Package 'splicelogic'

splicelogic: differential transcripts to splice events

Description

For more details on the features of splicelogic, read the vignette: browseVignettes(package = "splicelogic")

Author(s)

Maintainer: Beatriz Campillo [email protected] (ORCID)

Authors:

• Beatriz Campillo [email protected] (ORCID)

• Michael Love [email protected] (ORCID)

Other contributors:

• NIH NHGRI [funder]

• Wellcome Trust [funder]

See Also

Useful links:

• https://github.com/thelovelab/splicelogic

• https://thelovelab.github.io/splicelogic

• Report bugs at https://github.com/thelovelab/splicelogic/issues

---

Create mock GRanges data for splicing event testing

Description

Create mock GRanges data for splicing event testing

Usage

create_mock_data(
  n_genes = 1,
  n_tx_per_gene = 2,
  n_exons_per_tx = 5,
  coef_range = c(-1, 1),
  strand = c("+", "-", "*")
)

Arguments

n_genes	Number of genes to simulate
n_tx_per_gene	Number of transcripts per gene. Use 2 or more: the first transcript of each gene is given a negative estimate and the second a positive one, so a single transcript per gene leaves the set all-negative and the ⁠generate_*()⁠ helpers with nothing to modify.
n_exons_per_tx	Number of exons per transcript
coef_range	Range of coefficient values to sample from
strand	Strand to place every transcript on: "+" (default), "-", or "*". On "-" the exon ranks are reversed, so exon_rank 1 is the rightmost exon in genomic coordinates.

Value

A GRanges object with simulated transcripts and exons

Examples

# create mock data with 2 genes, 4 transcripts
# per gene, and 4 exons per transcript
gr <- create_mock_data(n_genes = 2, n_tx_per_gene = 4, n_exons_per_tx = 4)

# the same, on the minus strand
gr_minus <- create_mock_data(n_genes = 2, n_exons_per_tx = 4, strand = "-")

---

Default fill colours for splicelogic event types

Description

One pastel colour per event_type returned by the ⁠find_*()⁠ functions, chosen together with the soft blue and red plot_transcripts() uses for the up- and down-regulated transcripts, so the eleven stay as far apart as pastels allow, including under protanopia and deuteranopia. Pastels leave less room between colours than saturated ones, which is why event exons are also outlined and labelled with their type.

Usage

event_palette()

Value

A character vector of colours, named by event type.

Examples

event_palette()

---

Find splice events from annotated exons

Description

Functions to find different types of alternative splicing events from preprocessed GRanges exon data. Events include skipped exon (se), included exon (ie), mutatualy exclusive exons (mxe), retained intron (ri), spliced intron (si), alternative 5' and 3' splice sites (a5ss / a3ss), and alternative transcription start and end sites (atss / ates).

find_ie() and find_si() are the reverse of find_se() and find_ri(): the same comparison with the up and down transcripts swapped. A spliced intron is an intron of an up transcript that a down transcript retains, i.e. an intron that the up transcript splices out.

find_a5ss() / find_a3ss() do not require either boundary of an exon to be shared with its partner, so an exon that moved both boundaries is reported as an a5ss and an a3ss. A first exon has no acceptor and a last exon has no donor, so those boundaries never produce an a3ss / a5ss; find_atss() / find_ates() report them instead by comparing where each transcript begins and ends. exon_rank runs in transcription order, so this holds on both strands.

When an exon overlaps two or more exons of its partner (a retained or spliced intron), the boundaries facing the intron are left to find_ri() / find_si(); only the outer boundaries of the group are compared. An exon that retains an intron and uses a different outer splice site is therefore reported as both an ri (or si) and an a5ss / a3ss.

Usage

find_se(gr, type = c("over", "boundary", "identical"), inverse = FALSE, ...)

find_skipped_exons(
  gr,
  type = c("over", "boundary", "identical"),
  inverse = FALSE,
  ...
)

find_ie(gr, type = c("over", "boundary", "identical"), ...)

find_included_exons(gr, type = c("over", "boundary", "identical"), ...)

find_mxe(gr, type = c("over", "boundary", "identical"), ...)

find_mutually_exclusive_exons(
  gr,
  type = c("over", "boundary", "identical"),
  ...
)

find_ri(gr, inverse = FALSE)

find_retained_introns(gr, inverse = FALSE)

find_si(gr)

find_spliced_introns(gr)

find_a5ss(gr)

find_alternative_5_prime_splice_sites(gr)

find_a3ss(gr)

find_alternative_3_prime_splice_sites(gr)

find_atss(gr)

find_alternative_transcription_start_sites(gr)

find_ates(gr)

find_alternative_transcription_end_sites(gr)

find_all_events(
  gr,
  type = c("over", "boundary", "identical"),
  verbose = TRUE,
  ...
)

