sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
99fd439f36259e2457f4a0ba482ded47ba7f86c6fd50e683095e26258253d92c | R | 5,706 | 147 |
library("tidyverse")
library("sessioninfo")
library("DeconvoBuddies")
library("here")
## prep dirs ##
plot_dir <- here("plots", "13_PEC_deconvolution", "11_deconvo_plots_donor_subset")
if (!dir.exists(plot_dir)) dir.create(plot_dir, recursive = TRUE)
## load colors & shapes
load(here("processed-data","00_data_prep",... |
f0a1b95e57f8fedcda2d6baf808fe727c4e6dec4eb6d552c713ff92ba797c8a8 | R | 5,709 | 160 | #' Run differential rhythmicity analysis for RNA-seq data using DESeq2
#'
#' @inheritParams compareRhythms
#' @keywords internal
compareRhythms_deseq2 <- function(counts, exp_design, lengths, period,
rhythm_fdr, compare_fdr, amp_cutoff,
just_classify,... |
7240d323f8dd9535082b6261ef1504518986fb9de1299253ca1a6eac055cec3f | R | 5,713 | 138 | #----04_exclude_animals_v01_single_experiments----------------------------------
#-------------------------------------------------------------------------------
# Locomotor activity analysis for Reinhard et al. 2025 (10.1073/pnas.2506164122)
# Requirements:
# 1)scripts:
# 01_setup_v01
# 02_variables_an... |
5cb21d1359d886ef88bb5e9e8cd046a4f08b2897a905ef57821cb41ed3a6d843 | R | 5,714 | 142 |
## Create gene set file and gene size file from EWAS results table for use with MAGMA ##
library(biomaRt)
library(dplyr)
library(org.Hs.eg.db)
library(data.table)
#1. Load results ================================================================================================================
if(grepl(".rds", resF... |
25b9b95f5be6260cc90d434942297a8dc8b42d651583d17d848263803632b71a | R | 5,716 | 133 | #' Smooth Doublet Scores
#'
#' This function applies a distance-weighted kNN smoothing to single-cell
#' doublet probabilities, amplifying high-probability doublet clusters while
#' suppressing isolated noisy predictions.
#'
#' @param x A \code{SingleCellExperiment} object (containing the
#' 'scDblFinder.score' co... |
08c20217c29d881fa300f87ebc7434c7e3cebc1580c44b7a66e343be26a5b87a | R | 5,721 | 198 | # Siwei 21 Jun 2023
# Process all GAs (FASTQs trimmed (2 batches, 1 re-seqed)
# + previous ones from 2019)
# note the sample names are complex, need processing
# Jun 2023
# init
library(readr)
library(vcfR)
library(stringr)
library(ggplot2)
library(parallel)
library(MASS)
library(RColorBrewer)
library(grDevices)
#... |
328bca4f15e6ef7af0cf9870a9cd90ea6bba5614b3a3aaa3845c420c76195879 | R | 5,727 | 141 | # Data integration with Harmony
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# March 2022
# Based in the Harmony tutorial for integration with Seurat
# http://htmlpreview.github.io/?https://github.com/immunogenomics/harmony/blob/mast... |
062ce2c636759dc2114bc2b0d85b77e3371443e91b60aeb95708da81dbc930c0 | R | 5,732 | 199 | library(Seurat)
library(tidyverse)
library(here)
# load in metadata and dendrogram
meta <- readRDS(here("data", "human_meta.RDS"))
dend <- readRDS(here("data", "human_dend.RDS"))
dend_order <- labels(dend)
#Plot 1: layer heatmap
#check all SS clusters are represented
ss_cl <- meta %>%
filter(species_tech %>% ... |
9537fff3131bd0eff73608155dbfca7e6979df960c9a8df8b4855b68f5b61855 | R | 5,734 | 170 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
list2env(rjson::fromJSON(file = "00_configs.json"), envir = .GlobalEnv)
library(Seurat)
library(Signac)
library(ClustAssess)
library(qs)
library(rhdf5)
library(dplyr)
library(future)
library(doParallel)
library(ComplexHeatmap)
peak_folder <- file.path(project... |
56d85db1e2c2a17882821210066d0de2b173190403d986a8e7fdeea43fe00a81 | R | 5,741 | 131 | # Add clusters from Harmony to normalized Seurat object
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# May 2022, edited Dec 2022
# activate conda environment in ITHACA
# conda activate use_seurat_r4
# Open R
# R
# Load packages
li... |
b3c32dd24353903af5d7154582686b3860d7e840e7951b7e8c97c35fc7adb7c6 | R | 5,742 | 177 | # Install packages
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("clusterProfiler")
BiocManager::install("org.Mm.eg.db") # Mouse genome annotation package
set.seed(0)
# Load packages
library(clusterProfiler)
library(org.Mm.eg.db)
arqvo <- dir(... |
bdd7ce61e11aec6e22386a135c1316a64edd8193d56a92ae11b1f63b4e132d1b | R | 5,745 | 149 | #File to identified enriched terms in DE genes between neuronal classes using the pathfinR algorythm.
library(pathfindR)
library(openxlsx)
library(biomaRt)
library('org.Hs.eg.db')
library(writexl)
library(plyr)
library(tidyverse)
library(ComplexHeatmap)
library(circlize)
library(STRINGdb)
library(igraph)
protein_i... |
4a199927679ca9b45e6e18e0e4375b5e3efae1221f83c86f511527e2525c59d8 | R | 5,747 | 186 | # ====退院推移図データセット====
recoveredData <- reactive({
dataset <- mhlwSummary[, .(
陽性者 = sum(陽性者, na.rm = T),
回復者 = sum(退院者, na.rm = T),
重症者 = sum(重症者, na.rm = T),
死亡者 = sum(死亡者, na.rm = T)
), by = "日付"]
dataset <-
merge(
dataset,
confirmingData,
all.x = T,
by.x = "日付",
... |
6bba7ed60c381d979219e1b52c0674276ed9196a74b009ccb15cb83279970737 | R | 5,750 | 106 | ###ÕûÀí½á¹û
library(tidyr)
library(tidyverse)
library(openxlsx)
out_all_res <- data.frame()
out_null_res <- data.frame()
inputpath <- "/mnt/data/lijincheng/mGWAS/result/01UVMR/02mediator_outcome/final_with_conmix/with_conmix/"
outlist <- list.files(inputpath)
###metabolite 249
for(i in 1:249){
inputpath <- "/mnt/da... |
4c19144a8aea7077fb968c0cfc0460ef8a372af88ea69e601401271bdc447cdb | R | 5,757 | 159 | library(magrittr)
library(data.table)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggrepel)
library(Hmisc)
library(cowplot)
library(pROC)
library(stringr)
#genes that are relevant across drugs? sensitivity genes? essentiality?
