sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
3b279c23e3153bd661297ae842165cee8725239f82ebac491e3e8c0ff22dda03 | R | 6,805 | 142 | #!/usr/bin/env Rscript
library("optparse")
library(data.table)
library(tidyverse)
library(DESeq2)
run_standard_deseq = function(folder_of_featurecounts,
base_grep = "Ctrl",
contrast_grep = "TDPKD",
grep_pattern = "",
... |
02ac6be6bc737ae6c3d1b0cdc80c2b254d9997b469c2a0bfb8748c276ab92457 | R | 6,806 | 153 | ```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(WVPlots)
library(clue)
library(tidyverse)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggExtra)
library(cowplot)
theme_set(theme_cowplot())
library(corrplot)
library(visreg)
library(ggcorrplot)
library(ggseg3d)
library(ggseg)
library(ggseg... |
e20c82bd75a83674e1074e20b5fbcc55e90f485f1908c90591fd16a33add2f8e | R | 6,807 | 228 | #3
# primary and secondary cortex correlation check
From previous results, we know that only cluster1 and cluster2 have correlations detected. In this section, I am going to analyze the 1000 iterations result: (1) check with pvalue (2) check the idea range of coefficient (3) overlapped the results with ChIP-exo. Then ... |
289973d9ab70f598d8eec8b07125782d2ebe58312fde280b43ba730d61b6340f | R | 6,809 | 170 | #null simulation: take input data set
#1: expression data sets
#2: (cell) seed file, with each rows indicate the index of cells to be sampled as the fake cell type
#3: gs file
#4: gwas raw file
#5: process file: print out process of the program
#6: number of thread
#7: output file
#8: output directory: for temporary FU... |
5b7fbefb89d515ef9f14ef3784fc9721847a2c25e47e4960f922e88a21c64e78 | R | 6,812 | 250 | ### RegionsFlow Class Definition ###
#' S4 class for intron annotation data for a specific annotation version
#'
#' @slot FlowRegion A GRanges object. The intron ranges annotated with the
#' promoter information.
#' @slot introns A GRanges object. intron coordinates
#' @slot exonFlow A GRanges object. exon coordinate... |
cb3d25a2a1ae5de1bf57d46ad2020ffcee8157235e3757c2807f2301c2e72942 | R | 6,812 | 229 | #S3B
#3
# primary and secondary cortex correlation check
From previous results, we know that only cluster1 and cluster2 have correlations detected. In this section, I am going to analyze the 1000 iterations result: (1) check with pvalue (2) check the idea range of coefficient (3) overlapped the results with ChIP-exo. ... |
718d25469e8dc40afb2a3449d9ac810000194f30273c958732221775661b4a36 | R | 6,819 | 185 | # use Ensembl/BioMart, COSMIC and CellRanger annotation
# add EnsgID, chrom coordinates, chr.band, synonyms,
# description, phenotype description, COSMIC information about mutation
# and cell types where gene is diff. expressed or signature genes
# for all 17500 analysed genes
options(java.parameters = "-Xmx32g") #... |
43faa811df8be11c62905e02055e28f33c4b784cd2ef3e3135d14e939c9ddedb | R | 6,822 | 206 | library(scPagwas)
library(rtracklayer)
library(dplyr)
library(Seurat)
library(patchwork)
##Enrichment between traits and cell types within tissues
#prepare sc data
str <- "~/dat/cattle_scdata/Global atlas/All_rds/annotation_rds/"
setwd("~/dat/cattle_scdata/Global atlas/All_rds/annotation_rds")
list0 <- list.... |
cf4cbc090ff37bc558bdf56f32586c5fb5acffac660cab253afd5e4df24b744b | R | 6,823 | 147 | suppressPackageStartupMessages(library(optparse))
option_list = list(
make_option(c("-c", "--dexseq.targets"), type='character',
help="circRNA targets (ciri2)"),
make_option(c("-g", "--genes"), type='character',
help="Prepared gene database (bed)"),
make_option(c("-i", "--introns"), ... |
737c2f4ca4554c884e6c6264e30a384dc5e98a593140009eacd0220c26a56ac8 | R | 6,835 | 199 | # ncUCR overlapping lncRNA prognosis in glioma
# check the association of differential exp ncUCR genes and survival
setwd(dir = "D:/R_project/UCR_project/")
options(stringsAsFactors = FALSE)
rm(list = ls())
library(tidyverse)
library(forestplot)
library(ezcox)
library(grid)
# df <- read.delim(
# file = "01-data/51... |
52e5f0b6072cc136dbb7d4c58175a4afa69cde7b95b0d32ea87c9f08a30e7b95 | R | 6,843 | 121 | # library(tabulizer)
# library(data.table)
# library(purrr)
# library(zoo)
#
# dataPath <- "50_Data/MHLW/pcrByRegion.csv"
#
# location <- list(
# "20200324" = "https://www.mhlw.go.jp/content/10906000/000612050.pdf",
# "20200325" = "https://www.mhlw.go.jp/content/10906000/000612830.pdf",
# "20200326" = "https://... |
6fd6be1d04e29fde231eda14ea8de0a457241147da89178346aa3d294b4f3109 | R | 6,851 | 243 | # Siwei 26 Jul 2023
# Adjust vcf tranches to 99.9 to solve rs2027349 absence
# in new sequencing output issue
# Siwei 21 Jun 2023
# Process all NGN2s (FASTQs trimmed and the original NGN2-20
# + from 2019)
# note the sample names are complex, need processing (NGN2 and R21)
# Jun 2023
# init
library(readr)
library(vc... |
16e9365cb16190d69d3f012123db00ae78d383e8bccbc0416fbd801c0a4ccd2d | R | 6,853 | 200 | # ----------------------------------------------------------------------------
# Libraries and setup ----
library("argparse")
library("ComplexHeatmap")
library("ggplot2")
library("tidyverse")
library("Seurat")
library("PCAtools")
# Command line arguments ----
parser <- ArgumentParser(description = "Volcano plot for f... |
213bc09fc4fc8b9028c0fcf2e366b0adcc9d07915bc3f273cd296bf55a532a24 | R | 6,859 | 190 | library(Seurat)
library(Signac)
library(BPCells)
library(ggplot2)
object_sub <- readRDS("merged_object10/merged_object10.rds")
