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# antagonist
<!-- TOC start (generated with https://github.com/derlin/bitdowntoc) -->

- [How to cite this manuscript](#how-to-cite-this-manuscript)
- [Computational environment requirements](#computational-environment-requirements)
- [Installation](#installation)
- [Overview of the inputs](#overview-of-the-inputs)
- [Perturbagen signature library](#perturbagen-signature-library)
* [Download the Expanded CMap LINCS Resource 2020 signature files from clue.io:](#download-the-expanded-cmap-lincs-resource-2020-signature-files-from-clueio)
* [Chunk the signature files in `.RDS` objects](#chunk-the-signature-files-in-rds-objects)
* [Disease file format (csv, csv.gz or RDS)](#disease-file-format-csv-csvgz-or-rds)
- [Run the analyses](#run-the-analyses)
- [STEP 1: Run antagonism](#step-1-run-antagonism)
- [STEP 2: Aggregate and prioritize](#step-2-aggregate-and-prioritize)
- [STEP 3: The output](#step-3-the-output)
- [STEP 4: Additional figures](#step-4-additional-figures)
* [Showcasing a signature](#showcasing-a-signature)
* [Generating a gene-target prioritization plot](#generating-a-gene-target-prioritization-plot)

<!-- TOC end -->

antagonist
==========

A multithreaded R package and wrapper for gene target prioritization and computational drug repurposing.

<!-- TOC --><a name="how-to-cite-this-manuscript"></a>
# How to cite this manuscript

*If you use this package for gene target prioritization (GTP), cite::*

Voloudakis G, Vicari JM, Venkatesh S, Hoffman GE, Dobrindt K, Zhang W, Beckmann ND, Higgins CA, Argyriou S, Jiang S, Hoagland D, Gao L, Corvelo A, Cho K, Lee KM, Bian J, Lee JS, Iyengar SK, Luoh SW, Akbarian S, Striker R, Assimes TL, Schadt EE, Lynch JA, Merad M, tenOever BR, Charney AW; Mount Sinai COVID-19 Biobank; VA Million Veteran Program COVID-19 Science Initiative; Brennand KJ, Fullard JF, Roussos P. A translational genomics approach identifies IL10RB as the top candidate gene target for COVID-19 susceptibility. NPJ Genom Med. 2022 Sep 5;7(1):52. doi: [10.1038/s41525-022-00324-x](https://doi.org/10.1038/s41525-022-00324-x). PMID: [36064543](https://pubmed.ncbi.nlm.nih.gov/36064543/); PMCID: PMC9441828.
Expand All @@ -14,6 +38,7 @@ Voloudakis G, Lee KM, Vicari JM, Zhang W, Hoagland D, Venkatesh S, Bian J, Anyfa

So H-C, Chau CK-L, Chiu W-T, Ho K-S, Lo C-P, Yim SH-Y, et al. Analysis of genome-wide association data highlights candidates for drug repositioning in psychiatry. Nat Neurosci. 2017;20:1342–9. PMID:[28805813](https://pubmed.ncbi.nlm.nih.gov/28805813/)

