sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
c2d2ebff731c839d5a61b16fa264149bd5514ce4b00bfc2cabfdd6183559c35e | R | 5,938 | 151 | ###############################################################################################
# Script to clean confounder effects from expression data
# This script performs:
# 1. Loads preprocessed RSE data with outliers removed
# 2. Handles duplicate samples (keeps RiboZeroGold or highest RIN PolyA samples)
#... |
d1a20b46a3d305032c7acd08ba6eeb5d5b13cb26af1e60021b394c37629e34d7 | R | 5,938 | 109 | ##################################
# Inspect the longitudinal differentially accessible genes across malignant cell states
# Author: Kevin Johnson
##################################
library(tidyverse)
library(ggpubr)
library(EnvStats)
proj_dir <- "/vast/palmer/pi/verhaak/kcj28/care_idh_mut"
fig_dir <- file.pat... |
154a4c7aa47189292ddbf7935a8b9d843c5982d1fd8c716a8f2467f55d3be033 | R | 5,946 | 133 | #' banc-community — Build the BANC ↔ MANC per-neuron community lookup.
#'
#' Joins NBLAST + manual matches for community-curated downstream analyses.
#'
#' @section Reads:
#' - SeaTable `banc_meta`, `banc_manc_v1.2.1_nblast.feather`, `manc_neuprint_meta()`
#'
#' @section Writes:
#' - `banc_manc_community.csv`
####... |
7cb130f90ac6bf3d56e2f8860506a3ef147854f31fe20a2da7e1adec769650d6 | R | 5,947 | 161 | # Outline: The purpose of this script is to compute Bayes factors for mu and beta ERS and ERD, in a selected sensor (ususally C3) and in a set of labels (i.e., ROIs)
# Import packages
library(BayesFactor)
library(reticulate)
library(ggplot2)
library(scales)
library(effsize)
library(abind)
np <- import('numpy') # This ... |
0b35db8c2ffbc2545b8d051e047c3d66ac43523ad894f340454c564d3a4aafbe | R | 5,952 | 114 | # Load necessary libraries
library(ComplexHeatmap)
library(Cairo)
library(circlize) # For colorRamp2
# Choose
Dataset_H <- 'GSE160189_186538_Integration'
Dataset_M <- 'GSE185862'
row_order <- c("ExN_H", "InN_H", "Astro_H", "Micro_H", "Oligo_H", "OPC_H", "Endo_H")
column_order <- c("ExN_M", "InN_M", "Astro_M", "Mic... |
be5272af59f8ffbb8b8121bd97589b5a40c9eec282fada69ada89618ed02ce94 | R | 5,955 | 162 | ---
title: "Linear models for ECG data"
author: "Joonatan Palmu"
date: "`r format(Sys.time(), '%d.%m.%Y')`"
editor_options:
chunk_output_type: console
---
# Init
```{r options, include = FALSE}
knitr::opts_chunk$set(include = TRUE, echo = TRUE, message = FALSE, results='asis', cache=FALSE, warning=FALSE, collapse ... |
87f01e340e04cf7ff45463c4bf437af33f2a8292390f47d219646b1bda4e2ecd | R | 5,962 | 211 | ---
title: "Diffusion Decision Model (DDM)"
description: "A step-by-step DDM workflow in EMC2"
author: "Niek Stevenson"
output: rmarkdown::html_document
bibliography: refs.bib
vignette: >
%\VignetteIndexEntry{"Diffusion Decision Model (DDM)"}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r... |
922cc8dec4951055a75b5f08a5dcb4f64c4eb5abbf3ddba25d2b3e4997fe27f9 | R | 5,977 | 114 | # Moderate effect size genes project
# Author: Madison Caballero
# Desription: Parsing a variant file and reorganizing information into families.
library(data.table)
library(stringr)
args = commandArgs(trailingOnly=TRUE)
# Arg is family (Father_Mother) id
system(paste0("rm intermediates/", args[1], "_valid_probands.t... |
e052d8cde78a3f9de515ff8a24bdec6a27d7d10a71f1ef8a8c01548d8d6c7e51 | R | 5,981 | 104 | ReadTSV = function(tsv_file) {
# We need to filter out header lines beginning with '@';
# however, the standard 'fread("grep ...")' causes issues with the default Docker container, so we use a temporary file.
# See https://github.com/broadinstitute/gatk/issues/4140.
temp_file = tempfile()
system(spr... |
b82f723002234998dd20d8070aeecb49ba0f3ca9ee6eb41feec7c589691d57bb | R | 5,982 | 191 | # Environment ------------------------------------------------------------------
library(tidyverse)
library(lme4)
library(ggseg)
library(patchwork)
library(here)
library(glue)
library(patchwork)
source(here("src", "R", "plot_ggseg_brain.R"))
source(here("src", "R", "run_lda_workflow.R"))
.winsorize_sd <- function(x, ... |
034c0af064fe2786c5f38297f5050143d23869e210bd7cf030c661377d48fc3e | R | 5,983 | 222 | library(Seurat)
library(ggplot2)
library(scCustomize)
library(patchwork)
library(RColorBrewer)
library(viridis)
library(dplyr)
library(stringr)
#Use the diet Seurat object
data <- readRDS("diet_nodomap_metadata_updated_2510.rds")
DefaultAssay(data)
data <- SetIdent(data, value = "cluster_number")
table(Idents(data))
l... |
f7b382d43a18b348de598723f9e5ba7ffe97da2baf4fda37e2b34a3b1ceb859d | R | 5,985 | 190 | # Environment ------------------------------------------------------------------
library(tidyverse)
library(lme4)
library(ggseg)
library(patchwork)
library(here)
library(glue)
library(patchwork)
source(here("src", "R", "plot_ggseg_brain.R"))
source(here("src", "R", "run_lda_workflow.R"))
.winsorize_sd <- function(x, ... |
4a3dc0d56233b6c0904b8ca1b1854ea36e77697190d1a61069fd1ade794159cb | R | 5,987 | 158 | #' Plotting debarcoded sample yields as a function of the separation threshold
#'
#' @param bc.res The debarcoding results to plot
#' @param sep.threshold If provided, adds a red line corresponding to the separation
#' threshold in use
#'
#' @return Returns a ggplot object
#' @export
plot_barcode_separation <- functio... |
dc441193b9d14520464250b682754e137bc72d37b40f2fa5c96e5b17ff051e1c | R | 5,998 | 147 | #########################################
### Calculate accurate BANC skeletons ###
#########################################
source("banc/banc-startup.R")
# Direct us to the BANC dataset
choose_segmentation("flywire31") # must do this due to bug in skeletor R code?
