https://github.com/snikumbh/archr

archR: Identifying promoter sequence architectures de novo using NMF
https://github.com/snikumbh/archr

archr discovery nmf non-negative-matrix-factorization promoter-sequence-architectures r r-package scikit-learn sequence-architectures unsupervised-machine-learning

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archR: Identifying promoter sequence architectures de novo using NMF

• Host: GitHub
• URL: https://github.com/snikumbh/archr
• Owner: snikumbh
• License: gpl-3.0
• Created: 2019-05-24T15:53:01.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2021-07-01T13:38:24.000Z (over 3 years ago)
• Last Synced: 2024-11-11T13:43:01.474Z (2 months ago)
• Topics: archr, discovery, nmf, non-negative-matrix-factorization, promoter-sequence-architectures, r, r-package, scikit-learn, sequence-architectures, unsupervised-machine-learning
• Language: R
• Homepage: https://snikumbh.github.io/archR
• Size: 17.7 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Changelog: NEWS.md
  • License: LICENSE

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# archR

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Note: _This package is currently under development. So, please bear with me while I put the final blocks together. Thanks for your understanding!_ 

archR is an unsupervised, non-negative matrix factorization (NMF)-based algorithm for discovery of sequence architectures de novo.

Below is a schematic of archR's algorithm.



## Installation

### Python scikit-learn dependency

This package requires the Python module scikit-learn. Please see installation instructions [here](https://scikit-learn.org/stable/install.html).

### To install this package, use 

```r

if (!requireNamespace("remotes", quietly = TRUE)) {

    install.packages("remotes")   

}

remotes::install_github("snikumbh/archR", build_vignettes = FALSE)

``` 

### Usage

```r

# load package

library(archR)

library(Biostrings)

# Creation of one-hot encoded data matrix from FASTA file

# You can use your own FASTA file instead

inputFastaFilename <- system.file("extdata", "example_data.fa", 

                                  package = "archR", 

                                  mustWork = TRUE)

# Specifying dinuc generates dinucleotide features

inputSeqsMat <- archR::prepare_data_from_FASTA(inputFastaFilename,

                                                  sinuc_or_dinuc = "dinuc")

inputSeqsRaw <- archR::prepare_data_from_FASTA(inputFastaFilename, 

                                               raw_seq = TRUE)

nSeqs <- length(inputSeqsRaw)

positions <- seq(1, Biostrings::width(inputSeqsRaw[1]))

# Set archR configuration

# Most arguments have default values

archRconfig <- archR::archR_set_config(

        parallelize = TRUE,

        n_cores = 2,

        n_runs = 100,

        k_min = 1,

        k_max = 20,

        mod_sel_type = "stability",

        bound = 10^-6,

        chunk_size = 100,

	result_aggl = "ward.D",

	result_dist = "euclid",

        flags = list(debug = FALSE, time = TRUE, verbose = TRUE,

                     plot = FALSE)

        )

#

### Call/Run archR

archRresult <- archR::archR(config = archRconfig,

                               seqs_ohe_mat = inputSeqsMat,

                               seqs_raw = inputSeqsRaw,

                               seqs_pos = positions,

                               total_itr = 2,

			       set_ocollation = c(TRUE, FALSE))

```

# Contact

Comments, suggestions, enquiries/requests are welcome! Feel free to email [email protected] or [create an new issue](https://github.com/snikumbh/archR/issues/new)