VAMF - by Yiran Hou
"Varying-Censoring Aware Matrix Factorization for Single Cell RNA-Sequencing" \cite{Townes_2017}\cite{Townes_2017}\cite{Townes_2017} \cite{Townes_2017}\cite{Townes_2017}
• Paper DOI: https://doi.org/10.1101/166736
Single-cell RNA-sequencing suffers from high drop-out rate in gene level detection, leading to a high number of zero counts in the digital gene expression matrix. These zeroes do not faithfully represent low expression levels of genes and may bias factor estimation during log-transformation. Also, variation in per-cell zero counts may lead to spurious cluster detection that reflects technical variances (Hicks. 2017).
To model data censoring and per-cell zero-count variation at the same time, Townes et al. developed VAMF, Varying-Censoring Aware Matrix Factorization (Townes. 2017). They improved the censoring model developed in ZIFA, Zero-Inflated Factor Analysis (Pierson. 2015) by adding parameters accounting for cell-cell variation in censoring.
We ran Varying-Censoring Aware Matrix Factorization (VAMF) \cite{Townes_2017}.