ATpoint

pkgs <- c("KEGGREST", "org.Mm.eg.db", "tidyverse", "AnnotationDbi")
invisible(lapply(pkgs, function(x) suppressPackageStartupMessages(library(x, character.only = TRUE))))

pathway_id_2_name <- 
  keggList("pathway", "mmu") %>%
  enframe(name = "pathway_id", value = "pathway_name") %>%
  mutate(pathway_id = gsub("mmu", "", pathway_id),
         pathway_name = gsub(" - .*", "", pathway_name))

pathway_2_entrez <- 

ATpoint

#!/bin/bash

# Read existing md5sums and use to confirm integrity of fastq files

#SBATCH --nodes=1
#SBATCH --cpus-per-task=36
#SBATCH --partition=normal
#SBATCH --time=08:00:00
#SBATCH [email protected]
#SBATCH --job-name=md5checker

ATpoint

# Install R infrastructure
pkg_install <- c("reticulate", "zellkonverter")
BiocManager::install(pkg_install, update = FALSE)

# Install conda itself via the reticulate package, takes time...
reticulate::install_miniconda()

# Create an environment for velocity with a specific python version that is required
# to install a downgraded matplotlib, see below
reticulate::conda_create(envname = "velocity", python_version = "3.8.19")

ATpoint

geom_segment(data=metadata(sce)$grid.df, 
    mapping=aes(x=start.1, y=start.2, xend=end.1, yend=end.2), 
    size=1,
    arrow=arrow(length=unit(0.1, "inches"), type="closed"), inherit.aes = FALSE)

ATpoint

#' Read loom files into R as CsparseMatrix, depending on Matrix and hdf5r
#' @param path to file loom file on disk
#' @param include.ambiguous logical, whether to also read the "ambiguous" counts from velocyto, default FALSE
#'
readLoomMatrices <- function(file, include.ambiguous=FALSE) {
  
  require(Matrix)
  require(hdf5r)
  
  engine='hdf5r'

ATpoint

#!/bin/bash

# BundleAndValidate <FOLDER_TO_BUNDLE> <tar/tarReplaceLink/zip> <Destination>
#
# Tar a folder and validate integrity of the tarball. For this, compare the file listing of the input folder
# with the file listing of the tarball via md5sum. If identical assume tarball is fine.
# Option tar makes a tarball, tarReplacelink uses tar -h to replace links with pointing file and zip makes a zipball.
# Don't be surprised that it can take some time before you see the tarball at destination after submitting the job.
# The first step is a full listing of the origin file, and with many folders and files that can take a while.
# Uses mbuffer internally to buffer incoming tar stream.

ATpoint

#' Create unique combinations
#' 
#' To a given vector of numeric values add or subtract a constant value,
#' and return all possible combinations of that as a matrix
#' 
#' @param values a vector with numeri values
#' @param change_value a numeric value too add or subtract
#' 
#' @details
#' There are 2^length(values) possible combinations

ATpoint

reprex::reprex({
  d <- data.frame(a=1)
  
  # Works
  suppressMessages({library(AnnotationDbi); library(dplyr)})
  d %>% select(a)
  
  detach("package:AnnotationDbi", unload=TRUE)
  detach("package:dplyr", unload=TRUE)
  

ATpoint

# Retrieve REACTOME terms for human directly from the website, then map to mouse.
# Pull human to mouse mappings via biomaRt.

library(biomaRt)
library(data.table)
library(magrittr)
library(rtracklayer)
library(tidyverse)

options(timeout=999)

ATpoint

library(biomaRt)
library(data.table)
library(dplyr)
library(magrittr)

kegg <- sapply(c("mouse", "human"), function(x){
  
  if(x=="mouse"){
    
    dataset <- "mmusculus_gene_ensembl"