predictTxFeatures {SGSeq} | R Documentation |
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)
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)