seq_encoding_label: Encodes integer sequence for label classification.

In GenomeNet/deepG: Deep Learning for Genome Sequence Data

Encodes integer sequence for label classification.

Description

Returns encoding for integer or character sequence.

Usage

seq_encoding_label(
  sequence = NULL,
  maxlen,
  vocabulary,
  start_ind,
  ambiguous_nuc = "zero",
  nuc_dist = NULL,
  quality_vector = NULL,
  use_coverage = FALSE,
  max_cov = NULL,
  cov_vector = NULL,
  n_gram = NULL,
  n_gram_stride = 1,
  masked_lm = NULL,
  char_sequence = NULL,
  tokenizer = NULL,
  adjust_start_ind = FALSE,
  return_int = FALSE
)

Arguments

sequence	Sequence of integers.
maxlen	Length of predictor sequence.
vocabulary	Vector of allowed characters. Characters outside vocabulary get encoded as specified in ambiguous_nuc.
start_ind	Start positions of samples in sequence.
ambiguous_nuc	How to handle nucleotides outside vocabulary, either "zero", "empirical" or "equal". See train_model. Note that "discard" option is not available for this function.
nuc_dist	Nucleotide distribution.
quality_vector	Vector of quality probabilities.
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.
max_cov	Biggest coverage value. Only applies if use_coverage = TRUE.
cov_vector	Vector of coverage values associated to the input.
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)⁠.
masked_lm	If not NULL, input and target are equal except some parts of the input are masked or random. Must be list with the following arguments: mask_rate: Rate of input to mask (rate of input to replace with mask token). random_rate: Rate of input to set to random token. identity_rate: Rate of input where sample weights are applied but input and output are identical. include_sw: Whether to include sample weights. block_len (optional): Masked/random/identity regions appear in blocks of size block_len.
char_sequence	A character string.
tokenizer	A keras tokenizer.
adjust_start_ind	Whether to shift values in start_ind to start at 1: for example (5,11,25) becomes (1,7,21).
return_int	Whether to return integer encoding or one-hot encoding.

Value

A list of 2 tensors.

Examples

# use integer sequence as input
x <- seq_encoding_label(sequence = c(1,0,5,1,3,4,3,1,4,1,2),
                        maxlen = 5,
                        vocabulary = c("a", "c", "g", "t"),
                        start_ind = c(1,3),
                        ambiguous_nuc = "equal")

x[1,,] # 1,0,5,1,3

x[2,,] # 5,1,3,4,

# use character string as input
x <- seq_encoding_label(maxlen = 5,
                        vocabulary = c("a", "c", "g", "t"),
                        start_ind = c(1,3),
                        ambiguous_nuc = "equal",
                        char_sequence = "ACTaaTNTNaZ")

x[1,,] # actaa

x[2,,] # taatn

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