sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
698e143cd59bdd177df382cddb56c253a5405bda4d15ba061f3221c52c7aee47 | R | 697 | 17 | library(qs)
library(tidyverse)
library(Seurat)
library(magrittr)
library(ggwordcloud)
setwd("~/cortex/fig4/")
spatialMeta <- read_csv("spatialMeta.csv")
spatialMeta %<>% filter(str_detect(subclass,"SST"))
spatialMeta$deep_shallow <- ifelse(sst$cluster %in% c("SST_16", "SST_36", "SST_4", "SST_23", "SST_3", "SST_13", "S... |
3d0fe5ab8bb1a289317ea5fa00b2e5c5531901f16007348d88c80df9ce4c4bc2 | R | 698 | 22 | ## Fig1 UMAP plots ##
source("Scripts/lib.R")
## load pre-processed objects:
load("Processed_Objects/Inhibitory_datasets.Rdata")
EI_seurat <- readRDS("Processed_Objects/EXCIT_INHIBIT_cleaned_sub.rds")
## for the final plots in the paper the first UMAP axis was flipped
## F1c:
DimPlot(Inhibitory_datasets, reduction ... |
4f525dffaff84322e51d8192a2464b36aaeb19e8bd6f515658f0960e5c8bb6c2 | R | 698 | 21 |
# quantify for each fp, how many participants dropped out
# TODO: EEGNet and Sliding separately
# TODO for each experiment
data <- tar_read(data_eegnet)
experiments = unique(data$experiment)
# DEBUG
experiment <- experiments[1]
data_exp <- data[data$experiment == experiment,]
# make fp a variable as concatenation ... |
8c5f0d5006c01eb85d1d35fa6a2834317d86ad4337bd615759ba35f43f0d2adc | R | 707 | 17 | #' AMPEL Preprocessing
#'
#' Run the preprocessing as done in the AMPEL project.
#'
#' @param x `data.table`, in the format described in [`sbcdata`]
#' @return `data.table`, same as `x` but with additional columns `Excluded` and
#' `Label`. Also 6 attributes are added:
#' `"exclude_message"`, `"exclude_cases"`, `"exclu... |
6192f7a22daa9cbaeadf283cf2634a29048cce9c5813df5dbfd4684be18c3f1e | R | 752 | 25 | library(magrittr)
IN_DIR <- "/home/burkhart/Software/reticula/data/aim2/input/"
# Reaction Network
graph_targets.df <- read.table(paste(IN_DIR,"graph_targets.txt",sep=""),sep = " ")
graph_targets.df$V1 <- sample(graph_targets.df$V1)
write.table(graph_targets.df,
file=paste(IN_DIR,"shuffled_graph_target... |
6a922ce7e263addc04478baf3c5799ee4aa8b07f3a91cfff4b95f556ba94bd38 | R | 755 | 18 | library(dplyr)
library(magrittr)
DATA_DIR <- "/home/jgburk/PycharmProjects/reticula/data/tcga/output/"
incorrect_calls_df <- read.table(paste(DATA_DIR,"res_gnn_incorrect.csv",sep=""),sep = ",",header = TRUE)
colnames(incorrect_calls_df) <- c("Tissue", "Both_Misclass", "Only_Resnet_Misclass", "Only_GNN_Misclass")
n <... |
eb8e6cf6ea0ba4fe38c3589f681dd40609db0a4e6633069f6c9b08eaec6e9b8c | R | 761 | 24 | rm(list=ls())
####### load libraries
library(deSolve)
library(openxlsx)
library(SCIFER)
###### load functions
patient.id <- 'AX001' # specify patient ID here
age <- 63*365 # age in days
depth=10000 # sequencing depth (arbitrary, as we run in single-cell mode)
load("./RData/Mitchell_et_al/SNVs.RData")
snvs <- list(... |
306ebf7b8cb018b962a1b2e4dbb8c57d052e5b97b9d02e28e2be038b5ee5e2dc | R | 773 | 30 | ---
title: "Figure_5_Revisions"
output: html_notebook
---
```{r}
## Load the final probabilities
fate_df = read_csv("/wynton/group/paredes/Aunoy/f5_final_probs.csv")
```
```{r}
fate_df
```
```{r}
ixs = match(fate_df$barcode, colnames(cds))
fate_df$initial = colData(cds)$class[ixs]
```
```{r}
#library(ggpubr)
fate_... |
f3872edeefe3da31b072e47a9499bed395f63f8294e85e635b91ce9ebb8f996d | R | 804 | 34 |
#' Calculates the proportion of probes (CpGs) with power > 0.8
#'
#' @param allCellRes - a list of matrices with one matrix per cell type returned from running either calcDiff() or calcSamples()
#'
#' @return - a dataframe containing the proportion of probes with power > 0.8 where rows are either mean differences or n... |
fc7e7502d4e8ea9763e978d0955288dc54a7bc7b1ddcc70567a3aed893f4a65d | R | 807 | 28 | #!Rscript --vanilla --verbose
url <- 'https://cran.csie.ntu.edu.tw/'
## Install R package
install.packages("xml2", repos=url)
install.packages("BiocManager", repos=url)
install.packages("devtools", repos=url)
install.packages("pacman", repos=url)
BiocManager::install("Biobase")
BiocManager::install('yarn')
BiocManage... |
c301ec39bf11d767d577dafc375ccccdbb928a0182d12c3169b4ee33fd0c8425 | R | 809 | 20 | #### load packages ####
targetPackages <- c('tidyverse','arrow')
newPackages <- targetPackages[!(targetPackages %in% installed.packages()[,"Package"])]
