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SPECK (version 1.0.1)

Receptor Abundance Estimation using Reduced Rank Reconstruction and Clustered Thresholding

Description

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) , Stuart et al., (2019) , Butler et al., (2018) and Satija et al., (2015) . Method for the RRR is further detailed in: Erichson et al., (2019) and Halko et al., (2009) . Clustering method is outlined in: Song et al., (2020) and Wang et al., (2011) .

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Version

Install

install.packages('SPECK')

Monthly Downloads

218

Version

1.0.1

License

GPL (>= 2)

Maintainer

Azka Javaid

Last Published

October 5th, 2025

Functions in SPECK (1.0.1)

pbmc.rna.mat

Single cell RNA-sequencing (scRNA-seq) peripheral blood (PBMC) data sample.
speck

Abundance estimation for single cell RNA-sequencing (scRNA-seq) data.
ckmeansThreshold

Clustered thresholding of a vector.
randomizedRRR

Reduced rank reconstruction (RRR) of a matrix.
%>%

Pipe operator
SPECK-package

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