sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
8399014d5b5311cf03c1b4df1439e0822cf0054c5e15fa0b51835cd49201361f | R | 2,936 | 71 | ---
title: "R Notebook of rV2 manuscript figure S2 plots"
output: html_notebook
---
```{r Packagies}
library(tidyverse)
library(Seurat)
library(Signac)
library(qs)
source("AuxFunctions.R")
```
```{r Set parameters}
threads <- 6
```
```{r Load qs objects}
r1.data <- qread("../scATAC_data/E12_R1_DownstreamReady_nmm_.2... |
f8219a87ca5ccf339e9bc56630678b3bc0e2558a7d4b99e89bcbaaa7f7dc14e9 | R | 2,936 | 84 | # merge Biermann et al. meta data to the preprocessed data
# takes some minutes
library(Seurat)
library(ggplot2)
dataPath = "data/"
mergedOutFile = "seurat-allSamples-withMeta.rds"
mergedOutFile = paste0(dataPath,"seurat-allSamples-withMeta.rds")
rdsFiles = list.files(path = dataPath,pattern = "_sn_cb_DF.rds",recurs... |
ee5e78b8e510f8cda0697b50f276d4a98e45ed4511aeaa7558e3df51bf4cf7cb | R | 2,941 | 94 | library(DESeq2)
library(tidyverse)
library(Matrix)
library(igraph)
theme_set(theme_minimal(base_size = 16))
# vector of colours for plotting developmental stages
stage_colours <- c("nulliparous" = "grey",
"gestation d5.5" = "#a6cee3",
"gestation d9.5" = "#9ecae1",
... |
bf425313d0346ccfd8094afcd35e00e09d6c34b95037fceed3cc01cea0bc11a7 | R | 2,948 | 98 | rm(list=ls())
source('load_libraries.R')
source('aux_functions.R')
# bin data and compare overlap
compare_bins_overlap = function(data1, data2, bins = 10)
{
data1 = data1 %>%
mutate(importance = as.numeric(importance)) %>%
arrange(desc(importance)) %>%
mutate(bin = ntile(-(importance), bins))
data2 = dat... |
6caf719c02d4c0c9135afed04d9ff03fb3a28a5ffa2cfd79fdd40762ddc1ea7f | R | 2,953 | 101 | fluidRow(
column(
width = 12,
box(
title = i18n$t("プロジェクトメンバー"),
icon = icon("users"),
width = 6,
userList(
UserListItemWrappter(
image = "Icon/wei_su.jpg",
href = "https://twitter.com/swsoyee",
icon = "twitter",
title = "Wei_Su",
... |
6867c7cd143f3a26369801910dbf38dfab4fd4e10ba15e918d58ae0ea6a7f6c6 | R | 2,954 | 71 | #' AIF360 dataset
#' @description
#' Function to create AIF compatible dataset.
#' @param data_path Path to the input CSV file or a R dataframe.
#' @param favor_label Label value which is considered favorable (i.e. “positive”).
#' @param unfavor_label Label value which is considered unfavorable (i.e. “negative”).
#' @p... |
2f74175cd11dddf2f8e24fde41d32fd315a36731ade436d5536563608bf3c7f5 | R | 2,957 | 96 |
# 25 May 2022 Siwei
# process new astrocyte (AST) batch data (May 2022)
# note that this batch of AST bulk ATAC-seq data has lower quality
# since their original samples had been frozen-thawed.
# init
library(readr)
library(plyr)
library(dplyr)
library(stringr)
library(Rfast)
library(ggplot2)
library(RColorBrewe... |
19f628f5efc6f115544dc0b8397748daebb628111d1cada95da6bc8016c2c2a2 | R | 2,959 | 105 | # Siwei 18 Jun 2024
# test LDlinkR
# install.packages("LDlinkR")
# init ####
library(LDlinkR)
library(vcfR)
library(stringr)
rs10792832_proxy_hg38 <-
LDproxy(snp = "rs10792832",
pop = "CEU",
token = "ed9e7f4a5d87",
genome_build = "grch38", r2d = "r2")
# The master SNP genotype file ... |
6d2f830326c71ee6bcf41e1e21997ab7ed41b44c6db0e8dd94735ba7ada59355 | R | 2,960 | 68 |
## Comparison of Age linear regression statistics in an independent Illumina EPIC 27K fetal cortex cohort ##
# Fetal 27K cohort: Numata et al. (2012). DOI:10.1016/j.ajhg.2011.12.020
library(data.table)
library(ggplot2)
library(grid)
library(gridExtra)
#1. Load results ============================================... |
a931a8e06e3463b6ec07e9f382d960d3a8bc19d71ae4bdb4d21c7729146c2204 | R | 2,962 | 99 | rm(list=ls())
source('load_libraries.R')
source('aux_functions.R')
compare_bins_overlap = function(data1, data2, bins = 10)
{
# Add bin column to each dataset based on the importance
data1 = data1 %>%
mutate(importance = as.numeric(importance)) %>%
arrange(desc(importance)) %>%
mutate(bin = ntile(-(importa... |
a8f16c4c4a225cc2ba694d9815fb0b1f24c3c0eccd9744446f05da6c94eac0c1 | R | 2,966 | 79 | #R version 3.6.1
quartz<-function(width,height){windows(width, height)}
setwd("D:/2020 RV projection/wholebrain/RV SC no in situ/5432")
setwd("C:/SKS_Drive/2020 RV projection/Wholebrain/RV SC VGLUT2")
folder<-'D:/2020 RV projection/wholebrain/RV SC no in situ/5432/slide 4 section 2_2_1'
library(wholebrain)
flat.f... |
bca49e6cb4d2c89547c3ff305ba823fad8dd927670c3a84c3ddec00612454736 | R | 2,969 | 68 | # ***********************************************
# script to generate data for causal simulation
#
# Note: need to run null_sim_all.sh first
# ***********************************************
if (!require("here")) {
install.packages("here")
library("here")
}
if (!require("magrittr")) {
install.packages("magritt... |
c602e3b48d169b3a73d7feae18289a3c617b7dfd8816612fce618fd0544c4549 | R | 2,970 | 126 | ---
title: "QC & filtering P5"
