A frictionless, pipeable approach to dealing with summary statistics
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Updated
Jan 28, 2025 - HTML
A frictionless, pipeable approach to dealing with summary statistics
Estimate local SNP heritability and genetic covariance from GWAS summary association statistics.
The project focuses on the Exploratory Data Analysis (EDA) of the given Performance dataset, which includes marks obtained by students in different subjects.
This project carried out in R applies PCA for dimensionality reduction and K-Means for clustering on the IRIS dataset. It includes EDA, PCA variance analysis, and cluster evaluation using ggplot2 and factoextra. Additionally, it visualizes the impact of reducing dimensions on clustering.
Data Science Foundations II | Statistics Fundamentals for Data Science | Sampling for Data Science
Reviewing the (preliminary) summary statistics from the COVID19 Host Genetics Initiative meta-analysis of genome-wide association studies (GWAS).
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