diff --git a/latest/genindex.html b/latest/genindex.html
index b4b8585cb..af01dc001 100644
--- a/latest/genindex.html
+++ b/latest/genindex.html
@@ -241,13 +241,15 @@
C
compute_survival() (gpsea.analysis.temporal.Endpoint method)
-
-
|
+
- configure_cohort_creator() (in module gpsea.preprocessing)
- configure_default_protein_metadata_service() (in module gpsea.preprocessing)
+
+ - configure_hpo_term_analysis() (in module gpsea.analysis.pcats)
- configure_protein_metadata_service() (in module gpsea.preprocessing)
diff --git a/latest/objects.inv b/latest/objects.inv
index 66675c6b3..198bb1d2b 100644
Binary files a/latest/objects.inv and b/latest/objects.inv differ
diff --git a/latest/searchindex.js b/latest/searchindex.js
index 6aff8385e..13fee0c49 100644
--- a/latest/searchindex.js
+++ b/latest/searchindex.js
@@ -1 +1 @@
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"zero_vs_on": 6, "zero_vs_one_vs_two": 6, "\u03b1": 15, "\u03c72": 4}, "titles": ["gpsea package", "gpsea.analysis package", "gpsea.analysis.mtc_filter package", "gpsea.analysis.pcats package", "gpsea.analysis.pcats.stats package", "gpsea.analysis.predicate package", "gpsea.analysis.predicate.genotype package", "gpsea.analysis.predicate.phenotype package", "gpsea.analysis.pscore package", "gpsea.analysis.pscore.stats package", "gpsea.analysis.temporal package", "gpsea.analysis.temporal.endpoint package", "gpsea.analysis.temporal.stats package", "gpsea.config module", "gpsea.io module", "gpsea.model package", "gpsea.model.genome package", "gpsea.preprocessing package", "gpsea.util module", "gpsea.view package", "API reference", "GPSEA", "Installation", "Tutorial", "Statistical analyses", "Compare measurement values", "Compare genotype and phenotype groups", "Compare phenotype scores in genotype groups", "Survival analysis", "Genotype-Phenotype Correlations in Autosomal Recessive 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\ No newline at end of file
+Search.setIndex({"alltitles": {"API reference": [[20, null]], "Alternative phenopacket sources": [[34, "alternative-phenopacket-sources"]], "Analysis": [[25, "analysis"], [26, "analysis"], [27, "analysis"], [28, "analysis"]], "Background": [[35, "background"]], "Biallelic predicate": [[44, "biallelic-predicate"]], "Biallelic predicate genotype groups": [[44, "id8"]], "Building blocks": [[45, "building-blocks"]], "Categories": [[44, "categories"]], "Change length of an allele": [[32, "change-length-of-an-allele"]], "Choose the transcript": [[34, "choose-the-transcript"]], "Choose the transcript and protein of interest": [[34, "choose-the-transcript-and-protein-of-interest"]], "Cohort exploratory analysis": [[30, null]], "Cohort summary": [[30, "cohort-summary"]], "Compare genotype and phenotype groups": [[26, null]], "Compare measurement values": [[25, null]], "Compare phenotype scores in genotype groups": [[27, null]], "Compare the individuals with EGFR mutation": [[36, 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\ No newline at end of file
diff --git a/latest/tutorial.html b/latest/tutorial.html
index 51a514fad..9eb44c446 100644
--- a/latest/tutorial.html
+++ b/latest/tutorial.html
@@ -1213,39 +1213,20 @@ Prepare genotype and phenotype predicates) with a multiple testing correction.
-Phenotype MTC filter chooses the HPO terms to test according to several heuristics, which
+of a phenotype MT filter (PhenotypeMtcFilter) with a multiple testing correction.
+Phenotype MT filter chooses the HPO terms to test according to several heuristics, which
reduce the multiple testing burden and focus the analysis
-on the most interesting terms (see HPO MTC filter for more info).
+on the most interesting terms (see HPO MT filter for more info).
Then the multiple testing correction, such as Bonferroni or Benjamini-Hochberg,
is used to control the family-wise error rate or the false discovery rate.
See Multiple-testing correction for more information.
-In this example, we will use a combination of the HPO MTC filter (HpoMtcFilter)
-with Benjamini-Hochberg procedure (mtc_correction='fdr_bh')
-with a false discovery control level at (mtc_alpha=0.05):
->>> from gpsea.analysis.mtc_filter import HpoMtcFilter
->>> mtc_filter = HpoMtcFilter.default_filter(hpo)
->>> mtc_correction = 'fdr_bh'
->>> mtc_alpha = 0.05
-
- Choosing the statistical procedure for assessment of association between genotype and phenotype
-groups is the last missing piece of the analysis. We will use Fisher Exact Test:
->>> from gpsea.analysis.pcats.stats import FisherExactTest
->>> count_statistic = FisherExactTest()
-
- and we finalize the analysis setup by putting all components together
-into HpoTermAnalysis:
->>> from gpsea.analysis.pcats import HpoTermAnalysis
->>> analysis = HpoTermAnalysis(
-... count_statistic=count_statistic,
-... mtc_filter=mtc_filter,
-... mtc_correction=mtc_correction,
-... mtc_alpha=mtc_alpha,
-... )
+>>> from gpsea.analysis.pcats import configure_hpo_term_analysis
+>>> analysis = configure_hpo_term_analysis(hpo)
configure_hpo_term_analysis() configures the analysis
+that uses HPO MTC filter (HpoMtcFilter) for selecting HPO terms of interest,
+Fisher Exact test for computing nominal p values, and Benjamini-Hochberg for multiple testing correction.