Arguments

gr	A GRanges object with exon annotations, including 'tx_id', 'exon', and 'coef_col' metadata columns and preprocessed with preprocess().
type	How an exon flanking a candidate is matched to an exon of the partner transcript. One of: "over" (default) any overlap, with no coordinate in common required. The most permissive setting. "boundary" the two exons overlap and share the inner boundary of the flank, the splice site facing the candidate: the donor of the upstream flank and the acceptor of the downstream one. On "+" that is the end of the left exon and the start of the right one; on "-" the reverse. The outer boundaries may differ. "identical" the two exons are identical (same start and same end). The strictest setting.
inverse	If TRUE, swaps the up and down transcripts: find_se() then identifies included exons instead of skipped exons, and find_ri() spliced introns instead of retained introns.
...	Further arguments passed to GenomicRanges::findOverlaps() when matching flanking exons, e.g. minoverlap = 5L to require at least 5 bp of overlap. Do not pass select, which changes the return type. maxgap can also be used, to let "over" and "boundary" match exons that do not overlap at all.
verbose	If TRUE, prints progress messages. Default TRUE.

Value

A GRanges object with the detected exon ranges and the following additional metadata columns:

event_type

The type of splicing event detected (e.g. "se", "ie", "mxe", "ri", "si", "a5ss", "a3ss", "atss", "ates").

event_tx_id

Transcript ID of the paired transcript involved in the event.

event_estimate

DTU coefficient of the paired transcript.

event_<col>

One column per name in metadata(gr)$additional_columns, prefixed with event_, carrying the corresponding value from the paired transcript.

find_se(): skipped exons

find_ie(): included exons

find_mxe(): mutually exclusive exons

find_ri(): retained introns

find_si(): spliced introns

find_a5ss(): alternative 5' splice sites

find_a3ss(): alternative 3' splice sites

find_atss(): alternative transcription start sites — first exons that begin transcription at a different coordinate

find_ates(): alternative transcription end sites — last exons that end transcription at a different coordinate

find_all_events(): all detected events

Examples

# make some mock data and run the function
gr <- create_mock_data(n_genes = 2, n_tx_per_gene = 4, n_exons_per_tx = 4) |>
  preprocess(coef_col = "estimate") |>
  generate_se(n_events = 1)

# this should find the skipped exon events we generated
find_se(gr, type = "boundary")

find_ie(gr, type = "boundary")


# detect mutually exclusive exons
gr_mx <- create_mock_data(
  n_genes = 2, n_tx_per_gene = 4, n_exons_per_tx = 4
) |>
  preprocess(coef_col = "estimate") |>
  generate_mxe(n_events = 1)

find_mxe(gr_mx, type = "boundary")


# detect retained introns
gr_ri <- create_mock_data(
  n_genes = 2, n_tx_per_gene = 4, n_exons_per_tx = 4
) |>
  preprocess(coef_col = "estimate") |>
  generate_ri(n_events = 1)

find_ri(gr_ri)

# the same data with the signs flipped: the introns retained above are
# now spliced out by the up transcripts
gr_si <- gr_ri |> dplyr::mutate(estimate = -estimate)
find_si(gr_si)


# detect alternative 5' splice sites
gr_a5 <- create_mock_data(
  n_genes = 2, n_tx_per_gene = 4, n_exons_per_tx = 4
) |>
  preprocess(coef_col = "estimate") |>
  generate_a5ss(n_events = 1)

find_a5ss(gr_a5)


# detect alternative 3' splice sites
gr_a3 <- create_mock_data(
  n_genes = 2, n_tx_per_gene = 4, n_exons_per_tx = 4
) |>
  preprocess(coef_col = "estimate") |>
  generate_a3ss(n_events = 1)
find_a3ss(gr_a3)


gr_tss <- data.frame(
  seqnames = "chr1",
  start = c(1, 21, 41, 5, 21, 41),
  end = c(10, 30, 50, 10, 30, 50),
  strand = "+",
  gene_id = "g1",
  tx_id = rep(c("down", "up"), each = 3),
  exon_rank = rep(seq_len(3), 2),
  estimate = rep(c(-1, 1), each = 3)
) |>
  plyranges::as_granges() |>
  preprocess(coef_col = "estimate")

find_atss(gr_tss)


# detect all event types at once
gr_all <- create_mock_data(
  n_genes = 2, n_tx_per_gene = 4, n_exons_per_tx = 4
) |>
  preprocess(coef_col = "estimate") |>
  generate_se(n_events = 1)

find_all_events(gr_all, type = "boundary", verbose = FALSE)

---

Generate mock splice events in a GRanges object

Description

Functions to introduce specific types of alternative splicing events into mock GRanges data for testing purposes.