if(length(newPackages)) install.packages(newPackages, repos = "http://cran.us.r-project.org")
for(package in targetPackages) library(package, character.only = T)
#### ... |
df55914f99a82ff8e3ff43f06c95402199ce560b872b91641ab9bed4baba94e2 | R | 821 | 27 | library(dplyr)
library(data.table)
library(optparse)
# Load arguments ----------------------------------------------------------
# Environment: R 3.6.1
parse <- OptionParser()
option_list <- list(
make_option('--fam', type='character', help="plink-format .fam file", action='store')
)
args = commandArgs... |
80818c80320cf896444236f5fc1229f43ee1ac26ef487e72233f80c3e3d888e8 | R | 826 | 26 | library(dplyr)
library(data.table)
library(optparse)
# Load arguments ----------------------------------------------------------
# Environment: R 3.6.1
parse <- OptionParser()
option_list <- list(
make_option('--checklist', type='character', help="file for checklist", action='store')
)
args = commandAr... |
31e35f715c14f99ba7975c13d99e11855a3bf4c6039b3121d61988bdad809d1f | R | 828 | 16 | descriptionPlots <- function (counts, group, col , ggplot_theme = theme_gray())
{
if (!I("figures" %in% dir()))
dir.create("figures", showWarnings = FALSE)
barplotTotal(counts = counts, group = group, col = col, ggplot_theme = ggplot_theme)
barplotNull(counts = counts, group = group, col = col, ggpl... |
b747fc28b9844c06c32f5dedc64592703916e90ddc2d8db21d3622fe7c18cd06 | R | 828 | 32 |
args <- commandArgs(TRUE)
if(length(args)!=2)
{ print("Usage: \'R --slave --args <InputData> <output> < normalize.R\'")
q()
}
infile = args[[1]];
outfile = args[[2]];
#GConly = args[[3]];
library("MASS")
#if(GConly!="Y"&&GConly!="y"&&GConly!="N"&&GConly!="n"){
# print("<GC only> must be Y/... |
2b88d378ba4224a61a89b794d70a0c7d53402eaf6ac8448e625444c039d63ee3 | R | 831 | 21 | setwd("~/data/STEREO/GEF/bins")
library(qs)
cortexMeta <- read.delim("~/data/STEREO/AnalysisPlot/cortex")
chipRegion <- setNames(cortexMeta$region,cortexMeta$chip)
bin100Files <- list.files(".","100.qs")
chip = cortexMeta$chip[[1]]
dataCollect <- list()
for ( chip in cortexMeta$chip){
data <- bin100Files %>% str_s... |
d2d7813c88395052bed6450d237bd3e26957c7aef04d810b2adf572764969bf6 | R | 842 | 34 | output_format <- "github_document"
render_notebook <-
function(notebook_name, output_suffix = "", ...) {
output_file <- paste0(notebook_name, output_suffix, ".md")
rmarkdown::render(
glue::glue("{notebook_name}.Rmd"),
output_file = output_file,
output_dir = "knit_notebooks",
output_f... |
40883c0eeebea254965d8540b0c9253795ab2ce8337a146194ed012711ccc215 | R | 849 | 36 | library(SingleCellExperiment)
print("rds to csv")
args<-commandArgs(TRUE)
#print(args)
root_path = args[1]
rds_name = args[2]
save_path = args[3]
print(rds_name)
#print(save_path)
rds_path = paste(root_path, rds_name, ".rds", sep = "")
if(!file.exists(rds_path)){
rds_path = paste(root_path, rds_name, ".RDS", sep = ... |
866fd0a1b2cfc52b1351f3223d0156ce2f970af7f71e0cc2c695215e9506ac55 | R | 854 | 31 | library(shiny)
ui <- fluidPage(
# Add custom CSS for the scrolling text area
tags$head(
tags$style(HTML("
#scrollable_Text {
height: 400px; /* Set height to limit the vertical size */
overflow-y: auto; /* Enable vertical scrolling */
white-space: pre-wrap; /* Preserve whitespace */... |
098c04ad30e6a0aab56214c57eb38a9f7715fb17189ddb6b54621358361e0551 | R | 868 | 30 | test_that("count_cases", {
x <- data.table(
Id = c(rep(1, 3), 2),
Center = c("G", "G", "L", "L")
)
expect_identical(count_cases(x), 3L)
})
test_that("count_cbc", {
x <- data.table(
CRP = c(NA, NA, 100, NA),
HGB = c(6.5, 6, NA, NA),
HCT = c(0.3, 0.25, NA, 0.24)
... |
834556f25836a5c912807d2dd9f3d93ae06c0f583a9507193f2b13f34acd52f1 | R | 881 | 39 |
args <- commandArgs(TRUE)
if(length(args)!=2 && length(args)!=3)
{ print("Usage: \'R --slave --args <InputData> <FigFile> < plot_RC_vs_GC.R\'")
print("or")
print("Usage: \'R --slave --args <InputData> <FigFile> <Title> < plot_RC_vs_GC.R\'")
q()
}
infile = args[[1]];
figfile = args[[... |
aaf29eb2e56d4be45da21f84adbd59679f32e8b9b1be3e15790c86643a4e0fb9 | R | 896 | 30 | #' @title gtf2db
#' @description Generate GTF object from gtf file.
#' @param filename Path to gtf file.
#' @param use_utr Load CDS records or not. Default is FALSE.
#' @return A point to GTF struct.
#' @export
gtf2db <- function(filename = NULL, use_utr = FALSE) {
if (is.null(filename)) stop("No gtf file.")