# Read IDs
banc.meta <- banctable_query() %>%
dpl... |
78b5ee1e3668b27fd03faa84f7d35ec7073ea2e81ee8845e5b75828b030fb39a | R | 6,015 | 141 | #########################################################
# DISPLAY JITTER PLOT WITH FEATURE WEIGHTS FOR FACTOR 8 #
#########################################################
library(MOFA2)
library(dplyr)
library(ggplot2)
library(ggrepel)
library(scales)
library(ggthemes)
library(... |
1765b399a6d58773880f0618362f515b908345c10b1765ee7c3d5166e5892cfd | R | 6,018 | 158 | #packages
library(knitr)
library(dplyr)
library(ggplot2)
library(tidyr)
library(ragg)
#Load data
chr.frq.data <- read.csv("D:/Stats.stuff/chr.freq.csv")
chromosome_order <- c(as.character(1:22), 'X', 'Y')
table1 <- table(chr.frq.data$Sig.con.vs.ad)
table2 <- table(chr.frq.data$sig.ad.vs.add10)
freque... |
9c818aa1d24c4d329d728c7bcdb9cf9d786611e06ec865dfb951cf1e1ce67dda | R | 6,025 | 127 | library(dplyr)
library(tidyr)
library(readr)
############# Mouse ############
#### Create gene coverage matrix
gtf_path_M <- "/data/resource/reference/mouse/Mus_musculus.GRCm39.112.gtf"
gtf_table_M <- read.table(gtf_path_M, header = FALSE, skip = 5, sep = "\t", quote = "", comment.char = "")
colnames(gtf_table_M) <- ... |
53fa00d15611b5aeeb4c53914bcc3e3986635afcc8250d79b927a8d7115928fb | R | 6,027 | 166 | #!/usr/bin/env Rscript
# ============================================================================ #
## mgngtus_figureS3.R
## MGNG-TUS study: Make plots for Figure S3A-C.
## Copyright (C) Johannes Algermissen, University of Oxford, Oxford, UK, 2024-2025.
rm(list = ls())
# ==========================================... |
7c30af6ae1addb78201d6232703a637d575a346b93588fd3521350ff16f5dcff | R | 6,028 | 157 | library(ggsankey)
kegg_data <- ego@result
kegg_data2 <- kegg_data %>%
separate_rows(geneID,sep = "/")
kegg_data2$logFC <- d$avg_log2FC[match(kegg_data2$geneID,d$X)]
sankey <- kegg_data2
sankey <- sankey %>%
arrange(Description) %>%
mutate(Description = factor(Description, levels = unique(Description)))
sankey2 ... |
d183e90a5bb3440180a98370010e6f5a79ee03fea946b818b22e662228c1c4eb | R | 6,034 | 132 |
#makes diag plots for all .rds files in dirname and outputs to same dir
ml_plot = function(runname, datatype){
#label names based on data type
if(datatype %in% c("bind", "bindv2", "bindv3")){
xlabs = c("Error (log10(M))", "Observed Delta(log10(Ka)) Binding")
ylabs = c("Predicted Delta(log10(Ka)) Binding... |
b8921085562661f9a1d513020da8a6ac9c5915a726a89d80f9c1c59e67aa80ea | R | 6,036 | 110 | ses_theme <- theme(axis.line = element_line(linewidth = 0, colour = "black"), strip.background = element_rect(color = "black", fill = NA, size =1)) +
theme(panel.margin=unit(.1, "lines"), panel.border = element_rect(color = "black", fill = NA, size = 1), strip.background = element_rect(color = "black", fill = NA, ... |
0387fef0e011bce8394361755c268af49f26157b81d3e951ea4bb66817641e79 | R | 6,052 | 139 |
library(VariantAnnotation)
library(tidyverse)
## Parse snakemake
if(exists("snakemake")) {
vcff = snakemake@input[["vcf"]]
ssvcff = snakemake@input[["ssvcf"]]
mfcvf = snakemake@output[["mfcov"]]
genof = snakemake@output[["geno"]]
infof = snakemake@output[["info"]]
case_barcode = snakemake@wildcards[["case... |
61166c465b2a7ee2c014fa5e4a70bc4f7c87fe2d0101845db6f19f7a335fd2ae | R | 6,052 | 169 | #####################################################################
# Brain Tissue Gene Expression Prediction Training Pipeline (Basic Elastic Net)
#####################################################################
# Required packages
required_packages = c(
"data.table", "dplyr", "glmnet", "parallel",
... |
bf36b54006e8bb8d792bcee715d281a5b00870872d05d7f6aa75a1718cad57bf | R | 6,063 | 139 | ## ============================================================================