#ASTROCYTES
astro <- subset(object_sub, cell_type=="Astro")
astro <- FindVariableFeatures(astro)
astro <- ScaleData(astro)
astro <- RunPCA(astro)
astro <- FindNeighbors(astro,... |
1fb9c61234336302c6ebc4d70a3cce629f0b4e88bdcb23480a9c096344789669 | R | 6,871 | 162 | ---
title: "FPN_CAP_EIB_neurometabolites_12s"
author: "Asia Ferrari"
date: "2024-10-25"
output: html_document
---
```{r}
# This R script analyzes the relationship between neurochemical data (GABA, Glx) and behavioral data (EIB)
# in relation to network variables (INDegree, OUTDegree, Resilience, etc.). It inc... |
12f31da1204441feeed0d1ad848b3e23428bb4013acbcaa545d7a8b5df3d8b88 | R | 6,901 | 162 | ---
title: "aline-propensity-score"
author: "Alistair Johnson, Jesse Raffa"
date: "May 15, 2017"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Analysis of arterial line dataset
This notebook creates a propensity score using a dataset of patients with indwelling arte... |
49482a9f802f3268ddb9670721cf53f4b8ac2dc75e0a5d042db5d6c87a33c5ed | R | 6,902 | 135 | #' Map query dataset to the fetal whole brain atlas
#'
#' Wrapper function to map a query dataset onto the fetal whole brain atlas.
#' This function uses the Seurat algorithm to project the query dataset onto
#' the reference. Upon projection, the whole brain celltype annotation is then
#' predicted and the correspondi... |
dd72630c9ca713d1db678a1a5e40f02b74ff8938357c938109f6ed06b7179f99 | R | 6,911 | 181 | ---
title: "OD histogram analysis"
author: "C-M Svensson"
date: "2023-02-23"
output: pdf_document
---
```{r setup, include=FALSE}
rm(list = ls())
knitr::opts_chunk$set(echo = TRUE, fig.width = 12, fig.height = 12)
library(dplyr)
library(latex2exp)
library(tidyverse)
library(ggplot2)
library(readxl)
lib... |
57b82a87a0a0fbf56724210c19f704e67f5b8d55fb5aacc4cbf12f2af7b7b5a3 | R | 6,912 | 244 | # Siwei 02 Feb 2025
# plot new S 7a-b
# init ####
{
library(readxl)
library(stringr)
library(ggplot2)
library(scales)
library(reshape2)
library(RColorBrewer)
library(ggpubr)
library(dplyr)
library(data.table)
library(DescTools)
library(multcomp)
library(gridExtra)
}
# load raw data ####
... |
5f39c0d5749a7d737a5fabc1f9d3c2e13643ea4c20410e260895fce1dbbfe199 | R | 6,918 | 211 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
list2env(rjson::fromJSON(file = "00_configs.json"), envir = .GlobalEnv)
source(file.path(general_scripts_folder, "RNA", "create_seurat_from_cr_h5.R"))
library(ggplot2)
library(Seurat)
library(qs)
library(tidyverse)
library(Signac)
library(GenomicRanges)
librar... |
357197d147204a984c5d9fd6191f4f6351f233730e8e1e9a2090a3ecd4fdeeae | R | 6,923 | 140 | # Load Packages -------------------------------------------------------------------
library(tidyverse)
library(tximport)
library(readr)
# Load data ------------------------------------------------------------------------
tau1 <- read_table("/Volumes/big_dog/Direct_RNA_Sequencing/nanocount/counts_transcript_te/dmel-al... |
402caa240be88cd4d3319c4aeeae2dcbc61bd382423ee51302bd20ffda7091df | R | 6,924 | 186 | library(DESeq2)
library(ggplot2)
# plot C3 expression difference
astro_dds <- readRDS("DESeq2/filtered2/objects/Astrocytes_dds.RDS")
astro_counts <- counts(astro_dds, normalized=TRUE)
astro_counts <- astro_counts[,order(colnames(astro_counts))]
c3_counts <- astro_counts[rownames(astro_counts) == "C3",]
... |
673738842de9dd03d6f112fd7c6b264a272fbc88e642443985e5a2e029cc6547 | R | 6,936 | 130 |
## Comparison of bulk fetal "linear" and "non-linear" probe lists ##
library(data.table)
library(ggplot2)
library(cowplot)
library(gridExtra)
library(VennDiagram)
#1. Load results ================================================================================================================
reg <- readRDS(paste0(... |
4186121f2eb323d266d0f49a37404acc9fc51cc3f45230bed38c28e5ab7f4929 | R | 6,937 | 215 | # 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
# run MAST for exact exp. values
# adapt from Lexi's scDE code
# init ####
{
library(Seurat)
library(Signac)
library(edgeR)
library(future)
... |
c1f5b0eeeddb9f4af08c7dfa2786bd624deb257f60cb3ae839a09e8e055ed407 | R | 6,948 | 215 | library("SummarizedExperiment")
library("tidyverse")
library("ComplexHeatmap")
library("sessioninfo")
library("here")
## prep dirs ##
plot_dir <- here("plots", "06_marker_genes", "01_example_plots")
if (!dir.exists(plot_dir)) dir.create(plot_dir, recursive = TRUE)
set.seed(3042023)
#### prep example data ####
n_ct <... |
b6c3e508d98baabe89f2367e73d2951ff64b14fd54a482443e4a48e101a43eb9 | R | 6,950 | 166 | library(tidyverse)
source("scripts/fncs_plot_peka.R")
dbrn_tbl <- read_tsv("processed/peka/papa/2023-11_27_papa_cryptics_kmer6_window_250_distal_window_500_relpos_0.cleaned_6mer_distribution_genome.tsv")
# map comparison names to cleaned event types & region types
plot_clean_names <- tibble(comparison_name = c("blee... |
cd84f2966440b4eecb90493918d1393a29cfb733822252baaa52c01db548d9fc | R | 6,956 | 184 | # script for null simulation
if (!require("here")) {
install.packages("here")
library("here")
}
if (!require("magrittr")) {
install.packages("magrittr")
library("magrittr")
}
if(!require("tidyverse")) {
install.packages("tidyverse")
library("tidyverse")
}
if (!require("scran")) {
if (!requireNamespace("Bi... |
be3225217fe09699b8fdff2184269bc4f3e1ad63035636df6dbb01d34fd32639 | R | 6,958 | 195 | # This tests the cluster-based doublet discovery machinery.
