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bioc

bumphunter:Bump Hunter

Tools for finding bumps in genomic data

Maintained by Tamilselvi Guharaj. Last updated 5 months ago.

dnamethylationepigeneticsinfrastructuremultiplecomparisonimmunooncology

16 stars 11.61 score 210 scripts 43 dependents

henrikbengtsson

PSCBS:Analysis of Parent-Specific DNA Copy Numbers

Segmentation of allele-specific DNA copy number data and detection of regions with abnormal copy number within each parental chromosome. Both tumor-normal paired and tumor-only analyses are supported.

Maintained by Henrik Bengtsson. Last updated 1 years ago.

acghcopynumbervariantssnpmicroarrayonechanneltwochannelgenetics

7 stars 7.63 score 34 scripts 9 dependents

bioc

katdetectr:Detection, Characterization and Visualization of Kataegis in Sequencing Data

Kataegis refers to the occurrence of regional hypermutation and is a phenomenon observed in a wide range of malignancies. Using changepoint detection katdetectr aims to identify putative kataegis foci from common data-formats housing genomic variants. Katdetectr has shown to be a robust package for the detection, characterization and visualization of kataegis.

Maintained by Daan Hazelaar. Last updated 5 months ago.

wholegenomesoftwaresnpsequencingclassificationvariantannotation

5 stars 5.00 score 4 scripts

bioc

deltaGseg:deltaGseg

Identifying distinct subpopulations through multiscale time series analysis

Maintained by Diana Low. Last updated 5 months ago.

proteomicstimecoursevisualizationclustering

3.30 score 2 scripts

dazzimonti

profExtrema:Compute and Visualize Profile Extrema Functions

Computes profile extrema functions for arbitrary functions. If the function is expensive-to-evaluate it computes profile extrema by emulating the function with a Gaussian process (using package 'DiceKriging'). In this case uncertainty quantification on the profile extrema can also be computed. The different plotting functions for profile extrema give the user a tool to better locate excursion sets.

Maintained by Dario Azzimonti. Last updated 5 months ago.

2.70 score 10 scripts

daniellemccool

topdowntimeratio:Top-Down Time Ratio Segmentation for Coordinate Trajectories

Data collected on movement behavior is often in the form of time- stamped latitude/longitude coordinates sampled from the underlying movement behavior. These data can be compressed into a set of segments via the Top- Down Time Ratio Segmentation method described in Meratnia and de By (2004) <doi:10.1007/978-3-540-24741-8_44> which, with some loss of information, can both reduce the size of the data as well as provide corrective smoothing mechanisms to help reduce the impact of measurement error. This is an improvement on the well-known Douglas-Peucker algorithm for segmentation that operates not on the basis of perpendicular distances. Top-Down Time Ratio segmentation allows for disparate sampling time intervals by calculating the distance between locations and segments with respect to time. Provided a trajectory with timestamps, tdtr() returns a set of straight- line segments that can represent the full trajectory. McCool, Lugtig, and Schouten (2022) <doi:10.1007/s11116-022-10328-2> describe this method as implemented here in more detail.

Maintained by Danielle McCool. Last updated 3 years ago.

2.70 score 4 scripts

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