diff --git a/cytominer_eval/evaluate.py b/cytominer_eval/evaluate.py
index 59a3272..744f094 100644
--- a/cytominer_eval/evaluate.py
+++ b/cytominer_eval/evaluate.py
@@ -17,6 +17,7 @@
enrichment,
)
+
def evaluate(
profiles: pd.DataFrame,
features: List[str],
@@ -26,11 +27,11 @@ def evaluate(
similarity_metric: str = "pearson",
replicate_reproducibility_quantile: np.float = 0.95,
replicate_reproducibility_return_median_cor: bool = False,
- precision_recall_k: int = 10,
+ precision_recall_k: Union[int, List[int]] = 10,
grit_control_perts: List[str] = ["None"],
grit_replicate_summary_method: str = "mean",
mp_value_params: dict = {},
- enrichment_percentile: float = 0.5,
+ enrichment_percentile: Union[float, List[float]] = 0.99,
):
r"""Evaluate profile quality and strength.
@@ -85,7 +86,7 @@ def evaluate(
Only used when `operation='replicate_reproducibility'`. If True, then also
return pairwise correlations as defined by replicate_groups and
similarity metric
- precision_recall_k : {10, ...}, optional
+ precision_recall_k : int or list of ints {10, ...}, optional
Only used when `operation='precision_recall'`. Used to calculate precision and
recall considering the top k profiles according to pairwise similarity.
grit_control_perts : {None, ...}, optional
@@ -100,7 +101,7 @@ def evaluate(
Only used when `operation='mp_value'`. A key, item pair of optional parameters
for calculating mp value. See also
:py:func:`cytominer_eval.operations.util.default_mp_value_parameters`
- percentile : float, optional
+ enrichment_percentile : float or list of floats, optional
Only used when `operation='enrichment'`. Determines the percentage of top connections
used for the enrichment calculation.
"""
diff --git a/cytominer_eval/operations/enrichment.py b/cytominer_eval/operations/enrichment.py
index f8cb34d..37ab2ff 100644
--- a/cytominer_eval/operations/enrichment.py
+++ b/cytominer_eval/operations/enrichment.py
@@ -2,7 +2,7 @@
"""
import numpy as np
import pandas as pd
-from typing import List
+from typing import List, Union
import scipy
from .util import assign_replicates, calculate_grit, check_grit_replicate_summary_method
@@ -14,8 +14,10 @@
def enrichment(
- similarity_melted_df: pd.DataFrame, replicate_groups: List[str], percentile: 0.9,
-) -> dict:
+ similarity_melted_df: pd.DataFrame,
+ replicate_groups: List[str],
+ percentile: Union[float, List[float]],
+) -> pd.DataFrame:
"""Calculate the enrichment score. This score is based on the fisher exact odds score. Similar to the other functions, the closest connections are determined and checked with the replicates.
This score effectively calculates how much better the distribution of correct connections is compared to random.
@@ -28,7 +30,7 @@ def enrichment(
replicate_groups : List
a list of metadata column names in the original profile dataframe to use as
replicate columns.
- percentile : float
+ percentile : List of floats
Determines what percentage of top connections used for the enrichment calculation.
Returns
@@ -36,40 +38,48 @@ def enrichment(
dict
percentile, threshold, odds ratio and p value
"""
- # threshold based on percentile of top connections
- threshold = similarity_melted_df.similarity_metric.quantile(percentile)
-
+ result = []
replicate_truth_df = assign_replicates(
similarity_melted_df=similarity_melted_df, replicate_groups=replicate_groups
)
- # calculate the individual components of the contingency tables
- v11 = len(
- replicate_truth_df.query(
- "group_replicate==True and similarity_metric>@threshold"
+ # loop over all percentiles
+ if type(percentile) == float:
+ percentile = [percentile]
+ for p in percentile:
+ # threshold based on percentile of top connections
+ threshold = similarity_melted_df.similarity_metric.quantile(p)
+
