sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
9342219cd1d608b6a6fd9f5391d7e16dff572bf36bdf9c347d5700c19b86eba8 | R | 8,346 | 260 | # Siwei 11 Mar 2024
# Plot a list of genes from Alena's iMG expression data
# init ####
{
library(edgeR)
library(readr)
library(readxl)
library(Rfast)
library(factoextra)
library(dplyr)
library(stringr)
library(ggplot2)
library(RColorBrewer)
library(ggpubr)
library(reshape2)
library(sva)
}... |
ba95057885e3552c8dbb3f32fed230b9b131b3748d93f1b76e6d02ba28cf54df | R | 8,350 | 185 | fluidRow(
column(
width = 2,
descriptionBlock(
number = " ",
header = tagList(icon("torii-gate"), i18n$t("国内事例")),
text = i18n$t("チャーター便を含む")
)
),
column(
width = 1,
id = "domesticPCR",
descriptionBlock(
number = countup(sum(mhlwSummary[日付 == max(日付) & 分類 %in% 0:2]$... |
51ac0f51d65ed9611a8a813ca09998d76d7c2c49649da545f09c67cbd6ee051a | R | 8,354 | 208 | library(sesame)
library(readxl)
library(dplyr)
library(data.table)
library(tidyr)
library(parallel)
### Generate betas using SeSAMe
IDs <- read_excel("~/Downloads/scripts/MouseArrayMaster.xlsx", sheet = "Data") %>% dplyr::select(Prep, IDAT)
pfxes = searchIDATprefixes("/path/idats/") ## IDAT files location (GSE290585... |
353ad2718cc08017691aefb8d0382b9610433d5f98f2e0aafe632da9445cf308 | R | 8,364 | 177 | # ============================================================================ #
# Script: PCA and Correlation Analysis for MOCA/IADL Data with Demographic Merging
#
# Description:
# This script performs a principal component analysis (PCA) on patient cognitive
# data extracted from imaging records and merges it w... |
c4fba8629a532ed47f3607d986a23baf4f76f96ea0152da2d98fc41f4167ffc0 | R | 8,366 | 185 | library(limma)
##########################
## Define a subroutine ##
##########################
limmaTest <- function(inputData, outputLevel, fdrMethod, saveDir, suffix, comparison, comparisonName, columnNames, selColNames, design, contMatrix) {
fit = lmFit(inputData[, which(colnames(inputData) %in% selColName... |
6b93de01dd3aa404667dbfd892e50002cb84d785cbbe523ae3693931e6d88411 | R | 8,374 | 329 | #' Workbench Paths and Definitions
#'
#' Get workbench path depending on the host
#'
#' @description This function asks for the hosts and returns the appropriate path to wb_command binary
#'
#' @param par This is a test
#'
#' @return The path to workbench command
#'
#'
#' @keywords internal
#'
#' @export
#'
V = functi... |
603c947e81c1f610c7c2e2fb4d1f540eb88eae88ffd695dd57e0ce15d3d1c641 | R | 8,383 | 254 |
rm(list = ls())
library(EnhancedVolcano)
# Here is the differential expression table
res1 <- read.csv("output/DEGenes_bulk/ADvsResilient.csv")
colnames(res1)
res2 <- read.csv("output/DEGenes_bulk/ADvsControl.csv")
res3 <-read.csv("output/DEGenes_bulk/ResilientvsControl.csv")
sel_labs <- (res1[ res1$adj.P.Va... |
d3e5207c278d453b66d0ab225cad536dbefb7742c611409938777da81767fd04 | R | 8,401 | 252 | # Siwei 02 Jul 2024
# Import scRNA-seq data of previous microglia to identify potential samples
# use all PFC samples, check PICALM-AD expression, disregard genotype
# init ####
{
library(Seurat)
library(Signac)
library(edgeR)
library(future)
library(stringr)
library(harmony)
library(MAST)
library(... |
5b761c2b45fffa067f9057e241ec8610b787a7ac4f01195c7e536745e1fd7589 | R | 8,427 | 189 | library(MuSiC)
library(SingleCellExperiment)
library(BisqueRNA)
library(Biobase)
library(hspe)
library(rlist)
library(HiDecon)
load("Mathys23all.RData")
source("random.R")
set.seed(123)
reference.id <- sample(colnames(sim$Mathys23bulk),size=40)
ref.list.select <- sim$Mathys23ref[reference.id]
cell.counts <- sim$Mathys2... |
769c38cb04bf55144a44db6d8bac86b883e86fdde4fd5c731dc2c75690320f68 | R | 8,427 | 185 | # plot expression over all cell types of the cancer-relevant signature gene
# Figure 5 and Suppl Figure 5
options(java.parameters = "-Xmx32g") # to write/read big excel sheets
library(ggplot2)
library(xlsx)
figurePath = "Figures-and-Tables/"
cancerGenesMainPlot = paste0(figurePath, "Figure-5-cancer-gene-expr-perc-MB... |
8739c555721712b00643f5646876585cec05b5ce12d982b7072a61e4208aae40 | R | 8,434 | 230 | ---
title: "DP04 Gene Signature Enrichment"
author: "DanielZucha"
date: "`r Sys.Date()`"
output:
html_document:
toc: TRUE
toc_float:
collapsed: FALSE
toc_depth: 3
df_print: paged
params:
n_cores: 16
editor_options:
markdown:
wrap: 72
chunk_output_type: inline
---
Hi,
In this mar... |
8365638b064024726a501a2bb346fdeae03ef039f13c64a1e69e1c9c9c5d7418 | R | 8,448 | 200 | # 02 Normalize Impute.R
# 02 Normalize Impute.R
##########################################################
## Add batch info #
##########################################################
od1$batch <- "d1"
od2$batch <- "d2"
#########################################################... |
1649acc49961a0a578b78ca1812b99b0a6167cce9fbdb4eca1095118639101c1 | R | 8,451 | 243 | setwd("~/Documents/mixOmics/")
##### Upload Libraries
library(readr)
library(mixOmics)
library(dplyr)
library(readxl)
# Import dataset of groups -> Sample ID, Genotype, Diet, and Genotype-Diet interaction and define rownames
groups <- as.data.frame(read_excel("~/Documents/mixOmics/Tablas/Groups_full.xlsx",
