From 88c988fc1434586c1502895c101f76ba6476eac3 Mon Sep 17 00:00:00 2001 From: ielis Date: Thu, 21 Nov 2024 20:53:26 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20monarch-?= =?UTF-8?q?initiative/gpsea@218e35b1d3a098d74cdb75146df4a61bf3a7d7e4=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- latest/_modules/gpsea/analysis/_base.html | 106 ++++++++++++++---- .../_modules/gpsea/analysis/pcats/_impl.html | 39 +++---- .../gpsea/analysis/pcats/stats/_stats.html | 28 +++-- .../_modules/gpsea/analysis/pscore/_api.html | 12 +- .../gpsea/analysis/pscore/stats/_stats.html | 36 +++--- .../gpsea/analysis/temporal/_api.html | 12 +- .../gpsea/analysis/temporal/stats/_api.html | 4 +- .../gpsea/analysis/temporal/stats/_impl.html | 8 +- latest/apidocs/gpsea.analysis.html | 52 ++++++++- latest/apidocs/gpsea.analysis.pcats.html | 11 +- .../apidocs/gpsea.analysis.pcats.stats.html | 6 +- latest/apidocs/gpsea.analysis.pscore.html | 2 +- .../apidocs/gpsea.analysis.pscore.stats.html | 15 ++- latest/apidocs/gpsea.analysis.temporal.html | 2 +- .../gpsea.analysis.temporal.stats.html | 6 +- latest/apidocs/gpsea.html | 2 + latest/genindex.html | 30 ++++- latest/objects.inv | Bin 8144 -> 8201 bytes latest/searchindex.js | 2 +- 19 files changed, 272 insertions(+), 101 deletions(-) diff --git a/latest/_modules/gpsea/analysis/_base.html b/latest/_modules/gpsea/analysis/_base.html index 2fcac770..d45a9dbb 100644 --- a/latest/_modules/gpsea/analysis/_base.html +++ b/latest/_modules/gpsea/analysis/_base.html @@ -93,6 +93,61 @@

Source code for gpsea.analysis._base

 from ._partition import Partitioning
 
 
+
+[docs] +class StatisticResult: + """ + `StatisticResult` reports result of a :class:`~gpsea.analysis.Statistic`. + + It includes a statistic and a corresponding p value. + """ + + def __init__( + self, + statistic: typing.Optional[typing.Union[int, float]], + pval: float, + ): + if statistic is not None: + assert isinstance(statistic, (float, int)) + self._statistic = float(statistic) + else: + self._statistic = None + + if isinstance(pval, float) and (math.isnan(pval) or 0.0 <= pval <= 1.0): + self._pval = float(pval) + else: + raise ValueError( + f"`pval` must be a float in range [0, 1] but it was {pval}" + ) + + @property + def statistic(self) -> typing.Optional[float]: + return self._statistic + + @property + def pval(self) -> float: + return self._pval + + def __eq__(self, value: object) -> bool: + return ( + isinstance(value, StatisticResult) + and self._statistic == value._statistic + and self._pval == value._pval + ) + + def __hash__(self) -> int: + return hash((self._statistic, self._pval,)) + + def __str__(self) -> str: + return repr(self) + + def __repr__(self) -> str: + return f"StatisticResult(statistic={self._statistic}, pval={self._pval})"
+ + + +
+[docs] class Statistic(metaclass=abc.ABCMeta): """ Mixin for classes that are used to compute a nominal p value for a genotype-phenotype association. @@ -117,7 +172,8 @@

Source code for gpsea.analysis._base

         return NotImplemented
     
     def __hash__(self) -> int:
-        return hash((self._name,))
+        return hash((self._name,))
+
@@ -180,7 +236,7 @@

Source code for gpsea.analysis._base

         statistic: Statistic,
         n_usable: typing.Sequence[int],
         all_counts: typing.Sequence[pd.DataFrame],
-        pvals: typing.Sequence[float],
+        statistic_results: typing.Sequence[typing.Optional[StatisticResult]],
         corrected_pvals: typing.Optional[typing.Sequence[float]],
         mtc_correction: typing.Optional[str]
     ):
@@ -194,7 +250,7 @@ 

Source code for gpsea.analysis._base

         self._n_usable = tuple(n_usable)
         self._all_counts = tuple(all_counts)
 
-        self._pvals = tuple(pvals)
+        self._statistic_results = tuple(statistic_results)
         self._corrected_pvals = (
             None if corrected_pvals is None else tuple(corrected_pvals)
         )
@@ -212,7 +268,7 @@ 

Source code for gpsea.analysis._base

         for seq, name in (
             (self._n_usable, 'n_usable'),
             (self._all_counts, 'all_counts'),
-            (self._pvals, 'pvals'),
+            (self._statistic_results, 'statistic_results'),
         ):
             if len(self._pheno_predicates) != len(seq):
                 errors.append(
@@ -277,13 +333,21 @@ 

Source code for gpsea.analysis._base

         """
         return self._all_counts
 
+    @property
+    def statistic_results(self) -> typing.Sequence[typing.Optional[StatisticResult]]:
+        """
+        Get a sequence of :class:`~gpsea.analysis.StatisticResult` items with nominal p values and the associated statistic values
+        for each tested phenotype or `None` for the untested phenotypes.
+        """
+        return self._statistic_results
+
     @property
     def pvals(self) -> typing.Sequence[float]:
         """
         Get a sequence of nominal p values for each tested phenotype.
         The sequence includes a `NaN` value for each phenotype that was *not* tested.
         """
-        return self._pvals
+        return tuple(float('nan') if r is None else r.pval for r in self._statistic_results )
 