author: "Anne Hoffrichter, Eric Poisel, Lea Zillich"
date: "2021/11/02"
output:
html_document:
df_print: paged
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, tidy.opts=list(width.cutoff=80),tidy=TRUE, fig.asp=0.5, fig.width=12, warning = FALSE)
knitr::opts_knit$... |
31e6e47db5cb78dfb25feae0a0ee1f783bebcc4bf0151666391e1ec707a5267b | R | 2,971 | 126 | ---
title: "QC & filtering P1"
author: "Anne Hoffrichter, Eric Poisel, Lea Zillich"
date: "2021/11/02"
output:
html_document:
df_print: paged
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, tidy.opts=list(width.cutoff=80),tidy=TRUE, fig.asp=0.5, fig.width=12, warning = FALSE)
knitr::opts_knit$... |
36f62b261339371e97909d282a9fd9ef574cdf08e71ad7e3a133d6a8f2f572dc | R | 2,971 | 70 | source("/projects/ren-transposon/home/chz272/transposon/R_projects/Paired-map/R/cxzhu.R")
source("/projects/ren-transposon/home/chz272/transposon/R_projects/Paired-map/R/Paired-map.R")
library(Seurat)
setwd("/projects/ren-transposon/home/chz272/transposon/05.Paired-ChIP/20.NovaSeq/16.Integration/2020_10")
meta<-read.c... |
752a6973fd1edff3dabba1ebdbed362ba301a8f95a791b6a84460cf9237f010c | R | 2,971 | 126 | ---
title: "QC & filtering P3"
author: "Anne Hoffrichter, Eric Poisel, Lea Zillich"
date: "2021/11/02"
output:
html_document:
df_print: paged
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, tidy.opts=list(width.cutoff=80),tidy=TRUE, fig.asp=0.5, fig.width=12, warning = FALSE)
knitr::opts_knit$... |
c28b2a85c088517e8aa9c5ff87f73b027bef853ce2d265b676ac159aae4a355d | R | 2,971 | 126 | ---
title: "QC & filtering P2"
author: "Anne Hoffrichter, Eric Poisel, Lea Zillich"
date: "2021/11/02"
output:
html_document:
df_print: paged
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, tidy.opts=list(width.cutoff=80),tidy=TRUE, fig.asp=0.5, fig.width=12, warning = FALSE)
knitr::opts_knit$... |
e204029524d582af5ad1ee477cc44dd22ebeaa05e242061398919f103a370099 | R | 2,971 | 126 | ---
title: "QC & filtering P4"
author: "Anne Hoffrichter, Eric Poisel, Lea Zillich"
date: "2021/11/02"
output:
html_document:
df_print: paged
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, tidy.opts=list(width.cutoff=80),tidy=TRUE, fig.asp=0.5, fig.width=12, warning = FALSE)
knitr::opts_knit$... |
f0281aad6ae269515aa8b2463b0db17853df6a8f1c65a56f17d3d3a9ab6db56e | R | 2,971 | 126 | ---
title: "QC & filtering C7"
author: "Anne Hoffrichter, Eric Poisel, Lea Zillich"
date: "2021/11/02"
output:
html_document:
df_print: paged
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, tidy.opts=list(width.cutoff=80),tidy=TRUE, fig.asp=0.5, fig.width=12, warning = FALSE)
knitr::opts_knit$... |
155d902f0f618c8005aae88cc10205141ddd043e12f4f1155b6b799b2838506f | R | 2,973 | 126 | ---
title: "QC & filtering P6"
author: "Anne Hoffrichter, Eric Poisel, Lea Zillich"
date: "2021/11/02"
output:
html_document:
df_print: paged
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, tidy.opts=list(width.cutoff=80),tidy=TRUE, fig.asp=0.5, fig.width=12, warning = FALSE)
knitr::opts_knit$... |
6c638c098ef296071e1a72dc3baedbb12416c5fed6e5910a659d882387a1bc78 | R | 2,976 | 73 | ## Show the loading biases over reporter ions
## based on trimmed-mean of log2(intensity)
## Input arguments
## 1. inputFile: the path and name of "raw..._scan.txt" file
## 2. noiseLevel: currently, it is hard-coded as 1000
## 2. SNratio: signal-to-noise ratio for the loading-bias correction (specified in the ju... |
fd706c4fee468b52c61818251e3b92e69db48c19409f81fd3d779ebdde09a050 | R | 2,987 | 81 | library(tidyverse)
library(DESeq2)
sample_tbl <- read_csv("data/liu_facs/liu_facs_sample_sheet_dexseq_meta.csv")
counts <- read_tsv("processed/gene_exprn/2023-09-07_liu_facs_salmon_summarised_counts.tsv")
base_key="TDPpos"
contrast_key="TDPneg"
contrast_name="TDPneg_vs_TDPpos"
covs <- c("gender", "patient")
# convert... |
5de4ca298c4bce95df4f32205aeb8303a41d32212dc0c73822a95f9790a5ae86 | R | 2,988 | 89 | # integrate the preprocessed patient samples with RPCA
# takes a few hours
library(dplyr)
library(Seurat)
library(ggplot2)
library(parallel)
dataPath = "data/"
infile = "seurat-allSamples-withMeta.rds"
outfile = "seurat-integr-withMeta-neededCTs.rds"
paste0(dataPath,infile)
paste0(dataPath,outfile)
'%notin%' = Negat... |
231d8f6029a8897b7e6b5f541e3822abe5af0c9c5330e612cc5ba10169ccfe0d | R | 2,989 | 79 |
## Perform DMR analysis on bulk fetal Age EWAS results ##
#1. Load data ===================================================================================================================
load(paste0(PathToBetas, "fetalBulk_EX3_23pcw_n91.rdat"))
#2. Run DMR script =================================================... |
4e6d4bf6de05bc02395f2e2d857c4f186bcb46840457f70458f733356c7c01a4 | R | 2,991 | 80 | # GPT social perception: Preprocess the GPT4.1 data for frame experiment