... |
2d316b740132e1b7d99787c565f91d3d3cf37f91a0f813dca52d36b0ab934858 | R | 8,466 | 234 | # ÓÃÊý¾Ý¿âµÄÊý¾Ý̽Ë÷coding UCRÏà¹Ø»ùÒòµÄ±í´ïģʽ
setwd(dir = "D:/R_project/UCR_project/")
options(stringsAsFactors = FALSE)
rm(list = ls())
library(dbplyr)
library(pheatmap)
library(readxl)
library(stringr)
library(tidyverse)
library(biomaRt)
library(curl)
# human brain rpkm
human_rpkm <- read.table(file = "01-data/... |
a4ec596e771dacf601257dc0e4d0c44d69a1092748050898667592f35d5cbba7 | R | 8,467 | 177 | # FFERREIRA 12/11/2024
# BEPE - Nina Colaboration / Neanderthals project
# MAKE BUBBLE PLOTS FOR GENE ONTOLOGY BIOLOGICAL PROCESS (GO:BP)
## ShinyGO + REVIGO + chatGPT
# COMPARISON: NOVA1-ArAr-CTRL vs NOVA1-HuHu-CTRL
# Loads LIBs
library("RColorBrewer")
library("ggplot2")
library("ggtext")
library("tidyverse")
libra... |
bc91f717e8e34f721a28006cded690339525f99ed78f37488743a471dca9a49d | R | 8,469 | 374 | # Load required libraries
library(clusterProfiler)
library(org.Hs.eg.db)
library(enrichplot)
library(dplyr)
library(ggplot2)
library(enrichR)
library(xlsx)
# Function to perform pathway enrichment analysis
perform_pathway_enrichment <- function(de_genes, pathway_type, sig_threshold = 0.05) {
if (pathway_ty... |
801e12e956e11a4fe3f700ee888c5844f8422bb5e03880797387d9951dbc7b77 | R | 8,472 | 316 | # Siwei 18 Sept 2023
# Analyse Alena's RNASeq results in-house
# init ####
{
library(edgeR)
library(readr)
library(readxl)
library(Rfast)
library(factoextra)
library(dplyr)
library(stringr)
library(ggplot2)
library(RColorBrewer)
}
# load data ####
# ! Novogene "xls" files are actually tab-delimite... |
49fcad0e5771be1ed32214fc26332b32bafc14fd608089814c05d96b2d55026f | R | 8,498 | 199 | # Authors: Lauren Rylaarsdam, PhD; Andrew Adey, PhD
# 2024-2025
############################################################################################################################
#' @title dimEstimate
#' @description Estimate the nv value needed for singular value decomposition with irlba
#'
#' @param obj Ame... |
af55f27858b0db61c76018014d63bcd322f47c030597aa793034af1f62fa9cf8 | R | 8,512 | 264 | # Siwei 29 Feb 2024
# Make circos plots for Alena's PICALM paper
# init ####
library(readxl)
library(circlize)
library(RColorBrewer)
library(colorRamps)
library(viridis)
library(colorspace)
library(colorRamps)
library(stringr)
library(ggplot2)
# library(RColorBrewer)
## make line-dot plot #####
df_pathways_2_genes... |
37d3e270da6d272be6a07775f4d04f34df5c18915c4a76125fa0433f5be22b38 | R | 8,596 | 160 | options(stringsAsFactors = FALSE)
library(ggplot2)
library(reshape2)
library(dplyr)
library(stringr)
library(lme4)
library(lmerTest)
library(RColorBrewer)
library(ggpubr)
library(pheatmap)
library(grid)
# load META-CS indel count matrix
metacs.ds <- readRDS("data/indel_counts.metacs.ds.rds")
metacs.ss <- readRDS("dat... |
bfa983aa41c470a59860be971ddae88a686bdcd52ec2ae90ffc4fdb642db1510 | R | 8,619 | 197 | ##
## Calculate DESeq2 results, generate tables, heatmaps, and gene set enrichment.
##
deseq2_compare = function(deseq_dataset, contrast = NULL, name = NULL, genome = NULL) {
suppressPackageStartupMessages({
library(magrittr)
library(dplyr)
library(tidyr)
library(glue)
library(DESeq2)
libra... |
d7aec401d762cb04438a4fadec03ccb20f89b8487950db4a5a0d11fe708c13f4 | R | 8,667 | 238 | library(KFAS)
options(digits=10)
# should run this from the statsmodels/statsmodels directory
dta <- read.csv('datasets/macrodata/macrodata.csv')
obs <- diff(log(data.matrix(dta[c('realgdp','realcons','realinv')])))[1:9,]
T <- t(matrix(
c(-0.1119908792, 0.8441841604, 0.0238725303,
0.2629347724, 0.4996718412, -... |
8829bb1302eba7c842e92aa339d14d3c213bd9d1a2215724f646b59033d5f37c | R | 8,669 | 238 | library(scrattch.vis)
library(feather)
library(tidyverse)
library(colorspace)
options(stringsAsFactors = F)
# Setting working directory -----------------------------------------------
# this.dir <- dirname(parent.frame(2)$ofile)
# setwd(this.dir)
# Extract expression data for selected genes and t-types ------------... |
14c2045d39b6828e98ccf9a5a9d5ae34b344d9119b62f772195ec0c305ac7352 | R | 8,679 | 288 | # 10 Jan 2024 Siwei
# use is.nan instead of is.infinite as the value of div/0 is now NaN
# 19 Oct 2023 Siwei
# Write a loop to walk through all ready-made BAM files
# SplitCigarNReads skipped, should not cause major problem
# 06 Jan 2024 Siwei
# add a few lines so the code can run independently (i.e. multiple instanc... |
7ebded87ad12bd8b5e4d8eba4e49d5eab7ca8144fe86ab7e6ef62c29a40f00af | R | 8,692 | 237 | library("tidyverse")
library("data.table")
library("stringr")
library("openxlsx")
library("grDevices")
library(readxl)
library(dplyr)
library(MRcML)
library(MendelianRandomization)
library(TwoSampleMR)
library("doParallel") #¼ÓÔØdoParallel°üÓÃÓÚÖ®ºó×¢²á½ø³Ì
library("foreach") #µ¼Èëforeach°ü
##02 mibio ku... |
d7b501300e8d623ccf71e49222870b6e2ecdf73dec4eab69f8b5a08961ad58bf | R | 8,692 | 303 | #' PlotGenome
#'
#' Plot Allelic ratio along the genome for duplication detection.
