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1. 1. Normalize the reads by the length of feature, it generate reads per kilobase
      RPK_sfsf  = X_sf / L_f;
   2. Sum up all the RPKsf in a sample
      PRK_ss = ∑ ∑^F_f=1 FRPK_sf
   3. Generate a scaling factor for each sample by normalizing the PRK of the sample to the sum PRK of all the samples
      Ks=PRKSs=1SPRKS x TR / 10⁶Image Added,
      where TR is the total reads across all samples
   4. Divide raw reads by the scaling factor to get TPM
      TX_sfsf = X_sf/K_s
      

• Total count(Reads per million)
  TXsf = (10^₆ x X_sf)/TMR_s
  where X_sf here is the raw read of sample S on feature F, and
  TMR_s is the total mapped reads of sample S.
  If quantification is performed on an aligned reads data node, total mapped reads is the aligned reads.  If quantification is generated from imported read count text file, the total mapped reads is the sum of all feature reads in the sample.
  
  
• Upper quartile 
  The method is exactly the same as the LIMMA package [7].
  The following is the simple summarization of the calculation:

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A table that presents descriptive statistics on each sample, the last row is the grand statistics across all samples (Figure 4).

Expression signal

These box-whisker plots show the expression signal distribution for each sample before and after normalization. When you mouse over on each bar in the plot, a balloon would show detailed percentile information (Figure 5).

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