Usage

generate_se(gr, n_events = 1)

generate_mxe(gr, n_events = 1)

generate_ri(gr, n_events = 1)

generate_a5ss(gr, n_events = 1)

generate_a3ss(gr, n_events = 1)

generate_atss(gr, n_events = 1, mode = c("shift", "drop"))

generate_ates(gr, n_events = 1, mode = c("shift", "drop"))

Arguments

gr	A GRanges object with metadata columns: 'exon_rank', 'gene_id', 'tx_id', and 'estimate'.
n_events	Number of events to generate
mode	How to move the site: "shift" moves the terminal exon's outer boundary (so it still overlaps its partner), "drop" removes the terminal exon and re-ranks (so the new terminal exon does not overlap its partner at all).

Value

generate_se(): A GRanges object with skipped exon events introduced

generate_mxe(): A GRanges object with mutually exclusive exon events introduced

generate_ri(): A GRanges object with retained intron events introduced

generate_a5ss(): A GRanges object with alternative 5' splice site events introduced

generate_a3ss(): A GRanges object with alternative 3' splice site events introduced

generate_atss(): A GRanges object with alternative transcription start site events introduced

generate_ates(): A GRanges object with alternative transcription end site events introduced

Examples

gr <- create_mock_data(
  n_genes = 2, n_tx_per_gene = 4, n_exons_per_tx = 4
)
generate_se(gr, n_events = 1)


gr <- create_mock_data(
  n_genes = 2, n_tx_per_gene = 4, n_exons_per_tx = 4
)
generate_mxe(gr, n_events = 1)


gr <- create_mock_data(
  n_genes = 2, n_tx_per_gene = 4, n_exons_per_tx = 4
)
generate_ri(gr, n_events = 1)


gr <- create_mock_data(
  n_genes = 2, n_tx_per_gene = 4, n_exons_per_tx = 4
)
generate_a5ss(gr, n_events = 1)


gr <- create_mock_data(
  n_genes = 2, n_tx_per_gene = 4, n_exons_per_tx = 4
)
generate_a3ss(gr, n_events = 1)


gr <- create_mock_data(
  n_genes = 2, n_tx_per_gene = 4, n_exons_per_tx = 4
)
generate_atss(gr, n_events = 1)

# the non-overlapping variant: drop the first exon altogether
generate_atss(gr, n_events = 1, mode = "drop")


gr <- create_mock_data(
  n_genes = 2, n_tx_per_gene = 4, n_exons_per_tx = 4
)
generate_ates(gr, n_events = 1)

---

Extract sequences for GRanges

Description

Given a GRanges, look up the DNA or RNA sequence of each range in a reference genome. This is a simple helper function that rearranges the getSeq arguments and combines it with other helper functions to refer to genomes by name.

Usage

get_seq(gr, genome, as_rna = FALSE)

Arguments

gr	A GRanges object.
genome	Either a character string naming an installed BSgenome package (e.g. "hg38", or "BSgenome.Hsapiens.UCSC.hg38") or a BSgenome object. See ?getBSgenome for details.
as_rna	If TRUE, return an RNAStringSet (T -> U). Default FALSE returns a DNAStringSet.

Value

A DNAStringSet (or RNAStringSet if as_rna = TRUE), one entry per range in gr, in the same order. Assign it onto gr (e.g. gr$seq <- ...) to keep it as a metadata column.