+ # calculate the individual components of the contingency tables
+ v11 = len(
+ replicate_truth_df.query(
+ "group_replicate==True and similarity_metric>@threshold"
+ )
)
- )
- v12 = len(
- replicate_truth_df.query(
- "group_replicate==False and similarity_metric>@threshold"
+ v12 = len(
+ replicate_truth_df.query(
+ "group_replicate==False and similarity_metric>@threshold"
+ )
)
- )
- v21 = len(
- replicate_truth_df.query(
- "group_replicate==True and similarity_metric<=@threshold"
+ v21 = len(
+ replicate_truth_df.query(
+ "group_replicate==True and similarity_metric<=@threshold"
+ )
)
- )
- v22 = len(
- replicate_truth_df.query(
- "group_replicate==False and similarity_metric<=@threshold"
+ v22 = len(
+ replicate_truth_df.query(
+ "group_replicate==False and similarity_metric<=@threshold"
+ )
)
- )
- v = np.asarray([[v11, v12], [v21, v22]])
- r = scipy.stats.fisher_exact(v, alternative="greater")
- result = {
- "percentile": percentile,
- "threshold": threshold,
- "ods_ratio": r[0],
- "p-value": r[1],
- }
- return result
+ v = np.asarray([[v11, v12], [v21, v22]])
+ r = scipy.stats.fisher_exact(v, alternative="greater")
+ result.append(
+ {
+ "enrichment_percentile": p,
+ "threshold": threshold,
+ "ods_ratio": r[0],
+ "p-value": r[1],
+ }
+ )
+ result_df = pd.DataFrame(result)
+ return result_df
diff --git a/cytominer_eval/operations/precision_recall.py b/cytominer_eval/operations/precision_recall.py
index 4edfee2..5bd015d 100644
--- a/cytominer_eval/operations/precision_recall.py
+++ b/cytominer_eval/operations/precision_recall.py
@@ -4,7 +4,7 @@
import numpy as np
import pandas as pd
-from typing import List
+from typing import List, Union
from .util import assign_replicates, calculate_precision_recall
from cytominer_eval.transform.util import set_pair_ids, assert_melt
@@ -13,7 +13,7 @@
def precision_recall(
similarity_melted_df: pd.DataFrame,
replicate_groups: List[str],
- k: int,
+ k: Union[int, List[int]],
) -> pd.DataFrame:
"""Determine the precision and recall at k for all unique replicate groups
based on a predefined similarity metric (see cytominer_eval.transform.metric_melt)
@@ -27,7 +27,7 @@ def precision_recall(
replicate_groups : List
a list of metadata column names in the original profile dataframe to use as
replicate columns.
- k : int
+ k : List of ints or int
an integer indicating how many pairwise comparisons to threshold.
Returns
@@ -49,11 +49,16 @@ def precision_recall(
"{x}{suf}".format(x=x, suf=pair_ids[list(pair_ids)[0]]["suffix"])
for x in replicate_groups
]
-
- # Calculate precision and recall for all groups
- precision_recall_df = similarity_melted_df.groupby(replicate_group_cols).apply(
- lambda x: calculate_precision_recall(x, k=k)
- )
+ # iterate over all k
+ precision_recall_df = pd.DataFrame()
+ if type(k) == int:
+ k = [k]
+ for k_ in k:
+ # Calculate precision and recall for all groups
+ precision_recall_df_at_k = similarity_melted_df.groupby(
+ replicate_group_cols
+ ).apply(lambda x: calculate_precision_recall(x, k=k_))
+ precision_recall_df = precision_recall_df.append(precision_recall_df_at_k)
# Rename the columns back to the replicate groups provided
rename_cols = dict(zip(replicate_group_cols, replicate_groups))
diff --git a/cytominer_eval/tests/test_evaluate.py b/cytominer_eval/tests/test_evaluate.py
index 34af0b3..ed50551 100644
--- a/cytominer_eval/tests/test_evaluate.py
+++ b/cytominer_eval/tests/test_evaluate.py
@@ -111,11 +111,7 @@ def test_evaluate_replicate_reprod_return_cor_true():
assert np.round(med_cor_df.similarity_metric.max(), 3) == 0.949
assert sorted(med_cor_df.columns.tolist()) == sorted(
- [
- "Metadata_gene_name",
- "Metadata_pert_name",
- "similarity_metric",
- ]
+ ["Metadata_gene_name", "Metadata_pert_name", "similarity_metric",]
)