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
read_dat <- functio... |
cef1ae2cf7be50a38016f346b533008b7b48af67d619e8cbb40710b4aceccce3 | R | 5,758 | 115 | ```{r}
library(kronos)
library(tidyverse)
library(ggplot2)
setwd("C:/Users/U177202/PhD/Experiments_thesis/Youth_Jet_Lag_Experiment_2/YJL_Exp2_Microarrays/YJL_Exp2_KRONOS/YJL_Exp2_circadian_clock/YJL_Exp2_circadian_clock_ht_extra/Results")
```
```{r}
library(readxl)
yjl_ht_bmal1_new <- read_excel("C:/Users/U17... |
707839cc555e2955b0a91436c2056d7cd84f3b6ac502f48d0d86ef5fec0e8ea1 | R | 5,761 | 167 | setwd("~/mount/hpc_uni/mammary_gland_transcriptomes/test_pipeline/")
library(tidyverse)
theme_set(theme_minimal(base_size = 16))
regular <- read_tsv("results/salmon/stromal_t1_bc_M00323192/quant.sf")
diploid <- read_tsv("results/salmon_diploid/stromal_t1_bc_M00323192/quant.sf")
regular <- regular %>% select(gene = N... |
e36b437bc6eb1c2bb1f23807e176936424c7c87290b0b459f20de294780a4980 | R | 5,761 | 127 | # load libraries
library(tidyverse)
library(data.table)
library(Matrix)
library(Rfast)
library(matrixStats)
library(ggridges)
library(reticulate)
library(anndata)
library(gtools)
# source functions
source("/inkwell05/ameer/functions/0_source_functions.R")
# we will predict the significance (alpha = 0.05) of one slice... |
aad8b7593c71ab5e0e6ddd4a39ef19023661056f0c9dbb6146da32bd816736a4 | R | 5,765 | 121 | get_local_outcomes <- function(outcomes_df = outcomes_df)
{
library(data.table)
message("outcomes_df = dataframe from FR02, mibio,pathways;containing: exposures name id path")
raw_outcomes <- lapply(seq(1, dim(outcomes_df)[1]), function(i){
if(outcomes_df$exposures[i] == "FR02")
{
sp <- fread(outcomes_df... |
ba0817c6833a737fce2e43a364829710184f519fee4d4ebda6042a0873d45c26 | R | 5,772 | 169 | library(data.table)
library(dplyr)
library(tidyr)
library(purrr)
library(ggplot2)
library(grandR)
library(ggrastr)
library(glue)
give.n <- function(x){
return(c(y = median(x)*1.05, label = length(x)))
}
#' function to plot fitted half life curves in two conditions for specific genes
plot_hl <- function(go_of_inter... |
4425a68f5541bb307e7af410ad2e30442cd7cc27a9efe352fb40df4016178cf5 | R | 5,782 | 164 | #!/usr/bin/env R
#
# Analyze dispersion across cell types. Namely, plot the mean and var for genes.
#
library(SingleCellExperiment)
#----------
# set paths
#----------
sce.fpath <- file.path("/dcs04/lieber/lcolladotor/deconvolution_LIBD4030",
"DLPFC_snRNAseq/processed-data/sce/sce_DLPFC.Rdata"... |
2701cd4eacb3d70731f36afd5e0cf1eb02b2ee12d3dc6935630c33fcbe62ef7c | R | 5,784 | 168 | rm(list=ls())
set.seed(123)
source('load_libraries.R')
source('aux_functions.R')
library(dplyr)
library(ggplot2)
library(ggalluvial)
#Read and filter GO data
data=GSA.read.gmt('data/Human_GO_bp_with_GO_iea_symbol.gmt')
genesets=data$genesets
names(genesets)=data$geneset.descriptions
geneset_sizes = sapply(genesets, l... |
64b4351109e2a314ea1b308857661a85a4a7a4b94f8139ad9b894c2d0a0539b6 | R | 5,784 | 101 | # Load required R packages
library(data.table) # For efficient data reading, processing, and merging
library(MungeSumstats)# For GWAS data formatting and genome version conversion
# -------------------------- 1. Basic Parameter Setup --------------------------
# Output directory (for converted data and log file... |
1083c3ec80550c544b66499b1a2a9158a553a222d1913ca4820f1654f44101af | R | 5,785 | 156 | # Authors: Christian Wachinger based on ComBat code by Jean-Philippe Fortin