# library(scDblFinder); library(testthat); source("test-findDoubletClusters.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), ... |
d0c434e7b78ceb4b3a21abdf91199bcf07c917273ef1115cbce0063793e1ef58 | R | 6,981 | 174 | #' clusterStickiness
#'
#' Tests for enrichment of doublets created from each cluster (i.e. cluster's
#' stickiness). Only applicable with >=4 clusters.
#' Note that when applied to an multisample object, this functions assumes that
#' the cluster labels match across samples.
#'
#'
#' @param x A table of double statist... |
b3374572f58865c186a6f0def9ab2486df9986217138e06d33145feb78996be1 | R | 7,006 | 152 | observeEvent(input$sideBarTab, {
if (input$sideBarTab == 'aomori' && is.null(GLOBAL_VALUE$Aomori[[1]])) {
# GLOBAL_VALUE <- list(Aomori = list(
# summary = NULL,
# patient = NULL,
# callCenter = NULL,
# contact = NULL,
# updateTime = NULL
# )) # TEST
fi... |
6bc2e08d3fcf5d86031e116d51e9821e992652f0eda48a31517cf27981c09f72 | R | 7,026 | 193 | # Siwei 21 Oct 2023
# Transform raw input, calculate P val from 95% CI, and plot SE+P
# init #####
library(ggplot2)
library(RColorBrewer)
library(readr)
library(stringr)
library(dplyr)
# load data #####
# df_raw <-
# read_table("sum_2_plot_b4_transform.txt",
# col_names = FALSE)
df_raw <-
read_table... |
e9aa9bb7dc4d329a3e735034ae94b4544d8edbf5c1c43818d6673eb66e3a9b53 | R | 7,033 | 174 | library(tibble)
library(tidyverse)
library(Biobase)
library(SummarizedExperiment)
library(DescTools)
library(readxl)
multiplesheets <- function(fname) {
sheets <- readxl::excel_sheets(fname)
tibble <- lapply(sheets, function(x) readxl::read_excel(fname, sheet = x))
data_frame <- lapply(tibble, as.data.fr... |
0ac987542dc9793021aa2d6045323b45b3c88eb13245d1f3a574e040edcb94ed | R | 7,046 | 139 | # AIM ---------------------------------------------------------------------
# try to run harmony by merging the matrices from the individula objects. this will allow the skipping of the regular integration. the regular integration is needed as to run harmony the matrices should have the same number/order of the genes.
... |
c3e01c273464ff8180dd16193383c281f0b1ddf48205234582ad7efa62a42a5e | R | 7,051 | 191 | rm(list=ls())
source('load_libraries.R')
source('aux_functions.R')
cohorts=c("CMC","UCLA_ASD","Urban_DLPFC")
centrality="betweenness"
grn_path = "data/PEC2_NetMed_GRNs/"
# Read autism gene data
asd.risk = read.csv("data/SFARI-Gene_genes_01-23-2023release_03-02-2023export.csv", sep = ",") %>%
filter(gene.score == 1... |
2dd20029d5ea2093187ac7ec0ddb3937f85f61a817e7b95c6704f8aa477aa0bd | R | 7,059 | 184 | #' Retrieve introns from annotation
#'
#' @param refAnnotation.path path to reference annotation in gtf format
#'
#' @return regionFlow-class
#' @export
#' @import dplyr
#' @import GenomicRanges
#' @import IRanges
#' @import BiocGenerics
#' @importFrom magrittr %>%
#' @importFrom dplyr filter
#' @importFrom GenomicFeat... |
221af25209847c4af72e108a385a84b05d83dd6c3e651a8cdf8cbf0916e580e1 | R | 7,063 | 138 | ---
title: "Smoothed heatmaps for Figure 2B-2D, and S3B and S3D"
output: html_notebook
date: '2023-06-20'
---
```{r, include=FALSE, echo=FALSE}
library(Signac)
library(Seurat)
library(dplyr)
library(tidyverse)
library(RColorBrewer)
library(ComplexHeatmap)
library(qs)
library(circlize)
library(magick)
library(GenomicRa... |
3ff788134f699d39827702b20dd26a17cc13a55359a5053217df3c9f0b85c073 | R | 7,065 | 246 | # Siwei 09 Jun 2023
# Process all DNs (FASTQs trimmed + previous ones from 2019/21)
# Jun 2023
# init
library(readr)
library(vcfR)
library(stringr)
library(ggplot2)
library(parallel)
library(MASS)
library(RColorBrewer)
library(grDevices)
# load the table from vcf (the vcf has been prefiltered to include DP >= 20 o... |
248a31f8c291479bc0bb0539d4bcb98d7e579d518b051f8ab5b9f3d1cf9a009b | R | 7,078 | 132 | # function for plotting
plot_anywhere <- function(chr, start, end, gene_name = "",
SNPname = "", SNPposition = 1L,
mcols = 100, strand = "+",
x_offset_1 = 0, x_offset_2 = 0, ylimit = 800) {
cell_type <-
GRanges(seqnames = Rle(chr),
... |
a53beaaa11ce7c6d999acc6c7f12eec57ac78e0aa6e9dfe72c7774988c95527d | R | 7,085 | 161 | ```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(WVPlots)
library(clue)
library(tidyverse)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggExtra)
library(cowplot)
theme_set(theme_cowplot())
library(corrplot)
library(visreg)
library(ggcorrplot)
library(ggseg3d)
library(ggseg)
library(ggseg... |
9dafb6974ba528f7a254b0c51942588f965db8c31e44517b7b7810a6b5905149 | R | 7,086 | 127 |
library(tidyverse)
library(here)
library(jaffelab)
data_dir <- here("processed-data" , "08_bulk_deconvolution", "07_deconvolution_CIBERSORTx_prep")
list.files(data_dir)