db <- ... |
b1aa3baee7b32802fd4ef2ab334d05e7af68d1f67b4218e72c878b24a7ed3eee | R | 950 | 29 |
library(tictoc)
# speed test lme4 vs lmer
data <- tar_read(data_eegnet_exp, branches=1)
tic("lmerTest::lmer")
mod1 <- lmerTest::lmer(formula="accuracy ~ ref + ( ref | subject)",
control = lmerControl(optimizer = "optimx",
calc.derivs = FALSE,
... |
551276a8d54cdbb4f00891f4eaeccbe59388a4c26dfb342690c80331a09a8fc8 | R | 955 | 16 | library(magrittr)
OUT_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP035988/output/"
rxn2ensembls.nls <- readRDS(paste(OUT_DIR,"rxn2ensembls_nls.Rds",sep=""))
rxn_ensembl_counts.df <- rxn2ensembls.nls %>% lapply(.,length) %>% as.data.frame() %>% t() %>% as.data.frame()
rxn_ensembl_counts.df$V1 %>% hist(breaks=ma... |
960180aaeb660cd4e96c626182c33e4e99390ba45ef1e06d64038b4aaa47b53e | R | 961 | 24 | exploreCounts <- function (object, group, typeTrans = "VST", gene.selection = "pairwise",
col , batch,varInt,batchRem=FALSE)
{
if (class(object) == "DESeqDataSet") {
if (typeTrans == "VST")
counts.trans <- assay(varianceStabilizingTransformation(object))
else counts.trans <- assay(rlogTransformatio... |
44b1a5209fd7e91f59d25fc6599679e400bd53b56153cc572e60cacc84269017 | R | 967 | 28 | library(magrittr)
OUT_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP049593/output/"
IN_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP049593/input/"
GTEX_DIR <- "/home/jgburk/PycharmProjects/reticula/data/gtex/"
X <- readRDS(paste(OUT_DIR, "rxn_pca_nls.Rds", sep = ""))
Y <- readRDS(paste(OUT_DIR,"srp04... |
785ae24c72b5694a646af38b1e44e53317802fed7e1508e166f52eb816eee923 | R | 967 | 28 | library(magrittr)
OUT_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP042228/output/"
IN_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP042228/input/"
GTEX_DIR <- "/home/jgburk/PycharmProjects/reticula/data/gtex/"
X <- readRDS(paste(OUT_DIR, "rxn_pca_nls.Rds", sep = ""))
Y <- readRDS(paste(OUT_DIR,"srp04... |
805adf3454332641b58540ff35d4be573a29c4074dc819b73cd5cfd0e2322f0e | R | 967 | 28 | library(magrittr)
OUT_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP061240/output/"
IN_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP061240/input/"
GTEX_DIR <- "/home/jgburk/PycharmProjects/reticula/data/gtex/"
X <- readRDS(paste(OUT_DIR, "rxn_pca_nls.Rds", sep = ""))
Y <- readRDS(paste(OUT_DIR,"srp06... |
9eee63ba5cf65f7f1b840c8803e086f0db243eb2e686ca2ae9114f015cfdd99a | R | 967 | 28 | library(magrittr)
OUT_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP035988/output/"
IN_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP035988/input/"
GTEX_DIR <- "/home/jgburk/PycharmProjects/reticula/data/gtex/"
X <- readRDS(paste(OUT_DIR, "rxn_pca_nls.Rds", sep = ""))
Y <- readRDS(paste(OUT_DIR,"srp03... |
f2a74de4b0f439e768046e5647d779a6b9b2394d5908e578af4022d5dbd475bc | R | 967 | 28 | library(magrittr)
OUT_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP050223/output/"
IN_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP050223/input/"
GTEX_DIR <- "/home/jgburk/PycharmProjects/reticula/data/gtex/"
X <- readRDS(paste(OUT_DIR, "rxn_pca_nls.Rds", sep = ""))
Y <- readRDS(paste(OUT_DIR,"srp05... |
bdde3ee661fc262a2cbce98600982afb24f0667d488314d57794a0c10f20de05 | R | 968 | 29 | library(data.table)
run_all <- function(args){
bedgraph <- args[1]
sample <- args[2]
rnabed <- fread(bedgraph)
rnabed <- rnabed[V4 != 0]
rnabed_vec <- numeric(nrow(rnabed) * 2)
rnabed_vec[seq(1,length(rnabed_vec),by = 2)] <- rnabed$V2
rnabed_vec[seq(2,length(rnabed_vec),by = 2)] <- rnabed$V3
rnabed[,connect := ... |
5f36fc0794efb7bb48f479d2999f405e3642ef9ffe19a85d284dad0cefc9c717 | R | 973 | 46 |
# equationexport latex
library(equatiomatic)
# LM
model <- tar_read(sliding_LM)
# Give the results to extract_eq
extract_eq(model,
intercept = "beta", # make it beta_0 instead of alpha
#var_colors=c("blabla"="blue"),
#var_subscript_colors=c("blabla"=darkred)",
wrap=TRUE,... |
e438fa5f77b369af2bc9f973c87d92f1f29ed8619f19716a8692f86b71ea845e | R | 973 | 33 | library(plyr)
library(dplyr)
library(tidyverse)
library(tidyr)
library(reshape2)
library(data.table)
library(ggpubr)
library(Seurat)
# ADULT BARPLOT
nodes = c('Human', 'HC', 'HCGo', 'Great_Ape', 'Ape', 'cons')
dfL = list()
for(i in 1:length(nodes)){
fls = list.files(path = paste0('LDSC/TOP20K_EXPAND_... |
6a1b65e9782d64593fa522c930491227afe3930461b94941820fe8a6e426b84d | R | 981 | 38 | require(Signac)
require(Seurat)
require(Matrix)
require(EnsDb.Hsapiens.v86)
require(BSgenome.Hsapiens.UCSC.hg38)
require(dplyr)
require(readr)
args = commandArgs(trailingOnly = TRUE)
for (arg in args) {
split_arg <- strsplit(arg, "=")[[1]]
var_name <- split_arg[1]
var_value <- split_arg[2]
if(grepl(... |
4a0757b5c3372110b7620f4b6bfefb63aa6c129266006e83cf0686397999ea81 | R | 994 | 26 | library(DESeq2)
library(magrittr)
library(SummarizedExperiment)
start_time <- Sys.time()
IN_DIR <- "/home/jgburk/PycharmProjects/reticula/data/tcga/input/"
OUT_DIR <- "/home/jgburk/PycharmProjects/reticula/data/tcga/output/"
tcga.df <- readRDS(paste(OUT_DIR,"tcga_df.Rds",sep=""))
tcga.tissue.vec <- readRDS(paste(OUT... |
982d32ea0b5243233457584199e2e176ed5464238e78c6b4842176504752af27 | R | 994 | 23 | library(magrittr)
library(qs)
library(Seurat)
library(stringr)
library(tidyverse)
setwd("~/cortex/figS1-6/")
merge_seu <- readRDS("../SnRNA/SnRNA_seurat.RDS")
opcRNA <- merge_seu[,merge_seu$subclass == "OPC"]
geneName_id <- read.csv("../SnRNA/1_SnRNA_preprocessing/gene_kept.csv") %>% {setNames(.$gene_id,.$gene_name)}
... |
fdd56193a780f924fc24d9e4e1499d30d0e42ea538c9e824820bd84bacdf1df4 | R | 1,000 | 31 | library(magrittr)
IN_DIR <- "/home/burkhart/Software/reticula/data/aim2/input/"
# Reaction Network
edges.df <- read.table(paste(IN_DIR,"edges.txt",sep=""),sep = " ")
unique_nodes.vec <- c(edges.df$V1,edges.df$V2) %>% unique()
edges.df$V1 <- sample(unique_nodes.vec,nrow(edges.df),replace = TRUE)
edges.df$V2 <- samp... |
63593f91b99412a0de9cb90146e5418eae551d29d774dacc5b8f4794e50d5e78 | R | 1,006 | 47 | suppressPackageStartupMessages({
library(SingleCellExperiment)
library(SINCERA)
library(aricode)
})
set.seed(2021)
memory.limit(1e11)
################################ Run SINCERA ####################################
run_SINCERA <- function(data){
###Construct S4 object for sincera
data <- as.data.frame(da... |
88b3f3861987a2c7aad396527eb5f0446abfdf86796a1bb306c797faf9a081f0 | R | 1,011 | 24 | #' @title Signal matching and alignment
#' @description After inputting the average mass spectra information, this step
#' enables automatically matching the intensities between the experimentally
#' measured m/z and the targeted list with a tolerance of 5 ppm for each sample.
