---
title: "The depmap data"
author:
- name: Theo Killian
  affiliation: Computational Biology, UCLouvain
- name: Laurent Gatto
  affiliation: Computational Biology, UCLouvain
date: "`r Sys.Date()`"
output:
  BiocStyle::html_document:
    toc_float: true
vignette: >
  %\VignetteIndexEntry{depmap}
  %\VignetteEngine{knitr::rmarkdown}
  %\usepackage[utf8]{inputenc}
---

```{r style, echo = FALSE, results = 'asis'}
BiocStyle::markdown()
```

```{r, echo = FALSE}
suppressPackageStartupMessages(library("dplyr"))
```

# Introduction

The `depmap` package aims to provide a reproducible research framework
to cancer dependency data described by
[Tsherniak, Aviad, et al. "Defining a cancer dependency map." Cell 170.3 (2017): 564-576.](https://www.ncbi.nlm.nih.gov/pubmed/28753430).
The data found in the [depmap](https://bioconductor.org/packages/devel/data/experiment/html/depmap.html)
package has been formatted to facilitate the use of common R packages
such as `dplyr` and `ggplot2`. We hope that this package will allow
researchers to more easily mine, explore and visually illustrate
dependency data taken from the Depmap cancer genomic dependency study.

# Installation instructions

To install [depmap](https://bioconductor.org/packages/devel/data/experiment/html/depmap.html),
the [BiocManager](https://cran.r-project.org/web/packages/BiocManager/index.html)
Bioconductor Project Package Manager is required. If
[BiocManager](https://cran.r-project.org/web/packages/BiocManager/index.html) is
not already installed, it will need to be done so beforehand. Type (within R)
install.packages("BiocManager") (This needs to be done just once.)

```{r install, eval=FALSE}
install.packages("BiocManager")
BiocManager::install("depmap")
```

The `depmap` package fully depends on the `ExperimentHub` Bioconductor package,
which allows the data accessed in this package to be stored and retrieved from
the cloud.

```{r import_EH, message = FALSE}
library("depmap")
library("ExperimentHub")
```

# Available data

The [depmap](https://bioconductor.org/packages/devel/data/experiment/html/depmap.html)
package currently contains eight datasets available through `ExperimentHub`.

The data found in this R package has been converted from a "wide"
format `.csv` file to "long" format .rda file. None of the values taken
from the original datasets have been changed, although the columns
have been re-arranged.  Descriptions of the changes made are described
under the `Details` section after querying the relevant dataset.

```{r ehquery}
## create ExperimentHub query object
eh <- ExperimentHub()
query(eh, "depmap")
```

Each dataset has a `ExperimentHub` accession number, (e.g. *EH2260* refers to
the `rnai` dataset from the 19Q1 release).

## RNA inference knockout data

```{r, echo = FALSE}
rnai <- eh[["EH2260"]]
```

The `rnai` dataset contains the combined genetic dependency data for RNAi -
induced gene knockdown for select genes and cancer cell lines. This data
corresponds to the `D2_combined_genetic_dependency_scores.csv` file found in the
`r depmap::depmap_release()` depmap release and includes `r length(unique(rnai$gene))`
genes, `r length(unique(rnai$cell_line))` cell lines, 30 primary diseases and 31
lineages.

```{r, eval = FALSE}
## access `rnai_19Q1` by EH number
rnai <- eh[["EH2260"]]
```

```{r}
rnai
```

The `rnai` dataset can also be accessed by using the `depmap_rnai` function.

```{r}
# depmap_rnai()
```

```{r, echo = FALSE}
rm(rnai)
```

## CRISPR-Cas9 knockout data

```{r, echo = FALSE}
crispr <- eh[["EH2261"]]
```

The `crispr` dataset contains the (batch corrected CERES inferred gene effect)
CRISPR-Cas9 knockout data of select genes and cancer cell lines. This data
corresponds to the `gene_effect_corrected.csv` file from the `r depmap::depmap_release()`
depmap release. Data from this dataset includes `r length(unique(crispr$gene_name))`
genes, `r length(unique(crispr$cell_line))` cell lines, 26 primary diseases, 28
lineages.

```{r, eval = FALSE}
## access `crispr_19Q1` by EH number
crispr <- eh[["EH2261"]]
```

```{r}
crispr
```

The `crispr` dataset can also be accessed by using the `depmap_crispr` function.

```{r}
# depmap_crispr()
```

```{r, echo = FALSE}
rm(crispr)
```

