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• Absolute value
  TX_sf = | X_sf |
• Add
  TX_sf = X_sf + C  
  a constant value C needs to be specified
• Antilog
  TX_sf = bx_sf
  A log base value b needs to be specified from the drop-down list; any positive number can be specified when Custom value is chosen
• Arcsinh
  TX_sf =arcsinh (Xsf) 
  The hyperbolic arcsine (arcsinh) transformation is often used on flow cytometry data
• CLR (centered log ratio)
  TX_sf =ln((X_sf +1)/geom (X_sf +1) +1)
  geom is geometric mean of either observation or feature. This method can be applied on protein expression data.
• CPM (counts per million)
  TXsf = (10⁶ x X_sf)/TMR_s
  where Xsf here is the raw read of sample S on feature F, and TMRs 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.
• Divided by
  When mean, median, Q1, Q3, std dev, or sum is selected, the corresponding statistics will be calculated based on the transform on sample or features option
  Example: If transform on Samples is selected, Divide by mean is calculated as:
  TX_sf = X_sf/M_s
  where Ms is the mean of the sample.
  Example: If transform on Features is selected, Divide by mean is calculated as:
  TX_sf = X_sf/M_f
  where M_f is the mean of the feature.
• Log
  TX_sf = log_bX_sf
  A log base value b needs to be specified from the drop-down list; any positive number can be specified when Custom value is chosen
• Logit
  TX_sf=log_b(X_sf/(1-X_sf))
  A log base value b needs to be specified from the drop-down list; any positive number can be specified when Custom value is chosen
• Lower bound
  A constant value C needs to be specified,
  if X_sf is smaller than C, then TX_sf= C; otherwise, TX_sf = X_sf
• Median ratio (DESeq2 only), Median ratio (edgeR)
  These approaches are slightly different implementations of the method proposed by Anders and Huber (2010). The idea is as follows: for each feature, its expression is divided by the feature geometric mean expression across the samples. Then, for a given sample, one takes median of these ratios across the features and obtains a sample specific size factor. The normalized expression is equal to the raw expression divided by the size factor.
  Median ratio (DESeq2 only) is present in R, DESeq2 package, under the name of "ratio". This method should be selected if DESeq2 differential analysis will be used for downstream analysis, since it is not per million scale, not recommended to be used in any other differential analysis methods except for DESeq2.
  Median ratio (edgeR) is present in R, edgeR package under the name of “RLE”. It is very similar to Median ratio (DESeq2 only) method, but it uses per million scale.
• Multiply by
  TX_sf = X_sf x C
  A constant value C needs to be specified
• Poscounts (Deseq2 only)
  Deseq2 size factor estimate option. Comparing with Median ratio, poscount method can be used when all genes contain a sample with a zero. It calculates a modified geometric mean by taking the nth root of the product of the non-zero counts. It is not per million scale. Here is the details.
• Quantile normalization, a rank based normalization method.
  For instance, if transformation is performed on samples, it first ranks all the features in each sample.  Say vector V_s is the sorted feature values of sample S in ascending order, it calculates a vector that is the average of the sorted vectors across all samples --- V_m, then the values in V_s is replaced by the value in V_m in the same rank. Detailed information can be found in [1].
• Rank
  This transformation replaces each value with its rank in the list of sorted values. The smallest value is replaced by 1 and the largest value is replaced by the total number of non-missing values, N. If there are no tied values, the results in a perfectly uniform distribution. In the case of ties, all tied values receive the mean rank.
• Rlog
  This Regularied log transformation is the method implemented in DESeq2 package under the name of rlog. It applies a transformation to remove the dependence of the variance on mean. It should not be applied on zero inflated data such as single cell RNA-seq raw count data. The output of this task should not be used for differential expression analysis, but rather for data exploration, like clustering etc.
• Round
  Round the value to the nearest integer.

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