sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
df3e3dfb444a1b548918d3aafc12b206338026d2a0c0c6468012bc0c05fd9a27 | R | 6,426 | 191 | #!/usr/bin/env Rscript
#-------------------------------------------------
# Title: edgeR
# Author: Amanda Zacharias & CM
# Date: 2025-07-17
# Email: 19cm51@queensu.ca
#-------------------------------------------------
# Notes ------------------------------------------
# module load StdEnv/2023 r/4.5.0
#
#
#
# Options -... |
5b90253bc9cdba11f29793ef4d2848ce43b9dad7614af88de0510be4b6fe1adf | R | 6,429 | 142 | seizure <- c("NRP2,MAPK10,TRPC5,KCNJ6,HOMER1,CNTNAP2,KCNK1,ITPR1,PCDH15,MPC1,GRM1,NTRK2,NEUROD2,NF1,PLCL1,BDNF,SYN3,GABRG2,GAP43,NRN1,SPTBN2,KCNA4,SV2C,MALAT1,CACNA1H,GABRG3,PHACTR1,SCG2,CACNA1G,GABRA5,SV2B,CHGB,KCNQ3,KCNC2,SPNS2,HTR4,KCNT2")
seizure <- unlist(strsplit(seizure,","))
foo = function(x){
paste(toupper(s... |
c5f194399325655ae6e18ebc99111843f518eb7660892adb939a425d78867736 | R | 6,437 | 192 | #QC2: Use the nodose ganglia cell cluster specific asw to draw line graph to identify the best pc&res model
#Phox2b is the marker of nodose ganglia cells
#Identify the clusters where nodose ganglia cells are clustered ("Phox2b_cluster")
#Extract the pergroup_asw of Phox2b expressed clusters and average their asw from r... |
15f08eb4230a7594cd0a463fa6518bd84a2d5e64cfee8c0057c6a7f31a69de2d | R | 6,438 | 156 | # ENRICHMENT #
library(topGO)
library(tidyverse)
get_cat_long <- function(cat){
category_dictionary <- c(
"0" = "Unclassified",
"1" = "Neural and synaptic structure and signaling",
"2" = "Immune and inflammatory response",
"3" = "Metabolism and catabolism",
"4" = "Gene regulation",
"5" = "Ti... |
370afb17f14eb1b9ac41be0fb23ab7f3bcc904a62acbe7323e80cf0d7f51741a | R | 6,447 | 141 | ################################################################################
###############------ POSTERIOR PREDICTIVE CHECK FUNCTION -------###############
################################################################################
## Extracts generated quantities in a memory saving manner -----------------... |
88c12df5d471a93c6b954ee1c6424ffcc7dd00e439d9b93264ba4cd2fa53d958 | R | 6,449 | 189 | ---
title: "R Notebook"
output: html_notebook
---
This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code.
Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl... |
3da2174114026f7ec71d02e6b1dd22b0b1dc9845f409596d0df777850e490a0b | R | 6,454 | 155 | # Set working directory to project root
# setwd("path/to/project/root")
tissues = c("dlpfc")
full = sapply(tissues,function(x){
if(x!="amygdala"){
epixcan = get(load(paste0("./predictions/EpiXcan/EpiXcan_",x,"_testing_dataset_name_performance_selected.RData")))
epixcan = epixcan$gene
... |
1c4ab9c1372b1f5175a58f3e5172e4b74efe6c3bdaeffaf7593791f1fb69bdd8 | R | 6,456 | 193 | ---
title: "Compute Methylation-based Resilience Score (MRS)"
author: LW
date: "`r format(Sys.time(), '%Y-%m-%d')`"
output:
html_document:
toc: true
toc_float: true
number_sections: true
word_document: default
pdf_document: default
---
# Overview
This document computes a methylation-b... |
aeb63038f677ce571695125d455bbc6fee388f260e0ae7f1950d2472d9cf147d | R | 6,462 | 214 | runEigDecomp2 <- function(obj, num.eigs){
nmat = obj@jmat@nmat;
diag(nmat) = 0;
norm_p1 = nmat
d_norm1 <- Matrix::rowSums(norm_p1);
d_rot1 <- Diagonal(x = d_norm1 ^ -.5);
transitions <- as.matrix(d_rot1 %*% norm_p1 %*% d_rot1);
diag(transitions) = 0
message("-- using Adjacency spectral e... |
1077f7f89ff987e9fcf7b2513ea61d810347d1ffd05fc4e1adff004392e00ae0 | R | 6,465 | 148 | #' banc-tracing-cns-network — CNS-cluster / CNS-network curation + snapshot restore.
#'
#' Patches CNS cluster + CNS-network assignments from tracing sheets and
#' restores prior assignments from snapshots when needed.
#'
#' @section Reads:
#' - `BANC-project/data/cns_cluster/spectral_clustering_assignments_banc_591.... |
e37655f4158d7fac2096e8a5299eede8bd1662fb32fc8e1555efe4cceab77591 | R | 6,468 | 218 | #' qhet_mvmr
#'
#' Fits a multivariable Mendelian randomization model adjusting for weak instruments. The functions requires a formatted dataframe using the [`format_mvmr`] function, as well a phenotypic correlation matrix \code{pcor}. This should be obtained from individual level
#' phenotypic data, or constructed as ... |
768a25a13c8c15e3d2dea24aa5eb3a876be7e75c620891b403dea88ad9d3a377 | R | 6,471 | 216 | 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 ... |
e11b87ed34ff8ee07a257ac5939a2e0c1a6db9e797911715dae006e42f49088b | R | 6,473 | 175 | #' @title Save the output of lesymap.
