")
df_combined$Subject <- as.factor(df_combined$Subject)
df_combined$Gender <- as.factor(df_combined$Gender)
df_combined$ROI <- as.factor(df_combined$ROI)
df_combined$Time <- a... |
cc8e0523876c0e156047e89fe66cc041ce0113f8beba7fb76c47f8ce117ef581 | R | 8,674 | 239 | ---
title: "Train UMI-fy model"
author: "Christoph Hafemeister"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_float: true
code_folding: show
highlight: pygments
df_print: kable
link-citations: true
---
```{r setup, include = FALSE}
library('Matrix')
library('ggplot2')
library('knit... |
9ca6074bc7e7e293df0bb2b56cc34bcca37eafd1dc037f5d3d84d0257c92a197 | R | 8,719 | 185 | # It should be possible to pass some arguments as either unit() objects or as
# numbers. Numbers should be converted to unit(n, "lines"), while unit() objects
# should remain unchanged.
# A combination of units for some arguments and numbers for others should be
# possible.
#
# These arguments are:
# box.padding ... |
9be26318f54856efde76df59a293eb411c1e902d57e2719fa080ca11c104db90 | R | 8,736 | 267 |
addSNPs <-function(covstruc, SNPs, SNPSE = FALSE,parallel=FALSE,cores=NULL,GC="standard"){
print("Please note that an update was made on 11/21/19 that combine addSNPs and the multivariate GWAS functions into a single step. Therefore, addSNPs is no longer a necessary function.")
warning("Please note that an ... |
97b5853deb0635fcd4d4a10e4d43ab361896e9870b6c5dada06c6cf6594eedd5 | R | 8,767 | 175 | test_that("end-to-end run over the synthetic fixture produces correct schemas, sort order, and files", {
dir <- local_temp_dir()
fx <- make_synthetic_dataset(dir)
outs <- out_paths(local_temp_dir())
res <- run_eqtl_finemapping_files(
index_eqtl_file = fx$index_eqtl_file, cis_pairs_file = fx$cis_pairs_file,... |
eff54c1cdc6292ab3f0258a281e2cc9079d2a7cd2c8d93158bffe83b0a2c032b | R | 8,800 | 190 | ################################################################################
### Resolving strandedness in the GWAS samples.
### We discovered an issue in which the SNPs did not always match with the
### mm10 or C3HeB/FeJ references at their specific positions, so we
### consulted Dr. Jeff Smith. He had the s... |
922f328155107aef1ae1452adf4c08c7ec950977b383ae4ad7cd51404122230e | R | 8,849 | 264 | library(parallel)
library(igraph)
library(matrixcalc)
library(MASS)
install.packages("/Users/satabdisaha/Downloads/QUIC",
repos = NULL,
type = "source")
# library(QUIC)
library(Hmisc)
library(robustbase)
library(GENIE3)
library(fitdistrplus)
library(ZIM)
library(ggplot2); theme_set(... |
919832ba4e61e1398ae8b9c5a7596ade0cd7cb62894343b9263900b1c859afac | R | 8,900 | 289 | #!/usr/bin/env Rscript
# Subject-level all-critical ROI tests.
#
# Input: the CSV/TSV produced by 01_calculate_roi_isps.py.
# Output:
# - subject_level_summary.tsv: subject means by ROI x version (same/diff) x smltp.
# - subject_level_ttests.tsv: parametric paired t-test of WBSLD(same) vs WBSLD(diff),
# ... |
436799892904a75482505320a88b7fc514e77c398dcfea4c6c35e1922db009ec | R | 8,929 | 185 | # Function to generate a permutation map from a set of cortical regions of interest to itself,