# 1. Read data for each batch and add the frame names to the dataframes
# 2. Exclude rows that have nan data in at least one dataset
# 3. Exclude columns that dont have any variation from zero in at least one dataset
# 4. Calculate mean da... |
858883524fea0f4b443bc73301a7b276587e14ca3968a3702a3e10cb44542291 | R | 3,002 | 82 | #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")
}
#color
c... |
96bd8821a7c98c7796d437409c049c61816c957491e316379177d5b24cefdd9a | R | 3,002 | 106 | library("splatter")
library("scater")
library("DeconvoBuddies")
library("SingleCellExperiment")
library("tidyverse")
library("sessioninfo")
library("here")
## prep dirs ##
plot_dir <- here("plots", "06_marker_genes", "02_splat_example")
if (!dir.exists(plot_dir)) dir.create(plot_dir, recursive = TRUE)
## load our D... |
b3d52fdc0144a270272ad8d5a187b851fa237d7ece094b99b0d4a90775b6a5fa | R | 3,014 | 116 | #' EditVCF
#'
#' Edit the VCF (Variant Call Format) file, Creates file with SNPs data at the BAM directory called variantTable.csv
#' @param Directory The Path of to the BAM Directory, also the VCF file
#' @param Organism "Human" or "Mouse"
#' @param temp_dir "temp directory
#' @export
#' @return None
EditVCF <-
fun... |
89a402a60f9392107a91c5ae34a071e27e2eb857b8bf9212b3c580dc944deb86 | R | 3,016 | 59 | ### All the SNPs in the output from the exposure data will be queried against the requested outcomes
### Locally or in remote database using API
### several functions are required for harmonizing data: TwoSampleMR package, get_proxy, snp_replace_proxy
get_mv_harmonise_data_modifed <- function (exposure_dat, outcome_d... |
56a252967bf2ccbda49dc9ef221f99bda41a3828338434f538c6ff5c794dcd9a | R | 3,017 | 69 | # load packages
require(tidyverse)
require(Seurat)
# load Seurat object
sdata.align <- readRDS('sdata_align_snRNA-seq_singlet.rds')
# for each sample, count number of singlets per cell type
cluster.data <- sdata.align %>%
FetchData(vars = c('sample_id', 'genotype', 'sex', 'batch', 'cluster_label')) %>%
mutate(clu... |
50a812afbad048e19799154376f7309499a81989c13f64f111c2668278d57e3d | R | 3,018 | 67 | #' Calculate influential genes for a given trait and cell type using DFBETAS.
#'
#' @param ct A character string containing a valid cell type name in sscore.
#' @param sscore A dgeMatrix of seismic specificity scores where
#' each column is a cell type and row names are gene identifiers.
#' (Note: the identifiers used ... |
eed0b4d414563e629d5b444de5380e10dd9a736f633ff361e578470356127b15 | R | 3,021 | 100 | # install packages, if they're not already installed
packages <- c('argparse', 'psych', 'GPArotation')
new_packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new_packages)) {
install.packages(new_packages,
repos = "http://cran.us.r-project.org")
}
library('argparse')... |
1f93ca8e62066bae8f78dfc52ebf5db5f1508517f3ade0130e6478b53cb82e9e | R | 3,024 | 96 | library("tidyverse")
library("here")
library("jaffelab")
#### Get Data info ####
fastq <- list.files(here("raw-data", "bulkRNA"), recursive = TRUE, pattern = "*1.fastq.gz$")
fastq1_fn <- list.files(here("raw-data", "bulkRNA"), recursive = TRUE, pattern = "*1.fastq.gz$", full.names = TRUE)
fastq2_fn <- list.files(here... |
ba2381a617d3b87fe899d4bdd28a443d1c2b6b034bd98a494597cfd68a2853db | R | 3,032 | 113 | #!/usr/bin/env R
#
# Get the data and images for heatmap summaries of marker genes
library(pheatmap)
#-----------
# set params
#-----------
projid <- "dlpfc-ro1" # id of the current project
celltypevar <- "cellType_broad_k" # cell type from cluster assignment
donoridvar <- "BrNum" # brain id num corresponding to the... |
5d840076c922f9db9fb056c9fdac602ad1f17fe11252d343d2ed79c02d999c13 | R | 3,035 | 86 |
# pec_data = list(CMC = c(33792, 456460),
# DevBrain = c(33201, 102936),
# IsoHuB = c(33339, 29675),
# LIBD = c(33783, 52214),
# MultiomeBrain = c(33818, 141277),
# PTSDBrainomics = c(33877, 198572),
# SZBDMulti = c(34361, ... |
90e1bec1c6507adb8e0676e64711c5913bb47c16ff84c57ec193eb3884b3ee50 | R | 3,039 | 100 | library(ggplot2)
library(stringr)
library(plyr)
library(dplyr)
library(lubridate)
library(reshape2)
library(scales)
library(ggthemes)
library(Metrics)
data <- read.csv("r2plus1d_18_32_2_pretrained_test_predictions.csv", header = FALSE)
str(data)
dataNoAugmentation <- data[data$V2 == 0,]
str(dataNoAugmentation)
dat... |
9978bfbec0661ac6efa6076fcc748efcb1c5548cfd3dd7fce629dbc7a337fcee | R | 3,046 | 75 | library(tidyverse)
outdir <- "processed"
# find paths to per-event binding summaries
summary_paths <- list.files(path = "data",
pattern = "^2024-11-13_event\\.",full.names = T)
# extract event type from file name
summary_paths <- set_names(summary_paths, str_split_i(basename(summary_paths... |
9afc56e310e8fdfc187c379d6dbab6b1a6054d6584c37bcaf567e0dd0832cacd | R | 3,058 | 73 | # First run main.R until definition of Selles et al. Mixed Model (M_mm)
