sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
ac338bd9df339777f57ce4fc5ac637f720ab12d123a16d597f457ee82160be3e | R | 28,234 | 849 | ---
title: "Folate_ConBP_Females"
output: html_document
date: "2024-01-12"
---
Load RNA-sequencing data, Consensus between Brain and Placenta for males
```{r}
library(WGCNA)
library(readxl)
#only use samples that have
#No PCB ; No Folic Acid (0 / No)
#No PCB ; Yes Folic Acid (0 / Yes)
#Yes PCB ; No Folic Acid (0... |
1ed13f2caef8377d33614ce958295c9f72d7bcf6083018a55fd40feb8bbc20f1 | R | 28,278 | 920 | ---
title: "S5: behavioural, DDM-based analysis with ggdmc"
author: "I. S. Plank"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
number_sections: true
fontsize: 10pt
#geometry: margin=0.5in
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = T, warning = F, message = F, fig.align = 'cen... |
14a19f08519c51ca153307a9caa558a48ad9f40029d37b71d590437ed55017a2 | R | 28,288 | 822 | ---
title: "Male_ConsensusBrainPlacenta_PCBonly"
output: html_document
date: "2023-09-28"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
#Conserved modules between male brains and placenta
```{r}
# Display the current working directory
getwd();
# If necessary, change the path below to the dir... |
b29daf6d82d25d2195b47e098403530e568d34e6d47bbbd7fd63e0bf1770e498 | R | 28,292 | 864 |
#This code currently gets the log2 TMM expression of GDF15, then centers and scales it, and then compared the pc2 score (which also gets centerd and scaled) for each tissue
rm(list = ls())
#
# BiocManager::install("ComplexHeatmap")
# BiocManager::install("edgeR")
library(tidyverse)
library(edgeR)
library(corrr)
libr... |
1e73a630267842275bbded7a59a3e36063165882689713d931010f8a300900de | R | 28,329 | 689 | #####################################################################
# Helper Functions for Training Pipeline (Basic Elastic Net)
#####################################################################
# Helper function to get file paths
get_file_path = function(file_type, brain_region=NULL, chr=NULL, CONFIG) {
... |
a933d162af1036f7dba5f46b34c0d403851010908cc668405683f4d75b8b50b5 | R | 28,409 | 533 | # MODEL FITTING: whole-brain co-fluc amplitude DEPENDENT CHANGES IN regional co-fluctuation score.
library(dplyr)
library(R.matlab)
library(ggsegGlasser)
library(ggsegSchaefer)
library(ggseg)
library(ggplot2)
library(ggdensity)
library(ggseg3d)
library(cifti)
library(stringr)
library(factoextra)
library(matrixStats)
li... |
7b6441b869fbde0f73c705cbc14255d27c5296cbeb38eb09fff34de996e77ae1 | R | 28,433 | 894 | ---
title: "S4: behavioural, HGF-based analysis with brms"
author: "I. S. Plank"
date: "`r Sys.Date()`"
output:
pdf_document:
toc: true
number_sections: true
fontsize: 10pt
geometry: margin=0.5in
---
```{r settings, include=FALSE}
knitr::opts_chunk$set(echo = T, warning = F, message = F, fig.align = 'cen... |
1cd2252ce53083eea3ba949a6fe84c522ebbe769ce10ec3db33bb15335e2d527 | R | 28,469 | 741 | getuw_fl <- function(ds,
lambda1s = NULL,
lambda2s = NULL,
nlambda1 = 10,
nlambda2 = 10,
max.iter = 500,
iseed = NULL,
silent = FALSE) {
# dim
n2 <- nrow(ds)
p <- ncol... |
bac9e731784d582fe2512acdd34d75d17288abf613fba69e3d59977a64b10303 | R | 28,480 | 962 | root="C:/Users/Jun/Documents/R/AA_wSignal/"
require(readxl)
require("changepoint")
require("lubridate")
require("colorRamps")
freeze_rule=1 #length in seconds for a bout of immobility to trigger freezing
itilength=45 #in seconds
zbase=2 #in seconds
max_shock_time=15
arena=c(21,47,335,178) #xleft, ybot... |
116b45d389c244968d19b428e96792bfbbc60b3d54e4f7c94637affe8d551119 | R | 28,542 | 573 | ####*兴奋性神经元分析---------------------------
####****兴奋性神经元分亚型---------
####*****加载颜色-------
cols <- c(rgb(196,165,222,150,maxColorValue = 255),#L23
rgb(129,184,223,150,maxColorValue = 255),#456
rgb(246,202,229,150,maxColorValue = 255)) #L6
####*****提取数据--------
Excitatory <- readRDS(file = "./temporal... |
5a49c66c465b936a05b8092e7911737bc54f9844ff89c0493b36700a1fbef382 | R | 28,559 | 580 | #' @title perform_muscat_de_analysis
#'
#' @description \code{perform_muscat_de_analysis} Perform differential expression analysis via Muscat - Pseudobulking approach - FOR ONE CELLTYPE.