#### Example with sce_test ####
first_line <- readLines(file(here(data_dir, "sce_counts_test.txt"), "r"),n=1)
first_line_top25 <- readLines(file(he... |
af7b4508e8b9f3ac46425e54e1df286d87d123617f3222046b2417097e9fa112 | R | 7,094 | 127 |
library(GenomicRanges)
###functions
exonsToIntrons_v3 <- function(exons) {
exons_df <- as.data.frame(exons)
exons_df <- exons_df[order(exons_df$transcript_id, exons_df$start, exons_df$end), ]
exons_df$intron_start <- exons_df$end
exons_df$intron_end <- dplyr::lead(exons_df$start)
rm <- ... |
8cf3cf66df313553b29c1a2bf6846245780940a453147afdd7c6df08257bb55a | R | 7,109 | 166 | # Given the output of CIBERSORTx analysis for Chen et al. and Kraft et al.
# create plots of cell type fraction and heatmap with gene expression fold-changes
dataPath = "data/CIBERSORTx-output/"
figurePath = "Figures-and-Tables/"
# cell fraction files:
kraftMBMcellFractionFile = paste0( dataPath, "Kraft_MBM_ImputedC... |
ecad970800625f81fa7fb41a728d8bb6a61413170d658edb90d1c8b3c1107dde | R | 7,111 | 184 |
library("tidyverse")
library("sessioninfo")
library("DeconvoBuddies")
library("here")
## prep dirs ##
plot_dir <- here("plots", "07_GTEx", "03_deconvo_plots")
if (!dir.exists(plot_dir)) dir.create(plot_dir, recursive = TRUE)
## load colors
load(here("processed-data","00_data_prep","cell_colors.Rdata"), verbose = TRU... |
95ec1b6171a5537d382aa64032952aecfbe10576b9140732bfa2a6ac0a2dbf5f | R | 7,116 | 220 | library(DESeq2)
library(UpSetR)
library(tidyverse)
theme_set(theme_minimal(base_size = 14))
# Read Data ---------------------------------------------------------------
# imprinted genes from Tucci et al
tucci <- readxl::read_excel("data/external/tucci_et_al_sup1_imprinted_genes.xlsx",
she... |
969640048e744c6d69998d4717ed59b513d54fedd1f843a46c73415c70d7cd23 | R | 7,128 | 243 | # Siwei 11 Mar 2025
# plot for Nature appeal letter
# init ####
{
library(readxl)
library(stringr)
library(ggplot2)
library(scales)
library(reshape2)
library(RColorBrewer)
library(ggpubr)
library(dplyr)
library(data.table)
library(DescTools)
library(multcomp)
}
rm(list = ls())
set.seed(42)... |
d3e1086f41ab1ab8c57e0c492206f211273a72dd042a56763d8ba072207960f9 | R | 7,138 | 69 | library(tidyverse)
library(here)
library(sessioninfo)
sample_info_path = here('processed-data', '01_SPEAQeasy', 'data_info.csv')
design_description_text = paste(
"Assays were completed using 22 individual blocks of postmortem human DLPFC tissue collected across anterior (Ant), middle (Mid), and posterior (Post) po... |
05be0f9e490545c58da7a4c6ef759be18390c6b4751fbc87bd4ede59b0ffb7f5 | R | 7,140 | 218 | library(ggplot2)
library(Seurat)
library(bulkAnalyseR)
library(ComplexHeatmap)
library(dplyr)
source_folder <-
expr_folder <- file.path(source_folder, "07.Expr_matrix")
bulk_folder <- file.path(source_folder, "08.Bulkanalyser")
output_folder <- file.path(source_folder, "09.Figures")
if (!dir.exists(output_folder)) {
... |
042393a86f654f294e3b99abfcda7dd866c67cd1617ba04dd20b3ddbb3846916 | R | 7,160 | 115 | # this script's purpose is to summarise the results of our models run on only observed data
library(tidyverse) # version 2.0.0
library(gratia) # version 0.10.0
library(qvalue) # version 2.38.0
"%not_in%" <- Negate("%in%") # define not in operator
path = "/data/pt_life/ResearchProjects/LLammer/gamms/Results/observed_o... |
4f7e813baca8f968fef29fcaa25519614d4f24465ee3de064a0fd3b63e80ea68 | R | 7,160 | 183 | library(ggplot2)
library(twice)
data(hg19rmsk_info)
pbd_obj <- readRDS("data/pbd_obj.rds")
y_kznf <- kznf_infer %>% filter(age=="young")
overlaps <- readRDS("data/pbd_kznfs_tes_overlap.rds")
hsc1 <- pbd_obj$hmc1_corr %>%
filter(!pair %in% pbd_obj$ptc1_corr$pair) %>%
filter(!pair %in% pbd_obj$ppc1_corr$pair) %... |
ac79027e6e97fbe09aa552124f1a1737ee67c036b8f90c01044b9355cf40d38a | R | 7,163 | 179 | #Data from Blankenship 2011, assumed to be in same directory as this file
source("processing_functions.R")
dt=0.1
#Store data separately by phenotype- can be done in one big data frame, but this is more readable
WT_files=c("Blankenship2011_WT_01","Blankenship2011_WT_02","Blankenship2011_WT_03","Blankenship2011_WT_04"... |
5f5d596a601e5cb543d13a77f37b5dd717c51e354602f9a487d97400678113e9 | R | 7,186 | 175 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
list2env(rjson::fromJSON(file = "00_configs.json"), envir = .GlobalEnv)
library(Seurat)
library(qs)
library(future)
library(foreach)
library(dplyr)
library(gprofiler2)
library(foreach)
library(future.apply)
# 50 GB
options(future.globals.maxSize = 50 * 1024^3... |
633e4aebb3c54f28e2592fcb0e43d0b950ea0f18cf3fd47b07e331a819c56a64 | R | 7,186 | 131 | library(dplyr)
library(Seurat)
library(ggplot2)
library(clusterProfiler)
