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Make prediction for nucleotide sequence or entries in fasta/fastq file

Source: R/predict.R
predict_model.Rd

Removes layers (optional) from pretrained model and calculates states of fasta/fastq file or nucleotide sequence. Writes states to h5 or csv file (access content of h5 output with load_prediction function). There are several options on how to process an input file:

  • If "one_seq", computes prediction for sequence argument or fasta/fastq file. Combines fasta entries in file to one sequence. This means predictor sequences can contain elements from more than one fasta entry.

  • If "by_entry", will output a separate file for each fasta/fastq entry. Names of output files are: output_dir + "Nr" + i + filename + output_type, where i is the number of the fasta entry.

  • If "by_entry_one_file", will store prediction for all fasta entries in one h5 file.

  • If "one_pred_per_entry", will make one prediction for each entry by either picking random sample for long sequences or pad sequence for short sequences.

Usage

predict_model(
  model,
  output_format = "one_seq",
  layer_name = NULL,
  sequence = NULL,
  path_input = NULL,
  round_digits = NULL,
  filename = "states.h5",
  step = 1,
  vocabulary = c("a", "c", "g", "t"),
  batch_size = 256,
  verbose = TRUE,
  return_states = FALSE,
  output_type = "h5",
  padding = "none",
  use_quality = FALSE,
  quality_string = NULL,
  mode = "label",
  lm_format = "target_right",
  output_dir = NULL,
  format = "fasta",
  include_seq = FALSE,
  reverse_complement_encoding = FALSE,
  ambiguous_nuc = "zero",
  ...
)

Arguments

model

A keras model.

output_format

Either "one_seq", "by_entry", "by_entry_one_file", "one_pred_per_entry".

layer_name

Name of layer to get output from. If NULL, will use the last layer.

sequence

Character string, ignores path_input if argument given.

path_input

Path to fasta file.

round_digits

Number of decimal places.

filename

Filename to store states in. No file output if argument is NULL. If output_format = "by_entry", adds "nr" + "i" after name, where i is entry number.

step

Frequency of sampling steps.

vocabulary

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

batch_size

Number of samples used for one network update.

verbose

Boolean.

return_states

Return predictions as data frame. Only supported for output_format "one_seq".

output_type

"h5" or "csv". If output_format`` is "by_entries_one_file", "one_pred_per_entry"can only be"h5"`.

padding

Either "none", "maxlen", "standard" or "self".

  • If "none", apply no padding and skip sequences that are too short.

  • If "maxlen", pad with maxlen number of zeros vectors.

  • If "standard", pad with zero vectors only if sequence is shorter than maxlen. Pads to minimum size required for one prediction.

  • If "self", concatenate sequence with itself until sequence is long enough for one prediction. Example: if sequence is "ACGT" and maxlen is 10, make prediction for "ACGTACGTAC". Only applied if sequence is shorter than maxlen.

use_quality

Whether to use quality scores.

quality_string

String for encoding with quality scores (as used in fastq format).

mode

Either "lm" for language model or "label" for label classification.

lm_format

Either "target_right", "target_middle_lstm", "target_middle_cnn" or "wavenet".

output_dir

Directory for file output.

format

File format, "fasta", "fastq", "rds" or "fasta.tar.gz", "fastq.tar.gz" for tar.gz files.

include_seq

Whether to include input sequence in h5 file.

reverse_complement_encoding

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

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.

...

Further arguments for sequence encoding with seq_encoding_label.

Value

If return_states = TRUE returns a list of model predictions and position of corresponding sequences. If additionally include_seq = TRUE, list contains sequence strings. If return_states = FALSE returns nothing, just writes output to file(s).

Examples

if (FALSE) { # reticulate::py_module_available("tensorflow")
# make prediction for single sequence and write to h5 file
model <- create_model_lstm_cnn(maxlen = 20, layer_lstm = 8, layer_dense = 2, verbose = FALSE)
vocabulary <- c("a", "c", "g", "t")
sequence <- paste(sample(vocabulary, 200, replace = TRUE), collapse = "")
output_file <- tempfile(fileext = ".h5")
predict_model(output_format = "one_seq", model = model, step = 10,
             sequence = sequence, filename = output_file, mode = "label")

# make prediction for fasta file with multiple entries, write output to separate h5 files
fasta_path <- tempfile(fileext = ".fasta")
create_dummy_data(file_path = fasta_path, num_files = 1,
                 num_seq = 5, seq_length = 100,
                 write_to_file_path = TRUE)
model <- create_model_lstm_cnn(maxlen = 20, layer_lstm = 8, layer_dense = 2, verbose = FALSE)
output_dir <- tempfile()
dir.create(output_dir)
predict_model(output_format = "by_entry", model = model, step = 10, verbose = FALSE,
               output_dir = output_dir, mode = "label", path_input = fasta_path)
list.files(output_dir)
}