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Data generator for fasta/fastq files and label targets

Source: R/generator_csv.R
generator_fasta_label_header_csv.Rd

Iterates over folder containing fasta/fastq files and produces encoding of predictor sequences and target variables. Targets will be read from fasta headers or a separate csv file.

Usage

generator_fasta_label_header_csv(
  path_corpus,
  format = "fasta",
  batch_size = 256,
  maxlen = 250,
  max_iter = 10000,
  vocabulary = c("a", "c", "g", "t"),
  verbose = FALSE,
  shuffle_file_order = FALSE,
  step = 1,
  seed = 1234,
  shuffle_input = FALSE,
  file_limit = NULL,
  path_file_log = NULL,
  vocabulary_label = c("x", "y", "z"),
  reverse_complement = TRUE,
  ambiguous_nuc = "zero",
  proportion_per_seq = NULL,
  read_data = FALSE,
  use_quality_score = FALSE,
  padding = TRUE,
  skip_amb_nuc = NULL,
  max_samples = NULL,
  concat_seq = NULL,
  added_label_path = NULL,
  add_input_as_seq = NULL,
  target_from_csv = NULL,
  target_split = NULL,
  file_filter = NULL,
  use_coverage = NULL,
  proportion_entries = NULL,
  sample_by_file_size = FALSE,
  reverse_complement_encoding = FALSE,
  n_gram = NULL,
  n_gram_stride = 1,
  add_noise = NULL,
  return_int = FALSE,
  reshape_xy = NULL
)

Arguments

path_corpus

Input directory where fasta files are located or path to single file ending with fasta or fastq (as specified in format argument). Can also be a list of directories and/or files.

format

File format, either "fasta" or "fastq".

batch_size

Number of samples in one batch.

maxlen

Length of predictor sequence.

max_iter

Stop after max_iter number of iterations failed to produce a new batch.

vocabulary

Vector of allowed characters. Characters outside vocabulary get encoded as specified in ambiguous_nuc.

verbose

Whether to show messages.

shuffle_file_order

Logical, whether to go through files randomly or sequentially.

step

How often to take a sample.

seed

Sets seed for set.seed function for reproducible results.

shuffle_input

Whether to shuffle entries in every fasta/fastq file before extracting samples.

file_limit

Integer or NULL. If integer, use only specified number of randomly sampled files for training. Ignored if greater than number of files in path.

path_file_log

Write name of files to csv file if path is specified.

vocabulary_label

Character vector of possible targets. Targets outside vocabulary_label will get discarded.

reverse_complement

Boolean, for every new file decide randomly to use original data or its reverse complement.

ambiguous_nuc

How to handle nucleotides outside vocabulary, either "zero", "discard", "empirical" or "equal".

  • If "zero", input gets encoded as zero vector.

  • If "equal", input is repetition of 1/length(vocabulary).

  • If "discard", samples containing nucleotides outside vocabulary get discarded.

  • If "empirical", use nucleotide distribution of current file.

proportion_per_seq

Numerical value between 0 and 1. Proportion of sequence to take samples from (use random subsequence).

read_data

If TRUE the first element of input is a list of length 2, each containing one part of paired read. Maxlen should be 2*length of one read.

use_quality_score

Whether to use fastq quality scores. If TRUE input is not one-hot-encoding but corresponds to probabilities. For example (0.97, 0.01, 0.01, 0.01) instead of (1, 0, 0, 0).

padding

Whether to pad sequences too short for one sample with zeros.

skip_amb_nuc

Threshold of ambiguous nucleotides to accept in fasta entry. Complete entry will get discarded otherwise.

max_samples

Maximum number of samples to use from one file. If not NULL and file has more than max_samples samples, will randomly choose a subset of max_samples samples.

concat_seq

Character string or NULL. If not NULL all entries from file get concatenated to one sequence with concat_seq string between them. Example: If 1.entry AACC, 2. entry TTTG and concat_seq = "ZZZ" this becomes AACCZZZTTTG.

