Title: Repertoire Graphical Visualization
Version: 0.0.1
Description: Visualization platform for T cell receptor repertoire analysis output results. It includes comparison of sequence frequency among samples, network of similar sequences and convergent recombination source between species. Currently repertoire analysis is in early stage of development and requires new approaches for repertoire data examination and assessment as we intend to develop. No publication is available yet (will be available in the near future), Efroni (2021) <https:>.
License: MIT + file LICENSE
URL: https://github.com/systemsbiomed/RepertoiR
BugReports: https://github.com/systemsbiomed/RepertoiR/issues
Imports: circlize, grDevices, igraph, reshape2, stringdist, stringi, stringr
Suggests: testthat (≥ 3.0.0)
Config/testthat/edition: 3
Encoding: UTF-8
RoxygenNote: 7.1.2
NeedsCompilation: no
Packaged: 2021-10-22 09:16:57 UTC; User
Author: Ido Hasson ORCID iD [aut, cre], Sol Efroni [aut], Hagit Philip [aut], Alona Zilberberg [aut]
Maintainer: Ido Hasson <idoh@systemsbiomed.org>
Repository: CRAN
Date/Publication: 2021-10-25 07:00:21 UTC

Visualized for CR Sources

Description

Visualization of Two clones for their convergent recombination (CR) sources. Each sequence (NT) is represented as a colored bar (red for A, yellow for G, blue for T and green for C) linked to its translated amino acid sequence by a colored line, red for the first clone and blue for the second.

Usage

cr_source(clone1, clone2, ...)

Arguments

clone1

First vector of sequences, string-length is the same for each nucleotide sequence ('A', 'G', 'T', 'C').

clone2

Second vector of sequences, same string-length as for the first vector.

...

Any other arguments.

Value

No return value.

Examples

nt <- c("A", "G", "C", "T")
seq_len <- 15
seq_n <- c(12, 7)

# Create data
c1 <- replicate(seq_n[1],
                paste(sample(nt, seq_len, replace = TRUE), collapse = ''))
c2 <- replicate(seq_n[2],
                paste(sample(nt, seq_len, replace = TRUE), collapse = ''))

cr_source(c1, c2)

Visualized for CR Sources

Description

Visualization of Two clones for their convergent recombination (CR) sources. Each sequence (NT) is represented as a colored bar (red for A, yellow for G, blue for T and green for C) linked to its translated amino acid sequence by a colored line, red for the first clone and blue for the second.

Usage

## Default S3 method:
cr_source(clone1, clone2, ...)

Arguments

clone1

First vector of sequences, string-length is the same for each nucleotide sequence ('A', 'G', 'T', 'C').

clone2

Second vector of sequences, same string-length as for the first vector.

...

Any other arguments.

Value

No return value.

Examples

nt <- c("A", "G", "C", "T")
seq_len <- 15
seq_n <- c(12, 7)

# Create data
c1 <- replicate(seq_n[1],
                paste(sample(nt, seq_len, replace = TRUE), collapse = ''))
c2 <- replicate(seq_n[2],
                paste(sample(nt, seq_len, replace = TRUE), collapse = ''))

cr_source(c1, c2)

Sequences distance network

Description

Computes pairwise string distances among repertoire's sequences and visualize similar pairs as connected nodes, each sized by its frequency.

Usage

network(dataset, by, nrow, method, ...)

Arguments

dataset

A matrix or a data frame includes row names which are used as the compared sequences. Data set's numeric values determine node-size.

by

Index of column to set its values as node-size. first column is default (1).

nrow

Number of nodes to display. Default is 1000 nodes.

method

stringdist method to perform for distance dissimilarity calculation: "osa", "lv", "dl", "hamming", "lcs", "qgram", "cosine", "jaccard", "jw", "soundex". Default is Levenshtein distance ("lv").

...

Any additional arguments needed by the specialized methods.

Value

No return value.

Examples


aa <- c(
  "G", "A", "V", "L", "I", "P", "F", "Y", "W", "S",
  "T", "N", "Q", "C", "M", "D", "E", "H", "K", "R"
)
data <- matrix(rexp(1 / 2, n = 1000), ncol = 4)
cons <- sample(aa, 10)
aavec <- c()

while (length(aavec) < nrow(data)) {
  aaseq <- cons
  index <- sample(length(aaseq), sample(length(aaseq) / 3, 1))
  aaseq[index] <- sample(aa, length(index), replace = TRUE)
  aaseq <- paste0(aaseq, collapse = "")
  aavec <- unique(append(aavec, aaseq))
}

rownames(data) <- aavec
colnames(data) <- LETTERS[1:ncol(data)]

network(data, by = 3, nrow = 100)

Sequences distance network

Description

Computes pairwise string distances among repertoire's sequences and visualize similar pairs as connected nodes, each sized by its frequency.

Usage

## Default S3 method:
network(dataset, by = 1, nrow = 1000, method = "lv", ...)

Arguments

dataset

A matrix or a data frame includes row names which are used as the compared sequences. Data set's numeric values determine node-size.

by

Index of column to set its values as node-size. first column is default (1).

nrow

Number of nodes to display. Default is 1000 nodes.

method

stringdist method to perform for distance dissimilarity calculation: "osa", "lv", "dl", "hamming", "lcs", "qgram", "cosine", "jaccard", "jw", "soundex". Default is Levenshtein distance ("lv").

...

Any additional arguments needed by the specialized methods.

Value

No return value.

Examples

aa <- c(
  "G", "A", "V", "L", "I", "P", "F", "Y", "W", "S",
  "T", "N", "Q", "C", "M", "D", "E", "H", "K", "R"
)
data <- matrix(rexp(1 / 2, n = 1000), ncol = 4)
cons <- sample(aa, 10)
aavec <- c()

while (length(aavec) < nrow(data)) {
  aaseq <- cons
  index <- sample(length(aaseq), sample(length(aaseq) / 3, 1))
  aaseq[index] <- sample(aa, length(index), replace = TRUE)
  aaseq <- paste0(aaseq, collapse = "")
  aavec <- unique(append(aavec, aaseq))
}

rownames(data) <- aavec
colnames(data) <- LETTERS[1:ncol(data)]

network(data)

Sunflower repertoire graph

Description

Sequence frequency visualization among samples, displayed as rings of nodes inside each other.

Usage

sunflower(dataset, ...)

Arguments

dataset

Input object: a matrix or a data frame.

First column is located as the outer ring, the second is right after and so on to the last column as the inmost ring. Cell's numeric value determines node size.

...

Any other arguments.

Value

No return value.

Examples

data <- matrix(rexp(400,1/4), ncol = 4)
sunflower(data)

Default graph

Description

Default visualization of sequence frequencies among samples as rings inside each other.

Usage

## Default S3 method:
sunflower(dataset, ...)

Arguments

dataset

Input object: a matrix or a data frame.

First column is located as the outer ring, the second is right after and so on to the last column as the inmost ring. Cell's numeric value determines node size.

...

Any other arguments.

Value

No return value.

Examples

data <- matrix(rexp(400,1/4), ncol = 4)
sunflower(data)