---
title: "phantasusLite tutorial"
output:
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    toc: true
    toc_float: false
vignette: >
  %\VignetteIndexEntry{phantasusLite tutorial}
  %\VignetteEngine{knitr::rmarkdown}
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---

# Introduction

The `phantasusLite` package contains a set functions that facilitate working with public 
gene expression datasets originally developed for [phantasus package](https://bioconductor.org/packages/phantasus). Unlike `phantasus`, `phantasusLite` aims to limit the amount of dependencies.

The current functionality includes:

* Providing an interface to precomputed RNA-seq gene counts from ARCHS4 and DEE2 projects stored at remote HSDS repositories.
* Inferring sample groups for cases when they are not provided in the original metadata.
* Saving and loading expression matrices from GCT format.

# Installation

It is recommended to install the release version of the package from Bioconductor using the following commands:

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

BiocManager::install("phantasusLite")
```

Alternatively, the most recent version of the package can be installed from the GitHub repository:

```{r message=FALSE, eval=FALSE}
library(devtools)
install_github("ctlab/phantasusLite")
```

# Loading precomputed RNA-seq counts


```{r message=FALSE, warning=FALSE}
library(GEOquery)
library(phantasusLite)
```

Let's load dataset GSE53053 from GEO using `GEOquery` package:

```{r message=FALSE}
ess <- getGEO("GSE53053")
es <- ess[[1]]
```

RNA-seq dataset from GEO do not contain the expression matrix, thus `exprs(es)` is empty:

```{r}
head(exprs(es))
```

However, a number of precomputed gene count tables are available at HSDS server  '<https://alserglab.wustl.edu/hsds/>'. It features HDF5 files with counts
from ARCHS4 and DEE2 projects:

```{r}
url <- 'https://alserglab.wustl.edu/hsds/?domain=/counts'
getHSDSFileList(url)
```

GSE53053 dataset was sequenced from *Mus musculus* and we can get an expression matrix 
from the corresponding HDF5-file with DEE2 data:

```{r}
file <- "dee2/mmusculus_star_matrix_20240409.h5"
es <- loadCountsFromH5FileHSDS(es, url, file)
head(exprs(es))
```

Normally `loadCountsFromHSDS` can be used to automatically select the HDF5-file with the largest
number of quantified samples:

```{r}
es <- ess[[1]]
es <- loadCountsFromHSDS(es, url)
head(exprs(es))
```

The counts are different from the previous values as ARCHS4 counts were used -- ARCHS4 is prioritized when there are several files with the same number of samples:

```{r}
preproc(experimentData(es))$gene_counts_source
```

Further, gene symbols are also imported from ARCHS4 database and are available as feature data:
```{r}
head(fData(es))
```


# Inferring sample groups

For some of the GEO datasets, such as GSE53053, the sample annotation is not fully available.
However, frequently sample titles are structured in a way that allows to infer the groups.
For example, for GSE53053 we can see there are three groups: Ctrl, MandIL4, MandLPSandIFNg,
with up to 3 replicates:

```{r}
es$title
```

For such well-structured titles, `inferCondition` function can be used to automatically
identify the sample conditions and replicates:

```{r}
es <- inferCondition(es)
print(es$condition)
print(es$replicate)
```

# Working with GCT files

GCT text format can be used to store annotated gene expression matrices and load them in software 
such as [Morpheus](https://software.broadinstitute.org/morpheus/) or [Phantasus](https://ctlab.itmo.ru/phantasus/).

For example, we can save the `ExpressionSet` object that we defined previously:

```{r}
f <- file.path(tempdir(), "GSE53053.gct")
writeGct(es, f)
```

And the load the file back:

```{r}
es2 <- readGct(f)
print(es2)
```

# Session info

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