#' @param orderedTable The variable containing the output of the MajorMinorCalc function
#' @param Window an odd number determining the window of the moving average plot, usually 151
#' @param Ylim the plot y axes maximal limit, usually ... |
7e302f848ed3f249bf75219643a7e29e7afcb5d4be170da1fbde52aae980dd0b | R | 8,693 | 154 |
# The tximport pipeline.
# So this code has these following input that can be uptaken:
# (1) necessary: the father direcotry where all the salmon files folder locate + metadata that has sample_name column containing the name of each salmon folder.
# (2) necassary: specify the species. mouse/human will lead to the usa... |
cf6840d6be35c08742771309f7f45721adb339c9f76993ed18589f0934cf32ab | R | 8,698 | 192 | ---
title: "Additional Utilties"
output:
html_document: default
pdf_document:
latex_engine: xelatex
date: "2024-11-13"
author: "Lauren Rylaarsdam"
---
Some Amethyst utilities were not covered in the brain and pbmc vignettes for the sake of clarity. In this vignette,
we will cover those additional functions us... |
d4ca2d9d64762405a41a3de620929a7cfef27a83ea13bca1f3482dfd15e345ca | R | 8,726 | 209 |
## Test enrichment of gene lists within EWAS results ##
epicAnnotGeneList <- read.csv(paste0(refPath, "EPIC_annot_SFARI_SCHEMA.csv"), row.names=1)
sfari <- unique(epicAnnotGeneList$SFARI.Gene[-which(is.na(epicAnnotGeneList$SFARI.Gene)|epicAnnotGeneList$SFARI.Gene=='')])
schema <- unique(epicAnnotGeneList$SCHEMA.Gen... |
6896b7d769338810608fcafde1d487fb34e76522188c3118b0aba85ed8a261ab | R | 8,731 | 262 | #OPEN LIBRARIES
```{r}
library(kronos)
gg_kronos_sinusoid_noNA <- function(kronosOut, fill = "unique_group"){
requireNamespace("ggplot2")
d <- merge(kronosOut@input, kronosOut@to_plot, by="row.names", all=TRUE)[,-1]
d_noNA <- na.omit(d)
x_obs <- paste0(kronosOut@plot_info$time, ".x")
x_pred = paste0(k... |
229156ccff252bb881e60cbcdc272f2dca712159c6086d58f353e1cbe78d8d8f | R | 8,738 | 263 | library("tidyverse")
library("sessioninfo")
library("DeconvoBuddies")
library("here")
library("viridis")
library(spatialLIBD)
library(ggrepel)
# library("GGally")
## prep dirs ##
plot_dir <- here("plots", "08_bulk_deconvolution", "14_deconvo_plots_layer")
if (!dir.exists(plot_dir)) dir.create(plot_dir, recursive = TRU... |
732482eb78728e3584b273fbd0951eca18120091007d0539c9e984b7ea9bf488 | R | 8,749 | 270 | #!/usr/bin/env Rscript
print("##################################################")
print("# Contamination estimation with decontX #")
print("##################################################")
library("argparse")
parser <- ArgumentParser(description='Identifies contamination from factors such as ambient RN... |
4fac08a02835389f919fb3f7379e3c2d735ef18c647932d5d2eba6732a2be9f5 | R | 8,762 | 235 | ---
title: "R Notebook of E12R1 scATAC processing at clade level with pre-defined joitn feature space"
output: html_notebook
---
```{r Libraries, include=FALSE}
load.libs <- c(
"Signac",
"tidyverse",
"dplyr",
"BSgenome.Mmusculus.UCSC.mm10",
"RColorBrewer",
"future",
"GenomicRanges",
"EnsDb.Mmusculus.v7... |
4def7222b014c4fe02a700c6fff4dbcd6b9bb5db005cb23ab56b584ca825aca8 | R | 8,763 | 198 | #' Plot statistics for the first-tier mapping using \code{ComplexHeatmap}
#'
#' Plot statistics for the first-tier mapping using \code{ComplexHeatmap} (Fig 3)
#'
#' @param inpMat A matrix from \code{getWBstats()}, or a list of multiple matrices. If names detected in the list, they are shown in the legend as study nam... |
c96f949c8b9905cc493e256096e99dcaea2572c1eb2c42cf324ef0e5febfd314 | R | 8,769 | 262 | #OPEN LIBRARIES
```{r}
library(kronos)
gg_kronos_sinusoid_noNA <- function(kronosOut, fill = "unique_group"){
requireNamespace("ggplot2")
d <- merge(kronosOut@input, kronosOut@to_plot, by="row.names", all=TRUE)[,-1]
d_noNA <- na.omit(d)
x_obs <- paste0(kronosOut@plot_info$time, ".x")
x_pred = paste0(k... |
1d8b53ff148680f97241c98333ec91139e98e0e6596d9d4c9deba3c99915c973 | R | 8,782 | 198 | #' Plot statistics for the first-tier mapping using \code{ComplexHeatmap}
#'
#' Plot statistics for the first-tier mapping using \code{ComplexHeatmap} (Fig 3)
#'
#' @param inpMat A matrix from \code{getWBstats()}, or a list of multiple matrices. If names detected in the list, they are shown in the legend as study nam... |
6b43787b23222042ea7baa7540c6b77a1161014019ec1d31ed60fd1912b03f80 | R | 8,814 | 305 | # make disease/gene group plots for LDSC enrichment
# peaks enriched from bulk ATAC-seq Ast, MG, GA, NGN2
# focus on the enrichment of Als and Alz
# Siwei 26 Jun 2023
# init
library(ggplot2)
library(readr)
library(RColorBrewer)
library(stringr)
# load data #####
raw_df <-
read_delim("combined_output_Ast_MG_GA_NGN2_... |
8dbf0057e14060f4415d8fd1fa309320128f30e3c461464518c5c0528bad2dd5 | R | 8,818 | 173 | ################################################################################
# Script to plot from .csv files the regional brain maps of: (1) MIND networks
# and (2) effect sizes of MIND degree after stratifying by cognition and symptoms
#########################################################################... |
295c145f4efdc19212b3ac7c4b4c292214f67e52d1912ceb9ca1c53d708b312b | R | 8,824 | 216 |
library("SummarizedExperiment")
library("tidyverse")
library("EnhancedVolcano")
library("here")
library("sessioninfo")
library("ggrepel")
library("jaffelab")
library("UpSetR")
