Proper normalization is important as it ensures that other sources of variability are not mistakenly treated as real differences in datasets. TRANSIT provides various normalization methods, which are briefly described below:

  • TTR:
    Trimmed Total Reads (TTR), normalized by the total read-counts (like totreads), but trims top and bottom 5% of read-counts. This is the recommended normalization method for most cases as it has the beneffit of normalizing for difference in saturation in the context of resampling.
  • nzmean:
    Normalizes datasets to have the same mean over the non-zero sites.
  • totreads:
    Normalizes datasets by total read-counts, and scales them to have the same mean over all counts.
  • zinfnb:
    Fits a zero-inflated negative binomial model, and then divides read-counts by the mean. The zero-inflated negative binomial model will treat some empty sites as belonging to the “true” negative binomial distribution responsible for read-counts while treating the others as “essential” (and thus not influencing its parameters).
  • quantile:
    Normalizes datasets using the quantile normalization method described by Bolstad et al. (2003). In this normalization procedure, datasets are sorted, an empirical distribution is estimated as the mean across the sorted datasets at each site, and then the original (unsorted) datasets are assigned values from the empirical distribution based on their quantiles.
  • betageom:
    Normalizes the datasets to fit an “ideal” Geometric distribution with a variable probability parameter p. Specially useful for datasets that contain a large skew. See Beta-Geometric Correction .
  • nonorm:
    No normalization is performed.


In addition to choosing normalization for various analyses in the GUI, you can also call Transit to normalize wig files from the command-line, as shown in this example:


> python3 src/ normalize --help

usage: python3 src/ normalize <input.wig> <output.wig> [-n TTR|betageom]
   or: python3 src/ normalize -c <combined_wig> <output> [-n TTR|betageom]

> python3 src/ normalize Rv_1_H37RvRef.wig Rv_1_H37RvRef_TTR.wig -n TTR

> python3 src/ normalize Rv_1_H37RvRef.wig Rv_1_H37RvRef_BG.wig -n betageom

The normalize command now also works on combined_wig_ files too. If the input file is a combined_wig file, indicate it with a ‘-c’ flag.