#' @usage perform_muscat_de_analysis(sce, sample_id, celltype_id, group_id, batches, covariates, contrasts, expressed_df, assay_oi_pb... |
fb1966b0109b06ccb355efc21fb504c4a4cd005da88c58ae4f0668898dab7487 | R | 28,664 | 747 | ##########################################################################################
# Doublet Identification Methods
##########################################################################################
#' Add Doublet Scores to a collection of ArrowFiles or an ArchRProject
#'
#' For each sample in the Arr... |
c310c52dc5880314fd3f7b7e5abf1e3f375a8bf253942eb25cb65c5b267c8117 | R | 28,724 | 544 | ########################################
### Calculate rootpoints for neurons ###
########################################
source("banc/banc-startup.R")
library(R.utils)
message("##### Working out BANC roots #####")
numCores <- 1
# Direct us to the BANC dataset
bancr::choose_banc()
bc <- banctable_query("SELECT _id, s... |
9e64fd493d52e2bd47d4c33c68a8d9176f7f187adcf30e687b25d6ebb988ab07 | R | 28,929 | 955 | ---
title: "Expt 7 Ions and protein young mice"
output: html_document
date: "2025-09-19"
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(ggpubr)
library(ggplot2)
library(dplyr)
library(rstatix)
setwd("~/Library/Mobile Docume... |
f91475bfb6e2e27f9580ee9a2e3b34f035ebfa0005c9e2707e26258839dcd735 | R | 28,939 | 623 | ####*胶质细胞富集功能总结-------------
###***小胶质细胞每个亚群在海马体前后端差异基因--------
Microglial <- readRDS(file = "./Hippo\\subcluster\\Microglial\\anno\\Microglial_sub.rds")
celltype <- names(table(Microglial$Anno_Idents))
diff_micro_all <- data.frame(matrix(ncol = 5, nrow = 0))
for (i in 1:length(celltype)) {
data <- subset(Microglial,... |
ae1982225e1345bceb0bb9a14cab0010e980861795171b03c3231d0598e25be9 | R | 28,963 | 816 | library(Seurat)
library(SeuratObject)
library(dplyr)
library(Matrix)
library(ggplot2)
library(patchwork)
# Loading datasets
WT_data <- Read10X(data.dir = "WT")
WT <- CreateSeuratObject(counts = WT_data, project = "WT")
PINK1_data <- Read10X(data.dir = "PINK1")
PINK1 <- CreateSeuratObject(counts = PINK1_data, project ... |
75e262378c11458aa03052957a654d29a2a906b76a1caa4fbc4c71a7e610a11e | R | 28,991 | 762 | ---
title: "iDOR_transcriptome"
author: "Iza-Clarisse"
date: "10-02-25"
---
This file contains **your original code** with inline comments only. I did **not** change variable names, logic, or function calls. Any potential pitfalls are flagged with `# NOTE:` comments, but the code itself remains exactly as you wrote it... |
26bb4720e747fd306a5da01ee7dd24786b73395fe9befca8d4ca72d1963d1901 | R | 29,023 | 672 |
# These functions are used for visualization
#' Plot the QC metrics of an individual biological sample
#'
#' @param data_list A list of Seurat objects.
#' @param metrics A string - the name of the QC metrics.
#' @param group_by A string - the grouping variable in the metadata.
#' @param plot_type A string - the typ... |
1d35e67868b9162940b631a80ff1e6c5d5e13a57459bd7310fb9ca0fe0fb2abe | R | 29,083 | 697 | required_packages <- c(
"dplyr",
"data.table",
"stringi",
"stringr",
"ggplot2",
"ggpubr",
"CMplot",
"org.Hs.eg.db",
"grid",
"VennDiagram",
"GenomicRanges",
"Gviz"
)
load_required_packages <- function(packages) {
missing_packages <- packages[!vapply(packages, requireNamespace, logical(1), quie... |
24ceea107c913242696af307249b9ec3a1555fbff2ca0b0d73c91bd6f921ad70 | R | 29,106 | 636 | #' @importFrom dplyr %>%
#' @importFrom ComplexHeatmap ht_opt
#' @importFrom ComplexHeatmap Heatmap
#' @importFrom ComplexHeatmap HeatmapAnnotation
#' @importFrom rlang .data
#' @importFrom stats setNames
NULL
## exporting to inherit parameters below, plotEmbeddings()
#' @export
sccore::embeddingPlot
#' Get a vector ... |
ffd42d20b449628bbe10c43a88a2f9c2a9974a0169b60d745f5a2fbdbd5f8be7 | R | 29,123 | 551 | ###亚型分析-------------
###*兴奋性神经元----------
scRNA <- readRDS(file = "./scRNA_anno_cluster.rds")
metadata <- scRNA@meta.data
##meta.data添加信息
scRNA@meta.data$color[metadata$Anno_Idents %in% "Oligodendrocyte"] <- "#CCCCFF"
scRNA@meta.data$color[metadata$Anno_Idents %in% "Oligodendrocyte progenitor cell"] <- "#FFCC00"
scRNA@... |
2bb33b60ee185437694ce3200c4f339c9564486d84080dee77caaa0b884637a4 | R | 29,239 | 614 | #' Visualize 5 or 3-primes on a genome
#'
#' @export
plot_transcripts <-
function(visfrom, visto, plot.data, gene.plotdata, geom = geom_bar(), geom2 = NULL,
sum.counts.in.window=T, bin_width = 200, sum.fun='sum', add.all.pos=F, y.thresh=0, minus.strand.down=T,
genome=NA, add.genome.plot=T, flip... |
691f54e7017b9e2e0270c2d925e4c6f346ce1bbfb909ff890a4b5518134b3730 | R | 29,394 | 668 | library(data.table)
library(openxlsx)
library(protr)
source("R/combined_ml.R")
source("R/score_funs.R")
source("R/ml_predict.R")
#model driver, save plots to file
mai_antib_model_plotter = function(plot.type = "scatter", ml = "rf", feats = "mixed"){
pdf(file = paste0("output/antib_model_", plot.type, "_", ml, "... |
c4f049d72aaaef21a14453c0bbaa80f7a10047199d46c370f8af9de1fedd20b0 | R | 29,403 | 638 | ##################################
# Examine distributions of the CARE IDH-mutant cell states across tumor grade, tumor types, and time points
# Author: Kevin Johnson
##################################
# Reproduce the following figures: Fig3a, Fig3b, Fig3c, Fig3d, EDF7a, EDF8a, EDF8b, EDF8c
library(tidyverse)
library... |
0a22e6842bbf78e10536e9ed809b5f7caa30a1d0731f49302f42296bfd25668d | R | 29,507 | 950 | ---
title: "A. Villani's microglia RNAseq"
author: "Izaskun Mallona, UZH"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