#'
#' @description
#' Function to save the output of lesymap.
#'
#' @param lsm object obtained with lesymap()
#' @param saveDir folder to save to, will be
#' created if it doesn't exist.
#' @param infoFile (default='Info.txt') what should
#' be the filename of the file with informa... |
57e47937fdc17997dba904a9d60fd8fc6b7e418c2ab50fac4155f3c929c639c4 | R | 6,474 | 104 | #' Normalize edge weights between 0 and 1
#'
#' Transforms the edge weights of all networks to be in the range [0, 1].
#'
#' Normalizing the edge weights between 0 and 1 makes them interpretable as adjacencies and ensures that network concepts such as connectivity are applicable.
#'
#' There are 2 approaches for the no... |
ac9ff4d804178f06c7a4e816d1c9a1129647d3c71dc7de824ce6fd681b3d55f0 | R | 6,474 | 151 | #' banc-malecns-nblast-images — Render per-query BANC↔maleCNS NBLAST review PNGs.
#'
#' For efferent / motor / visceral BANC neurons lacking a maleCNS match.
#'
#' @section Reads:
#' - per-query NBLAST CSVs under `<banc.nblast.malecns.save.path>/results/<ver>`
#'
#' @section Writes:
#' - `<banc.nblast.malecns.save.... |
9e0765b2c6353a70a147fcadd68cc6af653cd594038fe8273a809c9d7ebc48a2 | R | 6,485 | 160 | #!/usr/bin/env Rscript
## 5b.maturation_euler.R
##
## Concern: replace Fig. 4c UpSet with a three-set Euler diagram
## of the maturation contrasts (Time_Ctrl, Time_G32A, Time_R403C).
##
## Produces Euler diagrams at FDR<0.05 (primary Fig. 4c replacement) and
## FDR<0.10 (relaxed-threshold robustness panel for Supplemen... |
c284c3563c86e22f1d7793c398c78c17188c2e0a769e98185e5525a2e9f732ca | R | 6,486 | 173 | ---
title: "Untitled"
output: html_document
date: "2026-04-17"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Load
```{r}
library(rstatix)
library(dplyr)
library(car)
library(GO.db)
library(Seurat)
library(readr)
library(stringr)
library(clusterProfiler)
library(scCustomize)
library(ggplot2)
... |
8d17985585cb257f020da56cb80553b61b470a74ee61f0f30c7596d023149ae8 | R | 6,504 | 133 | #' @title export2loom
#' @description Create .loom file with the results of the analysis to visualize them in http://scope.aertslab.org
#' @param scenicOptions scenicOptions object
#' @param dgem Digital expression matrix
#' @param hierarchy Labels for the hierarchy levels (up to three)
#' @return The .loom file (\code... |
de61c0398325fa9daed86b79a7ea5c1e2f130517fe82274ce737fe08db438af0 | R | 6,510 | 180 | ################################################################################
# Figure 3B with Individual Points
# Creates multi-panel grid of all tissue scatter plots (Factor1 vs GDF15)
# with individual sample points colored by DTHPLCE and copies individual
# tissue plots to the journal guidelines folder
#########... |
0994fa021a95638613fa784dfa0e181fe63da70788c70a510996af19731d42f0 | R | 6,519 | 209 | #!/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$add_argument("-i", "--input", required=TRUE, help="load clustered RData")
parser$add_argument("-p", "--... |
58e19ea0bb9454c0f0e58dee118d8d4f4ddc9c6fa969e2c73e564632f208f791 | R | 6,523 | 126 | here::i_am("scripts/2.validations/2.3.binding_site_enrichment_and_divergence/2.3.23.binding_site_divergence_across_species.R")
library(tidyverse)
library(here)
library(ggbeeswarm)
library(ggsignif)
wd <- here("data/validations/binding_site_enrichment_and_divergence/")
dir.create(here(wd, "figures"))
## Calculate bi... |
b51d52733bd0ba6af2e2c58ce2bda2ad12d85061a44b0b2379c86f1763a0a15b | R | 6,525 | 167 | #' Map points between FlyWire v1 and FAFB14 space (xyz nm)
#'
#' @description \code{flywire2fafb} maps points FlyWire->FAFB
#'
#' @details Note that you can also access FlyWire<->FAFB bridging registrations
#' via the \code{\link[nat.templatebrains]{xform_brain}} series of functions. This will allow you
#' to trans... |
078d066f09309113e715d643511c10494cfa265ebcc541a529fff8dab906b96d | R | 6,529 | 180 | #' Convert XYZ locations between FANC4 (autoseg) and FANC3 (CATMAID)
#'
#' @param xyz An Nx3 matrix of coordinates
#' @param ... additional arguments for \code{pbapply::pblapply} (used to chunk
#' requests for many points) and eventually the POST command against the
#' transform services server.
#' @inheritParams f... |
2098e5ea10911206762a53825d24a877ef0e57cae796b212d1d41d079ddd33bc | R | 6,530 | 233 | # Building a Prod-Ready, Robust Shiny Application.
#
# Each step is optional.
#
# 2. All along your project
## 2.1 Add modules
##
# golem::add_module( name = "my_first_module" ) # Name of the module
# golem::add_module( name = "my_other_module" ) # Name of the module
## 2.2 Add dependencies
# usethis::use_package( ... |
24baca0c4defeb0c7aede5386240c625f68762e544fbec2ba065095f1a36058d | R | 6,548 | 158 | #TODO: Convert regulons to GeneSet or a class?