@@ -134,6 +130,7 @@ def test_evaluate_precision_recall():
for k in ks:
+ # first test the function with k = float, later we test with k = list of floats
result = evaluate(
profiles=gene_profiles,
features=gene_features,
@@ -152,7 +149,7 @@ def test_evaluate_precision_recall():
result.query("recall == 1").shape[0]
== expected_result["gene"]["recall"][str(k)]
)
-
+ # test function with argument k = list of floats, should give same result as above
result = evaluate(
profiles=compound_profiles,
features=compound_features,
@@ -160,7 +157,7 @@ def test_evaluate_precision_recall():
replicate_groups=["Metadata_broad_sample"],
operation="precision_recall",
similarity_metric="pearson",
- precision_recall_k=k,
+ precision_recall_k=[k],
)
assert (
@@ -205,9 +202,7 @@ def test_evaluate_grit():
top_result = (
grit_results_df.sort_values(by="grit", ascending=False)
.reset_index(drop=True)
- .iloc[
- 0,
- ]
+ .iloc[0,]
)
assert np.round(top_result.grit, 4) == 2.3352
assert top_result.group == "PTK2"
@@ -233,9 +228,7 @@ def test_evaluate_grit():
top_result = (
grit_results_df.sort_values(by="grit", ascending=False)
.reset_index(drop=True)
- .iloc[
- 0,
- ]
+ .iloc[0,]
)
assert np.round(top_result.grit, 4) == 0.9990
diff --git a/cytominer_eval/tests/test_operations/test_enrichment.py b/cytominer_eval/tests/test_operations/test_enrichment.py
index 8f2ff08..b49d70a 100644
--- a/cytominer_eval/tests/test_operations/test_enrichment.py
+++ b/cytominer_eval/tests/test_operations/test_enrichment.py
@@ -42,25 +42,31 @@
def test_enrichment():
- result = []
- for p in np.arange(1, 0.97, -0.005):
- r = enrichment(
- similarity_melted_df=similarity_melted_df,
- replicate_groups=replicate_groups,
- percentile=p,
- )
- result.append(r)
- result_df = pd.DataFrame(result)
+ percent_list = np.arange(1, 0.97, -0.005)
+ result = enrichment(
+ similarity_melted_df=similarity_melted_df,
+ replicate_groups=replicate_groups,
+ percentile=percent_list,
+ )
# check for correct shape and starts with 1.0
- assert result_df.shape == (7, 4)
- assert result_df.percentile[0] == 1.0
+ assert result.shape == (7, 4)
+ assert result.enrichment_percentile[0] == 1.0
+ assert result.enrichment_percentile[1] == 0.995
# check if the higher percentiles are larger than the small one
- assert result_df.percentile[1] > result_df.percentile.iloc[-1]
+ assert result.enrichment_percentile[1] > result.enrichment_percentile.iloc[-1]
+
+ result_int = enrichment(
+ similarity_melted_df=similarity_melted_df,
+ replicate_groups=replicate_groups,
+ percentile=0.97,
+ )
+
+ assert result_int.enrichment_percentile[0] == result.enrichment_percentile.iloc[-1]
def test_compare_functions():
- percentile = 0.9
+ percent_list = [0.95, 0.9]
eval_res = evaluate(
profiles=df,
features=features,
@@ -68,11 +74,11 @@ def test_compare_functions():
replicate_groups=replicate_groups,
operation="enrichment",
similarity_metric="pearson",
- enrichment_percentile=percentile,
+ enrichment_percentile=percent_list,
)
enr_res = enrichment(
similarity_melted_df=similarity_melted_df,
replicate_groups=replicate_groups,
- percentile=percentile,
+ percentile=percent_list,
)
- assert enr_res == eval_res
+ assert enr_res.equals(eval_res)
diff --git a/cytominer_eval/tests/test_operations/test_precision_recall.py b/cytominer_eval/tests/test_operations/test_precision_recall.py
index 7e23b55..75f0bc7 100644
--- a/cytominer_eval/tests/test_operations/test_precision_recall.py
+++ b/cytominer_eval/tests/test_operations/test_precision_recall.py
@@ -39,22 +39,30 @@
def test_precision_recall():
- result = precision_recall(
+ result_list = precision_recall(
similarity_melted_df=similarity_melted_df,
replicate_groups=replicate_groups,
- k=10,
+ k=[5, 10],
)
- assert len(result.k.unique()) == 1