html_document:
toc: true
toc_float: true
code_folding: hide
code_download: true
number_sections: true
df_print: kable
theme: lumen
params:
seed: 665
---
```{r w... |
a7b490ccfb7047a13ae8055946e3b69295fc56ac3ae46b7f00daeebdbebcf538 | R | 29,761 | 807 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
######################
### load libraries ###
###... |
7f5e3166cc96199bc111275ba261937de0d53d4082ba862141758202b51b0661 | R | 29,770 | 569 | context("test MONSTER result")
test_that("MONSTER function works", {
system('curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/unittest_datasets/testDatasetMonster.RData')
load("./testDatasetMonster.RData")
data("yeast")
design <- c(rep(0,20),rep(NA,10),rep(1,20))
yeast$exp.cc[is.na(yeast$exp.cc)]... |
142f90749e492db1e5e1d5ae510041bae61d42803193a9053e51ed77f48fe54b | R | 29,779 | 950 | rm(list = ls())
library(dplyr)
library(plyr)
library(Seurat)
library(ggpubr)
library(reshape2)
library(tidyr)
library(tidyverse)
library(DropletQC)
source('~/SCRIPTS/utility_functions.R')
# Set directories
path_cr = '~/project/01_CELLRANGER_OUT/BAT_OUT/'
path_cb = '~/project/03_MATRIX_FROM_CELLBENDER/BAT_OUR/'
samps =... |
8b277d371c45f851f6dbaade2486664ae3980189378f43614a428c2010dac653 | R | 29,792 | 752 | getuw_ssl <- function(ds,
penalty = c("adaptive", "separable"),
lambda0s = NULL,
nlambda0 = 100,
lambda1 = NULL,
theta = 0.5,
a = 1,
b,
eps = 0.... |
b1919096cad50dc6ab8a77951f050abdbb688324752b7c60f19616809906fcba | R | 29,931 | 889 |
#This code currently gets the log2 TMM expression of GDF15, then centers and scales it, and then compared the pc2 score (which also gets centerd and scaled) for each tissue
rm(list = ls())
#
# BiocManager::install("ComplexHeatmap")
# BiocManager::install("edgeR")
library(tidyverse)
library(edgeR)
library(corrr)
libr... |
05b0ccaffe16f4a12b2fda3d495766cf661fae507a16d1c5562f184b65034262 | R | 29,941 | 768 | ###############################################################################
## Critical Period Trajectories: GSVA-Based Analysis ##
## D35 -> D65 Maturation Dynamics with Proper Pathway Enrichment Scores ##
##############################################################################... |
31fc113c3393771f1c084d73be417e488e7d4e81d48015c17a4106fd0cad5a48 | R | 29,954 | 681 |
# R411
.libPaths("/vast/projects/rdavuluri-group/psurana/conda_environments/r451/lib/R/library")
# Load required libraries
pacman::p_load(data.table, dplyr, biomaRt, GenomicRanges, Biostrings, BSgenome,
BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm39, caret)
##------------------------------... |
eaa9b05ad8c3c8f1ab0de0e083b98e102d896f3098a3ac4b58c6a3dc36691a1d | R | 30,181 | 854 | ## Validate the identified Enhancers
library(ggplot2)
library(ArchR)
addArchRThreads(threads = 10)
projdir <- "projrmSubset_subPeak"
dt.Enhancer.majorGroupExist <- readRDS(file.path(getwd(), "outdir_projrmSubset_subPeak/CRE_projrmSubset_subPeak","subC.enhancer_majorGroupExist.rds"))
#!!!!!!!!!!!
dt.exist <- dt.Enha... |
1c90414528950a9315bc2c5817b3a914c28526d2ea9d674876b3379ae95548ea | R | 30,193 | 825 | # KOLF2.1J-iTF paper
#cCRE identification
#Figure2 chromatin conformation
# open R
srun -n 10 -p interactive --pty /bin/bash
module load cluster/gsl/1.15
module load cluster/htslib/1.9-devel
module load cluster/build_tools/llvm/8.0
module load cluster/util/gcc/8.2
module load hdf5_18/1.8.21
conda activate r_env
#co... |
0387e98536ebfda59cb66b1bd890f0ce85c5f64694fad4599f4b74364254a6fe | R | 30,556 | 714 | library("Biobase")
library("preprocessCore")
library("locfit")
library("blme")
library("reshape")
# Contains the empty() function
library("plyr")
library("genefilter")
library("ggplot2")
library("MASS")
# Cut out 3 legacy parameters
simemIsogenic = function(screens,
covariate=NULL,
... |
ae19e5264183bbbc3244e7641cd77062f35a89ee60b6cb0f07d2aa81d6a8156f | R | 30,580 | 893 | ---
title: "ConsensusPlacentaComparisonPCBOnly"
output: html_document
date: "2023-08-30"
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
# Display the current working directory
getwd();
# If necessary, change the path below to the directory w... |
b0c47f4c335a888e2992455209153d3759e9ba623d6e7d3cc7aea5908663af48 | R | 30,671 | 618 | #' @importFrom rlang .data
#' @importFrom utils packageVersion
NULL
#' @keywords internal
extendMatrix <- function(mtx, col.names) {
new.names <- setdiff(col.names, colnames(mtx))
ext.mtx <- matrix(0, nrow=nrow(mtx), ncol=length(new.names))
colnames(ext.mtx) <- new.names
return(cbind(mtx, ext.mtx)[,col.names])... |
d5576861b2684cd56c8bf3f1f2b5d332f69df36685052c9ac8d0f7e63194c770 | R | 30,686 | 666 | ---
title: "Figure 6"
output: html_document
date: "2025-10-16"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library(rstatix)
library(dplyr)
library(ggplot2)
library(car)
library(GO.db)
library(Seurat)
library(scCustomize)
library(circlize)
library(ggplot2)
library(readr)
library(clust... |
cc041005fade38f14352ff3b5cfeb0f1955a59462fa300bd9224596eac87e56b | R | 30,692 | 784 | # author: Gavin Ha
# Dana-Farber Cancer Institute
# Broad Institute
# contact: <gavinha@gmail.com> or <gavinha@broadinstitute.org>
# date: March 17, 2017
#### EM (FWD-BACK) Algorithm ####
runEMclonalCN <- function(data, params,
#gParams = NULL, nParams = NULL, pParams = NULL, sParam... |
33841a823238d3171ec47ad69bdd38d249c46a7f474616d264e25f7333d6f519 | R | 30,715 | 585 | #' Run single variant or gene- or region-based score tests with SPA based on the linear/logistic mixed model.