     @property
     def corrected_pvals(self) -> typing.Optional[typing.Sequence[float]]:
@@ -341,13 +405,12 @@ 

Source code for gpsea.analysis._base

         return tuple(int(idx) for idx in np.argsort(vals) if selected[idx])
- @property def total_tests(self) -> int: """ Get total count of genotype-phenotype associations that were tested in this analysis. """ - return sum(1 for pval in self.pvals if not math.isnan(pval)) + return sum(1 for result in self._statistic_results if result is not None) @property def mtc_correction(self) -> typing.Optional[str]: @@ -363,7 +426,7 @@

Source code for gpsea.analysis._base

             and self._pheno_predicates == value._pheno_predicates \
             and self._n_usable == value._n_usable \
             and self._all_counts == value._all_counts \
-            and self._pvals == value._pvals \
+            and self._statistic_results == value._statistic_results \
             and self._corrected_pvals == value._corrected_pvals \
             and self._mtc_correction == value._mtc_correction
     
@@ -373,7 +436,7 @@ 

Source code for gpsea.analysis._base

             self._pheno_predicates,
             self._n_usable,
             self._all_counts,
-            self._pvals,
+            self._statistic_results,
             self._corrected_pvals,
             self._mtc_correction,
         ))
@@ -409,7 +472,7 @@

Source code for gpsea.analysis._base

         phenotype: Partitioning,
         statistic: Statistic,
         data: pd.DataFrame,
-        pval: float,
+        statistic_result: StatisticResult,
     ):
         super().__init__(gt_predicate, statistic)
 
@@ -421,12 +484,8 @@ 

Source code for gpsea.analysis._base

         )
         self._data = data
 
-        if isinstance(pval, float) and math.isfinite(pval) and 0.0 <= pval <= 1.0:
-            self._pval = float(pval)
-        else:
-            raise ValueError(
-                f"`pval` must be a finite float in range [0, 1] but it was {pval}"
-            )
+        assert isinstance(statistic_result, StatisticResult)
+        self._statistic_result = statistic_result
         
     @property
     def phenotype(self) -> Partitioning:
@@ -469,25 +528,34 @@ 

Source code for gpsea.analysis._base

         ]
+
+[docs] + def statistic_result(self) -> StatisticResult: + """ + Get statistic result with the nominal p value and the associated statistics. + """ + return self._statistic_result
+ + @property def pval(self) -> float: """ Get the p value of the test. """ - return self._pval + return self._statistic_result.pval def __eq__(self, value: object) -> bool: return isinstance(value, MonoPhenotypeAnalysisResult) \ and super(AnalysisResult, self).__eq__(value) \ and self._phenotype == value._phenotype \ - and self._pval == value._pval \ + and self._statistic_result == value._statistic_result \ and self._data.equals(value._data) def __hash__(self) -> int: return hash(( super(AnalysisResult, self).__hash__(), self._phenotype, - self._pval, + self._statistic_result, self._data, ))
diff --git a/latest/_modules/gpsea/analysis/pcats/_impl.html b/latest/_modules/gpsea/analysis/pcats/_impl.html index 5da0049f..f18d9bd2 100644 --- a/latest/_modules/gpsea/analysis/pcats/_impl.html +++ b/latest/_modules/gpsea/analysis/pcats/_impl.html @@ -99,7 +99,7 @@

Source code for gpsea.analysis.pcats._impl

 from ..mtc_filter import PhenotypeMtcFilter, PhenotypeMtcResult
 
 from .stats import CountStatistic
-from .._base import MultiPhenotypeAnalysisResult
+from .._base import MultiPhenotypeAnalysisResult, StatisticResult
 
 DEFAULT_MTC_PROCEDURE = 'fdr_bh'
 """
@@ -283,30 +283,31 @@ 

Source code for gpsea.analysis.pcats._impl

 
         return issues
 
-    def _compute_nominal_pvals(
+    def _compute_nominal_stats(
         self,
         n_usable: typing.Iterable[int],
         all_counts: typing.Iterable[pd.DataFrame],
-    ) -> np.ndarray:
-        pvals = []
+    ) -> typing.Sequence[typing.Optional[StatisticResult]]:
+        results = []
         for i, (usable, count) in enumerate(zip(n_usable, all_counts)):
             if usable == 0:
-                pvals.append(np.nan)
+                results.append(None)
             else:
                 try:
-                    pval = self._count_statistic.compute_pval(count)
-                    pvals.append(pval)
+                    stat_result = self._count_statistic.compute_pval(count)
+                    results.append(stat_result)
                 except ValueError as ve:
                     # TODO: add more context to the exception?
                     raise ve
 
-        return np.array(pvals)
+        return np.array(results)
 
     def _apply_mtc(
         self,
-        pvals: typing.Sequence[float],
+        stats: typing.Sequence[typing.Optional[StatisticResult]],
     ) -> typing.Sequence[float]:
         assert self._mtc_correction is not None
+        pvals = tuple(s.pval for s in stats if s is not None)
         _, corrected_pvals, _, _ = multitest.multipletests(
             pvals=pvals,
             alpha=self._mtc_alpha,
@@ -340,13 +341,13 @@ 

