BaseSet

Provides classes for working with sets

Keywords

bioconductor bioconductor-package package r r-package sets

Keywords from Contributors

interpretability standardization hack bioconductor-packages bioinformatics functional-similarity gene gene-sets pathway-analysis similarity

Last synced: 4 months ago · JSON representation ·

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Provides classes for working with sets

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Owner: ropensci
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Homepage: https://docs.ropensci.org/BaseSet
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bioconductor bioconductor-package package r r-package sets

Created about 7 years ago · Last pushed 11 months ago

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# BaseSet

The goal of BaseSet is to facilitate working with sets in an efficient way. 
The package implements methods to work on sets, doing intersection, union, complementary, power sets, cartesian product and other set operations in a tidy way. 


The package supports [classical](https://en.wikipedia.org/wiki/Set_(mathematics)) and [fuzzy](https://en.wikipedia.org/wiki/Fuzzy_set) sets. 
Fuzzy sets are similar to classical sets but there is some vagueness on the relationship between the element and the set. 


It also allows to import from several formats used in the life science world. 
Like the [GMT](https://software.broadinstitute.org/cancer/software/gsea/wiki/index.php/Data_formats#GMT:_Gene_Matrix_Transposed_file_format_.28.2A.gmt.29) and the [GAF](https://geneontology.org/docs/go-annotation-file-gaf-format-2.1/) or the [OBO format](https://obofoundry.org/) file for ontologies.

You can save information about the elements, sets and their relationship on the object itself. 
For instance origin of the set, categorical or numeric data associated with sets...

Watch BaseSet working on the [examples](#Examples) below and in the vignettes. 
You can also find [related packages](#Related-packages) and the differences with BaseSet. 
If you have some questions or bugs [open an issue](https://github.com/ropensci/BaseSet/issues) (remember the [Code of Conduct](#Code-of-Conduct))

# Installation

The package depends on some packages from Bioconductor. In order to install some of its dependencies you'll need first to install `{BiocManager}`:

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

You can install the latest version of BaseSet from [Github](https://github.com/ropensci/BaseSet) with:

```{r eval=FALSE}
BiocManager::install("ropensci/BaseSet", 
                     dependencies = TRUE, build_vignettes = TRUE, force = TRUE)
```

 
# Examples {#Examples}

```{r include=FALSE}
library("BaseSet")
```

## Sets

We can create a set like this:

```{r TidySet}
sets <- list(A = letters[1:5], B = c("a", "f"))
sets_analysis <- tidySet(sets)
sets_analysis
```

Perform typical operations like union, intersection. You can name the resulting set or let the default name:

```{r union-intersection}
union(sets_analysis, sets = c("A", "B")) 
# Or we can give a name to the new set
union(sets_analysis, sets = c("A", "B"), name = "D")
# Or the intersection
intersection(sets_analysis, sets = c("A", "B"))
# Keeping the other sets:
intersection(sets_analysis, sets = c("A", "B"), name = "D", keep = TRUE) 
```

And compute size of sets among other things:

```{r set_size}
set_size(sets_analysis)
```

The elements in one set not present in other:

```{r subraction}
subtract(sets_analysis, set_in = "A", not_in = "B", keep = FALSE)
```

Or any other verb from [dplyr](https://cran.r-project.org/package=dplyr). We can add columns, filter, remove them and add information about the sets:

```{r dplyr}
library("magrittr")
set.seed(4673) # To make it reproducible in your machine
sets_enriched <- sets_analysis %>% 
  mutate(Keep = sample(c(TRUE, FALSE), 7, replace = TRUE)) %>% 
  filter(Keep == TRUE) %>% 
  select(-Keep) %>% 
  activate("sets") %>% 
  mutate(sets_origin = c("Reactome", "KEGG"))
sets_enriched

# Activating sets makes the verb affect only them:
elements(sets_enriched)
relations(sets_enriched)
sets(sets_enriched)
```

