Package 'immApex'

Title: Tools for Adaptive Immune Receptor Sequence-Based Keras Modeling
Description: A set of tools to build tensorflow/keras3-based models in R from amino acid and nucleotide sequences focusing on adaptive immune receptors. The package includes pre-processing of sequences, unifying gene nomenclature usage, encoding sequences, and combining models. This package will serve as the basis of future immune receptor sequence functions/packages/models compatible with the scRepertoire ecosystem.
Authors: Nick Borcherding [aut, cre]
Maintainer: Nick Borcherding <[email protected]>
License: MIT + file LICENSE
Version: 1.1.0
Built: 2024-11-30 03:35:05 UTC
Source: https://github.com/bioc/immApex

Help Index

Adjacency matrix from amino acid or nucleotide sequences

Description

Calculate frequency of adjacency between residues along a set of biological sequences.

Usage

adjacencyMatrix(
  input.sequences = NULL,
  normalize = TRUE,
  sequence.dictionary = amino.acids
)

Arguments

input.sequences

The amino acid or nucleotide sequences to use
normalize

Return the values as a function of total number of residues (TRUE) or frequencies (FALSE)
sequence.dictionary

The letters to use in sequence generation (default are all amino acids)

Value

Adjacency matrix based on input.sequences.

Examples

new.sequences <- generateSequences(prefix.motif = "CAS",
                                   suffix.motif = "YF",
                                   number.of.sequences = 100,
                                   min.length = 8,
                                   max.length = 16)
                          
adj.matrix <- adjacencyMatrix(new.sequences,
                              normalize = TRUE)

Ensure clean gene nomenclature using IMGT annotations

Description

This function will format the genes into a clean nomenclature using the IMGT conventions.

Usage

formatGenes(
  input.data,
  region = "v",
  technology = NULL,
  species = "human",
  simplify.format = TRUE
)

Arguments

input.data

Data frame of sequencing data or scRepertoire outputs
region

Sequence gene loci to access - "v", "d", "j", or "c" or a combination using c("v", "d", "j")
technology

The sequencing technology employed - 'TenX', "Adaptive', or 'AIRR'
species

One or two word designation of species. Currently supporting: "human", "mouse", "rat", "rabbit", "rhesus monkey", "sheep", "pig", "platypus", "alpaca", "dog", "chicken", and "ferret"
simplify.format

If applicable, remove the allelic designation (TRUE) or retain all information (FALSE)

Value

A data frame with the new columns of formatted genes added.

Examples

data(immapex_example.data)
formatGenes(immapex_example.data[["TenX"]],
            region = "v",
            technology = "TenX")

Randomly Generate Amino Acid Sequences

Description

Use this to make synthetic amino acid sequences for purposes of testing code, training models, or noise.

Usage

generateSequences(
  prefix.motif = NULL,
  suffix.motif = NULL,
  number.of.sequences = 100,
  min.length = 1,
  max.length = 10,
  sequence.dictionary = amino.acids
)

Arguments

prefix.motif

Add defined amino acid/nucleotide sequence to the start of the generated sequences.
suffix.motif

Add defined amino acid/nucleotide sequence to the end of the generated sequences
number.of.sequences

Number of sequences to generate
min.length

Minimum length of the final sequence. The min.length may be adjusted if incongruent with prefix.motif/suffix.motif lengths
max.length

Maximum length of the final sequence
sequence.dictionary

The letters to use in sequence generation (default are all amino acids)

Value

A vector of generated sequences

Examples

generateSequences(prefix.motif = "CAS",
                  suffix.motif = "YF",
                  number.of.sequences = 100,
                  min.length = 8,
                  max.length = 16)

Geometric Encoder from Amino Acid Strings

Description

Use this to transform amino acid sequences into a geometric encoding of the sequence.

