| Title: | Splice event prediction and quantification from RNA-seq data |
|---|---|
| Description: | SGSeq is a software package for analyzing splice events from RNA-seq data. Input data are RNA-seq reads mapped to a reference genome in BAM format. Genes are represented as a splice graph, which can be obtained from existing annotation or predicted from the mapped sequence reads. Splice events are identified from the graph and are quantified locally using structurally compatible reads at the start or end of each splice variant. The software includes functions for splice event prediction, quantification, visualization and interpretation. |
| Authors: | Leonard Goldstein [cre, aut] |
| Maintainer: | Leonard Goldstein <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.41.0 |
| Built: | 2024-10-31 05:25:02 UTC |
| Source: | https://github.com/bioc/SGSeq |
High-level function for the prediction and quantification of splice junctions, exon bins and splice sites from BAM files.
analyzeFeatures(sample_info, which = NULL, features = NULL, predict = is.null(features), alpha = 2, psi = 0, beta = 0.2, gamma = 0.2, min_junction_count = NULL, min_anchor = 1, min_n_sample = 1, min_overhang = NA, annotation = NULL, max_complexity = 20, verbose = FALSE, cores = 1)analyzeFeatures(sample_info, which = NULL, features = NULL, predict = is.null(features), alpha = 2, psi = 0, beta = 0.2, gamma = 0.2, min_junction_count = NULL, min_anchor = 1, min_n_sample = 1, min_overhang = NA, annotation = NULL, max_complexity = 20, verbose = FALSE, cores = 1)
sample_info |
Data frame with sample information.
Required columns are “sample_name”, “file_bam”,
“paired_end”, “read_length”, “frag_length”
and “lib_size”. Library information can be obtained with
function |
which |
|
features |
|
predict |
Logical indicating whether transcript features should be predicted from BAM files |
alpha |
Minimum FPKM required for a splice junction to be included |
psi |
Minimum splice frequency required for a splice junction to be included |
beta |
Minimum relative coverage required for an internal exon to be included |
gamma |
Minimum relative coverage required for a terminal exon to be included |
min_junction_count |
Minimum fragment count required for a splice
junction to be included. If specified, argument |
min_anchor |
Integer specifiying minimum anchor length |
min_n_sample |
Minimum number of samples a feature must be observed in to be included |
min_overhang |
Minimum overhang required to suppress filtering or
trimming of predicted terminal exons (see the manual page for
|
annotation |
|
max_complexity |
Maximum allowed complexity. If a locus exceeds
this threshold, it is skipped, resulting in a warning.
Complexity is defined as the maximum number of unique predicted
splice junctions overlapping a given position.
High complexity regions are often due to spurious read alignments
and can slow down processing. To disable this filter, set to |
verbose |
If |
cores |
Number of cores available for parallel processing |
Splice junctions and exons are predicted from BAM files with
predictTxFeatures.
Known features can be provided as TxFeatures or
SGFeatures via argument features.
If features is not NULL and predict is
TRUE, known features are augmented with predictions.
Known and/or predicted transcript features are converted to splice
graph features. For details, see convertToSGFeatures.
Optionally, splice graph features can be annotated with respect to
a TxFeatures object provided via argument annotation.
For details, see the help page for function annotate.
Finally, compatible fragment counts for splice graph features are
obtained from BAM files with getSGFeatureCounts.
SGFeatureCounts object
Leonard Goldstein
path <- system.file("extdata", package = "SGSeq") si$file_bam <- file.path(path, "bams", si$file_bam) sgfc <- analyzeFeatures(si, gr)path <- system.file("extdata", package = "SGSeq") si$file_bam <- file.path(path, "bams", si$file_bam) sgfc <- analyzeFeatures(si, gr)
High-level function for the analysis of splice variants from
splice graph features. Splice variants are identified with
findSGVariants. Representative counts are obtained
and variant frequencies estimated with getSGVariantCounts.
analyzeVariants(object, maxnvariant = 20, include = "default", min_denominator = NA, min_anchor = 1, cores = 1)analyzeVariants(object, maxnvariant = 20, include = "default", min_denominator = NA, min_anchor = 1, cores = 1)
object |
|
maxnvariant |
If more than |
include |
Character string indicating whether identified splice variants should be filtered. Possible options are “default” (only include variants for events with all variants closed), “closed” (only include closed variants) and “all” (include all variants). |
min_denominator |
Integer specifying minimum denominator when
calculating variant frequencies. The total number of boundary-spanning
reads must be equal to or greater than |
min_anchor |
Integer specifiying minimum anchor length |
cores |
Number of cores available for parallel processing |
SGVariantCounts object
Leonard Goldstein
sgvc <- analyzeVariants(sgfc_pred)sgvc <- analyzeVariants(sgfc_pred)
Features in query are assigned transcript names and gene names
of structurally compatible features in subject (see below).
If a feature in query does not match any features in
subject, its geneName inherits from connected annotated features.
annotate(query, subject)annotate(query, subject)
query |
|
subject |
|
Feature matching is performed as follows: Query splice junctions are matched with identical subject splice junctions. Query splice sites are matched with splice sites implied by subject splice junctions. Query exon bins are matched with overlapping subject exons. Spliced boundaries of query exon bins must match spliced subject exon boundaries. Query exon bins cannot extend across spliced subject exon boundaries.
query with updated txName, geneName
column slots
Leonard Goldstein
sgf_annotated <- annotate(sgf_pred, txf_ann) sgv_annotated <- annotate(sgv_pred, txf_ann)sgf_annotated <- annotate(sgf_pred, txf_ann) sgv_annotated <- annotate(sgv_pred, txf_ann)
Functions counts and FPKM are used to extract counts and
FPKM values from SGFeatureCounts and SGVariantCounts
objects. Function variantFreq is used to access relative usage
estimates from SGVariantCounts objects.
FPKM(object, ...) FPKM(object, ...) <- value variantFreq(object) variantFreq(object) <- value ## S4 method for signature 'SGFeatureCounts' counts(object) ## S4 replacement method for signature 'SGFeatureCounts' counts(object) <- value ## S4 method for signature 'SGFeatureCounts' FPKM(object) ## S4 replacement method for signature 'SGFeatureCounts' FPKM(object) <- value ## S4 method for signature 'SGVariantCounts' counts(object, ...) ## S4 replacement method for signature 'SGVariantCounts' counts(object, ...) <- value ## S4 method for signature 'SGVariantCounts' FPKM(object, ...) ## S4 method for signature 'SGVariantCounts' variantFreq(object) ## S4 replacement method for signature 'SGVariantCounts' variantFreq(object) <- valueFPKM(object, ...) FPKM(object, ...) <- value variantFreq(object) variantFreq(object) <- value ## S4 method for signature 'SGFeatureCounts' counts(object) ## S4 replacement method for signature 'SGFeatureCounts' counts(object) <- value ## S4 method for signature 'SGFeatureCounts' FPKM(object) ## S4 replacement method for signature 'SGFeatureCounts' FPKM(object) <- value ## S4 method for signature 'SGVariantCounts' counts(object, ...) ## S4 replacement method for signature 'SGVariantCounts' counts(object, ...) <- value ## S4 method for signature 'SGVariantCounts' FPKM(object, ...) ## S4 method for signature 'SGVariantCounts' variantFreq(object) ## S4 replacement method for signature 'SGVariantCounts' variantFreq(object) <- value
object |
Object containing assay data |
... |
Arguments passed to method for |
value |
Replacement value |
Assay data for accessor functions or updated object for replacement functions.
