Analysis Methods¶
TRANSIT has analysis methods capable of analyzing Himar1 and Tn5 datasets. Below is a description of some of the methods.
Gumbel¶
The Gumbel can be used to determine which genes are essential in a single condition. It does a gene-by-gene analysis of the insertions at TA sites with each gene, makes a call based on the longest consecutive sequence of TA sites without insertion in the genes, calculates the probability of this using a Bayesian model.
Note
Intended only for Himar1 datasets.
How does it work?¶
Parameters¶
- Samples: Gumbel uses Metropolis-Hastings (MH) to generate samples of posterior distributions. The default setting is to run the simulation for 10,000 iterations. This is usually enough to assure convergence of the sampler and to provide accurate estimates of posterior probabilities. Less iterations may work, but at the risk of lower accuracy.
- Burn-In: Because the MH sampler many not have stabilized in the first few iterations, a “burn-in” period is defined. Samples obtained in this “burn-in” period are discarded, and do not count towards estimates.
- Trim: The MH sampler produces Markov samples that are correlated. This parameter dictates how many samples must be attempted for every sampled obtained. Increasing this parameter will decrease the auto-correlation, at the cost of dramatically increasing the run-time. For most situations, this parameter should be left at the default of “1”.
- Minimum Read: The minimum read count that is considered a true read. Because the Gumbel method depends on determining gaps of TA sites lacking insertions, it may be susceptible to spurious reads (e.g. errors). The default value of 1 will consider all reads as true reads. A value of 2, for example, will ignore read counts of 1.
- Replicates: Determines how to deal with replicates by averaging the read-counts or summing read counts across datasets. This should not have an affect for the Gumbel method, aside from potentially affecting spurious reads.
Outputs and diagnostics¶
The Gumbel method generates a tab-separated output file at the location chosen by the user. This file will automatically be loaded into the Results Files section of the GUI, allowing you to display it as a table. Alternatively, the file can be opened in a spreadsheet software like Excel as a tab-separated file. The columns of the output file are defined as follows:
Run-time¶
The Gumbel method takes on the order of 10 minutes for 10,000 samples. Run-time is linearly proportional to the ‘samples’ parameter, or length of MH sampling trajectory. Other notes: Gumbel can be run on multiple replicates; replicate datasets will be automatically merged.
Tn5Gaps¶
The Tn5Gaps method can be used to determine which genes are essential in a single condition for Tn5 datasets. It does an analysis of the insertions at each site within the genome, makes a call for a given gene based on the length of the most heavily overlapping run of sites without insertions (gaps), calculates the probability of this using a the Gumbel distribution.
Note
Intended only for Tn5 datasets.
How does it work?¶
This method is loosely is based on the original gumbel analysis method described in this paper:
Griffin, J.E., Gawronski, J.D., DeJesus, M.A., Ioerger, T.R., Akerley, B.J., Sassetti, C.M. (2011). High-resolution phenotypic profiling defines genes essential for mycobacterial survival and cholesterol catabolism. PLoS Pathogens, 7(9):e1002251.
The Tn5Gaps method modifies the original method in order to work on Tn5 datasets, which have significantly lower saturation of insertion sites than Himar1 datasets. The main difference comes from the fact that the runs of non-insertion (or “gaps”) are analyzed throughout the whole genome, including non-coding regions, instead of within single genes. In doing so, the expected maximum run length is calculated and a p-value can be derived for every run. A gene is then classified by using the p-value of the run with the largest number of nucleotides overlapping with the gene.
This method was tested on a salmonella Tn5 dataset presented in this paper:
Langridge GC1, Phan MD, Turner DJ, Perkins TT, Parts L, Haase J, Charles I, Maskell DJ, Peters SE, Dougan G, Wain J, Parkhill J, Turner AK. (2009). Simultaneous assay of every Salmonella Typhi gene using one million transposon mutants. Genome Res. , 19(12):2308-16.
This data was downloaded from SRA (located herei) , and used to make wig files (base and bile) and the following 4 baseline datasets were merged to make a wig file: (IL2_2122_1,3,6,8). Our analysis produced 415 genes with adjusted p-values less than 0.05, indicating essentiality, and the analysis from the above paper produced 356 essential genes. Of these 356 essential genes, 344 overlap with the output of our analysis.
Parameters¶
- Minimum Read: The minimum read count that is considered a true read. Because the Gumbel method depends on determining gaps of TA sites lacking insertions, it may be suceptible to spurious reads (e.g. errors). The default value of 1 will consider all reads as true reads. A value of 2, for example, will ignore read counts of 1.
- Replicates: Determines how to deal with replicates by averaging the read-counts or suming read counts accross datasets. This should not have an affect for the Gumbel method, aside from potentially affecting spurious reads.