# The original and present code is under the Artistic License 2.0.
# If using this code, make sure you agree and accept this license.
combatPP <- function(dat, batch, PC=NULL, mod=NULL, eb=TRUE, verbose=TRUE, parametric=TRUE){
dat <- as.ma... |
aed5f0618a410d4d2e8c46c0aad3cc4dfe98f18d3053a2901df0c5638fce5c01 | R | 5,788 | 101 |
library("SingleCellExperiment")
library("iSEE")
library("shiny")
sce <- readRDS("sce_nac_small.rds")
packageVersion("iSEE")
initial <- list()
################################################################################
# Settings for Reduced dimension plot 1
####################################################... |
ef0f315074d72f97d1b20caa12a3846777990c126182d4b09049660c8cddf213 | R | 5,788 | 101 |
library("SingleCellExperiment")
library("iSEE")
library("shiny")
sce <- readRDS("sce_hpc_small.rds")
packageVersion("iSEE")
initial <- list()
################################################################################
# Settings for Reduced dimension plot 1
####################################################... |
51b4c4d87bf4fc82733689972ff8b8f73701fd8a404f62f0e4f5f447967179a3 | R | 5,790 | 167 | library(fgsea)
library(gprofiler2)
library(tidyverse)
library(org.Hs.eg.db)
library(GO.db)
library(reactome.db)
# Prepare GMT files for pathway analysis
prepare_gmt_data <- function(min_size = 15, max_size = 500) {
# Filter gene sets by size
adjust_gene_set_size <- function(gene_set, min_size, max_size) {
... |
d761ff189a2cefac6cc6fc3f0d7565be1769327d408be600bfcd59dc3d1746a6 | R | 5,790 | 101 |
library("SingleCellExperiment")
library("iSEE")
library("shiny")
sce <- readRDS("sce_amyg_small.rds")
packageVersion("iSEE")
initial <- list()
################################################################################
# Settings for Reduced dimension plot 1
###################################################... |
5329e0fa6cf1d2fd3410e5f57872e64d2f2176eddeeb512ef3e79c7a34351cec | R | 5,792 | 101 |
library("SingleCellExperiment")
library("iSEE")
library("shiny")
sce <- readRDS("sce_sacc_small.rds")
packageVersion("iSEE")
initial <- list()
################################################################################
# Settings for Reduced dimension plot 1
###################################################... |
54a3b5e956552cd59c7def10b96b3792ed605c63777a609a8c4c1fefca17475a | R | 5,796 | 101 |
library("SingleCellExperiment")
library("iSEE")
library("shiny")
sce <- readRDS("sce_dlpfc_small.rds")
packageVersion("iSEE")
initial <- list()
################################################################################
# Settings for Reduced dimension plot 1
##################################################... |
9254e6616fcd3c60eb3fd7058a1146d42bccd5576f64e559b9ac5195538c888e | R | 5,796 | 155 | # This tests the doublet density machinery.
# library(scDblFinder); library(testthat); source("test-computeDoubletDensity.R")
set.seed(9900001)
ngenes <- 100
mu1 <- 2^rexp(ngenes)
mu2 <- 2^rnorm(ngenes)
counts.1 <- matrix(rpois(ngenes*100, mu1), nrow=ngenes)
counts.2 <- matrix(rpois(ngenes*100, mu2), nrow=ngenes)
co... |
0ad94b307ca9920d7f7b4dd0db42ad1f409d5f337763e508f4e6cb6c7887078b | R | 5,797 | 118 | #plot for the simulation results
##### load packages and results ####
if (!require("here")){
install.packages("here")
library("here")
}
if (!require("tidyverse")){
install.packages("tidyverse")
library("tidyverse")
}
if (!require("magrittr")){
install.packages("magrittr")
library("magrittr")
}
if (!require(... |
d1b8e2e250aef1574a27447b647da44e895f79a07628512b54061901f6fd51a9 | R | 5,809 | 132 | # GPT social perception: Calculate how similarly GPT4 evaluated video experiment data compared to real human participants
#
# Process:
# 1. We have approximately 10 human raters -> select all possible combinations of K raters, K = {1,2,3,4,5}
# 2. Calculate the average correlation between the left_out_group... |
b3d60a4d31ba68ce37e527f2a6b3131784d8f8df09ffc09606a9a113d7c94502 | R | 5,814 | 151 | # ----------------------------------------------------------------------------
# Libraries and setup ----
library("Seurat")
library("SeuratDisk")
library("tidyverse")
library("argparse")
# Command line arguments ----
parser <- ArgumentParser(description = "Integrate seurat object")
parser$add_argument('--input', '-i... |
35ca53d1dce6f4defb888a52749f217747d168eeadbd665c8d382eae2db7dced | R | 5,824 | 132 | # GPT social perception: Calculate how similarly GPT4 evaluated frame experiment data compared to real human participants.
#
# Process:
# 1. We have approximately 10 human raters select all possible combinations of K raters, K = {1,2,3,4,5}
# 2. Calculate the average correlation between the left_out_group a... |
de2a7b063e5a1db6b54d7ce759c8dd0c970e540df11efdb32ce427b4aee75cb4 | R | 5,826 | 148 | dev <- FALSE
if(dev) {
library(ggplot2)
library(tidyr)
work_dir <- "/home/ricoderks/Documents/LUMC/Projects/soda-light/"
feature_table <- read.csv(file = file.path(work_dir, "240220_features.csv"),
row.names = 1,
header = TRUE)
data_table <- read.csv(f... |
3a82924bce7ba5d3d89bbd0e36c97ef2608eb793f66d154982b187cfe3fa7023 | R | 5,839 | 132 | # GPT social perception: Calculate how similarly GPT4.1 evaluated video experiment data compared to real human participants
#
# Process:
# 1. We have approximately 10 human raters -> select all possible combinations of K raters, K = {1,2,3,4,5}
# 2. Calculate the average correlation between the left-out-gro... |
973aad070cb9057fc4480705e7a0139c1d7306f4e8d314c24dfa835f66e68e45 | R | 5,845 | 156 |
# devtools::install_github("Danko-Lab/BayesPrism/BayesPrism")
library("BayesPrism")
library("SingleCellExperiment")
library("here")
library("sessioninfo")
## get args
args = commandArgs(trailingOnly=TRUE)
marker_label <- args[1]
marker_file <- NULL
if(marker_label == "FULL"){
message("Using FULL gene-set")
} else... |
a567b0abf6ef5d789f04ab63211da607ce4df79f4355f4fb1a4b7c1617127071 | R | 5,851 | 142 | #' Version 2.0
#' Last modified on 20/01/2020
#' Script Task: Calculate alpha-diversity
#' Author: Ilias Lagkouvardos
#' Contributions by: Thomas Clavel, Sandra Reitmeier
#'
#' For meaningful comparisons of species richness across samples,
#' use of normalized sequence counts is expected.
