Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.04.06.535838v1?rss=1
Authors: Chang, C.-W., Schmid, K.
Abstract:
Landscape genomics is an emerging field of research that integrates genomic and environmental information to explore the drivers of evolution. Reliable data on the geographical origin of biological samples is a prerequisite for accurate landscape genomics studies. Traditionally, researchers discover potentially questionable samples using visualisation-based tools. However, such approaches cannot handle large sample sizes due to overlapping data points on a graph and can hinder reproducible research. To address this shortcoming, we developed Geo-Genetic outlier (GGoutlieR), an R package of a heuristic framework for detecting and isualising samples with unusual geo-genetic patterns. Outliers are identified by calculating empirical p-values for each sample, allowing users to identify them in data sets with thousands of samples. The package also provides a plotting function to display the geo-genetic patterns of outliers on a geographical map. GGoutlieR could significantly reduce the amount of data cleaning that researchers need to do before carrying out landscape genomics analyses.
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