Examples

gr <- GenomicRanges::GRanges(
    "chr1", IRanges::IRanges(start = c(1e6, 1.1e6), width = 50)
  )

  suppressPackageStartupMessages(
    library(Biostrings)
  )

  get_seq(gr, "hg38")
  get_seq(gr, "hg38", as_rna = TRUE)

  set.seed(123)
  gr <- create_mock_data(
    n_genes = 2, n_tx_per_gene = 3, n_exons_per_tx = 6
  ) |>
    generate_se(n_events = 1) |>
    GenomicRanges::shift(50e6) # move mock data
  
  skipped <- find_se(gr)

  suppressPackageStartupMessages(
    library(magrittr)
  )
  # magrittr pipe needed for the `.` placeholder below
  skipped %>% 
    plyranges::flank_upstream(100) %>%
    dplyr::mutate(seq = get_seq(., "hg38"))

---

Plot a per-transcript fold change for the transcripts of splicelogic events

Description

Draws one diverging bar per transcript appearing in tx_id or event_tx_id of events: left of zero for a negative value and right for a positive one. It is the companion to plot_transcripts(): given the same events the two plots share their row order and y limits, so they line up when placed side by side, e.g. with patchwork.

Usage

plot_transcript_lfc(
  events,
  tx_lfc,
  title = NULL,
  lfc_max = NULL,
  lfc_label = "log2 fold change",
  lfc_ends = NULL,
  bar_color = "grey30",
  transcript_label = FALSE
)

Arguments

events	A GRanges returned by find_all_events() or any ⁠find_*()⁠ function.
tx_lfc	Numeric vector named by transcript id, e.g. a log2 fold change from a differential expression analysis. Transcripts not named in it keep their row but get no bar.
title	Plot title. Default NULL.
lfc_max	Half-width of the x axis. NULL (default) uses the largest absolute value in the plot; pass a number to make plots comparable. Values beyond it are clipped at the axis edge.
lfc_label	x-axis title.
lfc_ends	Optional length-2 character vector naming the negative and positive ends of the axis, e.g. c("KD", "WT") for a log2(WT/KD). Default NULL draws no end labels.
bar_color	Colour of the bars. Default "grey30".
transcript_label	Whether to print the transcript ids on the y axis, for use on its own. Default FALSE, as plot_transcripts() already labels the rows.

Value

A ggplot object.

Examples

exons <- data.frame(
  seqnames = "chr1",
  start = c(1, 201, 401, 1, 401),
  end = c(100, 300, 500, 100, 500),
  strand = "+",
  gene_id = "g1",
  tx_id = c("down", "down", "down", "up", "up"),
  exon_rank = c(1, 2, 3, 1, 2),
  estimate = c(-1, -1, -1, 1, 1)
) |>
  plyranges::as_granges() |>
  preprocess(coef_col = "estimate")

events <- find_all_events(exons, verbose = FALSE)
tx_lfc <- c(down = -1.8, up = 0.6)

if (requireNamespace("ggplot2", quietly = TRUE)) {
  plot_transcript_lfc(events, tx_lfc, transcript_label = TRUE)
}

---

Plot the transcripts involved in splicelogic events

Description

Draws one row per transcript appearing in tx_id or event_tx_id of events. Transcripts are coloured by the sign of their estimate (blue when positive, red when negative), and the exons called as events are filled by event type, outlined, and labelled with the type. An exon called as several types at once, e.g. an a5ss and an a3ss when both of its boundaries moved, is filled with the first type and striped diagonally with the others. A bent arrow over the first exon marks where each transcript starts and which way it runs.

Usage

plot_transcripts(
  events,
  exons,
  title = NULL,
  up_color = "#1f77b4",
  down_color = "#d62728",
  event_colors = event_palette(),
  unknown_color = "#999999",
  rescale_introns = TRUE,
  new_intron_length = 50,
  flanking_length = c(50, 50),
  transcript_label = TRUE,
  label_events = TRUE,
  label_size = 2.6,
  legend = TRUE,
  tx_annot = NULL,
  stripe_multi_events = TRUE
)

Arguments

events	A GRanges returned by find_all_events() or any ⁠find_*()⁠ function.
exons	A flat GRanges of exons with a tx_id column holding every transcript in events, e.g. the preprocessed GRanges the events were found in.
title	Plot title. Default NULL.
up_color, down_color	Colours for transcripts with a positive and a negative estimate.
event_colors	Named vector mapping event type to fill colour. Default event_palette().
unknown_color	Fill for event types absent from event_colors.
rescale_introns	Whether gaps between exons are shortened to new_intron_length. Default TRUE.
new_intron_length	Length (bp) of each gap after rescaling. Default 50.
flanking_length	Length (bp) of the region drawn before the first and after the last exon. Default c(50, 50).
transcript_label	Whether to print the transcript id and estimate above each row. Default TRUE.
label_events	Whether to print the event type below each event exon. Default TRUE.
label_size	Size of the event labels. Default 2.6.
legend	Whether to draw the fill legend. Default TRUE.
tx_annot	Optional character vector named by transcript id, appended to that transcript's label (e.g. novel status). Transcripts not named in it keep the plain label.
stripe_multi_events	Whether an exon called as several event types is striped with the types its fill leaves out. Default TRUE; FALSE fills it with the first type alone, and drops the others from the legend.

