Jeffrey Pullin

@jeffreypullin.bsky.social

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PhD Student, MRC Biostatistics Unit University of Cambridge Gates Cambridge Scholar Bioinformatics, genetics, single-cell, statistics Australian 🇦🇺

Statistical power of negative binomial and linear model methods across the OneK1K dataset a) Number of eQTLs detected by the quasar linear model and negative binomial GLM with adjusted profile likelihood dispersion estimation methods across all cell types in the OneK1K dataset. b) Number of eGenes detected by the quasar linear model and negative binomial GLM with adjusted profile likelihood dispersion estimation methods across all cell types in the OneK1K dataset.
Histograms of Pearson correlation of − log10 transformed variant-level p-values for each gene, correlating the output of output of quasar against that uses the same statistical model (LM: tensorQTL, NB-GLM : jaxQTL, LMM: apex. All results are computed for the B IN cluster. b) Speed of methods across the three representative cell types. All methods were run on CPUs. Methods are labelled by the options used to run them: for tensorQTL and jaxQTL ‘cis’ computes significance at the level of genes while ‘cis nominal’ computes significance at the level of variants.
Bar charts of number of discoveries across different tools and thresholds in a paper about eQTL mapping