#' For normalized richness ca... |
991d039779608fd0bbec4da51d94d8ea48f9a5226ed12e492b39d8a47bec0b9b | R | 5,857 | 132 | # GPT social perception: Calculate how similarly GPT4.1 evaluated frame experiment data compared to real human participants.
#
# Process:
# 1. We have approximately 10 human raters select all possible combinations of K raters, K = {1,2,3,4,5}
# 2. Calculate the average correlation between the left_out_group... |
3e15463017fa03ffe45bbcd9f884d68964c40702844251db1081ec047b2e45b8 | R | 5,866 | 86 | library(dplyr)
library(Seurat)
library(ggplot2)
library(EnhancedVolcano)
library(clusterProfiler)
library(org.Mm.eg.db)
# Load TM integrated data
sSC.integrated <- readRDS("/project/Campbell_Lab/yl7mfw/Data Analysis/20240620_TwoSpecies_OtherPlot/20240728_Species and Cluster markers/20240729_Orthologous_sSCintegrated.r... |
696e58001ba7340888aa09e55ec7cc27adc8a738f3439960822ee0ad4a33966d | R | 5,871 | 131 | if (!require("here")) {
install.packages("here")
library("here")
}
if (!require("tidyverse")) {
install.packages("tidyverse")
library("tidyverse")
}
if (!require("magrittr")) {
install.packages("magrittr")
library("magrittr")
}
if (!require("seismicGWAS")) {
if (!requireNamespace("devtools", quietly = TRU... |
3aa132dc32884787fd865489c2efffaef8b1b9e84d15e4cf1bac2299a6b42e15 | R | 5,874 | 136 | observeEvent(input$sideBarTab, {
if (input$sideBarTab == 'iwate' && is.null(GLOBAL_VALUE$Iwate[[1]])) {
# GLOBAL_VALUE <- list(Iwate = list(
# summary = NULL,
# patient = NULL,
# updateTime = NULL
# )) # TEST
fileList <- list.files(paste0(DATA_PATH, 'Pref/Iwate/'))
... |
c9828e528d67614d0b253faa695a558aea924a12d4f3c5417525da45edf62162 | R | 5,874 | 160 |
# install.packages("hspe_0.1.tar.gz")
library("hspe")
library("SingleCellExperiment")
library("jaffelab")
library("tidyverse")
library("here")
library("sessioninfo")
## get args
args = commandArgs(trailingOnly=TRUE)
marker_label <- args[1]
## not using txt list of marker genes, methods needs named list
marker_sets ... |
ede3e6c39512825a5875b1ad3be36cd3aa089d57ee5e0dda9db46200dc79887d | R | 5,879 | 163 | #!/usr/bin/env Rscript
#### Seurat CCA integration and LISI score
#### Author: Jana Biermann, PhD
library(Seurat)
library(dplyr)
library(ggplot2)
library(ggrastr)
library(gplots)
library(lisi)
library(tidyr)
library(magrittr)
library(viridis)
library(scales)
colBP <- c('#A80D11', '#008DB8')
colSCSN <- c('#E1AC24', '... |
9907b16ced76431dbdee8a0c49fafc1707414b006f2d583a6bc9ef9313606cd2 | R | 5,887 | 219 | ---
title: "CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project - cognitive genes: cell-type enrichment"
author: "Isabel Castanho"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_float:
collapsed: false
toc_depth: 4
code_folding: hide
---
---
... |
66ea58a520be843a076158f64033030a388a73638f1e6e2d85803f34f1a51e08 | R | 5,900 | 123 | #Analysis of Tabula muris data set using Spearman's correlation model
##### 1. load packages and data#######
###load packages
if (!require("here")) {
install.packages("here")
library("here")
}
if (!require("magrittr")) {
install.packages("magrittr")
library("magrittr")
}
if (!require("tidyverse")) {
install.... |
15636a52a90765f2d7ffeded7c7f59e6e89ba456e39c91a229406fa0eff671c3 | R | 5,910 | 178 | library(tidyverse)
library(data.table)
library(sessioninfo)
setDTthreads(1)
gwas_files <- list.files(path = "NAc_GWAS/", pattern = ".txt", full.names = TRUE)
aoi <- fread(gwas_files[1])
cpd <- fread(gwas_files[2])
dpw <- fread(gwas_files[3])
sc <- fread(gwas_files[4])
si <- fread(gwas_files[5])
bim <- fread("filter... |
2dab2b7f304e4be256342297012842bd1a6dda64b3942aa21e669678a8038b7b | R | 5,915 | 131 | #' Run differential rhythmicity analysis for normalized data using linear mixed effect model (lme4)
#'
#' @inheritParams compareRhythms
#' @keywords internal
compareRhythms_cosinor <- function(data, exp_design, period, rhythm_fdr,
compare_fdr, amp_cutoff, just_classify, longitudinal) {... |
33a42bea2b9f0a6855ef875b6da80c50e447029ff42375505d2ee77c7701f96c | R | 5,920 | 201 | # make figures for Alena PICALM paper
# Siwei 22 May 2024
# init ####
{
library(readxl)
library(stringr)
library(ggplot2)
library(scales)
library(reshape2)
library(RColorBrewer)
library(ggpubr)
}
## Western blot quantify ####
df_raw <-
read_excel("table_western.xlsx",
sheet = 1)
... |
25e1dc13f6d02fd368975cd7e00b3c0127e53bc3f445a7678e9c509e64f0e0a0 | R | 5,921 | 171 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
list2env(rjson::fromJSON(file = "00_configs.json"), envir = .GlobalEnv)
library(Seurat)
library(Signac)
library(ClustAssess)
library(qs)
library(rhdf5)
library(dplyr)
library(future)
library(doParallel)
library(ComplexHeatmap)
library(ggplot2)
first_group <- ... |
4dc102fb696a6394c5e86641a8c77eb46703336af0bce53eb65ba8f5d9c5fc39 | R | 5,925 | 179 | #!/usr/bin/env R