## Alpha diversity comparison between two groups
## - Reads a wide-format diversity-index file (one column per group)
## - Performs IQR-based outlier removal (optional)
## - Tests group differences with Kruskal-Wallis + pairwise Wilcoxon (F... |
69b7b609fdcd786e1507e5b0d5962040e49575080f16e967b62d0f1b5fd77e3a | R | 6,066 | 193 | # Environment ------------------------------------------------------------------
library(tidyverse)
library(lme4)
library(ggseg)
library(patchwork)
library(here)
library(glue)
library(patchwork)
source(here("src", "R", "plot_ggseg_brain.R"))
source(here("src", "R", "run_lda_workflow.R"))
.winsorize_sd <- function(x, ... |
becaabe68bcddf88623bed16eaeb99d836dbcab50541847db1cd2ec36aae4076 | R | 6,067 | 175 | packages <- c("ape", "nlme", "ggplot2", "ggimage", "rsvg")
installed <- installed.packages()
for (pkg in packages) {
if (!pkg %in% rownames(installed)) {
install.packages(pkg)
}
}
# Load required libraries
library(ape) # For phylogenetic tree handling
library(nlme) # For gls() and correlation struct... |
e9d09f5dab2028fc75e7f28d8e76c41f337648fce160faa81c3b0215ea791642 | R | 6,076 | 148 | here::i_am("scripts/4.paper_figures_and_tables/suppl.figureS7.R")
library(tidyverse)
library(cowplot)
library(ggtree)
library(CroCoNet)
library(RColorBrewer)
library(here)
wd <- here("data/neural_differentiation_dataset/CroCoNet_analysis/")
source(here("scripts/4.paper_figures/helper_functions.R"))
fig_dir <- here("d... |
b8691d0b074e06ea00a8ffb72c6bdae41e94224f8d613829d7d8506ace5ac894 | R | 6,077 | 162 |
cover_dt_weighted <- function(dt,
weight_col = NULL,
by = c("seqnames", "strand", "sample"),
return = c("rle", "pos"),
nworkers = 1L,
# zero-fill options
... |
2aaba42c6751b074f423d44c3b7f43ff9fe972ae321fd5feba571b89cb70d522 | R | 6,081 | 218 | ---
title: "LC-MS/MS analysis RAD50"
author: "Thomas Boutet"
date: "11/14/2023"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# libraries
```{r}
library(ggplot2)
library(ggrepel)
library(dplyr)
library(magick)
library(cowplot)
library(patchwork)
library(stringr)
```
# p... |
636ee15d2002ae5b279604e9090a89c77df3d4467d14e9c6f0afc13aa7c238f5 | R | 6,081 | 170 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
######################
### load libraries ###
###... |
79a6c282d88c7878b707eaf556b0395c822fa483692f9fcf8689d625c788b763 | R | 6,082 | 154 | ---
title: "Adjustment of Alignment Strength with conos"
output:
rmarkdown::html_vignette:
vignette: >
%\VignetteIndexEntry{"Adjustment of Alignment Strength with conos"}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
This tutorial uses the same data and methods as the main [Conos Walkthrou... |
14f313acb2e7b82f36438b3856d0d7f5ad22ca032abb076764160567afea3941 | R | 6,083 | 156 |
## Figure S2 ####
tell_user('done.\nCreating Figure S2...')
resx=300
png('display_items/figure-s2.png',width=6.5*resx,height=3.0*resx,res=resx)
layout_matrix = matrix(c(1,2,2), nrow=1, byrow=T)
layout(layout_matrix)
panel = 1
### A CRL abundance ####
par(mar=c(3,4,3,1))
pep_stds = read_tsv('data/crl_pepstds.tsv', ... |
07f3dcf4a690d159dc9b3403193f16034f87729d1a0070f4af8aedcbe37dcc84 | R | 6,084 | 144 | # plot SPP1 locus: HiC and ATAC data highligthing loci of interest
setwd('/work/tdietl/collabs/Anita/')
hg38 = c('chr4', 87850650, 87993426)
macFas6 = c('chr5', 85478479, 85611097)
hic_tracks <- list(hg38=c("hic.H_IPSC.score_k100"),
macFas6=c("hic.M_IPSC.score_k100"))
hic_track_names <- list(hg3... |
8a99e754abd404c101778ee0183ec109e87305cd8fb4acabae9d0e2cbdcad335 | R | 6,085 | 209 | # Note: These codes were transferred from RVenn 1.1.0.
# The author is Turgut Yigit Akyol.
# Method for intersection =======================
#' Intersection of many sets.
#'
#' \code{overlap} returns the same elements of the sets in a \code{Venn} object.
#'
#' @param venn (Required) A \code{Venn} object.
