Package: l1kdeconv 1.2.0
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.
Authors:
l1kdeconv_1.2.0.tar.gz
l1kdeconv_1.2.0.zip(r-4.7-any)l1kdeconv_1.2.0.zip(r-4.6-any)l1kdeconv_1.2.0.zip(r-4.5-any)
l1kdeconv_1.2.0.tgz(r-4.6-any)l1kdeconv_1.2.0.tgz(r-4.5-any)
l1kdeconv_1.2.0.tar.gz(r-4.7-any)l1kdeconv_1.2.0.tar.gz(r-4.6-any)
l1kdeconv_1.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
l1kdeconv/json (API)
| # Install 'l1kdeconv' in R: |
| install.packages('l1kdeconv', repos = c('https://zhaoli2017.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:463499db63. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 145 | ||
| source / vignettes | OK | 173 | ||
| linux-release | OK | 134 | ||
| macos-release | OK | 217 | ||
| macos-oldrel | OK | 202 | ||
| windows-devel | OK | 82 | ||
| windows-release | OK | 90 | ||
| windows-oldrel | OK | 105 | ||
| wasm-release | OK | 105 |
Exports:getclusterrangesgmmplotmultigmmmanydatamultigmmplotmultigmmsamedistribumultigmmsamedistribulikmultigmmsamedistribumultirmoutlier1dsplitgrp
Dependencies:askpassbase64encbslibcachemclicpp11crosstalkcurldata.tabledigestdplyrevaluatefarverfastmapfontawesomefsgenericsggplot2gluegtablehighrhtmltoolshtmlwidgetshttrisobandjquerylibjsonlitekernlabknitrlabelinglaterlatticelazyevallifecyclemagrittrMASSMatrixmemoisemimemixtoolsnlmeopensslotelpillarpkgconfigplotlypromisespurrrR6rappdirsRColorBrewerRcpprlangrmarkdownS7sassscalessegmentedstringistringrsurvivalsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Get the Cluster Ranges in a Vector of 1D Coordinates | getclusterranges |
| Plot the Fit Results of 2-Component Gaussian Mixture Model | gmmplot |
| Split the input dataset into several sub list to deconvolution. | multigmmmanydata |
| Plot the Fit Results of aggregate 2-Component Gaussian Mixture Model | multigmmplot |
| Fit Multi 2-Component Gaussian Mixture Model in same distribution with a Fixed Proportion | multigmmsamedistribu |
| The sum of Log-Likelihoods of 1D Multi Same Distribution Gaussian Mixture Model | multigmmsamedistribulik |
| Split the input dataset into several sub list to deconvolution. | multigmmsamedistribumulti |
| Remove the Outliers in a Vector of 1D Coordinates | rmoutlier1d |
| Split a list with size n into groups with at least m elements | splitgrp |