,
Sol Efroni [aut],
Hagit Philip [aut],
Alona Zilberberg [aut]| Title: | Repertoire Graphical Visualization |
|---|---|
| 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:>. |
| Authors: | Ido Hasson [aut, cre] ,
Sol Efroni [aut],
Hagit Philip [aut],
Alona Zilberberg [aut] |
| Maintainer: | Ido Hasson <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.0.1 |
| Built: | 2025-02-20 03:29:16 UTC |
| Source: | https://github.com/systemsbiomed/repertoir |
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.
cr_source(clone1, clone2, ...)cr_source(clone1, clone2, ...)
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. |
No return value.
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)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)
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.
## Default S3 method: cr_source(clone1, clone2, ...)## Default S3 method: cr_source(clone1, clone2, ...)
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. |
No return value.
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)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)
Computes pairwise string distances among repertoire's sequences and visualize similar pairs as connected nodes, each sized by its frequency.
network(dataset, by, nrow, method, ...)network(dataset, by, nrow, method, ...)
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. |
No return value.
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)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)
Computes pairwise string distances among repertoire's sequences and visualize similar pairs as connected nodes, each sized by its frequency.
## Default S3 method: network(dataset, by = 1, nrow = 1000, method = "lv", ...)## Default S3 method: network(dataset, by = 1, nrow = 1000, method = "lv", ...)
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. |
No return value.
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)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)
Sequence frequency visualization among samples, displayed as rings of nodes inside each other.
sunflower(dataset, ...)sunflower(dataset, ...)
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. |
No return value.
data <- matrix(rexp(400,1/4), ncol = 4) sunflower(data)data <- matrix(rexp(400,1/4), ncol = 4) sunflower(data)
Default visualization of sequence frequencies among samples as rings inside each other.
## Default S3 method: sunflower(dataset, ...)## Default S3 method: sunflower(dataset, ...)
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. |
No return value.
data <- matrix(rexp(400,1/4), ncol = 4) sunflower(data)data <- matrix(rexp(400,1/4), ncol = 4) sunflower(data)