#' @param ... |
7e3a84e1d2d5a9ce0a0c83433d66d19fad49c70b2f79aca54dc7f43dfa2d0209 | R | 6,089 | 144 | ## Source data need to be imported as "data". See README file on github for further instructions.
library("easypackages")
libraries("tidyr", "dplyr","openxlsx", "readxl")
# Start from scratch, data import and
{
data_NT <- read_excel("Supplementary File S1 (input file for sign prediction and rendomization).xlsx", sh... |
8d27898effec65b123254b5cc9ba98d071315820395a6a5673bb328a2c9bf52c | R | 6,092 | 181 | ################################################################################
###############----------- TASK SIMULATION FUNCTION -------------###############
################################################################################
## Simulates task data given a set of parameters --------------------------... |
7e92fe50f2f99becf4c6e4962987a944e7929987f223686fe7b76bd2cfd35b8b | R | 6,095 | 197 | library(shiny)
library(bslib)
library(bsicons)
library(DT)
theme <- bs_theme(
version = 5,
bg = "#070B14",
fg = "#E5E7EB",
primary = "#3B82F6",
base_font = font_google("Inter"),
heading_font = font_google("Inter"),
border_radius = "0.9rem"
)
stepper_ui <- function(active = 1) {
steps <- c("Upload", "F... |
053d6b58c028e494375c341e46caeb677076cc113b0f8042c69795b054a75e47 | R | 6,096 | 137 | ##
##Load regNet package
##
#- See https://github.com/seifemi/regNet for details how to install regNet.
library( regNet )
##
##Set the following paths according to your system
##
#- Must be modified to fit to your own system
#- Central project path prefix for regNet to store outputs of regNet
centralProjectPathPrefix... |
588553baac8c60d994b3371e6692846a87ed35c910f2d419dfbf81a5f3151880 | R | 6,099 | 158 | rm(list = ls())
############################################
# Packages
############################################
pkgs_cran <- c("openxlsx", "ggplot2", "reshape2")
for (pkg in pkgs_cran) {
if (!requireNamespace(pkg, quietly = TRUE)) {
install.packages(pkg, dependencies = TRUE)
}
library(pkg, character.on... |
51b2d25e17dbff7aba8012cd241b0b7026d6f3bf904837c606e81b8df837a062 | R | 6,113 | 257 | ---
title: "Add probes to comb-p results"
author:
- David lukacsovich^[University of Miami]
- Lily Wang^[University of Miami]
date: "`r Sys.Date()`"
output:
rmarkdown::html_document:
highlight: pygments
theme: lumen
toc: true
number_sections: true
df_print: paged
code_download: false
... |
c00b898dcf5085d712a5beaae3a21a176bdced94ae65396a89214f6827a2227f | R | 6,119 | 257 | ---
title: "Add probes to comb-p results"
author:
- David lukacsovich^[University of Miami]
- Lily Wang^[University of Miami]
date: "`r Sys.Date()`"
output:
rmarkdown::html_document:
highlight: pygments
theme: lumen
toc: true
number_sections: true
df_print: paged
code_download: false
... |
bcbda6a4937e1ebb6aec880b60be4ada2dc8004fe6af95b10ffa3439daaa648b | R | 6,122 | 164 | #' Identify the image limits
#'
#' This function is useful for automatically cropping the images. It finds
#' the edge points (min and max on both the X and Y axis) in pixels based
#' on a particular image. This function takes advantage of the known design
#' of Visium slides as documented at
#' <https://support.10xgen... |
9414079d68facb94edcb4bd45a1abc65c725bab3978e232c0126ae0ea7150ef0 | R | 6,124 | 166 | # =================================================================================================
# DotPlot_allow_dups()
# -------------------------------------------------------------------------------------------------
# Purpose:
# A wrapper around Seurat::DotPlot() that accepts duplicate gene names in the featur... |
ad06ddf002e3e435181013b3fa2811a74724d1941e589c30297cfd6dd75cf3d9 | R | 6,124 | 147 | #' RESCUE
#'
#' Y_\{G x J\} ~= X_\{G x K\} alpha_\{K x J\} + R_\{G x J\}
#'
#' @param Y.count Gene x Grid (or Spot) raw count matrix
#' @param X.count Gene x Cell raw count matrix from reference data
#' @param X.cluster vector of cell type annotations for each cell from reference data
#' @param C.grid vector of tuning ... |
d4bf48ec1bf39f71064211e7b1c57e984187bb045314c352d3435aba8c81abd7 | R | 6,127 | 143 | library(ggplot2)
#### Define count matrices to process ####
count_matrices <- c(
"merged_matrix_R",
"adjusted_merged_matrix_R"
)
#### Read coldata (once, before loop) ####
coldata_df <- read.table("/home/joonho345/1_Epilepsy_RNA/RNA_Animal/02.Quantification/filtered_coldata_R.txt",
sep = ... |
51a36faab09714010ee6a7a1e4df57eaca1131720c5784b59b4a1bc04d41e2c7 | R | 6,133 | 158 | library(edgeR)
library(biomaRt)
library(nichenetr)
# Get FADS1 neg genes i.e. geneset of interest
Neg_net <- readRDS("my_big_negative_GTEx_DLPFC_list.RData")
Neg_net <- Neg_net$ENSG00000149485_ %>%
as.data.frame()
Neg_net <- Neg_net$c..ENSG00000187961....ENSG00000236423....ENSG00000097021....ENSG00000116329...
mart ... |
c30fef8be1b748aae3bf51b29b9cd756ce6b05db4c86806725bdcaa160cf5e20 | R | 6,136 | 162 | library(tidyverse)
# Helper functions for constructing yHat of interest from asreml model coefficients output #
################################################################################
# Function to extract the specific part after the second colon
extract_third_part <- function(name) {
parts <- unlist(strsp... |
af6187169cf334e43a7d8b7677dddf2fe43272d73447757aaa11a32d8f2cedc6 | R | 6,144 | 84 | #' Get transcriptional regulators
#'
#' Selects relevant transcriptonal regulators by intersecting highly variable genes with known transcriptional regulators from the specified source(s). These regulators can be used in subsequent steps of the CroCoNet pipeline to assemble modules around them.
