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Low-expression feature

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Multiple test correction can be performed on the p-values of each comparison, with FDR step-up being the default (1). If you check the Other options like Storey q-value (2), an extra column with q-values will be added to the report.In the step up method,   and Bonferroni are provided, select one method at a time; None means no multiple test correct will be performed.

FDR step-up:

Suppose there are n p-values (n is the number of features). The p-values are sorted by ascending order and m represents the rank of a p-value. The calculation compares p-value*(n/m) with the specified alpha level, and the cut-off p-value is the one that generates the last product that is less than the alpha level. The goal of step up method is to find:

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In order to find K* , start with S_n* and then go up the list until you find the first step up value that is less or equal to alpha.

In the Storey q-value method, :

q-value is the minimum "positive false discovery rate" (pFDR) that can occur when rejecting a statistic.

For an observed statistic T=t and nested set of rejection area {C},

Bonferroni:

Suppose there are n p-values (n is the number of features), the expected number of Type I errors would be given by Image Added, thus the significance level of each individual test should be adjusted to Image Added. Alternatively the p-values should be adjusted as p_B=p*n, p_{B is Bonferroni corrected p-value. If pB is greater than 1, it is set to 1}

Report option

This section configures how to select the best model for a feature. There are two options for Model selection criterion: AICc (Akaike Information Criterion corrected) and AIC (Akaike Information Criterion). AICc is recommended for small sample size, while AIC is recommended for medium and large sample size What about large samples?(3). Note that when sample size grows from small to medium, AICc converges to AIC. Taking the AICc/AIC value into account, GSA considers the model with the lowest information criterion as the best choice.

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