Discussion

COMBAT removed batch effects, but did so by removing any genes that were expressed in less than 20% of cells. Thus, those interested in rare cell types that occur in less than a fifth of the data will find COMBAT to remove the signal of interest as well as the technical signal.

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
 •      GitHub link: https://github.com/singlecell-batches/vamf
 
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}