#' @title regulonsToGeneSet
#' @description Converts the regulons stored as incidence matrix to gene sets (e.g. list of genes)
#' @param incidMat Incidence matrix with TFs as rows and GENES as columns
#' @return List of genes per regulon
#' @examples
#' regulonsToGeneSe... |
49d81cb89f11d2c10b4bf430ec6e5cff21b263375c4ad62000817d7ff21c519e | R | 6,552 | 170 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
options(tibble.print_min = 5, tibble.print_max = 5)
```
# RECOMBINE
##... |
344260c03d6c2587e27bd28ec2cb54b388a54ce9219040ededf599abcdbb972b | R | 6,553 | 154 | ###combine driver mutations of cancers
library(dplyr)
files <- list.files("data/Onco_mutations_OncoVar_TCGA/",full.names = T)
dt <- do.call(rbind,lapply(files,data.table::fread,data.table=F))
files_gene <- list.files("data/Onco_genes_OncoVar_TCGA/",full.names = T)
dt_genes <- do.call(rbind,lapply(files_gene,data.table... |
8097a326cad48dc33182878fae101c52384612ff46634f6ba545076d61d7bb0c | R | 6,565 | 143 | gene_sets <- list(
M_gene_set_GG_GO0014004_16 = M_gene_set_GG_GO0014004_16,
M_gene_set_GG_GO0014014_59 = M_gene_set_GG_GO0014014_59,
M_gene_set_GG_GO0014015_94 = M_gene_set_GG_GO0014015_94,
M_gene_set_GG_GO0042063_410 = M_gene_set_GG_GO0042063_410,
M_gene_set_GG_GO0048708_94 = M_gene_set_GG_GO0048708_94,
M_... |
582a00b69fbeb69f51b036f05e9f86dd6a1efcf28dfbf01baa7175d374eac628 | R | 6,568 | 156 | #' banc-update-ntpred — Push neurotransmitter predictions to SeaTable.
#'
#' Reads `banc_ntpred_<src>.feather` (from `banc-calculate-ntpred.R`) and
#' pushes per-neuron predictions to SeaTable's `neurotransmitter_predicted_v<N>`
#' column (v1 reserved for older detector; v2/v3 populated from
#' `--source v2/v3` runs). ... |
c955146d5100c3e2813d6d2ae505d71710519b944d381dddea4b1128e57fe276 | R | 6,576 | 220 | 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 ... |
367fdbb95d8880f570a85db115f7a0c33ee119cd7094b01d04922c7854992c6a | R | 6,586 | 153 | #IMPORTANT INFORMATION BEFORE STARTING
#.bam and .bai files should have the same name and should have identical paths as this .R file
#This script exports 2 different results in pzfx (Prism) or xlsx (Excel) format.
#file named as "-frequency-of-sequences" shows frequency of all sequences present in library
#file name... |
7fcbe6e61cf7bcfe0c7bca79e15a7758237541b92865c66cf5ba669bbae1eb19 | R | 6,607 | 220 | 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 ... |
a9156ef319076ad0bdcbaeec94b9f3b52e06b62c496519c413027366e6db5afa | R | 6,607 | 175 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
#################################################... |
ef9e0bf534fed2236e53634a3fe46c3d4e348d1b1cc079a1899c0027b356aba0 | R | 6,607 | 174 | # =============================================================================
# Supplementary Figure 8 — STS Control Analysis: Learning
# =============================================================================
#
# Paper: ReFOFA (NCOMMS-24-11279)
# Sample: Experimental group only, all stimuli (faces + animals),... |
28bec90efd3fa977af25b4476de263532b6da790a7baa7873e9aaff44fa7abbd | R | 6,612 | 146 | here::i_am("scripts/2.validations/2.7.POU5F1_LTR7_enrichment/2.7.3.calculate_enrichment_near_POU5F1_module_members.R")
library(tidyverse)
library(plyranges)
library(here)
wd <- here("data/validations/POU5F1_LTR7_enrichment/")
fig_dir <- here(wd, "figures/")
dir.create(fig_dir)
# all network genes
all_genes <- readR... |
8195e78ebdb87c772e90eeb617eff0a68744a7f5fc9cecc8fb1f46c7b90005e8 | R | 6,614 | 143 | ## Side chain analysis across 2 in vitro studies
## Remove LPC-O and LPE-O with 1 or 2 double bonds, as well as LPC-P and LPE-P with 0 or 1 double bond due to annotation ambiguity.