#'
#' @param bgenFile character. Path to bgen file. Currently version 1.2 with 8 bit compression is supported
#' @param bgenFileIndex character. Path to the .bgi file (index of the bgen file)
#' @param sampleFi... |
829db97748f9795d24a7927685080fc03b48ef021e5bdd29f462c3d467b5dd80 | R | 30,725 | 638 | ---
title: "Using spatialLIBD with 10x Genomics public datasets"
author:
- name: Abby Spangler
affiliation:
- &libd Lieber Institute for Brain Development, Johns Hopkins Medical Campus
email: aspangle@gmail.com
- name: Brenda Pardo
affiliation:
- *libd
- &lcg Licenciatura de Ciencias Genómi... |
d8b67f0f9062c5874214f9db894343cde4ae3a5910cc1aac4ba86b143e0d32b1 | R | 30,734 | 762 | # ==============================================================================
# Script 23: Figure 5 - RNAi Differential Dependencies (ILC vs NST)
# ==============================================================================
# Description: Performs gene dependency analysis comparing ILC vs NST cell lines
# ... |
89365cd8a9a9484e873963c75cbd37001aa6cec365d63e7f08b54f0129e8d88f | R | 30,741 | 684 | # ITS Tau correlation with the oscillatory and rhythmic alpha variables ---------------------------
## Load the required packages -------------------------------------------------
required_packages <- c(
"tidyverse", # Load this first because it includes ggplot2 and dplyr
"ggpubr",
"psych",
"reshape2",
"flex... |
c4bb67ab95bc034955594a877943b7cb9304ec0d3c23f9d5d934a3808fbfd48e | R | 30,757 | 728 | #' Information Criteria and Log Marginal Likelihood
#'
#' Returns the BPIC/DIC and optionally marginal deviance (-2 x log marginal likelihood) for a list of samples objects.
#'
#' Computes DIC and BPIC using a deviance based on either (a) the data likelihood
#' only ("conditional", default) or (b) the joint likelihood ... |
1ab7087ca6077f6b6e753e053e3babd55d3fc4489f1f43f0d40319278c7921bd | R | 30,768 | 783 | library(gplots)
enrichment_plot <- function(rankings, components, bin=0.01,
title = '',xtitle = '', ytitle = ''){
rank_order <- abs(rankings[order(abs(rankings), decreasing = TRUE)])
thresh <- seq(from = 0, to = max(rank_order), by = bin)
y <- c()
for (i in thresh){
... |
4d4ee266386827f76fea11b7dad00c3d50f9fe0924870c477a700d7f19c0e852 | R | 30,835 | 1,000 | # ===============================================================
# Visualization Module - PCA (Biplot)
# ===============================================================
# Helper to detect categorical columns ----------------------------------------
.is_categorical <- function(x) {
is.factor(x) || is.character(x) ||... |
a66a520a88ed375f983c801174ff64ca10c75e7cf0601f42bd08a3bc344b4c7d | R | 30,907 | 628 | ---
title: "acesinabcd_acesproxy"
author: "FB"
date: "2024-03-11"
output:
html_document: default
word_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## ACEs proxy scores for ABCD data
Running this code will create an ACEs proxy score for Baseline, Yr1, Yr2, and Yr3.
Need... |
7309b0c38918b46937af7202e09f357d06fa05e1b630357f3b04925c59a70b6e | R | 30,912 | 540 | ##################################
# 10x snRNAseq analysis of preprocessing approaches for CARE-IDH-mutant oligodendrogliomas
# Author: Kevin Johnson
# Date Updated: 2026.03.18
##################################
library(tidyverse)
library(RColorBrewer)
library(viridis)
library(Seurat)
library(harmony)
library(Matrix)
... |
181a19d3343c2da0831273c7e6a78a2505a5c207a4ff3f04c0f78ff81926f5d4 | R | 31,017 | 505 | #' Prune modules
#'
#' Prunes the initial modules by keeping only the best targets of each transcriptional regulator.
#'
#' 3 methods are implemented to choose the best targets:
#' \itemize{
#' \item{topN: Takes a fixed number of targets per regulator with the highest regulator-target adjacencies (for details see \cod... |
85bdf884c63abbd5f831dd73118beca1941907fb30ba558a813a144e5aeacf0c | R | 31,069 | 664 | #' banc-cell-info — Compile and publish BANC cell-info reference tables to GCS.
#'
#' Brain hemilineage table, NT / neuropeptide controlled vocabularies, plus
#' other annotation reference CSVs.
