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updates readme
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@KrystynaGrzesiak
KrystynaGrzesiak committed Jul 22, 2024
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5 changes: 4 additions & 1 deletion README.Rmd
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Expand Up @@ -21,7 +21,10 @@ knitr::opts_chunk$set(

## Overview

The kmerFilters is an R package providing tools for simulating k-mer data and benchmarking various filtering techniques. It is designed to assist researchers and practitioners in evaluating and comparing different approaches for preprocessing sequence data, particularly for applications such as protein function prediction.
k-mers (n-grams) refer to k-length substrings derived from longer sequences, which can be continuous (representing a block of subsequent residues) or discontinuous (where the wildcards representing the gaps between residues are allowed). In biological applications, k-mers are used for various purposes, including genome assembly, sequence alignment, motif discovery, variant calling, phylogenetics, and CRISPR target identification. Due to the vast number of variables introduced by k-mer notation, we need tools to filter the variables.

In this package we provide tools for simulating k-mer data and benchmarking various filtering techniques. It is designed to assist researchers and practitioners in evaluating and comparing different approaches for preprocessing sequence data, particularly for applications such as protein function prediction.


## Features

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71 changes: 40 additions & 31 deletions README.md
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Expand Up @@ -11,10 +11,19 @@ coverage](https://codecov.io/gh/BioGenies/kmerFilters/branch/main/graph/badge.sv

## Overview

The kmerFilters is an R package providing tools for simulating k-mer
data and benchmarking various filtering techniques. It is designed to
assist researchers and practitioners in evaluating and comparing
different approaches for preprocessing sequence data, particularly for
k-mers (n-grams) refer to k-length substrings derived from longer
sequences, which can be continuous (representing a block of subsequent
residues) or discontinuous (where the wildcards representing the gaps
between residues are allowed). In biological applications, k-mers are
used for various purposes, including genome assembly, sequence
alignment, motif discovery, variant calling, phylogenetics, and CRISPR
target identification. Due to the vast number of variables introduced by
k-mer notation, we need tools to filter the variables.

In this package we provide tools for simulating k-mer data and
benchmarking various filtering techniques. It is designed to assist
researchers and practitioners in evaluating and comparing different
approaches for preprocessing sequence data, particularly for
applications such as protein function prediction.