setwd(rstudioapi::getActiveProject())
rm(list = ls())
library(tidyverse)
library(ggpubr)
library(ggrepel)
source("./Scripts/Function_script... |
549ce02acc0d1c16d41ae6d62b2ce08da1ca4217bb98a13906ed4d7db5ed42ba | R | 6,618 | 210 | # Environment ------------------------------------------------------------------
library(tidyverse)
library(lme4)
library(ggseg)
library(patchwork)
library(here)
library(glue)
library(ggraph)
library(igraph)
source(here("src", "R", "plot_ggseg_brain.R"))
source(here("src", "R", "run_lda_workflow.R"))
# Data I/O ----... |
c6d6bed6f9997dc55a67d344db4cf36122470d18f125742e7f68f51a301496bb | R | 6,619 | 207 | # Environment ------------------------------------------------------------------
library(tidyverse)
library(lme4)
library(ggseg)
library(patchwork)
library(here)
library(glue)
library(ggraph)
library(igraph)
source(here("src", "R", "plot_ggseg_brain.R"))
source(here("src", "R", "run_lda_workflow.R"))
# Data I/O ----... |
79e26f64f813830a0b19d05f218e7684e32847321126c5849a4cf09c34558e35 | R | 6,622 | 99 | library(dplyr)
#### choose groups to compare ####
####### COLDATA_3 #######
coldata_df_M_KAI_IH_IPSI_A_HA <- coldata_df_M %>% filter(TYPE_2 == "M_KAI_IH_IPSI_A_HA")
filtered_samples_M_KAI_IH_IPSI_A_HA <- rownames(coldata_df_M_KAI_IH_IPSI_A_HA)
adjusted_df_M_KAI_IH_IPSI_A_HA <- adjusted_df_M[, colnames(adjusted_df_M)... |
ed5d583b3b3171ee73f7be98f6f5dbba5bb10fd6b3f76cdde0b1d689908bea33 | R | 6,622 | 118 | here::i_am("scripts/2.validations/2.2.pathway_enrichment/2.2.2.summarize_Reactome_results.R")
library(tidyverse)
library(ggpubr)
library(ggtext)
library(here)
wd <- here("data/validations/pathway_enrichment/")
dir.create(paste0(wd, "figures/"))
# load all regulators
regulators <- readRDS(here("data/neural_different... |
42f73003ebbe813f82027ae3a5814b63876fa29bad4855ebc9a8557f647366b7 | R | 6,624 | 144 | #' Find reciprocal top hits
#'
#' Identifies reciprocal top hits and high scoring cell type pairs. This
#' function calls the newer topHitsByStudy function.
#'
#' @param cell_NV matrix of celltype-to-celltype AUROC scores
#' (output from \code{\link{MetaNeighborUS}})
#' @param dat a SummarizedExperiment object contain... |
c082220d08de9f9cd11e151d7226af86c5e7c321da04bbe1c3f8448b0ce2dcdc | R | 6,625 | 203 | # create parser object
suppressPackageStartupMessages(library("argparse"))
parser <- ArgumentParser()
parser$add_argument("-i", "--input", required=TRUE, help="list of summit bed")
parser$add_argument("-g", "--genome", default="hg38", help="genome")
parser$add_argument("-blacklist", "--blacklist", required=TRUE, help... |
811c70be6842b5f93ae545615957e8fd2acc80b54bba37fbc8edc001b3f9ff35 | R | 6,628 | 158 | # ==============================================================================
# Script 07: Supplementary Figure 6 - Alterations in Key Pathways (ICLE)
# ==============================================================================
# Description: Generates pathway-level alteration matrix and heatmap for ICLE
# ... |
4bdaa7fc5bbb5461ef4f3456c708b5e0675de77666324536d54a38439cfc4231 | R | 6,631 | 113 | library(data.table)
library(parallel)
library(dplyr)
library(purrr)
library(limma)
# Set working directory to project root
# setwd("path/to/project/root")
#####
### Params
####
model="CIS_EpiXcan"
model_cis = "CIS"
model_epi = "EpiXcan"
### Params
target_file_path = c("./genotype/")
site ... |
50d87f6f2786b2055348fe43ffd3197e95d8e93c3e2806c4bc3c65c640b8046b | R | 6,633 | 132 | # Load necessary libraries
library(dplyr)
library(pheatmap)
library(Cairo)
library(ComplexHeatmap)
library(circlize) # For colorRamp2
################################################
################ GO terms ###################
################################################
# Choose Species
Species <- 'Mouse'
Spec... |
e7b29abec8f00c63f535e10aeb2342c04dc7fb9b06d65642a40abd37afb004da | R | 6,636 | 246 | ## lasso_model_omnibus.R
##
## For a given design matrix X_design and response Y, this function:
## 1) runs de-sparsified lasso (lasso.proj),
## 2) computes group-wise MinP (Westfall–Young) and iART-A statistics,
## 3) combines them via ACATO to obtain omnibus p-values for Group 1 and Group 2.
##
## Additiona... |
b81750c839e564ad3fac3b62ba17ddb6670d4c2d3f4c0ef898b381592c73f2c3 | R | 6,650 | 135 | library(here)
library(dplyr)
# load summary of top hits from Macosko/Zeng pretained models
df1 = read.delim(here('results/cross_species_studies/cross_species_studies_mapping_summary_Macosko.csv'), sep = ',')
df2 = read.delim(here('results/cross_species_studies/cross_species_studies_mapping_summary_Zeng.csv'), sep = ',... |
f36a75ec1ad5ae562243e82a67bea1b9cb38ef2a6594d353691ef08fd1203c6a | R | 6,653 | 121 | #!/usr/bin/env Rscript
library(Matrix)
library(Seurat)
library(tidyverse)
library(readxl)
library(Augur)
source("scRNAseq_funs.R")
IntegratedCsa <- readRDS("IntegratedCsa.RDS")
print(sprintf("There are %s cells from Csa.", nrow(IntegratedCsa@meta.data)))
###########################################################... |
9847dc101150f60d3688b20d77d4a5b8d7ce91424298025f04c8b2520081f291 | R | 6,661 | 193 | #' @title Prediction of new cases from lesymap output
#'
#' @description
#' Uses an existing lesyamp object output from your
#' analysis to predict new cases.