Usage

geometricEncoder(
  input.sequences,
  method.to.use = "BLOSUM62",
  theta = pi/3,
  verbose = TRUE
)

Arguments

input.sequences

The set of amino acid sequences
method.to.use

The method or approach for the conversion:

  • BLOcks SUbstitution Matrices: BLOSUM45, BLOSUM50, BLOSUM62, BLOSUM80, BLOSUM100

  • Point Accepted Mutation Matrices: PAM30, PAM40, PAM70, PAM120, PAM250

theta

The angle for geometric transformation
verbose

Print messages corresponding to the processing step

Value

Geometric encoded amino acid sequences in a matrix

Examples

new.sequences <- generateSequences(prefix.motif = "CAS",
                                   suffix.motif = "YF",
                                   number.of.sequences = 100,
                                   min.length = 8,
                                   max.length = 16)
                          
sequence.matrix <- geometricEncoder(new.sequences, 
                                    method.to.use = "BLOSUM62",
                                    theta = pi/3)

Get IMGT Sequences for Specific Loci

Description

Use this to access the ImMunoGeneTics (IMGT) sequences for a specific species and gene loci. More information on IMGT can be found at imgt.org.

Usage

getIMGT(
  species = "human",
  chain = "TRB",
  sequence.type = "aa",
  frame = "inframe",
  region = "v",
  max.retries = 3,
  verbose = TRUE
)

Arguments

species

One or two-word common designation of species.
chain

Sequence chain to access, e.g., TRB or IGH.
sequence.type

Type of sequence - aa (amino acid) or nt (nucleotide).
frame

Designation for all, inframe, or inframe+gap.
region

Gene loci to access.
max.retries

Number of attempts to fetch data in case of failure.
verbose

Print messages corresponding to the processing step.

Value

A list of allele sequences.

Examples

TRBV_aa <- getIMGT(species = "human",
                   chain = "TRB",
                   frame = "inframe",
                   region = "v",
                   sequence.type = "aa", 
                   max.retries = 3)

Extract Immune Receptor Sequences

Description

Use this to extract immune receptor sequences from a Single-Cell Object or the output of combineTCR and combineBCR.

Usage

getIR(input.data, chains, sequence.type = "aa")

Arguments

input.data

Single-cell object or the output of combineTCR and combineBCR from scRepertoire
chains

Immune Receptor chain to use - TRA, TRB, IGH, or IGL
sequence.type

Extract amino acid (aa) or nucleotide (nt) sequences

Value

A data frame of nucleotide or amino acid sequences

A list of amino acid properties

Description

A list of amino acid properties that are used for propertyEncoder function.

This includes:

  • atchleyFactors

  • crucianiProperties

  • FASGAI

  • kideraFactors

  • MSWHIM

  • ProtFP

  • stScales

  • tScales

  • VHSE

  • zScales

Usage

data("immapex_AA.data")

Value

List of 10 amino acid properties for 20 amino acids

List of amino acid substitution matrices

Description

A list of amino acid substitution matrices, using the Point Accepted Matrix (PAM) and BLOck SUbstitution Matrix (BLOSUM) approaches. A discussion and comparison of these matrices are available at PMID: 21356840.

  • BLOSUM45

  • BLOSUM50

  • BLOSUM62

  • BLOSUM80

  • BLOSUM100

  • PAM30

  • PAM40

  • PAM70

  • PAM120

  • PAM250

Usage

data("immapex_blosum.pam.matrices")

Value

List of 10 substitution matrices

Example contig data for Apex

Description

Contains a collection of bulk or paired TCR sequences in the respective formats in the form of a list from the following sources:

More information on the data formats are available: AIRR, Adaptive, and TenX.

Usage

data("immapex_example.data")

Value

List of 3 example data sets for 10x, AIRR and Adaptive contigs.

A list of IMGT gene names by genes, loci, and species

Description

A list of regularized gene nomenclature to use for converting for data for uniformity. Data is organize by gene region, loci and species. Not all species are represented in the data and pseudogenes have not been removed.

Usage

data("immapex_gene.list")

Value

List of gene nomenclature by region, loci, and species.

Infer portions of the CDR Loop from Vgene data

Description

Use this isolate sequences from the CDR loop using the V gene annotation. When there are multiple V gene matches for a single gene, the first allelic sequence is used.

