nexonsAnalysis

R functions for analysis and graphical representation of output from nexons

devtools::install_github("laurabiggins/nexonsAnalysis")
library(nexonsAnalysis)

Example data and plot

Using nexons gtf output

Nexons can produce output in 2 different formats. One we will refer to as the “default nexons output”, the other is in gtf format. This package was originally designed to work with the gtf format, but it can also use the default nexons output. The example below using the plot_wrapper() function is a very simple example that does not allow many arguments to be specified and will only work with the nexons gtf file output.

file <- system.file("extdata", "nexons_sirv5_f15.gtf", package = "nexonsAnalysis")
plot_wrapper(file, min_count = 5, quant_plot = TRUE)

FALSE [1] "No ordering of y axis specified"

The above example can be reproduced using the individual functions that plot_wrapper() is a wrapper around. This allows more options to be specified. The order_splices argument in draw_splice_picture accepts one of (one of ‘score’, ‘name’, NULL) default NULL. This refers to the ordering of the splices on the y axis (on the left hand plot), ‘score’ will sort data by score (highest at the top), ‘name’ will sort data alphabetically (with unknowns at the bottom). draw_splice_picture also allows a particular gene to be specified with the gene argument.

file <- system.file("extdata", "nexons_sirv5_f15.gtf", package = "nexonsAnalysis")
nexons_output <- readr::read_delim(file)
parsed_splices <- parse_nexons_gtf(nexons_output, min_count = 3)
draw_splice_picture(parsed_splices, quant = TRUE, order_splices = "score", gene="SIRV5")

Using default nexons output

For one sample at a time

file <- system.file("extdata", "sirv5.txt", package = "nexonsAnalysis")
nexons_output <- readr::read_delim(file)
parsed_splices <- parse_default_nexons(nexons_output, score_column = "seqs_sirv5_minimap.sam")
draw_splice_picture(parsed_splices, quant = TRUE, order_splices = "name")

For one sample - not supplying score_column

file <- system.file("extdata", "sirv5.txt", package = "nexonsAnalysis")
nexons_output <- readr::read_delim(file)
parsed_splices <- parse_default_nexons(nexons_output)
draw_splice_picture(parsed_splices, quant = FALSE, order_splices = "name")

For multiple samples

Use purrr::map to iterate over multiple datasets.

file <- system.file("extdata", "sirv5.txt", package = "nexonsAnalysis")
nexons_output <- readr::read_delim(file)
# get names of datasets - there are 2 in this file
count_columns <- tail(colnames(nexons_output), n=2)
parsed_splices <- purrr::map(count_columns, parse_default_nexons, nexons_output=nexons_output)
p <- purrr::map(parsed_splices, draw_splice_picture, quant=TRUE, order_splices = "score")

Adding titles to the plots

file <- system.file("extdata", "sirv5.txt", package = "nexonsAnalysis")
nexons_output <- readr::read_delim(file)
# get names of datasets - there are 2 in this file
count_columns <- tail(colnames(nexons_output), n=2)
parsed_splices <- purrr::map(count_columns, parse_default_nexons, nexons_output=nexons_output) |>
  purrr::set_names(count_columns)
p <- purrr::imap(parsed_splices, ~ draw_splice_picture(.x, title_text=.y, quant=TRUE, order_splices = "score"))

Flagging up potentially truncated reads

The function identifyPotentialTruncations identifies whether transcripts might be truncations of other transcripts. It adds an additional column to the tibble named truncation_origin. The flexibility parameter refers to the difference in location (number of bases) to still be classified as a match. For exact matches set flexibility to 0. Default is 10.

file <- system.file("extdata", "nexons_sirv5_f15_trunc.txt", package = "nexonsAnalysis")
nexons_output <- readr::read_delim(file)
parsed_splices <- parse_nexons_gtf(nexons_output, min_count = 3)
parsed_with_trunc <- identifyPotentialTruncations(parsed_splices, flexibility = 10)
DT::datatable(parsed_with_trunc)

draw_splice_picture(parsed_splices, quant = TRUE, order_splices = "name")

Flagging up truncations

This also works with the default nexons output as well as the gtf output.

file <- system.file("extdata", "sirv5.txt", package = "nexonsAnalysis")
nexons_output <- readr::read_delim(file)
parsed_splices <- parse_default_nexons(nexons_output, score_column = "seqs_sirv5_minimap.sam")
parsed_default_with_trunc <- identifyPotentialTruncations(parsed_splices, flexibility = 10)
DT::datatable(parsed_default_with_trunc)