Differential analysis & tests / Chi-square test
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The chi-square test of independence asks whether two categorical variables are associated, e.g. genotype and phenotype, or variant type and impact class. Give raw data (one observation per row) or a ready-made contingency table of counts.
Expected count = row total × column total / grand total. If more than 20% of cells have expected counts below 5, or any is below 1, the chi-square approximation is unreliable and the page says so; for 2×2 tables use Fisher’s exact test.
Yates’ continuity correction is applied to 2×2 tables by default (as in common statistics packages) and can be switched off. Cramér’s V (0 to 1) is the effect size.
The heatmap shows adjusted (standardised) residuals: above about 2 means clearly more observations than expected under independence, below about −2 clearly fewer — this points to the cells driving the result.
Goodness-of-fit mode tests whether observed counts follow given proportions (uniform if none are given).
Pearson K. (1900) Philos Mag 50:157–175; Yates F. (1934) J R Stat Soc Suppl 1:217–235; Agresti A. (2013) Categorical Data Analysis (adjusted residuals). χ² and P match scipy.stats.chi2_contingency / chisquare; adjusted residuals match statsmodels Table.standardized_resids.
Up to 200,000 rows of raw data.
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