Usage

inferCDR(
  input.data,
  reference = NULL,
  chain = "TRB",
  technology = NULL,
  sequence.type = "aa",
  sequences = c("CDR1", "CDR2")
)

Arguments

input.data

Data frame output of formatGenes
reference

IMGT reference sequences from getIMGT
chain

Sequence chain to access, like TRB or IGH
technology

The sequencing technology employed - TenX, Adaptive, AIRR, or Omniscope
sequence.type

Type of sequence - aa for amino acid or nt for nucleotide
sequences

The specific regions of the CDR loop to get from the data, such as CDR1.

Value

A data frame with the new columns of CDR sequences added.

Examples

# Getting the Sequence Reference
data(immapex_example.data)
TRBV_aa <- getIMGT(species = "human",
                   chain = "TRB",
                   frame = "inframe",
                   region = "v",
                   sequence.type = "aa") 
                    
# Ensuring sequences are formatted to IMGT                   
TenX_formatted <- formatGenes(immapex_example.data[["TenX"]],
                              region = "v",
                              technology = "TenX")
             
# Inferring CDR loop elements           
TenX_formatted <- inferCDR(TenX_formatted,
                           chain = "TRB", 
                           reference = TRBV_aa,
                           technology = "TenX", 
                           sequence.type = "aa",
                           sequences = c("CDR1", "CDR2"))

Randomly Mutate Sequences of Amino Acids

Description

Use this to mutate or mask sequences for purposes of testing code, training models, or noise.

Usage

mutateSequences(
  input.sequences,
  n.sequences = 1,
  mutation.rate = 0.01,
  position.start = NULL,
  position.end = NULL,
  sequence.dictionary = amino.acids
)

Arguments

input.sequences

The amino acid or nucleotide sequences to use
n.sequences

The number of mutated sequences to return
mutation.rate

The rate of mutations to introduce into sequences
position.start

The starting position to mutate along the sequence Default = NULL will start the random mutations at position 1
position.end

The ending position to mutate along the sequence Default = NULL will end the random mutations at the last position
sequence.dictionary

The letters to use in sequence mutation (default are all amino acids)

Value

A vector of mutated sequences

Examples

sequences <- generateSequences(prefix.motif = "CAS",
                               suffix.motif = "YF",
                               number.of.sequences = 100,
                               min.length = 8,
                               max.length = 16)
                                
mutated_sequences <- mutateSequences(sequences, 
                                     n.sequence = 1,
                                     position.start = 3,
                                     position.end = 8)

One Hot Encoder from Amino Acid or Nucleotide Strings

Description

Use this to transform amino acid or nucleotide sequences into a one hot encoding of the sequence.

Usage

onehotEncoder(
  input.sequences,
  max.length = NULL,
  motif.length = 1,
  convert.to.matrix = TRUE,
  sequence.dictionary = amino.acids,
  padding.symbol = ".",
  verbose = TRUE
)

Arguments

input.sequences

The amino acid or nucleotide sequences to use
max.length

Additional length to pad, NULL will pad sequences to the max length of input.sequences
motif.length

The length of the amino acid residues to encode - a motif.length = 1 produces single amino acid encodings
convert.to.matrix

Return a matrix (TRUE) or a 3D array (FALSE)
sequence.dictionary

The letters to use in sequence generation (default are all amino acids). This will be overrode if using a motif approach (motif.length > 1)
padding.symbol

Symbol to use for padding at the end of sequences
verbose

Print messages corresponding to the processing step

Value

One hot encoded sequences in a matrix or 3D array

Examples

new.sequences <- generateSequences(prefix.motif = "CAS",
                                   suffix.motif = "YF",
                                   number.of.sequences = 100,
                                   min.length = 8,
                                   max.length = 16)
                          
sequence.matrix <- onehotEncoder(new.sequences, 
                                 convert.to.matrix = TRUE)

Adding Position-Specific Information to Sequences

Description

Use this calculate positional encoding for recurrent neural networks using sin/cos and position information.

Usage

positionalEncoder(number.of.sequences, latent.dims = NULL)

Arguments

number.of.sequences

The number of sequences to generate position information
latent.dims

The number of latent dimensions.

Value

A matrix of values

Examples

position.info <- positionalEncoder(number.of.sequences = 1000, 
                                   latent.dims = 64)

Position Probability Matrix for Amino Acid or Nucleotide Sequences

Description

Use this to generate a position-probability or weight matrix for a set of given sequences.