#### Set up ####
## dirs
plot_dir <- here("plots", "09_bulk_DE", "11_GO_analysis")
if(!dir.exists(plot_dir)) dir.create(plot_dir, recursive =... |
adca947daabe294418a0ff71ffba7c7d98f4271ff6e65ccde4cd1ec7fdcdd697 | R | 8,857 | 274 | ## Load plink before starting R:
# module load plink/1.90b6.6
## Also load twas fusion code
# module load fusion_twas/github
library("SummarizedExperiment")
library("jaffelab")
library("data.table")
library("sessioninfo")
library("getopt")
library("BiocParallel")
library("tidyr")
library("here")
## For styling this s... |
0c6084614d7016998c9f68d867a24402fde43c35d72161328932cac0779d6b81 | R | 8,859 | 248 | library(magrittr)
library(data.table)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggrepel)
library(Hmisc)
library(cowplot)
library(pROC)
library(stringr)
library(RColorBrewer)
library(netresponse)
library(igraph)
library(ComplexHeatmap)
library(circlize)
#genes that are relevant across drugs? sensitivity gen... |
ca344a751f2ebecb9e1173e84e0fa6a65aff52b7fbe76b325eaa3534022a618b | R | 8,864 | 221 | #' utils.r
#'
#' Utility functions for the dhcp dataset
#'
#'
#' Make thresholded t-statistic maps.
#'
#' Performs one-sample t-test and writes tmap rds, cifti, and png for reporting
#' # SAVE ACTIVATIONS (BIN MASK) AS CIFTI NOT CURRENTLY WORKING (templateICAr 0.2.3)
#'
#' @import ciftiTools
#'
#' @param ses_pair DHCP... |
48c52beb6525e212bc619e62fac5aae6128f7cce2ca75f7ec37ad0ec11004e86 | R | 8,884 | 289 | # 19 Dec 2023 Siwei
# Reconstruct the Velmeshev raw object from scratch (mtx files)
# Will integrate as instructed in their github code
# https://github.com/velmeshevlab/dev_hum_cortex/blob/main/snRNAseq_integration
# init ####
{
library(Seurat)
library(Signac)
library(readr)
library(future)
library(parallel... |
d2ecc64e53d7a2f5a5561cda9107a8e024d63d9068d4e94f3d1da4f45764f699 | R | 8,894 | 283 |
rm(list = ls())
library(EnhancedVolcano)
# Here is the differential expression table
res1 <- read.csv("output/DEGenes_bulk/ADvsResilient.csv")
colnames(res1)
res2 <- read.csv("output/DEGenes_bulk/ADvsControl.csv")
res3 <-read.csv("output/DEGenes_bulk/ResilientvsControl.csv")
sel_labs <- (res1[ res1$adj.P.Va... |
df41433ee382c06f58b421be0a7dee2d607fbb4b2775a76ed2c460eda556f223 | R | 8,927 | 220 | library(tidyverse)
library(glue)
library(zoo)
#' convert peka _distribution table to long format with one row per position and kmer
peka_wide_to_long <- function(df, kmers, first_posn_idx = 14) {
# all remaining columns after first are the position cols
coord_cols <- colnames(df)[14:length(colnames(df))]
df ... |
334e9affc2c82bd42208e3fff9862f99d1f88683d193628fcaae0bb26ecfc7da | R | 8,953 | 244 | # =============================================================================
# 细胞注释脚本
# =============================================================================
# 功能：对细胞进行注释和差异表达分析
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 <- list.files("./")
for (file in... |
798ff0c0a368d63cdf254735b2771126a67ded4c76e6561384dec75c0d90f2cb | R | 8,959 | 241 | library("tidyverse")
library("data.table")
library("stringr")
library("openxlsx")
library("grDevices")
library(readxl)
library(dplyr)
library(MRcML)
library(MendelianRandomization)
library(TwoSampleMR)
library("doParallel") #¼ÓÔØdoParallel°üÓÃÓÚÖ®ºó×¢²á½ø³Ì
library("foreach") #µ¼Èëforeach°ü
blood_doubl... |
f2ddd33980e73727060dd006190d714aa7e473554adbe9e9eeef0fe8db3bcd82 | R | 8,962 | 159 | #Make plots (part 2) ###############################################################################################
#Libraries
library(Seurat)
library(tidyverse)
library(RColorBrewer)
#Load mapped Seurat data with reductions
df = LoadSeuratRds("3_integrated_samples/integrated_with_reductions.rds")
#Create column wi... |
639abebee7a2026c44b2640693c657eb41cc58bf724e1e162eb82febee27d4ea | R | 8,986 | 238 | library(AnnotationDbi)
library(gprofiler2)
library(dplyr)
library(xlsx)
library(parallel)
library(ggplot2)
library(stringr) # for str_wrap to wrap labels
library(gridExtra)
library(grid)
library(rrvgo) # for TreeMap
library(png)
dataPath = "data/"
figurePath = "Figures-and-Tables/"
figure4file = paste0(figurePath,"Fig... |
6a117726622cae412d81928166707e6e79a8681ffdf46715233b800affd0a8fa | R | 8,988 | 250 | ####
library(readxl)
library(jaffelab)
library(readr)
library(pracma)
dir.create("star_pdfs")
options(width=100)
## read in reference data
ref = read_excel("raw_data/Star_expected_expression_revisionMNT.xlsx")
ref = as.data.frame(ref[,1:5])
ref_mat = as.matrix(ref[,2:5])
rownames(ref_mat) = ref$Population
## read in... |
f0b1975843cf80493bfe64e26eb61ac040417cc91c40657236c8b1cbe0b41984 | R | 8,993 | 221 | # Author: Somnath Tagore, Ph.D. Title: Running KINOMO for performing Non-negative Matrix Factorization using gene expression data
# Script Name: kinomo_run.R
# Last Updated: 01/24/2022
#Instructions
#The KINOMO repsository can be accessed via https://github.com/IzarLab/KINOMO.git
#Convert the gene expression data (ra... |
2af764ce26304faaff5b3586933d157c1f965626f8873a9ca2c2f9db054798be | R | 9,026 | 172 | #' Run differential rhythmicity analysis
#'
#' The differential rhythmicity analysis is run with a call to this function. To
#' execute this function, the three necessary ingredients are the timeseries
#' data, the experimental design and parameters to choose and tune the method.
