#rgstrap - Twitter Hashtag

almost 3 years ago

Researchers at @BakerResearchAu have developed a computational tool (#RGStraP) to estimate #RNAseq-based genetic principal components (RG-PCs) & assess whether RG-PCs can be used to control for population structure in #geneexpression analyses. https://t.co/v79Q8Kqfyv

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Muhamad Fachrul, PhD @F_Azr

almost 4 years ago

Results from #RGStraP achieved high SNP-level genotype concordance and correlations of genetic principal components (PCs) when compared to paired genotyping array. PCs from both array and RNAseq (RG-PCs) showed subpopulation clustering based on self-reported ethnicity. (6/9)

F_Azr's tweet photo. Results from #RGStraP achieved high SNP-level genotype concordance and correlations of genetic principal components (PCs) when compared to paired genotyping array.

PCs from both array and RNAseq (RG-PCs) showed subpopulation clustering based on self-reported ethnicity.

(6/9) https://t.co/F3Pb7SWOt9

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Muhamad Fachrul, PhD @F_Azr

almost 4 years ago

We constructed the #RGStraP pipeline, which can capture population structure (genotype calls or 🧬 principal components) directly from RNAseq (https://t.co/v3BAZmolnU) We tested RGStraP on a dataset of whole-blood samples from Nepal, a diverse-yet-understudied population. (5/9)

F_Azr's tweet photo. We constructed the #RGStraP pipeline, which can capture population structure (genotype calls or 🧬 principal components) directly from RNAseq (https://t.co/v3BAZmolnU)

We tested RGStraP on a dataset of whole-blood samples from Nepal, a diverse-yet-understudied population. (5/9) https://t.co/PeqpacaUH3

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Muhamad Fachrul, PhD @F_Azr

almost 4 years ago

My 2nd preprint is up! We demonstrated a straightforward technique of capturing 🧬 structure solely from RNAseq data, which can then be used to control for population stratification in downstream analyses @minouye271 https://t.co/poHS2pnKjD Small thread below! #RGStraP (1/9)

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