#'
#' @param lsm object of class lesymap from previous analysis
#'
#' @param lesions.list list of antsImages, or a vector of
#' filenames, or a single antsImage ... |
86f40470dd14b4015ede859deea1891395e1c0ea6e4d6dc0b8cc0a50950b1d1d | R | 6,669 | 151 | #' Return the soma or root position of MANC bodyids
#'
#' @details Currently there are three sources of soma information, the
#' \code{\link{mancsomapos}} data.frame distributed with the malevnc package,
#' a key value annotation stored in DVID and and point annotations stored in
#' Clio. Currently DVID is consid... |
71623cba1730efc5e746c47455c11e7c8b4960c1ce4e150825aef6312c09fb1c | R | 6,670 | 175 | #################################
# R script for exploring gene expression in the single cell dataset.
#################################
# remove all the objects from the R session
rm(list=ls())
#set the working directory:
# ***********update "WD" with the path to your working directory on your computer:
WD="P... |
1b04db8b607aeb3768c841a3799d02ef7a50c38589d86e104366ac761fa84f66 | R | 6,686 | 156 | here::i_am("scripts/2.validations/2.5.expression_pattern_divergence/2.5.1.calculate_expression_pattern_divergence.R")
library(tidyverse)
library(BiocManager)
library(SingleCellExperiment)
library(variancePartition)
library(here)
wd <- here("data/validations/expression_pattern_divergence/")
dir.create(wd)
## Calcula... |
68b575744d2969963e62a1d8a898b6d0ec850e816abb54afe09a183b30db3b3c | R | 6,695 | 152 | here::i_am("scripts/2.validations/2.4.sequence_divergence/2.4.2.calculate_sequence_divergence.R")
library(tidyverse)
library(plyranges)
library(Biostrings)
library(ggrepel)
library(here)
wd <- here("data/validations/sequence_divergence/")
fig_dir <- here(wd, "figures/")
dir.create(fig_dir)
## Get the longest (C)CDS... |
f937b1dce860488cc7e885a2762def6c72b2fc8c8efd394b2b0b77be839a9a54 | R | 6,696 | 172 | # ============================================================
# Inhibitory neurons (ROSMAP): CHCHD10 ~ Braak (NO covariates)
# Per-donor aggregation from a single Seurat object
# No AD/NC grouping or coloring
# ============================================================
suppressPackageStartupMessages({
library(Seu... |
14cb5652c418dc46d114c0da25c68ae1de3f06bdc6114ddf6c646bb6a852d9cd | R | 6,704 | 229 | library(ggplot2)
path='/Users/menezeslf/Documents/LabManuscripts/del3-67 eye/hg38_loops/'
files0=sapply(strsplit(dir(path),split='.hg38',fixed=TRUE), function(x) x[1])
files=paste0(path,dir(path))
df=data.frame('source'=character(),'chrA'=character(),'startA'=numeric(),'endA'=numeric(),'chrB'=character(),'startB'=nu... |
f2edaf34fded6657695b24f9f4f0001c81fc915cb1b874642910b794e7af4f11 | R | 6,706 | 185 | # ==============================================================
# Script: Immune cells — merge clinical, QC, and DEG (wilcox)
# Modified: run fully UNFILTERED DEG and save CSV + RDS
# ==============================================================
suppressPackageStartupMessages({
library(Seurat)
library(dplyr)
l... |
1a8adff0e4cc31c2f315bb31e38d09510da0b441f051ab5a1b12d528a6303e7c | R | 6,711 | 153 | #' banc-hemibrain-nblast-images — Render per-query BANC↔hemibrain NBLAST review PNGs.
#'
#' @section Reads:
#' - per-query NBLAST CSVs under `<banc.nblast.hemibrain.save.path>/results_with_mirrored/<ver>`
#' - BANC + hemibrain BANC-space meshes
#'
#' @section Writes:
#' - `<banc.nblast.hemibrain.save.path>/images... |
28dde4f10b9e64a60cf7efb281c40cb07d1a2bfdef98bbd21ce9cc752ddb9ab0 | R | 6,712 | 126 | # function to find differential spatial signaling scores (differential S3)
# input: mFISH seurat object, with cell identity columns, assay, slots, networks, data directories, case and control dataset name, shortlist cell identity pairs and ligand-receptor pairs
# output: for all ligand-receptors and all cell identity p... |
f50284c7b3b95d3dddf2e1bac266208427b560315443f5d434bbf793ad89881e | R | 6,715 | 211 | # Environment ------------------------------------------------------------------
library(tidyverse)
library(lme4)
library(ggseg)
library(patchwork)
library(here)
library(glue)
library(ggraph)
library(igraph)
library(tidymodels)
source(here("src", "R", "plot_ggseg_brain.R"))
source(here("src", "R", "run_lda_workflow.R"... |
54a1f18713bec9603206aa2b4323abaeb0a5d899c74c075e763cc430b539aaab | R | 6,716 | 262 | ---
title: "Quality Control"
author: "bcbio"
date: "`r Sys.Date()`"
output:
html_document:
code_folding: hide
df_print: paged
highlights: pygments
number_sections: true
self_contained: true
theme: default
toc: true
toc_float:
collapsed: true
smooth_sc... |
107cc5cd8d0a59f7fcf10a0d9c49044e0b09737f464fee698d949e366952c54e | R | 6,730 | 290 | ---
title: "KM plots of aging-associated CpGs in external cohort (ADNI)"
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
df_print: page... |
ca4a6650165b80c66a1cfe1a49c581e4a56ad6d0441630271a3aa9e66c49bebb | R | 6,730 | 171 | #!/usr/bin/env Rscript
# Load required packages
require(metafor)
require(metap)
require(dplyr)
require(purrr)
# Load configuration and utilities
source("config.R")
source("utils.R")
# Parse command line arguments
args <- commandArgs(trailingOnly = TRUE)
if (length(args) != 1) {
stop("Usage: Rscript ... |
c5aefdce6cdcfa98683d335543394a43f124074b6f142eba22cd89fc46346a89 | R | 6,737 | 230 | 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 ... |
de40307e547182a77f522ee30ccfe7f03bfff7e3f286d0f0bc926ef5dd5fe4bb | R | 6,741 | 171 | ---
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 ... |
dfe13b36c9af331183f914345a9ed2b0b624392c59f229467ea99c019ea18f51 | R | 6,751 | 188 | library(dplyr)
library(pheatmap)
library(viridis)
library(RColorBrewer)
#Plotting the top10 -log(p-value) of NGN3, NGN9, NGN16, NGN18
leftright <- c("Oxidative Phosphorylation",
"Mitochondrial Dysfunction",
"Respiratory electron transport",
"RHO GTPase cycle",
... |
c838a1dc5af6044f82842e24e3639eb03802a1ce852f544e3f5a0622f915c24f | R | 6,763 | 159 | #' banc-fanc-nblast-images — Render per-query BANC↔FANC NBLAST review PNGs.