# this scripts store prediction performance of the XGB ensemble applied at
# different times post-stroke
weeks <- c(1, 6, 13)
names <- c("Week 1", "Week 6", "Week 13")
perf_wks_xgb <- data.frame()
baseline_ARAT <- test_xgb %>%
group_by(Number)... |
b010ca9aa9fe5aa78307364ad834e98ced42fd2703c008616b32cab64dd63b4d | R | 3,062 | 116 | # ÓÃbedtools closest²éÕÒintergenic UCR×î½üµÄcoding»ùÒò£¬×öGO¸»¼¯·ÖÎö
# ûÓп¼ÂǷDZàÂë»ùÒòÊÇÒòΪ·Ç±àÂë²»ÄÜ×ögo·ÖÎö£¬¶øÇҷDZàÂëÒ²ÊÇͨ¹ý²éÕÒtarget coding gene×ögo
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(ggplot2)
intergenic_coding_gene <- read.table(
file = "01-data/11.1-Get... |
9014a9130af45236bfb9d3c96eb1743793416ad7b7746a055e887cc716f984d2 | R | 3,063 | 97 | library(tidyverse)
library(here)
meta_human <- readRDS(here("data", "human_meta.RDS"))
meta_chimp <- readRDS(here("data", "chimp_meta.RDS"))
meta_gorilla <- readRDS(here("data", "gorilla_meta.RDS"))
meta_rhesus <- readRDS(here("data", "rhesus_meta.RDS"))
meta_marmoset <- readRDS(here("data", "marmoset_meta.RDS"))
me... |
8112bc6dd2105c5c3679c9e0ce6174f60f87f087e8aa7306f5929d0235662aca | R | 3,064 | 97 | # make disease/gene group plots for LDSC enrichment on scARC
# peaks enriched from psedobulk exp
# Siwei 28 Jul 2022
# init
library(ggplot2)
library(readr)
library(RColorBrewer)
library(stringr)
# import data
df_raw <-
# read_delim("results_4_R.txt",
# delim = "\t", escape_double = FALSE,
# ... |
c046031f1f9c64aee575a73e9853e2b2c8c284851ffb6e69120438d221870ab2 | R | 3,065 | 88 | #File to identified enriched terms in DE genes between neuronal types 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)
#regions ref... |
c24662d354c350596edb61d31faf01ce3bf28c1b739b464fa8df6995f861d649 | R | 3,071 | 58 | #### Figure S5H - Heatmap ####
#### Expression changes of neurotransmitter receptors and transporters in astrocytes ####
library(matrixStats)
library(gplots)
library(stringr)
library(here)
# human astrocyte DEGs:
h_c <- read.table(here("data/astrocytes", "Astro_human_vs_chimp_sig_genes.csv"), sep=",", header=TRUE)
h... |
d54c08fe12fe3754a4b326464c9e6b47791a7661d7054442cc1295b367162b39 | R | 3,078 | 79 | library("DeconvoBuddies")
library("SingleCellExperiment")
library("tidyverse")
library("ggrepel")
library("here")
library("sessioninfo")
#### prep dirs ####
data_dir <- here("processed-data", "12_other_input_deconvolution", "07_find_markers_Mathys")
if(!dir.exists(data_dir)) dir.create(data_dir)
plot_dir <- here("plo... |
cb50a40940c5f406839f7277483c157b8f4472f2c75a192912466aae43730d55 | R | 3,082 | 82 | library("DeconvoBuddies")
library("SingleCellExperiment")
library("tidyverse")
library("ggrepel")
library("here")
library("sessioninfo")
#### prep dirs ####
data_dir <- here("processed-data", "12_tran_deconvolution", "01_find_markers_Tran")
if(!dir.exists(data_dir)) dir.create(data_dir)
plot_dir <- here("plots", "12_... |
79e0eb96acb96f53de2ccf6279a237ef404997f1ded9f469c2236389feae0ea2 | R | 3,086 | 90 | library(lme4)
library(R2nparray)
files = list.files(path="results", pattern="lme...csv")
rslt = list()
# Check the code with a sequence of data files with different
# dimensions and random effects structures.
for (file in files) {
# Fit a model with independent random effects (irf=TRUE) or
# dependent rando... |
cfb0ed66a1edcc97b6818e0d1520704fefaebd110cfcfcf23206cf06be60b1e0 | R | 3,086 | 101 | # ÀûÓÃSEEKR̽Ë÷ncRNA UCRÏà¹ØµÄlncRNAµÄ¹¦ÄÜ
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(Biostrings)
# get bed file
lncRNA_with_name <- read.table(
file = "01-data/33-SEEKR_exploration_of_lncrna_function/Homo_lncRNA_with_gene_name_1.bed",
sep = "\t"
) # 5757
lncRNA_without_... |
7453215d4e0928349f72ccde4b11547deefbd94f0de442e191c68eb870d5481b | R | 3,100 | 77 |
library("SingleCellExperiment")
library("MuSiC")
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 {
marker_file <- args[2]
stopifnot(file.exists(marker_file))
... |
9d63fb8da6b285ec9081e082a0db1e3e1d147881902690e0f7453b288010a022 | R | 3,108 | 85 | library(data.table)
library(sparkline)
source(file = "01_Settings/Path.R", local = T, encoding = "UTF-8")
jMobility <- fread(paste0(DATA_PATH, "Google/Global_Mobility_Report.Japan.csv"))
nameJa <- unique(jMobility$nameJa)
prefResultList <- data.table()
for (pref in nameJa) {
prefDt <- jMobility[nameJa == pref]
c... |
72b77ed583d58668cada28de917ef6eb97d1edfd8da41e2397e9d932172faf4d | R | 3,110 | 101 | library(dmrff)
options(mc.cores=4)
source("functions.r")
library(peakRAM)
stats <- read.csv(text="n.sites,n.samples,dat,peak,coverage
10000,100,NA,NA,NA
10000,200,NA,NA,NA
10000,400,NA,NA,NA
20000,800,NA,NA,NA
40000,1600,NA,NA,NA
40000,3200,NA,NA,NA")
for (i in 1:nrow(stats)) {
cat(date(), stats$n.sites[i], st... |
e8e5ba347bf727be8983795bdd278069b438a9489590e1e7a1621f28fa05e7a5 | R | 3,113 | 97 | snp_replace_proxy <- function(dat, snp_proxy, type = "exposure", build = "37", pop = "EUR")