#
# Simulate the bulk RNA-seq dataset and write functions for analysis
# of differential expression results.
#
library(DESeq2)
library("ggplot2")
#-----------------
# helper functions
#-----------------
random_bulkdata <- function(design.str, num.lib = 2, num.prep = 3,
... |
a2a774ee3c8863649464f5f93d2c1f75d37aec391c0f11d515357ee41eac168a | R | 5,929 | 151 | library("tidyverse")
library("colorblindr")
library("ggthemes")
# library("RColorBrewer")
library("here")
#### Load data ####
# DLPFC snRNA-seq cell colors
# load("/dcs04/lieber/lcolladotor/deconvolution_LIBD4030/DLPFC_snRNAseq/processed-data/03_build_sce/cell_type_colors.Rdata", verbose = TRUE)
cell_type_colors_all... |
d00e15ce5efdf4d135fdac8118f6cbe8679713b2c40cccf2a31187206488fd37 | R | 5,932 | 150 | rm(list=ls())
source('load_libraries.R')
source('aux_functions.R')
# Read in databases and preprocess data
db_complete = read.csv("data/drugbank.tsv", sep = "\t") # download drugbank
dgib=read.csv("data/interactions.tsv",sep="\t") # from DGIB
dgib$drug_claim_name=tolower(dgib$drug_claim_name)
# find compounds that ar... |
2b5e988875efeef73764af9fda70c722e4f4eab3f23ce9848897631e8a3fa967 | R | 5,946 | 162 | library(tidyverse)
library(decoupleR)
library(tidytext)
source("scripts/helpers.R")
# What is the overlap between ELK1/ELK4 ChIP-seq targets and known signalling pathways?
# Is inferred activity a consequence of signalling pathway induction upon TDP-43 KD, or due specifically to ELK1/ELK4?
progeny_top100 <- read_tsv... |
6e0b4cdf3091bd026bc673f42a3eb2123840effc5871211b429c119bebe4812d | R | 5,946 | 155 | #!/usr/bin/env Rscript
suppressPackageStartupMessages({
library(optparse)
library(dplyr)
})
options(dplyr.summarise.inform = FALSE)
option_list = list(
make_option(c("-e", "--ens_var"), type="character", default=NULL,
help="ENS_ID variable"),
make_option(c("-m", "--proportion_min"), type="chara... |
47edaf805a7d3830dc75d169e33427bde6ddccf3d63913b1bb9c53ca9b8907c3 | R | 5,948 | 183 | observeEvent(input$linePlot, {
if ((input$linePlot == "vaccine") && is.null(GLOBAL_VALUE$vaccine)) {
vaccine <- fread(file = "50_Data/MHLW/vaccine.csv")
vaccine$date <- as.Date(as.character(vaccine$date), format = "%Y%m%d")
vaccine$total <- rowSums(vaccine[, 2:ncol(vaccine)])
vaccine[, `:=` (
me... |
7e13fe7c0617219292a3293b13bd055cefed21d46d6b9826428de594b68297b6 | R | 5,957 | 174 | get_six_UVMR_from_bulk_harmonised <- function(dat = dat,
exposure = "",
outcome = "",
outpath = "")
{
source("/mnt/data/lijincheng/mGWAS/result/02MRBMA/MRBMA_function/function/se... |
2b2377bb64b0accbc9fb1c9d663be12181aced214e758593641dd31036858d50 | R | 5,958 | 114 | library(tidyverse)
library(lubridate)
# =====データ読み込み=====
# 都道府県別新規感染者数
df1 <- read_csv("50_Data/byDate.csv") %>% pivot_longer(-1, names_to = "name_ja", values_to = "new_cases")
df1[is.na(df1)] <- 0
# 都道府県別新規死亡者数
df2 <- read_csv("50_Data/death.csv") %>% pivot_longer(-1, names_to = "name_ja", values_to = "new_deaths")... |
913027bbf8e8e713982f73b2add08881f1a3153213869dc4884f0a63f6b9bd8d | R | 5,966 | 95 | library(here)
library(SummarizedExperiment)
library(tidyverse)
#### confirm issue in SPEAQeasy output ####
load(here("processed-data", "01_SPEAQeasy", "round2_v40_2023-04-05", "count_objects", "rse_gene_Human_DLPFC_Deconvolution_n113.Rdata"), verbose = TRUE)
# load(here("processed-data","01_SPEAQeasy","round2_v40_2022... |
dbaf28c0ff15d403de2eda7d734d08e07494548397dadae419dab15738960421 | R | 5,973 | 88 | context("compareRhythms_edgeR")
load("test_data_rnaseq.rda")
exp_design_batch <- cbind(exp_design, batch, stringsAsFactors=TRUE)
test_that("edger analysis works for default params", {
results <- compareRhythms(countsFromAbundance, exp_design, method = "edger")
expect_s3_class(results, "data.frame")
expect_name... |
940bc8544ddd333adddfd2e5c3dcfb451b419156cf50b03318382dde94916851 | R | 5,978 | 176 | library(tidyverse)
library(pheatmap)
library(EnhancedVolcano)
library(circlize)
set.seed(1234)
#' Process DEG results for comparison
#' @param deg_data List of DEG results from MAST analysis
#' @param external_data List of external dataset results
#' @param z_score_cutoff Cutoff for z-score filtering
process_compariso... |
345c3d6a9664d8da50641c4627a5367ea2f17940e1010920ed60d13e48785fdd | R | 5,987 | 191 | ---
title: "iCLIP Intron enrichments in features"
author: "Michael Rauer"
date: "`r format(Sys.time(), '%d %B, %Y')`"
params:
rmd: "iCLIP.introns_analysis.Rmd"
output:
html_document:
code_folding: hide
df_print: paged
fig_caption: yes
number_sections: yes
tables: yes
toc: yes
toc_float:... |
236851e42c411b93f8892c65a9bd4223a7ad52d627d3f31e63f81fbb796dac5f | R | 5,998 | 151 | # R version 4.4.2
library(Seurat) # version 5.1.0
library(AUCell) # version 1.28.0