#'
#' @param Intensity The theore... |
04e62a26ef33879475957e2ff29176b43068cac9c5eb0c785169dc6a74d056a7 | R | 1,015 | 26 | library(DESeq2)
library(magrittr)
library(SummarizedExperiment)
start_time <- Sys.time()
IN_DIR <- "/home/jgburk/PycharmProjects/reticula/data/gtex/input/"
OUT_DIR <- "/home/jgburk/PycharmProjects/reticula/data/gtex/output/"
gtex.df <- readRDS(paste(OUT_DIR,"gtex_df.Rds",sep=""))
gtex.tissue.detail.vec <- readRDS(pa... |
eefef135f6254205143126a4b6c54eb112e709140f07caf4ad63a956f3471cf4 | R | 1,024 | 27 | #' @title Selection of ions with sufficient imaging signals
#'
#' @description If the intensity of the 12C-metabolite ion is found to be less
#' than the set minimum count in a specific number of samples, this ion and
#' its corresponding isotopologue group will be removed.
#'
#' @param df The table generated b... |
96f28e4d6a42f9e9162f2ea827eec92237e6c0403c67d2aa16f55d25c86ccc67 | R | 1,054 | 15 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
Rtsne_cpp <- function(X, no_dims, perplexity, theta, verbose, max_iter, distance_precomputed, Y_in, init, stop_lying_iter, mom_switch_iter, momentum, final_momentum, eta, exaggeration_factor, n... |
60cadb007fc60074f20f38daf6bc09246696eff33f01cb41338c154eed267171 | R | 1,060 | 33 | ################################################################################
# Written by James M Roe, Ph.D.
# Center for Lifespan Changes in Brain and Cognition, Department of Psychology
# University of Oslo, Oslo, Norway
# November 12, 2020
#------------------------------------------------------------------------... |
0e614cd00904c22e46d482eeb121a5a3cb748e457b4d72a95a8c125d37acc63d | R | 1,064 | 26 | library(DESeq2)
library(magrittr)
library(SummarizedExperiment)
start_time <- Sys.time()
IN_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP035988/input/"
OUT_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP035988/output/"
srp035988.df <- readRDS(paste(OUT_DIR,"srp035988_df.Rds",sep=""))
srp035988.tissue... |
259fd0a990b36fb7d5ac004299264496f6af9cb75c13737415a9b98f27c95b4f | R | 1,064 | 26 | library(DESeq2)
library(magrittr)
library(SummarizedExperiment)
start_time <- Sys.time()
IN_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP061240/input/"
OUT_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP061240/output/"
srp061240.df <- readRDS(paste(OUT_DIR,"srp061240_df.Rds",sep=""))
srp061240.tissue... |
64beb0ceb3a312c906faa38b67f21473cccd79ee0542ce38ce82593ec0208fde | R | 1,064 | 26 | library(DESeq2)
library(magrittr)
library(SummarizedExperiment)
start_time <- Sys.time()
IN_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP050223/input/"
OUT_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP050223/output/"
srp050223.df <- readRDS(paste(OUT_DIR,"srp050223_df.Rds",sep=""))
srp050223.tissue... |
7e73c7785d9135b0659efe5445f583c443cbf299ee692b09386f5a41086785f0 | R | 1,064 | 26 | library(DESeq2)
library(magrittr)
library(SummarizedExperiment)
start_time <- Sys.time()
IN_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP042228/input/"
OUT_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP042228/output/"
srp042228.df <- readRDS(paste(OUT_DIR,"srp042228_df.Rds",sep=""))
srp042228.tissue... |
89dd2bc9ffdd11e5e187ed8e2f241dc20d39d612ad98610d849dcc1e246d39e5 | R | 1,064 | 26 | library(DESeq2)
library(magrittr)
library(SummarizedExperiment)
start_time <- Sys.time()
IN_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP049593/input/"
OUT_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP049593/output/"
srp049593.df <- readRDS(paste(OUT_DIR,"srp049593_df.Rds",sep=""))
srp049593.tissue... |
4c1ca50669f107f263d04a29ab30661688b7d049e9bfca8f3f94654580e0c2ae | R | 1,075 | 39 | rm(list=ls())
####### load libraries
library(deSolve)
library(openxlsx)
library(SCIFER)
###### load functions
sample <- "AN02255" # specify sample ID
sample.info <- read.xlsx("MetaData/Supplementary Tables.xlsx", startRow = 7, sheet = 8)
sample.info$Age <- as.numeric(sample.info$Age)
sample.info$Average.coverage <- a... |
5642aa758618c9eda2daa891a43b0d04028192012e1a0a3cdf02fed2905a5239 | R | 1,089 | 34 | #https://github.com/chanzuckerberg/single-cell-curation/blob/main/notebooks/curation_api/R/get_datasets_R.ipynb
setwd("~/cortex/SnRNA/2_codePreprocessingExternalData/")
library(readr)
library(httr)
library(stringr)
library(rjson)
library(tidyverse)
library(magrittr)
domain_name <- "cellxgene.cziscience.com"
site_url ... |
6dc1d07d1a49c13fdc01c9495ff54581e4eaced2708469b05b3a80847ac4139f | R | 1,093 | 26 | #### load packages ####
targetPackages <- c('sangerseqR','annotate')
newPackages <- targetPackages[!(targetPackages %in% installed.packages()[,"Package"])]
if(length(newPackages)) install.packages(newPackages, repos = "http://cran.us.r-project.org")
for(package in targetPackages) library(package, character.only = T)
# ... |
100436cd64c77617060ddfc18a893d301f418d86f0c5218fd0c7e4989e067557 | R | 1,106 | 36 | ## Script used to bootstrap R-universe build.
