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'))

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.

4 exports 0.36 score 150 dependencies 1 dependents 2 scripts 268 downloads

Last updated 10 months agofrom:650b0f8698. Checks:OK: 1 WARNING: 1. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Sep 13 2024
R-4.5-linux	WARNING	Sep 13 2024

Exports:%>%ckmeansThreshold randomizedRRR speck

Dependencies:abind askpass base64enc BH bitops bslib cachem caTools Ckmeans.1d.dp cli cluster codetools colorspace commonmark cowplot cpp11 crayon crosstalk curl data.table deldir digest dotCall64 dplyr dqrng evaluate fansi farver fastDummies fastmap fitdistrplus FNN fontawesome fs future future.apply generics ggplot2 ggrepel ggridges globals glue goftest gplots gridExtra gtable gtools here highr htmltools htmlwidgets httpuv httr ica igraph irlba isoband jquerylib jsonlite KernSmooth knitr labeling later lattice lazyeval leiden lifecycle listenv lmtest magrittr MASS Matrix matrixStats memoise mgcv mime miniUI munsell nlme openssl parallelly patchwork pbapply pillar pkgconfig plotly plyr png polyclip progressr promises purrr R6 RANN rappdirs rbibutils RColorBrewer Rcpp RcppAnnoy RcppArmadillo RcppEigen RcppHNSW RcppProgress RcppTOML Rdpack reshape2 reticulate rlang rmarkdown ROCR rprojroot RSpectra rsvd Rtsne sass scales scattermore sctransform Seurat SeuratObject shiny sitmo sourcetools sp spam spatstat.data spatstat.explore spatstat.geom spatstat.random spatstat.sparse spatstat.univar spatstat.utils stringi stringr survival sys tensor tibble tidyr tidyselect tinytex utf8 uwot vctrs viridisLite withr xfun xtable yaml zoo

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 Sep 13 2024.

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

Citation

Readme and manuals

Help Manual

Help page	Topics
Clustered thresholding of a vector.	ckmeansThreshold
Single cell RNA-sequencing (scRNA-seq) peripheral blood (PBMC) data sample.	pbmc.rna.mat
Reduced rank reconstruction (RRR) of a matrix.	randomizedRRR
Abundance estimation for single cell RNA-sequencing (scRNA-seq) data.	speck