outlier_process: Processing outliers

In dasper: Detecting abberant splicing events from RNA-sequencing data

Description

The set of functions prefixed with "outlier_" are used to detect outliers. They are designed to be run after you have extracted your junctions and coverage based features, in the order outlier_detect, outlier_aggregate. Or, alternatively the wrapper function outlier_process can be used to run the 2 functions stated above in one go. For more details of the individual functions, see "Details".

Usage

outlier_aggregate(
  junctions,
  samp_id_col = "samp_id",
  bp_param = BiocParallel::SerialParam()
)

outlier_detect(
  junctions,
  feature_names = c("score", "coverage_score"),
  bp_param = BiocParallel::SerialParam(),
  ...
)

outlier_process(
  junctions,
  feature_names = c("score", "coverage_score"),
  samp_id_col = "samp_id",
  bp_param = BiocParallel::SerialParam(),
  ...
)

Arguments

junctions	junction data as a RangedSummarizedExperiment-class object.
samp_id_col	name of the column in the SummarizedExperiment that details the sample ids.
bp_param	a BiocParallelParam-class instance denoting whether to parallelise the calculating of outlier scores across samples.
feature_names	names of assays in junctions that are to be used as input into the outlier detection model.
...	additional arguments passed to the outlier detection model (isolation forest) for setting parameters.

Details

outlier_process wraps all "outlier_" prefixed functions in dasper. This is designed to simplify processing of the detecting outlier junctions for those familiar or uninterested with the intermediates.

outlier_detect will use the features in assays named feature_names as input into an unsupervised outlier detection algorithm to score each junction based on how outlier-y it looks in relation to other junctions in the patient. The default expected score and coverage_score features can be calculated using the junction_process and coverage_process respectively.

outlier_aggregate will aggregate the outlier scores into a cluster-level. It will then rank each cluster based on this aggregated score and annotate each cluster with it's associated gene and transcript.

Value

DataFrame with one row per cluster detailing each cluster's associated junctions, outlier scores, ranks and genes.

Functions

• outlier_aggregate: Aggregate outlier scores from per junction to cluster-level

• outlier_detect: Detecting outlier junctions

See Also

for more details on the isolation forest model used: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html

Examples

##### Set up txdb #####

# use GenomicState to load txdb (GENCODE v31)
ref <- GenomicState::GenomicStateHub(
    version = "31",
    genome = "hg38",
    filetype = "TxDb"
)[[1]]

##### Set up BigWig #####

# obtain path to example bw on recount2
bw_path <- recount::download_study(
    project = "SRP012682",
    type = "samples",
    download = FALSE
)[[1]]

# cache the bw for speed in later
# examples/testing during R CMD Check
bw_path <- dasper:::.file_cache(bw_path)


##### junction_process #####

junctions_processed <- junction_process(
    junctions_example,
    ref,
    types = c("ambig_gene", "unannotated"),
)

##### coverage_process #####

junctions_w_coverage <- coverage_process(
    junctions_processed,
    ref,
    coverage_paths_case = rep(bw_path, 2),
    coverage_paths_control = rep(bw_path, 3)
)

##### outlier_detect #####

junctions_w_outliers <- outlier_detect(junctions_w_coverage)

##### outlier_aggregate #####

outlier_scores <- outlier_aggregate(junctions_w_outliers)

##### outlier_process #####

# this wrapper will obtain outlier scores identical to those
# obtained through running the individual wrapped functions shown below
outlier_processed <- outlier_process(junctions_w_coverage)

# the two objects are equivalent
all.equal(outlier_processed, outlier_scores, check.attributes = FALSE)

dasper documentation built on Nov. 8, 2020, 7:28 p.m.