Leonard Goldstein
x <- counts(sgfc_pred) y <- FPKM(sgfc_pred) u <- counts(sgvc_pred, option = "variant5p") v <- FPKM(sgvc_pred, option = "variant5p")x <- counts(sgfc_pred) y <- FPKM(sgfc_pred) u <- counts(sgvc_pred, option = "variant5p") v <- FPKM(sgvc_pred, option = "variant5p")
Convert transcript features (predicted from RNA-seq data or extracted from transcript annotation) to splice graph features.
convertToSGFeatures(x, coerce = FALSE)convertToSGFeatures(x, coerce = FALSE)
x |
|
coerce |
Logical indicating whether transcript features should be coerced to splice graph features without disjoining exons and omitting splice donor and acceptor sites |
Splice junctions are unaltered. Exons are disjoined into non-overlapping exon bins. Adjacent exon bins without a splice site at the shared boundary are merged.
Entries for splice donor and acceptor sites (positions immediately upstream and downstream of introns, respectively) are added.
In the returned SGFeatures object, column type takes
values “J” (splice junction), “E” (exon bin),
“D” (splice donor) or “A” (splice acceptor).
Columns splice5p and splice3p indicate mandatory
splices at the 5' and 3' end of exon bins, respectively
(determining whether reads overlapping exon boundaries must be
spliced at the boundary to be considered compatible).
splice5p (splice3p) is TRUE if the first (last)
position of the exon coincides with a splice acceptor (donor)
and it is not adjacent to a neighboring exon bin.
Each feature is assigned a unique feature and gene identifier,
stored in columns featureID and geneID,
respectively. The latter indicates features that belong to the
same gene, represented by a connected component in the splice graph.
SGFeatures object
Leonard Goldstein
sgf <- convertToSGFeatures(txf_ann)sgf <- convertToSGFeatures(txf_ann)
Convert a TxDb object or a GRangesList of exons
grouped by transcripts to a TxFeatures object.
convertToTxFeatures(x)convertToTxFeatures(x)
x |
|
If x is a GRangesList, transcript names and gene names
can be specified as character vectors in metadata
columns txName and geneName, respectively.
If missing, transcript names are based on names(x).
For import from GFF format, use function importTranscripts.
In the returned TxFeatures object, column type takes
values “J” (splice junction), “I” (internal exon),
“F” (5'/first exon), “L” (3'/last exon) or “U”
(unspliced).
TxFeatures object
Leonard Goldstein
gr <- GRanges(c(1, 1), IRanges(c(1, 201), c(100, 300)), c("+", "+")) grl <- split(gr, 1) txf <- convertToTxFeatures(grl)gr <- GRanges(c(1, 1), IRanges(c(1, 201), c(100, 300)), c("+", "+")) grl <- split(gr, 1) txf <- convertToTxFeatures(grl)
Export features to BED format. Splice sites are not included.
exportFeatures(features, file)exportFeatures(features, file)
features |
|
file |
Character string specifying output file |
NULL
Leonard Goldstein
## Not run: exportFeatures(txf_pred, "txf.bed") exportFeatures(sgf_pred, "sgf.bed") ## End(Not run) NULL## Not run: exportFeatures(txf_pred, "txf.bed") exportFeatures(sgf_pred, "sgf.bed") ## End(Not run) NULL
Identify splice variants from splice graph.
findSGVariants(features, maxnvariant = 20, annotate_events = TRUE, include = c("default", "closed", "all"), cores = 1)findSGVariants(features, maxnvariant = 20, annotate_events = TRUE, include = c("default", "closed", "all"), cores = 1)
features |
|
maxnvariant |
If more than |
annotate_events |
Logical indicating whether identified
splice variants should be annotated in terms of canonical events.
For details see help page for |
include |
Character string indicating whether identified splice variants should be filtered. Possible options are “default” (only include variants for events with all variants closed), “closed” (only include closed variants) and “all” (include all variants). |
cores |
Number of cores available for parallel processing |
SGVariants object
Leonard Goldstein
sgv <- findSGVariants(sgf_pred)sgv <- findSGVariants(sgf_pred)
Obtain paired-end status, median aligned read length, median aligned insert size and library size from BAM files.
getBamInfo(sample_info, yieldSize = NULL, cores = 1)getBamInfo(sample_info, yieldSize = NULL, cores = 1)
sample_info |
Data frame with sample information including
mandatory columns “sample_name” and “file_bam”.
Column “sample_name” must be a character vector. Column
“file_bam” can be a character vector or |
yieldSize |
Number of records used for obtaining library
information, or |
cores |
Number of cores available for parallel processing |
BAM files must have been generated with a splice-aware alignment program that outputs the custom tag ‘XS’ for spliced reads, indicating the direction of transcription. BAM files must be indexed.
Library information can be inferred from a subset of BAM records
by setting the number of records via argument yieldSize.
Note that library size is only obtained if yieldSize is NULL.
sample_info with additional columns “paired_end”,
“read_length”, “frag_length”, and “lib_size”
if yieldSize is NULL
Leonard Goldstein
path <- system.file("extdata", package = "SGSeq") si$file_bam <- file.path(path, "bams", si$file_bam) ## data.frame as sample_info and character vector as file_bam si <- si[, c("sample_name", "file_bam")] si_complete <- getBamInfo(si) ## DataFrame as sample_info and BamFileList as file_bam DF <- DataFrame(si) DF$file_bam <- BamFileList(DF$file_bam) DF_complete <- getBamInfo(DF)path <- system.file("extdata", package = "SGSeq") si$file_bam <- file.path(path, "bams", si$file_bam) ## data.frame as sample_info and character vector as file_bam si <- si[, c("sample_name", "file_bam")] si_complete <- getBamInfo(si) ## DataFrame as sample_info and BamFileList as file_bam DF <- DataFrame(si) DF$file_bam <- BamFileList(DF$file_bam) DF_complete <- getBamInfo(DF)
Compatible counts are obtained for each sample and combined into
an SGFeatureCounts object.
getSGFeatureCounts(sample_info, features, min_anchor = 1, counts_only = FALSE, verbose = FALSE, cores = 1)getSGFeatureCounts(sample_info, features, min_anchor = 1, counts_only = FALSE, verbose = FALSE, cores = 1)
sample_info |
Data frame with sample information.
Required columns are “sample_name”, “file_bam”,
“paired_end”, “read_length”, “frag_length”
and “lib_size”. Library information can be obtained with
function |
features |
|
min_anchor |
Integer specifiying minimum anchor length |
counts_only |
Logical indicating only counts should be returned |
verbose |
If |
cores |
Number of cores available for parallel processing |
codeSGFeatureCounts object, or integer matrix of counts
if counts_only = TRUE
Leonard Goldstein
path <- system.file("extdata", package = "SGSeq") si$file_bam <- file.path(path, "bams", si$file_bam) sgfc <- getSGFeatureCounts(si, sgf_pred)path <- system.file("extdata", package = "SGSeq") si$file_bam <- file.path(path, "bams", si$file_bam) sgfc <- getSGFeatureCounts(si, sgf_pred)
For splice variants, obtain counts of compatible fragments spanning
the start and/or end of each variant.
