STEPS: Selective Trends Evidenced by Penetrance Surge

Description Usage Arguments Details Value Author(s) See Also

Description

This function identifies and prioritize Selective Trends Evidenced by Penetrance Surge in a CGH array series. STEPS is an alternative to the Minimal Common Region (MCR) algorithms, with the aim to identify regions frequently amplified or deleted.

Usage

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  STEPS(segPenetrance, dpen = 2, vpen = 0.8, gpen = 0.3, threshold = NA,
    nested = c("merge", "flag", "none"), digits = 3, chromEnd = FALSE, quiet = FALSE)

Arguments

segPenetrance

A data.frame, as a single element from the list returned by the penetrance function.

dpen

Single numeric value, penalty to apply to penetrance increases.

vpen

Single numeric value, penalty to apply to penetrance differences between wide boundaries.

gpen

Single numeric value, penalty to apply to genomic assymetry.

threshold

Single numeric value, minimum STEPS score to filter results. 0 is the less stringent threshold to use, as negative scores correspond to assymetric STEPS (ascending only on a side). Higher values will return less results (focusing on the most significant ones), however scoring and boundaries of the results will not be impacted.

nested

Single character value, defining how to deal with overlapping STEPS. "merge" will only keep for each set of overlapping STEPS the one with the highest score, "flag" will preserve all the STEPS but add a "nest" column with a distinct ID for each nest, and "none" won't do anything about this.

digits

Single integer value, to be passed to round for score computations.

chromEnd

Single logical value, whether to consider chromosome ending as a penetrance drop or not.

quiet

Single logical value, whether to throw diagnosis messages or not.

Details

When a specific gene alteration induces a cell selection (like in tumors), it leads to different altered fragments from a patient to an other. All these fragments have a region in common : the region containing the selecting gene (the Minimal Common Region). Such patterns can be extracted from the penetrance, as they lead to 'stairway' patterns in specific locations.

This function crawls along the penetrance from every available starting point, computing in both directions a score : a descending step grants the penetrance difference (in percents) while an ascending step penalizes by the penetrance difference multiplied by penalty. In each direction, the maximal score is used as boundary, and a total STEPS score for the starting point is computed as 2 * (leftMax + rightMax) - abs(leftMax - rightMax).

The greatest scores highlight symetric STEPS with high descending paths on both sides.

Value

Returns a subset of segPenetrance with the following additionnal columns :

score

Numeric, the two-side score for the described starting point (see 'Details').

leftBoundary

Integer, position considered as the left boundary of the stairway pattern.

leftScore

Numeric, score for the left side of the STEPS (see 'Details').

rightBoundary

Integer, position considered as the right boundary of the stairway pattern.

rightScore

Numeric, score for the right side of the STEPS (see 'Details').

Author(s)

Sylvain Mareschal

See Also

penetrance, SRA


cghRA documentation built on May 2, 2019, 3:34 a.m.