Receptor Abundance Estimation using Reduced Rank Reconstruction and Clustered Thresholding R package SPECK version 1.0.1 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) <doi:10.48550/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>. Software Depends: R (>= 3.5) Imports: Ckmeans.1d.dp, magrittr, Matrix (>= 1.6.1.1), rsvd, Seurat Suggests: ggplot2, gridExtra, knitr, rmarkdown, SeuratObject, usethis Azka Javaid <azka.javaid.gr@dartmouth.edu> Comprehensive R Archive Network (CRAN) H. Robert Frost [aut], Azka Javaid [aut, cre] GPL (>= 2) 2025-10-05 application/tgz https://CRAN.R-project.org/package=SPECK doi:10.32614/CRAN.package.SPECK