getmstatistic - Quantifying Systematic Heterogeneity in Meta-Analysis

Quantifying systematic heterogeneity in meta-analysis using R. The M statistic aggregates heterogeneity information across multiple variants to, identify systematic heterogeneity patterns and their direction of effect in meta-analysis. It's primary use is to identify outlier studies, which either show "null" effects or consistently show stronger or weaker genetic effects than average across, the panel of variants examined in a GWAS meta-analysis. In contrast to conventional heterogeneity metrics (Q-statistic, I-squared and tau-squared) which measure random heterogeneity at individual variants, M measures systematic (non-random) heterogeneity across multiple independently associated variants. Systematic heterogeneity can arise in a meta-analysis due to differences in the study characteristics of participating studies. Some of the differences may include: ancestry, allele frequencies, phenotype definition, age-of-disease onset, family-history, gender, linkage disequilibrium and quality control thresholds. See <https://magosil86.github.io/getmstatistic/> for statistical statistical theory, documentation and examples.

Last updated 4 years ago

getmstatisticgwasheartgenes214heterogeneitymeta-analysismstatisticoutlier-studiesstatasystematic-heterogeneity

4.41 score 3 stars 17 scripts 229 downloads

bumblebee - Quantify Disease Transmission Within and Between Population Groups

A simple tool to quantify the amount of transmission of an infectious disease of interest occurring within and between population groups. 'bumblebee' uses counts of observed directed transmission pairs, identified phylogenetically from deep-sequence data or from epidemiological contacts, to quantify transmission flows within and between population groups accounting for sampling heterogeneity. Population groups might include: geographical areas (e.g. communities, regions), demographic groups (e.g. age, gender) or arms of a randomized clinical trial. See the 'bumblebee' website for statistical theory, documentation and examples <https://magosil86.github.io/bumblebee/>.

Last updated 3 years ago

epidemiologygeneticsheterogeneityphylogeneticstransmission

3.88 score 15 scripts 205 downloads

getspres - SPRE Statistics for Exploring Heterogeneity in Meta-Analysis

An implementation of SPRE (standardised predicted random-effects) statistics in R to explore heterogeneity in genetic association meta- analyses, as described by Magosi et al. (2019) <doi:10.1093/bioinformatics/btz590>. SPRE statistics are precision weighted residuals that indicate the direction and extent with which individual study-effects in a meta-analysis deviate from the average genetic effect. Overly influential positive outliers have the potential to inflate average genetic effects in a meta-analysis whilst negative outliers might lower or change the direction of effect. See the 'getspres' website for documentation and examples <https://magosil86.github.io/getspres/>.

Last updated 4 years ago

3.70 score 9 scripts 186 downloads