Source code for gpsea.analysis.pcats._impl

         )
 
         # 2 - Compute nominal p values
-        pvals = self._compute_nominal_pvals(n_usable=n_usable, all_counts=all_counts)
+        stats = self._compute_nominal_stats(n_usable=n_usable, all_counts=all_counts)
 
         # 3 - Apply Multiple Testing Correction
         if self._mtc_correction is None:
             corrected_pvals = None
         else:
-            corrected_pvals = self._apply_mtc(pvals=pvals)
+            corrected_pvals = self._apply_mtc(stats=stats)
 
         return MultiPhenotypeAnalysisResult(
             gt_predicate=gt_predicate,
@@ -355,7 +356,7 @@ 

Source code for gpsea.analysis.pcats._impl

             pheno_predicates=pheno_predicates,
             n_usable=n_usable,
             all_counts=all_counts,
-            pvals=pvals,
+            statistic_results=stats,
             corrected_pvals=corrected_pvals,
         )
@@ -379,7 +380,7 @@

Source code for gpsea.analysis.pcats._impl

         pheno_predicates: typing.Iterable[PhenotypePolyPredicate[hpotk.TermId]],
         n_usable: typing.Sequence[int],
         all_counts: typing.Sequence[pd.DataFrame],
-        pvals: typing.Sequence[float],
+        statistic_results: typing.Sequence[typing.Optional[StatisticResult]],
         corrected_pvals: typing.Optional[typing.Sequence[float]],
         mtc_filter_name: str,
         mtc_filter_results: typing.Sequence[PhenotypeMtcResult],
@@ -391,7 +392,7 @@ 

Source code for gpsea.analysis.pcats._impl

             statistic=statistic,
             n_usable=n_usable,
             all_counts=all_counts,
-            pvals=pvals,
+            statistic_results=statistic_results,
             corrected_pvals=corrected_pvals,
             mtc_correction=mtc_correction,
         )
@@ -503,7 +504,7 @@ 

Source code for gpsea.analysis.pcats._impl

             cohort_size=cohort_size,
         )
 
-        pvals = np.full(shape=(len(n_usable),), fill_value=np.nan)
+        results = np.full(shape=(len(n_usable),), fill_value=None)
         corrected_pvals = None
 
         mtc_mask = np.array([r.is_passed() for r in mtc_filter_results])
@@ -511,16 +512,16 @@ 

Source code for gpsea.analysis.pcats._impl

             # We have at least one HPO term to test.
 
             # 3 - Compute nominal p values
-            pvals[mtc_mask] = self._compute_nominal_pvals(
+            results[mtc_mask] = self._compute_nominal_stats(
                 n_usable=slice_list_in_numpy_style(n_usable, mtc_mask),
                 all_counts=slice_list_in_numpy_style(all_counts, mtc_mask),
             )
 
             # 4 - Apply Multiple Testing Correction
             if self._mtc_correction is not None:
-                corrected_pvals = np.full(shape=pvals.shape, fill_value=np.nan)
+                corrected_pvals = np.full(shape=results.shape, fill_value=np.nan)
                 # Do not test the p values that have been filtered out.
-                corrected_pvals[mtc_mask] = self._apply_mtc(pvals=pvals[mtc_mask])
+                corrected_pvals[mtc_mask] = self._apply_mtc(stats=results[mtc_mask])
 
         return HpoTermAnalysisResult(
             gt_predicate=gt_predicate,
@@ -529,7 +530,7 @@ 

Source code for gpsea.analysis.pcats._impl

             pheno_predicates=pheno_predicates,
             n_usable=n_usable,
             all_counts=all_counts,
-            pvals=pvals,
+            statistic_results=results,
             corrected_pvals=corrected_pvals,
             mtc_filter_name=self._mtc_filter.filter_method_name(),
             mtc_filter_results=mtc_filter_results,
diff --git a/latest/_modules/gpsea/analysis/pcats/stats/_stats.html b/latest/_modules/gpsea/analysis/pcats/stats/_stats.html
index 864afde4..5a9a0265 100644
--- a/latest/_modules/gpsea/analysis/pcats/stats/_stats.html
+++ b/latest/_modules/gpsea/analysis/pcats/stats/_stats.html
@@ -90,9 +90,9 @@ 

Source code for gpsea.analysis.pcats.stats._stats

import numpy as np import pandas as pd -import scipy +from scipy.stats import fisher_exact -from ..._base import Statistic +from ..._base import Statistic, StatisticResult
@@ -150,7 +150,7 @@

Source code for gpsea.analysis.pcats.stats._stats

def compute_pval( self, counts: pd.DataFrame, - ) -> float: + ) -> StatisticResult: pass
@@ -190,12 +190,19 @@

Source code for gpsea.analysis.pcats.stats._stats

def compute_pval( self, counts: pd.DataFrame, - ) -> float: + ) -> StatisticResult: if counts.shape == (2, 2): - _, pval = scipy.stats.fisher_exact(counts.values, alternative="two-sided") - return pval + result = fisher_exact(counts.values, alternative="two-sided") + return StatisticResult( + statistic=result.statistic, + pval=result.pvalue, + ) elif counts.shape == (2, 3): - return self._fisher_exact(counts.values) + pval = self._fisher_exact(counts.values) + return StatisticResult( + statistic=None, + pval=pval, + ) else: raise ValueError(f'Unsupported counts shape {counts.shape}')
@@ -239,11 +246,12 @@

