The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

l1kdeconv: Deconvolution for LINCS L1000 Data

LINCS L1000 is a high-throughput technology that allows the gene expression measurement in a large number of assays. However, to fit the measurements of ~1000 genes in the ~500 color channels of LINCS L1000, every two landmark genes are designed to share a single channel. Thus, a deconvolution step is required to infer the expression values of each gene. Any errors in this step can be propagated adversely to the downstream analyses. We present a LINCS L1000 data peak calling R package l1kdeconv based on a new outlier detection method and an aggregate Gaussian mixture model. Upon the remove of outliers and the borrowing information among similar samples, l1kdeconv shows more stable and better performance than methods commonly used in LINCS L1000 data deconvolution.

Version: 1.2.0
Depends: R (≥ 3.2.0)
Imports: stats, mixtools, ggplot2
Published: 2017-07-08
DOI: 10.32614/CRAN.package.l1kdeconv
Author: Zhao Li[aut], Peng Yu[aut, cre]
Maintainer: Zhao Li <lizhao.informatics at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: l1kdeconv results

Documentation:

Reference manual: l1kdeconv.pdf

Downloads:

Package source: l1kdeconv_1.2.0.tar.gz
Windows binaries: r-devel: l1kdeconv_1.2.0.zip, r-release: l1kdeconv_1.2.0.zip, r-oldrel: l1kdeconv_1.2.0.zip
macOS binaries: r-release (arm64): l1kdeconv_1.2.0.tgz, r-oldrel (arm64): l1kdeconv_1.2.0.tgz, r-release (x86_64): l1kdeconv_1.2.0.tgz, r-oldrel (x86_64): l1kdeconv_1.2.0.tgz
Old sources: l1kdeconv archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=l1kdeconv to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.