If we set an FDR threshold of 0.1%, this approach identifies around 1300 highly

variable genes.

The output of this variance modelling can be used as input to a `denoisePCA()`

function to compute "denoised" principal components for clustering and other

downstream analyses (details not shown here; please see the `simpleSingleCell`

workflow).

#### High Dropout Genes

An alternative to finding HVGs is to identify genes with unexpectedly high numbers of zeros.

The frequency of zeros, known as the "dropout rate", is very closely related to expression level

in scRNASeq data. Zeros are the dominant feature of single-cell RNASeq data, typically accounting

for over half of the entries in the final expression matrix. These zeros predominantly result

from the failure of mRNAs failing to be reversed transcribed [(Andrews and Hemberg, 2016)](http://www.biorxiv.org/content/early/2017/05/25/065094). Reverse transcription

is an enzyme reaction thus can be modelled using the Michaelis-Menten equation:

An alternative to finding HVGs is to identify genes with unexpectedly high

numbers of zeros. The frequency of zeros, known as the "dropout rate", is very

closely related to expression level in scRNASeq data. Zeros are the dominant

feature of single-cell RNASeq data, typically accounting for over half of the

entries in the final expression matrix. These zeros predominantly result from

the failure of mRNAs failing to be reversed transcribed [(Andrews and Hemberg,

Plot the expression of the features for each of the other methods. Which appear to be differentially expressed? How consistent are the different methods for this dataset?