---
title: "eds: Low-level reader function for Alevin EDS format"
date: "`r format(Sys.Date(), '%m/%d/%Y')`"
output: rmarkdown::html_document
vignette: |
  %\VignetteIndexEntry{eds: Low-level reader function for Alevin EDS format}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

# eds package for reading in Alevin EDS format

The R package `eds` provides a single function `readEDS` for
efficiently reading Alevin's EDS format for single cell count data
into R, utilizing the sparse matrix format in the `Matrix` package.

**Note:** `eds` provides a low-level function `readEDS` which most
users will not need to use. Most users and developers will likely
prefer to use `tximport` (for importing matrices) or `tximeta` (for
easy conversion to *SingleCellExperiment* objects).  This package is
primarily developed in order to streamline the dependency graph for
other packages.

# About EDS

EDS is an accronym for *Efficient single cell binary Data Storage*
format for the cell-feature count matrices.

For more details on the EDS format see the following repository:

<https://github.com/COMBINE-lab/EDS>

# Simple example of reading EDS into R:

The following example is the same as round in `?readEDS`, first we
point to EDS files as output by Alevin:

```{r}
library(tximportData)
library(eds)
dir0 <- system.file("extdata",package="tximportData")
samps <- list.files(file.path(dir0, "alevin"))
dir <- file.path(dir0,"alevin",samps[3],"alevin")
quant.mat.file <- file.path(dir, "quants_mat.gz")
barcode.file <- file.path(dir, "quants_mat_rows.txt")
gene.file <- file.path(dir, "quants_mat_cols.txt")
```

`readEDS()` requires knowing the number of cells and genes, which we
find by reading in associated barcode and gene files. Again, note that
a more useful convenience function for reading in Alevin data is
`tximport` (matrices) or `tximeta` (for easy conversion to
SingleCellExperiment). 

```{r}
cell.names <- readLines(barcode.file)
gene.names <- readLines(gene.file)
num.cells <- length(cell.names)
num.genes <- length(gene.names)
```

Finally, reading in the sparse matrix is accomplished with:

```{r}
mat <- readEDS(
    numOfGenes=num.genes,
    numOfOriginalCells=num.cells,
    countMatFilename=quant.mat.file)
```

# Session info

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