Outputs and diagnostics¶
The Tn5Gaps method generates a tab-separated output file at the location chosen by the user. This file will automatically be loaded into the Results Files section of the GUI, allowing you to display it as a table. Alternatively, the file can be opened in a spreadsheet software like Excel as a tab-separated file. The columns of the output file are defined as follows:
| Column Header | Column Definition |
|---|---|
| ORF | Gene ID. |
| Name | Name of the gene. |
| Desc | Gene description. |
| k | Number of Transposon Insertions Observed within the ORF. |
| n | Total Number of TA dinucleotides within the ORF. |
| r | Length of the Maximum Run of Non-Insertions observed. |
| ovr | The number of nucleotides in the overlap with the longest run partially covering the gene. |
| lenovr | The length of the above run with the largest overlap with the gene. |
| pval | P-value calculated by the permutation test. |
| padj | Adjusted p-value controlling for the FDR (Benjamini-Hochberg). |
| call | Essentiality call for the gene. Depends on FDR corrected thresholds. Essential or Non-Essential. |
Run-time¶
The Tn5Gaps method takes on the order of 10 minutes. Other notes: Tn5Gaps can be run on multiple replicates; replicate datasets will be automatically merged.
HMM¶
The HMM method can be used to determine the essentiality of the entire genome, as opposed to gene-level analysis of the other methods. It is capable of identifying regions that have unusually high or unusually low read counts (i.e. growth advantage or growth defect regions), in addition to the more common categories of essential and non-essential.
Note
Intended only for Himar1 datasets.
How does it work?¶
Parameters¶
The HMM method automatically estimates the necessary statistical parameters from the datasets. You can change how the method handles replicate datasets:
- Replicates: Determines how the HMM deals with replicate datasets by either averaging the read-counts or summing read counts across datasets. For regular datasets (i.e. mean-read count > 100) the recommended setting is to average read-counts together. For sparse datasets, it summing read-counts may produce more accurate results.
Output and Diagnostics¶
| Column # | Column Definition |
|---|---|
| 1 | Coordinate of TA site |
| 2 | Observed Read Counts |
| 3 | Probability for ES state |
| 4 | Probability for GD state |
| 5 | Probability for NE state |
| 6 | Probability for GA state |
| 7 | State Classification (ES = Essential, GD = Growth Defect, NE = Non-Essential, GA = Growth-Defect) |
| 8 | Gene(s) that share(s) the TA site. |
| Column Header | Column Definition |
|---|---|
| Orf | Gene ID |
| Name | Gene Name |
| Desc | Gene Description |
| N | Number of TA sites |
| n0 | Number of sites labeled ES (Essential) |
| n1 | Number of sites labeled GD (Growth-Defect) |
| n2 | Number of sites labeled NE (Non-Essential) |
| n3 | Number of sites labeled GA (Growth-Advantage) |
| Avg. Insertions | Mean insertion rate within the gene |
| Avg. Reads | Mean read count within the gene |
| State Call | State Classification (ES = Essential, GD = Growth Defect, NE = Non-Essential, GA = Growth-Defect) |
Run-time¶
Re-sampling¶
The re-sampling 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 read-counts that are significantly different across conditions.
Note
Can be used for both Himar1 and Tn5 datasets
How does it work?¶
This technique has yet to be formally published in the context of differential essentiality analysis. Briefly, the read-counts at each genes are determined for each replicate of each condition. The total read-counts 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 read-counts is determined. This forms a null distribution, from which a p-value is calculated for the original, observed difference in read-counts.
Parameters¶
The resampling method is non-parametric, 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 p-values calculated will have, at the expense of longer computation time. The re-sampling 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 read-counts.
- 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: By default resampling will ignore sites that are zero across all the datasets (i.e. completely empty), which is useful for decreasing running time (specially for large datasets like Tn5). This option allows the user to include these empty rows.
- 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.
Output and Diagnostics¶
The re-sampling method outputs a tab-delimited file with results for each gene in the genome. P-values are adjusted for multiple comparisons using the Benjamini-Hochberg procedure (called “q-values” or “p-adj.”). A typical threshold for conditional essentiality on is q-value < 0.05.
| Column Header | Column Definition |
|---|---|
| Orf | Gene ID. |
| Name | Name of the gene. |
| Description | 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. |
| p-value | P-value calculated by the permutation test. |
| p-adj. | Adjusted p-value controlling for the FDR (Benjamini-Hochberg) |
Run-time¶
A typical run of the re-sampling method with 10,000 samples will take around 45 minutes (with the histogram option ON). Using the adaptive resampling option, the run-time is reduced to around 10 minutes.
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.
- nonorm:
- No normalization is performed.