---
title: "Obtaining mouse reference data from sorted cell populations"
author: "Friederike Dündar"
date: "8/9/2019"
output: 
  BiocStyle::html_document
---

```{r setup, echo=FALSE, include=FALSE}
library(BiocStyle)
knitr::opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE)
```

# Overview

Dvir Aran's original `r Githubpkg("dviraran/SingleR")` github repository contains Robjects with **normalized expression values from reference data sets** such as those collected by Blueprint & Encode, the Human Primary Cell Atlas (HPCA), and the Immunological Genome Project (ImmGen).
These expression data are based on bulk RNA-seq or microarrays from purified cell populations or single-cell RNA-seq of individual tissues.
Every sample represents the transcriptome of a specific cell type; this data is therefore well suited to be used as a general training data set for the typical _SingleR_ analysis.

# Data retrieval

For now, we're going to retrieve the processed data from the legacy _SingleR_ repository:

```{r retrieve_from_github}
dataset <- "mouse.rnaseq" 
full.url <- sprintf("https://github.com/dviraran/SingleR/blob/master/data/%s.rda?raw=true", dataset)

library(BiocFileCache)
bfc <- BiocFileCache(ask=FALSE)
ref <- bfcrpath(bfc, full.url)

env <- new.env()
load(ref, envir = env)
ref.set <- get(dataset, envir = env)
names(ref.set)
```

The original objects contain numerous nested lists.
We only need the matrix of normalized expression values and the labels assigned to each sample/cell.

# Data extraction 

Extract the normalized expression matrix:

```{r get_logcounts}
logcounts <- ref.set$data
```

Extract cell labels, which represent the metadata:

```{r get_coldata}
library(S4Vectors)
coldata <- DataFrame(row.names = colnames(logcounts),
    label.main = ref.set$main_types,
    label.fine = ref.set$types)
```

# Saving to file

Saving counts and metadata to upload them to `r Biocpkg("ExperimentHub")`.

```{r save_for_ExpHub}
path <- file.path("SingleR", dataset, "1.0.0")
dir.create(path, showWarnings = FALSE, recursive = TRUE)

## saving counts
saveRDS(logcounts, file = file.path(path, "logcounts.rds"))

## saving metadata
saveRDS(coldata, file = file.path(path, "coldata.rds"))
```

# Session info

```{r}
sessionInfo()
```