#'
#' This approach comb... |
4a4981cf11380632efacd03eeb418c593dcdaab035681c5d0b68fd1a33e607c3 | R | 6,148 | 262 | ---
title: "Read in ADNI DNA methylation data"
subtitle: "Filter to tripl Aplus samples"
author:
- David lukacsovich^[University of Miami]
- Lily Wang^[University of Miami]
date: "`r Sys.Date()`"
output:
rmarkdown::html_document:
highlight: pygments
theme: yeti
toc: true
number_sections: true
... |
933359f0584cf40699fb75e3d86c58e021786d51ca6db6b5112a8b80cd423a4c | R | 6,149 | 208 | ---
title: "Response Models"
description: "Ordered and multinomial response models in EMC2"
author: "Niek Stevenson"
output: rmarkdown::html_document
bibliography: refs.bib
vignette: >
%\VignetteIndexEntry{"Response Models"}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
editor_options:
markdown:... |
c3560f009a62223eac86bb6f3480d2ff5d1722689beb659b76dab43c9e9ae5f3 | R | 6,150 | 118 | library(Seurat)
library(Matrix)
library(Cairo)
#########################################################
#### Integration of two human datasets ####
seurat_human_1 <- GSE160189_hippo_int
seurat_human_2 <- GSE186538_hippo_int
integration_name <- 'GSE160189_186538_Integration'
## Find common genes between the two datas... |
6354d2707602f537149ca5dc5859b7d153fdc025807668f18a87e6f9d689ef0f | R | 6,159 | 178 | #' @title T-tests for symptom mapping (fast)
#'
#' @description
#' Lesion to symptom mapping performed on a prepared matrix.
#' T-tests are performed using each column of the
#' matrix to split the behavioral scores in two groups. If
#' var.equal=TRUE the Welch test is performed instead.
#' This function relies on TTfa... |
a20e2389bb87e4b142f8fac0071692cc6e5d16bbaf592664d7d7f332d27877c6 | R | 6,161 | 193 | .oo_model_list <- function(model) {
if (is.function(model)) {
return(model())
}
model
}
.oo_expanded_designs <- function(dadm, row_idx = NULL, model = NULL) {
if (!is.null(attr(dadm, "design_pool"))) {
out <- get_designs_expanded(dadm, model, order = "stored")
if (!is.null(row_idx)) {
out <- ... |
e739acfb34db84b52a8af8e91b4cd7c06e1b875e05191bfff627e2c2c7042215 | R | 6,163 | 195 | # Load libraries
suppressPackageStartupMessages({
library(ggplot2)
library(ggrepel)
library(ggpubr)
library(data.table)
library(RColorBrewer)
library(tidyverse)
library(preprocessCore)
library(future.apply)
library(DESeq2)
library(pheatmap)
library(sva)
})
dir.create("dge")
load("futcounts/Expression_Input.RData")
p... |
d62ed2489f9b785ec3eb0d73d3be6d66c03b987306b84f71c1e4c555f9064f59 | R | 6,164 | 134 | ###############################
### GET HEMIBRAIN META DATA ###
###############################
source("banc/banc-startup.R")
library(neuprintr)
# Get the meta data
all.neurons.meta <- neuprint_search("Traced",field="status",dataset="hemibrain:v1.2.1")
all.neurons.meta <- subset(all.neurons.meta, statusLabel!="Leaves"... |
4c148421812dff874ad3d9e63139c0104bd88babfa1d4914c8faa425ce1f1252 | R | 6,169 | 120 | ###########################################################
### SUPERSEDED 2026-05-21 — use banc/metrics/banc-calculate-completion.R
### (section: "Proportion-on-cell ECDF") instead. That version is
### banc.version-aware and reads the 10 GB v2-enriched parquet
### (banc_meta.R output) rather than the raw 30 GB v821 CS... |
7ef641c2caf6769a95def866b73329841fb187072356b41ab61a7e3194548010 | R | 6,170 | 109 | library('BayesCircIsotropy')
library('R.matlab')
library('REdaS')
library(tidyr)
library(dplyr)
# Computes the bayes factor in favor of the alternative hypothesis of von Mises
# distributed data.
# This version uses a conjugate prior.
# conj_prior has mu0, R0, and c0, in that order.
# prior 12 as in the ar... |
564bd067a77f0f44150c347d9d4d0c2f72b4c116a12c4cbb527ca815c75b3205 | R | 6,174 | 148 | #' selectSolutions.R
#' author: Gavin Ha
#' Dana-Farber Cancer Institute
#' Broad Institute
#' contact: <gavinha@gmail.com> or <gavinha@broadinstitute.org>
#' date: May 14, 2017
library(optparse)
option_list <- list(
make_option(c("--ploidyRun2"), type = "character", help = "Directory containing TitanCNA results... |
96a2754202b4659395582d497d335a917e9ddde00942f229c90acd30f12bb1ef | R | 6,175 | 168 | # script to check for inconsistencies in banc_meta
source("banc/curation/banc_curation_init.R")
# get banc_meta
banc_meta <- banctable_query()
# get franken meta
franken_meta <- franken_meta()
# select only the relevant columns of banc_meta
# make sure columns with multiple entries are sorted
banc_meta_sel <- banc_... |
bc4b1cfef23b1b32acd788ecd8cbd5469bf12887f228a3f893e2af15c7cd215e | R | 6,187 | 145 | ################################################################################
###############----------- MODEL COMPARISON FUNCTION ------------###############
################################################################################
## Conducts model comparison in a memory saving manner ---------------------... |
eb52d6aa4e322b53d4e5d3148ffa85ec31106db283649465d074a3ff416e41a1 | R | 6,189 | 167 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
# load influence scores
######################
#... |
7d3d7b3b11a22705b0df9ed6a0ba92cf059ac5ffe1e7c93db9e4da61bf0cfcd2 | R | 6,191 | 143 | # =================================================================================================
# apply_qc_thresholds()
# -------------------------------------------------------------------------------------------------
# Purpose:
# Apply the QC thresholds generated by qc_plots_by_sample() to a Seurat object and ... |
894cededdc05d4c876482c80140d34e37b71a18b098b1b46ee97e4f2dfcb8034 | R | 6,192 | 133 | library(foreach)
library(dplyr)
remove(list = ls())
#### Change Working Directory if Necessary ####
#setwd("~/FC-Network-Replicability-Effects")
# Set atlas parcellation directory path
base_directory <- getwd()
#### change the paths for your local paths ####
setwd(paste0(base_directory,"/Data Preprocessing/Metadata"... |
ec9d1b124578e9eb38535b242e9ebe0b961e77ec8472675a32384b418a22e49a | R | 6,192 | 171 | #' banc-nblast-search — Find BANC neurons matching a target cell_type via compiled NBLAST.