## Execute git commands to initialize git submodules
system("git submodule init")
system("git submodule update")
## core
file.copy("../src", "./src/", recursive = TRUE)
file.copy("../include", "./src/", recursive = TRUE)
file.copy("../amalgamation", "./src/", recursive = ... |
0678ec6df9cc19b4a786f6cb4aba2489c136f7da7954c130c6d075a8b1b1adc6 | R | 1,121 | 58 |
# diagnostics
library(gridExtra)
library(broom)
library(broom.mixed)
library(ggplot2)
fm1 <- tar_read(eegnet_HLM_exp, branches=1)[[1]]
data <- augment(fm1)
# Residuals vs. Fitted plot
p1 <- ggplot(data = data, aes(x = .fitted, y = .resid)) +
geom_point() +
geom_smooth(method = "loess", se = FALSE) +
labs(x = ... |
9c6eed3638a8dc9bdf71804b614cfb8a2846666e36f065e30054e43a8ef74822 | R | 1,131 | 53 | ## Install dependencies of R package for testing. The list might not be
## up-to-date, check DESCRIPTION for the latest list and update this one if
## inconsistent is found.
pkgs <- c(
## CI
"pkgbuild",
"roxygen2",
"XML",
"cplm",
"e1071",
## suggests
"knitr",
"rmarkdown",
"ggplot2",
"DiagrammeR",
... |
19ea0a152957c744256885e9babe2a33c84f01cffba5a8178a200c2de8e7d4a3 | R | 1,132 | 42 | rm(list=ls())
####### load libraries
library(deSolve)
library(openxlsx)
library(SCIFER)
###### load functions
patient.id <- 'N1' # specify patient ID here
sort <- "CD34" # specify cell sort here, should be either CD34, MNC, MNC_minus_T, PB_gran
sample.info <- read.xlsx("MetaData/Supplementary Tables.xlsx", sheet = 2,... |
2318c45af415158bcdaf7414c72e0f1e3bcd799aa622d6acbfc113966b05da18 | R | 1,137 | 32 | # Run after running 'analysis/02_diffbind_e16.R' and 'tables/scripts/tableS3.R'
library(readr)
library(tidyverse)
library(dplyr)
# READ IN DIFFBIND RESULTS: ----------------------------------------------------
diffbind_res_df <- read.csv("tables/table_S3_atacseq_e16_diffbind_results.csv")
# Create dataframes for CTR... |
b0d7d3077679f05cf7bcae1fc42d59a1b6da033b8015b81950845c5ccf8c9c9d | R | 1,137 | 36 |
library(ggplot2)
library(ggsankey)
library(dplyr)
library(magrittr)
library(paletteer)
# own data
data <- tar_read(data_eegnet)
data %<>% filter(subject == "sub-001") %>%
filter(experiment == "N170") %>%
select(-c(subject, accuracy, experiment))
# now change the names of all columns with the replacements
names(... |
c874f95fc5796db88a9a7239e42ee1d06f1e6c688bb9483109d00721bec2d879 | R | 1,148 | 34 | library(tidyverse)
extract_bids <- function(d, column){
d |>
mutate(
modality = if_else(str_detect({{column}}, "T1w"), "T1w", "bold"),
sub = str_extract({{column}}, "(?<=sub-)[[:digit:]]+"),
ses = str_extract({{column}}, "(?<=ses-)[[:digit:]]+"),
task = str_extract({{column}}, "(?<=task-)... |
358b0dc6e8c525ef3b63f3804f07c99c24b240ba44579c405a810963ac80e44e | R | 1,153 | 42 | rm(list=ls())
####### load libraries
library(deSolve)
library(openxlsx)
library(SCIFER)
###### load functions
patient.id <- 'N1' # specify patient ID here
sort <- "CD34" # specify cell sort here, should be either CD34, MNC, MNC_minus_T, PB_gran
sample.info <- read.xlsx("MetaData/Supplementary Tables.xlsx", sheet = 2,... |
de89813ad170b97f5904c9872f48e9513e4c6d9595a37830cb2557c66658c49d | R | 1,169 | 18 | library(Seurat)
library(tidyverse)
setwd("~/cortex/figS1-6/")
Meta <- read_csv("../SnRNA/3_mergingDatasets/SnRNA_Meta.csv")
submeta <- Meta %>% filter(str_detect(class,"exc"))
# figS1b-1
{ggplot(submeta, aes(x = region, fill = subclass)) + geom_bar(position = "fill") + scale_fill_manual(values = subclass_color) + cow... |
54450db0b1de9fb0c110a0428d6104f2d77d6621bdb6e68c3d4e1c03d5042fff | R | 1,174 | 30 | library(NAIR)
# set working directory
setwd('/Users/lung/Documents/Projects/deep_learning/PepTCR-Net/')
# directory of input data
dir <- './datasets'
# your data file path
data_dir <- file.path('./datasets/mira_train_data.csv')
# read data file path
data <- read.csv(data_dir)
# output data file path
distance <- 0
ou... |
a80edaec077e793a3bd1903535e987df96205f39d99af1c9a8f78dc844543de8 | R | 1,191 | 50 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
#' Compute Euclidean distance matrix by rows
#'
#' Used in consmx function
#'
#' @param x A numeric matrix.