#'
#' @section Reads:
#' - FAFB `flytable_query` (hemilineage source)
#' - SeaTable `banc_meta`
#'
#' @section Writes:
#'... |
94666452b3811a53148eda6788b96ccd6f3e8750ffa06ad054b99a44fb241856 | R | 31,110 | 626 | ##*星形胶质细胞---------
scRNA <- readRDS(file = "./Hippo\\scRNA_anno_cluster.rds")
table(scRNA$Anno_Idents)
Astrocyte <- subset(scRNA,Anno_Idents %in% "Astrocyte")
table(Astrocyte$group)
metadata <- Astrocyte@meta.data
dim(metadata)
metadata <- metadata[,-c(6:18,20)]
Astrocyte <- CreateSeuratObject(counts = Astrocyte@assays... |
10aa0787bb9b0b92af2b09309c4e5d1d31e6f97a965f9cc651dc8b2e048148b5 | R | 31,124 | 647 | #!/usr/bin/env Rscript
# ============================================================================ #
## 02_mgngtus_plot.R
## MGNG-TUS study: plot behaviour (responses, repetitions, RTs) as bar and line plots.
## Copyright (C) Johannes Algermissen, University of Oxford, Oxford, UK, 2024-2025.
rm(list = ls())
# ====... |
3707058fc37926d6ba59858da2ed2662c7458496e0b4c688ac10d2751ad5d300 | R | 31,241 | 1,009 | library(readxl)
# library(preprocessCore)
# tximport tools
salmon_process <- function(dir = getwd(), samples_file = "samples.xlsx", tx_to_gene_file, DGE = FALSE, output = TRUE,
output_data_file = "summarized.txt", output_session_file = "sessionInfo.txt",
s... |
37c1c00e0b794b024c0a59e1115496c3367565459e4bcf390b2af58b055435a2 | R | 31,246 | 827 | # load packages
library(patchwork)
library(Seurat)
library(BPCells)
library(rhdf5)
library(DropletUtils)
library(DropletQC)
library(dplyr)
library(Matrix)
library(ggplot2)
library(plyr)
library(tidyverse)
library(tidyr)
library(ggpubr)
library(reshape2)
library(rio)
library(data.table)
library(harmony)
library(DESeq2)
... |
8344d1a7e60176472b46a332fcd0c0c59b4b4528c3ea4ca05f4f4075b3a09d55 | R | 31,294 | 697 | #' banc-update-celltypes — Derive cell types from cross-dataset matches and push to SeaTable.
#'
#' For each matched dataset (FAFB, MANC, maleCNS, hemibrain, FANC), look
#' up the matched neuron's cell_type and set the dataset-specific column.
#' Also sets `cell_type` itself per the cascade (FAFB > MANC > hemibrain > F... |
f905eb1973a69cd604f4f3ca07bc538c474b25ce028544ecafd3bdf60a622f34 | R | 31,320 | 549 | ##################################
# 10x snRNAseq analysis of preprocessing approaches for CARE-IDH-mutant astrocytomas
# Author: Kevin Johnson
# Date Updated: 2026.03.17
##################################
library(tidyverse)
library(RColorBrewer)
library(viridis)
library(Seurat)
library(harmony)
library(Matrix)
librar... |
3ffc67491010774973142ec5f19ea43182977bf7b5c1b9f07d1fd0ff590d251d | R | 31,453 | 1,077 | ---
title: "ConsensusBrainEnrichRComparisons"
output: html_document
date: "2023-05-22"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library(WGCNA)
library(readxl)
#took out samples that had Folic Acid
F_Brain <- read_excel("/Users/kchau/Documents/PEBBLES/RNA-seq_ComparisonBrain_PCBon... |
694cae1c6d397359bd6b9199c2a715d4fffd09c096ea24afcafb284a57a9d432 | R | 31,923 | 570 | #set working directory ====
setwd("/athena/ganlab/scratch/lif4001/DVPV/DF_2ndRound")
#install.packages
library(Seurat)
library(ggplot2)
library(DoubletFinder)
library(SoupX)
#for loading Cell Ranger counts:
all <- readRDS(file = "/athena/ganlab/scratch/lif4001/DVPV/DF_1stRound/DVPV_CTX_311_QC.rds")
#Doublet finder (no... |
34ce095e7388b9e13a3033170d609654d7af6aa34ac21f1122b56fe43bd78216 | R | 31,987 | 766 | #' banc-bm-asymmetry — Diagnose BM sensory L/R asymmetry and propose retypes.
#'
#' For all BANC sensory neurons whose cell_type starts with `BM_`, report
#' L/R count asymmetry and propose retype corrections by scoring each
#' BANC neuron's downstream cell_type connectivity profile against FAFB BM
#' type centroids (c... |
0a6e80f12fe08a9a9514fc01748d4beb724881ba1205fc038a500a2aaa868b97 | R | 32,030 | 681 | library(openxlsx)
library(seqinr)
library(data.table)
#4/10/23
#selects two hundred variants to experiment on based on various criteria
#selects n.top most dangerous specific variant pairs, looks for n.sing top most dangerous single mutations
#and selects all combinations (single or paired) without dupes, for each var... |
7cdea0d590475128d15c7f9a0cb2011e51657b2ed62d50065a9caf291bf79198 | R | 32,159 | 650 | ##################################
# Purpose: Analyze the myeloid cell abundance across relevant features such as grade and time point
# Author: Kevin Johnson
##################################
library(tidyverse)
library(ggpubr)
library(EnvStats)
proj_dir <- "/vast/palmer/pi/verhaak/kcj28/care_idh_mut/"
fig_dir... |
27e788c521efffdf42eac1dbd5fc6e1feeca024f3f7ae208e615f2cbf2d8bf9c | R | 32,185 | 736 | ##***伪时间轨迹分析------------
Excitatory_sub <- readRDS(file = "./Hippo\\subcluster\\Excitatory\\anno\\Excitatory_sub.rds")
cols<- c("#AAD0E3","#277AB4","#FABF74","#FB7D1A","#CAB4D6","#B5DF90","#3AA12F","#693C9A")
table(Excitatory_sub$group)
A_sub <- subset(Excitatory_sub,group %in% "A")
table(A_sub$Anno_Idents)
table(Excit... |
ee7f155a4ec06a9823f138405367eb762fbba8e80c813f5af689b24aefd54146 | R | 32,230 | 867 | ---
title: "S1: behavioural, conventional analysis with brms"
author: "I. S. Plank"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
number_sections: true
fontsize: 10pt
#geometry: margin=0.5in
---
```{r settings, include=FALSE}
knitr::opts_chunk$set(echo = T, warning = F, message = F, fig.align =... |
0fec081ba0edd1ca3bc338d978c87b7222228b8ee9ff835728ff0249c004b7c2 | R | 32,236 | 508 | # ----------------------------------------------------------------