#'
#' Manual: set `search_cell_type` + `search_dataset` at the top; returns
#' the top-N BANC NBLAST hits.
#'
#' @section Reads:
#' - per-dataset compiled NBLAST feathers
#######################################################... |
ecaafbb15b4fd8e43a3e3f7e8f465ac38a2b241e76cb233291c685e8f20f5e19 | R | 6,192 | 147 | #' banc-manc-nblast-images — Render per-query BANC↔MANC NBLAST review PNGs.
#'
#' @section Reads:
#' - per-query NBLAST CSVs under `<banc.nblast.manc.save.path>/results/<ver>`
#' - BANC + MANC BANC-space meshes
#'
#' @section Writes:
#' - `<banc.nblast.manc.save.path>/images/<ver>/todo/*.png`
#'
#' @section Invok... |
2129fdd0766d452bb756203824bad7ef3946f78408270c55e6342a5af6fb1727 | R | 6,196 | 180 | ---
title: "Feasibility and Acceptability Analysis"
author: "Dr Emily Stanyer"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
df_print: paged
params:
data_dir: "Data/raw/feasibility"
fig_dir: "Figures/feasibility"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE... |
36ee276791f33cfa2e438192fbe6a060779484c8f3c3b6f757709ba525a7e425 | R | 6,198 | 170 | # ============================================================================
# Figure 2E: Dose Response — with individual replicate data points
# Journal requirement: show individual data points behind mean ± SEM
# ============================================================================
library(here)
# Source t... |
4bcd3c97b4bcd8ddc11340bb678d7eca3523e1371f63ae0e801b1e94a4d09cc3 | R | 6,198 | 183 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
######################
### load libraries ###
###... |
9e405e13d512945722d08b1b1e94eb35f858906109108965e1a2e3120fba7d64 | R | 6,199 | 121 | # 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(ggseg3d)
library(cifti)
library(stringr)
library(factoextra)
library(matrixStats)
library(scales)
libra... |
6e489affb9f13190345d5288ca46b4cc19f0b5db804d5f2050f9b1c844ab8d73 | R | 6,201 | 196 | ---
title: "Follow-up Data Analysis"
author: "Dr Emily Stanyer"
date: "`r Sys.Date()`"
output: html_document
params:
data_path: "Data/raw/follow_up/follow_up_data.csv"
output_dir: "Output/follow_up/"
fig_dir: "Figures/follow_up"
---
```{r setup, include=FALSE}
library(here)
# Define directories relative to the P... |
c8b301bdc18dee6f278220be3f7a248428b20af7185e714573faf69ad3e58248 | R | 6,209 | 145 | library(ggplot2)
#### Define count matrices to process ####
count_matrices <- c(
"merged_matrix_M",
"adjusted_merged_matrix_M"
)
#### Read coldata (once, before loop) ####
coldata_df <- read.table("/home/joonho345/1_Epilepsy_RNA/RNA_Animal/02.Quantification/filtered_coldata_M.txt",
sep = ... |
02b911ad915e1b2e068d1fb0f9b4b73d8ee5c5509e8c1ac19b915d25e586c258 | R | 6,214 | 216 |
## this imports the headers of a fast file quickly
get_fasta_headers <- function(fasta.ref) {
headers <- system(paste0("grep '^>' ", fasta.ref), intern = TRUE)
return(headers)
}
## this imports one sequence (according to the header argument) from a fasta file quickly
extract_sequence_by_header <- function(h... |
550b8eea7d6d8b3f80c6db9265715caa8fdeb540e53f4f1ad94a7092f61d19d3 | R | 6,221 | 206 | ########################################################################################
### Cladogram - Endobiotics Cohort Proteomic GUSome
########################################################################################
### Usage
# Rscript ggtree_Endo_14_MPX.r
###############################################... |
6490c72c7821c3cea1b465f33d02423631bb6ea39f238ab1b5ebc2df5bd72941 | R | 6,233 | 139 | ### KO efficiency assessed by specific substrate and product level of target enzyme
rm(list=ls())
library(tidyverse)
library(ggpubr)
setwd(rstudioapi::getActiveProject())
source("./Scripts/Function_scripts/Pre-processing_functions.R")
load(file = "./Output_Data/KO_LUT_paper.order2024-02-08.Rdata")
load("./Output_Data/... |
dd7d7eeaeda489d19ac5996b694b4e380df269b96f30b257347c07fceff98f98 | R | 6,245 | 104 | ################################################################################
# This R file contains codes to extract individual-level and injection venue-level parameters for HIV transmission model.