# while (approximately) preserving contiguity and hemispheric symmetry.
# The function is based on a rotation of the FreeSurfer projection of coordinates
# of a set of regions of interest on the sphere.
#
# Inputs:
# coord.l... |
bd15bf602c896d3e53454ba8e612f6c0d0fadbb2c5dabb3efebdfa932d0f94cf | R | 8,988 | 164 |
setwd('~/Desktop/Research/ciona/spatialTranscriptomics/myData/')
# slide 1
slide1 <- read.csv('ciona_brain_nc1/spatial/tissue_positions_list.csv', header = FALSE)
slide_1_1 <- slide1
slide_1_2 <- slide1
slide_1_3 <- slide1
slide_1_1[slide_1_1$V6 > 4000,]$V2 <- 0
slide_1_2[(slide_1_2$V6 < 4000) | (slide_1_2$V6 > 70... |
a67fb227b6c18b5bdd49bdc4d3f97834ea636689f3b9cb4a9863222f540850b0 | R | 8,993 | 302 | ############################################################
# Compare GMV weight maps across cohorts/domains
# Rigorous comparison using cocor + figures
# Author: Yuan Zhang
# Date: 2026-03-24
############################################################
rm(list = ls())
library(Hmisc)
library(corrplot)
library(ggplo... |
da8b8c175f211a48c8da3e473c2ea77f17e69dec8380897cd45e121cddd6e29d | R | 9,009 | 142 | ### Harmonizing connectivity matrices for efficiency analysis
#########################################################
### (A) Installing and loading required packages
#########################################################
if (!require("dplyr")) {
install.packages("dplyr", dependencies = TRUE)
library(dplyr)
}... |
aa80365f606ed1e07cd3a6f2ea6840f22c8120040bb065051b56eee343d15c76 | R | 9,014 | 145 | ---
title: "Variance Stabilizing Transformation"
author: "Christoph Hafemeister"
date: "`r Sys.Date()`"
output:
html_document:
highlight: pygments
---
```{r setup, include = FALSE}
library('Matrix')
library('ggplot2')
library('reshape2')
library('sctransform')
library('knitr')
knit_hooks$set(optipng = hook_opti... |
b21395caf121433b87456a69097d596f8a7d59f211a8516053a2449c72440fab | R | 9,014 | 174 | ## 11_itgb2_csf_pleocytosis.R
##
## Why ITGB2 is detected more often in MS than in control CSF, and why that is not an
## MS-specific property of the protein.
##
## Background. ITGB2 (CD18) is the one Tier-1 candidate whose Astral CSF measurement is
## substantially incomplete: it is quantified in 700/978 MS (71.6%) bu... |
556359975f67bde45f9c605552eba3c83466a40fa27fed3f0ab57fa89ba18409 | R | 9,074 | 263 | #' @include GeneSetAnalysis.R
#'
NULL
#' @param parent ID or name of the parent (top-level) gene set in the GO/Reactome database.
#' This restricts the analysis to a subset of gene sets. Default: NULL.
#' @param dataset (GO) Alias for 'parent'. Default: NULL.
#' @param root (GO) Specifies which root category to use:
#... |
8ecd2c2f478a3d09bb4208ac0501859a549e95eeb2f9a6a22521b2491e59d1d1 | R | 9,121 | 275 | ---
title: "Quick Start-Up Guide"
author: "Yichao Hua"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Quick Start-Up Guide}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
---
## Quick Start-Up Guide {#quick-start-up-guide}
This quick start-up guide provi... |
e28a268127413340420d7f82a78b0e57829e37f4495e7de934f012f1b375b2a0 | R | 9,129 | 239 | # write all source data to file
rm(list=ls())
# Load packages ----
library("here")
library("magrittr")
library("tidyverse")
library("sgof")
library("openxlsx")
# results dir ----
resdir = here("sourceData")
loadResults = function(files) {
df_models = lapply(files, function(f) {
read_table(f) |> mutate(src = ... |
c50b8733d8127c9d2a9b9ada050be1d04a63f577b0ab94f2c8c29572a5b23ee8 | R | 9,149 | 332 | ---
title: "Survey the expression of beat/side othorlogs in clonal raider ants Ooceraea biroi"
output: html_notebook
---
# Load the packages.
```{r}
library(tidyverse)
library(magrittr)
library(ggridges)
library(RColorBrewer)
library(viridis)
library(gplots)
library(pheatmap)
library(grid)
library(gridExtra)
libr... |
3d1c94f8ab318382895203a138109f215ca650accbbdb16bb34f3aa3b10bb6ca | R | 9,150 | 224 | # Internal output-table builders and writer for run_eqtl_finemapping_files().
