The main inference function for running SuShiE.

Parameters:

Xs: List[Array | ndarray | bool | number | bool | int | float | complex]¶

Genotype data for multiple ancestries.

ys: List[Array | ndarray | bool | number | bool | int | float | complex]¶

Phenotype data for multiple ancestries.

covar: List[Array | ndarray | bool | number | bool | int | float | complex] | None = None¶

Covariate data for multiple ancestries.

L: int = 10¶

Inferred number of eQTLs for the gene.

no_scale: bool = False¶

Do not scale the genotype and phenotype. Default is to scale.

no_regress: bool = False¶

Do not regress covariates on genotypes. Default is to regress.

no_update: bool = False¶

Do not update the effect size prior. Default is to update.

pi: Array | ndarray | bool | number | bool | int | float | complex | None = None¶

The probability prior for one SNP to be causal (\(\pi\) in Model Description). Default is \(1\) over the number of SNPs by specifying it as None.

resid_var: List[float] | None = None¶

Prior residual variance (\(\sigma^2_e\) in Model Description). Default is \(0.001\) by specifying it as None.

effect_var: List[float] | None = None¶

Prior causal effect size variance (\(\sigma^2_{i,b}\) in Model Description). Default is \(0.001\) by specifying it as None.

rho: List[float] | None = None¶

Prior effect size correlation (\(\rho\) in Model Description). Default is \(0.1\) by specifying it as None.

max_iter: int = 500¶

The maximum iteration for optimization. Default is \(500\).

min_tol: float = 0.0001¶

The convergence tolerance. Default is \(10^{-4}\).

threshold: float = 0.95¶

The credible set threshold. Default is \(0.95\).

purity: float = 0.5¶

The minimum pairwise correlation across SNPs to be eligible as output credible set. Default is \(0.5\).

purity_method: str = 'weighted'¶

The method to compute purity across ancestries. Default is weighted.

max_select: int = 500¶

The maximum number of selected SNPs to compute purity. Default is \(500\).

min_snps: int = 100¶

The minimum number of SNPs to fine-map. Default is \(100\).

no_reorder: bool = False¶

Do not re-order single effects based on Frobenius norm of alpha-weighted posterior mean square. Default is to re-order.

seed: int = 12345¶

The randomization seed for selecting SNPs in the credible set to compute purity. Default is \(12345\).

Returns:

A SuShiE result object that contains prior (Prior), posterior (Posterior), cs, pip, elbo, and elbo_increase.

Return type:

SushieResult