ED1 <- function(x) {
.Call('_SC3_ED1', PACKAGE = 'SC3', x)
}
#' Compute Euclid... |
bdcebd5d001a2a163e98411bc6bf7dd8cf46628b018148372cc7fd56cf1f13ba | R | 1,193 | 35 | ---
output: html_document
editor_options:
chunk_output_type: inline
---
```{r, message=FALSE}
library(tidyverse)
source("~/Dropbox (OHSU)/Saunders Lab's shared workspace/arpy/manuscripts/2023_Thai2P4M_FeigeYoung/ms_analyses/1_karl_analysis/r_functions_paths/_color_palettes_tha2P4M.R")
source("~/Dropbox (OHSU)/Saun... |
ebb3ca362c7642c8ad78411ffc48a046a694c9eea80eb75bb234b107f96db51d | R | 1,202 | 40 | GetAssayData1 <- function(object, assay = NULL, layer = "counts")
{
assay <- assay %||% DefaultAssay(object)
old.assay <- DefaultAssay(object)
DefaultAssay(object) <- assay
if (packageVersion("Seurat") < "5") {
data <- GetAssayData(object, slot = layer)
} else {
data <- NULL
layers <- Layers(objec... |
6c3c2e031fbf4404feafe63afa977a1e55aec3d472ffc1aef99d03060ee9c0c4 | R | 1,212 | 35 | library(DESeq2)
library(magrittr)
library(EnhancedVolcano)
ALPHA <- 0.05
OUT_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP050223/output/"
dds <- readRDS(paste(OUT_DIR,"dds.Rds",sep=""))
dds$Tissue <- relevel(dds$Tissue,
ref= "tissue: normal thymus")
dds_de <- DESeq(dds,
... |
8317ed8d524242b79d641a115bfe4edb1de283b1897c6574c1d358922d827474 | R | 1,223 | 17 | #!/usr/bin/env Rscript
library(tidyverse)
library(ggplot2)
library(Seurat)
library(MAST)
#run diff expr
seurat_obj <- readRDS("/n/groups/walsh/indData/Maya/FCD_project/analysis/2_Analyze_Full_Object/Output/Seurat_Objects/8_processed_seurat_obj_integrated_azimuthAnnotations_ourAnnotations_doubletsFiltered_FCD1Filtered... |
055ffaf7ec814445f5f677079650f4bd896b48ff2a7da4accf93af9e3c126a5c | R | 1,233 | 67 | ---
title: "Inspect profiles"
author: "Shantanu Singh"
date: "June 2021"
---
## Load libraries
```{r message=FALSE}
library(ggplot2)
library(glue)
library(magrittr)
library(tidyverse)
```
## Data overview
### Read profiles
```{r message=FALSE}
batch_id <- "NCP_PROGENITORS_1"
platemap <- "BR_NCP_PROGENITORS_1"
pl... |
12c416dad01b0adcd5df0cdfedf1086f0b8b9df1b60cf325675a576d1e77fbb0 | R | 1,245 | 42 | ## Fig 3: Risk Score Estimation using ssGSEA
# Load libraries
suppressPackageStartupMessages({
library(escape)
library(Seurat)
library(SingleCellExperiment)
library(GSEABase)
library(dplyr)
})
# Load data (assumes preprocessed Seurat object or expression matrix named `data`)
# Replace `data` below with the ... |
d57623cd87e59161bb88da951138ed57f1601ed1419a6b6b732aa35523c7311d | R | 1,249 | 36 | bedanno <- function(chr = NULL, start = NULL, end = NULL, strand = NULL, gtf = NULL, upstream = 1000, downstream = 1000)
{
sl <- .Call("anno_bed", chr, start, end, strand, gtf, upstream, downstream)
sl
}
#' @title annoBED
#' @description Annotate gene name for bed region. Before annotation you should run ParseBED t... |
026f770f0f98a8c73f11f3add8e8f5320def773b1c3b9aa833f6f60f8ffcd3cb | R | 1,252 | 59 | # table export
library(xtable)
library(dplyr)
library(tidyr)
## F TEST
data = tar_read(eegnet_HLM_emm_omni_comb)
# all experiments listed in one row as string concat
#df_new <- data %>%
# filter(p.fdr < 0.5) %>%
# group_by(`model term`) %>%
# summarize(significant_experiments = toString(experiment))
thisLa... |
e9163253ce07c4b735d158d3ed44f9ad5ff28ae8869d458920e497dc600a26d5 | R | 1,254 | 60 | suppressPackageStartupMessages({
library(cidr)
library(aricode)
library(SingleCellExperiment)
})
set.seed(2021)
memory.limit(1e11)
############################## Run CIDR ###################################
run_CIDR <- function(dt){
sData <- scDataConstructor(dt)
sData <- determineDropoutCandidates(sData)... |
a9931f5299ea884b401feeaee42dd43213570b422a22d975ae2d02db5173c1e2 | R | 1,258 | 46 | #' @title ImputationByWeight
#' @param X Expression counts.
#' @param cells Cells to imputate.
#' @param W Weight matrix.
#' @param filter Value below this cutoff will be filtered. Used to reduce density of matrix.
#' @export
ImputationByWeight <- function(X = NULL, cells = NULL, W = NULL, filter = 0.001)
{
if (is.nu... |
8c954d8b8c6937cf4bbf05b74f6dbfc60355d402cf1c0f1db4155af9f164610a | R | 1,268 | 48 |
#infile = "/home/ruibin/software/normalization/normalization/normalization_v0.1.6/tmp/binfiltering_l3MTjXX.txt"
#infile = "/home/ruibin/software/normalization/normalization/normalization_v0.1.6/tmp/binfiltering_7p7rzvv.txt"
#outfile = "/home/ruibin/software/normalization/normalization/normalization_v0.1.6/tmp/bin... |
148da944eb795695d8dbc4e122f1bf120e368f8219207ccf738af3aa51d125d5 | R | 1,275 | 30 | library(DESeq2)
library(magrittr)
library(SummarizedExperiment)
start_time <- Sys.time()
IN_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP050223/input/"
OUT_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP050223/output/"
SRP050223_DATA_FIL <- "rse_gene(2).Rdata"
ensembl2rxns.df <- read.table(paste(IN_... |
2b607d00a040ed21eda5944d047ed66c10417fe869e2e3c32cadd896aeb497c7 | R | 1,276 | 33 | ---
title: "Save Summary File for GEO"
author: "Arpy"
date: '2024-12-24'
output: html_document
---
```{r libraries and functions, message=FALSE}
library(tidyverse)
library(readxl)
library(xlsx)
source("~/OHSU Dropbox/Saunders Lab's shared workspace/arpy/manuscripts/2023_Thai2P4M_FeigeYoung/ms_analyses/1_karl_analysi... |
37e4377f0822fd213192c087a07016c39fcb1fd81b512510da0700fd7d222058 | R | 1,277 | 43 | #' @title annoTX
#' @description Annotate gene name and genomic locations for transcript id.
#' @param object Seurat object.