#'
#' @param data A matrix of log2 expr... |
86e287dbd0f1424af591d3f57ee614f7b4d3d0b403c688ce3c202800d7c2fb52 | R | 9,043 | 193 | library(tidyverse)
# Script to re-process PAPA results tables and add additional output
# 1. Combined df containing results for all experiments (alogn with per-experiment)
# 2. De-duplicating gene name values (due to a stupid design decision on my part)
# 3. Re-annotating bleedthrough events if called as novel extensi... |
f189610b3f8c3c7ad8be2ee1c02b22a5927ded6715d6989b1aafd907aeb95595 | R | 9,043 | 214 |
library("tidyverse")
library("sessioninfo")
library("BayesPrism")
library("here")
## prep dirs ##
data_dir <- here("processed-data", "08_bulk_deconvolution", "03_get_est_prop")
if (!dir.exists(data_dir)) dir.create(data_dir, recursive = TRUE)
#### data details ####
## dataset properties
dataset_lt <- tibble(Dataset ... |
7f9dc33de9961ce1d11b1f709073b4997cb03dc818f20e23f4c1aaddd34e34e9 | R | 9,059 | 224 | ---
title: "APA x iCLIP"
output: html_notebook
---
```{r}
library(ggsci)
library(ggplot2)
theme_Publication <- function(base_size=14, base_family="Helvetica") {
library(grid)
library(ggthemes)
(theme_foundation(base_size=base_size, base_family=base_family)
+ theme(plot.title = element_text(fac... |
453fb5df5709778c5f60349eabf44956dcc826a10c8e48136b732cff89c8c5f6 | R | 9,061 | 300 | # Functions and plot themes for autopsy SARS-CoV-2 analyses:
# 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 = el... |
de57e4f5a21647d794407aec56e2064c7beade24effa1b656fe4b029b15fa72a | R | 9,079 | 257 | # Load required packages
library(data.table)
library(dplyr)
library(stringr)
library(purrr)
library(ggplot2)
library(ggrepel)
library(readr)
# -------------------------- 1. Set Up Working Environment --------------------------
# Replace these paths with your actual directories
gwas_path <- "[Path to your GW... |
c3e6edb4c73e93dda930d19df75b8bb70d30320c67d0b28b4b9530abfe79a917 | R | 9,086 | 264 | require(optparse)
require(tidyverse)
require(ggpubr)
require(cowplot)
require(scattermore)
require(extrafont)
# variables
SETS_MAIN = c(
'experimentally_derived_regulons_pruned_w_viper_networks-EX',
'experimentally_derived_regulons_pruned-bulkgenexpr',
'experimentally_derived_regulons_pruned-bulkscgenexpr'... |
158efaa107d0d0ad7ba4b674c8bc3eed6ee33524af75bb57095c7dddf6b7e65d | R | 9,088 | 220 | library(dplyr)
library(Seurat)
library(ggplot2)
library(clusterProfiler)
library(nichenetr)
library(org.Mm.eg.db)
library(readxl)
# Set up directory
setwd("/project/Campbell_Lab/yl7mfw/Data Analysis/20250317_Integrating birds OT with mammalian SC")
# Load HTM data
HTM.integrated <- readRDS("/sfs/gpfs/tardis/project/C... |
ab3154fd559b2443cb066351ca11cbaa6c0c904bd08fa54c743ae05c65e4bf41 | R | 9,090 | 208 | library(tidyverse)
library(fgsea)
source("../riboseq/helpers.R") # load in get_ranked_gene_list
set.seed(123)
dbrn_tbl <- read_tsv("processed/peka/papa/2023-11-27_papa_cryptics_kmer6_window_250_distal_window_500_relpos_0.cleaned_6mer_distribution_genome_simple.tsv")
# remove bleedtrhough events, too low n to be reliab... |
5a89bb1acdbea9336673c32bad50e77c5d8e056632c5f449db4974cc70945bd8 | R | 9,098 | 240 |
library("tidyverse")
library("SummarizedExperiment")
library("here")
library("sessioninfo")
#### Plot Setup ####
plot_dir = here("plots","00_data_prep","02_update_experiment_tile")
if(!dir.exists(plot_dir)) dir.create(plot_dir)
pos_df <- tibble(Position = c("Anterior", "Middle", "Posterior"),
pos =... |
69115f74820e8d7b9fe76bb5f13ea75b01c1d51cbee14945b5fe09f07e9ed8ca | R | 9,106 | 173 | ```{r}
# BAR-seq coronal data
# data is shrunk by removing image stitching-related artefacts (cf. Xiaoyin's email)
# data is quality controlled by keeping cells with genes/cell >= 5 and reads/cell >= 20
# data alongside CCF and slide coordinates are saved and can be used for analysis
# load libraries
suppressPackageS... |
687d2e94731ab0e6c26f3b0dbc9ed2b300548cff393d5bc6ae10cedb020b96c2 | R | 9,114 | 270 | # Analysis of QC metrics
library(tidyverse)
library(patchwork)
theme_set(theme_minimal() + theme(text = element_text(size = 16)))
cell_colours <- RColorBrewer::brewer.pal(6, "Dark2")
names(cell_colours) <- c("adipocytes", "basal", "endothelial", "luminal differentiated", "luminal progenitors", "stromal")
# Read da... |
7d33c51a697b8230b7376e6964beae68764a00a7148efbd094e0c82b669d2c69 | R | 9,121 | 240 | # 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... |
1ae7bc5e400a50fd44bba3448e1128053c0377dd9bfbb4803298e865617244a1 | R | 9,131 | 231 | library("tidyverse")
library("data.table")
library("readxl")
library("dplyr")
library("TwoSampleMR")
library("stringr")
library("openxlsx")
library("grDevices")
library(readxl)
library(dplyr)
library(MRcML)
library(MendelianRandomization)
###loop
library("doParallel") #¼ÓÔØdoParallel°üÓÃÓÚÖ®ºó×¢²á½ø³Ì
library("for... |
0fef44d004d9247d01ce0766bc4d0aebcd544036bfe2ee15a93f3ebf11597092 | R | 9,137 | 219 | library(limma)
library(MASS)
library(FNN)
##################
## Data loading ##
##################
# Input file is a "raw_..._scan.txt" file generated from itraq.pl main script
## Extract a suffix from the input file name
suffix = tail(unlist(strsplit(inputFile, "/")), 1);
suffix = gsub("raw_", "", suffix)
... |
334443a5f8bc4f918e366198a56efdd48da34189884377730d8872aa99dcf11c | R | 9,176 | 195 | # Differential expression analysis was performed on GEO2R portal.
