@@ -240,7 +240,7 @@ In this step we removed empty droplet, load the spliced matrix in a Seurat objec
### Normalization
Sanity infers the log expression levels Xgc of gene g in cell c by filtering out the Poisson noise on the UMI count matrix Ngc of gene g in cell c. The LTQ *x<sub>gc</sub>* of gene *g* in cell *c* corresponds to the estimated logarithm of the fraction of mRNAs in cell *c* that belong to gene *g*. The LTQs are thus normalized such that *Σ<sub>g</sub> exp(x<sub>gc</sub>) = 1* for each cell *c*. In order to get an estimate of the number of mRNAs for gene *g* in cell *c* one would thus need to multiply *exp(x<sub>gc</sub>)* by the estimated total number of mRNAs *M* in the cell.
Sanity infers the log expression levels *x<sub>gc</sub>* of gene *g* in cell *c* by filtering out the Poisson noise on the UMI count matrix Ngc of gene g in cell c. The LTQ *x<sub>gc</sub>* of gene *g* in cell *c* corresponds to the estimated logarithm of the fraction of mRNAs in cell *c* that belong to gene *g*. The LTQs are thus normalized such that *Σ<sub>g</sub> exp(x<sub>gc</sub>) = 1* for each cell *c*. In order to get an estimate of the number of mRNAs for gene *g* in cell *c* one would thus need to multiply *exp(x<sub>gc</sub>)* by the estimated total number of mRNAs *M* in the cell.
