generator_fasta_lm: Language model generator for fasta/fastq files

In GenomeNet/deepG: Deep Learning for Genome Sequence Data

Language model generator for fasta/fastq files

Description

Iterates over folder containing fasta/fastq files and produces encoding of predictor sequences and target variables. Will take a sequence of fixed size and use some part of sequence as input and other part as target.

Usage

generator_fasta_lm(
  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,
  reverse_complement = FALSE,
  output_format = "target_right",
  ambiguous_nuc = "zeros",
  use_quality_score = FALSE,
  proportion_per_seq = NULL,
  padding = TRUE,
  added_label_path = NULL,
  add_input_as_seq = NULL,
  skip_amb_nuc = NULL,
  max_samples = NULL,
  concat_seq = NULL,
  target_len = 1,
  file_filter = NULL,
  use_coverage = NULL,
  proportion_entries = NULL,
  sample_by_file_size = 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.
reverse_complement	Boolean, for every new file decide randomly to use original data or its reverse complement.
output_format	Determines shape of output tensor for language model. Either "target_right", "target_middle_lstm", "target_middle_cnn" or "wavenet". Assume a sequence "AACCGTA". Output correspond as follows ⁠"target_right": X = "AACCGT", Y = "A"⁠ ⁠"target_middle_lstm": X = (X_1 = "AAC", X_2 = "ATG"), Y = "C"⁠ (note reversed order of X_2) ⁠"target_middle_cnn": X = "AACGTA", Y = "C"⁠ ⁠"wavenet": X = "AACCGT", Y = "ACCGTA"⁠
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.
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).
proportion_per_seq	Numerical value between 0 and 1. Proportion of sequence to take samples from (use random subsequence).
padding	Whether to pad sequences too short for one sample with zeros.
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).
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.
target_len	Number of nucleotides to predict at once for language model.
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).
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

# create dummy fasta files
path_input_1 <- tempfile()
dir.create(path_input_1)
create_dummy_data(file_path = path_input_1,
                  num_files = 2,
                  seq_length = 8,
                  num_seq = 1,
                  vocabulary = c("a", "c", "g", "t"))

gen <- generator_fasta_lm(path_corpus = path_input_1, batch_size = 2,
                                   maxlen = 7)
z <- gen()
dim(z[[1]])
z[[2]]

GenomeNet/deepG documentation built on Dec. 24, 2024, 12:11 p.m.