Usage

probabilityMatrix(
  input.sequences,
  max.length = NULL,
  convert.PWM = FALSE,
  background.frequencies = NULL,
  sequence.dictionary = amino.acids,
  padding.symbol = ".",
  verbose = TRUE
)

Arguments

input.sequences

The amino acid or nucleotide sequences to use
max.length

Additional length to pad, NULL will pad sequences to the max length of input.sequences
convert.PWM

Convert the matrix into a positional weight matrix using log likelihood
background.frequencies

Provide amino acid or nucleotide frequencies for the positional weight matrix. If NULL, assumes uniform likelihood.
sequence.dictionary

The letters to use in sequence generation (default are all amino acids)
padding.symbol

Symbol to use for padding at the end of sequences
verbose

Print messages corresponding to the processing step

Value

A matrix with position specific probabilities or weights

Examples

new.sequences <- generateSequences(prefix.motif = "CAS",
                                   suffix.motif = "YF",
                                   number.of.sequences = 100,
                                   min.length = 8,
                                   max.length = 16)
                          
PPM.matrix <- probabilityMatrix(new.sequences)

Encoder from Amino Acid String by Properties

Description

Use this to transform amino acid sequences a a matrix by amino acid properties derived from dimensional reduction strategies

Usage

propertyEncoder(
  input.sequences,
  max.length = NULL,
  method.to.use = NULL,
  convert.to.matrix = TRUE,
  summary.function = NULL,
  padding.symbol = ".",
  verbose = TRUE
)

Arguments

input.sequences

The amino acid sequences to use
max.length

Additional length to pad, NULL will pad sequences to the max length of input.sequences
method.to.use

The method or approach to use for the conversion:

  • Individual sets: atchleyFactors, crucianiProperties, FASGAI, kideraFactors, MSWHIM, ProtFP, stScales, tScales, VHSE, zScales"

  • Multiple Sets: c("atchleyFactors", "VHSE")

convert.to.matrix

Return a matrix (TRUE) or a 3D array (FALSE)
summary.function

Return a matrix that summarize the amino acid method/property Available summaries include: "median", "mean", "sum", variance ("vars"), or Median Absolute Deviation ("mads")
padding.symbol

Symbol to use for padding at the end of sequences
verbose

Print messages corresponding to the processing step

Value

Converted amino acid sequences by property in a matrix or 3D array

Examples

new.sequences <- generateSequences(prefix.motif = "CAS",
                                   suffix.motif = "YF",
                                   number.of.sequences = 100,
                                   min.length = 8,
                                   max.length = 16)
                          
sequence.matrix <- propertyEncoder(new.sequences, 
                                   method.to.use = "VHSE",
                                   convert.to.matrix = TRUE)

One Hot Decoder from One Hot Encoded Matrix or 3D Array

Description

Use this to transform one hot encoded sequences back into amino acid or nucleotide sequences.

Usage

sequenceDecoder(
  sequence.matrix,
  encoder = "onehotEncoder",
  aa.method.to.use = NULL,
  call.threshold = 0.5,
  sequence.dictionary = amino.acids,
  padding.symbol = ".",
  remove.padding = TRUE
)

Arguments

sequence.matrix

The encoded sequences to decode in an array or matrix
encoder

The method to prepare the sequencing information - "onehotEncoder" or "propertyEncoder"
aa.method.to.use

The method or approach to use for the conversion:

  • Individual sets: atchleyFactors, crucianiProperties, FASGAI, kideraFactors, MSWHIM, ProtFP, stScales, tScales, VHSE, zScales"

  • Multiple Sets: c("atchleyFactors", "VHSE")

call.threshold

The relative strictness of sequence calling with higher values being more stringent
sequence.dictionary

The letters to use in sequence generation (default are all amino acids)
padding.symbol

Symbol to use for padding at the end of sequences
remove.padding

Remove the additional symbol from the end of decoded sequences

Value

Decoded amino acid or nucleotide sequences

Examples

new.sequences <- generateSequences(prefix.motif = "CAS",
                                   suffix.motif = "YF",
                                   number.of.sequences = 100,
                                   min.length = 8,
                                   max.length = 16)
                          
sequence.matrix <- onehotEncoder(new.sequences, 
                                 convert.to.matrix = TRUE)
                                 
decoded.sequences <- sequenceDecoder(sequence.matrix,
                                     padding.symbol = ".")