Counts can be obtained from an SGFeatureCounts object
or from BAM files. Only one of the two arguments feature_counts
or sample_info must be specified. Local estimates of relative
usage are calculated at the start and/or end of each splice variant.
For splice variants with relative usage estimates at both start and end,
these are combined by taking a weighted mean, where weights are
proportional to the total number of reads spanning the respective
boundary.
getSGVariantCounts(variants, feature_counts = NULL, sample_info = NULL, min_denominator = NA, min_anchor = 1, verbose = FALSE, cores = 1)getSGVariantCounts(variants, feature_counts = NULL, sample_info = NULL, min_denominator = NA, min_anchor = 1, verbose = FALSE, cores = 1)
variants |
|
feature_counts |
|
sample_info |
Data frame with sample information.
Required columns are “sample_name”, “file_bam”,
“paired_end”, “read_length”, “frag_length”
and “lib_size”. Library information can be obtained with
function |
min_denominator |
Integer specifying minimum denominator when
calculating variant frequencies. The total number of boundary-spanning
reads must be equal to or greater than |
min_anchor |
Integer specifiying minimum anchor length |
verbose |
If |
cores |
Number of cores available for parallel processing |
SGVariantCounts object
Leonard Goldstein
sgvc_from_sgfc <- getSGVariantCounts(sgv_pred, sgfc_pred) path <- system.file("extdata", package = "SGSeq") si$file_bam <- file.path(path, "bams", si$file_bam) sgvc_from_bam <- getSGVariantCounts(sgv_pred, sample_info = si)sgvc_from_sgfc <- getSGVariantCounts(sgv_pred, sgfc_pred) path <- system.file("extdata", package = "SGSeq") si$file_bam <- file.path(path, "bams", si$file_bam) sgvc_from_bam <- getSGVariantCounts(sgv_pred, sample_info = si)
Import GFF file and generate a GRangesList of transcripts
suitable as input for functions convertToTxFeatures or
predictVariantEffects.
importTranscripts(file, tag_tx = "transcript_id", tag_gene = "gene_id")importTranscripts(file, tag_tx = "transcript_id", tag_gene = "gene_id")
file |
Character string specifying input GFF file |
tag_tx |
GFF attribute tag for transcript identifier |
tag_gene |
GFF attribute tag for gene identifier |
GRangesList of exons grouped by transcipts with
metadata columns txName, geneName, cdsStart, cdsEnd.
Leonard Goldstein
## Not run: tx <- importTranscripts(file) ## End(Not run) NULL## Not run: tx <- importTranscripts(file) ## End(Not run) NULL
SGFeatureCounts objectCreate SGFeatureCounts object from rowRanges, colData and counts.
makeSGFeatureCounts(rowRanges, colData, counts, min_anchor = 1)makeSGFeatureCounts(rowRanges, colData, counts, min_anchor = 1)
rowRanges |
|
colData |
Data frame with sample information |
counts |
Integer matrix of counts |
min_anchor |
Integer specifiying minimum anchor length |
SGFeatureCounts object
Leonard Goldstein
sgfc <- makeSGFeatureCounts(sgf_pred, si, matrix(0L, length(sgf_pred), nrow(si)))sgfc <- makeSGFeatureCounts(sgf_pred, si, matrix(0L, length(sgf_pred), nrow(si)))
Merge features, typically after feature prediction in multiple samples.
mergeTxFeatures(..., min_n_sample = 1)mergeTxFeatures(..., min_n_sample = 1)
... |
one or more |
min_n_sample |
Minimum number of samples a feature must be observed in to be included |
Merged features are the union of splice junctions and internal exons. For terminal exons with shared spliced boundary, the longest exon is retained.
TxFeatures object with merged features
Leonard Goldstein
txf_merged <- mergeTxFeatures(txf_ann, txf_pred)txf_merged <- mergeTxFeatures(txf_ann, txf_pred)
Plot read coverage and splice junction read counts for an individual sample or averaged across samples.
plotCoverage(x, geneID = NULL, geneName = NULL, eventID = NULL, which = NULL, sample_info = NULL, sizefactor = NA, toscale = c("exon", "none", "gene"), color = "darkblue", ylim = NULL, label = NULL, nbin = 200, summary = mean, curvature = 1, main = NULL, min_anchor = 1, cores = 1)plotCoverage(x, geneID = NULL, geneName = NULL, eventID = NULL, which = NULL, sample_info = NULL, sizefactor = NA, toscale = c("exon", "none", "gene"), color = "darkblue", ylim = NULL, label = NULL, nbin = 200, summary = mean, curvature = 1, main = NULL, min_anchor = 1, cores = 1)
x |
|
geneID |
Single gene identifier used to subset |
geneName |
Single gene name used to subset |
eventID |
Single event identifier used to subset |
which |
|
sample_info |
Data frame with sample information.
If |
sizefactor |
Numeric vector with length equal to the number of
samples in |
toscale |
Controls which parts of the splice graph are drawn to scale. Possible values are “none” (exonic and intronic regions have constant length), “exon” (exonic regions are drawn to scale) and “gene” (both exonic and intronic regions are drawn to scale). |
color |
Color used for plotting coverages |
ylim |
Numeric vector of length two, determining y-axis range used for plotting coverages. |
label |
Optional y-axis label |
nbin |
Number of bins for plotting coverages |
summary |
Function used to calculate per-bin coverage summaries |
curvature |
Numeric determining curvature of plotted splice junctions. |
main |
Plot title |
min_anchor |
Integer specifiying minimum anchor length |
cores |
Number of cores available for parallel processing. |
data.frame with information on splice junctions included
in the splice graph
Leonard Goldstein
## Not run: par(mfrow = c(4, 1)) for (j in seq_len(4)) plotCoverage(sgfc_pred[, j]) ## End(Not run) NULL## Not run: par(mfrow = c(4, 1)) for (j in seq_len(4)) plotCoverage(sgfc_pred[, j]) ## End(Not run) NULL
Plot splice graph and heatmap of expression values.
plotFeatures(x, geneID = NULL, geneName = NULL, which = NULL, tx_view = FALSE, cex = 1, assay = "FPKM", include = c("junctions", "exons", "both"), transform = function(x) { log2(x + 1) }, Rowv = NULL, distfun = dist, hclustfun = hclust, margin = 0.2, RowSideColors = NULL, square = FALSE, cexRow = 1, cexCol = 1, labRow = colnames(x), col = colorRampPalette(c("black", "gold"))(256), zlim = NULL, heightPanels = c(1, 2), ...)plotFeatures(x, geneID = NULL, geneName = NULL, which = NULL, tx_view = FALSE, cex = 1, assay = "FPKM", include = c("junctions", "exons", "both"), transform = function(x) { log2(x + 1) }, Rowv = NULL, distfun = dist, hclustfun = hclust, margin = 0.2, RowSideColors = NULL, square = FALSE, cexRow = 1, cexCol = 1, labRow = colnames(x), col = colorRampPalette(c("black", "gold"))(256), zlim = NULL, heightPanels = c(1, 2), ...)
