Here, we adapt an established meta-analysis framework to address the problem of drug repurposing using an ensemble of disease signatures.

Our meta-analysis pipeline is general, and applicable to any disease context; it can be applied to improve the results of signature-based drug repurposing by leveraging the large number of disease signatures in the public domain.

Here, we design a meta-analysis pipeline that takes in a large set of disease signatures and then identifies drugs that consistently reverse deleterious gene changes.

We show that our meta-analysis pipeline increases the reproducibility of top drug hits, and then extensively characterize new lung cancer drug candidates in silico.

CMapBatch meta-analysis strategy: From individual cancer gene signatures to candidate therapeutics

Our CMapBatch meta-analysis pipeline comprises the following steps (Fig.

CMapBatch meta-analysis pipeline.

Next, we apply meta-analysis to identify which drugs are consistently ranked as the best candidates across all disease signatures.

Thus, we perform the meta-analysis at a later step: our method combines lists of drugs rather than lists of genes.

First, we conducted a meta-analysis using CMapBatch to identify drugs that reverse the transcriptional changes seen with lung cancer across 21 gene signatures (see Table 1).

CADMI expression performed comparably to the SE score in a meta-analysis , however, it was outperformed by pathological tumour stage (CADMI expression vs stage: p = 0.03).

We found that the SE score improved over stage Ia/b alone in a meta-analysis across 765 stage I lung ade-nocarcinomas (SE score+stage vs stage: p = 0.025), whereas CADMI expression made no improvement over stage Ia/b (CADMI expression+stage vs stage: p = 0.13, Fig.

Sub-staging by size is currently the standard clinical approach to stratify stage I tumours, however, on meta-analysis we found that this stratification, unlike signalling entropy was not significantly prognostic over the stage I stratum (Fig.

Meta-analysis revealed that signalling entropy is prognostic across both ER positive and ER negative samples (ER positive: c-indeX = 0.63, 95% CI = (0.604, 0.657),p = 8.5e — 15, ER negative: c-indeX = 0.57, 95% CI = (0.538, 0.602), p = 0.032, Fig.

In a meta-analysis over the 10 breast cancer validation sets we found that unlike signalling entropy Mam-maPrint was not significantly prognostic over ER negative samples (Fig.

Meta-analysis comparison of signalling entropy with OncotypeDX.

The overall concordance indices were derived via meta-analysis using a random effects model.

Meta-analysis across 10 data sets reveals that signalling entropy performs comparably to OncotypeDX across (A) ER positive samples and (B) ER negative samples.

In this meta-analysis across 148 studies, we ask whether it is possible to identify patterns that differentiate five emotion categories—fear, anger, disgust, sadness, and happiness—in a way that is consistent across studies.

Bayesian Spatial Point Processes (BSPP) for Neuroimaging Meta-Analysis

The BSPP is built on a hierarchical marked independent cluster process designed for functional neuroimaging meta-analysis [38].

Meta-analysis is uniquely suited to addressing our two questions because it examines findings from many studies and laboratories that utilize different procedures, stimuli, and samples.

The BSPP model differs from standard univariate [12,13,39] and co-activation based [40,41] approaches to meta-analysis in several fundamental ways.

We test model performance against the reported impact of PCV7 on childhood IPD in high-income countries from a recent meta-analysis .

We conducted a literature review and meta-analysis to obtain the odds of pre-PCV7 VT carriage in the respective settings.

Where multiple studies on nasopharyngeal carriage were conducted within different subsets of the same population that were monitored for IPD, the results from those studies were combined via a Bayesian random effects meta-analysis .

Respective studies were pooled through Bayesian random effects meta-analysis to provide a single estimate of the proportion of VT among carriers for each setting (Table 1 and $2 Fig).

Where the proportion of VT carriers was derived through the Bayesian meta-analysis we drew the bootstrap samples from the respective posterior distribution instead (S2 Fig).