## Features
Expand Down Expand Up @@ -67,16 +76,16 @@ motifs <- generate_motifs(alphabet = alph,

motifs
#> [[1]]
#> [1] "d" "_" "c" "_" "_" "_" "d"
#> [1] "d" "_" "_" "_" "_" "_" "b"
#>
#> [[2]]
#> [1] "d" "_" "_" "_" "_" "d" "_" "a" "_" "d"
#> [1] "c" "_" "_" "d" "c"
#>
#> [[3]]
#> [1] "a" "_" "_" "d"
#> [1] "b" "_" "c" "_" "_" "c"
#>
#> [[4]]
#> [1] "c" "_" "d" "_" "_" "a"
#> [1] "c" "_" "_" "b" "_" "_" "c"
```

Using simulated motifs we can simulate positive and negative sequences
Expand All @@ -89,31 +98,31 @@ results <- generate_kmer_data(n_seq = 20,
motifs = motifs,
n_injections = 4)
results
#> 20 x 14659 sparse Matrix of class "dgCMatrix"
#> 20 x 14940 sparse Matrix of class "dgCMatrix"
#> [[ suppressing 33 column names 'a', 'd', 'b' ... ]]
#>
#> [1,] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 . . . 1 1 1 1 1 1 1 1 1 1 1 . . ......
#> [2,] 1 1 1 1 1 1 1 . 1 1 1 1 1 1 . . . 1 1 1 1 1 1 1 1 . 1 1 . 1 . 1 1 ......
#> [3,] 1 1 1 1 1 1 . 1 . 1 1 1 1 1 1 . . . . 1 1 1 1 1 1 . . 1 . 1 . 1 1 ......
#> [4,] 1 1 1 1 1 1 1 1 1 1 1 1 . 1 1 . 1 1 . . 1 1 1 1 1 . 1 1 . 1 . 1 1 ......
#> [5,] 1 1 1 1 1 1 . . 1 1 . 1 1 1 1 1 1 . 1 . 1 1 1 . . 1 . 1 . 1 . . . ......
#> [6,] 1 1 1 1 1 1 1 1 1 . 1 1 1 1 1 1 1 . . . 1 1 1 1 1 1 . . 1 1 . . 1 ......
#> [7,] 1 1 1 1 1 1 1 . 1 1 1 1 . 1 1 . . . . . 1 1 1 . 1 . . 1 . 1 . . 1 ......
#> [8,] 1 1 1 1 1 1 1 . . 1 1 1 . 1 1 . . 1 1 . 1 1 1 . 1 . 1 1 1 1 . . 1 ......
#> [9,] 1 1 1 1 . 1 1 1 1 1 1 1 1 1 1 1 1 . 1 . 1 1 1 . 1 1 . 1 1 1 . 1 . ......
#> [10,] 1 1 1 1 1 1 . 1 1 1 . . . 1 1 1 1 . 1 1 1 1 . 1 1 . . 1 . 1 1 . 1 ......
#> [11,] 1 1 1 1 1 1 . . 1 1 . 1 1 . 1 . 1 . 1 1 1 1 . . 1 1 1 . . 1 . . . ......
#> [12,] 1 1 1 1 . . . 1 1 1 1 1 1 1 1 1 1 . . . 1 . 1 1 1 1 1 . 1 1 . 1 . ......
#> [13,] 1 1 1 1 1 1 1 1 . 1 1 1 . 1 . . . 1 1 1 . 1 1 1 . . . 1 1 . . 1 1 ......
#> [14,] 1 1 1 1 . . . 1 1 1 . 1 1 1 1 1 1 . 1 . 1 1 1 1 1 1 . 1 . . 1 1 1 ......
#> [15,] 1 1 1 1 1 1 1 . . 1 1 1 . 1 1 1 . . 1 . . 1 . 1 1 . 1 1 . 1 1 1 1 ......
#> [16,] 1 1 1 1 . . . 1 . 1 1 1 1 1 . 1 1 1 . 1 1 1 1 1 . 1 1 . 1 1 1 1 . ......
#> [17,] 1 1 1 1 1 . . 1 1 . . 1 1 1 1 . . 1 1 1 . . 1 1 1 . 1 . 1 1 . 1 1 ......
#> [18,] 1 1 1 1 1 . 1 1 1 1 1 1 1 1 1 . 1 1 1 1 . . . . 1 1 1 1 . 1 1 1 1 ......
#> [19,] 1 1 1 1 . 1 1 1 . . 1 1 1 1 . 1 1 . 1 1 1 . . . 1 . . 1 1 . 1 1 1 ......
#> [20,] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 . 1 1 . . 1 1 . . 1 1 1 1 1 . 1 1 1 ......
#> [1,] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 . . . . . . 1 1 1 1 1 1 1 1 1 1 1 1 . ......
#> [2,] 1 1 1 1 . 1 1 1 1 1 1 1 1 1 1 1 1 . . . 1 1 . 1 1 1 1 1 . 1 . 1 1 ......
#> [3,] 1 1 1 1 . 1 . 1 . . 1 1 1 1 1 1 1 1 1 1 1 1 1 1 . 1 1 1 1 . 1 1 1 ......
#> [4,] 1 1 1 1 . 1 1 1 1 1 . 1 1 . 1 1 1 1 1 1 . 1 1 1 . 1 . 1 1 1 1 1 1 ......
#> [5,] 1 1 1 1 1 1 1 . 1 1 1 1 1 1 . 1 1 1 . . 1 1 1 . 1 1 . 1 1 1 1 1 . ......
#> [6,] 1 1 1 1 1 . 1 1 1 1 1 1 . 1 . 1 . 1 1 . 1 1 . 1 . . . 1 1 1 1 . 1 ......
#> [7,] 1 1 1 1 1 1 . 1 1 . 1 1 1 1 1 1 . 1 1 1 . 1 1 . 1 1 . 1 1 1 1 . . ......
#> [8,] 1 1 1 1 1 . 1 . 1 1 1 1 . 1 . 1 1 . . . 1 . . 1 1 . 1 1 . 1 1 1 . ......
#> [9,] 1 1 1 1 1 1 . 1 1 1 1 1 1 1 . 1 . . 1 1 1 . . 1 1 . 1 1 1 1 1 1 1 ......
#> [10,] 1 1 1 1 1 1 1 1 . 1 1 1 1 . . . 1 1 . . 1 1 1 . 1 1 1 1 . 1 1 1 . ......
#> [11,] 1 1 1 1 . . . 1 . 1 . 1 1 1 1 1 1 1 1 . 1 1 1 1 1 1 1 1 1 . 1 . . ......
#> [12,] 1 1 1 1 1 1 1 . . 1 1 1 1 1 1 1 1 . 1 . 1 1 . . 1 1 1 1 . 1 1 1 . ......
#> [13,] 1 1 1 1 1 1 1 . 1 1 . 1 . 1 1 . 1 1 1 1 1 1 . . . 1 . 1 1 1 1 1 1 ......
#> [14,] 1 1 1 1 1 . 1 1 1 . 1 1 1 1 1 1 . . 1 1 1 1 1 1 . 1 1 1 1 1 . 1 . ......
#> [15,] 1 1 . 1 1 1 . 1 . 1 1 . 1 . . . 1 . 1 1 1 . 1 . 1 . 1 . 1 1 . 1 1 ......
#> [16,] 1 1 1 1 . 1 1 . 1 1 . 1 . 1 . 1 1 1 1 . 1 . 1 . . 1 . 1 . 1 1 . . ......
#> [17,] 1 1 1 1 1 1 1 1 1 1 1 1 . 1 1 . . 1 1 . 1 1 1 1 1 1 . . 1 1 1 1 1 ......
#> [18,] 1 1 1 1 1 1 1 1 1 1 . 1 1 . 1 . . 1 1 1 1 1 . 1 . 1 . 1 1 1 . 1 1 ......
#> [19,] 1 1 1 1 . . 1 1 . 1 . 1 1 1 1 1 1 1 1 1 . 1 . 1 . . . 1 1 1 1 . 1 ......
#> [20,] 1 1 1 1 1 . 1 1 1 . 1 1 1 1 1 1 . 1 1 1 1 1 . . . 1 1 1 1 1 1 . 1 ......
#>
#> .....suppressing 14626 columns in show(); maybe adjust 'options(max.print= *, width = *)'
#> .....suppressing 14907 columns in show(); maybe adjust 'options(max.print= *, width = *)'
#> ..............................
```

Expand All @@ -128,7 +137,7 @@ For example, the following code:

``` r
get_target_additive(results)
#> [1] 1 1 1 1 1 1 1 0 1 1 0 1 0 0 0 0 1 0 0 0
#> [1] 0 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
```

creates a binary response variable based on the logistic regression
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