setwd("from_GEO2R_portal/")
# R version 4.2.2
library(ggplot2) # version 3.4.2
library(EnhancedVolcano) # version 1.16.0
# GSE165595, expression of serine-related genes and neurotransmitters ----
# read norm counts
glioma = read.delim(file = gzfile('GSE1... |
d9af17ff5f8fd7570c25d362abd62fdf5cdca91fa4afa63c79e51d2469b5e4ff | R | 9,178 | 221 | .libPaths("/Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library")
library(ggplot2)
library(parallel)
library(gamlss)
library(tidyverse)
library(rcompanion)
fit_braincharts_model <- function(roi, dat, sw_dir, out_dir, plotting, ncores = 1){
#' fit_braincharts_model(roi, data, sw_dir, out_dir)
#'
... |
7c731cfbc929ada5deb622bb817485832836b578d2e96e2346ff9b81dd0c0373 | R | 9,183 | 305 | observeEvent(input$sideBarTab, {
if (input$sideBarTab == "route") {
# GLOBAL_VALUE <- list(signateDetail = NULL) # TEST
GLOBAL_VALUE$signateDetail <-
fread(file = paste0(DATA_PATH, "resultSignateDetail.csv"))
GLOBAL_VALUE$signateLink <-
fread(file = paste0(DATA_PATH, "resultSignateLink.csv"))
... |
02ccfdac99d28a53fbe6364b9b5d7192ba1ab25bcdac1cdb275ac7799fe94db4 | R | 9,193 | 197 | ---
title: "FPN_CAP_behavior_12s_36s"
author: "Asia Ferrari"
date: "2024-11-10"
output: html_document
---
```{r}
# Load necessary libraries
library(dplyr)
library(ggplot2)
library(readr)
library(readxl)
library(MASS)
library(knitr)
library(kableExtra)
library(car)
library(multcomp)
library(stats)
li... |
ea902df8e86065081723aed23df04ed2a148bdd565a40e0d845c3c936e94f804 | R | 9,204 | 331 | rm(list = ls())
library(stringr)
library(ggplot2)
library(RColorBrewer)
library(patchwork)
library(dplyr)
library(DirichletReg)
library(tibble)
library(car)
#cell_counts <- read.csv("Qupath_DLPFC/pourya_reannotated.csv")
cell_counts <- read.csv("Qupath_DLPFC/annotations_processed_V2.csv")
temp <- as.data.frame.matr... |
310410cd9dec6e79fb84f711dd35766a45cdceaf8286abadcf12e348d898b465 | R | 9,235 | 163 | #generate plots for results of the Tabula sapiens data set
##### load packages and results from scdrs/ours/fuma/magma #####
if (!require("here")) {
install.packages("here")
library("here")
}
if (!require("tidyverse")) {
install.packages("tidyverse")
library("tidyverse")
}
if (!require("magrittr")) {
install.p... |
c07dc29b0ad47d814678bdf9483a9122e05aee8054bbf2da87ec1d838502c8e8 | R | 9,236 | 312 | # 20 Dec 2023 Siwei
# Will integrate as instructed in their github code
# https://github.com/velmeshevlab/dev_hum_cortex/blob/main/snRNAseq_integration
# Subset the reconstructed Velmeshev object into Ex and IN subsets
# Need to make the plots separately
# init ####
{
library(Seurat)
library(readr)
library(fut... |
6f127886348a7808d1a2666ff94ff63edcff96508e3c092d69d6ceed891d1d8f | R | 9,253 | 196 | ---
title: "FPN_CAP_behavior_12s"
author: "Asia Ferrari"
date: "2024-11-10"
output: html_document
---
```{r}
# Load necessary libraries
library(dplyr)
library(ggplot2)
library(readr)
library(readxl)
library(MASS)
library(knitr)
library(kableExtra)
library(car)
library(multcomp)
library(stats)
librar... |
b018bae32868b749404f84ab9b67e58f5694ce755b8c178f72670cb1cdeb8c54 | R | 9,274 | 249 | # fig 3A and figure S5
# check the 1000 iterations of KRAB-ZNFs (with random gene sets) to TEs
library(dplyr)
library(purrr)
library(ggplot2)
library(tidyr)
library(foreach)
library(doParallel)
get_count_parallel <- function(dir_path){
# Create a vector of file names
file_names <- paste0(dir_path, 1:1000, "_... |
59dff67d98c293b62a7455a5bb56ed5637aa480f20a37be833ceed57da2d5fae | R | 9,275 | 253 | # ----------------------------------------------------------------------------
# Libraries and setup ----
library("hdf5r")
library("rhdf5")
