---
title: "G&T-seq Mouse Embryo (8-cell stage)"
date: "`r BiocStyle::doc_date()`"
vignette: |
  %\VignetteIndexEntry{GT-seq Mouse Embryo}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
output:
    BiocStyle::html_document:
      toc_float: true
package: SingleCellMultiModal
bibliography: ../inst/REFERENCES.bib
---

# Installation

```{r,eval=FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("SingleCellMultiModal")
```

## Load

```{r,include=TRUE,results="hide",message=FALSE,warning=FALSE}
library(SingleCellMultiModal)
library(MultiAssayExperiment)
```

# G&T-seq: parallel sequencing data of single-cell genomes and transcriptomes

G&T-seq is a combination of Picoplex amplified gDNA sequencing (genome) and
SMARTSeq2 amplified cDNA sequencing (transcriptome) of the same cell.
For more information, see @Macaulay2015.

## Downloading datasets

The user can see the available dataset by using the default options

```{r}
GTseq("mouse_embryo_8_cell", mode = "*", dry.run = TRUE)
```

Or by simply running:

```{r}
GTseq()
```

## Obtaining the data

To obtain the actual datasets:

```{r,message=FALSE}
gts <- GTseq(dry.run = FALSE)
gts
```

## Exploring the data structure

Check available metadata for each of the 112 mouse embryo cells assayed by G&T-seq:

```{r}
colData(gts)
```

Take a peek at the `sampleMap`:

```{r}
sampleMap(gts)
```

## Copy numbers

To access the integer copy numbers as detected from scDNA-seq:

```{r}
head(assay(gts, "genomic"))[, 1:4]
```

## RNA-seq

To access raw read counts as quantified from scRNA-seq:

```{r}
head(assay(gts, "transcriptomic"))[, 1:4]
```

For protocol information, see @Macaulay2016.

# sessionInfo

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

# References