#'
#' @section Reads:
#' - per-query NBLAST CSVs under `<banc.nblast.fanc.save.path>/results/<ver>`
#' - BANC + FANC BANC-space meshes
#'
#' @section Writes:
#' - `<banc.nblast.fanc.save.path>/images/<ver>/todo/*.png`
#'
#' @section Invok... |
095f5ea87aef61c8ae9319aa2d7e39e9fddd31bf754c20bccc326ee654a897a6 | R | 6,777 | 178 | #!/usr/bin/env Rscript
suppressPackageStartupMessages({
if(!require(optparse)){ install.packages("optparse"); library(optparse) }
if(!require(data.table)){ install.packages("data.table"); library(data.table) }
if(!require(tidyverse)){ install.packages("tidyverse"); library(tidyverse) }
if(!require(ggplot2)){ i... |
5586e0aefa4e9b9b58d3c15c9cfca3cfa7b548c8a73d6c71d3e4735827e3e276 | R | 6,783 | 209 | #title: "Post-hoc ANOVA test Peri/resection cavity area"
#author: "Mona Zimmermann"
#date: ""
#output: html_document
#### Email: m.l.m.zimmermann@amsterdamumc.nl
#### Purpose of script: Statistical analysis for the post hoc test of clinical
#### relevance in activity changes for the peri-resection cavity
##... |
76931b1eff8a4bcfb627ee4992e59efc2c62d248873bacf7468228bef13ea541 | R | 6,789 | 308 | ---
title: "Prepare pseudo-bulk data and conduct DE analysis"
author: "Wei Sun"
date: "`r Sys.Date()`"
output:
html_document:
theme: journal
highlight: tango
toc: true
toc_depth: 3
toc_float:
collapsed: true
smooth_scroll: false
number_sections: false
df_print: paged
---
```{r s... |
161619c51ee12e1d6abb5b7558d7391cc353cd775dd44e585da4663ca9660956 | R | 6,791 | 247 | # ============================================================
# KPCA design matrix construction
#
# Three KPCA-based design matrices are constructed:
# - P : pre-image–guided truncation using r_P components
# - PA : adaptive truncation within the P set
# - A : fully adaptive truncation based on global eigenval... |
44c40d7a456e3a0262d131e63de7315c7f0c8836ce44bae096d2c4c184ff7ee4 | R | 6,793 | 208 | ## Simulating an ordinal trait with genetic association
## Strategy:
## simulate a large population with an ordinal association (K categories,
## proportions per category, and a defined minor allele frequency and
## odds ratio, under proportional odds assumption).
## Then, select samples from the large ... |
b4d38ce76efa50f24343fbe29b1e1b0580be7d5e6f03850622013a57702c82a8 | R | 6,817 | 217 | #### Section: Plot Context Initialization ####
initialize_anova_plot_context <- function(data, info, layout_values) {
factor1 <- info$factors$factor1
factor2 <- info$factors$factor2
order1 <- info$orders$order1
order2 <- info$orders$order2
if (!is.null(factor1) && !is.null(order1) && factor1 %in% na... |
81cb6e273368165f680d6b6574c43e822a66e51e742e4428cb9c22b9bf1b4302 | R | 6,829 | 191 | ################################################################################
# Marker-based dot plot for graph-based clusters
#
# - No absolute/local paths are hard-coded or printed
# - Input/output locations are provided via command-line arguments
# - Cluster labels remain unlabeled (numeric IDs only)
#
# Usage:
#... |
c6361c2b67039f5ad3c368a40961cbd39568501b7c7a09642de96723d71a676b | R | 6,832 | 158 | ##############################
### Run ArchR differential gene accessibility on multiome ATAC data for Myeloid cells
### Author: Kevin Johnson
##############################
## Myeloid cell states gene differential gene accessibility
# Specify directories
workdir <- "/vast/palmer/pi/verhaak/kcj28/care_idh_mut/results... |
2df9c1a4d895806800d318b521d96b998dee023cfb9b2cd4468b13697e34908f | R | 6,844 | 141 | # ==============================================================================
# Script 06: Supplementary Figure 5 - Key ILC vs NST Alterations (Patient Tumors)
# ==============================================================================
# Description: Builds frequency table and barplot of LumA ILC vs NST alterat... |
a795fcfa0e9fd19489ffda4327e8da0f3e3ed19edf792e439efd743215d7cf73 | R | 6,852 | 147 | #' Create Marker-free Visualization
#'
#' Generates a heatmap representing correlation coefficients.
#' Includes options for clustering and tiered significance markers (***, **, *).
#'
#' @param ann An object containing correlation data (`ann@results$marker_free$corr`) and p-values.
