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41 changes: 28 additions & 13 deletions README.Rmd
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Expand Up @@ -13,48 +13,63 @@ knitr::opts_chunk$set(
)
```

## r4msigdb
# r4msigdb

<!-- badges: start -->
[![R-CMD-check](https://github.com/snowGlint/r4msigdb/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/snowGlint/r4msigdb/actions/workflows/R-CMD-check.yaml)
[![test-coverage](https://github.com/snowGlint/r4msigdb/actions/workflows/test-coverage.yaml/badge.svg)](https://github.com/snowGlint/r4msigdb/actions/workflows/test-coverage.yaml)
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The goal of r4msigdb is to query for the MSigDB.
[MSigDB](https://www.gsea-msigdb.org/gsea/msigdb/) is a widely used gene set database in bio-research. However, navigating and querying pathways of interest on its website can be challenging. This R package is designed to facilitate more convenient and efficient querying of pathways either based on specific genes or using regular expression patterns to match pathway names.

## Installation
# Installation

You can install the development version of r4msigdb like so:

```{r, eval=FALSE}
devtools::install('snowGlint/r4msigdb')
```

## Example
# Querying Pathways

This is a basic example which shows you how to query for the MSigDB:
## Search for Pathways Related to a Specific Topic

```{r example}
To retrieve pathways related to a specific topic (e.g., cell programmed death):

```{r example1}
library(r4msigdb)
query(species = 'Hs', pathway = 'FERROPTOSIS')
query(species = 'Hs', pathway = 'OPTOSIS') |> head()
```

This will return a list of pathways associated with `OPTOSIS` and the genes involved in each pathway.

## Search for Pathways Related to Specific Genes

If you want to find pathways related to specific genes (e.g., PTPRC and TP53):

```{r example2}
query(species = 'Hs', symbols = c('PTPRC', 'TP53')) |> head()
```

You can unlist the symbol column using `.unlist = TRUE`
This will provide pathways where the specified genes are involved.

## Custom Query

Advanced users familiar with data.table can perform custom queries. For example, to retrieve all pathways `collection_name == 'H'`:

```{r}
query(species = 'Hs', .unlist = TRUE) |> head()
query(species = 'Hs')[.('H')] |> head()
```

You could query for the MSigDB by your own way if you are familiar with `data.table`
You can also use `.unlist = TRUE` to unlist the symbols column in any of the above query methods.

```{r}
query(species = 'Hs')[.('H'), by = .(collection_name)] |> head()
query(species = 'Hs', .unlist = TRUE) |> head()
```

## GSEA
# GSEA

```{r, message=FALSE}
```{r, eval=FALSE}
library(fgsea)
library(data.table)
palette <- c("#440154FF", "#31688EFF", "#26828EFF", "#6DCD59FF", "#FDE725FF")
Expand Down
94 changes: 63 additions & 31 deletions README.md
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@@ -1,36 +1,67 @@

<!-- README.md is generated from README.Rmd. Please edit that file -->

## r4msigdb
# r4msigdb

<!-- badges: start -->

[![R-CMD-check](https://github.com/snowGlint/r4msigdb/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/snowGlint/r4msigdb/actions/workflows/R-CMD-check.yaml)
[![test-coverage](https://github.com/snowGlint/r4msigdb/actions/workflows/test-coverage.yaml/badge.svg)](https://github.com/snowGlint/r4msigdb/actions/workflows/test-coverage.yaml)
<!-- badges: end -->

The goal of r4msigdb is to query for the MSigDB.
[MSigDB](https://www.gsea-msigdb.org/gsea/msigdb/) is a widely used gene
set database in bio-research. However, navigating and querying pathways
of interest on its website can be challenging. This R package is
designed to facilitate more convenient and efficient querying of
pathways either based on specific genes or using regular expression
patterns to match pathway names.

## Installation
# Installation

You can install the development version of r4msigdb like so:

``` r
devtools::install('snowGlint/r4msigdb')
```

## Example
# Querying Pathways

This is a basic example which shows you how to query for the MSigDB:
## Search for Pathways Related to a Specific Topic

To retrieve pathways related to a specific topic (e.g., cell programmed
death):

``` r
library(r4msigdb)
query(species = 'Hs', pathway = 'FERROPTOSIS')
query(species = 'Hs', pathway = 'OPTOSIS') |> head()
#> Key: <collection_name, standard_name>
#> collection_name standard_name symbol
#> <char> <char> <list>
#> 1: C2:CP:WIKIPATHWAYS WP_FERROPTOSIS GCLC,GCLM,CP,ATG5,ACSL4,TFRC,...
#> 2: C5:GO:BP GOBP_FERROPTOSIS SLC39A7,SLC7A11,TMEM164,GPX4,AIFM2
#> collection_name standard_name
#> <char> <char>
#> 1: C2:CGP ALCALA_APOPTOSIS
#> 2: C2:CGP BROCKE_APOPTOSIS_REVERSED_BY_IL6
#> 3: C2:CGP CONCANNON_APOPTOSIS_BY_EPOXOMICIN_DN
#> 4: C2:CGP CONCANNON_APOPTOSIS_BY_EPOXOMICIN_UP
#> 5: C2:CGP DEBIASI_APOPTOSIS_BY_REOVIRUS_INFECTION_DN
#> 6: C2:CGP DEBIASI_APOPTOSIS_BY_REOVIRUS_INFECTION_UP
#> symbol
#> <list>
#> 1: HCCS,MATK,FAS,CYFIP2,ELOVL1,PFKP,...
#> 2: DPM1,RALA,PHTF2,ADIPOR2,CD44,SH2D2A,...
#> 3: ICA1,ETV1,TRAPPC6A,DNASE1L1,TMSB10,HDAC9,...
#> 4: TAC1,IFRD1,TSPAN9,GCLM,FAS,CD44,...
#> 5: LASP1,BLTP2,METTL13,CD9,NISCH,BRCA1,...
#> 6: AK2,CDC27,ACSM3,ZFX,TAC1,IFRD1,...
```

This will return a list of pathways associated with `OPTOSIS` and the
genes involved in each pathway.