{
stopifnot(nrow(dat) == 1)
stopifnot("SNP" %in% names(dat))
stopifnot(paste0("effect_allele.",type) %in% names(dat))
stopifnot(paste0("eaf.",type) %in% names(dat))
stopifnot(build %in% c("37","38"))
# Create and ... |
ee6ccaf47995cd0e10787a6e44f7af17891b97aab7cd0f220ecb7b9cb0e50de0 | R | 3,119 | 80 | #!/usr/bin/env Rscript
### title: DGE (Differential gene expression) in CD8+ T cells comparing expanded vs non-expanded TCRs
### author: Jana Biermann, PhD
library(dplyr)
library(Seurat)
library(ggplot2)
library(gplots)
library(ggrepel)
library(hypeR)
library(DropletUtils)
'%notin%' <- Negate('%in%')
colExp<-c('#C82... |
4381b99863a0bd798d63caa3ceba6958d28c020816609162f76984d21cbc0da6 | R | 3,127 | 72 | munge_sce_mat = function(data_obj, mapping_df, assay_name = "all",multi_mapping = "sum") {
#check if the assay exists
if ( assay_name != "all" & !assay_name %in% SummarizedExperiment::assayNames(data_obj)) {
stop("The assay you are indicating does not exist")
}
#check if the feature name is correct
if( i... |
07fc1c8c3b365880bc761ab1797beaae5eb34b2c865e33fe609a12fe0198f292 | R | 3,129 | 105 | # =============================================================================
# 巨噬细胞与CPTC细胞通讯分析脚本
# =============================================================================
# 功能：巨噬细胞亚群与CPTC细胞的细胞通讯分析，识别特定细胞类型间的通讯模式
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 ... |
a5beaee5025da9e5c7d407037a4915098b272cb7b32d06727b9d2c682b745aa8 | R | 3,136 | 102 | set.seed(123)
library(DESeq2)
library(dplyr)
library(readr)
library(tidyr)
library(purrr)
library(ggplot2)
library(stringr)
library(tibble)
library(ggrepel)
library(apeglm)
cds_counts <- list.files("data/gene_cds_counts", pattern = "_results.txt$",full.names = T) %>%
set_names(str_remove(basename(.), "_featureCounts... |
4c7a123c90d7f818732218a8d03bcde68648aa39a2aac6af1bb223b1462aa378 | R | 3,140 | 104 | # =============================================================================
# 差异表达分析脚本
# =============================================================================
# 功能：进行差异表达分析
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 lf)... |
69649942ea2c1657f10f25ff43205d4b42c5b8e95f06ef0dbeb83f37f72f753c | R | 3,147 | 87 |
library("SingleCellExperiment")
library("BisqueRNA")
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 {
marker_file <- args[2]
stopifnot(file.exists(marker_file)... |
fc80cb3219c0b46993a9f86934366a55b832f089c076f852f10d9f137429ecac | R | 3,150 | 102 | ##
## Generate a heatmap for a matrix of values with specified genes/rows and samples/columns.
##
plot_heatmap = function(mat, row_subset, col_subset, title, col_groups = NULL, file_prefix = "heatmap") {
suppressPackageStartupMessages({
library(glue)
library(pheatmap)
library(RColorBrewer)
})
# c... |
879b130bf672b54ef693ca55f3cfb22728dc29a58e7351c1a8b7ccba076f497c | R | 3,151 | 87 | setwd("STARmap_AllenVISp/")
library(Seurat)
library(ggplot2)
starmap <- readRDS("data/seurat_objects/20180505_BY3_1kgenes.rds")
starmap.imputed <- readRDS("data/seurat_objects/20180505_BY3_1kgenes_imputed.rds")
allen <- readRDS("data/seurat_objects/allen_brain.rds")
# remove HPC from starmap
class_labels <- ... |
7635a8c3ba61226c771d8452e134b36fcb2d41cfdf9be500bbef054186cced57 | R | 3,154 | 93 | # test_lowess_r_output.R
#
# Generate outputs for unit tests
# for lowess function in cylowess.pyx
#
# May 2012
#
# test_simple
x_simple = 0:19
# Standard Normal noise
noise_simple = c(-0.76741118, -0.30754369,
0.39950921, -0.46352422, -1.67081778,
0.6595567 , 0.66367639, -2.04388585... |
ad2c777c3ed5e3590ed83fedab253bf063be9d7809a1442fef26c721a00128b3 | R | 3,155 | 95 | # ----------------------------------------------------------------------------
# Libraries and setup ----
library("argparse")
library("ggplot2")
library("tidyverse")
# Command line arguments ----
parser <- ArgumentParser(description = "Differential expression analyses")
parser$add_argument('--metadata', '-m',
... |
70d88c816fe14f10b572715f67a2426d06227b8f6d1fd26915882b0fa6b90d54 | R | 3,158 | 64 | preprocess_mutation <- function(metadata, mutation_path, output){
# read in metadata
metadata <- read.table(metadata, sep="\t", header=T)
# read in annotated mutation table
mutation <- read.table(mutation_path)
colnames(mutation) <- c("region", "gene", "chr", "start", "end", "ref", "alt",
... |
1bd7df6e6627606be1f351a1350788d52654a10169bd1719964bcd557781c6b6 | R | 3,161 | 93 | library(tidyverse)
library(formattable)
# Setting working directory -----------------------------------------------
this.dir <- dirname(parent.frame(2)$ofile)
setwd(this.dir)
# This function is taken from https://github.com/renkun-ken/formattable/issues/26
export_formattable <- function(f, file, width = "100%", h... |
3e3c913373fba272ca05ba21b057504f49557b1f477bbbc4e3c2502c3105406d | R | 3,171 | 64 | pred.viz.ann <- function (cur_pat){