library(GSEABase) # version 1.68.0
library(tidyverse) # version 2.0.0
library(ggvenn) # version 0.1.10
# calculate molecular subtype enrichment scores for each cell using AUCell ----
# seurat object containing scRNA-seq data of our pati... |
6e744495bcc328881c9f21ae7966efe3b18ea0e8bd982f5db1794fff258ec02c | R | 5,999 | 149 | # зÖÀàºÍ·ÖÀà±ýͼ
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(dbplyr)
library(tidyverse)
UCR_coding <- read.table(file = "02-analysis/16-New_classification/01-UCR_from_protein_coding_gene.bed",
sep = "\t")
write.table(UCR_coding$V8, file = "02-analysis/16-New_classific... |
371bf56c15f574a53b92440e60f4f6368060b343752428358cce161787c2e096 | R | 6,004 | 133 | # Load necessary libraries
library(dplyr)
library(ggplot2)
library(gridExtra)
library(ggdist)
library(RColorBrewer)
# Set global settings
my_colors <- brewer.pal(12, "Set3")
# Load preprocessed data
AllDataSumPerPart <- read.csv("..../BehaviourDataSumPerPart_withBiomarkers.csv")
AllDataSumPerPartStop <- read.csv("...... |
1553daead8be2811e04b438a8e463c63742af41795422d5a988f7c30dc50c9c4 | R | 6,010 | 136 |
setwd("/project/Campbell_Lab/yl7mfw/Data Analysis/20240619_Integration_Clu21")
# load packages up into R
library(dplyr)
library(Seurat)
library(ggplot2)
library(googleVis)
Species <- readRDS("/project/Campbell_Lab/yl7mfw/Data Analysis/20240419_Three Species Integration_Orthologs and TSDB3/20240422_5_sSC.Neurons.rds"... |
9a91b1e6cda4de5192953f1b550d540ebc2b76c53b972435d978ccb65defab24 | R | 6,020 | 182 | # plot prep and filter lists:
PlotTheme = theme_bw() +
theme(axis.line = element_line(colour = "black"),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border ... |
75198bdf4085f424bf6079662de66193c087bd0f74b4901031b9ee978c074030 | R | 6,021 | 205 | # Siwei 24 Jan 2025
# plot new Ext. Fig 5b
# init ####
{
library(readxl)
library(stringr)
library(ggplot2)
library(scales)
library(reshape2)
library(RColorBrewer)
library(ggpubr)
library(dplyr)
library(data.table)
library(DescTools)
library(multcomp)
library(gridExtra)
}
## Fig_3B ####
d... |
b144cfaf7a0ed09dcf3f982e453d6be4bf4350f959497d27d8927d7c621e8b85 | R | 6,021 | 191 | # ÔÚhuman_brain_down_devASµÄÐòÁÐÖвéÕÒËùÓÐhexamerµÄÊýÄ¿
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(Biostrings)
# count down devAS hexamers -----------------------------------------------
# get human brain down devAS fasta
human_brain_down_devAS <- readDNAStringSet(
filepa... |
8d34b8caded97099f937ee0e5e672aa7d9922664557d80d9a2d68930f2beba34 | R | 6,029 | 187 | observeEvent(input$sideBarTab, {
if (input$sideBarTab == "google" && is.null(GLOBAL_VALUE$Google[[1]])) {
# GLOBAL_VALUE <- list(Google = list(
# mobility = fread(paste0(DATA_PATH, "Google/Global_Mobility_Report.Japan.csv"))
# )) # TEST
GLOBAL_VALUE$Google <- list(
mobility = fread(paste... |
97aba5985a0fc3b4a7bc593f37374c334b67069ed39c03f7ece0662c328976fc | R | 6,038 | 162 | library(magrittr)
library(data.table)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggrepel)
library(Hmisc)
library(cowplot)
library(pROC)
library(stringr)
#genes that are relevant across drugs? sensitivity genes? essentiality?
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
###################... |
1bbc076f1c2d626e4be6e4378aedb99120bd0f1d5c7768e7695a4750e74ccb92 | R | 6,044 | 150 | observeEvent(input$sideBarTab, {
if (input$sideBarTab == "miyagi" && is.null(GLOBAL_VALUE$Miyagi[[1]])) {
# GLOBAL_VALUE <- list(Miyagi = list(
# summary = NULL,
# patient = NULL,
# updateTime = NULL
# )) # TEST
fileList <- list.files(paste0(DATA_PATH, "Pref/Miyagi/"))
... |
ea1d0e290c7fa8650b4e59e4a7dcbc4488a5ccb106abff91398309961972f096 | R | 6,074 | 242 | #!/usr/bin/env R
#
# Learning DESeq2 analysis and formatting scripts to do evaluation of
# DE results, figure generation, etc.
#
# BiocManager::install("DESeq2")
BiocManager::install("airway")
library(DESeq2)
## indep hyp weighting
#library(IHW)
## example datasets
#library(airway)
#library(GEOquery)
#library(tximpo... |
4e9a48cb97a7a6cd379a08a9f87f821958e59f6f1d42d49d2968747e6b981921 | R | 6,094 | 122 | # Authors: Andrew Adey, PhD; Lauren Rylaarsdam, PhD
# 2024-2025
############################################################################################################################
### Nearest Neighbor Label Xfer
#' @title transferLabelsNN
#' @description Transfer metadata labels based on nearest neighbor analy... |
4c6af4604487d15bed12ed5fcf357222da358d379c1c1293056543d51e4d894c | R | 6,095 | 182 | #' Check UK Biobank field IDs
#'
#' @description Check if provided field IDs are valid and return all possible phenotype names in the UK Biobank RAP
#'
#' @return Returns a vector of strings (valid phenotypes).