added_label_path

Path to file with additional input labels. Should be a csv file with one column named "file". Other columns should correspond to labels.

add_input_as_seq

Boolean vector specifying for each entry in added_label_path if rows from csv should be encoded as a sequence or used directly. If a row in your csv file is a sequence this should be TRUE. For example you may want to add another sequence, say ACCGT. Then this would correspond to 1,2,2,3,4 in csv file (if vocabulary = c("A", "C", "G", "T")). If add_input_as_seq is TRUE, 12234 gets one-hot encoded, so added input is a 3D tensor. If add_input_as_seq is FALSE this will feed network just raw data (a 2D tensor).

target_from_csv

Path to csv file with target mapping. One column should be called "file" and other entries in row are the targets.

target_split

If target gets read from csv file, list of names to divide target tensor into list of tensors. Example: if csv file has header names "file", "label_1", "label_2", "label_3" and target_split = list(c("label_1", "label_2"), "label_3"), this will divide target matrix to list of length 2, where the first element contains columns named "label_1" and "label_2" and the second entry contains the column named "label_3".

file_filter

Vector of file names to use from path_corpus.

use_coverage

Integer or NULL. If not NULL, use coverage as encoding rather than one-hot encoding and normalize. Coverage information must be contained in fasta header: there must be a string "cov_n" in the header, where n is some integer.

proportion_entries

Proportion of fasta entries to keep. For example, if fasta file has 50 entries and proportion_entries = 0.1, will randomly select 5 entries.

sample_by_file_size

Sample new file weighted by file size (bigger files more likely).

reverse_complement_encoding

Whether to use both original sequence and reverse complement as two input sequences.

n_gram

Integer, encode target not nucleotide wise but combine n nucleotides at once. For example for n=2, "AA" -> (1, 0,..., 0), "AC" -> (0, 1, 0,..., 0), "TT" -> (0,..., 0, 1), where the one-hot vectors have length length(vocabulary)^n.

n_gram_stride

Step size for n-gram encoding. For AACCGGTT with n_gram = 4 and n_gram_stride = 2, generator encodes (AACC), (CCGG), (GGTT); for n_gram_stride = 4 generator encodes (AACC), (GGTT).

add_noise

NULL or list of arguments. If not NULL, list must contain the following arguments: noise_type can be "normal" or "uniform"; optional arguments sd or mean if noise_type is "normal" (default is sd=1 and mean=0) or min, max if noise_type is "uniform" (default is min=0, max=1).

return_int

Whether to return integer encoding or one-hot encoding.

reshape_xy

Can be a list of functions to apply to input and/or target. List elements (containing the reshape functions) must be called x for input or y for target and each have arguments called x and y. For example: reshape_xy = list(x = function(x, y) {return(x+1)}, y = function(x, y) {return(x+y)}) . For rds generator needs to have an additional argument called sw.

Value

A generator function.

Examples

if (FALSE) { # reticulate::py_module_available("tensorflow")
path_input <- tempfile()
dir.create(path_input)
# create 2 fasta files called 'file_1.fasta', 'file_2.fasta'
create_dummy_data(file_path = path_input, 
                  num_files = 2,
                  seq_length = 5,
                  num_seq = 1,
                  vocabulary = c("a", "c", "g", "t"))
dummy_labels <- data.frame(file = c('file_1.fasta', 'file_2.fasta'), # dummy labels
                           label1 = c(0, 1),
                           label2 = c(1, 0))
target_from_csv <- tempfile(fileext = '.csv')
write.csv(dummy_labels, target_from_csv, row.names = FALSE)
gen <- generator_fasta_label_header_csv(path_corpus = path_input, batch_size = 2, 
                                        maxlen = 5, target_from_csv = target_from_csv)
z <- gen()
dim(z[[1]])
z[[2]]
}