## Fuzzy sets

In [fuzzy sets](https://en.wikipedia.org/wiki/Fuzzy_set) the elements are vaguely related to the set by a numeric value usually between 0 and 1.
This implies that the association is not guaranteed.

```{r fuzzy}
relations <- data.frame(sets = c(rep("A", 5), "B", "B"), 
                        elements = c("a", "b", "c", "d", "e", "a", "f"),
                        fuzzy = runif(7))
fuzzy_set <- tidySet(relations)
fuzzy_set
```

The equivalent operations performed on classical sets are possible with fuzzy sets:

```{r fuzzy-operations}
union(fuzzy_set, sets = c("A", "B")) 
# Or we can give a name to the new set
union(fuzzy_set, sets = c("A", "B"), name = "D")
# Or the intersection
intersection(fuzzy_set, sets = c("A", "B"))
# Keeping the other sets:
intersection(fuzzy_set, sets = c("A", "B"), name = "D", keep = TRUE) 
```

Assuming that the fuzzy value is a probability, we can calculate which is the probability of having several elements:

```{r prob}
# A set could be empty
set_size(fuzzy_set)
# The more probable size of the sets:
set_size(fuzzy_set) %>% 
  group_by(sets) %>% 
  filter(probability == max(probability))
# Probability of belonging to several sets:
element_size(fuzzy_set)
```

With fuzzy sets we can filter at certain levels (called alpha cut):

```{r alphaCut}
fuzzy_set %>% 
  filter(fuzzy > 0.5) %>% 
  activate("sets") %>% 
  mutate(sets_origin = c("Reactome", "KEGG"))
```

# Related packages {#related}

There are several other packages related to sets, which partially overlap with BaseSet functionality:

 - [`{sets}`]( https://CRAN.R-project.org/package=sets)  
 Implements a more generalized approach, that can store functions or lists as an element of a set (while BaseSet only allows to store a character or factor), but it is harder to operate in a tidy/long way. Also the operations of intersection and union need to happen between two different objects, while a single TidySet object (the class implemented in BaseSet) can store one or thousands of sets.

 - [`{GSEABase}`](https://bioconductor.org/packages/GSEABase/)  
 Implements a class to store sets and related information, but it doesn't allow to store fuzzy sets and it is also quite slow as it creates several classes for annotating each set. 
  
 - [`{BiocSet}`](https://bioconductor.org/packages/BiocSet/)  
 Implements a tidy class for sets but does not handle fuzzy sets. It also has less functionality to operate with sets, like power sets and cartesian product. BiocSet was influenced by the development of this package. 

 - [`{hierarchicalSets}`](https://CRAN.R-project.org/package=hierarchicalSets)  
 This package is focused on clustering of sets that are inside other sets and visualizations. However, BaseSet is focused on storing and manipulate sets including hierarchical sets.
 
 - [`{set6}`](https://cran.r-project.org/package=set6)
 This package implements different classes for different type of sets including fuzzy sets, conditional sets. However, it doesn't handle information associated to elements, sets or relationship. 
 
# Why this package? {#why}

On bioinformatics when looking for the impact of an experiment enrichment methods are applied.
This involves obtaining several sets of genes from several resources and methods.
Usually these curated sets of genes are taken at face value. 
However, there are several resources of sets and they [do not agree between them](https://doi.org/10.1186/1471-2105-14-112), regardless they are used without considering any uncertainty on sets composition. 


Fuzzy theory has long studied sets whose elements have degrees of membership and/or uncertainty. 
Therefore one way to improve the methods involve using fuzzy methods and logic on this field. 
As I couldn't find any package that provided methods for this I set on creating it (after trying to [expand](https://github.com/llrs/GSEAdv) the existing one I knew).

This package is intended to be easy to use for someone who is working with collections of sets but flexible about the methods and logic it can use. 
To be consistent, the standard fuzzy logic is the default but it might not be the right one for your data. 
Consider changing the defaults to match with the framework the data was obtained with. 

# Code of Conduct {#CoC}

Please note that this package is released with a [Contributor
Code of Conduct](https://ropensci.org/code-of-conduct/). 
By contributing to this project, you agree to abide by its terms.