library(org.Mm.eg.db)
library(readxl)
# Set up directory
setwd("/sfs/gpfs/tardis/project/Campbell_Lab/yl7mfw/Data Analysis/20240730_NewPlot/Fig7")
# Load HTM dataset
sSC.integrated <- readRDS("/sfs/gpfs/tardis/project/Campbell_Lab/yl7mfw/Data An... |
6559eeaf554c1e22304105dc2ad5ffebf114300a3cd7a012bf23d68eca68c29c | R | 7,189 | 273 | #Computing species richness and diversity.
# Definitions from http://www.flutterbys.com.au/stats/tut/tut13.2.html and http://www.metagenomics.wiki/pdf/definition/alpha-beta-diversity
#and http://www2.uaem.mx/r-mirror/web/packages/vegan/vignettes/diversity-vegan.pdf and https://grunwaldlab.github.io/analysis_of_microb... |
54d073167c30eb6caafaa6782f4f01ec5a8a9f3928ed2d0c6eff18f09067b5c8 | R | 7,190 | 145 | ################################################
################################################
### Volcano Plot of Genes in Top Term (Mito) ###
################################################
################################################
# Load required packages
if (!require("BiocManager", quietly = TRUE)) {ins... |
2fbef526893e6d544dae9b194256f4ece3d6172d4593a75e40ec4f0fbd1c6e04 | R | 7,192 | 204 | # Siwei 14 Jul 2023
# Use DiffBind to identify the most accessible peaks of the three
# neuronal types
# for the maximum separation power
# init #####
library(readr)
library(DiffBind)
library(stringr)
library(RColorBrewer)
library(factoextra)
# make the input sample sheet
df_sample_sheet <-
data.frame(bamReads = ... |
39d05407c01099e3f813a8a194270a24bd3240181d653ddeadb32b03b0df59b5 | R | 7,195 | 168 | ```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(WVPlots)
library(clue)
library(tidyverse)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggExtra)
library(cowplot)
theme_set(theme_cowplot())
library(corrplot)
library(visreg)
library(ggcorrplot)
library(ggseg3d)
library(ggseg)
library(scale... |
581479be0fee820f91d2e1bde8d05f3dc6c4b8958a3de91dcdf607eb363cbf26 | R | 7,196 | 157 | ## Load plink before starting R
# module load plink/1.90b6.6
# R
## Now run R code
library('data.table')
library('SummarizedExperiment')
library('sessioninfo')
library("tidyverse")
# system("wget https://www.dropbox.com/s/f60w61ifky3wkwh/20116_0.gwas.imputed_v3.both_sexes.tsv.bgz?dl=1")
# system("mv 20116_0.gwas.impu... |
65f0b851b739034d5e71815c218975f57b9eecd802bb0262b53b971eacfac9b1 | R | 7,196 | 186 | ## WGCNA - associate networks with traits
# last change: LZ 2023-11
library(WGCNA)
library(data.table)
library(dplyr)
library(readr)
library(missMethyl)
library(clusterProfiler)
library(org.Hs.eg.db)
library(enrichplot)
library(ggplot2)
DF <- data.frame
setwd("/path/to/WGCNA")
options(stringsAsFactors = FALSE)
... |
8bfef54ea55438cb0b1e20e2e411224249b49a8a9bf5e8f42647667d48948ecc | R | 7,219 | 127 |
library("SingleCellExperiment")
library("iSEE")
library("shiny")
sce_small <- readRDS("sce_{{regionlower}}_small.rds")
cell_colors <- readRDS("cell_colors_{{regionlower}}.rds")
stopifnot(packageVersion("iSEE") >= "2.4.0")
## Related to https://github.com/iSEE/iSEE/issues/568
colData(sce_small) <- cbind(
colData(... |
4ce5df47e567aec7385e5558f03e3afc44dd81dc67307addc2c7ef2eb914c876 | R | 7,223 | 141 | options(stringsAsFactors = FALSE)
library(ggplot2)
library(reshape2)
library(dplyr)
library(stringr)
library(lme4)
library(lmerTest)
library(RColorBrewer)
library(ggpubr)
#### PTA SNV burden vs. age ####
df <- read.table("data/TableS3_PTA_burden.tsv", header=T, sep="\t")
df$Case_ID <- as.character(df$Case_ID)
mycols ... |
d94d765ec1a815cd97380195e306f61b431739afbe908926e609543aa2d70629 | R | 7,230 | 182 | explore_sce_original <- function(sce_original) {
print("Dimensions:")
print(dim(sce_original))
print("Number of unique cell names:")
print(length(unique(colnames(sce_original))))
print("Repeated cell names:")
col_tab <- table(colnames(sce_original))
print(col_tab[col_tab > 1])
print("Num... |
9227a4b614305223ec87f9da664a2156dea60d19b8500f817c9098a7c47cda66 | R | 7,234 | 180 | # Quality metrics and filtering using Seurat
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# January 2022
# Based in the Seurat Tutorial by the Sajita Lab
# activate conda environment in ITHACA
# conda activate use_seurat_r4
# Open R... |
2e9fe23fcd5d0cb117a0bf5d567cc0cfff24fe945ffa2b4fa60a222494699153 | R | 7,257 | 182 |
plot_type_egc_scatter <- function(mcatac, cell_types, const_threshold = NULL, limits = c(-17, -12), pointsize = 2, plot_ablines = TRUE, plot_const_line = FALSE, const_line_value = -14.5, use_theme = TRUE, log_transform = TRUE, base_type = "Epiblast") {
mc_egc <- as.matrix(mcatac@egc)
if (log_transform){
... |
062ea10798449cb695c9331831f737792ac0b1dc66243b4908e4d7306f14e7ab | R | 7,272 | 199 | ##
