deletions | additions       diff --git a/section_Normalization_Methods_The_number__.tex b/section_Normalization_Methods_The_number__.tex  index 167f358..e5897e1 100644  --- a/section_Normalization_Methods_The_number__.tex  +++ b/section_Normalization_Methods_The_number__.tex 
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%-----------------------  Total Count &   $\frac{gene\,read\,count}{total\,read\,number}$ &  inappropriate if some genes are are only expressed in one condition unique to  or extremely highly expressed \\ in one condition, but not the other\\  %\tabularnewline \midrule  %-----------------------  Trimmed Mean of M-values (TMM) &   1. calculate gene-wise $log_2$ fold changes (= M-values): $M_g = \frac{log_2( \frac{obs.\,gene\,count_1}{total\,read\,number_1} )}{log_2( \frac{obs.\,gene\,count_2}{total\,read\,number_2} )} $; 2. trimming: removal of upper and lower 30\%; 3. precision weighing: the inverse of the estimated variance is used to account for lower variance of genes with larger counts  %$log_2(TMM^r_{gk}) = \frac{\displaystyle\sum_{g \in G^*} w^r_{gk}M^r_{gk}}{\displaystyle\sum_{g \in G^*} w^r_{gk}} $   & more robust than total count normalization;  details in \citet{RobinsonOshlack2010}\\ %\tabularnewline \midrule  %-----------------------  DESeq DESeq's size factor  & 1. for each gene, the geometric mean of read counts across all samples is calculated; 2. every gene count is divided by the geometric mean; 3. for each sample, the median of these ratios (skipping the genes with a geometric mean of zero) is used as the sample's size factor  & \\ more robust than total count normalization; details in \citet{Anders2010}\\  %\tabularnewline \midrule  %-----------------------  Upper quartile & & \\