- assert result.k.unique()[0] == 10
+ result_int = precision_recall(
+ similarity_melted_df=similarity_melted_df,
+ replicate_groups=replicate_groups,
+ k=5,
+ )
+
+ assert len(result_list.k.unique()) == 2
+ assert result_list.k.unique()[0] == 5
# ITGAV has a really strong profile
assert (
- result.sort_values(by="recall", ascending=False)
+ result_list.sort_values(by="recall", ascending=False)
.reset_index(drop=True)
.iloc[0, :]
.Metadata_gene_name
== "ITGAV"
)
- assert all(x in result.columns for x in replicate_groups)
+ assert all(x in result_list.columns for x in replicate_groups)
+
+ assert result_int.equals(result_list.query("k == 5"))
diff --git a/cytominer_eval/transform/util.py b/cytominer_eval/transform/util.py
index db5d782..b957248 100644
--- a/cytominer_eval/transform/util.py
+++ b/cytominer_eval/transform/util.py
@@ -7,7 +7,13 @@
def get_available_eval_metrics():
r"""Output the available eval metrics in the cytominer_eval library"""
- return ["replicate_reproducibility", "precision_recall", "grit", "mp_value", "enrichment"]
+ return [
+ "replicate_reproducibility",
+ "precision_recall",
+ "grit",
+ "mp_value",
+ "enrichment",
+ ]
def get_available_similarity_metrics():
diff --git a/demos/CellPainting_Demo.ipynb b/demos/CellPainting_Demo.ipynb
index 790cdfb..adaf1cf 100644
--- a/demos/CellPainting_Demo.ipynb
+++ b/demos/CellPainting_Demo.ipynb
@@ -17,10 +17,18 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 1,
"id": "sophisticated-creek",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/Users/mbornhol/git/mycyto/cytominer-eval/demos\n"
+ ]
+ }
+ ],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
@@ -49,7 +57,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 2,
"id": "growing-blond",
"metadata": {},
"outputs": [
@@ -65,7 +73,7 @@
"text/plain": " Metadata_Plate Metadata_Well Metadata_WellCol Metadata_WellRow \\\n0 SQ00014618 A01 1 A \n1 SQ00014618 A02 2 A \n2 SQ00014618 A03 3 A \n3 SQ00014618 A04 4 A \n4 SQ00014618 A05 5 A \n\n Metadata_cell_line Metadata_gene_name Metadata_pert_name \\\n0 HCC44 EMPTY EMPTY \n1 HCC44 MCL1 MCL1-5 \n2 HCC44 AKT1 AKT1-1 \n3 HCC44 KRAS KRAS-2B \n4 HCC44 AKT1 AKT1-2 \n\n Cells_AreaShape_Center_Y Cells_AreaShape_Compactness \\\n0 -0.894997 -1.515696 \n1 -0.479926 0.246423 \n2 -0.635578 0.416772 \n3 1.024707 0.645336 \n4 -2.036443 0.159822 \n\n Cells_AreaShape_Eccentricity ... Nuclei_Texture_SumEntropy_RNA_5_0 \\\n0 -1.787667 ... 0.107581 \n1 0.629901 ... 0.165935 \n2 0.039795 ... -1.358338 \n3 0.714847 ... -0.975661 \n4 0.736176 ... -0.575835 \n\n Nuclei_Texture_SumVariance_AGP_20_0 Nuclei_Texture_SumVariance_AGP_5_0 \\\n0 -0.659049 -0.676846 \n1 1.999006 1.204036 \n2 0.155556 -0.112177 \n3 -0.931362 -0.809894 \n4 0.093271 -0.298606 \n\n Nuclei_Texture_SumVariance_DNA_10_0 Nuclei_Texture_SumVariance_DNA_20_0 \\\n0 -1.229791 -1.336051 \n1 0.560228 0.686189 \n2 -1.258864 -1.394609 \n3 -1.526434 -1.541661 \n4 -0.564712 -0.512144 \n\n Nuclei_Texture_SumVariance_DNA_5_0 Nuclei_Texture_Variance_AGP_5_0 \\\n0 -1.125138 -0.972360 \n1 0.601634 1.154001 \n2 -1.277509 -0.128419 \n3 -1.482596 -1.004789 \n4 -0.513650 -0.438356 \n\n Nuclei_Texture_Variance_DNA_10_0 Nuclei_Texture_Variance_DNA_20_0 \\\n0 -1.393856 -1.244227 \n1 0.596441 0.680359 \n2 -1.359460 -1.299859 \n3 -1.817028 -1.887102 \n4 -0.562306 -0.276279 \n\n Nuclei_Texture_Variance_DNA_5_0 \n0 -1.308729 \n1 0.715469 \n2 -1.396879 \n3 -1.681831 \n4 -0.547497 \n\n[5 rows x 956 columns]",
"text/html": "
\n\n