#
# All functions in this file are internal (not exported).
#' @keywords internal
#' @noRd
get_scalar <- function(row, col, default = NA) {
if (col %in% names(row)) row[[col]][1] else default
}
#' @keywords internal
#' @noRd
get_opt_colum... |
3e68880fdbf06e04ca1f778b654d333d7ae8b1fc6b0c84489c6bab2ce91c5005 | R | 9,150 | 269 | ---
title: "R Notebook"
output: html_notebook
---
<!--
# ED Fig 2i - Frequency of actin waves per axon and minor neurite,
# before and after polarization (DIV-3 polarized neurons).
## What this file does
For each DIV-3 polarized neuron movie:
1. Reads soma-actin + actin-wave-frame CSVs.
2. Classifies wave-receiving... |
bc4ad1c2ac5073068eeb3e4f96cc887afcd32b97e56dd0838d54b0175fdbea3f | R | 9,150 | 153 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
# Functions -------------------------------------------------------------------------------------
file.edit("chen_loubiere_2025_git/function/augmentation_function_tiling_sliding_window.R") # For ATAC-Seq peaks
file.edit("chen_loubiere_2025_git/function/comput... |
f7289c8b62639081e87410b5bf4a82c5eebf4946bd1681253a50e1907333c6a9 | R | 9,152 | 309 | ---
title: "R Notebook"
output: html_notebook
---
<!--
# Para-aminoblebbistatin somatic PA-Rac1: neurite length-difference comparison
## What this file does
Reads per-cell neurite-length CSVs from PA-Rac1 photoactivation at the soma
in WT neurons treated with 40 uM para-aminoblebbistatin (`csv_file` from
`Path_1`, l... |
e345a6589aa3eaa13c67a47230be30124d5cca185b00eae661946574d1b21974 | R | 9,163 | 298 | #!/usr/bin/env Rscript
# Author_and_contribution: Niklas Mueller-Boetticher; created template
# Author_and_contribution: Lucie Pfeiferova; functions for Giotto HMRF spatial domain exploring
# Author_and_contribution: Søren Helweg Dam; created environment setup script, updated environment yaml, added configs, tidied co... |
e9f0076f21cc44930a16bc077aaff52c5257caa81bd91cab0e25029c4c9bab0d | R | 9,163 | 268 | #' @include generics.R
#'
NULL
#' @param seu A Seurat object. Only applicable when using the Seurat method.
#' @param features Features to be plotted, which can include gene expression or any other data that can be retrieved using the `FetchData()` function. Only applicable for the Seurat method.
#' @param group.by A ... |
d6acdbe734fcc1c6efd22e7d814e5cc304adedd4558749d21f11fa5ef4bc6a05 | R | 9,239 | 207 | # brain-maintenance-lgcm: trivariate latent growth curve model and brain
# maintenance index, companion code for Menze et al. (2026).
#
# Copyright (C) 2026 The authors of Menze et al. (2026).
#
# This program is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License... |
5eedbb09ca51d7f03da48b8a3f3782a25e462e6a9c0d76f7ae263b9a4ef88215 | R | 9,264 | 246 | # ==============================================================================
# Script: 4_segmentation_stats.R
# Manuscript relevance: 3.1, Table S1, Table S4
# ==============================================================================
# PURPOSE:
# Summarize the input to / output of the linear segmentation pip... |
978f756dd6924825dde5f254e57fc821d5c300d754960a121ca5929b2df76ed8 | R | 9,281 | 287 | ########################################################################
# Common functions for RWRtoolkit.
########################################################################
load_network <- function(path_to_edgelist,
type = NULL,
name = NULL,
... |
e04f7c54c2e2bccf196da7f93bb305a634ff3d76978a0087c5a89e4a2543dbe6 | R | 9,281 | 193 | #' @title Run harmony pipeline on a Seurat Object
#' @description This function performs normalization, feature selection, scaling,
#' PCA, batch correction using Harmony, and optional UMAP and clustering on a Seurat object.
#' It is useful for integrating data across batches or technical sources.
#'
#' @param SeuratOb... |
b758adfdc7ada60cd09f8d71b0903ed7e6e2714899bfa299d4397d3b499824aa | R | 9,307 | 345 | ---
title: "R Notebook"
output: html_notebook
---
<!--
# EB3 integrated tip intensity per neurite (before / CK-666 / washout)
## What this file does
Reads ComDet trajectory CSVs (`Tra_file` from `Path_1`, lines 49, 63;
subfolder enumeration at line 242) and saved `df_list.RData` cache (line
250) for the neurite-tip ... |
dee2fb3ad1469fc8e1392a8512df375e1665e60c9a89a03c954deeaab89e2939 | R | 9,313 | 214 | context("geom_text_repel_just")
my.cars <- mtcars[c(TRUE, FALSE, FALSE, FALSE), ]
my.cars$car.names <- rownames(my.cars)
p <- ggplot(my.cars, aes(wt, mpg, label = car.names)) +
geom_point(colour = "red") +
expand_limits(x = c(1, 7), y = c(12, 24))
test_that("center with rotation", {
vdiffr::expect_doppelganger... |
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