#' @param meta A data frame contai... |
3e67794fd062bba3c3c4ef39cdf7692f67019375eeadf59e046c8992463f3f85 | R | 6,856 | 100 | library(neuprintr)
library(malevnc)
library(igraph)
library(nat)
library(RCy3)
#find neurons that make up an indirect pathway between two neurons of interest,
#by their contribution to indirect connectivity strength
#uses the get_partners_by_multistep_conn function with inputids and outputids specified
#for a given p... |
bf15af0281ed968e9f438cb55d85a1d7a0ab00c563efea89069620988346b080 | R | 6,871 | 144 | #This script is to compare our clustering and nucseq annotation to publicly available datasets
library(Seurat)
library(ggplot2)
library(dplyr)
library(stringr)
#1. read in data
data<-readRDS('~/OneDrive - University of Cambridge/NodoMap/datasets/diet_nodomap.RDS')
#Read in Kupari annotations
kupari<-read.delim('~/One... |
a5e2b9133f07e57826e4204ab903b995e538806b30e6a1c6ea89e215357febd2 | R | 6,872 | 202 | library(Seurat)
library(miloR)
library(SingleCellExperiment)
library(scater)
library(scran)
library(dplyr)
library(patchwork)
load("Astro_clusters.RData")
counts <- Astro_sub@assays$RNA
subset_FADS1@assays$RNA <- counts
subset_fad <- subset_FADS1
DefaultAssay(subset_fad) <- 'RNA'
subset_fad <- as.SingleCellExperime... |
7b73abe0bf9c55af8590272ba9adc858d108eafcf804e51fff0dc7644bf72d26 | R | 6,874 | 186 | # This code has been adapted from the wrsk package ((Brodinova et al (2017))
GenSynData<- function(size_clusters,
p_inf,
p_noise,
mu_clusters,
s_clusters,
out_unif_range,
out_n,
... |
79bb8fb1b2d5ab7cc92ffb09e0b4fd233415ae85dc39f449c63ecd27ad8f645d | R | 6,877 | 198 | library(dplyr)
library(pROC)
library(ROCR)
library(ggplot2)
library(stringr)
library(paletteer)
library(patchwork)
all_file_paths <- "D:\\work2\\t2dAndComplicationsUkb\\data\\processed\\lgbmNonMlr3_2ndEd" %>%
list.files(path = ., pattern = ".*", full.names = TRUE, recursive = TRUE)
aucPIndex <- which(str_d... |
6e44b746c141dfb98597a934d799a2fd2975a1d7c5ae4022261d9f0f58f8cdca | R | 6,879 | 206 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
# Cluster DNs, use various distances and explore ... |
804446a0c71e05d09f6fa4c45e7d0970323dff83fc42e9b85c18600fe86b581b | R | 6,880 | 206 | #
# sleuth: inspect your RNA-Seq with a pack of kallistos
#
# Copyright (C) 2015 Harold Pimentel, Nicolas Bray, Pall Melsted, Lior Pachter
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software F... |
322750013e4172d7f6d513bd0fb7ef4560f3d26f565fb0dfa9ba3cc973ff543e | R | 6,887 | 184 | #------------------------------------------------------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... |
fda107cc93033f9dbafe7f8e3be1ce51d334f42740b7dc68002c21925eb3929e | R | 6,898 | 288 | ---
title: "Recalculate ROSMAP data DMR medians"
author:
- David Lukacsovich^[University of Miami]
- Lily Wang^[University of Miami]
date: "`r format(Sys.time(), '%d %B %Y')`"
output:
rmarkdown::html_document:
highlight: breezedark
theme: lumen
toc: true
number_sections: true
df_print: paged
... |
fd998faee4bfd3d6ca5add2c30fd0fb68724c2ee1be7de07b3bf8b5643a31b4f | R | 6,903 | 159 | #' banc-fafb-nblast-images — Render per-query BANC↔FAFB NBLAST review PNGs.
#'
#' For BANC neurons lacking `fafb_match`. `BANC_NBLAST_REDO=TRUE` to regenerate.
#'
#' @section Reads:
#' - per-query NBLAST CSVs under `<banc.nblast.fafb.save.path>/results/<ver>`
#' - BANC + FAFB BANC-space meshes
#'
#' @section Writes... |
d54f8e89856a2629d8c196e39662762363082d666c6fb4e38920d10b8752ddd9 | R | 6,904 | 198 | ### Data Preprocessing - Creating a long form dataframe from the FC matrices ###
# Install packages
#install.packages('stringr')
#install.packages('tidyverse')
#install.packages('readxl')
#install.packages('reshape2')
# Packages
library(stringr)
library(tidyverse)
library(readxl)
library(reshape2)
######### Setting ... |
08c05735daa051d900eb2017829ee96c91181af576208f736716abc5726ff58e | R | 6,909 | 181 | here::i_am("scripts/2.validations/2.7.POU5F1_LTR7_enrichment/2.7.2.get_LTR7_positions_and_info.R")
library(plyranges)
library(tidyverse)
library(rtracklayer)
library(GenomeInfoDb)
library(DBI)
library(RMySQL)
library(here)
wd <- here("data/validations/POU5F1_LTR7_enrichment/")
# Get LTR7 and HERV-int elements from ... |
22ac72943af61ebbc7267eda11b9c48bd35224948381d72b7fd887c65c51a311 | R | 6,929 | 162 | setwd("./SD/SD2")
source("SD2_utiles.R")
library(anndata)
library(scater)
###helper functions
generate_spots = function (se_obj, clust_vr,lower = 2, upper = 10, n = 1000, verbose = TRUE)
{
if (is(se_obj) != "Seurat")
stop("ERROR: se_obj must be a Seurat object!")