x |
|
geneID |
Single gene identifier used to subset |
geneName |
Single gene name used to subset |
which |
|
tx_view |
Plot transcripts instead of splice graph (experimental) |
cex |
Scale parameter for feature labels and annotation |
assay |
Name of assay to be plotted in the heatmap |
include |
Include “exons”, “junctions” or “both” in the heatmap |
transform |
Transformation applied to assay data |
Rowv |
Determines order of rows. Either a vector of values used to
reorder rows, or |
distfun |
Distance function used for hierarchical clustering of rows (samples) |
hclustfun |
Clustering function used for hierarchical clustering of rows (samples) |
margin |
Width of right-hand margin as fraction of width of the
graphics device. Ignored if |
RowSideColors |
Character vector (or list of character vectors)
with length(s) equal to |
square |
Logical, if |
cexRow |
Scale factor for row (sample) labels |
cexCol |
Scale factor for column (feature) labels |
labRow |
Character vector of row (sample) labels |
col |
Heatmap colors |
zlim |
Range of values for which colors should be plotted,
if |
heightPanels |
Numeric vector of length two indicating height of the top and bottom panels. |
... |
further arguments passed to |
data.frame with information on exon bins and
splice junctions included in the splice graph
Leonard Goldstein
## Not run: sgfc_annotated <- annotate(sgfc_pred, txf_ann) plotFeatures(sgfc_annotated) ## End(Not run) NULL## Not run: sgfc_annotated <- annotate(sgfc_pred, txf_ann) plotFeatures(sgfc_annotated) ## End(Not run) NULL
Plot the splice graph implied by splice junctions and exon bins.
Invisibly returns a data.frame with details of plotted
features, including genomic coordinates.
plotSpliceGraph(x, geneID = NULL, geneName = NULL, eventID = NULL, which = NULL, toscale = c("exon", "none", "gene"), label = c("id", "name", "label", "none"), color = "gray", color_novel = color, color_alpha = 0.8, color_labels = FALSE, border = "fill", curvature = NULL, ypos = c(0.5, 0.1), score = NULL, score_color = "darkblue", score_ylim = NULL, score_ypos = c(0.3, 0.1), score_nbin = 200, score_summary = mean, score_label = NULL, ranges = NULL, ranges_color = "darkblue", ranges_ypos = c(0.1, 0.1), main = NULL, tx_view = FALSE, tx_dist = 0.2, short_output = TRUE)plotSpliceGraph(x, geneID = NULL, geneName = NULL, eventID = NULL, which = NULL, toscale = c("exon", "none", "gene"), label = c("id", "name", "label", "none"), color = "gray", color_novel = color, color_alpha = 0.8, color_labels = FALSE, border = "fill", curvature = NULL, ypos = c(0.5, 0.1), score = NULL, score_color = "darkblue", score_ylim = NULL, score_ypos = c(0.3, 0.1), score_nbin = 200, score_summary = mean, score_label = NULL, ranges = NULL, ranges_color = "darkblue", ranges_ypos = c(0.1, 0.1), main = NULL, tx_view = FALSE, tx_dist = 0.2, short_output = TRUE)
x |
|
geneID |
Single gene identifier used to subset |
geneName |
Single gene name used to subset |
eventID |
Single event identifier used to subset |
which |
|
toscale |
Controls which parts of the splice graph are drawn to scale. Possible values are “none” (exonic and intronic regions have constant length), “exon” (exonic regions are drawn to scale) and “gene” (both exonic and intronic regions are drawn to scale). |
label |
Format of exon/splice junction labels, possible values are “id” (format E1,... J1,...), “name” (format type:chromosome:start-end:strand), “label” for labels specified in metadata column “label”, or “none” for no labels. |
color |
Color used for plotting the splice graph. Ignored if features
metadata column “color” is not |
color_novel |
Features with missing annotation are
highlighted in |
color_alpha |
Controls color transparency |
color_labels |
Logical indicating whether label colors should be the same as feature colors |
border |
Determines the color of exon borders, can be “fill” (same as exon color), “none” (no border), or a valid color name |
curvature |
Numeric determining curvature of plotted splice junctions. |
ypos |
Numeric vector of length two, indicating the vertical
position and height of the exon bins in the splice graph,
specificed as fraction of the height of the plotting region
(not supported for |
score |
|
score_color |
Color used for plotting scores |
score_ylim |
Numeric vector of length two, determining y-axis range for plotting scores |
score_ypos |
Numeric vector of length two, indicating the vertical position and height of the score panel, specificed as fraction of the height of the plotting region |
score_nbin |
Number of bins for plotting scores |
score_summary |
Function used to calculate per-bin score summaries |
score_label |
Label used to annotate score panel |
ranges |
|
ranges_color |
Color used for plotting ranges |
ranges_ypos |
Numeric vector of length two, indicating the vertical position and height of the ranges panel, specificed as fraction of the height of the plotting region |
main |
Plot title |
tx_view |
Plot transcripts instead of splice graph (experimental) |
tx_dist |
Vertical distance between transcripts as fraction of height of plotting region |
short_output |
Logical indicating whether the returned data frame should only include information that is likely useful to the user |
By default, the color of features in the splice graph is
determined by annotation status (see arguments color,
color_novel) and feature labels are generated automatically
(see argument label). Alternatively, colors and labels can
be specified via metadata columns “color” and
“label”, respectively.
data.frame with information on exon bins and
splice junctions included in the splice graph
Leonard Goldstein
## Not run: sgf_annotated <- annotate(sgf_pred, txf_ann) plotSpliceGraph(sgf_annotated) ## End(Not run) ## Not run: sgv_annotated <- annotate(sgv_pred, txf_ann) plotSpliceGraph(sgv_annotated) ## End(Not run) NULL## Not run: sgf_annotated <- annotate(sgf_pred, txf_ann) plotSpliceGraph(sgf_annotated) ## End(Not run) ## Not run: sgv_annotated <- annotate(sgv_pred, txf_ann) plotSpliceGraph(sgv_annotated) ## End(Not run) NULL
Plot splice graph and heatmap of splice variant frequencies.
plotVariants(x, eventID = NULL, tx_view = FALSE, cex = 1, transform = function(x) { x }, Rowv = NULL, distfun = dist, hclustfun = hclust, margin = 0.2, RowSideColors = NULL, square = FALSE, cexRow = 1, cexCol = 1, labRow = colnames(x), col = colorRampPalette(c("black", "gold"))(256), zlim = c(0, 1), heightPanels = c(1, 2), expand_variants = FALSE, ...)plotVariants(x, eventID = NULL, tx_view = FALSE, cex = 1, transform = function(x) { x }, Rowv = NULL, distfun = dist, hclustfun = hclust, margin = 0.2, RowSideColors = NULL, square = FALSE, cexRow = 1, cexCol = 1, labRow = colnames(x), col = colorRampPalette(c("black", "gold"))(256), zlim = c(0, 1), heightPanels = c(1, 2), expand_variants = FALSE, ...)