## Search for Pathways Related to Specific Genes

If you want to find pathways related to specific genes (e.g., PTPRC and
TP53):

``` r
query(species = 'Hs', symbols = c('PTPRC', 'TP53')) |> head()
#> Key: <collection_name, standard_name>
#> collection_name standard_name
Expand All @@ -51,27 +82,15 @@ query(species = 'Hs', symbols = c('PTPRC', 'TP53')) |> head()
#> 6: DPM1,GCLC,M6PR,RECQL,GCFC2,PDK2,...
```

You can unlist the symbol column using `.unlist = TRUE`
This will provide pathways where the specified genes are involved.

``` r
query(species = 'Hs', .unlist = TRUE) |> head()
#> Key: <collection_name, standard_name>
#> collection_name standard_name symbol
#> <char> <char> <char>
#> 1: C1 MT MT-CO2
#> 2: C1 MT MT-ND2
#> 3: C1 MT MT-CO1
#> 4: C1 MT MT-ND3
#> 5: C1 MT MT-ND4
#> 6: C1 MT MT-ATP6
```
## Custom Query

You could query for the MSigDB by your own way if you are familiar with
`data.table`
Advanced users familiar with data.table can perform custom queries. For
example, to retrieve all pathways `collection_name == 'H'`:

``` r
query(species = 'Hs')[.('H'), by = .(collection_name)] |> head()
#> Warning: Ignoring by/keyby because 'j' is not supplied
query(species = 'Hs')[.('H')] |> head()
#> Key: <collection_name, standard_name>
#> collection_name standard_name
#> <char> <char>
Expand All @@ -91,12 +110,27 @@ query(species = 'Hs')[.('H'), by = .(collection_name)] |> head()
#> 6: ADIPOR2,BRCA1,DCBLD2,CROCC,IL2RB,ATP6V0A4,...
```

## GSEA
You can also use `.unlist = TRUE` to unlist the symbols column in any of
the above query methods.

``` r
query(species = 'Hs', .unlist = TRUE) |> head()
#> Key: <collection_name, standard_name>
#> collection_name standard_name symbol
#> <char> <char> <char>
#> 1: C1 MT MT-CO2
#> 2: C1 MT MT-ND2
#> 3: C1 MT MT-CO1
#> 4: C1 MT MT-ND3
#> 5: C1 MT MT-ND4
#> 6: C1 MT MT-ATP6
```

# GSEA

``` r
library(fgsea)
library(data.table)
#> Warning: package 'data.table' was built under R version 4.3.3
palette <- c("#440154FF", "#31688EFF", "#26828EFF", "#6DCD59FF", "#FDE725FF")

data(exampleRanks)
Expand All @@ -118,5 +152,3 @@ enrichplot::gseaplot2(gseaR, sortedgsea[["ID"]][1:5],
pvalue_table = FALSE
)
```

<img src="man/figures/README-unnamed-chunk-5-1.png" width="100%" />
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56 changes: 46 additions & 10 deletions vignettes/introduction.Rmd
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Expand Up @@ -18,31 +18,67 @@ knitr::opts_chunk$set(
library(r4msigdb)
```

## Basic usage
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```

# r4msigdb

[MSigDB](https://www.gsea-msigdb.org/gsea/msigdb/) is a widely used gene set database in bio-research. However, navigating and querying pathways of interest on its website can be challenging. This R package is designed to facilitate more convenient and efficient querying of pathways either based on specific genes or using regular expression patterns to match pathway names.

# Installation

You can install the development version of r4msigdb like so:

```{r, eval=FALSE}
devtools::install('snowGlint/r4msigdb')
```

This is a basic example which shows you how to query for the MSigDB:
# Querying Pathways

```{r example}
## Search for Pathways Related to a Specific Topic

To retrieve pathways related to a specific topic (e.g., cell programmed death):

```{r example1}
library(r4msigdb)
query(species = 'Hs', pathway = 'FERROPTOSIS')
query(species = 'Hs', pathway = 'OPTOSIS') |> head()
```

This will return a list of pathways associated with `OPTOSIS` and the genes involved in each pathway.

## Search for Pathways Related to Specific Genes

If you want to find pathways related to specific genes (e.g., PTPRC and TP53):

```{r example2}
query(species = 'Hs', symbols = c('PTPRC', 'TP53')) |> head()
```

You can unlist the symbol column using `.unlist = TRUE`
This will provide pathways where the specified genes are involved.

## Custom Query

Advanced users familiar with data.table can perform custom queries. For example, to retrieve all pathways `collection_name == 'H'`:

```{r}
query(species = 'Hs', .unlist = TRUE) |> head()
query(species = 'Hs')[.('H')] |> head()
```

You could query for the MSigDB by your own way if you are familiar with `data.table`
You can also use `.unlist = TRUE` to unlist the symbols column in any of the above query methods.

```{r}
query(species = 'Hs')[.('H'), by = .(collection_name)] |> head()
query(species = 'Hs', .unlist = TRUE) |> head()
```

## GSEA
# GSEA

```{r message=FALSE}
```{r, eval=FALSE}
library(fgsea)
library(data.table)
palette <- c("#440154FF", "#31688EFF", "#26828EFF", "#6DCD59FF", "#FDE725FF")
Expand Down

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