# The following script loads and processes data from:
#
# Sinha, Rashmi, et al. "Fecal microbiota, fecal metabolome, and
# colorectal cancer interrelations."
# PloS one 11.3 (2016): e0152126.
#
# The output tables are:
# 1) metadata - subject meta... |
ba3c08a44fa87cb5e9287db52c2aa9c19572d0c6870a2adcbe95ab11d896670b | R | 32,261 | 901 | library(dplyr)
library(factoextra)
library(tidyr)
library(cluster)
library(ggrepel)
library(ggplot2)
library(tidyverse)
library(nlme)
library(mgcv)
require(lme4)
library(purrr)
require("emmeans")
library(broom.mixed)
library(lmerTest)
library(easystats)
setwd("")
data <- read.csv("PsychENCODE-W5-bulk-RPKM-data-scRNA-... |
e6c3b7258e76957810b2a817d10df220137e5c18d9452b9db56ebfe0ffef9813 | R | 32,306 | 797 | # author: Gavin Ha
# Dana-Farber Cancer Institute
# Broad Institute
# contact: <gavinha@gmail.com> or <gavinha@broadinstitute.org>
# date: July 26, 2018
# data is the output format of TITAN cytoBand = {T,
# F} alphaVal = [0,1] geneAnnot is a dataframe with
# 4 columns: geneSymbol, chr, start, stop spacing
# i... |
2e44421257314bf285555eeef85f316e3be48f37440c00048b32bdb7315afd6c | R | 32,381 | 811 | ###############################################################################
## Analysis pipeline – human bulk RNA-seq (refactored 2025-05-19) ##
###############################################################################
## Key improvements ----------------------------------------------------------
#... |
26088bbbbc4651b4cde9247ce0b9e813ab433dda184af97f6378bc1fb4cc3590 | R | 32,573 | 693 | library(ggplot2)
library(Seurat)
library(tibble)
library(SCENIC)
library(SCopeLoomR)
library(dplyr)
library(scales)
library(stringr)
library(VennDiagram)
cherry_proportion_plot <- function(seurat_obj, mcherry, subtype, cell_line="all") {
if (cell_line == "all") {
a <- as.data.frame(table(seurat_obj@meta.data[,mc... |
b01f45f7891ac1ad2bec56dbb827231e249d7a23ee78487d19a0877ad4bbbec4 | R | 32,698 | 1,233 | #feature selection#Lasso
rm(list=ls())
install.packages("glmnet")
library(glmnet)
setwd("your_pathway")
data1 <- read.csv("youdata.csv",row.names = 1,header = TRUE)
dim(data1)
head(data1)
names(data1)
x <- as.matrix(data1[,1:50])
y <- as.numeric(data1[,51])
set.seed(99)
alpha1_fit <- glmnet(x,y,... |
ae19e6f56f9d43b0d90579df6dabf2a523fc16dd26828db95003114e8a32fa0d | R | 32,850 | 814 | pmwgs <- function(dadm, type, pars = NULL, prior = NULL,
nuisance = NULL, nuisance_non_hyper = NULL, ...) {
if(is.data.frame(dadm)) dadm <- list(dadm)
if(is.null(pars)) pars <- names(sampled_pars(attr(dadm[[1]], "prior")))
if(is.null(prior)) prior <- attr(dadm[[1]], "prior")
dadm <- extractDad... |
f27b991dca2a026fe1a53b653db99f94eec5480990be56c089193c8c0d05f8d8 | R | 32,886 | 928 | # open R
srun -n 10 -p interactive --pty /bin/bash
module load cluster/gsl/1.15
module load cluster/htslib/1.9-devel
module load cluster/build_tools/llvm/8.0
module load cluster/util/gcc/8.2
module load hdf5_18/1.8.21
conda activate r_env
#conda deactivate
R
# Load required libraries
library(csaw)
library(edgeR)
l... |
ba26dc2f719bcad7eedf632b39b43ef298b8e4eb705ed7ec7a4a1c13097fed8d | R | 32,978 | 979 | ---
title: "S4: behavioural, HGF-based analysis with brms"
author: "I. S. Plank"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
number_sections: true
fontsize: 10pt
#geometry: margin=0.5in
---
```{r settings, include=FALSE}
knitr::opts_chunk$set(echo = T, warning = F, message = F, fig.align = 'c... |
45d2f37b56043a63b438fca36aca63d289f9e4cc51f66ee30243727388f684ed | R | 32,988 | 1,032 | #' @title Manhattan plot
#'
#' @description `fig_manhattan` creates a Manhattan plot for genomic markers
#' from across the genome, e.g. results from genome-wide association studies.
