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"

ATpoint

# Murine peripheral blood cell numbers based on:
# https://www.stemcell.com/media/files/wallchart/WA10011-Frequencies_Percentages_Mouse_Immune_Cell_Types.pdf

library(magrittr)
library(patchwork)
library(tidyverse)

options(ggplot2.discrete.fill=c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", 
                                "#0072B2", "#D55E00", "#CC79A7"))

ATpoint

#' Take output of UCell::ScoreSignatures_UCell() and return a vector of labels with highest score.
#' If the highest score per row is unique (only one value is the highest value) return that label.
#' If all scores are zero return "NA".
#' If more than one value is the highest value return "ambiguous".
#' library(UCell)
#' data(sample.matrix)
#' gene.sets <- list(Tcell_signature = c("CD2","CD3E","CD3D"),
#'                   Myeloid_signature = c("SPI1","FCER1G","CSF1R"))
#' scores <- ScoreSignatures_UCell(sample.matrix, features=gene.sets)
#' convertUCell(scores)

ATpoint

cb <-  c("#E69F00", "#0072B2", "#009E73", 
         "#CC79A7", "#56B4E9", "#D55E00", 
         "#661100", "#F0E442", "#332288",
         "gray40", "black")

library(ggplot2)

ggplot(data.frame(x=c(rep(c(1,2,3), each=3), 4,4), y=1:length(l), color=LETTERS[1:length(l)]),
       aes(x=x, y=y, color=color)) + 
geom_point(size=10, show.legend=FALSE) +

ATpoint

---
title: "https://www.biostars.org/p/461026/"
author: "ATpoint"
output: html_document
---

# Load data

```{r loadData}