mantispy.pp.normalize

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mantispy.pp.normalize#

mantispy.pp.normalize(adata, method='mad_robustize', by='Metadata_Plate', reference=None, epsilon=1e-18, keep_raw=False, layer=None, key_added=None, copy=False)#

Normalize features within groups, optionally fitting on reference rows only.

Parameters:

  • adata (AnnData) – Object to normalize.

  • method (str (default: 'mad_robustize')) – "mad_robustize" computes (x - median) / (1.4826 * MAD + epsilon), "standardize" computes (x - mean) / sd, and "robustize" computes (x - median) / IQR.

  • by (str | list[str] | None (default: 'Metadata_Plate')) – Column(s) defining the groups statistics are computed within, usually the plate. None fits one set of statistics globally.

  • reference (str | None (default: None)) – Rows to fit on: None for all, "negcon" for Metadata_Control, or the name of a boolean obs column.

  • epsilon (float (default: 1e-18)) – Added to the MAD, matching pycytominer’s mad_robustize_epsilon. Unused by the other methods.

  • keep_raw (bool (default: False)) – Store the pre-normalization matrix in layers["raw"]. Off by default, because the layer doubles memory and the raw table is already on disk.

  • layer (str | None (default: None)) – Read this layer instead of X.

  • key_added (str | None (default: None)) – Write to layers[key_added] instead of overwriting X.

  • copy (bool (default: False)) – Return a normalized copy instead of normalizing in place.

Return type:

AnnData | None

Returns:

None, or the normalized copy when copy=True. Writes X or layers[key_added], and var["degenerate_scale"], or var["degenerate_scale_<key_added>"] when writing to a layer, which flags features that have no spread in some group or no reference values to centre on there, and comes with a warning; drop those features before computing distances.

Raises:

ValueError – If method is unknown, or reference selects no rows at all or none in some group.

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