Source code for gpsea.analysis.pcats.stats._stats

p_0 /= Decimal(math.factorial(table[i][j])) p = [0] - self._dfs(mat, pos, row_sum, col_sum, p_0, p) + FisherExactTest._dfs(mat, pos, row_sum, col_sum, p_0, p) return float(p[0]) - def _dfs(self, mat, pos, r_sum, c_sum, p_0, p): + @staticmethod + def _dfs(mat, pos, r_sum, c_sum, p_0, p): (xx, yy) = pos (r, c) = (len(r_sum), len(c_sum)) @@ -307,7 +315,7 @@

Source code for gpsea.analysis.pcats.stats._stats

pos_new = (0, yy + 1) else: pos_new = (xx + 1, yy) - self._dfs(mat_new, pos_new, r_sum, c_sum, p_0, p) + FisherExactTest._dfs(mat_new, pos_new, r_sum, c_sum, p_0, p) def __eq__(self, value: object) -> bool: return isinstance(value, FisherExactTest) diff --git a/latest/_modules/gpsea/analysis/pscore/_api.html b/latest/_modules/gpsea/analysis/pscore/_api.html index be870f46..944f6a88 100644 --- a/latest/_modules/gpsea/analysis/pscore/_api.html +++ b/latest/_modules/gpsea/analysis/pscore/_api.html @@ -89,7 +89,7 @@

Source code for gpsea.analysis.pscore._api

 from ..predicate.genotype import GenotypePolyPredicate
 from .stats import PhenotypeScoreStatistic
 
-from .._base import MonoPhenotypeAnalysisResult, Statistic
+from .._base import MonoPhenotypeAnalysisResult, Statistic, StatisticResult
 from .._partition import ContinuousPartitioning
 
 
@@ -215,9 +215,9 @@ 

Source code for gpsea.analysis.pscore._api

         phenotype: PhenotypeScorer,
         statistic: Statistic,
         data: pd.DataFrame,
-        pval: float,
+        statistic_result: StatisticResult,
     ):
-        super().__init__(gt_predicate, phenotype, statistic, data, pval)
+        super().__init__(gt_predicate, phenotype, statistic, data, statistic_result)
         assert isinstance(phenotype, PhenotypeScorer)
 
 
@@ -291,7 +291,7 @@

Source code for gpsea.analysis.pscore._api

             f"phenotype_scorer={self._phenotype}, "
             f"statistic={self._statistic}, "
             f"data={self._data}, "
-            f"pval={self._pval})"
+            f"statistic_result={self._statistic_result})"
         )
 
     def __repr__(self) -> str:
@@ -366,14 +366,14 @@ 

Source code for gpsea.analysis.pscore._api

         y = data.loc[
             data[MonoPhenotypeAnalysisResult.GT_COL] == y_key, MonoPhenotypeAnalysisResult.PH_COL
         ].to_numpy(dtype=float)  # type: ignore
-        pval = self._statistic.compute_pval(scores=(x, y))
+        result = self._statistic.compute_pval(scores=(x, y))
 
         return PhenotypeScoreAnalysisResult(
             gt_predicate=gt_predicate,
             phenotype=pheno_scorer,
             statistic=self._statistic,
             data=data,
-            pval=pval,
+            statistic_result=result,
         )
diff --git a/latest/_modules/gpsea/analysis/pscore/stats/_stats.html b/latest/_modules/gpsea/analysis/pscore/stats/_stats.html index 2f5d1aa6..f5da41d5 100644 --- a/latest/_modules/gpsea/analysis/pscore/stats/_stats.html +++ b/latest/_modules/gpsea/analysis/pscore/stats/_stats.html @@ -86,7 +86,7 @@

Source code for gpsea.analysis.pscore.stats._stats

from scipy.stats import mannwhitneyu, ttest_ind -from ..._base import Statistic +from ..._base import Statistic, StatisticResult
@@ -104,13 +104,13 @@

Source code for gpsea.analysis.pscore.stats._stats

def compute_pval( self, scores: typing.Collection[typing.Sequence[float]], - ) -> float: + ) -> StatisticResult: pass
def __eq__(self, value: object) -> bool: return super().__eq__(value) - + def __hash__(self) -> int: return super().__hash__()
@@ -128,7 +128,7 @@

Source code for gpsea.analysis.pscore.stats._stats

See :ref:`phenotype-score-stats` for an example usage. """ - + def __init__(self): super().__init__( name="Mann-Whitney U test", @@ -139,19 +139,22 @@

Source code for gpsea.analysis.pscore.stats._stats

def compute_pval( self, scores: typing.Collection[typing.Sequence[float]], - ) -> float: + ) -> StatisticResult: assert len(scores) == 2, 'Mann-Whitney U rank test only supports 2 categories at this time' - + x, y = scores x = MannWhitneyStatistic._remove_nans(x) y = MannWhitneyStatistic._remove_nans(y) - _, pval = mannwhitneyu( + statistic, pval = mannwhitneyu( x=x, y=y, alternative='two-sided', ) - return pval
+ return StatisticResult( + statistic=float(statistic), + pval=float(pval), + )
@staticmethod @@ -162,7 +165,7 @@

Source code for gpsea.analysis.pscore.stats._stats

def __eq__(self, value: object) -> bool: return isinstance(value, MannWhitneyStatistic) - + def __hash__(self) -> int: return 23
@@ -191,9 +194,13 @@