<!-- TOC --><a name="computational-environment-requirements"></a>
# Computational environment requirements
1. A linux computer (package has been developed and tested in linux; may work in other operating systems but it hasn't been tested)
2. R>=4.0
Expand All @@ -26,6 +51,7 @@ sudo apt-get install libmpfr-dev
```


<!-- TOC --><a name="installation"></a>
# Installation
```
devtools::install_github("DiseaseNeuroGenomics/antagonist") # link for the center's repository
Expand All @@ -39,15 +65,18 @@ install.packages('remotes')
remotes::install_github('RGLab/cytolib')
```

<!-- TOC --><a name="overview-of-the-inputs"></a>
# Overview of the inputs
1. [The perturbagen signature library](#-perturbagen-signature-library): a data.frame with known transcriptional signatures for compounds/shRNAs, etc, in this case LINCS
2. A disease signature: a data.frame with genes and their respective changees (can be logFC, z-score, effect sizes, etc.)
3. A recipe file: this is only required with job schedulers such as IBM's LSF; the wiki will be updated in the future for such applications.

<!-- TOC --><a name="perturbagen-signature-library"></a>
# Perturbagen signature library
We are currently using the Expanded CMap LINCS Resource 2020 signature files from clue.io. For installation of the perturbagen library, a total of ~67GB are required (11.5 GB after deleting intermediate files). We are using level5 signatures (see picture with different levels below).
![LINCS signature level overview](/data-raw/readme.images/L1000_Lvl5.png)

<!-- TOC --><a name="download-the-expanded-cmap-lincs-resource-2020-signature-files-from-clueio"></a>
## Download the Expanded CMap LINCS Resource 2020 signature files from clue.io:
We are currently using the version last updated on 11/23/201 (created on 11/20/2020) which can be downloaded [here](https://clue.io/data/CMap2020#LINCS2020).

Expand Down Expand Up @@ -76,6 +105,7 @@ Out of the 1,201,944 signatures the vast majority are not considered reproducibl
For our projects we include all `is_gold` signatures and do no filtering based on `is_exemplar` status. However, filtering for both will half the computational costs.


<!-- TOC --><a name="chunk-the-signature-files-in-rds-objects"></a>
## Chunk the signature files in `.RDS` objects
This is done for easier batch processing and improved IO performance when running multiple versions.
```
Expand All @@ -85,6 +115,7 @@ antagonist::split_gctx(parent.signature.dir = signature.dir)
```
The `level5_*.gctx` files can now be safely deleted.

<!-- TOC --><a name="disease-file-format-csv-csvgz-or-rds"></a>
## Disease file format (csv, csv.gz or RDS)
The input data frame usually is a TWAS/GFI/DGE output file. If another file is used then some column name changes are needed to work as expected. The required columns are as follows (with default names):

Expand All @@ -103,6 +134,7 @@ For the purposes of the tutorial we will use

!!! Add file for testing !!!

<!-- TOC --><a name="run-the-analyses"></a>
# Run the analyses
Setting up the variables and loading the package
> For the tutorial, we will use the genetically regulated gene expression for Rheumatoid Arthritis
Expand All @@ -113,6 +145,7 @@ signature.dir <- "ExpandedCMapLINCS2020/"
disease.sig.file <- system.file("extdata", "sample.datasets/RA.epixcan.csv.gz", package="antagonist")
```

<!-- TOC --><a name="step-1-run-antagonism"></a>
# STEP 1: Run antagonism
For one trait-tissue combination, it takes about 23,800 thread-minutes on an Intel 10th gen core.
> For testing if the pipeline is running, setting `prototyping = 10`, for example, which means that only 10/300 signatures will be used from each signature file would allow to see if there are any errors.
Expand All @@ -128,6 +161,7 @@ noperm = 3 # this is just for the tutorial to reduce run times
)
```

<!-- TOC --><a name="step-2-aggregate-and-prioritize"></a>
# STEP 2: Aggregate and prioritize

```
Expand All @@ -136,6 +170,7 @@ aggregate_and_prioritize()

> Please note that the default way of meta-analyzing the results is pulling all the tissues or cell types (whatever is in model_ID) together.
<!-- TOC --><a name="step-3-the-output"></a>
# STEP 3: The output
This is the out put folder structure, if there are more than one tissues parsed, then
```
Expand Down Expand Up @@ -216,9 +251,11 @@ Average rank (`AvgRank`) distribution plots are also generated for diagnostic pu

![GTP AvgRank Distribution](/data-raw/readme.images/RA_gtp_AvgRank_distribution_landscape.png)

<!-- TOC --><a name="step-4-additional-figures"></a>
# STEP 4: Additional figures
Additional figures can be prepared

<!-- TOC --><a name="showcasing-a-signature"></a>
## Showcasing a signature
For example, how does actinomycin D transcriptional signature antagonize the RA disease signature?

Expand All @@ -235,6 +272,7 @@ showcase_method_cdr_gtp(

![actinomycin D in RA](/data-raw/readme.images/RA.STARNET_BLD.actinomycin-d.CRCGN004_PC3_6H.BRD-A42383464-001-04-8.10.png)

<!-- TOC --><a name="generating-a-gene-target-prioritization-plot"></a>
## Generating a gene-target prioritization plot
```
gtp_pvalue_qqplot( ### Parameters
Expand Down

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