pub struct MArrayLm {Show 24 fields
pub n_genes: usize,
pub n_samples: usize,
pub n_coef: usize,
pub coefficients: Vec<f64>,
pub stdev_unscaled: Vec<f64>,
pub sigma: Vec<f64>,
pub df_residual: Vec<f64>,
pub cov_coefficients: Vec<f64>,
pub pivot: Vec<usize>,
pub rank: usize,
pub design: Vec<f64>,
pub amean: Option<Vec<f64>>,
pub contrasts: Option<Vec<f64>>,
pub df_prior: Option<Vec<f64>>,
pub s2_prior: Option<Vec<f64>>,
pub var_prior: Option<Vec<f64>>,
pub proportion: Option<f64>,
pub s2_post: Option<Vec<f64>>,
pub t: Option<Vec<f64>>,
pub df_total: Option<Vec<f64>>,
pub p_value: Option<Vec<f64>>,
pub lods: Option<Vec<f64>>,
pub f_stat: Option<Vec<f64>>,
pub f_p_value: Option<Vec<f64>>,
}Expand description
A linear model fit, growing as the pipeline runs over it.
Everything before MArrayLm::contrasts comes from lm_fit. After
contrasts_fit the coefficient axis is the contrast axis, and
MArrayLm::n_coef counts contrasts rather than design columns.
Fields§
§n_genes: usizeNumber of genes.
n_samples: usizeNumber of samples in the design.
n_coef: usizeWidth of the coefficient axis: design columns, or contrasts once
contrasts_fit has run.
coefficients: Vec<f64>Coefficients, row-major n_genes * n_coef. NaN where not estimable.
stdev_unscaled: Vec<f64>Unscaled standard deviations, row-major n_genes * n_coef.
sigma: Vec<f64>Residual standard deviation per gene.
df_residual: Vec<f64>Residual degrees of freedom per gene.
cov_coefficients: Vec<f64>Unscaled covariance of the estimable coefficients, row-major
rank * rank, or n_coef * n_coef after contrasts_fit.
pivot: Vec<usize>Design column indices, accepted ones first. Length is the original
n_coef, which after contrasts_fit is no longer this struct’s.
rank: usizeRank of the design.
design: Vec<f64>Design matrix, row-major n_samples * pivot.len().
amean: Option<Vec<f64>>Row means of the response, one per gene. limma’s Amean.
None when the caller had none to give, which makes
EBayesTrend::Amean an error rather than a silent no-trend fit.
contrasts: Option<Vec<f64>>Contrast matrix, column-major original n_coef * n_coef, once
contrasts_fit has run.
df_prior: Option<Vec<f64>>Prior degrees of freedom. Length one, or n_genes for a robust fit.
s2_prior: Option<Vec<f64>>Prior variance. Length one, or n_genes for a trended fit.
var_prior: Option<Vec<f64>>Prior variance of the coefficients, one per coefficient. The
B-statistic’s v0.
proportion: Option<f64>Assumed proportion of differentially expressed genes.
s2_post: Option<Vec<f64>>Posterior variance per gene.
t: Option<Vec<f64>>Moderated t, row-major n_genes * n_coef.
df_total: Option<Vec<f64>>Total degrees of freedom per gene, residual plus prior, capped.
p_value: Option<Vec<f64>>Two-sided p-values, row-major n_genes * n_coef.
lods: Option<Vec<f64>>Log-odds of differential expression, row-major n_genes * n_coef.
f_stat: Option<Vec<f64>>Moderated F per gene. None when the design is not full rank.
f_p_value: Option<Vec<f64>>P-value of MArrayLm::f_stat.
Implementations§
Source§impl MArrayLm
impl MArrayLm
Sourcepub fn from_lm_fit(
fit: LmFitResult,
design: &[f64],
n_coef: usize,
n_samples: usize,
amean: Option<Vec<f64>>,
) -> Result<Self, EdgeErrors>
pub fn from_lm_fit( fit: LmFitResult, design: &[f64], n_coef: usize, n_samples: usize, amean: Option<Vec<f64>>, ) -> Result<Self, EdgeErrors>
Wraps an lm_fit result, with the response’s row means alongside.
§Params
fit- Whatlm_fitreturneddesign- Row-major design,n_samples * n_coefn_coef- Number of design columnsn_samples- Number of samplesamean- Row means of the response, one per gene, orNone.voomreturns them ascrate::limma::voom::VoomResult::amean.
§Returns
The fit object with every empirical Bayes field unset, or
EdgeErrors::LengthMismatch if design or amean disagrees with the
fit’s shape.
Trait Implementations§
Auto Trait Implementations§
impl Freeze for MArrayLm
impl RefUnwindSafe for MArrayLm
impl Send for MArrayLm
impl Sync for MArrayLm
impl Unpin for MArrayLm
impl UnsafeUnpin for MArrayLm
impl UnwindSafe for MArrayLm
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