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add test for mann whitney on ptm data
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janni.roebbecke committed Jun 25, 2024
1 parent 9d056a4 commit d5194c0
Showing 1 changed file with 99 additions and 16 deletions.
115 changes: 99 additions & 16 deletions tests/protzilla/data_analysis/test_differential_expression.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,8 @@
import pytest

from protzilla.data_analysis.differential_expression import anova, linear_model, t_test
from protzilla.data_analysis.differential_expression_mann_whitney import mann_whitney_test_on_intensity_data
from protzilla.data_analysis.differential_expression_mann_whitney import mann_whitney_test_on_intensity_data, \
mann_whitney_test_on_ptm_data
from protzilla.data_analysis.plots import create_volcano_plot


Expand Down Expand Up @@ -63,8 +64,8 @@ def diff_expr_test_data():


def test_differential_expression_linear_model(
diff_expr_test_data,
show_figures,
diff_expr_test_data,
show_figures,
):
test_intensity_df, test_metadata_df = diff_expr_test_data
test_alpha = 0.05
Expand Down Expand Up @@ -108,8 +109,8 @@ def test_differential_expression_linear_model(
assert p_values_rounded == corrected_p_values
assert log2fc_rounded == log2_fc
assert (
list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique())
== differentially_expressed_proteins
list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique())
== differentially_expressed_proteins
)
assert current_out["corrected_alpha"] == test_alpha

Expand Down Expand Up @@ -161,13 +162,13 @@ def test_differential_expression_student_t_test(diff_expr_test_data, show_figure

assert p_values_rounded == corrected_p_values
assert (
list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique())
== differentially_expressed_proteins
list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique())
== differentially_expressed_proteins
)
assert current_out["corrected_alpha"] == test_alpha
assert (
list(current_out["significant_proteins_df"]["Protein ID"].unique())
== significant_proteins
list(current_out["significant_proteins_df"]["Protein ID"].unique())
== significant_proteins
)


Expand Down Expand Up @@ -218,13 +219,13 @@ def test_differential_expression_welch_t_test(diff_expr_test_data, show_figures)

assert p_values_rounded == corrected_p_values
assert (
list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique())
== differentially_expressed_proteins
list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique())
== differentially_expressed_proteins
)
assert current_out["corrected_alpha"] == test_alpha
assert (
list(current_out["significant_proteins_df"]["Protein ID"].unique())
== significant_proteins
list(current_out["significant_proteins_df"]["Protein ID"].unique())
== significant_proteins
)


Expand Down Expand Up @@ -389,9 +390,10 @@ def test_differential_expression_anova(show_figures):

assert assertion_p_values == p_values_rounded

def test_differential_expression_mann_whitney_on_intensitys(
diff_expr_test_data,
show_figures,

def test_differential_expression_mann_whitney_on_intensity(
diff_expr_test_data,
show_figures,
):
test_intensity_df, test_metadata_df = diff_expr_test_data
test_alpha = 0.05
Expand Down Expand Up @@ -443,3 +445,84 @@ def test_differential_expression_mann_whitney_on_intensitys(
)
assert current_out["corrected_alpha"] == test_alpha


@pytest.fixture
def ptm_test_data():
test_amount_list = (
["Sample1", 1, 1, 10, 1, 100],
["Sample2", 2, 2, 10, 1, 100],
["Sample3", 3, 3, 10, 1, 100],
["Sample4", 4, 4, 10, 1, 100],
["Sample5", 5, 5, 10, 1, 100],
["Sample6", 6, 3, 11, 111, 100],
["Sample7", 7, 4, 12, 222, 100],
["Sample8", 8, 5, 13, 333, 100],
["Sample9", 9, 6, 14, 444, 100],
["Sample10", 10, 7, 15, 555, 100],
)
test_amount_df = pd.DataFrame(
data=test_amount_list,
columns=["Sample", "Oxidation", "Acetyl", "GlyGly", "Phospho", "Unmodified"],
)

test_metadata_list = (
["Sample1", "Group1"],
["Sample2", "Group1"],
["Sample3", "Group1"],
["Sample4", "Group1"],
["Sample5", "Group1"],
["Sample6", "Group2"],
["Sample7", "Group2"],
["Sample8", "Group2"],
["Sample9", "Group2"],
["Sample10", "Group2"],
)
test_metadata_df = pd.DataFrame(
data=test_metadata_list,
columns=["Sample", "Group"],
)

return test_amount_df, test_metadata_df


def test_differential_expression_mann_whitney_on_ptm(
ptm_test_data,
show_figures,
):
test_amount_df, test_metadata_df = ptm_test_data
test_alpha = 0.05

current_input = dict(
ptm_df=test_amount_df,
metadata_df=test_metadata_df,
grouping="Group",
group1="Group1",
group2="Group2",
multiple_testing_correction_method="Benjamini-Hochberg",
alpha=test_alpha,
p_value_calculation_method="auto",
)
current_out = mann_whitney_test_on_ptm_data(**current_input)

expected_corrected_p_values = [0.0132, 0.1423, 0.0132, 0.0132, 1.00000]
expected_u_statistics = [0.0, 4.5, 0.0, 0.0, 12.5]
expected_log2_fc = [1.415, 0.737, 0.3785, 8.3794, 0.0]

expected_significant_ptms = ["Oxidation", "GlyGly", "Phospho"]

p_values_rounded = [
round(x, 4) for x in current_out["corrected_p_values_df"]["corrected_p_value"]
]
u_statistics = current_out["u_statistic_df"]["u_statistic"]
log2_fc_rounded = [
round(x, 4) for x in current_out["log2_fold_change_df"]["log2_fold_change"]
]

assert p_values_rounded == expected_corrected_p_values
assert all(u_statistics == expected_u_statistics)
assert log2_fc_rounded == expected_log2_fc
assert (
list(current_out["significant_ptm_df"]["PTM"].unique())
== expected_significant_ptms
)
assert current_out["corrected_alpha"] == test_alpha

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