# Interpretable, annotated visualisations of model output.
# Arguments:
# <cur_pat> an integer specifying the patient number
# Requires:
# <pred_xgb> data frame with bootstrap prediction results (output predict.XGB)
# extract/init data
Tout <- 180 # day number... |
24dee76893461bc49f46cc404e9743956eee9007b7587669d987acd696e474cf | R | 3,176 | 66 | library(SingleCellExperiment)
library(tidyverse)
library(here)
library(magrittr)
##### generate random seed for real data ####
load(here("data","expr","Tabula_muris","facs_clean.rda"))
#random sample cell types
facs_obj_sce <- facs_obj_sce[,!is.na(facs_obj_sce$cell_ontology_class)]
facs_obj_sce$cellid <- paste0("cell... |
18d1916dba74e055cc3e4fcc68448a3452eec6e0371c365aa164c213030b7538 | R | 3,183 | 87 | # AnJa/MNT segment for lifting coordintes from hg38 > hg19
# 21Aug2020 === === ===
###
library(rtracklayer)
library(GenomicRanges)
BiocManager::install("liftOver")
gene_df = read.delim("GRCh38_Ensembl-93_GENES_all-33538.gene.loc",header=FALSE)
colnames(gene_df)= c("GeneID", "Chr", "Start", "End", "Strand", "Symbol")
... |
fd7b2b03a80f1b8e7cb8f750f172c8a60391e845d653f0e6cd1a6dd74a7cf1b5 | R | 3,190 | 86 | mapMetabolomes <- function(fluxAll) {
# Map metabolome data onto fluxes
#
# INPUT:
# fluxAll: A data frame containing flux data, assumed to have an 'ID' column
# OUTPUT:
# A data frame containing mapped metabolome data, with associated metadata
# Load metabolome data and filter based on flux results
#
# .. Autho... |
1c4f1185a0e8c3e5456e76bb7f1c04f631fca71ddb1dbcdac13e86b623f608e5 | R | 3,191 | 82 |
## Plotting functions specific to FANS EWAS results ##
library(ggplot2)
library(viridis)
library(dplyr)
options(scipen=999) #remove scientific notation
ESscatter_fetalVSadult <- function(df, age.group, type='Point', bins=120){
# scatter
if(type=='Point'){
p <- {if(age.group=='Fetal'){
ggplot(df %>% arrange(Sign... |
7fcd270eec3f901a2b909b8f173f0200e3c72a63d841c593a676c71c25fcc05b | R | 3,195 | 90 | library("DESeq2")
library("edgeR")
library("limma")
library("tidyverse")
library("PCAtools")
source(file.path(".", "scripts/markers_sp1.R"))
source(file.path(".", "scripts/funcs_custom_heatmaps.R"))
fig5_markers <- fig_5_markers()
all_markers <- unname(unlist(fig5_markers))
marker_df <- tibble(gene = unlist(fig5_mark... |
a7bdd637ff09198027108da96b1891d9ba08b8b32d2e198ad0ce9bd53d43c5ab | R | 3,195 | 98 | ls.packages = c(
"brms", # Bayesian lmms
"tidyverse", # tibble stuff
"SBC" # plots for checking computational faithfulness
)
lapply(ls.packages, library, character.only=TRUE)
# set cores
options(mc.cores = parallel::detectCores())
# set... |
bde9c5ebbff3458a32cfa1a69d1517ac3932ab4b4606962760bab39259e50fb5 | R | 3,198 | 64 | #### Figure S6F ####
#### Heatmap of human microglia divergent genes in SynGO database ####
library(matrixStats)
library(gplots)
library(stringr)
library(here)
# human astrocyte DEGs:
h_c <- read.table(here("data/microglia", "Micro-PVM_human_vs_chimp_sig_genes.csv"), sep=",", header=TRUE)
h_c <- h_c[h_c$padj<0.01 & ... |
11ea15070656b403f39a017cc315afcbf40d23a3e10ac36c03c276acac5bd49c | R | 3,200 | 147 | library(EpiEstim)
library(incidence)
library(data.table)
createRtColumn <- function(data) {
RtTable <- t(data.frame(sapply(
colnames(data)[2:ncol(data)],
function(pref) {
createRtValue(data, pref)
}
)))
colnames(RtTable) <- c("Rt", "display")
RtTable <- data.table(RtTable, keep.rownames = TR... |
7b5391cb160a7840e01979194902ab0b59c5cb7cda7d73722c225d7e7a1abc7c | R | 3,207 | 108 | process_micro_outcome_bulk_harmonsed <- function(i) {
source("/mnt/data/lijincheng/mGWAS/result/01UVMR_immune_BBB/get_bulk_harmonised_data.R")
message("input = a table containg local microbiome GWAS summary data, names including:exposures name id path")
if(micro_exp$exposures[i] == "FR02") {
sp <- fread(mi... |
08d8a744a0928f4ff9a003f4e22f0769424a7d9c5b09b59d936f3d3161166dc9 | R | 3,210 | 130 | #!/usr/bin/env R