Details

All events passed in are drawn on one panel, so they must come from a single gene; events from several genes are an error. Subset first, or use plot_transcripts_by_gene() for one plot per gene and plot_transcripts_by_pair() for one plot per transcript pair. A single event, e.g. plot_transcripts(events[1], exons), draws its two transcripts.

A per-transcript value such as a fold change is drawn separately by plot_transcript_lfc(). Given the same events, the two plots share their row order and y limits, so they line up side by side.

Value

A ggplot object.

Examples

# one gene: "down" has three exons, "up" skips the middle one
exons <- data.frame(
  seqnames = "chr1",
  start = c(1, 201, 401, 1, 401),
  end = c(100, 300, 500, 100, 500),
  strand = "+",
  gene_id = "g1",
  tx_id = c("down", "down", "down", "up", "up"),
  exon_rank = c(1, 2, 3, 1, 2),
  estimate = c(-1, -1, -1, 1, 1)
) |>
  plyranges::as_granges() |>
  preprocess(coef_col = "estimate")

events <- find_all_events(exons, verbose = FALSE)

if (requireNamespace("ggplot2", quietly = TRUE)) {
  plot_transcripts(events, exons)
}

---

Plot the transcripts involved in splicelogic events, one plot per gene

Description

Splits events by gene_id and draws each gene with plot_transcripts().

Usage

plot_transcripts_by_gene(events, exons, genes = NULL, title = NULL, ...)

Arguments

events	A GRanges returned by find_all_events() or any ⁠find_*()⁠ function, with a gene_id column.
exons	A flat GRanges of exons with a tx_id column holding every transcript in events, e.g. the preprocessed GRanges the events were found in.
genes	Optional character vector of gene ids to restrict to.
title	Optional prefix for the plot titles, which are otherwise the gene id.
...	Passed to plot_transcripts().

Value

A named list of ggplot objects, one per gene.

Examples

exons <- data.frame(
  seqnames = "chr1",
  start = c(1, 201, 401, 1, 401),
  end = c(100, 300, 500, 100, 500),
  strand = "+",
  gene_id = "g1",
  tx_id = c("down", "down", "down", "up", "up"),
  exon_rank = c(1, 2, 3, 1, 2),
  estimate = c(-1, -1, -1, 1, 1)
) |>
  plyranges::as_granges() |>
  preprocess(coef_col = "estimate")

events <- find_all_events(exons, verbose = FALSE)

if (requireNamespace("ggplot2", quietly = TRUE)) {
  plots <- plot_transcripts_by_gene(events, exons)
  plots[["g1"]]
}

---

Plot the transcripts involved in splicelogic events, one plot per pair

Description

Splits events by transcript pair, so each plot shows two transcripts and every event between them. The pair is unordered, so events reported from either side land in the same plot.

Usage

plot_transcripts_by_pair(events, exons, title = NULL, ...)

Arguments

events	A GRanges returned by find_all_events() or any ⁠find_*()⁠ function. A tx_pair column, if present, is used as the grouping as is.
exons	A flat GRanges of exons with a tx_id column holding every transcript in events, e.g. the preprocessed GRanges the events were found in.
title	Optional prefix for the plot titles, which are otherwise the transcript pair.
...	Passed to plot_transcripts().

Value

A named list of ggplot objects, one per transcript pair.

Examples

exons <- data.frame(
  seqnames = "chr1",
  start = c(1, 201, 401, 1, 401),
  end = c(100, 300, 500, 100, 500),
  strand = "+",
  gene_id = "g1",
  tx_id = c("down", "down", "down", "up", "up"),
  exon_rank = c(1, 2, 3, 1, 2),
  estimate = c(-1, -1, -1, 1, 1)
) |>
  plyranges::as_granges() |>
  preprocess(coef_col = "estimate")

events <- find_all_events(exons, verbose = FALSE)

if (requireNamespace("ggplot2", quietly = TRUE)) {
  plots <- plot_transcripts_by_pair(events, exons)
  plots[["down-up"]]
}

---

Prepare exon ranges from a TxDb and DTU results table

Description

Extracts exon ranges from a TxDb object, merges them with differential transcript usage (DTU) results, and returns a flat GRanges ready for preprocess.