Generate Tokenized Sequences from Amino Acid String

Description

Use this to transform amino acid sequences into tokens in preparing for deep learning models.

Usage

tokenizeSequences(
  input.sequences,
  add.startstop = TRUE,
  start.token = "!",
  stop.token = "^",
  max.length = NULL,
  convert.to.matrix = TRUE,
  verbose = TRUE
)

Arguments

input.sequences

The amino acid or nucleotide sequences to use
add.startstop

Add start and stop tokens to the sequence
start.token

The character to use for the start token
stop.token

The character to use for the stop token
max.length

Additional length to pad, NULL will pad sequences to the max length of input.sequences
convert.to.matrix

Return a matrix (TRUE) or a vector (FALSE)
verbose

Print messages corresponding to the processing step

Value

Tokenize sequences in a matrix or vector

Examples

new.sequences <- generateSequences(prefix.motif = "CAS",
                                   suffix.motif = "YF",
                                   number.of.sequences = 100,
                                   min.length = 8,
                                   max.length = 16)
                          
sequence.matrix <- tokenizeSequences(new.sequences, 
                                    add.startstop = TRUE,
                                    start.token = "!",
                                    stop.token = "^", 
                                    convert.to.matrix = TRUE)

Generate Similar Sequences using Variational Autoencoder

Description

Use this to simulate sequences using a variational autoencoder (VAE) and perturbation of the probability distributions.

Usage

variationalSequences(
  input.sequences,
  encoder.function = "onehotEncoder",
  aa.method.to.use = NULL,
  number.of.sequences = 100,
  encoder.hidden.dim = c(128, 64),
  decoder.hidden.dim = NULL,
  latent.dim = 16,
  batch.size = 16,
  epochs = 50,
  learning.rate = 0.001,
  epsilon.std = 1,
  call.threshold = 0.2,
  activation.function = "relu",
  optimizer = "adam",
  disable.eager.execution = FALSE,
  sequence.dictionary = amino.acids,
  verbose = TRUE
)

Arguments

input.sequences

The amino acid or nucleotide sequences to use
encoder.function

The method to prepare the sequencing information - "onehotEncoder" or "propertyEncoder"
aa.method.to.use

The method or approach to use for the conversion:

  • Individual sets: atchleyFactors, crucianiProperties, FASGAI, kideraFactors, MSWHIM, ProtFP, stScales, tScales, VHSE, zScales"

  • Multiple Sets: c("atchleyFactors", "VHSE")

number.of.sequences

Number of sequences to generate
encoder.hidden.dim

A vector of the neurons to use in the hidden layers for the encoder portion of the model
decoder.hidden.dim

A vector of the neurons to use in the hidden layers for the decoder portion of the model. If NULL assumes symmetric autoencoder
latent.dim

The size of the latent dimensions
batch.size

The batch size to use for VAE training
epochs

The number of epochs to use in VAE training
learning.rate

The learning rate to use in VAE training
epsilon.std

The epsilon to use in VAE training
call.threshold

The relative strictness of sequence calling with higher values being more stringent
activation.function

The activation for the dense connected layers
optimizer

The optimizer to use in VAE training
disable.eager.execution

Disable the eager execution parameter for tensorflow.
sequence.dictionary

The letters to use in sequence mutation (default are all amino acids)
verbose

Print messages corresponding to the processing step

Value

A vector of mutated sequences

Examples

sequences <- generateSequences(prefix.motif = "CAS",
                               suffix.motif = "YF",
                               number.of.sequences = 100,
                               min.length = 8,
                               max.length = 16)
                                
new.sequences <- variationalSequences(sequences, 
                                      encoder = "onehotEncoder",
                                      encoder.hidden.dim = c(256, 128),
                                      latent.dim = 16,
                                      batch.size = 16)