#
# Author: Sean Maden
#
# Tests the difference in between-cell type dist for different marker sets.
#
# Note: this is part of the project "scprism.findtypes"
#
# Note: lapply() is unhappy without some placeholder arg (e.g. function() versus function(ii))
# when it is called from inside of a function,... |
b7720a8eedbb5a331b43bfe07bf6c697363970e5934400ff21df55464652cbb3 | R | 3,218 | 94 | # ÓÃintergenic/intronic/type II×öGO¸»¼¯·ÖÎö
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(dbplyr)
library(readxl)
library(stringr)
library(ggprism)
# coding UCR GO -----------------------------------------------------------
coding_UCR <- read_xlsx(path = "01-data/10-GO_enrichme... |
0131495e6d43d03f20bcd4efd783aa20c363490e20b1f5de6c12d39bdcdaef11 | R | 3,222 | 66 |
## Test assocation of global DNA methylation with Age in bulk fetal cortex ##
library(data.table)
epicManifest <- fread(paste0(refPath, "MethylationEPIC_v-1-0_B4.csv"), skip=7, fill=TRUE, data.table=F) # Illumina EPIC manifest
#1. Load data =========================================================================... |
15a422c2b829fcfa3b28d34a83da63b17ad20417e90fad696306064fe373d5a2 | R | 3,226 | 84 | library(tidyverse)
parse_coverage <- function(file, flank_interval, average = TRUE) {
f <- read_tsv(file, col_names = c("chr", "start", "end", "name", ".", "strand", "position", "coverage"), show_col_types = F)
# make sure positions are strand-aware
f$position[f$strand == "-"] <- (2 + flank_interval*2) - f$posit... |
3dba3df1ae8065087ad907d4ddabaf1477c2bc60ba3e310fd4e7b3077cce09c2 | R | 3,239 | 92 | dta <- read.table('data.dat', header=TRUE)
dta$Duration <- factor(dta$Duration)
dta$Weight <- factor(dta$Weight)
dta$logDays <- log(dta$Days + 1) # Use log days to "stabilize" variance
attach(dta)
library(car)
source('/home/skipper/statsmodels/statsmodels/tools/topy.R')
sum.lm = lm(logDays ~ Duration * Weight, contra... |
7c8fda8636385ef77c6962cbb2640942662f7885298a7bc01bd0690a42130c07 | R | 3,240 | 76 | #' Render Table of Contents
#'
#' A simple function to extract headers from an RMarkdown or Markdown document
#' and build a table of contents. Returns a markdown list with links to the
#' headers using
#' [pandoc header identifiers](http://pandoc.org/MANUAL.html#header-identifiers).
#'
#' WARNING: This func... |
cd1a32866c6fbc71a1aff9f81accbdbda11c6ce2b98a9c5660021767f86d0262 | R | 3,241 | 189 | prefectureNameMapJa <- c(
"北海道",
"青森県",
"岩手県",
"宮城県",
"秋田県",
"山形県",
"福島県",
"茨城県",
"栃木県",
"群馬県",
"埼玉県",
"千葉県",
"東京都",
"神奈川県",
"新潟県",
"富山県",
"石川県",
"福井県",
"山梨県",
"長野県",
"岐阜県",
"静岡県",
"愛知県",
"三重県",
"滋賀県",
"京都府",
"大阪府",
"兵庫県",
"奈良県",
"和歌山県",
"鳥取県",
"島根県",
"岡... |
9e2d1b49da02a002e2812c9e373837f84b72ae8eb8239dbb9936cac9d39a3a8c | R | 3,243 | 66 | options(stringsAsFactors = FALSE)
library(ggplot2)
library(reshape2)
library(dplyr)
library(stringr)
library(lme4)
library(lmerTest)
library(RColorBrewer)
library(ggpubr)
library(grid)
library(pheatmap)
#### PTA unsupervised clustering ####
df <- read.table("data/TableS3_PTA_burden.tsv", header=T, sep="\t")
df$Case_I... |
774907687a1dbe3b4b520a71a975cd54e5e80ac33bf20995c5bda969fdbc84a1 | R | 3,244 | 89 | # Normalization and Scaling using Seurat
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# July 2022
# Based in the Seurat Tutorial by the Sajita Lab
# activate conda environment in ITHACA
# conda activate use_seurat_r4
# Open R
# R
s... |
e0e61292bc7260810a32bb5fadf50d6abba79c1a210e697c1ea1179f84978169 | R | 3,247 | 103 | # setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
setwd(".")
list2env(rjson::fromJSON(file = "00_configs.json"), envir = .GlobalEnv)
library(Seurat)
library(qs)
library(foreach)
library(Signac)
library(ClustAssess)
objects_folder <- file.path(project_folder, "objects", "R", "seurat")
qc_folder <- file.path... |
838b4c1f0fb1d60c504fd9035a753bf4e0a97a89d241edbdcc0fa981cffe8357 | R | 3,248 | 91 | # =============================================================================
# 疾病样本质控脚本
# =============================================================================
# 功能：读取疾病样本（Dys_fascia）的10X数据，创建Seurat对象，进行质控过滤
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 <-... |
8e28552a9784f73ff4e55e0736cf6916856c271d73e8f8249e2642ad4ce9e39a | R | 3,253 | 85 | args=(commandArgs(TRUE))
var1 <- args[1]
var1
options(echo=FALSE)
setwd(args[2])
filename <- paste("all",var1,".txt",sep="")
myData <- read.table(file = filename, sep="\t", header=FALSE)
colnames(myData) <- c("Gene_Expr", "Tumor_Type", "Group")
myData$`Tumor_Type` <- as.character(myData$`Tumor_Type`)
myData$`Tumor_T... |
a33fde3ae08bed3079516409b85ea74e7778cf79b85526ea9d0e009c02f2e288 | R | 3,254 | 87 | # load libraries
library(tidyverse)
library(data.table)
library(Matrix)
library(Rfast)
library(matrixStats)
library(ggridges)
library(reticulate)
library(anndata)
# source functions
source("/inkwell05/ameer/functions/0_source_functions.R")
# our purpose is to compute and save cross-expression profiles (p-values and c... |
c7fa88bd8fd2a9e3b1e66328a701f366700bf32a68f3cd001c9d41e8701236b1 | R | 3,256 | 123 | ---
title: "DP01 Create Seurats"
author: "Daniel Zucha"
date: "2025-04-22"
output: html_document
---
Hi,
In this markdown we will load the published data and create seurat objects as deposited by Alsema et al 2024.