#'
#' @details This plotting function plots a Manhattan plot for genomic markers
#' from across the genome. The default is to truncat... |
ae0c7285fc2423f80341f2cd225833e43e04805f0c1c6630c58fc6c279e5543b | R | 33,151 | 926 | ---
title: "Supplementary materials"
subtitle: 'Predicting emotional valence in autism: a preregistered study in the Bayesian Brain framework'
author: "Plank et al."
date: "`r Sys.Date()`"
output:
pdf_document:
toc: false
number_sections: true
latex_engine: xelatex
extra_dependencies: ["float"]
fonts... |
e345426d90224542de682bc796e36119b0aae723e8e8143f44084f57ec6c686c | R | 33,187 | 454 | # unit-tests for UNAGI
context("test UNAGI functions")
test_that("[UNAGI] BreadthFirstSearchU() function yields expected results", {
# Construct a starting network, which will be modified.
# Here, we expect genes A and B to be connected after 1 hop via TF2, genes A and D
# to be connected after 1 hop via TF3, ... |
48307948939f13d0f430b672ba8f896516a4191b5d3ecb03b3461d87a5c31dc7 | R | 33,234 | 1,003 | ---
title: "Folate_ConMF_Placentas"
output: html_document
date: "2023-12-19"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Load RNA-sequencing data
```{r}
library(WGCNA)
library(readxl)
#only use samples that have
#No PCB ; No Folic Acid (0 / No)
#No PCB ; Yes Folic Acid (0 / Yes)
#Yes PCB ... |
d8f703bf6fe8c07f5b7007a0f5df4a2ddca067e2a250d1afa51ad98a39de7b85 | R | 33,246 | 664 | #############################################################################################
# SCRIPT TO GENERATE A HEATMAP WITH THE TOP ENRICHED TERMS (BASED ON P-VALUE) FOR EACH VIEW #
#############################################################################################
library(MOFA2)
library(reshape2)
... |
bf0358841b16699790a153ea50a31ddad5f365045d925d02e3fd0b8acbc5580b | R | 33,287 | 717 | library(Seurat)
library(ggplot2)
library(scCustomize)
library(patchwork)
library(RColorBrewer)
library(viridis)
library(dplyr)
library(stringr)
library(pheatmap)
# load data ---------------------------------------------------------------
data <- readRDS("diet_nodomap_metadata_updated_2510.rds")
DefaultAssay(data) = "o... |
2a97314983d668fda0e2379287856050ecc2b6cdcd34e220c147e6ccdf9b7b5f | R | 33,419 | 821 | #####################################################################
# Helper Functions for Training Pipeline
#####################################################################
# Helper function to get file paths
get_file_path = function(file_type, brain_region=NULL, chr=NULL, CONFIG) {
pattern = CONFIG$fi... |
5edd924b6f5279c768919a373a588b1f41c56fa71ed78df821b844c0314f7d10 | R | 33,438 | 821 | #####################################################################
# Helper Functions for Training Pipeline
#####################################################################
# Helper function to get file paths
get_file_path = function(file_type, brain_region=NULL, chr=NULL, CONFIG) {
pattern = CONFIG$fi... |
514cdb25b55dcc55657cbe057800d49050f7136be2603a8bc8c94392722d4326 | R | 33,457 | 827 | # Read Group file and split variants by functional annotations
read_groupfile <- function(groupfile_name, gene_name) {
lines <- readLines(groupfile_name)
# Pre-filter to only the gene's lines via fixed-string startsWith
# (regex strsplit on every line is the dominant cost for large group files)
prefix_... |
c866676e4e42ef701cd9008477a5e3e4897fc64081e48fa5730ffb94d9947463 | R | 33,603 | 1,006 | ---
title: "Supplementary materials"
#subtitle: 'Predicting emotional valence in autism: a preregistered study in the Bayesian Brain framework'
author: "Plank et al."
date: "`r Sys.Date()`"
output:
pdf_document:
toc: false
number_sections: true
latex_engine: xelatex
fontsize: 10pt
geometry: margin=0.5in
... |
38cf241285b06f9f2bfbd89a28b93b93bcc98cc71ddc69c7a8e28917b7852da0 | R | 33,641 | 749 | #' banc-alignment-plots — Generate publication-quality validation plots for the optic-lobe alignment.
#'
#' Faceted figures comparing BANC alignment assignments to FAFB ground truth
#' for the optic lobe. Falls back to per-side outputs if no bilateral
#' `banc_optic_both_alignment_optic_lobe_full.csv` is found.
#'
#' @... |
796803aea27417d7315198ce49bd9f4d3e7f4dda3acbd433efd7c5109cebe38e | R | 33,656 | 825 | #' Add Group Coverages to an ArchRProject object
#'
#' This function will merge cells within each designated cell group for the generation of pseudo-bulk replicates and then
#' merge these replicates into a single insertion coverage file.
#'
#' @param ArchRProj An `ArchRProject` object.