Usage

prepare_exons(
  txdb,
  dtu_table,
  coef_col,
  tx_id_col = "tx_id",
  gene_id_col = "gene_id",
  verbose = TRUE
)

Arguments

txdb	A TxDb object (from GenomicFeatures).
dtu_table	A data.frame or tibble with DTU results. Must contain columns for transcript ID, gene ID, and a coefficient.
coef_col	Column name in dtu_table with the coefficient / effect size values.
tx_id_col	Column name in dtu_table with transcript IDs matching the TxDb transcript names. Default "tx_id".
gene_id_col	Column name in dtu_table with gene IDs. Default "gene_id".
verbose	Whether to print progress messages. Default TRUE.

Value

A GRanges object with metadata columns: gene_id, tx_id, exon_rank, the coefficient column, and any additional columns from dtu_table.

Examples

library(AnnotationHub)
library(AnnotationDbi)
library(GenomicFeatures)
library(tibble)

ah <- AnnotationHub()
txdb <- ah[["AH84134"]] # fly TxDb (Drosophila melanogaster)

# build a simulated DTU table from the TxDb transcripts
txps <- txdb |>
  AnnotationDbi::select(
    keys(txdb, "TXID"), c("TXNAME", "GENEID"), "TXID"
  ) |>
  tibble::as_tibble() |>
  dplyr::select(tx_id = TXNAME, gene_id = GENEID)|>
  dplyr::filter(!is.na(gene_id))

sim_dtu_table <- txps |>
  dplyr::mutate(
    padj = runif(dplyr::n()),
    effect_est = rnorm(dplyr::n())
  )

fly_exons <- prepare_exons(
  txdb, sim_dtu_table, coef_col = "effect_est", verbose = TRUE
)

---

Prepare exons from two transcript partitions

Description

Combines two transcript partitions (up- and down-regulated) and assigns an estimate coefficient: +1 to up and -1 to down. Accepts either GRanges objects or character vectors of transcript IDs (in which case txdb is required to look up exon coordinates). The result is ready to pass to preprocess with coef_col = "estimate".

Usage

prepare_exons_by_partition(
  up,
  down,
  txdb = NULL,
  tx_id_col = "TXNAME",
  verbose = TRUE
)

Arguments

up	A GRanges object or character vector of transcript IDs for the upregulated partition (assigned estimate = +1).
down	A GRanges object or character vector of transcript IDs for the downregulated partition (assigned estimate = -1).
txdb	A TxDb object (from GenomicFeatures). Required when up and down are character vectors of transcript IDs.
tx_id_col	The keytype in txdb matching the transcript IDs in up and down. Default "TXNAME". Only used when up and down are character vectors. See AnnotationDbi::keytypes(txdb) for available options.
verbose	Whether to print progress messages. Default TRUE.

Details

When up and down are GRanges, both must have exon_rank, gene_id, and tx_id metadata columns. Extra columns are kept; if one object lacks a column present in the other, those entries receive NA.

Value

A combined GRanges object with an estimate column (+1 for up, -1 for down), ready for preprocess.

Examples

# GRanges input
gr <- create_mock_data(n_genes = 1, n_tx_per_gene = 4, n_exons_per_tx = 4)
gr <- generate_se(gr, n_events = 1)
gr_down <- gr[gr$estimate < 0]
gr_up <- gr[gr$estimate > 0]
prepare_exons_by_partition(gr_up, gr_down) |>
  preprocess(coef_col = "estimate")

---

Preprocess input GRanges object for splicing event calculation

Description

This function checks that the input is a valid GRanges object with required metadata columns, then adds a unique key, the number of exons per transcript, and an 'internal' flag for each exon.

Usage

preprocess(gr, coef_col, method_string = NULL, additional_columns = NULL)

Arguments

gr	A GRanges object with metadata columns: 'exon_rank', 'gene_id', 'tx_id', 'coef'.
coef_col	The name of the metadata column indicating upregulated (+1) and downregulated (-1) exons.
method_string	The Differential Transcript Usage (DTU) method used to obtain the coef_col, for annotation purposes (optional).
additional_columns	A character vector of metadata column names to record for downstream use. Stored in metadata(result)$additional_columns (optional).

Value

A GRanges object with added 'key', 'nexons', and 'internal' columns.

Examples

# create mock data and run preprocessing
gr <- create_mock_data(n_genes = 2, n_tx_per_gene = 4, n_exons_per_tx = 4) |>
  preprocess(coef_col = "estimate", method_string = "mock_method")

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