ascat | This repository provides the ASCAT R package ( v3 | Genomics library

by VanLoo-lab HTML Version: v3.1.2 License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | ascat Summary

ascat is a HTML library typically used in Artificial Intelligence, Genomics applications. ascat has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

This repository provides the ASCAT R package (v3.0) that can be used to infer tumour purity, ploidy and allele-specific copy number profiles. ASCAT is described in detail in: Allele-specific copy number analysis of tumors. Van Loo P et al. PNAS (2010). This repository also contains the code underlying additional publication: Allele-specific multi-sample copy number segmentation. Ross EM, Haase K, Van Loo P & Markowetz F. Bioinformatics (2020).

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Support

ascat has a low active ecosystem.

It has 135 star(s) with 79 fork(s). There are 12 watchers for this library.

It had no major release in the last 12 months.

There are 0 open issues and 99 have been closed. On average issues are closed in 14 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ascat is v3.1.2

Quality

ascat has no bugs reported.

Security

ascat has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

ascat does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

ascat releases are available to install and integrate.

Installation instructions are available. Examples and code snippets are not available.

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ascat Key Features

No Key Features are available at this moment for ascat.

ascat Examples and Code Snippets

No Code Snippets are available at this moment for ascat.

Community Discussions

Trending Discussions on ascat

how to get the y axis in layout stacked bokeh plots again aligned with bokeh version >= 1.1

IndexError: too many indices for array when plot winds netcdf

QUESTION

how to get the y axis in layout stacked bokeh plots again aligned with bokeh version >= 1.1

Asked 2020-Jan-17 at 06:58

I wrote a bokeh based package, to plot cluster heatmaps with as many horizontal and vertical annotation bars as needed.

https://gitlab.com/biotransistor/bokehheat

Everything worked fine till bokeh version 1.1. Since bokeh version 1.1 the horizontal annotation bar align no longer properly. This is a major problem for my package.

Is there a way to get the before version 1.1 behavior back with more recent versions of bokeh? (the latest version is bokeh 1.4.)

This is a code example to show what I mean:

...

ANSWER

Answered 2020-Jan-17 at 06:58

Your best bet with the new layout system is to set a common min_border_left value that is large enough to accommodate either axis.

Source https://stackoverflow.com/questions/59781641

QUESTION

IndexError: too many indices for array when plot winds netcdf

Asked 2017-Dec-03 at 10:13

Hi I am getting the following error when I try to plot winds in matplotlib.

Follow my code:

...

ANSWER

Answered 2017-Dec-03 at 10:13

The variable wind_speed has 4 dimensions, ncdump -h of your file:

short wind_speed(time, depth, latitude, longitude) ;

By reading it as:

Source https://stackoverflow.com/questions/47608101

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install ascat

You can download it from GitHub.

Support

Custom10k, IlluminaASA, IlluminaGSAv3, Illumina109k, IlluminaCytoSNP, IlluminaCytoSNP850k, Illumina610k, Illumina660k, Illumina700k, Illumina1M, Illumina2.5M, IlluminaOmni5, Affy10k, Affy100k, Affy250k_sty, Affy250k_nsp, AffyOncoScan, AffyCytoScanHD, AffySNP6, HumanCNV370quad, HumanCore12, HumanCoreExome24, HumanOmniExpress12 and IlluminaOmniExpressExome. Because arrays have a defined set of SNP probes, with a fairly constant rate of heterozygous probes accross individuals, useful metrics in ascat.predictGermlineGenotypes can be inferred from some cases (with no or very few CN changes). However, sequencing data is subjected to massive variations because of design, coverage and/or artefacts. Therefore, we are not able to provide pre-defined metrics for unmatched sequencing data.

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