library("scRepertoire")
library("Seurat")
library("SeuratDisk")
library("tidyverse")
library("argparse")
set.seed(7620)
# Command line arguments ----
parser <- ArgumentParser(de... |
1334e3090ab916433a02b229f26addbfbd34f229b1c4a019adb9d44ef40f90ed | R | 9,286 | 233 | # prep combined TCGA and Tempus data frame ------------------------------------
# R version 4.2.2
library(biomaRt) # version 2.54.0
library(rtracklayer) # version 1.58.0
library(dplyr) # version 1.1.2
## read Tempus data ----
P1 = read.delim(file = 'read_counts/htseq_P1.txt', header = FALSE, row.names = 1)
P3 = read.de... |
f2b542e1b9a2070db3ba9ab2f7be4dca3cac505adefcce1b3ac39f3ec7d4af88 | R | 9,286 | 302 | # Siwei 03 Jul 2023 #####
# plot a large PCA include MG, Ast, GA, and possibly NGN2
# ATAC-Seq data using the count matrix of Kosoy et al. (syn26207321)
# (microglia regulome)
# init #####
library(readr)
library(edgeR)
library(Rfast)
library(factoextra)
library(Rtsne)
library(irlba)
library(stringr)
library(dplyr)... |
19c5ca4c6a4fdb7568e927f15cae5ec61df1e993d86ed8a0c253b0257dba2c98 | R | 9,290 | 175 | # ¶ÔGRCh38»ùÒò×éµÄ»ùÒò×é×¢ÊÍÎļþ½øÐÐÕûÀí£¬ÓÃÓÚÖ®ºóµÄGO¸»¼¯·ÖÎö
rm(list = ls())
setwd(dir = "D:/R_project/UCR_project")
library(tidyverse)
library(data.table)
GRCh38.gtf <- fread(input = "02-analysis/07-UCR_Classification/Homo_sapiens.GRCh38.110.gtf",
sep = "\t")
colnames(GRCh38.gtf) <- c("Chrom"... |
8c62d0a535820fb15e4b41cde4c4962aa3bf8614d600bbcdd5c4ac4e5a9dd4f9 | R | 9,306 | 221 | library(Seurat)
library(harmony)
library(future)
library(BiocParallel)
library(scDblFinder)
library(igraph)
library(leidenAlg)
library(reticulate)
Sys.setenv(RETICULATE_PYTHON="/usr/bin/python3")
library(reticulate)
sc<-import("scanpy")
set.seed(1234)
snRNA<-readRDS(file="objects/snRNA/snRNA_merged_only.RDS")
snRNA.l... |
30216eccc2c030a98bef34e500845475932153bc494aabe3afbf530dcd41c159 | R | 9,319 | 283 | # Siwei 19 Sept 2024
# Import scRNA-seq data of GSE254025
# found another source of raw_h5ad, claimed to be annotated
# Check the expression of PICALM in risk vs non-risk cells
# also compare Alena's iMG snRNA-seq with GSE254025's HOMEO, LDAM, DAM populations
# run hierachical clustering
# init ####
{
library(Seurat... |
b3f424336fcf19f6ebbd3be1b840a0250a6b71fb3af1e5f78ee0ef7dd2db867e | R | 9,329 | 263 | ####
library(readxl)
library(jaffelab)
library(readr)
library(pracma)
library(RColorBrewer)
options(width=100)
dir.create("triangle_pdfs")
## read in reference data
ref = read_excel("raw_data/Triangle_expected_expression_revisionMNT.xlsx")
ref = as.data.frame(ref[,1:5])
ref_mat = as.matrix(ref[,2:5])
rownames(ref_mat... |
0136bdcc1940294acc2929a4b8c60f00bf86bfb4cda641094cb68956cac4e02c | R | 9,343 | 311 | source(
file = "global.R",
local = T,
encoding = "UTF-8"
)
shinyUI(
dashboardPage(
skin = "red",
title = i18n$t("新型コロナウイルス感染速報"),
options = list(sidebarExpandOnHover = TRUE),
header = dashboardHeader(
title = paste0("🦠 ", i18n$t("新型コロナウイルス感染速報")),
titleWidth = 350,
controlbar... |
954684860db2548bbfcb4d27a22cb84d9c0ef8ca09c044a2a77882dc9db44094 | R | 9,349 | 148 | # this script produces plots for analyses that relied on observed data only
library(tidyverse) # version 2.0.0
library(gratia) # version 0.10.0
library(patchwork) # version 1.3.0
library(MetBrewer) # version 0.2.0
# this script produces composite plots of the partial effects of LSNS on our outcomes comparing them to ... |
c396d9d85eec3b0c528864be1efa6fba19f185697ef98295e603357f8930cc4d | R | 9,365 | 187 | library(ggplot2)
library(corrplot)
library(psych)
library(plotrix)
perf_data = read.delim("mechanical_perfusion_donor_data.tsv", sep = "\t")
perf_data$Age = as.numeric(perf_data$Age..Private.)
