mantispy.pp.feature_select

Contents

mantispy.pp.feature_select#

mantispy.pp.feature_select(adata, operations=('variance_threshold', 'correlation_threshold', 'drop_na_columns', 'blocklist'), min_variance=1e-06, freq_cut=0.05, unique_cut=0.01, corr_threshold=0.9, corr_method='pearson', na_cutoff=0.05, outlier_cutoff=500.0, blocklist='default', noise_removal_perturb_groups='Metadata_Perturbation', noise_removal_stdev_cutoff=0.8, key_added='selected', copy=False)#

Flag the features worth keeping.

Parameters:

  • adata (AnnData) – Object to select features on. Usually well-level profiles.

  • operations (Sequence[str] (default: ('variance_threshold', 'correlation_threshold', 'drop_na_columns', 'blocklist'))) – Which operations to run, from OPERATIONS. The default omits frequency_threshold, drop_outliers and noise_removal, matching pycytominer’s own default.

  • min_variance (float (default: 1e-06)) – variance_threshold: keep features with variance above this.

  • freq_cut (float (default: 0.05)) – frequency_threshold: drop a feature when the count of its second most common value divided by the count of its most common is below this. Either this rule or unique_cut drops a feature.

  • unique_cut (float (default: 0.01)) – frequency_threshold: drop a feature when its share of distinct values is below this.

  • corr_threshold (float (default: 0.9)) – correlation_threshold: drop one member of every pair correlated above this.

  • corr_method (str (default: 'pearson')) – correlation_threshold: "pearson" or "spearman".

  • na_cutoff (float (default: 0.05)) – drop_na_columns: drop features missing in more than this fraction of rows.

  • outlier_cutoff (float (default: 500.0)) – drop_outliers: drop features whose absolute value exceeds this.

  • blocklist (str | Sequence[str] (default: 'default')) – blocklist: "default" for the bundled list, or explicit names. Matched against the current names and against var["original_name"], so it works either side of standardize_feature_names().

  • noise_removal_perturb_groups (str (default: 'Metadata_Perturbation')) – noise_removal: obs column grouping replicates.

  • noise_removal_stdev_cutoff (float (default: 0.8)) – noise_removal: drop features whose within-group standard deviation, averaged over groups, is above this. An absolute threshold on the scale normalize left the values on, so it is only meaningful next to the normalization that produced them; pycytominer’s default assumes whole-plate standardization.

  • key_added (str (default: 'selected')) – Name of the boolean var column to write.

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

Return type:

AnnData | None

Returns:

None, or the modified copy. Writes var[key_added] and a per-operation count of removals to uns["mantispy"]["feature_select"], each count being what that operation removes on its own. Nothing is dropped; use subset_features() for that.

Raises:

  • ValueError – If operations names an operation that is not in OPERATIONS.

  • KeyError – If noise_removal is requested but noise_removal_perturb_groups is not an obs column.

Notes

Every operation judges the full feature set, so each count in uns["mantispy"]["feature_select"] says what that operation alone would remove and is the same whatever order operations runs in. The counts therefore overlap: a feature that is both constant and mostly missing is counted by variance_threshold and by drop_na_columns, and the counts sum to more than the number of features actually removed, which is n_vars minus var[key_added].sum().

correlation_threshold is the most expensive operation. pycytominer uses pandas.DataFrame.corr, one Cython pass per column pair. Here the pairs come from chunked matrix products over blocks of columns, so no n_vars ** 2 array is held in memory.

Contents