Source code for gpsea.analysis.pscore.stats._stats

def compute_pval( self, scores: typing.Collection[typing.Sequence[float]], - ) -> float: + ) -> StatisticResult: + """ + :Returns: + a tuple with the p-value and the t-statistic + """ assert len(scores) == 2, 'T test only supports 2 categories at this time' - + x, y = scores res = ttest_ind( a=x, b=y, @@ -201,12 +208,15 @@

Source code for gpsea.analysis.pscore.stats._stats

nan_policy="omit", ) - return res.pvalue
+ return StatisticResult( + statistic=float(res.statistic), + pval=float(res.pvalue), + )
def __eq__(self, value: object) -> bool: return isinstance(value, TTestStatistic) - + def __hash__(self) -> int: return 31
diff --git a/latest/_modules/gpsea/analysis/temporal/_api.html b/latest/_modules/gpsea/analysis/temporal/_api.html index e033787d..ef78fd0d 100644 --- a/latest/_modules/gpsea/analysis/temporal/_api.html +++ b/latest/_modules/gpsea/analysis/temporal/_api.html @@ -95,7 +95,7 @@

Source code for gpsea.analysis.temporal._api

from ._util import prepare_censored_data
 from .stats import SurvivalStatistic
 
-from .._base import MonoPhenotypeAnalysisResult
+from .._base import MonoPhenotypeAnalysisResult, StatisticResult
 from .._partition import ContinuousPartitioning
 
 
@@ -159,14 +159,14 @@ 

Source code for gpsea.analysis.temporal._api

endpoint: Endpoint,
         statistic: SurvivalStatistic,
         data: pd.DataFrame,
-        pval: float,
+        statistic_result: StatisticResult,
     ):
         super().__init__(
             gt_predicate=gt_predicate,
             phenotype=endpoint,
             statistic=statistic,
             data=data,
-            pval=pval,
+            statistic_result=statistic_result,
         )
         assert isinstance(endpoint, Endpoint)
 
@@ -224,7 +224,7 @@ 

Source code for gpsea.analysis.temporal._api

f"endpoint={self._phenotype}, "
             f"statistic={self._statistic}, "
             f"data={self._data}, "
-            f"pval={self._pval})"
+            f"statistic_result={self._statistic_result})"
         )
 
     def __repr__(self) -> str:
@@ -285,14 +285,14 @@ 

Source code for gpsea.analysis.temporal._api

survivals[gt_cat].append(survival)
 
         vals = tuple(survivals[gt_cat] for gt_cat in gt_predicate.get_categorizations())
-        pval = self._statistic.compute_pval(vals)
+        result = self._statistic.compute_pval(vals)
 
         return SurvivalAnalysisResult(
             gt_predicate=gt_predicate,
             endpoint=endpoint,
             statistic=self._statistic,
             data=data,
-            pval=pval,
+            statistic_result=result,
         )
diff --git a/latest/_modules/gpsea/analysis/temporal/stats/_api.html b/latest/_modules/gpsea/analysis/temporal/stats/_api.html index 4ecf9de5..326b6b04 100644 --- a/latest/_modules/gpsea/analysis/temporal/stats/_api.html +++ b/latest/_modules/gpsea/analysis/temporal/stats/_api.html @@ -83,7 +83,7 @@

Source code for gpsea.analysis.temporal.stats._api

import abc import typing -from ..._base import Statistic +from ..._base import Statistic, StatisticResult from .._base import Survival @@ -105,7 +105,7 @@

Source code for gpsea.analysis.temporal.stats._api

def compute_pval( self, scores: typing.Collection[typing.Sequence[Survival]], - ) -> float: + ) -> StatisticResult: """ Compute p value for the collection of survivals being sampled from the same source distribution. diff --git a/latest/_modules/gpsea/analysis/temporal/stats/_impl.html b/latest/_modules/gpsea/analysis/temporal/stats/_impl.html index 08226522..71fcb0d2 100644 --- a/latest/_modules/gpsea/analysis/temporal/stats/_impl.html +++ b/latest/_modules/gpsea/analysis/temporal/stats/_impl.html @@ -84,6 +84,7 @@

Source code for gpsea.analysis.temporal.stats._impl

from scipy import stats +from ..._base import StatisticResult from .._base import Survival from .._util import prepare_censored_data from ._api import SurvivalStatistic @@ -109,7 +110,7 @@

Source code for gpsea.analysis.temporal.stats._impl

def compute_pval( self, scores: typing.Collection[typing.Iterable[Survival]], - ) -> float: + ) -> StatisticResult: """ Compute p value for survivals being sourced from the same distribution. @@ -127,7 +128,10 @@

Source code for gpsea.analysis.temporal.stats._impl

alternative="two-sided", ) - return float(result.pvalue)
+ return StatisticResult( + statistic=float(result.statistic), + pval=float(result.pvalue), + )
def __eq__(self, value: object) -> bool: diff --git a/latest/apidocs/gpsea.analysis.html b/latest/apidocs/gpsea.analysis.html index 2b10c018..70c2f3d3 100644 --- a/latest/apidocs/gpsea.analysis.html +++ b/latest/apidocs/gpsea.analysis.html @@ -103,7 +103,7 @@

gpsea.analysis package

-class gpsea.analysis.AnalysisResult(gt_predicate: GenotypePolyPredicate, statistic: Statistic)[source]
+class gpsea.analysis.AnalysisResult(gt_predicate: GenotypePolyPredicate, statistic: Statistic)[source]

Bases: object

AnalysisResult includes the common parts of results of all analyses.

