File size: 4,685 Bytes
0161e74 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 | from .._types import MetricBestValue, MetricType
from ._anndata import (
ClusteringAgreement,
discrimination_score,
edistance,
mae,
mae_delta,
mse,
mse_delta,
pearson_delta,
)
from ._de import (
DEDirectionMatch,
DENsigCounts,
DESigGenesRecall,
DESpearmanLFC,
DESpearmanSignificant,
compute_pr_auc,
compute_roc_auc,
de_overlap_metric,
)
from ._registry import MetricRegistry
metrics_registry = MetricRegistry()
metrics_registry.register(
name="pearson_delta",
metric_type=MetricType.ANNDATA_PAIR,
description="Pearson correlation between mean differences from control",
best_value=MetricBestValue.ONE,
func=pearson_delta,
)
metrics_registry.register(
name="mse",
metric_type=MetricType.ANNDATA_PAIR,
description="Mean squared error of each perturbation from control.",
best_value=MetricBestValue.ZERO,
func=mse,
)
metrics_registry.register(
name="mae",
metric_type=MetricType.ANNDATA_PAIR,
description="Mean absolute error of each perturbation from control.",
best_value=MetricBestValue.ZERO,
func=mae,
)
metrics_registry.register(
name="mse_delta",
metric_type=MetricType.ANNDATA_PAIR,
description="Mean squared error of each perturbation-control delta.",
best_value=MetricBestValue.ZERO,
func=mse_delta,
)
metrics_registry.register(
name="mae_delta",
metric_type=MetricType.ANNDATA_PAIR,
description="Mean squared error of each perturbation-control delta.",
best_value=MetricBestValue.ZERO,
func=mae_delta,
)
for distance_metric in ["l1", "l2", "cosine"]:
metrics_registry.register(
name=f"discrimination_score_{distance_metric}",
metric_type=MetricType.ANNDATA_PAIR,
description=f"Determines similarity of each pred representation to real via normalized rank: {distance_metric}",
best_value=MetricBestValue.ONE,
func=discrimination_score,
kwargs={"metric": distance_metric},
)
metrics_registry.register(
name="pearson_edistance",
metric_type=MetricType.ANNDATA_PAIR,
best_value=MetricBestValue.ONE,
description="Calculates the pearson correlation coefficient between all pred and real edistance from controls",
func=edistance,
)
for metric in ["overlap", "precision"]:
for n in [None, 50, 100, 200, 500]:
repr = n if n else "N"
metrics_registry.register(
name=f"{metric}_at_{repr}",
metric_type=MetricType.DE,
description=f"Overlap metric ({metric}) of top {repr} DE genes",
best_value=MetricBestValue.ONE,
func=de_overlap_metric,
kwargs={"k": n, "metric": metric},
)
metrics_registry.register(
name="de_spearman_sig",
metric_type=MetricType.DE,
description="Spearman correlation on number of significant DE genes",
best_value=MetricBestValue.ONE,
func=DESpearmanSignificant, # type: ignore
is_class=True,
)
metrics_registry.register(
name="de_direction_match",
metric_type=MetricType.DE,
description="Agreement in direction of DE gene changes",
best_value=MetricBestValue.ONE,
func=DEDirectionMatch, # type: ignore
is_class=True,
)
metrics_registry.register(
name="de_spearman_lfc_sig",
metric_type=MetricType.DE,
description="Spearman correlation on log fold changes of significant genes",
best_value=MetricBestValue.ONE,
func=DESpearmanLFC, # type: ignore
is_class=True,
)
metrics_registry.register(
name="de_sig_genes_recall",
metric_type=MetricType.DE,
description="Recall of significant genes",
best_value=MetricBestValue.ONE,
func=DESigGenesRecall, # type: ignore
is_class=True,
)
metrics_registry.register(
name="de_nsig_counts",
metric_type=MetricType.DE,
description="Counts of significant genes",
best_value=MetricBestValue.NONE,
func=DENsigCounts, # type: ignore
is_class=True,
)
metrics_registry.register(
name="pr_auc",
metric_type=MetricType.DE,
description="Computes precision-recall for significant recovery",
best_value=MetricBestValue.ONE,
func=compute_pr_auc,
)
metrics_registry.register(
name="roc_auc",
metric_type=MetricType.DE,
description="Computes ROC AUC for significant recovery",
best_value=MetricBestValue.ONE,
func=compute_roc_auc,
)
metrics_registry.register(
name="clustering_agreement",
metric_type=MetricType.ANNDATA_PAIR,
description="Clustering agreement between real and predicted perturbation centroids",
best_value=MetricBestValue.ONE,
func=ClusteringAgreement, # type: ignore
is_class=True,
)
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