x |
|
eventID |
Single event identifier used to subset |
tx_view |
Plot transcripts instead of splice graph (experimental) |
cex |
Scale parameter for feature labels and annotation |
transform |
Transformation applied to splice variant frequencies |
Rowv |
Determines order of rows. Either a vector of values used to
reorder rows, or |
distfun |
Distance function used for hierarchical clustering of rows (samples) |
hclustfun |
Clustering function used for hierarchical clustering of rows (samples) |
margin |
Width of right-hand margin as fraction of width of the
graphics device. Ignored if |
RowSideColors |
Character vector (or list of character vectors)
with length(s) equal to |
square |
Logical, if |
cexRow |
Scale factor for row (sample) labels |
cexCol |
Scale factor for column (feature) labels |
labRow |
Character vector of row (sample) labels |
col |
Heatmap colors |
zlim |
Range of values for which colors should be plotted,
if |
heightPanels |
Numeric vector of length two indicating height of the top and bottom panels. |
expand_variants |
Experimental option - leave set to |
... |
further arguments passed to |
data.frame with information on exon bins and
splice junctions included in the splice graph
Leonard Goldstein
## Not run: sgvc_annotated <- annotate(sgvc_pred, txf_ann) plotVariants(sgvc_annotated) ## End(Not run) NULL## Not run: sgvc_annotated <- annotate(sgvc_pred, txf_ann) plotVariants(sgvc_annotated) ## End(Not run) NULL
Splice junctions and exons are predicted for each sample and merged
across samples. Terminal exons are filtered and trimmed, if applicable.
For details, see the help pages for
predictTxFeaturesPerSample, mergeTxFeatures,
and processTerminalExons.
predictTxFeatures(sample_info, which = NULL, alpha = 2, psi = 0, beta = 0.2, gamma = 0.2, min_junction_count = NULL, min_anchor = 1, max_complexity = 20, min_n_sample = 1, min_overhang = NA, verbose = FALSE, cores = 1)predictTxFeatures(sample_info, which = NULL, alpha = 2, psi = 0, beta = 0.2, gamma = 0.2, min_junction_count = NULL, min_anchor = 1, max_complexity = 20, min_n_sample = 1, min_overhang = NA, verbose = FALSE, cores = 1)
sample_info |
Data frame with sample information.
Required columns are “sample_name”, “file_bam”,
“paired_end”, “read_length”, “frag_length”
and “lib_size”. Library information can be obtained with
function |
which |
|
alpha |
Minimum FPKM required for a splice junction to be
included. Internally, FPKMs are converted to counts, requiring arguments
|
psi |
Minimum splice frequency required for a splice junction to be included |
beta |
Minimum relative coverage required for an internal exon to be included |
gamma |
Minimum relative coverage required for a terminal exon to be included |
min_junction_count |
Minimum fragment count required for a splice
junction to be included. If specified, argument |
min_anchor |
Integer specifiying minimum anchor length |
max_complexity |
Maximum allowed complexity. If a locus exceeds
this threshold, it is skipped, resulting in a warning.
Complexity is defined as the maximum number of unique predicted
splice junctions overlapping a given position.
High complexity regions are often due to spurious read alignments
and can slow down processing. To disable this filter, set to |
min_n_sample |
Minimum number of samples a feature must be observed in to be included |
min_overhang |
Minimum overhang required to suppress filtering or
trimming of predicted terminal exons (see the manual page for
|
verbose |
If |
cores |
Number of cores available for parallel processing |
TxFeatures object
Leonard Goldstein
path <- system.file("extdata", package = "SGSeq") si$file_bam <- file.path(path, "bams", si$file_bam) txf <- predictTxFeatures(si, gr)path <- system.file("extdata", package = "SGSeq") si$file_bam <- file.path(path, "bams", si$file_bam) txf <- predictTxFeatures(si, gr)
The effect of a splice variant is predicted for individual protein-coding transcripts.
predictVariantEffects(sgv, tx, genome, fix_start_codon = TRUE, output = c("short", "full"), cores = 1)predictVariantEffects(sgv, tx, genome, fix_start_codon = TRUE, output = c("short", "full"), cores = 1)
sgv |
|
tx |
|
genome |
|
fix_start_codon |
Logical indicating whether the annotated start codon should be considered fixed and the variant transcript should not be scanned for alternative start codons |
output |
Character string indicating whether short results or full results (with additional columns) should be returned |
cores |
Number of cores available for parallel processing |
data.frame with rows corresponding to a
variant-transcript pair. The output includes columns for variant
identifier, transcript name, gene name, type of alteration at the
RNA and protein level, and variant description at the RNA and
protein level in HGVS notation. For output = "full"
additional columns are returned. These include the full-length RNA
and protein sequence for the reference and variant transcript.
Event start and end coordinates in the full output are 0- and
1-based, respectively (to allow for description of deletions).
Coordinates for the last junction in a transcript refer to the
last base of the second-to-last exon.
Leonard Goldstein
require(BSgenome.Hsapiens.UCSC.hg19) seqlevelsStyle(Hsapiens) <- "NCBI" predictVariantEffects(sgv_pred, tx, Hsapiens)require(BSgenome.Hsapiens.UCSC.hg19) seqlevelsStyle(Hsapiens) <- "NCBI" predictVariantEffects(sgv_pred, tx, Hsapiens)
Predicted terminal exons are processed as described under Details.
processTerminalExons(features, min_overhang = NA)processTerminalExons(features, min_overhang = NA)
features |
|
min_overhang |
Minimum overhang required to suppress filtering or
trimming of predicted terminal exons (see Details). Use |
Processing of terminal exon predictions is done in two steps: (1) terminal exons that share a splice site with an internal exon are filtered, and (2) remaining terminal exons that overlap other exons are trimmed.
predictTxFeatures predicts flanking terminal exons for each
identified splice junction. This ensures that each splice junction
has a flanking exon after merging with mergeTxFeatures.
This approach results in many predicted terminal exons that
share a splice site with predicted internal exons (often contained
within them or with a short overhang due to incorrect alignments).
Most of these are not real terminal exons and are filtered before
further analysis. Filtering based on the overhang is controlled with
argument min_overhang.
Some of the remaining predicted terminal exons overlap other exons
such that their unspliced boundary shows a short overhang with
respect to a spliced boundary of the overlapping exon. Often these
exon extensions into an intron are due to incorrect alignments.
Terminal exons with overhang smaller than min_overhang are
trimmed such that their trimmmed unspliced boundary coincides with
the spliced boundary of the overlapping exon.
TxFeatures object with processed features
Leonard Goldstein
txf_processed <- processTerminalExons(txf_ann)txf_processed <- processTerminalExons(txf_ann)
Creates an instance of S4 class SGFeatureCounts for storing
compatible splice graph feature counts.
SGFeatureCounts(x)SGFeatureCounts(x)
x |
|
SGFeatureCounts object
Leonard Goldstein
sgfc <- SGFeatureCounts()sgfc <- SGFeatureCounts()
Creates an instance of S4 class SGFeatures for storing
splice graph features.