## Perform gene set enrichment analysis from a data frame of genes and ranks.
##
gse_fgsea = function(stats_df, gene_col, rank_col, species, title = "", pos_label = "Pos", neg_label = "Neg", file_prefix = "gse") {
suppressPackageStartupMessages({
library(magrittr)
library(dplyr)
library(tidyr)
l... |
1420fdeb41f5435172a9bfc8f63dd519ab1075a18ec2d662a80fad66c1020676 | R | 7,273 | 200 | # ²é¿´UCRºÍRFµÄ»ùÒòµÄ×éÖ¯ÌØÒìÐÔ
# tau¼ÆË㹫ʽÀ´Ô´£º
# https://academic.oup.com/bioinformatics/article/21/5/650/220059
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(stringr)
library(ggview)
GTEx_exp <- read.table(file = "01-data/22-Tissue_specificity_across_different_organs/GTEx_... |
0f7e253d62f75491260b2afb9ac46f29663d7c5c5b583c4a58f72943eb9bf758 | R | 7,293 | 274 | ---
title: "DP02 Annotating Spatial Clusters"
author: "DanielZucha"
date: "2024-12-30"
output: html_document
---
Hi,
Having created the Seurat object, we use BayesSpace clustering to introduce spatially-aware clustering.
This process is only applicable to the Visium-based [Alsema et al. 2024](https://www.nature.co... |
a46846d35de79b2393821e10a5d3a3a243d4430863ca0fae87e7687f2e9ff163 | R | 7,295 | 251 | # Siwei 26 Jan 2025
# plot Fig. 3e
# init ####
{
library(readxl)
library(stringr)
library(ggplot2)
library(scales)
library(reshape2)
library(RColorBrewer)
library(ggpubr)
library(ggridges)
library(dplyr)
library(data.table)
library(DescTools)
library(multcomp)
library(gridExtra)
libr... |
d4713e910fbcc66f25f099ec26fcc3417e5d32b5b28d4b1fc73281b9fd9faea6 | R | 7,298 | 119 | ###ÕûÀí½á¹û
library(tidyr)
library(tidyverse)
library(openxlsx)
out_all_res <- data.frame()
out_null_res <- data.frame()
inputpath <- "/mnt/data/lijincheng/mGWAS/result/04replication/Kunkle/pathways/"
outlist <- list.files(inputpath)
##ÌÞ³ýÒÔÏÂpathawyÒòΪֻÓÐÒ»¸öSNP(empty 1,2,4,5)
#COLANSYN.PWY..colanic.acid.buildin... |
7f1818a6ddb09efc29e6684e7913e7e84998f42c4dcacf1c93daf7a54614be65 | R | 7,302 | 148 | ### Author: Dr Ricardo De Paoli-Iseppi. Creation date: 26/02/2021 ###
### Short script to count risk gene data from curated GWAS Catalog data ###
# Requires: GWAS Catalog of MHD of interest (.csv) and validation gene lists (.csv) e.g. MAGMA, TWAS, SMR (optional).
# Validation evidence lists require minimum colnames 'P... |
e403214c4ced1411043bd2919285695665bb3cc12a286148203f0669814bd49d | R | 7,303 | 148 | ### Author: Dr Ricardo De Paoli-Iseppi. Creation date: 26/02/2021 ###
