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 = 'https://cloud.r-project.org')
Datasets:
  • pbmc.rna.mat - Single cell RNA-sequencing (scRNA-seq) peripheral blood (PBMC) data sample.

On CRAN:

Conda:

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 212 downloads 4 exports 150 dependencies

Last updated 1 years agofrom:650b0f8698. Checks:1 OK, 2 WARNING. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 12 2025
R-4.5-linuxWARNINGMar 12 2025
R-4.4-linuxWARNINGMar 12 2025

Exports:%>%ckmeansThresholdrandomizedRRRspeck

Dependencies:abindaskpassbase64encBHbitopsbslibcachemcaToolsCkmeans.1d.dpcliclustercodetoolscolorspacecommonmarkcowplotcpp11crayoncrosstalkcurldata.tabledeldirdigestdotCall64dplyrdqrngevaluatefansifarverfastDummiesfastmapfitdistrplusFNNfontawesomefsfuturefuture.applygenericsggplot2ggrepelggridgesglobalsgluegoftestgplotsgridExtragtablegtoolsherehighrhtmltoolshtmlwidgetshttpuvhttricaigraphirlbaisobandjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelazyevalleidenbaselifecyclelistenvlmtestmagrittrMASSMatrixmatrixStatsmemoisemgcvmimeminiUImunsellnlmeopensslparallellypatchworkpbapplypillarpkgconfigplotlyplyrpngpolyclipprogressrpromisespurrrR6RANNrappdirsrbibutilsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppProgressRcppTOMLRdpackreshape2reticulaterlangrmarkdownROCRrprojrootRSpectrarsvdRtsnesassscalesscattermoresctransformSeuratSeuratObjectshinysitmosourcetoolsspspamspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstringistringrsurvivalsystensortibbletidyrtidyselecttinytexutf8uwotvctrsviridisLitewithrxfunxtableyamlzoo

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

Azka Javaid and H. Robert Frost

Rendered fromSPECKVignette.Rmdusingknitr::rmarkdownon Mar 12 2025.

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

Citation

To cite package ‘SPECK’ in publications use:

Frost H, Javaid A (2023). SPECK: Receptor Abundance Estimation using Reduced Rank Reconstruction and Clustered Thresholding. R package version 1.0.0, https://CRAN.R-project.org/package=SPECK.

Corresponding BibTeX entry: 

  @Manual{,
    title = {SPECK: Receptor Abundance Estimation using Reduced Rank
      Reconstruction and Clustered Thresholding},
    author = {H. Robert Frost and Azka Javaid},
    year = {2023},
    note = {R package version 1.0.0},
    url = {https://CRAN.R-project.org/package=SPECK},
  }