# The synthetic datasets are in the forms of .csv and .rds.
# Columns in the synthetic dataset resemble the actual ... |
07434c1329cddcd70a837f5802276f68fa210f811e009716f4e647df3c1cb0e1 | R | 6,252 | 105 | #' Function performs single inner fold nested cross-validation step.
#'
#' 1. Split data frame to training and test sets.
#' 2. Perform model parameter tuning in training data using n-fold inner cross-validation
#' 3. Test tuned model using independent test data
#'
#' @param
#'
#' @return
#'
#' @export
nested_xgboost_c... |
91511b3f6f61864f9e224646e2eb978381d7ba571f946cae1df4d7071c16ba7f | R | 6,254 | 218 | #!/usr/bin/env Rscript
suppressPackageStartupMessages(library("argparse"))
# create parser object
parser <- ArgumentParser()
# specify our desired options
# by default ArgumentParser will add an help option
parser$add_argument("-i", "--input", required=TRUE, help="input doublets scores")
parser$add_argument("-m", "-... |
0a098fd8a0e531409ec93bcea0da62601f8840222aa48d84cdfcb5ef50514e7c | R | 6,277 | 211 | #### Section: Model Fitting & Preparation ####
prepare_stratified_anova <- function(
df,
responses,
model,
factor1_var = NULL,
factor1_order = NULL,
factor2_var = NULL,
factor2_order = NULL,
stratification = NULL,
stratify_var = NULL,
strata_order = NULL
) {
req(df, responses,... |
8a4a97b11126b0ebc79b1499654530928a1f20ec99e942e3474195151ab39c31 | R | 6,279 | 164 | #------------------------------------------------------SETUP-----------------------------------------------------#
# Load libraries
library(tidyverse)
library(data.table)
library(BuenColors)
library(Seurat)
library(irlba)
library(Matrix)
library(future)
library(reticulate)
library(ggrastr)
library(tidytext)
library(ma... |
e6ce2f561abcec527dff7cbfa506acbd6abb041edde984444c7c50311aa33f9a | R | 6,282 | 161 |
#' These are wrapper functions for the ov.from.bam2 function, linear and parallel, respectively
#'
#' @export
import.bams <- function( bamfiles, bamnames, write.filtered, bam.all=data.frame(NULL), force.create=F,
filtering=c('all.reads.w.supp.ali', 'only.supp.ali.of.reads'), ...) {
nbam <... |
77899934df84de522dcafac2d8a750b839ebbfb95dedff694c5cb05aa2893f9d | R | 6,290 | 194 | # Make bed files to run LDSC
# 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
... |
77b3650573679298757bdfb85438f5c9850864ce34aebb8a168353eec7d6ebe0 | R | 6,291 | 155 | # Functions related to graph visualization of AUROCs.
#' Convert AUROC matrix into a graph.
#'
#' This representation is a useful alternative for heatmaps for large datasets
#' and sparse AUROC matrices (MetaNeighborUS with one_vs_best = TRUE)
#'
#' @param best_hits Matrix of AUROCs produced by MetaNeighborUS.
#' @par... |
a46b68c07e7fff5612c98b7f347e04fbbccf84a6d3052e1fdfcd622d8a08b975 | R | 6,302 | 170 | #------------------------------------------------------SETUP-----------------------------------------------------#
# Load libraries
library(tidyverse)
library(data.table)
library(BuenColors)
library(Seurat)
library(irlba)
library(Matrix)
library(future)
library(reticulate)
library(ggrastr)
library(tidytext)
library(ma... |
bb7d013a493c8bb7dcebba53a3b6df0eceadc6446e5929a6bd68dab53fbeb9e2 | R | 6,309 | 162 | ---
title: "Intersect_GO"
author: "Iza"
date: "2025-02-14"
output: html_document
---
This document is your **exact** script with added comments only. I did **not** change any code, names, thresholds, or paths. Caveats are marked with `# NOTE:` inside the chunks.