#'
#' @author Luke Pilling
#'
#' @name fields_to_phenos
#'
#' @param fields A vector of character strings. Th... |
fbc8210ca651e44850f2ce49bef264b6bc2f1678583976c767dd8fd97b3177e4 | R | 6,098 | 144 | library("SummarizedExperiment")
library("here")
library("tidyverse")
library("SingleCellExperiment")
library("sessioninfo")
#### Plot Setup ####
plot_dir = here("plots","00_data_prep","data_standards")
if(!dir.exists(plot_dir)) dir.create(plot_dir)
pos_df <- tibble(Position = c("Anterior", "Middle", "Posterior"),
... |
8ebf4a2e6202a357e536edfcd6f0acecdce2a7c87b9ef97d08cd36aa443ae3b1 | R | 6,107 | 176 | #' Update UK Biobank field with `title` and `label` from the schema
#'
#' @description Variables such as education and ethnicity are provided as integers but have specific codes.
#'
#' The UK Biobank schema are machine-readable dictionaries and mappings defining the internal structure of the online Showcase. https://bi... |
4baa4eb5acc463a3e10b3681ebd63525d108a27367f006ab03c8ac3e3b6d9c7d | R | 6,110 | 117 | #----06_load_data_from_RDS_v01--------------------------------------------------
#-------------------------------------------------------------------------------
# Locomotor activity analysis for Reinhard et al. 2025 (10.1073/pnas.2506164122)
# Requirements:
# 1)scripts:
# 01_setup_v01
# 02_variables_an... |
e69bbb9f5283242d54efd8dc1d3f6cc9917c0e408254d0f9aeeca1f98835ebad | R | 6,132 | 138 | if (!require("here")) {
install.packages("here")
library("here")
}
if (!require("magrittr")) {
install.packages("magrittr")
library("magrittr")
}
if(!require("tidyverse")) {
install.packages("tidyverse")
library("tidyverse")
}
#load results
Kunkle_hc.micro_dfbetas <- read.table(here("results","Saunders","i... |
dac8f9ff6515674209d496d0cdd92721ac98d7b922e2196961ed90acf1da58c4 | R | 6,137 | 176 | # ÕûÀíËùÓеÄhuman essential gene
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
# »ñµÃhuman deg -------------------------------------------------------------
deg_eukaryotes <- read.csv(file = "01-data/21-Human_essential_gene/deg_eukaryotes.csv",
sep = ";", he... |
24d07d68a3564da077ce01b5f5fe86b267d5ee93c090ce9c49655f7370f77635 | R | 6,159 | 161 | #!/usr/bin/env R
#
# Analyze dispersion across cell types. Namely, plot the mean and var for genes.
#
library(SingleCellExperiment)
expt.str <- "dlpfc-ro1"
save.fpath.rds <- save.fpath.plot <- "/users/smaden/"
#-----
# load
#-----
# load snrnaseq
base.path <- "/dcs04/lieber/lcolladotor/deconvolution_LIBD4030/"
sce.... |
f508137c84cb6ea6fcc038edb0cade15c384211939be13234993db3b842c5977 | R | 6,167 | 136 | observeEvent(input$sideBarTab, {
if (input$sideBarTab == 'kanagawa' && is.null(GLOBAL_VALUE$Kanagawa[[1]])) {
# GLOBAL_VALUE <- list(Kanagawa = list(
# summary = NULL,
# updateTime = NULL
# )) # TEST
fileList <- list.files(paste0(DATA_PATH, 'Pref/Kanagawa/'))
indexName ... |
5395b0874031ccd3706159ba89867f1178560478a6f39d7d6f1b3b41a84b3dce | R | 6,174 | 207 | # =============================================================================
# 巨噬细胞亚群追踪分析脚本
# =============================================================================
# 功能：巨噬细胞亚群提取、重新聚类、细胞类型注释，并进行CytoTRACE分化轨迹分析
print("Hello world!")
rm(list = ls())
# 加载配置文件
source("../config.R")
# 设置工作目录
setwd(ENV_DIR)
lf <... |
aba7dc08d87de84ab153a0b363dbb3aa2d3de87c6cd185e99629063dc51764d0 | R | 6,178 | 220 | # Load required packages
# install.packages("scales")
# install.packages("ggplot2")
library(ggplot2)
library(dplyr)
library(tidyr)
library(readr)
##### Violin Plot #####
# Define stages
stages <- c("NAT", "CAG", "IM", "PGAC", "Metastasis")
# Read and reshape data
combined_data <- lapply(stages, functio... |
e634f0ebbb770b971d19073d41ab3669bbfaa75672735452201959ac5614f1d2 | R | 6,179 | 129 | suppressPackageStartupMessages(library(optparse))
option_list = list(
make_option(c("-g", "--gtf"), type='character',
help="genome annotation (gtf)"),
make_option(c("--transcript_table"), type='character', default = NULL,
help="Table with target transcript_ids. Requires column \'trans... |
81bba8fc377aa3a4b37fa91557d54c1cfbf009b21e2719580491a5442ca36115 | R | 6,180 | 124 | get_local_clumped_exposures <- function(exposures_df = exposures_df, p_threhold = 1e-5)
{
message("exposures_df = dataframe from FR02, mibio,pathways;containing: exposures name id path")
message("p_threhold = 1e-5, for gut microbiota")
raw_exposures <- lapply(seq(1, dim(exposures_df)[1]), function(i){
if(expos... |
deaa490e0e7df03d5fc6ffb2c0d7286d156cfdcb382f0eb4f0fcd783c33e4ae7 | R | 6,182 | 184 | # organize the vep results
setwd(dir = "D:/R_project/UCR_project/")
options(stringsAsFactors = FALSE)
rm(list = ls())
library(tidyverse)
library(ggplot2)
library(stringr)
library(cowplot)
library(ggview)