@@ -114,7 +114,7 @@
-property statistic: Statistic
+property statistic: Statistic

Get the statistic which computed the (nominal) p values for this result.

@@ -122,7 +122,7 @@
-class gpsea.analysis.MonoPhenotypeAnalysisResult(gt_predicate: GenotypePolyPredicate, phenotype: Partitioning, statistic: Statistic, data: DataFrame, pval: float)[source]
+class gpsea.analysis.MonoPhenotypeAnalysisResult(gt_predicate: GenotypePolyPredicate, phenotype: Partitioning, statistic: Statistic, data: DataFrame, statistic_result: StatisticResult)[source]

Bases: AnalysisResult

MonoPhenotypeAnalysisResult reports the outcome of an analysis that tested a single genotype-phenotype association.

@@ -175,6 +175,12 @@ where both genotype and phenotype columns are available (i.e. not None or NaN).

+
+
+statistic_result() StatisticResult[source]
+

Get statistic result with the nominal p value and the associated statistics.

+
+
property pval: float
@@ -185,7 +191,7 @@
-class gpsea.analysis.MultiPhenotypeAnalysisResult(gt_predicate: GenotypePolyPredicate, pheno_predicates: Iterable[PhenotypePolyPredicate[P]], statistic: Statistic, n_usable: Sequence[int], all_counts: Sequence[DataFrame], pvals: Sequence[float], corrected_pvals: Sequence[float] | None, mtc_correction: str | None)[source]
+class gpsea.analysis.MultiPhenotypeAnalysisResult(gt_predicate: GenotypePolyPredicate, pheno_predicates: Iterable[PhenotypePolyPredicate[P]], statistic: Statistic, n_usable: Sequence[int], all_counts: Sequence[DataFrame], statistic_results: Sequence[StatisticResult | None], corrected_pvals: Sequence[float] | None, mtc_correction: str | None)[source]

Bases: Generic[P], AnalysisResult

MultiPhenotypeAnalysisResult reports the outcome of an analysis that tested the association of genotype with two or more phenotypes.

@@ -226,6 +232,13 @@

The rows correspond to the phenotype categories, and the columns represent the genotype categories.

+
+
+property statistic_results: Sequence[StatisticResult | None]
+

Get a sequence of StatisticResult items with nominal p values and the associated statistic values +for each tested phenotype or None for the untested phenotypes.

+
+
property pvals: Sequence[float]
@@ -273,6 +286,37 @@
+
+
+class gpsea.analysis.Statistic(name: str)[source]
+

Bases: object

+

Mixin for classes that are used to compute a nominal p value for a genotype-phenotype association.

+
+
+property name: str
+

Get the name of the statistic (e.g. Fisher Exact Test, Logrank test).

+
+ +
+ +
+
+class gpsea.analysis.StatisticResult(statistic: int | float | None, pval: float)[source]
+

Bases: object

+

StatisticResult reports result of a Statistic.

+

It includes a statistic and a corresponding p value.

+
+
+property statistic: float | None
+
+ +
+
+property pval: float
+
+ +
+

Subpackages

diff --git a/latest/apidocs/gpsea.analysis.pcats.html b/latest/apidocs/gpsea.analysis.pcats.html index 92599f41..9c12fd05 100644 --- a/latest/apidocs/gpsea.analysis.pcats.html +++ b/latest/apidocs/gpsea.analysis.pcats.html @@ -129,7 +129,7 @@
-class gpsea.analysis.pcats.MultiPhenotypeAnalysisResult(gt_predicate: GenotypePolyPredicate, pheno_predicates: Iterable[PhenotypePolyPredicate[P]], statistic: Statistic, n_usable: Sequence[int], all_counts: Sequence[DataFrame], pvals: Sequence[float], corrected_pvals: Sequence[float] | None, mtc_correction: str | None)[source]
+class gpsea.analysis.pcats.MultiPhenotypeAnalysisResult(gt_predicate: GenotypePolyPredicate, pheno_predicates: Iterable[PhenotypePolyPredicate[P]], statistic: Statistic, n_usable: Sequence[int], all_counts: Sequence[DataFrame], statistic_results: Sequence[StatisticResult | None], corrected_pvals: Sequence[float] | None, mtc_correction: str | None)[source]

Bases: Generic[P], AnalysisResult

MultiPhenotypeAnalysisResult reports the outcome of an analysis that tested the association of genotype with two or more phenotypes.

@@ -170,6 +170,13 @@

The rows correspond to the phenotype categories, and the columns represent the genotype categories.

+
+
+property statistic_results: Sequence[StatisticResult | None]
+

Get a sequence of StatisticResult items with nominal p values and the associated statistic values +for each tested phenotype or None for the untested phenotypes.