perf_data$PMI = as.numeric(gsub(" hours", "", perf_data$Total.PMI))
perf_data$`Amount Perfused` = as.numeric(gsub("L", "", pe... |
f4f183862218fb6cefd7f91760af587493ca446d7206caa8ab17a2a5ac1fdc2a | R | 9,367 | 220 | ---
title: "2. A quick application of coloclization analysis"
date: "2023-05-01"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Quick_start}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
lang: en-US
---
```{r, include = FALSE}
knitr::opts_chunk$set(
echo=TRUE,
progress =FALS... |
119245e3c11739faca9c09996e7ca8999984e1049e49c577e97ae6e1bf6d7dfc | R | 9,415 | 189 | # function for plotting with interactionTrack
# Siwei 25 Dec 2019
plot_anywhere_interact_seg <- function(chr, start, end, gene_name = "",
SNPname = "", SNPposition = 1L,
mcols = 100, strand = "+",
x_offset_1 = 0, x... |
8aa9bc990d49a8a8d993ce69f360f92c58861b356fad41e330adfb2015598817 | R | 9,415 | 207 | ```{r}
# MERFISH brain receptor map
suppressPackageStartupMessages(library(xfun))
pkgs = c("SingleCellExperiment","tidyverse","data.table","dendextend","fossil","gridExtra","gplots","metaSEM","foreach","Matrix","grid","spdep","diptest","ggbeeswarm","Signac","metafor","ggforce","anndata","reticulate","scales",
... |
b2b0548188156201f68cdc294641aeabf1c394435bccac70029ae8680f78efe8 | R | 9,415 | 177 | # function for plotting rs2027349 site
# revised from plot_anywhere
# Use OverlayTrack to combine data tracks
plot_AsoC_composite <- function(chr, start, end, gene_name = "",
mcols = 100, strand = "+",
x_offset_1 = 0, x_offset_2 = 0, ylimit = 400,
... |
cd603213c033b8a8bfe836e6faf358fc1f88705fdc7bb402979d96a807325895 | R | 9,425 | 262 | #!/usr/bin/env Rscript
### calculate diversity ###
# install required packages
required_pkg <- c("optparse", "ape", "rbiom", "compositions", "BiocManager")
a <- sapply(required_pkg, function(x) { if (!requireNamespace(x, quietly = TRUE))
install.packages(x, repos = "http://cran.us.r-project.org")
})
if (! "microb... |
47024f64970d6ab0b7b8a48dbeecbdee2c2f1122e9869b4f801e48226515d664 | R | 9,426 | 238 | ---
title: "LP label transfer annotation"
author: "Yuanming Liu"
date: "2024/7/17"
output: html_document
---
# Enter commands in R to install Seurat
rm(list = ls())
# load them up into R
library(dplyr)
library(Seurat)
library(ggplot2)
library(hdf5r)
library(googleVis)
# Set up directory
setwd("/project/Campb... |
65c70bb4795c9b7d5f4533c1e57e19d4e974c908357c6e0d751635ea8e2c010d | R | 9,445 | 226 | ---
title: "Convert tree shrew orthologous genes to mouse genes"
author: "Yuanming Liu"
date: "2024/4/14"
output: Orthl_document
---
# R version 4.3.1 (2023-06-16 ucrt)
# Platform: x86_64-w64-mingw32/x64 (64-bit)
# Running under: Windows 11 x64 (build 22631)
# Matrix products: default
... |
c1afcc1668a3cbef88b618cbe397140fda3f708a38a7df0aca7d88fa7868b195 | R | 9,453 | 210 | #!/usr/bin/env Rscript
### title: Counting number of IG combinations in B cells
### authors: Jana Biermann,PhD; Yiping Wang, PhD
library(Seurat)
library(destiny)
library(dplyr)
library(ggplot2)
library(cowplot)
library(scater)
library(SingleCellExperiment)
library(gplots)
library(viridis)
library(ggvenn)
library(ggal... |
8e62e6a17db74d62664db9d54db6d8c405db42aeca00561be07f3aee04efa9c8 | R | 9,455 | 221 | library(tidyverse)
library(ggprism)
# Calculate a group-wise empirical p-value
# Null hypothesis = no difference in values between the control group and the treatment group i.e. they come from the same distribution
# test statistic = number (fraction) of times control samples are further from the control mean (absolut... |
4ce0e360801098e25c95f6b3997432806e4607f19d4c44238997be078b115296 | R | 9,470 | 305 | # Siwei 16 Jan 2025
# reshape Alena's new batch 2 data and plot
{
library(stringr)
# library(Seurat)
library(parallel)
library(future)
# library(glmGamPoi)
# library(edgeR)
library(data.table)
library(readr)
library(readxl)
library(dplyr)
library(tidyr)
library(reshape2)
library(scales... |
15212b30b9aa4468ec3a5ef94c3ab959c8a93ec1e637f3a25bcb2a8c9063046d | R | 9,473 | 218 | #' Create a polygenic score
#'
#' @description Use user-provided list of genetic variants with weights for a trait to create a polygenic score. Uses the imputed BGEN files (field 22828) or WGS DRAGEN BGEN files (field 24309) data and load as data.frame
#'
#' Uses plink2 to create the score (https://www.cog-genomics.org... |
cc7d595b6d4308f7d624ea92b5fbf6e0b2fda69fe8e29fb15e67c3c6901e5f4b | R | 9,495 | 215 | library(tidyverse)
#' run bedtools slop on a BED file to extend interval by user-specified distance
extend_bed <- function(bed, chromsizes, flank_interval, outfile, bedtools_path = "/home/sam/mambaforge-pypy3/envs/pybioinfo/bin/bedtools") {
if (!dirname(outfile) == ".") {
system(paste("mkdir -p", dirname(outf... |
0dcb4d1fba49c4352cdebb68e3f143a7a96b33ba094add4cafd7aaad1ab70e18 | R | 9,523 | 202 | library(matrixStats) # 1.3.0
library(dplyr) # 1.1.4
# functions to calculate the fold of IQR
cal.fold <- function(x, Q1, Q3, IQR) {
if (x > Q3) {
return((x - Q3) / IQR)
} else if (x >= Q1) {
return(0)
} else {
return((x - Q1) / IQR)
}
}
cal.folds.rowwise <- function(a.ro... |
1f14313dd47acad7a4b47c73ae2fcf6da382d2517f46a175e05f8335635a56fa | R | 9,531 | 270 |
library("SummarizedExperiment")
library("tidyverse")
library("sessioninfo")
library("here")
library("jaffelab")
library("recount")
# library("viridis")
library("ggrepel")
library("GGally")
# library("vsn")
## prep dirs ##
plot_dir <- here("plots", "10_bulk_vs_sn_DE", "02_explore_sn_v_bulk")
if (!dir.exists(plot_dir))... |
b9a1af8847b673e4b62aa256f39bb1db20f267791b2b4e1ebfb2cf822bafa105 | R | 9,540 | 303 | # Siwei 03 Jul 2023 #####
# plot a large PCA include MG, Ast, GA, and possibly NGN2
# ATAC-Seq data using the count matrix of Kosoy et al. (syn26207321)
# (microglia regulome)
# init #####
library(readr)
library(edgeR)
library(Rfast)
library(factoextra)
library(Rtsne)
library(irlba)
library(stringr)
library(dplyr)... |
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