\n \n \n | \n Metadata_Plate | \n Metadata_Well | \n Metadata_WellCol | \n Metadata_WellRow | \n Metadata_cell_line | \n Metadata_gene_name | \n Metadata_pert_name | \n Cells_AreaShape_Center_Y | \n Cells_AreaShape_Compactness | \n Cells_AreaShape_Eccentricity | \n ... | \n Nuclei_Texture_SumEntropy_RNA_5_0 | \n Nuclei_Texture_SumVariance_AGP_20_0 | \n Nuclei_Texture_SumVariance_AGP_5_0 | \n Nuclei_Texture_SumVariance_DNA_10_0 | \n Nuclei_Texture_SumVariance_DNA_20_0 | \n Nuclei_Texture_SumVariance_DNA_5_0 | \n Nuclei_Texture_Variance_AGP_5_0 | \n Nuclei_Texture_Variance_DNA_10_0 | \n Nuclei_Texture_Variance_DNA_20_0 | \n Nuclei_Texture_Variance_DNA_5_0 | \n
\n \n \n \n 0 | \n SQ00014618 | \n A01 | \n 1 | \n A | \n HCC44 | \n EMPTY | \n EMPTY | \n -0.894997 | \n -1.515696 | \n -1.787667 | \n ... | \n 0.107581 | \n -0.659049 | \n -0.676846 | \n -1.229791 | \n -1.336051 | \n -1.125138 | \n -0.972360 | \n -1.393856 | \n -1.244227 | \n -1.308729 | \n
\n \n 1 | \n SQ00014618 | \n A02 | \n 2 | \n A | \n HCC44 | \n MCL1 | \n MCL1-5 | \n -0.479926 | \n 0.246423 | \n 0.629901 | \n ... | \n 0.165935 | \n 1.999006 | \n 1.204036 | \n 0.560228 | \n 0.686189 | \n 0.601634 | \n 1.154001 | \n 0.596441 | \n 0.680359 | \n 0.715469 | \n
\n \n 2 | \n SQ00014618 | \n A03 | \n 3 | \n A | \n HCC44 | \n AKT1 | \n AKT1-1 | \n -0.635578 | \n 0.416772 | \n 0.039795 | \n ... | \n -1.358338 | \n 0.155556 | \n -0.112177 | \n -1.258864 | \n -1.394609 | \n -1.277509 | \n -0.128419 | \n -1.359460 | \n -1.299859 | \n -1.396879 | \n
\n \n 3 | \n SQ00014618 | \n A04 | \n 4 | \n A | \n HCC44 | \n KRAS | \n KRAS-2B | \n 1.024707 | \n 0.645336 | \n 0.714847 | \n ... | \n -0.975661 | \n -0.931362 | \n -0.809894 | \n -1.526434 | \n -1.541661 | \n -1.482596 | \n -1.004789 | \n -1.817028 | \n -1.887102 | \n -1.681831 | \n
\n \n 4 | \n SQ00014618 | \n A05 | \n 5 | \n A | \n HCC44 | \n AKT1 | \n AKT1-2 | \n -2.036443 | \n 0.159822 | \n 0.736176 | \n ... | \n -0.575835 | \n 0.093271 | \n -0.298606 | \n -0.564712 | \n -0.512144 | \n -0.513650 | \n -0.438356 | \n -0.562306 | \n -0.276279 | \n -0.547497 | \n
\n \n
\n
5 rows × 956 columns
\n
"
},
- "execution_count": 6,
+ "execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
@@ -89,7 +97,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 3,
"id": "alpine-pattern",
"metadata": {},
"outputs": [
@@ -97,7 +105,7 @@
"data": {
"text/plain": "['Metadata_Plate',\n 'Metadata_Well',\n 'Metadata_WellCol',\n 'Metadata_WellRow',\n 'Metadata_cell_line',\n 'Metadata_gene_name',\n 'Metadata_pert_name']"
},
- "execution_count": 7,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@@ -138,7 +146,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 4,
"id": "super-witness",
"metadata": {},
"outputs": [],
@@ -168,7 +176,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 5,
"id": "handed-rating",
"metadata": {},
"outputs": [
@@ -177,7 +185,7 @@
"text/plain": " cell_line percent_matching\n0 HCC44 0.109077\n1 A549 0.065802\n2 ES2 0.114925",
"text/html": "\n\n
\n \n \n | \n cell_line | \n percent_matching | \n
\n \n \n \n 0 | \n HCC44 | \n 0.109077 | \n
\n \n 1 | \n A549 | \n 0.065802 | \n
\n \n 2 | \n ES2 | \n 0.114925 | \n
\n \n
\n
"
},
- "execution_count": 9,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -189,7 +197,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 6,
"id": "employed-oracle",
"metadata": {},
"outputs": [
@@ -205,7 +213,7 @@
"text/plain": " Metadata_gene_name Metadata_pert_name similarity_metric cell_line\n0 AKT1 AKT1-1 0.240928 HCC44\n1 AKT1 AKT1-2 0.178413 HCC44\n2 ARID1B ARID1B-1 0.220200 HCC44\n3 ARID1B ARID1B-2 0.133993 HCC44\n4 ATF4 ATF4-1 0.797046 HCC44",
"text/html": "\n\n
\n \n \n | \n Metadata_gene_name | \n Metadata_pert_name | \n similarity_metric | \n cell_line | \n
\n \n \n \n 0 | \n AKT1 | \n AKT1-1 | \n 0.240928 | \n HCC44 | \n
\n \n 1 | \n AKT1 | \n AKT1-2 | \n 0.178413 | \n HCC44 | \n
\n \n 2 | \n ARID1B | \n ARID1B-1 | \n 0.220200 | \n HCC44 | \n
\n \n 3 | \n ARID1B | \n ARID1B-2 | \n 0.133993 | \n HCC44 | \n
\n \n 4 | \n ATF4 | \n ATF4-1 | \n 0.797046 | \n HCC44 | \n