#' @param assay Work assay.
#' @param gtf GTF object, load by gtf2db.
#' @param gene.name Tag name for gene name. Will be created after annotation. Default is "gene_name".
#' @return Annotated S... |
1206daef8ef340e3c3a5438dba5fa6d3456994beb6c8956119c888d868c29381 | R | 1,287 | 36 | # READ DDS OBJECT FROM RDS FILE: -----------------------------------------------
dds <- readRDS(
file = rds_deseq2_results_e17)
# EXTRACT TRANSFORMED VALUES: --------------------------------------------------
vsd <- vst(dds, blind=FALSE)
# CUSTOMIZE A PCA PLOT: ------------------------------------------------------... |
46a8ebdc15afca8a6d15bff5e4223f75bf1267279bae52b43fc22c3abb19e461 | R | 1,300 | 40 | library(lmerTest)
packages <- c("dplyr", "ggplot2", "ggsignif", "readr", "lmerTest", "emmeans", "magrittr", "ggpubr", "data.table",
"tidyverse", "tidyquant", "ggdist", "ggthemes", "broom", "dplyr", "purrr", "rstatix", "tidyr")
lapply(packages, require, character.only = TRUE)
data <- get_preprocess_data("eegnet.csv... |
f9fc060dd2590ed9dd37a5febf11273a9013d2db6fbdacdfd1c95a71c81c5f69 | R | 1,300 | 44 | #' Function to convert an igraph into a tibble for nodes or edges
#'
#' \code{oIG2TB} is supposed to convert an igraph into a tibble for nodes or edges.
#'
#' @param ig an "igraph" object
#' @param what what to extract. It can be "edges" for edges and "nodes" for nodes
#' @return
#' a tibble object
#' @note none
#' @ex... |
a251b713d7eb039457421d42d755040c07ecb4d621f1bc07c9eca165ba004c2d | R | 1,310 | 37 | # READ DDS OBJECT FROM RDS FILE: -----------------------------------------------
dds <- readRDS(
file = rds_deseq2_results)
# EXTRACT TRANSFORMED VALUES: --------------------------------------------------
vsd <- vst(dds, blind=FALSE)
# CUSTOMIZE A PCA PLOT: --------------------------------------------------------
#... |
1f8c4921c8871ff5d79fb0054f8306712e9f4c50137e65986faa8c4209b8bbdc | R | 1,311 | 49 |
# CHORD diagram to visualize set interactions in Models
#
library(circlize)
# Create an adjacency matrix:
# a list of connections between 20 origin nodes, and 5 destination nodes:
numbers <- sample(c(1:1000), 100, replace = T)
data <- matrix( numbers, ncol=5)
rownames(data) <- paste0("orig-", seq(1,20))
colnames(da... |
d0afd690631e4cfca70e9e4cd8692dd2591a5efd45b28ad6648fd1be9c110ded | R | 1,312 | 48 | library(readxl)
library(dplyr)
library(ggplot2)
library(tidyr)
library(ggstatsplot)
library(patchwork)
file_path <- "~/R_scripts/final_peak_analysis_summary_all.xlsx"
colnames(data) <- tolower(colnames(data))
data <- data %>%
rename(
peak_caller = method,
histone_mark = histone,
sample = sample
)
expe... |
374bc1a6ac640cdbd68e50e559ac80c4b279f62bb1455c3c90da851da4efc1a7 | R | 1,314 | 32 | library(DESeq2)
library(magrittr)
library(SummarizedExperiment)
start_time <- Sys.time()
IN_DIR <- "/home/jgburk/PycharmProjects/reticula/data/tcga/input/"
OUT_DIR <- "/home/jgburk/PycharmProjects/reticula/data/tcga/output/"
TCGA_DATA_FIL <- "rse_gene(4).Rdata"
ensembl2rxns.df <- read.table(paste(IN_DIR,"Ensembl2Re... |
3e812657f02b8d906cdb3ae463efaa608a6745793a5b669667bbe2c8a874e7d9 | R | 1,334 | 20 | library(Seurat)
library(tidyverse)
setwd("~/cortex/SnRNA/3_mergingDatasets")
subclass_color <-c(AST = "#665C47", ENDO = "#604B47", ET = "#CEC823", CHANDELIER = "#E25691",
`L2-L3 IT LINC00507` = "#07D8D8", `L3-L4 IT RORB` = "#09B2B2",
`L4-L5 IT RORB` = "#69B199", `L6 CAR3` = "#898... |
64f79fe037d222d0ffe97cbadca173dd77a56e04a9d75bbbb64509fc12a2b993 | R | 1,345 | 47 | #' @title annoGene
#' @description Annotate genomic locations for gene names.
#' @param object Seurat object.
#' @param assay Work assay.
#' @param gtf GTF object, load by gtf2db.
#' @param gene.name Tag name for gene name. If not set, use rownames instead and will create gene_name tag in the meta table.
#' @return Ann... |
a7191fa735c7b5d6a15219e2d43925e7b2f7427ff2a015e9a4fd27d1453e61e3 | R | 1,356 | 38 | # Script to compute a permutation test, no replacement, to compare means
# Created EBM 3/06/2022
# Use: list1 <- first sample list of numbers
# list2 <- second sample list of numbers
# tails <- one or two tailed computation
# showgraph <- 'y' or 'n' to show histogram of shuffle results
permtest_mean <- fun... |
c0989bf2f07d9c6a213b4af4182a4aa5be056f0032eeddcd2823009b090da4a1 | R | 1,369 | 23 | #' @rdname Rtsne
#' @export
Rtsne_neighbors <- function(index, distance, dims=2, perplexity=30, theta=0.5,
max_iter=1000,verbose=getOption("verbose", FALSE),
Y_init=NULL,
stop_lying_iter=ifelse(is.null(Y_init),250L,0L),
mom_switch_iter=ifelse(is.null(Y_init),250L,0L),
momentum=0.... |
e61c2ae950606f9847315b2b07bcea6f5a2f21df91b936c951f35b71a98e046e | R | 1,385 | 42 |
# plot ecdf with colored dots for top pipelines
data <- tar_read(data_tsum)
best_data = data.frame()
for (experiment_val in c("ERN", "LRP", "MMN", "N170", "N2pc", "N400", "P3")){
newdata <- data %>%
#group_by(ref, hpf, lpf, emc, mac, base, det, ar) %>%
#summarize(tsum = mean(tsum)) %>%
filter(experi... |
bd2c85440195eb63e64ecd9b90650e36026db521f75a0e40e3cea1a704bcd08a | R | 1,399 | 22 | #' Calculate the dot product between all the possible combinations of foldchanges from diferent clusters.