+
+
property pvals: Sequence[float]
@@ -236,7 +243,7 @@
-class gpsea.analysis.pcats.HpoTermAnalysisResult(gt_predicate: GenotypePolyPredicate, statistic: CountStatistic, mtc_correction: str | None, pheno_predicates: Iterable[PhenotypePolyPredicate[TermId]], n_usable: Sequence[int], all_counts: Sequence[DataFrame], pvals: Sequence[float], corrected_pvals: Sequence[float] | None, mtc_filter_name: str, mtc_filter_results: Sequence[PhenotypeMtcResult])[source]
+class gpsea.analysis.pcats.HpoTermAnalysisResult(gt_predicate: GenotypePolyPredicate, statistic: CountStatistic, mtc_correction: str | None, pheno_predicates: Iterable[PhenotypePolyPredicate[TermId]], n_usable: Sequence[int], all_counts: Sequence[DataFrame], statistic_results: Sequence[StatisticResult | None], corrected_pvals: Sequence[float] | None, mtc_filter_name: str, mtc_filter_results: Sequence[PhenotypeMtcResult])[source]

Bases: MultiPhenotypeAnalysisResult[TermId]

HpoTermAnalysisResult includes the HpoTermAnalysis results.

On top of the attributes of MultiPhenotypeAnalysisResult parent, diff --git a/latest/apidocs/gpsea.analysis.pcats.stats.html b/latest/apidocs/gpsea.analysis.pcats.stats.html index f67c3aca..4a23df55 100644 --- a/latest/apidocs/gpsea.analysis.pcats.stats.html +++ b/latest/apidocs/gpsea.analysis.pcats.stats.html @@ -106,7 +106,7 @@

class gpsea.analysis.pcats.stats.CountStatistic(name: str)[source]
-

Bases: Statistic

+

Bases: Statistic

CountStatistic calculates a p value for a contingency table produced by a pair of discrete random variables.

@@ -148,7 +148,7 @@

Supports shape
-abstract compute_pval(counts: DataFrame) float[source]
+abstract compute_pval(counts: DataFrame) StatisticResult[source]

@@ -170,7 +170,7 @@

Supports shape
-compute_pval(counts: DataFrame) float[source]
+compute_pval(counts: DataFrame) StatisticResult[source]
diff --git a/latest/apidocs/gpsea.analysis.pscore.html b/latest/apidocs/gpsea.analysis.pscore.html index 93416cc7..c75a2d87 100644 --- a/latest/apidocs/gpsea.analysis.pscore.html +++ b/latest/apidocs/gpsea.analysis.pscore.html @@ -164,7 +164,7 @@
-class gpsea.analysis.pscore.PhenotypeScoreAnalysisResult(gt_predicate: GenotypePolyPredicate, phenotype: PhenotypeScorer, statistic: Statistic, data: DataFrame, pval: float)[source]
+class gpsea.analysis.pscore.PhenotypeScoreAnalysisResult(gt_predicate: GenotypePolyPredicate, phenotype: PhenotypeScorer, statistic: Statistic, data: DataFrame, statistic_result: StatisticResult)[source]

Bases: MonoPhenotypeAnalysisResult

PhenotypeScoreAnalysisResult is a container for PhenotypeScoreAnalysis results.

The PhenotypeScoreAnalysisResult.data property provides a data frame diff --git a/latest/apidocs/gpsea.analysis.pscore.stats.html b/latest/apidocs/gpsea.analysis.pscore.stats.html index 429f0e56..df00422a 100644 --- a/latest/apidocs/gpsea.analysis.pscore.stats.html +++ b/latest/apidocs/gpsea.analysis.pscore.stats.html @@ -104,13 +104,13 @@

class gpsea.analysis.pscore.stats.PhenotypeScoreStatistic(name: str)[source]
-

Bases: Statistic

+

Bases: Statistic

PhenotypeScoreStatistic calculates a p value for 2 or more phenotype score groups computed by a PhenotypeScorer.

-abstract compute_pval(scores: Collection[Sequence[float]]) float[source]
+abstract compute_pval(scores: Collection[Sequence[float]]) StatisticResult[source]
@@ -126,7 +126,7 @@

See Compare phenotype scores in genotype groups for an example usage.

-compute_pval(scores: Collection[Sequence[float]]) float[source]
+compute_pval(scores: Collection[Sequence[float]]) StatisticResult[source]
@@ -141,8 +141,13 @@

The NaN phenotype score values are ignored.

-compute_pval(scores: Collection[Sequence[float]]) float[source]
-
+compute_pval(scores: Collection[Sequence[float]]) StatisticResult[source] +
+
Returns:
+

a tuple with the p-value and the t-statistic

+
+
+
diff --git a/latest/apidocs/gpsea.analysis.temporal.html b/latest/apidocs/gpsea.analysis.temporal.html index 90b3fe66..02fb33c5 100644 --- a/latest/apidocs/gpsea.analysis.temporal.html +++ b/latest/apidocs/gpsea.analysis.temporal.html @@ -139,7 +139,7 @@
-class gpsea.analysis.temporal.SurvivalAnalysisResult(gt_predicate: GenotypePolyPredicate, endpoint: Endpoint, statistic: SurvivalStatistic, data: DataFrame, pval: float)[source]
+class gpsea.analysis.temporal.SurvivalAnalysisResult(gt_predicate: GenotypePolyPredicate, endpoint: Endpoint, statistic: SurvivalStatistic, data: DataFrame, statistic_result: StatisticResult)[source]

Bases: MonoPhenotypeAnalysisResult

SurvivalAnalysisResult includes the results of a SurvivalAnalysis.