## WES copy number data

```{r, echo = FALSE}
copyNumber <- eh[["EH2262"]]
```

The `copyNumber` dataset contains the WES copy number data, relating to the
numerical log-fold copy number change measured against the baseline copy number
of select genes and cell lines. This dataset corresponds to the
`public_19Q1_gene_cn.csv` from the `r depmap::depmap_release()` depmap release.
This dataset includes `r length(unique(copyNumber$gene_name))` genes,
`r length(unique(copyNumber$cell_line))` cell lines, 38 primary diseases and 33
lineages.

```{r, eval = FALSE}
## access `copyNumber_19Q1` by EH number
copyNumber <- eh[["EH2262"]]
```

```{r}
copyNumber
```

The `copyNumber` dataset can also be accessed by using the `depmap_copyNumber`
function.

```{r}
# depmap_copyNumber()
```

```{r, echo = FALSE}
rm(copyNumber)
```

## CCLE Reverse Phase Protein Array data

```{r, echo = FALSE}
RPPA <- eh[["EH2263"]]
```

The `RPPA` dataset contains the CCLE Reverse Phase Protein Array (RPPA) data
which corresponds to the `CCLE_RPPA_20180123.csv` file from the `r depmap::depmap_release()`
depmap release. This dataset includes 214 genes, `r length(unique(RPPA$cell_line))`
cell lines, 28 primary diseases, 28 lineages.

```{r, eval = FALSE}
## access `RPPA_19Q1` by EH number
RPPA <- eh[["EH2263"]]
```

```{r}
RPPA
```

The `RPPA` dataset can also be accessed by using the `depmap_RPPA` function.

```{r}
# depmap_RPPA()
```

```{r, echo = FALSE}
rm(RPPA)
```

## CCLE RNAseq gene expression data

```{r, echo = FALSE}
TPM <- eh[["EH2264"]]
```

The `TPM` dataset contains the CCLE RNAseq gene expression data. This shows
expression data only for protein coding genes (using scale log2(TPM+1)). This
data corresponds to the `CCLE_depMap_19Q1_TPM.csv` file from the `r depmap::depmap_release()`
depmap release. This dataset includes `r length(unique(TPM$gene_name))` genes,
`r length(unique(TPM$cell_line))` cell lines, 33 primary Diseases, 32 lineages.

```{r, eval = FALSE}
## access `TPM_19Q1` by EH number
TPM <- eh[["EH2264"]]
```

```{r}
TPM
```

The `TPM` dataset can also be accessed by using the `depmap_TPM` function.

```{r}
# depmap_TPM()
```

```{r, echo = FALSE}
rm(TPM)
```

## Cancer cell lines

```{r, echo = FALSE}
metadata <- eh[["EH2266"]]
```

The `metadata` dataset contains the metadata about all of the cancer cell lines.
It corresponds to the `depmap_19Q1_cell_lines.csv` file found in the `r depmap::depmap_release()`
depmap release. This dataset includes 0 genes, `r length(unique(metadata$cell_line))`
cell lines, 38 primary diseases and 33 lineages.

```{r, eval = FALSE}
## access `metadata_19Q1` by EH number
metadata <- eh[["EH2266"]]
```

```{r}
metadata
```

The `metadat` dataset can also be accessed by using the `depmap_metadata`
function.

```{r}
# depmap_metadata()
```

```{r, echo = FALSE}
rm(metadata)
```

## Mutation calls

```{r, echo = FALSE}
mutationCalls <- eh[["EH2265"]]
```

The `mutationCalls` dataset contains all merged mutation calls (coding region,
germline filtered) found in the depmap dependency study. This dataset
corresponds with the `depmap_19Q1_mutation_calls.csv` file found in the 
`r depmap::depmap_release()` depmap release and includes 
`r length(unique(mutationCalls$gene_name))` genes,
`r length(unique(mutationCalls$depmap_id))` cell lines, 37 primary diseases and
33 lineages.

```{r, eval = FALSE}
## access `mutationCalls_19Q1` by EH number
mutationCalls <- eh[["EH2265"]]
```

```{r}
mutationCalls
```

The `mutationCalls` dataset can also be accessed by using the
`depmap_mutationCalls` function.

```{r}
# depmap_mutationCalls()
```

```{r, echo = FALSE}
rm(mutationCalls)
```


# The [Broad Institute](https://depmap.org/portal/download/) data

If desired, the original data from which the
[depmap](https://bioconductor.org/packages/devel/data/experiment/html/depmap.html)
package were derived from can be downloaded from the [Broad Institute](https://depmap.org/portal/download/)
website. The instructions on how to download these files and how the data was
transformed and loaded into the [depmap](https://bioconductor.org/packages/devel/data/experiment/html/depmap.html)
package can be found in the `make_data.R` file found in `./inst/scripts`. (It
should be noted that the original uncompressed *.csv* files are >1.5GB in
total and take a moderate amount of time to download remotely.)

# Session information

```{r echo = FALSE}
sessionInfo()