### Short script to count risk gene data from curated GWAS Catalog data ###
# Requires: GWAS Catalog of MHD of interest (.csv) and validation gene lists (.csv) e.g. MAGMA, TWAS, SMR (optional).
# Validation evidence lists require minimum colnames 'P... |
e5cf7ec230c80f0870dd2467521b05374b459b2ac66d55fda525bdfca83732c5 | R | 7,316 | 227 | #' Print R object contents to python expression
#'
#' @param object with names attribute names(object) will be written as python assignments
#' @param prefix string string that is prepended to the variable names
#' @param blacklist list of strings names that are in the blacklist are ignored
#' @param trans named list (... |
0ce2bf8b092bd5816cd6af8e2cf956aa4952a39f680bee503315fe68ce840163 | R | 7,328 | 268 |
library(org.Hs.eg.db)
library(openxlsx)
library(clusterProfiler)
library(dplyr)
library(clusterProfiler)
library(diffEnrich)
kegg_hsa <- get_kegg('hsa')
source("disease_specific_genes_function.R")
cells=c("Astrocyte","Neurons")
neuropro=read.table("neuropro_v1.txt", sep="\t", header=T, fill=T)
df_neuropo=as.dat... |
3ad38ced3e7d3fd393aa03eee27f71861eab5329200a4bd240fa112fb0784703 | R | 7,328 | 137 | ---
title: Scoring potential doublets from simulated densities
package: scDblFinder
author:
- name: Aaron Lun
email: infinite.monkeys.with.keyboards@gmail.com
date: "`r Sys.Date()`"
output:
BiocStyle::html_document
vignette: |
%\VignetteIndexEntry{4_computeDoubletDensity}
%\VignetteEngine{knitr::rmarkdown}
%... |
1cc0bc8a96a3440b87d642ec287fc63b826c03c604f46db29c663c514b9fb17a | R | 7,331 | 203 |
# devtools::install_github("Danko-Lab/BayesPrism/BayesPrism")
library("BayesPrism")
library("SingleCellExperiment")
library("here")
library("sessioninfo")
plot_dir <- here("plots" , "08_bulk_deconvolution", "06_deconvolution_BayesPrism")
if(!dir.exists(plot_dir)) dir.create(plot_dir, recursive = TRUE)
#### load DLP... |
bf06e359370151f5d489c7c16d1fef09a35fe6086fadb0acd7f96a049cdaf70e | R | 7,336 | 186 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
list2env(rjson::fromJSON(file = "00_configs.json"), envir = .GlobalEnv)
library(Seurat)
library(Signac)
library(ClustAssess)
library(qs)
library(rhdf5)
library(dplyr)
library(future)
library(doParallel)
library(ComplexHeatmap)
library(ggplot2)
marker_path <- ... |
6c7744731a6be0f35ca61a3c7686a4fb7d08c61d8f811ef6805d3b8ddca323c9 | R | 7,339 | 262 | # Siwei 02 Feb 2025
# plot new S 7c-d
# init ####
{
library(readxl)
library(stringr)
library(ggplot2)
library(scales)
library(reshape2)
library(RColorBrewer)
library(ggpubr)
library(dplyr)
library(data.table)
library(DescTools)
library(multcomp)
library(gridExtra)
}
# load raw data ####
... |
1631d1bdf0d69051a3bc22c472dd12beb39fd1c9dfbf41ad0046a69eced63d71 | R | 7,350 | 219 | require(optparse)
require(tidyverse)
require(ggpubr)
require(cowplot)
require(ggvenn)
require(extrafont)
require(scattermore)
# variables
# formatting
LINE_SIZE = 0.25
FONT_SIZE = 2 # for additional labels
FONT_FAMILY = "Arial"
PAL_SINGLE_DARK = "darkgreen"
# Development
# -----------
# ROOT = here::here()
# RAW_D... |
149f86c94f9e5ea9da116851f5ff60e16cef9d5c776c8b02ed575a3e9f5edd01 | R | 7,352 | 145 | options(stringsAsFactors = FALSE)
library(ggplot2)
library(reshape2)
library(dplyr)
library(stringr)
library(lme4)
library(lmerTest)
library(RColorBrewer)
library(ggpubr)
#### PTA Indel burden vs. age ####
df <- read.table("data/TableS3_PTA_burden.tsv", header=T, sep="\t")
df <- df[df$Clinical!="AD",]
df$Case_ID <- a... |
2408bf96534607cba7772911a047ba57896e0127c6cd8a5107513784870e89c1 | R | 7,355 | 164 | # Run CellChat to explore cell-cell communication - major cell types
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# March 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github.com/jinwork... |
36f6ef9483699db7951b22983d4b755e60e38d8af262e75747048df08a2b3030 | R | 7,355 | 164 | # Run CellChat to explore cell-cell communication - major cell types
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# March 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github.com/jinwork... |
3d06aea19694e3063b555c63d9d2444274dc745bc4ffb00e1d410a5dcb78b45c | R | 7,360 | 164 | # Run CellChat to explore cell-cell communication - major cell types
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# March 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github.com/jinwork... |
9dd8e80bc2cff420aa68e87887d37d9f4cbf1f05bf06defba0cfe4fef33c0990 | R | 7,360 | 164 | # Run CellChat to explore cell-cell communication - major cell types
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# March 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github.com/jinwork... |
19657850a917b8a7208db727c01efc77ae3961130a6a5571b7f070ec987de235 | R | 7,364 | 149 |
library("SingleCellExperiment")
library("iSEE")
library("shiny")
sce_small <- readRDS("sce_amy_small.rds")
cell_colors <- readRDS("cell_colors_amy.rds")
stopifnot(packageVersion("iSEE") >= "2.4.0")
## Related to https://github.com/iSEE/iSEE/issues/568
colData(sce_small) <- cbind(
colData(sce_small)[, !colnames(c... |
9387d4ef86812dc32caee0819f38e5c5e7fe2ef70119aa6db1a9f3d1b56fe6f3 | R | 7,364 | 164 | # Run CellChat to explore cell-cell communication - major cell types
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# March 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github.com/jinwork... |
9c0ca94c8e2c6f15eaa58127ffc15ca6985de3f6730593492fae4a4ee7ad121c | R | 7,364 | 149 |
library("SingleCellExperiment")
library("iSEE")
library("shiny")
sce_small <- readRDS("sce_hpc_small.rds")
cell_colors <- readRDS("cell_colors_hpc.rds")
stopifnot(packageVersion("iSEE") >= "2.4.0")
## Related to https://github.com/iSEE/iSEE/issues/568
colData(sce_small) <- cbind(