\n \n
\n
"
},
- "execution_count": 10,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -219,7 +227,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 7,
"id": "welsh-attempt",
"metadata": {},
"outputs": [
@@ -233,9 +241,9 @@
},
{
"data": {
- "text/plain": ""
+ "text/plain": ""
},
- "execution_count": 11,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -275,9 +283,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
- "id": "under-western",
- "metadata": {},
+ "execution_count": 9,
"outputs": [],
"source": [
"precision_recall_at_k = []\n",
@@ -285,24 +291,30 @@
"for cell_line in df.Metadata_cell_line.unique():\n",
" cell_line_df = df.query(\"Metadata_cell_line == @cell_line\")\n",
" \n",
- " for k in [2, 3, 5, 10, 15, 25]:\n",
+ " k_list = [2, 3, 5, 10, 15, 25]\n",
" \n",
- " precision_recall_results = evaluate(\n",
- " profiles=cell_line_df,\n",
- " features=features,\n",
- " meta_features=meta_features,\n",
- " replicate_groups=[\"Metadata_gene_name\", \"Metadata_pert_name\"],\n",
- " operation=\"precision_recall\",\n",
- " similarity_metric=\"pearson\",\n",
- " precision_recall_k=k\n",
- " ).assign(cell_line=cell_line)\n",
- " \n",
- " precision_recall_at_k.append(precision_recall_results)"
- ]
+ " precision_recall_results = evaluate(\n",
+ " profiles=cell_line_df,\n",
+ " features=features,\n",
+ " meta_features=meta_features,\n",
+ " replicate_groups=[\"Metadata_gene_name\", \"Metadata_pert_name\"],\n",
+ " operation=\"precision_recall\",\n",
+ " similarity_metric=\"pearson\",\n",
+ " precision_recall_k=k_list\n",
+ " ).assign(cell_line=cell_line)\n",
+ "\n",
+ " precision_recall_at_k.append(precision_recall_results)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ }
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 11,
"id": "mighty-experiment",
"metadata": {},
"outputs": [
@@ -318,7 +330,7 @@
"text/plain": " Metadata_gene_name Metadata_pert_name k precision recall cell_line\n0 AKT1 AKT1-1 2.0 0.0 0.0 HCC44\n1 AKT1 AKT1-2 2.0 0.0 0.0 HCC44\n2 ARID1B ARID1B-1 2.0 0.0 0.0 HCC44\n3 ARID1B ARID1B-2 2.0 0.0 0.0 HCC44\n4 ATF4 ATF4-1 2.0 0.0 0.0 HCC44",
"text/html": "\n\n
\n \n \n | \n Metadata_gene_name | \n Metadata_pert_name | \n k | \n precision | \n recall | \n cell_line | \n
\n \n \n \n 0 | \n AKT1 | \n AKT1-1 | \n 2.0 | \n 0.0 | \n 0.0 | \n HCC44 | \n
\n \n 1 | \n AKT1 | \n AKT1-2 | \n 2.0 | \n 0.0 | \n 0.0 | \n HCC44 | \n
\n \n 2 | \n ARID1B | \n ARID1B-1 | \n 2.0 | \n 0.0 | \n 0.0 | \n HCC44 | \n
\n \n 3 | \n ARID1B | \n ARID1B-2 | \n 2.0 | \n 0.0 | \n 0.0 | \n HCC44 | \n
\n \n 4 | \n ATF4 | \n ATF4-1 | \n 2.0 | \n 0.0 | \n 0.0 | \n HCC44 | \n
\n \n
\n
"
},
- "execution_count": 13,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -332,7 +344,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 12,
"id": "ranking-valentine",
"metadata": {},
"outputs": [
@@ -346,9 +358,9 @@
},
{
"data": {
- "text/plain": ""
+ "text/plain": ""
},
- "execution_count": 14,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -388,7 +400,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 13,
"id": "juvenile-scholar",
"metadata": {},
"outputs": [
@@ -396,7 +408,7 @@
"data": {
"text/plain": "['Chr2-1', 'Chr2-4', 'Chr2-5', 'Chr2-2', 'Chr2-3', 'Chr2-6']"
},
- "execution_count": 15,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -409,7 +421,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 14,
"id": "suitable-medicine",
"metadata": {},
"outputs": [],
@@ -437,7 +449,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 15,
"id": "interior-brave",
"metadata": {},
"outputs": [
@@ -450,10 +462,10 @@
},
{
"data": {
- "text/plain": " Metadata_pert_name mp_value cell_line permutations\n0 AKT1-1 0.1 HCC44 10\n1 AKT1-2 0.1 HCC44 10\n2 ARID1B-1 0.1 HCC44 10\n3 ARID1B-2 0.2 HCC44 10\n4 ATF4-1 0.0 HCC44 10",