#'
#' `foldchangeComposition()` returns a dataframe containing the best top similarities between all possible pairs of single cell samples.
#'
#' This function will perform the dot product of each possible combinat... |
8a0637fc9d7033fcebad4ec5605496c6c3be649eb0511b9f5e3d27ced4d9f491 | R | 1,415 | 31 | calcDiffTotal <- function (complete, alpha = 0.05)
{
nDiffTotal<- matrix(NA, ncol = 5, nrow = length(complete),
dimnames = list(names(complete), c("Test vs Ref","min baseMean", "# down",
"# up", "# total")))
for (name in names(compl... |
4cb1d3484d00dc3fac86e5f68eb63d1c23f0ff993c612996bf452c304e6072ee | R | 1,427 | 51 | test_that(".merge_df_lab", {
x <- data.table(
Id = rep(1:4, c(1, 3, 2, 1)),
Center = rep(c("G", "L"), c(6, 1)),
Time = c(0, 0, 0, 1, 0, 0, 0),
CRP = c(NA, NA, 100, NA, NA, NA, 5),
HGB = c(7, 6, NA, 5, 6.4, 6.5, NA),
HCT = c(0.3, 0.4, NA, 0.4, 0.3, 0.4, NA)
)
r... |
303b9e7bb87c7de8909ea73fb680acc8a31a4c2041b58e242e205685e2921c95 | R | 1,432 | 39 | library(viridis)
library(pheatmap)
library(RColorBrewer)
combined.df <- readRDS("~/combined_df.Rds")
tissue.vec <- readRDS("~/tissue_vec.Rds")
datasource.vec <- readRDS("~/datasource_vec.Rds")
colFun <- colorRampPalette(RColorBrewer::brewer.pal(10,"Paired"))
annotation_col.df <- data.frame(Datasource = datasource.ve... |
4ad57810a71c9911dff75515bdfc66cd2299db9749b37e6d6c2b7cbd1c56e5d7 | R | 1,432 | 39 | library(magrittr)
OUT_DIR <- "/home/jgburk/PycharmProjects/reticula/data/tcga/output/"
IN_DIR <- "/home/jgburk/PycharmProjects/reticula/data/tcga/input/"
GTEX_DIR <- "/home/jgburk/PycharmProjects/reticula/data/gtex/"
X <- readRDS(paste(OUT_DIR, "rxn_pca_nls.Rds", sep = ""))
Y <- readRDS(paste(OUT_DIR,"tcga_tissue_vec... |
5e44e97f50465bc8bdb359057cf45fd50dc8d54fd7bb76002588fc4e022a044a | R | 1,436 | 39 | library(viridis)
library(pheatmap)
library(RColorBrewer)
combined.df <- readRDS("~/combined_df.Rds")
tissue.vec <- readRDS("~/tissue_vec.Rds")
datasource.vec <- readRDS("~/datasource_vec.Rds")
colFun <- colorRampPalette(RColorBrewer::brewer.pal(10,"Paired"))
annotation_col.df <- data.frame(Datasource = datasource.ve... |
38331f0655be93d33e2df54e278521ff59d68b586bafd1463b2f37d5353c29c4 | R | 1,440 | 67 | require(rphast)
require(ape)
require(dplyr)
require(parallel)
require(Biostrings)
require(ggpubr)
require(seqinr)
require(phangorn)
require(msa)
source('SCRIPTS/Functions.R')
args = commandArgs(trailingOnly = TRUE)
for (arg in args) {
split_arg <- strsplit(arg, "=")[[1]]
var_name <- split_arg[1]
... |
5e2dd30a8cfa1e84b8785cda63efd9a5ce52e1def2af362d8161471834699da7 | R | 1,443 | 24 | ################################################################################
# Check differences in means and variances before ComBat (in controls) #
################################################################################
i_controls = which(X$patient == 0)
m = lm(stai ~ country + age + sex, data = ... |
2b4585486b018397ae1b6c39ee519ddeb6094621ebaa4e054088f8e69d7c5635 | R | 1,451 | 30 | library(tidyverse)
library(EnhancedVolcano)
library(magrittr)
library(ggprism)
setwd("~/cortex/fig3/")
resFiles <- list.files("./fig3g/","neuronsubclass_results.csv",full.names = T,recursive = T)
region_color <- c("#2F4587", "#8562AA", "#EC8561", "#B97CB5", "#D43046", "#F0592B",
"#ED4A96", "#593C97... |
5176666152a962baf394865d65e6132c3923bf101bf78460f1181f45c6d5d592 | R | 1,451 | 42 | # Install and load required packages
install_and_load <- function(package) {
if (!requireNamespace(package, quietly = TRUE)) {
install.packages(package, dependencies = TRUE)
}
library(package, character.only = TRUE)
}
# List of packages to install and load
packages <- c(
"Matrix", "Seurat", "ggplot2", "gri... |
4114d198e72c204c2d9899f76cf527b67e2ff3c64415ed53bbb99087bfbb57ca | R | 1,452 | 37 | library(DESeq2)
library(magrittr)
library(SummarizedExperiment)
start_time <- Sys.time()
IN_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP049593/input/"
OUT_DIR <- "/home/jgburk/PycharmProjects/reticula/data/SRP049593/output/"
SRP049593_DATA_FIL <- "rse_gene(1).Rdata"
ensembl2rxns.df <- read.table(paste(IN_... |
c60dd96b35fa83b35d1e74808f9f97c2b47ab4ca2e1b0a5da1ff10ffe7c88ba8 | R | 1,462 | 25 | volcanoPlot <- function (complete, alpha = 0.05, outfile = TRUE,fc.cutoff=1)
{
ncol <- ifelse(length(complete) <= 4, ceiling(sqrt(length(complete))),
3)
nrow <- ceiling(length(complete)/ncol)
if (outfile)
png(filename = "figures/volcanoPlot.png", width = cairoSizeWrapper(1800 *
ncol... |
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