What it does
The resampling method is a comparative analysis the allows that can be used to determine conditional essentiality of genes. It is based on a permutation test, and is capable of determining readcounts that are significantly different across conditions.
This technique has yet to be formally published in the context of differential essentiality analysis. Briefly, the readcounts at each genes are determined for each replicate of each condition. The total readcounts in condition A is subtracted from the total read counts at condition B, to obtain an observed difference in read counts. The TA sites are then permuted for a given number of “samples”. For each one of these permutations, the difference is readcounts is determined. This forms a null distribution, from which a pvalue is calculated for the original, observed difference in readcounts.
Note : Can be used for both Himar1 and Tn5 datasets
Inputs
Input files for Resampling need to be:
 .wig files : Tabulated files containing one column with the TA site coordinate and one column with the read count at this site.
 annotation .prot_table : Annotation file generated by the Convert Gff3 to prot_table for TRANSIT tool.
Parameters
Optional Arguments:
s <integer> 
= Number of samples. Default: 
 10000 

n <string> 
= Normalization method. Default: 
 TTR 

h 
= Output histogram of the permutations for each gene. Default: 
 Off. 

a 
= Perform adaptive resampling. Default: 
 Off. 

ez 
= Exclude rows with zero accross conditions. Default: 
 Off 

pc 
= Pseudocounts to be added at each site. Default: 
 0 

l 
= Perform LOESS Correction; Helps remove possible genomic position bias. Default: 
 Off. 

r <string> 
= How to handle replicates. Sum, Mean. Default: 
 r Mean 

iN <float> 
= Ignore TAs occuring at given fraction of the N terminus. Default: 
 0.0 

iC <float> 
= Ignore TAs occuring at given fraction of the C terminus. Default: 
 0.0 

ctrl_lib 
= String of letters representing library of control files in order e.g. 'AABB' Default: 
 empty. Letters used must also be used in exp_lib. If nonempty, resampling will limit permutations to withinlibraries. 

exp_lib 
= String of letters representing library of experimental files in order e.g. 'ABAB' Default: 
 empty. Letters used must also be used in ctrl_lib. If nonempty, resampling will limit permutations to withinlibraries. 

The resampling method is nonparametric, and therefore does not require any parameters governing the distributions or the model. The following parameters are available for the method:
 Samples: The number of samples (permutations) to perform. The larger the number of samples, the more resolution the pvalues calculated will have, at the expense of longer computation time. The resampling method runs on 10,000 samples by default.
 Output Histograms:Determines whether to output .png images of the histograms obtained from resampling the difference in readcounts.
 Adaptive Resampling: An optional “adaptive” version of resampling which accelerates the calculation by terminating early for genes which are likely not significant. This dramatically speeds up the computation at the cost of less accurate estimates for those genes that terminate early (i.e. deemed not significant). This option is OFF by default.
 Include Zeros: Select to include sites that are zero. This is the preferred behavior, however, unselecting this (thus ignoring sites that) are zero accross all dataset (i.e. completely empty), is useful for decreasing running time (specially for large datasets like Tn5).
 Normalization Method: Determines which normalization method to use when comparing datasets. Proper normalization is important as it ensures that other sources of variability are not mistakenly treated as real differences. See the Normalization section for a description of normalization method available in TRANSIT.
 TTR (Default) : Trimmed Total Reads (TTR), normalized by the total readcounts (like totreads), but trims top and bottom 5% of readcounts. 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 nonzero sites.
 totreads : Normalizes datasets by total readcounts, and scales them to have the same mean over all counts.
 zinfnb : Fits a zeroinflated negative binomial model, and then divides readcounts by the mean. The zeroinflated negative binomial model will treat some empty sites as belonging to the “true” negative binomial distribution responsible for readcounts 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 BetaGeometric Correction .
 nonorm : No normalization is performed.
Outputs
The resampling method outputs a tabdelimited file with results for each gene in the genome. Pvalues are adjusted for multiple comparisons using the BenjaminiHochberg procedure (called “qvalues” or “padj.”). A typical threshold for conditional essentiality on is qvalue < 0.05.
Column Header 
Column Definition 
Orf 
Gene ID 
Name 
Gene Name 
Desc 
Gene Description 
N 
Number of TA sites in the gene. 
TAs Hit 
Number of TA sites with at least one insertion. 
Sum Rd 1 
Sum of read counts in condition 1. 
Sum Rd 2 
Sum of read counts in condition 2. 
Delta Rd 
Difference in the sum of read counts. 
pvalue 
Pvalue calculated by the permutation test. 
padj. 
Adjusted pvalue controlling for the FDR (BenjaminiHochberg) 
More Information
See TRANSIT documentation