- "text/html": "\n\n
\n \n \n | \n Metadata_pert_name | \n mp_value | \n cell_line | \n permutations | \n
\n \n \n \n 0 | \n AKT1-1 | \n 0.1 | \n HCC44 | \n 10 | \n
\n \n 1 | \n AKT1-2 | \n 0.1 | \n HCC44 | \n 10 | \n
\n \n 2 | \n ARID1B-1 | \n 0.1 | \n HCC44 | \n 10 | \n
\n \n 3 | \n ARID1B-2 | \n 0.2 | \n HCC44 | \n 10 | \n
\n \n 4 | \n ATF4-1 | \n 0.0 | \n HCC44 | \n 10 | \n
\n \n
\n
"
+ "text/plain": " Metadata_pert_name mp_value cell_line permutations\n0 AKT1-1 0.2 HCC44 10\n1 AKT1-2 0.2 HCC44 10\n2 ARID1B-1 0.1 HCC44 10\n3 ARID1B-2 0.1 HCC44 10\n4 ATF4-1 0.0 HCC44 10",
+ "text/html": "\n\n
\n \n \n | \n Metadata_pert_name | \n mp_value | \n cell_line | \n permutations | \n
\n \n \n \n 0 | \n AKT1-1 | \n 0.2 | \n HCC44 | \n 10 | \n
\n \n 1 | \n AKT1-2 | \n 0.2 | \n HCC44 | \n 10 | \n
\n \n 2 | \n ARID1B-1 | \n 0.1 | \n HCC44 | \n 10 | \n
\n \n 3 | \n ARID1B-2 | \n 0.1 | \n HCC44 | \n 10 | \n
\n \n 4 | \n ATF4-1 | \n 0.0 | \n HCC44 | \n 10 | \n
\n \n
\n
"
},
- "execution_count": 17,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -467,7 +479,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 16,
"id": "verbal-beatles",
"metadata": {},
"outputs": [
@@ -476,22 +488,22 @@
"output_type": "stream",
"text": [
"/Users/mbornhol/miniconda3/envs/noncyto/lib/python3.8/site-packages/pandas/core/arraylike.py:358: RuntimeWarning: divide by zero encountered in log10\n",
- "/Users/mbornhol/miniconda3/envs/noncyto/lib/python3.8/site-packages/plotnine/layer.py:324: PlotnineWarning: stat_density : Removed 434 rows containing non-finite values.\n"
+ "/Users/mbornhol/miniconda3/envs/noncyto/lib/python3.8/site-packages/plotnine/layer.py:324: PlotnineWarning: stat_density : Removed 444 rows containing non-finite values.\n"
]
},
{
"data": {
"text/plain": "",
- "image/png": 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\n"
+ "image/png": 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\n"
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
- "text/plain": ""
+ "text/plain": ""
},
- "execution_count": 18,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -544,7 +556,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 17,
"id": "respective-constant",
"metadata": {},
"outputs": [],
@@ -557,7 +569,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 18,
"id": "accepting-basement",
"metadata": {},
"outputs": [],
@@ -583,7 +595,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 19,
"id": "complex-settle",
"metadata": {},
"outputs": [
@@ -599,7 +611,7 @@
"text/plain": " perturbation group grit cell_line\n0 AKT1-1 AKT1 0.918037 HCC44\n1 AKT1-2 AKT1 0.858680 HCC44\n2 ARID1B-1 ARID1B 0.304972 HCC44\n3 ARID1B-2 ARID1B 0.096940 HCC44\n4 ATF4-1 ATF4 -0.122386 HCC44",
"text/html": "\n\n
\n \n \n | \n perturbation | \n group | \n grit | \n cell_line | \n
\n \n \n \n 0 | \n AKT1-1 | \n AKT1 | \n 0.918037 | \n HCC44 | \n
\n \n 1 | \n AKT1-2 | \n AKT1 | \n 0.858680 | \n HCC44 | \n
\n \n 2 | \n ARID1B-1 | \n ARID1B | \n 0.304972 | \n HCC44 | \n
\n \n 3 | \n ARID1B-2 | \n ARID1B | \n 0.096940 | \n HCC44 | \n
\n \n 4 | \n ATF4-1 | \n ATF4 | \n -0.122386 | \n HCC44 | \n
\n \n
\n
"
},
- "execution_count": 21,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
@@ -613,7 +625,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 20,
"id": "ceramic-teens",
"metadata": {},
"outputs": [
@@ -634,9 +646,9 @@
},
{
"data": {
- "text/plain": ""
+ "text/plain": ""
},
- "execution_count": 22,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
@@ -662,34 +674,44 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 21,
+ "outputs": [],
+ "source": [
+ "percent_list = [0.99, 0.98, 0.97, 0.96]\n",
+ "\n",
+ "enrichment_result = evaluate(\n",
+ " profiles=df,\n",
+ " features=features,\n",
