Normalization

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.

Command-line

You can call Transit to normalize wig files or combined_wig files from the command-line. (It will automatically determine the type of input file.)

Usage:
    > python3 transit.py normalize <wig_file or combined_wig_file> <output_file> [Optional Arguments]

    Optional Arguments:
    --n <string>  :=  Normalization method (e.g. TTR, betageom, etc). Default: --n TTR

GUI

Normalization may be applied to combined_wig files loaded into the GUI by selecting the following sequence of menu items: ‘Pre-processing’->’Generate’->’A normalized combined_wig file using…’

Examples

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

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

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