SGFeatures(x, type = mcols(x)$type, splice5p = mcols(x)$splice5p, splice3p = mcols(x)$splice3p, featureID = mcols(x)$featureID, geneID = mcols(x)$geneID, txName = mcols(x)$txName, geneName = mcols(x)$geneName)SGFeatures(x, type = mcols(x)$type, splice5p = mcols(x)$splice5p, splice3p = mcols(x)$splice3p, featureID = mcols(x)$featureID, geneID = mcols(x)$geneID, txName = mcols(x)$txName, geneName = mcols(x)$geneName)
x |
|
type |
Character vector or factor taking value |
splice5p |
Logical vector indicating a mandatory splice at the 5' end of an exon bin (determining whether reads extending across the 5' boundary must be spliced to be considered compatible) |
splice3p |
Logical vector indicating a mandatory splice at the 3' end of an exon bin (determining whether reads extending across the 3' boundary must be spliced to be considered compatible) |
featureID |
Integer vector of feature IDs |
geneID |
Integer vector of gene IDs |
txName |
|
geneName |
|
SGFeatures extends GRanges with column slot type
specifying feature type. type is a factor with levels
J (splice junction), E (exon bin),
D (splice donor), A (splice acceptor).
splice5p and splice3p are logical vectors indicating
mandatory splices at the 5' and 3' end of an exon bin, respectively.
These are used to determine whether reads extending across the
5' and 3' boundaries of an exon bin must be spliced at the boundary
to be considered compatible with the exon bin.
featureID and geneID are integer vectors representing
unique identifiers for features and genes (connected components in
the splice graph).
txName and geneName are CharacterLists storing
transcript and gene annotation, respectively.
SGFeatures object
Leonard Goldstein
sgf <- SGFeatures()sgf <- SGFeatures()
Creates an instance of S4 class SGVariantCounts for storing
splice variant counts.
SGVariantCounts(x)SGVariantCounts(x)
x |
|
SGVariantCounts object
Leonard Goldstein
sgvc <- SGVariantCounts()sgvc <- SGVariantCounts()
Creates an instance of S4 class SGVariants for storing
splice variants.
SGVariants(x)SGVariants(x)
x |
|
SGVariants includes columns as described below.
from and to indicate the variant start and end,
respectively. from nodes are splice donors (“D”)
or transcript starts (“S”). to nodes are splice
acceptors (“A”) or transcript ends (“E”).
type and featureID describe the variant in
terms of the splice graph features that make up the variant.
segmentID specifies unique identifiers labelling
unbranched segments of the splice graph.
closed5p indicates whether nodes in the variant can be
reached from nodes outside of the variant exclusively through the
from node.
closed3p indicates whether nodes in the variant can reach
nodes outside of the variant exclusively through the to node.
closed5pEvent indicates whether nodes in the event can
be reached from nodes outside of the event exclusively through the
from node.
closed3pEvent indicates whether nodes in the event can
reach nodes outside of the event exclusively through the to
node.
geneID has the same interpretation as for
SGFeatures.
eventID and variantID are unique identifiers for
each event and variant, respectively.
featureID5p and featureID3p indicate representative
features used for variant quantification at the start and end of the
variant, respectively.
featureID5pEvent and featureID3pEvent indicate the
ensemble of representative features at the start and end of the event,
respectively.
txName indicates structurally compatible transcripts.
geneName behaves as for SGFeatures.
variantType indicates whether a splice variant is
consistent with a canonical splice event (for a list of possible
values, see the manual page for annotateSGVariants).
variantName provides a unique name for each splice variant
(for details, see the manual page for makeVariantNames).
SGVariants object
Leonard Goldstein
sgv <- SGVariants()sgv <- SGVariants()
Accessor and replacement functions for metadata columns.
type(x) <- value txName(x) txName(x) <- value geneName(x) geneName(x) <- value featureID(x) featureID(x) <- value geneID(x) geneID(x) <- value splice5p(x) splice5p(x) <- value splice3p(x) splice3p(x) <- value from(x) <- value to(x) <- value segmentID(x) segmentID(x) <- value variantID(x) variantID(x) <- value eventID(x) eventID(x) <- value closed5p(x) closed5p(x) <- value closed3p(x) closed3p(x) <- value closed5pEvent(x) closed5pEvent(x) <- value closed3pEvent(x) closed3pEvent(x) <- value variantType(x) variantType(x) <- value variantName(x) variantName(x) <- value featureID5p(x) featureID5p(x) <- value featureID3p(x) featureID3p(x) <- value featureID5pEvent(x) featureID5pEvent(x) <- value featureID3pEvent(x) featureID3pEvent(x) <- value ## S4 method for signature 'Features' type(x) ## S4 method for signature 'Paths' type(x) ## S4 method for signature 'Counts' type(x) ## S4 replacement method for signature 'Features' type(x) <- value ## S4 replacement method for signature 'Paths' type(x) <- value ## S4 replacement method for signature 'Counts' type(x) <- value ## S4 method for signature 'Features' txName(x) ## S4 method for signature 'Paths' txName(x) ## S4 method for signature 'Counts' txName(x) ## S4 replacement method for signature 'Features' txName(x) <- value ## S4 replacement method for signature 'Paths' txName(x) <- value ## S4 replacement method for signature 'Counts' txName(x) <- value ## S4 method for signature 'Features' geneName(x) ## S4 method for signature 'Paths' geneName(x) ## S4 method for signature 'Counts' geneName(x) ## S4 replacement method for signature 'Features' geneName(x) <- value ## S4 replacement method for signature 'Paths' geneName(x) <- value ## S4 replacement method for signature 'Counts' geneName(x) <- value ## S4 method for signature 'SGFeatures' featureID(x) ## S4 method for signature 'Paths' featureID(x) ## S4 method for signature 'Counts' featureID(x) ## S4 replacement method for signature 'SGFeatures' featureID(x) <- value ## S4 replacement method for signature 'Paths' featureID(x) <- value ## S4 replacement method for signature 'Counts' featureID(x) <- value ## S4 method for signature 'SGFeatures' geneID(x) ## S4 method for signature 'Paths' geneID(x) ## S4 method for signature 'Counts' geneID(x) ## S4 replacement method for signature 'SGFeatures' geneID(x) <- value ## S4 replacement method for signature 'Paths' geneID(x) <- value ## S4 replacement method for signature 'Counts' geneID(x) <- value ## S4 method for signature 'SGFeatures' splice5p(x) ## S4 method for signature 'SGSegments' splice5p(x) ## S4 method for signature 'SGFeatureCounts' splice5p(x) ## S4 replacement method for signature 'SGFeatures' splice5p(x) <- value ## S4 replacement method for signature 'SGSegments' splice5p(x) <- value ## S4 replacement method for signature 'SGFeatureCounts' splice5p(x) <- value ## S4 method for signature 'SGFeatures' splice3p(x) ## S4 method for signature 'SGSegments' splice3p(x) ## S4 method for signature 'SGFeatureCounts' splice3p(x) ## S4 replacement method for signature 'SGFeatures' splice3p(x) <- value ## S4 replacement method for signature 'SGSegments' splice3p(x) <- value ## S4 replacement method for signature 'SGFeatureCounts' splice3p(x) <- value ## S4 method for signature 'Paths' segmentID(x) ## S4 method for signature 'SGVariantCounts' segmentID(x) ## S4 replacement method for signature 'Paths' segmentID(x) <- value ## S4 replacement method for signature 'SGVariantCounts' segmentID(x) <- value ## S4 method for signature 'Paths' from(x) ## S4 method for signature 'SGVariantCounts' from(x) ## S4 replacement method for signature 'Paths' from(x) <- value ## S4 replacement method for signature 'SGVariantCounts' from(x) <- value ## S4 method for signature 'Paths' to(x) ## S4 method for signature 'SGVariantCounts' to(x) ## S4 replacement method for signature 'Paths' to(x) <- value ## S4 replacement method for signature 'SGVariantCounts' to(x) <- value ## S4 method for signature 'SGVariants' eventID(x) ## S4 method for signature 'SGVariantCounts' eventID(x) ## S4 replacement method for signature 'SGVariants' eventID(x) <- value ## S4 replacement method for signature 'SGVariantCounts' eventID(x) <- value ## S4 method for signature 'SGVariants' variantID(x) ## S4 method for signature 'SGVariantCounts' variantID(x) ## S4 replacement method for signature 'SGVariants' variantID(x) <- value ## S4 replacement method for signature 'SGVariantCounts' variantID(x) <- value ## S4 method for signature 'SGVariants' closed5p(x) ## S4 method for signature 'SGVariantCounts' closed5p(x) ## S4 replacement method for signature 'SGVariants' closed5p(x) <- value ## S4 replacement method for signature 'SGVariantCounts' closed5p(x) <- value ## S4 method for signature 'SGVariants' closed3p(x) ## S4 method for signature 'SGVariantCounts' closed3p(x) ## S4 replacement method for signature 'SGVariants' closed3p(x) <- value ## S4 replacement method for signature 'SGVariantCounts' closed3p(x) <- value ## S4 method for signature 'SGVariants' closed5pEvent(x) ## S4 method for signature 'SGVariantCounts' closed5pEvent(x) ## S4 replacement method for signature 'SGVariants' closed5pEvent(x) <- value ## S4 replacement method for signature 'SGVariantCounts' closed5pEvent(x) <- value ## S4 method for signature 'SGVariants' closed3pEvent(x) ## S4 method for signature 'SGVariantCounts' closed3pEvent(x) ## S4 replacement method for signature 'SGVariants' closed3pEvent(x) <- value ## S4 replacement method for signature 'SGVariantCounts' closed3pEvent(x) <- value ## S4 method for signature 'SGVariants' variantName(x) ## S4 method for signature 'SGVariantCounts' variantName(x) ## S4 replacement method for signature 'SGVariants' variantName(x) <- value ## S4 replacement method for signature 'SGVariantCounts' variantName(x) <- value ## S4 method for signature 'SGVariants' variantType(x) ## S4 method for signature 'SGVariantCounts' variantType(x) ## S4 replacement method for signature 'SGVariants' variantType(x) <- value ## S4 replacement method for signature 'SGVariantCounts' variantType(x) <- value ## S4 method for signature 'SGVariants' featureID5p(x) ## S4 method for signature 'SGVariantCounts' featureID5p(x) ## S4 replacement method for signature 'SGVariants' featureID5p(x) <- value ## S4 replacement method for signature 'SGVariantCounts' featureID5p(x) <- value ## S4 method for signature 'SGVariants' featureID3p(x) ## S4 method for signature 'SGVariantCounts' featureID3p(x) ## S4 replacement method for signature 'SGVariants' featureID3p(x) <- value ## S4 replacement method for signature 'SGVariantCounts' featureID3p(x) <- value ## S4 method for signature 'SGVariants' featureID5pEvent(x) ## S4 method for signature 'SGVariantCounts' featureID5pEvent(x) ## S4 replacement method for signature 'SGVariants' featureID5pEvent(x) <- value ## S4 replacement method for signature 'SGVariantCounts' featureID5pEvent(x) <- value ## S4 method for signature 'SGVariants' featureID3pEvent(x) ## S4 method for signature 'SGVariantCounts' featureID3pEvent(x) ## S4 replacement method for signature 'SGVariants' featureID3pEvent(x) <- value ## S4 replacement method for signature 'SGVariantCounts' featureID3pEvent(x) <- valuetype(x) <- value txName(x) txName(x) <- value geneName(x) geneName(x) <- value featureID(x) featureID(x) <- value geneID(x) geneID(x) <- value splice5p(x) splice5p(x) <- value splice3p(x) splice3p(x) <- value from(x) <- value to(x) <- value segmentID(x) segmentID(x) <- value variantID(x) variantID(x) <- value eventID(x) eventID(x) <- value closed5p(x) closed5p(x) <- value closed3p(x) closed3p(x) <- value closed5pEvent(x) closed5pEvent(x) <- value closed3pEvent(x) closed3pEvent(x) <- value variantType(x) variantType(x) <- value variantName(x) variantName(x) <- value featureID5p(x) featureID5p(x) <- value featureID3p(x) featureID3p(x) <- value featureID5pEvent(x) featureID5pEvent(x) <- value featureID3pEvent(x) featureID3pEvent(x) <- value ## S4 method for signature 'Features' type(x) ## S4 method for signature 'Paths' type(x) ## S4 method for signature 'Counts' type(x) ## S4 replacement method for signature 'Features' type(x) <- value ## S4 replacement method for signature 'Paths' type(x) <- value ## S4 replacement method for signature 'Counts' type(x) <- value ## S4 method for signature 'Features' txName(x) ## S4 method for signature 'Paths' txName(x) ## S4 method for signature 'Counts' txName(x) ## S4 replacement method for signature 'Features' txName(x) <- value ## S4 replacement method for signature 'Paths' txName(x) <- value ## S4 replacement method for signature 'Counts' txName(x) <- value ## S4 method for signature 'Features' geneName(x) ## S4 method for signature 'Paths' geneName(x) ## S4 method for signature 'Counts' geneName(x) ## S4 replacement method for signature 'Features' geneName(x) <- value ## S4 replacement method for signature 'Paths' geneName(x) <- value ## S4 replacement method for signature 'Counts' geneName(x) <- value ## S4 method for signature 'SGFeatures' featureID(x) ## S4 method for signature 'Paths' featureID(x) ## S4 method for signature 'Counts' featureID(x) ## S4 replacement method for signature 'SGFeatures' featureID(x) <- value ## S4 replacement method for signature 'Paths' featureID(x) <- value ## S4 replacement method for signature 'Counts' featureID(x) <- value ## S4 method for signature 'SGFeatures' geneID(x) ## S4 method for signature 'Paths' geneID(x) ## S4 method for signature 'Counts' geneID(x) ## S4 replacement method for signature 'SGFeatures' geneID(x) <- value ## S4 replacement method for signature 