library(ggprism)
library(sysfonts)
library(showtext)
lwd_pt <- .pt*72.27/96
myTheme <- theme(
panel.grid = ele... |
0f605d62420755d767805acbaaf0cbfc60bcdf96199eff0d52a0eb4bf6ff2a35 | R | 6,183 | 210 | #!/usr/bin/env Rscript
#load pkgs
suppressPackageStartupMessages({
require(ampvis2)
require(tidyverse)
require(data.table)
})
functions_file <- "MiDAS_genusfunctions.csv"
#load config
cli::cat_line(
"reformat.R: Reading config.json file"
)
config <- jsonlite::read_json(
"config.json",
simplifyVector = TRU... |
31ea3f06933ded1b5a27294079840d8a31b607418adabeb21581fe02260dc059 | R | 6,211 | 176 | library(Seurat)
library(Signac)
library(patchwork)
file.dir <- "./"
files.set <- c("Pool_1", "Pool_2", "Pool_3", "Pool_4", "Pool_5", "Pool_6",
"Pool_7", "Pool_8", "Pool_9", "Pool_10", "Pool_11", "Pool_12",
"Pool_13", "Pool_14", "Pool_15", "Pool_16", "Pool_17")
fil... |
4b58c483df39daab764d1cc45c932720f3586a9fe74c1bb5f55bd26f3af2f3cd | R | 6,218 | 182 | # ²é¿´ncRNA UCRÏà¹ØµÄlncRNAµÄgwRVIS
# ÏÈ¿´¿´ËùÓеÄncRNA UCRÊDz»ÊǶ¼ÂäÔÚgwRVIS»®¶¨µÄ·¶Î§ÀÈç¹ûÊǾͲ»ÐèÒª»ñµÃµ¥¼î»ùµÄgwRVISÁË
setwd(dir = "D:/R_project/UCR_project/")
options(stringsAsFactors = FALSE)
rm(list = ls())
library(tidyverse)
library(ggplot2)
library(ggview)
library(ggpubr)
library(ggsci)
library(ggbeeswarm... |
3466296ff895a81b775db5182fa84207d8bf5214c89f536dbfe7d1b6e849579f | R | 6,225 | 143 | observeEvent(input$sideBarTab, {
if (input$sideBarTab == 'ibaraki' && is.null(GLOBAL_VALUE$Ibaraki[[1]])) {
# GLOBAL_VALUE <- list(Ibaraki = list(
# summary = NULL,
# patient = NULL,
# updateTime = NULL
# )) # TEST
fileList <- list.files(paste0(DATA_PATH, 'Pref/Ibaraki... |
47335c53e04b75b404c9ea7df59765fc00877e409ac9a9e7233766cf2c337ecc | R | 6,225 | 167 | # based on Biermann pipeline, code/Melanoma_Brain_Metastasis-main/Initial_processing/1_Individual_seurat_analysis.R
# normalize, find variable genes, scale, PCA, UMAP, find neighbors, find clusters
# mtRNA, doubledFinder + scrublet
# filter cells: 500-10000 genes per cell, 1000-60000 counts per cell, max 10% mt, sin... |
5dfc9b4ba29d39471eb6eb710d354a117e5c0f0747f740e7e6f486a132db6816 | R | 6,225 | 134 | # This R script prepares two files (All TAXA and All OTUS) as inputs for the Serial-Group-Comparisons Script.
#************************
# A total of 4 files is required for merging.
# 1. A file containing the alpha-diversity measures.
# 2. A file with the normalized relative abundances of OTUs across samples.
# 3. A f... |
016fc1924a9b57e58b4076564e98b5cb7644ad86747bd663bd7d22afd7f39d22 | R | 6,229 | 153 | # Siwei 29 Mar 2024
# Plot CLU1 SNP rs1532278
# init #####
library(Gviz)
library(rtracklayer)
library(BSgenome)
library(BSgenome.Hsapiens.UCSC.hg38)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(ensembldb)
library(org.Hs.eg.db)
library(grDevices)
library(gridExtra)
library(RColorBrewer)
library(readr)
library... |
31972a68caaa32b88f3a00cc85894e8f4a8b4bc58aef3df467b3d19ef9d506a5 | R | 6,237 | 175 | xgb.learn.curve <- function (dat_train, dat_test, X, grid, IDvarn = "Number", seed = 49789){
# this function creates a learning curve for a given model
# Arguments:
# <dat_train> a data frame containing the train data, including patient ID and outcome
# <dat_test> a data frame containing the test d... |
c3ff043c973c9ecfa0b518ace15da4297c4141830cf80f6c9ed606a03664a406 | R | 6,239 | 154 | library(optparse)
option_list <- list(make_option(c("-r", "--regions"),
type="character",
help="Path to BED file of single-nucleotide intervals for which to extend and get coverage from iCLIP peaks file"),
make_option(c("-i","--iclip")... |
a779027fda0f9ca0b0e30ebe2f4eda2e2bf37cc3e4e6597e0404962dfca844af | R | 6,242 | 147 | #!/usr/bin/env Rscript
### title: RNA velocity plots in R
### author: Jana Biermann, PhD
library(plyr)
library(dplyr)
library(ggplot2)
library(ggrastr)
library(scales)
library(viridis)
library(patchwork)
library(reticulate)
### Choose one label
label <- 'MBM_sn'
label <- 'MPM_sn'
# Load python libraries
scv <- imp... |
62c621a4135a3c39b4afa2df82f3750b4e7688c575c9a3a09043de1a34959d25 | R | 6,250 | 177 | require(optparse)
require(tidyverse)
require(ggpubr)
require(cowplot)
require(scattermore)
require(extrafont)
# variables
# formatting
LINE_SIZE = 0.25
FONT_SIZE = 2 # for additional labels
FONT_FAMILY = "Arial"
# Development
# -----------
# ROOT = here::here()
# RAW_DIR = file.path(ROOT,'data','raw')
# PREP_DIR = ... |
de8802724dd77dfb92d2c427e23d1fc8468f64626d0f92a645633a0a801ed47c | R | 6,252 | 164 | # Plot relevant pathways from CellChat results - cell subtypes
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# April 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github.com/jinworks/Cell... |
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