if (!is.character(clust_vr))
stop("ERRO... |
a098d66d6df5ca17a88251d98dbf317028438c73e3e6d4d4f27f03e8a8184c9f | R | 6,948 | 239 | #!/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="list of summit bed")
parser$add_argument("-g", "--ge... |
f5eff15cc5d56221619df2ed79d6095526cfe6523be7d265754eee83ebdb28f6 | R | 6,952 | 158 | #PCA
#http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual#R_clustering
#http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual#clustering_pca
#http://faculty.ucr.edu/~tgirke/Documents/R_BioCond/My_R_Scripts/my.colorFct.R
args<-commandArgs(TRUE)
RCSU_file = sprintf("%s", args[1])
vir_taxa_file = sprintf("%s"... |
1ef319c912e0b10e41449edd6805a8150887fb5d2dfb3ddcc0257b81d57eef93 | R | 6,953 | 182 | library(ggExtra)
library(ggplot2)
library(tidyr)
library(MetaNeighbor)
library(conflicted)
library(magrittr)
library(reshape2)
library(dplyr)
library(here)
library(readr)
library(tmod)
conflict_prefer("count", "dplyr")
conflict_prefer("select", "dplyr")
conflict_prefer("filter", "dplyr")
conflicts_prefer(dplyr::rename)... |
42df7710c307795187bacfcf8642e4f636551e96cd3730066426b69db6df7167 | R | 6,961 | 221 | #' @title GENI plots test data for the Manhattan plot
#'
#' @description An R object containing formatted results from a genome-wide
#' association study (GWAS) of interleukin-6 levels with *p*-values < `1e-3`
#' ([GCST90274815](https://www.ebi.ac.uk/gwas/studies/GCST90274815)).
#'
#' @name geni_test_manhattan
#'
#... |
45bd30507d73fc38562c62439f9c62be6519c72701e05656bbc5584783a23a80 | R | 6,962 | 156 | #' banc-meta-fix — One-off spot-fixes for BANC metadata issues.
#'
#' Ad-hoc fixer script for the franken / flywire / manc metas, run by hand
#' when a class of metadata bugs surfaces. Not part of any automated run.
#'
#' @section Reads:
#' - `<banc.meta.save.path>/flywire_meta.csv`
#' - `<banc.meta.save.path>/manc... |
11e1470564f447b5a68370d8aac5563dba761fc23f40973831b792bbaf963aea | R | 6,964 | 199 | ---
title: "quantanx_PRS"
output: html_document
authors: "Rujia Wang & Megan Skelton"
---
https://github.com/zhilizheng/SBayesRC
# Prepare summary statistics for SBayesRC
Created by Tian Xie 2023/06/12
```{r sbayesrc prep}
library(data.table)
library(dplyr)
#install.packages("https://github.com/zhilizheng/SBayesRC/... |
9039e0553bfa8b03b91b9f6416ccf6dc2ef55fda409ebea31c35e9c2b651f1dc | R | 6,969 | 205 | #!/usr/bin/env Rscript
###############################################
# Manoj M Wagle (USydney, MIT CSAIL)
###############################################
suppressPackageStartupMessages({
library(ggplot2)
library(data.table)
library(ggridges)
library(rlang)
})
args <- commandArgs(trailingOnly = TR... |
324f28cd060f997031e04bf97985abccf064a0bdbc3421e8a04c0162162b3421 | R | 6,981 | 93 |
plotfun.TRs.only <- function(samples=NA, visfrom = NA, visto = NA, vline= NULL, vline2 = NULL, # breakseq = 1500,
breakseq=NULL, ylim=NULL, cagefr.clust=NULL,
annot_fill_column = annot_fill_column, palette=colorvec, tr.palette = colorvec,
... |
5803168fb47bec9a9f695dc817c8d1bd8a1c61c295e58c9059d84220da830751 | R | 6,981 | 211 | # Load required libraries
library(Seurat)
library(patchwork)
library(ggplot2)
library(dplyr)
# Load individual Seurat objects into the environment
E12.5_1 <- readRDS(".../E12.5_1.RDS")
E12.5_2 <- readRDS(".../E12.5_2.RDS")
E12.5_3 <- readRDS(".../E12.5_3.RDS")
E13.5_1 <- readRDS(".../E13.5_1.RDS")
E13.5_2 <- readRDS("... |
c347e6fd3bd60211bc9b7ce43e29f4cecbaa5cce458d9620be9241b1fc3a263a | R | 6,983 | 182 | #load libraries
library(gwaRs)
library(ggplot2)
library(dplyr)
library(ggrepel)
#load in data
#brain MTG
Ext.DMR.Data <- read.csv("D:/ManhattanPlot/Ext.DMRs.con.vs.ad.meth.csv")
#Brain PFC
#Ext.DMR.Data <- read.csv("D:/Second.Ext.Meth.Data.MtSinai.csv")
Int.DMR.Data <- read.csv("D:/ManhattanPlot/Int.CONvsAD... |
5cb37281a607273644b9e11274a6258811baaaf5a0c171bc8c42b0be3b4e3ee4 | R | 6,987 | 171 | #安装包
install.packages('Seurat')
install.packages(c('dplyr','patchwork'))
install.packages('BiocManager')
BiocManager::install('scater')
install.packages('ggplot2')
#加载包
library(Seurat)
library(dplyr)
library(patchwork)
library(cowplot)
library(tidyverse)
library(ggplot2)
#导入数据
pbmc.data <- Read10X... |
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