seqArchR

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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!

seqArchR is an unsupervised, non-negative matrix factorization (NMF)-based algorithm for discovery of sequence architectures de novo. Below is a schematic of seqArchR's algorithm.

Installation

Python scikit-learn dependency

This package requires the Python module scikit-learn. Please see installation instructions here.

To install this package, use

```r if (!requireNamespace("remotes", quietly = TRUE)) { install.packages("remotes")
}

remotes::installgithub("snikumbh/seqArchR", buildvignettes = FALSE) ```

Usage

```r

load package

library(seqArchR) 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 = "seqArchR", mustWork = TRUE)

Specifying dinuc generates dinucleotide features

inputSeqsMat <- seqArchR::preparedatafromFASTA(inputFastaFilename, sinucor_dinuc = "dinuc")

inputSeqsRaw <- seqArchR::preparedatafromFASTA(inputFastaFilename, rawseq = TRUE)

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

Set seqArchR configuration

Most arguments have default values

seqArchRconfig <- seqArchR::setconfig( parallelize = TRUE, ncores = 2, nruns = 100, kmin = 1, kmax = 20, modseltype = "stability", bound = 10^-6, chunksize = 100, resultaggl = "ward.D", resultdist = "euclid", flags = list(debug = FALSE, time = TRUE, verbose = TRUE, plot = FALSE) )

Call/Run seqArchR

seqArchRresult <- seqArchR::seqArchR(config = seqArchRconfig, seqsohemat = inputSeqsMat, seqsraw = inputSeqsRaw, seqspos = positions, totalitr = 2, setocollation = c(TRUE, FALSE))

```

Contact

Comments, suggestions, enquiries/requests are welcome! Feel free to email sarvesh.nikumbh@gmail.com or create an new issue