Package: SPECK 1.0.0

Azka Javaid

SPECK: Receptor Abundance Estimation using Reduced Rank Reconstruction and Clustered Thresholding

Surface Protein abundance Estimation using CKmeans-based clustered thresholding ('SPECK') is an unsupervised learning-based method that performs receptor abundance estimation for single cell RNA-sequencing data based on reduced rank reconstruction (RRR) and a clustered thresholding mechanism. Seurat's normalization method is described in: Hao et al., (2021) <doi:10.1016/j.cell.2021.04.048>, Stuart et al., (2019) <doi:10.1016/j.cell.2019.05.031>, Butler et al., (2018) <doi:10.1038/nbt.4096> and Satija et al., (2015) <doi:10.1038/nbt.3192>. Method for the RRR is further detailed in: Erichson et al., (2019) <doi:10.18637/jss.v089.i11> and Halko et al., (2009) <arxiv:0909.4061>. Clustering method is outlined in: Song et al., (2020) <doi:10.1093/bioinformatics/btaa613> and Wang et al., (2011) <doi:10.32614/RJ-2011-015>.

Authors:H. Robert Frost [aut], Azka Javaid [aut, cre]

SPECK_1.0.0.tar.gz
SPECK_1.0.0.tar.gz(r-4.5-noble)SPECK_1.0.0.tar.gz(r-4.4-noble)
SPECK_1.0.0.tgz(r-4.4-emscripten)
SPECK.pdf |SPECK.html
SPECK/json (API)

# Install 'SPECK' in R:
install.packages('SPECK', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • pbmc.rna.mat - Single cell RNA-sequencing (scRNA-seq) peripheral blood (PBMC) data sample.

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.48 score 1 packages 3 scripts 172 downloads 4 exports 150 dependencies

Last updated 1 years agofrom:650b0f8698. Checks:OK: 1 WARNING: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 12 2024
R-4.5-linuxWARNINGDec 12 2024

Exports:%>%ckmeansThresholdrandomizedRRRspeck

Dependencies:abindaskpassbase64encBHbitopsbslibcachemcaToolsCkmeans.1d.dpcliclustercodetoolscolorspacecommonmarkcowplotcpp11crayoncrosstalkcurldata.tabledeldirdigestdotCall64dplyrdqrngevaluatefansifarverfastDummiesfastmapfitdistrplusFNNfontawesomefsfuturefuture.applygenericsggplot2ggrepelggridgesglobalsgluegoftestgplotsgridExtragtablegtoolsherehighrhtmltoolshtmlwidgetshttpuvhttricaigraphirlbaisobandjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelazyevalleidenlifecyclelistenvlmtestmagrittrMASSMatrixmatrixStatsmemoisemgcvmimeminiUImunsellnlmeopensslparallellypatchworkpbapplypillarpkgconfigplotlyplyrpngpolyclipprogressrpromisespurrrR6RANNrappdirsrbibutilsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppProgressRcppTOMLRdpackreshape2reticulaterlangrmarkdownROCRrprojrootRSpectrarsvdRtsnesassscalesscattermoresctransformSeuratSeuratObjectshinysitmosourcetoolsspspamspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstringistringrsurvivalsystensortibbletidyrtidyselecttinytexutf8uwotvctrsviridisLitewithrxfunxtableyamlzoo

Receptor Abundance Estimation using SPECK: An Application to the GSE164378 Peripheral Blood Mononuclear Cells (PBMC) data

Rendered fromSPECKVignette.Rmdusingknitr::rmarkdownon Dec 12 2024.

Last update: 2022-10-16
Started: 2022-10-13