The genotype categories and survival are reported in the data data frame with the following structure:

diff --git a/latest/apidocs/gpsea.analysis.temporal.stats.html b/latest/apidocs/gpsea.analysis.temporal.stats.html index b24a0ccf..59a4ba0f 100644 --- a/latest/apidocs/gpsea.analysis.temporal.stats.html +++ b/latest/apidocs/gpsea.analysis.temporal.stats.html @@ -105,13 +105,13 @@
class gpsea.analysis.temporal.stats.SurvivalStatistic(name: str)[source]
-

Bases: Statistic

+

Bases: Statistic

SurvivalStatistic calculates a p value for 2 or more survival groups computed by a SurvivalMetric.

-abstract compute_pval(scores: Collection[Sequence[Survival]]) float[source]
+abstract compute_pval(scores: Collection[Sequence[Survival]]) StatisticResult[source]

Compute p value for the collection of survivals being sampled from the same source distribution.

@@ -127,7 +127,7 @@ A two-sided alternative hypothesis is tested.

-compute_pval(scores: Collection[Iterable[Survival]]) float[source]
+compute_pval(scores: Collection[Iterable[Survival]]) StatisticResult[source]

Compute p value for survivals being sourced from the same distribution.

Parameters:
diff --git a/latest/apidocs/gpsea.html b/latest/apidocs/gpsea.html index cc9f64bd..95f0853c 100644 --- a/latest/apidocs/gpsea.html +++ b/latest/apidocs/gpsea.html @@ -114,6 +114,8 @@

SubpackagesAnalysisResult
  • MonoPhenotypeAnalysisResult
  • MultiPhenotypeAnalysisResult
  • +
  • Statistic
  • +
  • StatisticResult
  • Subpackages
  • diff --git a/latest/genindex.html b/latest/genindex.html index cbacb570..aee3971e 100644 --- a/latest/genindex.html +++ b/latest/genindex.html @@ -1135,6 +1135,8 @@

    N

  • (gpsea.analysis.pscore.DeVriesPhenotypeScorer property)
  • (gpsea.analysis.pscore.MeasurementPhenotypeScorer property) +
  • +
  • (gpsea.analysis.Statistic property)
  • (gpsea.model.Disease property)
  • @@ -1309,10 +1311,10 @@

    P

  • postnatal_days() (gpsea.model.Age static method)
  • - - + - +
    • statistic (gpsea.analysis.AnalysisResult property) + +
    • +
    • statistic_result() (gpsea.analysis.MonoPhenotypeAnalysisResult method) +
    • +
    • statistic_results (gpsea.analysis.MultiPhenotypeAnalysisResult property) + +
    • +
    • StatisticResult (class in gpsea.analysis)
    • Status (class in gpsea.model)
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"641del": 23, "65": 26, "658_660del": 23, "664": 25, "668c": [23, 30], "67": 26, "678g": 23, "680_681insctgagaataat": 23, "69": [25, 26], "7": [1, 3, 6, 10, 15, 23, 30, 35], "709c": [23, 30], "71": [23, 26], "710g": [23, 30], "72": 23, "741216622359659": 25, "75": 26, "755": 23, "7703831604944444": 26, "7735491022101784": 23, "78": 26, "781a": 23, "787g": 23, "798del": 23, "8": [8, 15, 17, 18, 23, 25, 26, 30, 34, 35], "80": [26, 34], "833333333333336": 26, "835c": 23, "84": 26, "85": [23, 30], "873c": 23, "880g": 23, "89": 26, "9": [23, 25, 26, 34], "90": [23, 30, 35], "905del": 23, "93": 26, "939del": 23, "94": 23, "95": [23, 35], "97": 26, "9876g": 15, "99": [26, 35], "A": [2, 3, 6, 7, 8, 10, 12, 15, 17, 19, 21, 23, 30, 32, 34, 35, 37, 39, 41, 44], "AND": [6, 27, 45], "AT": 32, "As": [5, 7, 27, 30, 41, 45], "But": 30, "By": [2, 19, 23, 35, 40], "For": [2, 4, 7, 8, 14, 15, 16, 23, 25, 26, 27, 28, 29, 30, 32, 34, 35, 36, 40, 44, 45], "INS": 15, "If": [2, 3, 6, 11, 13, 15, 17, 18, 23, 26, 35, 37], "In": [2, 4, 8, 15, 17, 21, 23, 26, 27, 28, 29, 30, 33, 34, 35, 37, 40, 41, 44], "It": [3, 4, 23, 30, 35, 43], "NO": 7, "NOT": [2, 15, 16, 25, 45], "No": [1, 3, 16, 23, 30, 34, 40], "Not": 15, "OR": [6, 25, 27, 44, 45], "On": [3, 7, 34], "One": [28, 37, 45], "That": 22, "The": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 43, 44, 45], "Then": [15, 22, 23, 34], "There": [15, 23, 24], "These": [6, 15], "To": [17, 22, 23, 26, 27, 34, 35, 45], "_": [27, 28, 34, 40], "__eq__": 6, "__hash__": 6, "__init__": [15, 17], "__repr__": 6, "__str__": 6, "_api": [2, 3], "_cohort": 15, "_io": 17, "_measur": 25, "_missing_implies_phenotype_exclud": 7, "_stat": 3, "_t": 15, "_tempor": 15, "_term_id": 2, "_variant": 44, "a_label": [6, 23, 25, 26, 27, 28, 44], "a_pred": [6, 23, 25, 26, 27, 28, 44], "aa": 6, "aat": 32, "ab": 6, "abbrevi": [19, 35], "abdomen": 31, "abdomin": 31, "abil": 30, "abnorm": [8, 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