```{r}
# NOTE: These RDS/CSV paths contain repeated "GO... |
1e210c873328a18fea97e3b04afc18c2320cadcfa642c8db7bda5bfa07a087aa | R | 6,311 | 183 | ---
title: "ldsc summary"
output: pdf_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE, fig.align='center')
```
```{r}
library(tidyverse)
require(forcats)
library(RColorBrewer)
library(pheatmap)
library(qvalue)
library(viridis)
library(dendsort)
sort_hclust <- fu... |
0da056cc6253907b61fb697e5cd082a1b092846a7da8b4ee6caa55dbb59ebb46 | R | 6,314 | 135 | # Load required functions from external scripts
source("functions/count_occurrences.R") # Function to count categorical variable values
source("functions/multiple_entries_to_vector.R") # Function to split comma-separated values
source("functions/save_figure.R") # Wrapper for saving figures to fi... |
c0dff4fdf5ab565e7dcd536f55c4b44b8a0c7192b1680be54ceb8a798b49bd20 | R | 6,325 | 153 | #load libraries
library(gwaRs)
library(ggplot2)
library(dplyr)
library(ggrepel)
#load in data
Ext.DMR.Data <- read.csv("D:/ManhattanPlot/Ext.DMRs.con.vs.ad.meth.csv")
Int.DMR.Data <- read.csv("D:/ManhattanPlot/Int.CONvsAD.DMRs.both.csv")
ext.2 <- read.csv("D:/Second.Ext.Meth.Data.MtSinai.csv")
#prepare dat... |
d2a08a564e49e1096f9f52440f174bb2448ff3af86b219f88cf004c8cf42977f | R | 6,327 | 176 | #####################################################################
# INGENE GTEx Prediction Pipeline
#####################################################################
# Applies trained INGENE models to GTEx data to generate predictions
#####################################################################
... |
5ba9f0f03dc0bd3ecf6585ad3b91cada02de3776a1d6d2b6f866210668e34c08 | R | 6,335 | 116 | ---
title: "Accessing the chunked graph server"
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
## tl;dr
To set up programmatic access to FlyWire, use `flywire_set_token()` as follows:
```{r, eval=FALSE}
library(fafbseg)
flywire_set_token()
```
and follow the on-screen p... |
c402aca38f95e707d9e16cba8e34b2e6d3f28e0bf12468cb5875675fbefbf6a9 | R | 6,339 | 243 | rm(list = ls())
############################################
# Packages
############################################
pkgs_cran <- c("openxlsx", "readxl", "ggplot2", "tidyr", "dplyr", "reshape2", "rstatix")
for (pkg in pkgs_cran) {
if (!requireNamespace(pkg, quietly = TRUE)) {
install.packages(pkg, dependencies ... |
5d8bcacb2f3378464c49ef5fed19903dd2c34bc5cde7216218f2d0619f1f26a6 | R | 6,342 | 144 | library(ggplot2)
library(ggforce)
rm(list=ls())
options(digits = 4) # by default, print results to four decimal digits
set.seed(1823) #for replication - the year Trinity College was founded!
#replace the empty quotes with the file path to the location where you downloaded the files from Zenodo below
path = ''
screen... |
98f9ddaa2837558b257a2480079154ec9e1ea86749f83956172b55d365e08e93 | R | 6,348 | 180 | #' Extract significant genes
#'
#' From the layer-level modeling results, this function extracts the top `n`
#' significant genes. This is the workhorse function used by
#' [sig_genes_extract_all()] through which we obtain the information that can
#' then be used by functions such as [layer_boxplot()] for constructing
... |
22ebdae3007c99f522f1fa0b9738f25398f565d99af622c13c20d06a7496bae2 | R | 6,368 | 146 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
# Libraries
source("banc/banc-startup.R")
# Sett... |
afc7055151c99708af6b9e3e6961bfc090c88d1c68624234ec93988cb5e966e0 | R | 6,381 | 156 | #' Import cNMF results then export to an NMF object
#'
#' @param prefix \code{character} The run_name parameter from cNMF run
#' @param metrics \code{character} Either score or TPM, default is score
#' @param minRank \code{numeric} The minimum rank from cNMF run
#' @param maxRank \code{numeric} the maximum rank from cN... |
65c65dfda68d0ab51364c15e1932b41d3c340c0e8834c446954bacbbaa28686d | R | 6,388 | 138 | # ==============================================================================
# Script 09: Figure 2D - FMI CDH1 Alterations (Local vs Distant Metastasis)
# ==============================================================================
# Description: Foundation Medicine breast diagnostic cohort: CDH1 alteration
# ... |
58c8d7eef3900c13f7df343a72a1d4f8f356651f218e1450dc90c467a3a9367f | R | 6,390 | 192 | ################################################################################
# Faceted violin plots of gene expression
#
# - No hard-coded absolute/local paths
# - Input/output are provided via command-line arguments
# - Assumes an input table with columns: Gene, Expression, Group
# - Produces a horizontal violin ... |
9d0ac19484f1e3ac8646c2b879ac7a048d9739c71d27f9d68cf52bd5c56a8ffc | R | 6,403 | 151 | library(Seurat)
library(ggplot2)
library(patchwork)
library(dplyr)
library(data.table)
library(viridis)
data_dir <-"Br2720_all/Br2720_ant2/"
brain <-Load10X_Spatial(data.dir = data_dir, filename= 'filtered_feature_bc_matrix.h5')
# Change the co-ordinates from characters to integers
brain@images[["slice1"]]@coordinat... |
9d7e5144acddd54cb1a17708183575462333de024a780a24287b5b74f46b4453 | R | 6,412 | 200 | .ordered_interval_prob <- function(lower, upper, pars, cdf_fun) {
cdf_fun(upper, pars) - cdf_fun(lower, pars)
}
.ordered_cut_transform <- function(raw_cut, lR) {
nr <- length(levels(lR))
if (length(raw_cut) %% nr != 0) {
stop("cut vector length must be divisible by the number of response levels")
}
cut ... |
9cb13282d267e9b7556c26fce4be15b07d7a19593945d7357130aff6f26a7c2b | R | 6,421 | 184 | ---
title: "saraswathy_neurons"
output: html_document
date: "2026-02-18"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Load Libraries
```{r}
library(ggplot2)
library(dplyr)
library(tidyr)
library(Seurat)
library(readr)
```
Load datasets and visualize
```{r}
# saraswathy dataset
saraswathy_Ne... |
0a4527a420bd98ee6e95e8dd3e1c9bbbf53946b6221507ac4abf28fb98c0a55b | R | 6,424 | 145 | library(parallel)
# library(pbapply)
#version 2
#optimizes meta-parameters using random search for given ml model according to best RMSE
#writes tried parameter sets and error stats to file and returns best pred.df from crossval
ml_tuner = function(x, y, filename, which.fold = 0, ...){
#unpack ellipses
inlist =... |
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