colData(sce_small)[, !colnames(c... |
bae923336ca66394c19ed2941261a0ac44cb4ef6f227977e95b480b649072676 | R | 7,364 | 149 |
library("SingleCellExperiment")
library("iSEE")
library("shiny")
sce_small <- readRDS("sce_nac_small.rds")
cell_colors <- readRDS("cell_colors_nac.rds")
stopifnot(packageVersion("iSEE") >= "2.4.0")
## Related to https://github.com/iSEE/iSEE/issues/568
colData(sce_small) <- cbind(
colData(sce_small)[, !colnames(c... |
6c275bb5d22a613062bb1d6df951e266fd08b76faf21b143081ba0fd36c766c5 | R | 7,365 | 164 | # Run CellChat to explore cell-cell communication - major cell types
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# March 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github.com/jinwork... |
ba7e16e0032bf11fd26f113ece347e0e2f551cf4b3577aac442bff44774efc17 | R | 7,365 | 164 | # Run CellChat to explore cell-cell communication - major cell types
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# March 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github.com/jinwork... |
a5cc3e02cf2651416985ac964a1bff264f584ef03511354886225da21693363a | R | 7,367 | 149 |
library("SingleCellExperiment")
library("iSEE")
library("shiny")
sce_small <- readRDS("sce_sacc_small.rds")
cell_colors <- readRDS("cell_colors_sacc.rds")
stopifnot(packageVersion("iSEE") >= "2.4.0")
## Related to https://github.com/iSEE/iSEE/issues/568
colData(sce_small) <- cbind(
colData(sce_small)[, !colnames... |
d0c603eac298d65d111fe4e220211d2305d8ba67d787738603ca73f8747abcb8 | R | 7,367 | 194 | #5B
library(twice)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggpubr)
load("data/mayoTEKRABber_balance.RData")
# cerebellum expression data load
cbe_gene <- mayoTEKRABber$cbeDE$normalized_gene_counts %>% data.frame()
cbe_kznfs <- cbe_gene[kznf_infer$external_gene_name, ]
cbe_TE <- mayoTEKRABber$cbeDE$norm... |
60448fa07e99afc2d0c62a3db25473cf0fcfe7750bda29a79b805dac8d73191e | R | 7,370 | 149 |
library("SingleCellExperiment")
library("iSEE")
library("shiny")
sce_small <- readRDS("sce_dlpfc_small.rds")
cell_colors <- readRDS("cell_colors_dlpfc.rds")
stopifnot(packageVersion("iSEE") >= "2.4.0")
## Related to https://github.com/iSEE/iSEE/issues/568
colData(sce_small) <- cbind(
colData(sce_small)[, !colnam... |
f19b5566d48c3e864fcdfca46b4f4e628a51afbd325059b4c8b64a2644debc64 | R | 7,370 | 164 | # Run CellChat to explore cell-cell communication - major cell types
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# March 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github.com/jinwork... |
e2d864cbc762925dae67472dd378de8fea378bea6cb7092ee7c42bf0f60fbbae | R | 7,375 | 164 | # Run CellChat to explore cell-cell communication - major cell types
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# March 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github.com/jinwork... |
e0891fda223128a00e2986ba8d8c4f9ed81e77e272957cf139774b4f1e831529 | R | 7,380 | 232 | #' Get required genomic tools ready for use
#'
#' @return NA
#'
#' @author Luke Pilling
#'
#' @name prep_tools
#'
#' @noRd
prep_tools <- function(
get_plink=FALSE,
get_plink2=FALSE,
get_bgen=FALSE,
get_tabix=FALSE,
verbose=FALSE,
very_verbose=FALSE
) {
# check tools directory exists
if (! dir.exists("~/_ukbr... |
a13f1667b773b3543ba5272b2c00782c859d209b96a816a2c3507c312628bfea | R | 7,419 | 243 | #' Prepare Standardized Missingness Data
#'
#' Extracts relevant columns from a peptide dataset and annotates with a source label (e.g., "LFQ", "LBQ").
#'
#' @param data A data frame with peptide data.
#' @param sample_col Column representing the sample or run (unquoted).
#' @param peptide_col Column representing the p... |
2dc6a06f3ef68fa55b4c63d6b57b020301c37045e2d3654cf0d17306d25c11a2 | R | 7,441 | 147 |
## 4. WGCNA - extract hub probes ##
library(WGCNA)
library(data.table)
library(tidyverse)
library(magrittr)
library(cowplot)
#1. Load data used in GPMethylation =============================================================================================
load(paste0(dataPath, "FetalBrain_Betas_noHiLowConst.RData"... |
775e0bba87fc82471bb1bb715530d70529a7f5594454c688864ca8581e06266b | R | 7,452 | 187 | # Load required library
library(dplyr)
# -------------------------------------------------------------------------
# Compare overlapping DEGs and pathways between:
# - Brain organoid study
# - Post-mortem tissue study (Molecular Neurology article)
# ---------------------------------------------------------------------... |
a4fd14fa79b6ffc638af2a2a232f3d8a5a86df2b2c36ca985127f24f76feb37e | R | 7,476 | 122 | #library(magrittr)
library(tidyr)
library(data.table)
library(biomaRt)
library(org.Hs.eg.db)
source_folder <- "/servers/iss-corescratch/am3019/250220_SP_alex_bulk/X204SC25013974-Z01-F001"
expr_folder <- file.path(source_folder, "07.Expr_matrix")
bulk_folder <- file.path(source_folder, "08.Bulkanalyser")
output_folder ... |
1a894438bb4771033bef9363c0c7f0c9ea5e90e9a0add70fcb9c1ab3f725dee5 | R | 7,485 | 182 |
library("tidyverse")
library("sessioninfo")
library("BayesPrism")
library("here")
## prep dirs ##
data_dir <- here("processed-data", "13_PEC_deconvolution", "04_get_est_prop")
if (!dir.exists(data_dir)) dir.create(data_dir, recursive = TRUE)
#### data details ####
## dataset properties
dataset_lt <- tibble(Dataset =... |
a066939aed2b7775229a258e8be22f33efd771e130863780b8027e055dbed3b2 | R | 7,509 | 234 | # =============================================================================
# 深度学习特征整合分析脚本
# =============================================================================
# 功能：结合深度学习特征重要性与DGE结果，筛选关键基因
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(".... |
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