Table 1 MA, DM and baseline methods included in the experimentations. For each method a synthetic description is provided describing its main characteristics
Approach | Method | Description |
|---|---|---|
Meta-Analysis | Fisher | Combines p-values in a statistic that follows a χ 2 distribution. |
Stouffer | Transforms p-values in Z-scores and merges them with a weighted average | |
Fixed-Effects | Assumes all studies measure the same effect and combines estimates with a weighted average | |
Random-Effects | Combines estimated effects by assuming that each study measures a biased version of the true effect | |
FR-Effects | Estimates whether Fixed or Random-Effects assumptions hold and use one of the two methods accordingly | |
Rank-Product | Combines statistics’ ranks by multiplication. | |
Data-Merging | SVA | Provides surrogate variables that approximate the effect of confounding factors and batch-effects present in the data |
Combat | Assumes additive and multiplicative batch-effects and estimates them by pooling information across genes | |
RMA | Normalizes data across expression profiles using Quantile Normalization | |
RMA-Combat | Applies RMA and Combat one after the other | |
Scaling | Scales the value of each gene in each study to have zero mean and unitary standard deviation | |
No-Correction | Merges samples from all studies in a single dataset without any correction | |
Baseline | Single-Datasets | Computes the performance that is expected by analyzing a single, randomly chosen dataset |
Random-Guessing | Produces randomly sampled correlation values |