Now we can perform the analysis and investigate the results.
>>> result = analysis.compare_genotype_vs_phenotypes(
... cohort=cohort,
diff --git a/latest/user-guide/analyses/phenotype-groups.html b/latest/user-guide/analyses/phenotype-groups.html
index 0bd94d7d0..acf7503df 100644
--- a/latest/user-guide/analyses/phenotype-groups.html
+++ b/latest/user-guide/analyses/phenotype-groups.html
@@ -233,54 +233,76 @@ Phenotype predicatesTrue path rule.
-
-Statistical test
+
+Statistical analysis
We will use Fisher exact test (FET) to test the association
between genotype and phenotype groups, as described previously.
+In the case of this cohort, we can test association between having a frameshift variant and one of 369 HPO terms.
+However, testing multiple hypotheses on the same dataset increases the risk of finding
+a significant association solely by chance.
+GPSEA uses a two-pronged strategy to reduce the number of tests and, therefore, mitigate this risk:
+a phenotype multiple testing (MT) filter and multiple testing correction (MTC).
+Phenotype MT filter selects a (sub)set of HPO terms for testing,
+for instance only the user-selected terms (see SpecifyTermsStrategy)
+or the terms selected by HpoMtcFilter.
+Multiple testing correction then adjusts the nominal p values for the increased risk
+of false positive G/P associations.
+The available MTC procedures are listed in the Multiple-testing correction procedures section.
+We must pick one of these to perform genotype-phenotype analysis.
+
+Default analysis
+We recommend using HPO MT filter (HpoMtcFilter) as a phenotype MT filter
+and Benjamini-Hochberg for multiple testing correction.
+The default analysis can be configured with configure_hpo_term_analysis() convenience method.
+>>> from gpsea.analysis.pcats import configure_hpo_term_analysis
+>>> analysis = configure_hpo_term_analysis(hpo)
+
+ Custom analysis
+If the defaults do not work, we can configure the analysis manually.
+First, we choose a phenotype MT filter (e.g. HpoMtcFilter):
+>>> from gpsea.analysis.mtc_filter import HpoMtcFilter
+>>> mtc_filter = HpoMtcFilter.default_filter(hpo, term_frequency_threshold=.2)
+
+ then a statistical test (e.g. Fisher Exact test):
>>> from gpsea.analysis.pcats.stats import FisherExactTest
>>> count_statistic = FisherExactTest()
FET will compute a p value for each genotype phenotype group.
-
-
-Multiple testing correction
-In the case of this cohort, we could test association between having a frameshift variant and one of 369 HPO terms.
-However, testing multiple hypotheses on the same dataset increases the risk of finding a significant association
-by chance.
-GPSEA uses a two-pronged strategy to mitigate this risk - a phenotype MTC filter and multiple testing correction.
-Here we will use a combination of the HPO MTC filter (HpoMtcFilter)
-with Benjamini-Hochberg procedure (mtc_correction='fdr_bh')
-with a false discovery control level set to 0.05 (mtc_alpha=0.05):
->>> from gpsea.analysis.mtc_filter import HpoMtcFilter
->>> mtc_filter = HpoMtcFilter.default_filter(hpo, term_frequency_threshold=0.2)
->>> mtc_correction = 'fdr_bh'
+and we finalize the setup by choosing a MTC procedure
+(e.g. fdr_bh for Benjamini-Hochberg) along with the MTC alpha:
+>>> mtc_correction = 'fdr_bh'
>>> mtc_alpha = 0.05
Final analysis
-We finalize the analysis setup by putting all components together
-into HpoTermAnalysis:
+The final HpoTermAnalysis is created as:
>>> from gpsea.analysis.pcats import HpoTermAnalysis
>>> analysis = HpoTermAnalysis(
... count_statistic=count_statistic,
... mtc_filter=mtc_filter,
-... mtc_correction=mtc_correction,
-... mtc_alpha=mtc_alpha,
+... mtc_correction='fdr_bh',
+... mtc_alpha=0.05,
... )
Analysis
-We can now execute the analysis:
+We can now test associations between the genotype groups and the HPO terms:
>>> result = analysis.compare_genotype_vs_phenotypes(
... cohort=cohort,
... gt_predicate=gt_predicate,
@@ -292,8 +314,8 @@ Analysis Thanks to phenotype MTC filter, we only tested 24 out of 369 terms.
-We can learn more by showing the MTC filter report:
+Thanks to phenotype MT filter, we only tested 24 out of 369 terms.
+We can learn more by showing the MT filter report:
>>> from gpsea.view import MtcStatsViewer
>>> mtc_viewer = MtcStatsViewer()
>>> mtc_report = mtc_viewer.process(result)
diff --git a/latest/user-guide/index.html b/latest/user-guide/index.html
index 5305cfaa3..81d37a82f 100644
--- a/latest/user-guide/index.html
+++ b/latest/user-guide/index.html
@@ -175,7 +175,7 @@
- Background
- Implementation in GPSEA
diff --git a/latest/user-guide/mtc.html b/latest/user-guide/mtc.html
index 61d0d7173..cc4ab4e5e 100644
--- a/latest/user-guide/mtc.html
+++ b/latest/user-guide/mtc.html
@@ -61,7 +61,7 @@
- Background
- Implementation in GPSEA
@@ -126,9 +126,9 @@ Implementation in GPSEA |