```{r libraries}
library(Seurat)
library(tidyverse)
library(qs2)
```
Load Samples and create individ... |
b4a71bd17c024cf1cbb2aba106192ee0d3dfc23c94fc43aa44f83e3e7b3b89bd | R | 3,260 | 65 | #### Figure S5B ####
#### Heatmap of human astrocyte divergent genes in SynGO database ####
library(matrixStats)
library(gplots)
library(stringr)
library(here)
# human astrocyte DEGs:
h_c <- read.table(here("data/astrocytes", "Astro_human_vs_chimp_sig_genes.csv"), sep=",", header=TRUE)
h_c <- h_c[h_c$padj<0.01 & (h_... |
bd643f8c94f0fa585194b1d81db1622108fc925f54de36ab1abf19939f3495eb | R | 3,263 | 88 | options(max.print = 1000)
options(stringsAsFactors = FALSE)
options(scipen = 999)
library(data.table)
library(openxlsx)
args = commandArgs(trailingOnly=TRUE) # bam file name, class annotations
## load class annotations
class_anno <- read.xlsx(args[2])
rownames(class_anno) <- gsub(" ", ".", gsub(":", ".", gsub("-",... |
69b1fd54785c77fcfde9102e325098aa6c2b04148587ab991b6be179cf2dee7a | R | 3,265 | 94 |
library("SingleCellExperiment")
library("Matrix")
library("dplyr")
library("here")
library("sessioninfo")
library("scater")
library("org.Hs.eg.db")
#### load Mathy's data #####
## read in data
mathys_dir <- "/dcs05/lieber/marmaypag/legacySingleCell_Tran_Maynard_Neuron_2021_LIBD001/Mathys/"
list.files(mathys_dir)
pd ... |
c9cc82050b95c431828fcd8994f9d2668d2b0f696ebf95b9f601cbdc1e04ed2c | R | 3,270 | 117 | ##
## Replacement for DESeq2::plotPCA() with sample labels and custom condition colors.
## Sample names will be shown next to each dot with minimal overlap.
## The axis will display proportion of variance for each principal component.
## Tested using DESeq2 versions 1.12 to 1.26.
##
deseq2_pca = function(object, intg... |
4c51c951b1b1c34c619439bb230676ea88be2c898ac350e7e682f5284cb26af5 | R | 3,271 | 86 | #!/usr/bin/env Rscript
#### Fine cell type annotation of B cell subset for MBM_sn
#### Author: Jana Biermann, PhD
library(dplyr)
library(Seurat)
library(ggplot2)
library(gplots)
library(viridis)
'%notin%' <- Negate('%in%')
path.ct <- 'data/cell_type_DEG/MBM_sn/bcells/'
filename <- 'MBM_sn_bcells'
seu <- readRDS('da... |
eb48a02f9e1d88165622644b613d1ba87548dc86c77286877f445870a392064c | R | 3,271 | 95 | #Install and load packages
required_packages <- c("networkD3", "htmlwidgets", "dplyr", "tidyverse", "webshot", "jsonlite")
for (pkg in required_packages) {
if (!requireNamespace(pkg, quietly = TRUE)) install.packages(pkg)
library(pkg, character.only = TRUE) # Load package (with messages)
}
#Required data
df <- r... |
5eb807973eb4a7a58dac1fdc384a3ae0bd7a817d94426ed47e9f65992e79407e | R | 3,280 | 85 | library(tidyverse)
library(ggrepel)
set.seed(123)
corticali3_kinetic <- read_tsv("processed/2023-08-22_i3cortical_slamseq_grandr_kinetics.tsv")
# ranks GW
corticali3_kinetic %>%
mutate(hl_rank = rank(desc(log2FoldHalfLife)),
synth_rank = rank(desc(log2FoldSynthesis))) %>%
arrange(synth_rank) %>% View()
... |
3165a7e7649ad952fdcd0e90fc6cfa746893470a0f139180e00fb577559fb028 | R | 3,287 | 112 | # 14 Jan 2024
# make Fig 3A/B
# init ####
library(readxl)
library(ggplot2)
library(RColorBrewer)
library(reshape2)
# load data ####
df_raw_Fig3a <-
read_excel("Fig_3a.xlsx")
df_to_plot <-
df_raw_Fig3a
colnames(df_to_plot) <-
c("Gene", "CW20107", "KOLF2.2J", "CD-14")
df_to_plot <-
melt(df_to_plot)
colnames(... |
704fd854bc94f3495dffbfc4d47c6d5a8e0a56f6a4ce340a84f408123ed9c5d1 | R | 3,293 | 118 | # 25 May 2022 Siwei
# merge new (May 2022) + old (Jun 2021) MG batch data
# init
library(readr)
library(plyr)
library(dplyr)
library(stringr)
library(Rfast)
library(ggplot2)
library(RColorBrewer)
# load data
ASoC_df_raw <-
read_delim("DP20_data_files/non_500bp_intersected/MG_28_lines_merged_peaks_filtered_03J... |
5309fbbe9ead0b5d321e41bf1bb5274763f67d75a6e06bb6175690eaff153de9 | R | 3,303 | 85 | library(dmrff)
options(mc.cores=4)
source("functions.r")
## construct a random dataset
set.seed(20180220)
n.sites <- 1000
n.samples <- 100
manifest <- generate.manifest(n.sites)
dataset <- generate.dataset(n.samples, manifest)
## show methylation correlation structure
r <- sapply(2:nrow(dataset$methylation), functi... |
eb0866ff3569eec18df861f5368228d4150484b467740efe548e00f5d9098c05 | R | 3,311 | 102 | #----------------------------------#
# Fit DESeq model for standard #
# differential expression analysis #
#----------------------------------#
library(DESeq2)
# Read Data ---------------------------------------------------------------
# DDS object with gene-level quantification
dds <- readRDS("results/DESeqData... |
11aed2e5b4e917a2561c020894ae372a6f869a6a514c8e0cb7a1a4a6573b7996 | R | 3,318 | 85 | #########################################################################
#########################################################################
### Perform Principal Component Analysis by Multi-Dimensional Scaling ###
#########################################################################
########################... |
dce05e082d3bf61f3a44b07a234673e10526e7fe96d005139b702c4ae09769b5 | R | 3,319 | 88 | # Aggregate single cell expression to pseudobulk expression by cell type and patient
# In each case, only keep genes with min. 10 cells with real expression values
options(java.parameters = "-Xmx32g") # to write excel sheets
library(Seurat)
library(parallel)
library(xlsx)
cellTypeGranularity = "cell_type_int"
minNum... |
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