'Paths' geneID(x) <- value ## S4 replacement method for signature 'Counts' geneID(x) <- value ## S4 method for signature 'SGFeatures' splice5p(x) ## S4 method for signature 'SGSegments' splice5p(x) ## S4 method for signature 'SGFeatureCounts' splice5p(x) ## S4 replacement method for signature 'SGFeatures' splice5p(x) <- value ## S4 replacement method for signature 'SGSegments' splice5p(x) <- value ## S4 replacement method for signature 'SGFeatureCounts' splice5p(x) <- value ## S4 method for signature 'SGFeatures' splice3p(x) ## S4 method for signature 'SGSegments' splice3p(x) ## S4 method for signature 'SGFeatureCounts' splice3p(x) ## S4 replacement method for signature 'SGFeatures' splice3p(x) <- value ## S4 replacement method for signature 'SGSegments' splice3p(x) <- value ## S4 replacement method for signature 'SGFeatureCounts' splice3p(x) <- value ## S4 method for signature 'Paths' segmentID(x) ## S4 method for signature 'SGVariantCounts' segmentID(x) ## S4 replacement method for signature 'Paths' segmentID(x) <- value ## S4 replacement method for signature 'SGVariantCounts' segmentID(x) <- value ## S4 method for signature 'Paths' from(x) ## S4 method for signature 'SGVariantCounts' from(x) ## S4 replacement method for signature 'Paths' from(x) <- value ## S4 replacement method for signature 'SGVariantCounts' from(x) <- value ## S4 method for signature 'Paths' to(x) ## S4 method for signature 'SGVariantCounts' to(x) ## S4 replacement method for signature 'Paths' to(x) <- value ## S4 replacement method for signature 'SGVariantCounts' to(x) <- value ## S4 method for signature 'SGVariants' eventID(x) ## S4 method for signature 'SGVariantCounts' eventID(x) ## S4 replacement method for signature 'SGVariants' eventID(x) <- value ## S4 replacement method for signature 'SGVariantCounts' eventID(x) <- value ## S4 method for signature 'SGVariants' variantID(x) ## S4 method for signature 'SGVariantCounts' variantID(x) ## S4 replacement method for signature 'SGVariants' variantID(x) <- value ## S4 replacement method for signature 'SGVariantCounts' variantID(x) <- value ## S4 method for signature 'SGVariants' closed5p(x) ## S4 method for signature 'SGVariantCounts' closed5p(x) ## S4 replacement method for signature 'SGVariants' closed5p(x) <- value ## S4 replacement method for signature 'SGVariantCounts' closed5p(x) <- value ## S4 method for signature 'SGVariants' closed3p(x) ## S4 method for signature 'SGVariantCounts' closed3p(x) ## S4 replacement method for signature 'SGVariants' closed3p(x) <- value ## S4 replacement method for signature 'SGVariantCounts' closed3p(x) <- value ## S4 method for signature 'SGVariants' closed5pEvent(x) ## S4 method for signature 'SGVariantCounts' closed5pEvent(x) ## S4 replacement method for signature 'SGVariants' closed5pEvent(x) <- value ## S4 replacement method for signature 'SGVariantCounts' closed5pEvent(x) <- value ## S4 method for signature 'SGVariants' closed3pEvent(x) ## S4 method for signature 'SGVariantCounts' closed3pEvent(x) ## S4 replacement method for signature 'SGVariants' closed3pEvent(x) <- value ## S4 replacement method for signature 'SGVariantCounts' closed3pEvent(x) <- value ## S4 method for signature 'SGVariants' variantName(x) ## S4 method for signature 'SGVariantCounts' variantName(x) ## S4 replacement method for signature 'SGVariants' variantName(x) <- value ## S4 replacement method for signature 'SGVariantCounts' variantName(x) <- value ## S4 method for signature 'SGVariants' variantType(x) ## S4 method for signature 'SGVariantCounts' variantType(x) ## S4 replacement method for signature 'SGVariants' variantType(x) <- value ## S4 replacement method for signature 'SGVariantCounts' variantType(x) <- value ## S4 method for signature 'SGVariants' featureID5p(x) ## S4 method for signature 'SGVariantCounts' featureID5p(x) ## S4 replacement method for signature 'SGVariants' featureID5p(x) <- value ## S4 replacement method for signature 'SGVariantCounts' featureID5p(x) <- value ## S4 method for signature 'SGVariants' featureID3p(x) ## S4 method for signature 'SGVariantCounts' featureID3p(x) ## S4 replacement method for signature 'SGVariants' featureID3p(x) <- value ## S4 replacement method for signature 'SGVariantCounts' featureID3p(x) <- value ## S4 method for signature 'SGVariants' featureID5pEvent(x) ## S4 method for signature 'SGVariantCounts' featureID5pEvent(x) ## S4 replacement method for signature 'SGVariants' featureID5pEvent(x) <- value ## S4 replacement method for signature 'SGVariantCounts' featureID5pEvent(x) <- value ## S4 method for signature 'SGVariants' featureID3pEvent(x) ## S4 method for signature 'SGVariantCounts' featureID3pEvent(x) ## S4 replacement method for signature 'SGVariants' featureID3pEvent(x) <- value ## S4 replacement method for signature 'SGVariantCounts' featureID3pEvent(x) <- value
x |
Object containing metadata column |
value |
Replacement value |
S4 classes defined in the SGSeq package contain metadata columns
that store information for each element in the object. For example, class
TxFeatures contains a column type that indicates feature
type. The specific columns contained in an object depend on its class.
Content of metadata column for accessor functions or updated object for replacement functions.
Leonard Goldstein
head(type(txf_ann)) head(type(sgf_ann))head(type(txf_ann)) head(type(sgf_ann))
Creates an instance of S4 class TxFeatures for storing
transcript features.
TxFeatures(x, type = mcols(x)$type, txName = mcols(x)$txName, geneName = mcols(x)$geneName)TxFeatures(x, type = mcols(x)$type, txName = mcols(x)$txName, geneName = mcols(x)$geneName)
x |
|
type |
Character vector or factor, taking value
|
txName |
|
geneName |
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TxFeatures extends GRanges with column slot type
specifying feature type. type is a factor with levels
J (splice junction), I (internal exon),
F (5' terminal exon), L (3' terminal exon),
U (unspliced transcript).
txName and geneName are CharacterLists storing
transcript and gene annotation, respectively.
TxFeatures object
Leonard Goldstein
gr <- GRanges(1, IRanges(101, 200), "+") txf <- TxFeatures(gr, type = "J")gr <- GRanges(1, IRanges(101, 200), "+") txf <- TxFeatures(gr, type = "J")
Update object created with previous version of SGSeq.
## S4 method for signature 'SGVariants' updateObject(object, ..., verbose = FALSE) ## S4 method for signature 'SGVariantCounts' updateObject(object, ..., verbose = FALSE)## S4 method for signature 'SGVariants' updateObject(object, ..., verbose = FALSE) ## S4 method for signature 'SGVariantCounts' updateObject(object, ..., verbose = FALSE)
object |
Object to be updated |
... |
Additional arguments |
verbose |
Should a warning message be generated |
Updated object
Leonard Goldstein