Owner

Name: rOpenSci
Login: ropensci
Kind: organization
Email: info@ropensci.org
Location: Berkeley, CA

Website: https://ropensci.org/
Twitter: rOpenSci
Repositories: 307
Profile: https://github.com/ropensci

Citation (CITATION.cff)

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type: software
license: MIT
title: 'BaseSet: Working with Sets the Tidy Way'
version: 0.0.17.9004
abstract: Implements a class and methods to work with sets, doing intersection, union,
  complementary sets, power sets, cartesian product and other set operations in a
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authors:
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      "provider": {
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        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
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      "sameAs": "https://CRAN.R-project.org/package=rmarkdown"
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    {
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        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=spelling"
    },
    {
      "@type": "SoftwareApplication",
      "identifier": "testthat",
      "name": "testthat",
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      "provider": {
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        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=testthat"
    }
  ],
  "softwareRequirements": [
    {
      "@type": "SoftwareApplication",
      "identifier": "R",
      "name": "R",
      "version": ">= 4.0.0"
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    {
      "@type": "SoftwareApplication",
      "identifier": "dplyr",
      "name": "dplyr",
      "version": ">= 1.0.0",
      "provider": {
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        "name": "Comprehensive R Archive Network (CRAN)",
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      },
      "sameAs": "https://CRAN.R-project.org/package=dplyr"
    },
    {
      "@type": "SoftwareApplication",
      "identifier": "magrittr",
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      "sameAs": "https://CRAN.R-project.org/package=magrittr"
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      "identifier": "methods",
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}

GitHub Events

Total

Create event: 2
Release event: 1
Issues event: 6
Watch event: 1
Delete event: 1
Issue comment event: 5
Push event: 12

Last Year

Create event: 2
Release event: 1
Issues event: 6
Watch event: 1
Delete event: 1
Issue comment event: 5
Push event: 12

Committers

Last synced: about 1 year ago

All Time

Total Commits: 618
Total Committers: 5
Avg Commits per committer: 123.6
Development Distribution Score (DDS): 0.015

Past Year

Commits: 3
Committers: 1
Avg Commits per committer: 3.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
llrs	l**a@g**m	609
Lluís	l****s	3
Anna Krystalli	a**i@g**m	2
Lluís Revilla	l**a@i**s	2
github-actions[bot]	4****]	2

Committer Domains (Top 20 + Academic)

irsicaixa.es: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 69
Total pull requests: 3
Average time to close issues: 2 months
Average time to close pull requests: 23 minutes
Total issue authors: 4
Total pull request authors: 2
Average comments per issue: 1.19
Average comments per pull request: 1.33
Merged pull requests: 3
Bot issues: 1
Bot pull requests: 0

Past Year

Issues: 3
Pull requests: 0
Average time to close issues: 3 days
Average time to close pull requests: N/A
Issue authors: 2
Pull request authors: 0
Average comments per issue: 1.33
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

llrs (64)
maelle (2)
mjsteinbaugh (1)
todo[bot] (1)

Pull Request Authors

llrs (2)
annakrystalli (1)

Top Labels

Issue Labels

rOpenSci review (15) enhancement (8) todo :spiral_notepad: (6) bug (1) question (1) good first issue (1)

Pull Request Labels

Packages

Total packages: 1
Total downloads:
- cran 343 last-month

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 6
Total maintainers: 1

cran.r-project.org: BaseSet

Working with Sets the Tidy Way

Homepage: https://github.com/ropensci/BaseSet
Documentation: http://cran.r-project.org/web/packages/BaseSet/BaseSet.pdf
License: MIT + file LICENSE
Latest release: 1.0.0
published 11 months ago

Versions: 6
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 343 Last month

Rankings

Forks count: 14.9%

Stargazers count: 18.7%

Average: 27.9%

Dependent packages count: 29.8%

Dependent repos count: 35.5%

Downloads: 40.6%

Maintainers (1)

lluis.revilla@gmail.com

Last synced: 5 months ago

BaseSet

Science Score: 57.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.Rmd

Owner

Citation (CITATION.cff)

CodeMeta (codemeta.json)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

cran.r-project.org: BaseSet

Rankings

Maintainers (1)