#' @param groupBy The name of t... |
db555bd9ab7ba4dbee1a30e790d51dea2d94bf77de7a72c0c2a9c675e3bd4586 | R | 33,783 | 712 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
######################
### load libraries ###
###... |
a7f670c2c386c52b1771f0d65cc93f2684cfc724b987106d7b1acd6e730db141 | R | 33,889 | 776 | context("MultiNicheNet pipeline")
organism = "human"
if(organism == "human"){
options(timeout=1000)
lr_network = readRDS(url("https://zenodo.org/record/5884439/files/lr_network_human_21122021.rds"))
lr_network = lr_network %>% dplyr::rename(ligand = from, receptor = to) %>% distinct(ligand, receptor) %>% filter(l... |
e0890a11d6328c646da7ed3e915a7cd57a2b049748dd655c53857a2a27684f1d | R | 33,892 | 722 | ---
title: "A defined community of core gut microbiota members promotes cognitive performance in honey bees"
author: "Amelie Cabirol, CNRS-University of Toulouse"
date: "11/02/2022"
output:
html_document:
toc: yes
toc_float:
collapsed: no
top_depth: 3
pdf_document:
toc: yes
---
... |
15175205ce57a45c0c32f3e33619d34c54bbd14ab11aa2facd1f44520dbe4140 | R | 33,900 | 910 | ---
title: "A defined community of core gut microbiota members promotes cognitive performance in honey bees"
author: "Andrew Quinn (University of Lausanne) and Amelie Cabirol (CNRS-University of Toulouse)"
date: "04/12/2025"
output: html_document
keep_tex: yes
always_allow_html: true
---
# Introduction
T... |
4892dac8acf8ecd017d6b11e3b5bbe62615ae9288a9b1ec90da73d6c54fe4d73 | R | 34,227 | 1,220 |
local({
# the requested version of renv
version <- "1.0.7"
attr(version, "sha") <- NULL
# the project directory
project <- Sys.getenv("RENV_PROJECT")
if (!nzchar(project))
project <- getwd()
# use start-up diagnostics if enabled
diagnostics <- Sys.getenv("RENV_STARTUP_DIAGNOSTICS", unset = "FALS... |
2212186db11625759b4b2d7bf91cf38b00b61926b9384b68756b870e600e56aa | R | 34,414 | 874 | #####################################################################
# INGENE Training Helper Functions
#####################################################################
# This file contains helper functions for the INGENE training pipeline.
# Key components:
# - Cross-validation and model evaluation function... |
8d263773651d43001853a567e320c5a5ba2b7c1e729d7d6400701f292ce5a140 | R | 34,447 | 1,015 | ---
title: "Folate_ConMF_Brains"
output: html_document
date: "2023-11-30"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Load RNA-sequencing data
```{r}
library(WGCNA)
library(readxl)
#only use samples that have
#No PCB ; No Folic Acid (0 / No)
#No PCB ; Yes Folic Acid (0 / Yes)
#Yes PCB ; ... |
972074ad660fe12e8674afb20c881a6eb7bfcd31a297b7d0b2ecaf0077e031a2 | R | 34,496 | 919 | ---
title: "Tutorial 02: Neuron Morphology"
author: "Alexander Bates"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_float: true
toc_depth: 3
code_folding: show
theme: cosmo
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = TRUE,
warning = FALSE,
message = FALSE,
... |
1ad9102b722e7dd7624cad0e5de0bb98359ffb08dcd39520fe32d33acd87f1a6 | R | 34,682 | 688 | ---
title: "MultiNicheNet analysis: MIS-C pairwise comparison - wrapper function"
author: "Robin Browaeys"
package: "`r BiocStyle::pkg_ver('multinichenetr')`"
output:
BiocStyle::html_document
output_dir: "/Users/robinb/Work/multinichenetr/vignettes"
vignette: >
%\VignetteIndexEntry{MultiNicheNet analysis: MIS-C pa... |
ab51555f448df085219e88dbeb4f83a0738b0d8d6e24255c7aa85b4390506d0e | R | 34,702 | 861 | #This code currently gets the log2 TMM expression of GDF15, then centers and scales it, and then compared the pc2 score (which also gets centerd and scaled) for each tissue
# Uncommented out many of the "print" plots
rm(list = ls())
# BiocManager::install("edgeR")
library(tidyverse)
library(edgeR)
library(corrr)
li... |
444161390c3c1851b9166f362e8753861ffb5774044a3fa8db98fd147794e269 | R | 34,735 | 688 | ---
title: "Running SCENIC"
subtitle: SCENIC (Single Cell rEgulatory Network Inference and Clustering)
output:
html_document:
number_sections: no
toc: yes
toc_float: yes
css: corrected.css
results: hold
html_notebook:
toc: yes
pdf_document:
toc: yes
bibliography: references.bib
vignett... |
b934617d69226969fec1cc24c04085eec20fa8603d7e7060d3d2eb33c174bcca | R | 34,772 | 727 | #' Given a set of genes of interest, full bipartite networks with scores (one network for each sample), a significance
#' cutoff for statistical testing, and a hop constraint, BLOBFISH finds a subnetwork of
#' significant edges connecting the genes.
#' @param geneSet A character vector of genes comprising the targets o... |
ac9b4e3856f74bcb06a739c62c33df608e03df9f034c9732b0ccfcf3b54bdd29 | R | 35,099 | 1,086 | ####################################################################
# Transcription Factor Deviation Methods
####################################################################
#' Add a matrix of deviations for a given peakAnnotation to Arrow Files in ArchRProject
#'
#' This function will compute peakAnnotation dev... |
f639049a0d7120118f7721630007d746a060f409bc3e501a0f4ef9f8b14196f3 | R | 35,431 | 867 | #' banc-class-fixes — Propagate classification columns for bates-sourced neurons.
#'
#' For BANC neurons whose `cell_type_source` contains "bates", pull
#' downstream classification columns from the matched FAFB neuron in
#' `franken_meta` (primary) or the matched maleCNS neuron (fallback). Writes
#' the propagation pl... |
d54f797e9dbbed8d077fd9b74d7443c660f07202ab73b141a60b2a43ae0a9bcc | R | 35,469 | 839 | ##################################################################
# Requirements for generic data extraction function:
# - extract arbitrary subset of screens, identified by:
# cellLineId, condition, any combination of filtering criteria organized as a list (variable => vars)
#
# - optionally: add data column(s) to ... |
12ef3d26231b88a201a2c1879384cb00167f5ff21c3e830265cef81d8dff6e2f | R | 35,651 | 769 | #' @title Lesion to Symptom Mapping
#'
#' @description
#' Lesymap uses univariate and multivariate methods to map
#' functional regions of the brain that, when lesioned,
#' cause specific cognitive deficits. It requires is
#' a set of binary lesion maps (nifti files) in template
#' space and the vector of corresponding... |
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