+ " meta_features=meta_features,\n",
+ " replicate_groups=[\"Metadata_gene_name\"],\n",
+ " operation=\"enrichment\",\n",
+ " similarity_metric=\"pearson\",\n",
+ " enrichment_percentile= percent_list,\n",
+ ")"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
"outputs": [
{
"data": {
- "text/plain": " percentile ods_ratio\n0 0.99 3.685582\n1 0.98 2.597346\n2 0.97 2.116626\n3 0.96 1.875379",
- "text/html": "\n\n
\n \n \n | \n percentile | \n ods_ratio | \n
\n \n \n \n 0 | \n 0.99 | \n 3.685582 | \n
\n \n 1 | \n 0.98 | \n 2.597346 | \n
\n \n 2 | \n 0.97 | \n 2.116626 | \n
\n \n 3 | \n 0.96 | \n 1.875379 | \n
\n \n
\n
"
+ "text/plain": " enrichment_percentile ods_ratio\n0 0.99 3.685582\n1 0.98 2.597346\n2 0.97 2.116626\n3 0.96 1.875379",
+ "text/html": "\n\n
\n \n \n | \n enrichment_percentile | \n ods_ratio | \n
\n \n \n \n 0 | \n 0.99 | \n 3.685582 | \n
\n \n 1 | \n 0.98 | \n 2.597346 | \n
\n \n 2 | \n 0.97 | \n 2.116626 | \n
\n \n 3 | \n 0.96 | \n 1.875379 | \n
\n \n
\n
"
},
- "execution_count": 24,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "result = []\n",
- "for p in np.arange(0.99, 0.96, -0.01):\n",
- " r = evaluate(\n",
- " profiles=df,\n",
- " features=features,\n",
- " meta_features=meta_features,\n",
- " replicate_groups=[\"Metadata_gene_name\"],\n",
- " operation=\"enrichment\",\n",
- " similarity_metric=\"pearson\",\n",
- " enrichment_percentile = p,\n",
- " )\n",
- " result.append(r)\n",
- "\n",
- "result_df = pd.DataFrame(result)\n",
- "result_df[[\"percentile\",\"ods_ratio\"]]"
+ "enrichment_result[[\"enrichment_percentile\",\"ods_ratio\"]]"
],
"metadata": {
"collapsed": false,
@@ -708,7 +730,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 23,
"id": "cathedral-familiar",
"metadata": {},
"outputs": [
@@ -721,10 +743,10 @@
},
{
"data": {
- "text/plain": " Metadata_gene_name Metadata_pert_name median_correlation cell_line \\\n0 AKT1 AKT1-1 0.240928 HCC44 \n1 AKT1 AKT1-2 0.178413 HCC44 \n2 ARID1B ARID1B-1 0.220200 HCC44 \n3 ARID1B ARID1B-2 0.133993 HCC44 \n4 ATF4 ATF4-1 0.797046 HCC44 \n\n precision_recall_k precision recall mp_value mp_value_permutations \\\n0 25.0 0.16 0.133333 0.118 1000 \n1 25.0 0.08 0.066667 0.154 1000 \n2 25.0 0.00 0.000000 0.091 1000 \n3 25.0 0.00 0.000000 0.114 1000 \n4 25.0 0.00 0.000000 0.002 1000 \n\n perturbation group grit \n0 AKT1-1 AKT1 0.918037 \n1 AKT1-2 AKT1 0.858680 \n2 ARID1B-1 ARID1B 0.304972 \n3 ARID1B-2 ARID1B 0.096940 \n4 ATF4-1 ATF4 -0.122386 ",
- "text/html": "\n\n
\n \n \n | \n Metadata_gene_name | \n Metadata_pert_name | \n median_correlation | \n cell_line | \n precision_recall_k | \n precision | \n recall | \n mp_value | \n mp_value_permutations | \n perturbation | \n group | \n grit | \n
\n \n \n \n 0 | \n AKT1 | \n AKT1-1 | \n 0.240928 | \n HCC44 | \n 25.0 | \n 0.16 | \n 0.133333 | \n 0.118 | \n 1000 | \n AKT1-1 | \n AKT1 | \n 0.918037 | \n
\n \n 1 | \n AKT1 | \n AKT1-2 | \n 0.178413 | \n HCC44 | \n 25.0 | \n 0.08 | \n 0.066667 | \n 0.154 | \n 1000 | \n AKT1-2 | \n AKT1 | \n 0.858680 | \n
\n \n 2 | \n ARID1B | \n ARID1B-1 | \n 0.220200 | \n HCC44 | \n 25.0 | \n 0.00 | \n 0.000000 | \n 0.091 | \n 1000 | \n ARID1B-1 | \n ARID1B | \n 0.304972 | \n
\n \n 3 | \n ARID1B | \n ARID1B-2 | \n 0.133993 | \n HCC44 | \n 25.0 | \n 0.00 | \n 0.000000 | \n 0.114 | \n 1000 | \n ARID1B-2 | \n ARID1B | \n 0.096940 | \n
\n \n 4 | \n ATF4 | \n ATF4-1 | \n 0.797046 | \n HCC44 | \n 25.0 | \n 0.00 | \n 0.000000 | \n 0.002 | \n 1000 | \n ATF4-1 | \n ATF4 | \n -0.122386 | \n
\n \n
\n
"
+ "text/plain": " Metadata_gene_name Metadata_pert_name median_correlation cell_line \\\n0 AKT1 AKT1-1 0.240928 HCC44 \n1 AKT1 AKT1-2 0.178413 HCC44 \n2 ARID1B ARID1B-1 0.220200 HCC44 \n3 ARID1B ARID1B-2 0.133993 HCC44 \n4 ATF4 ATF4-1 0.797046 HCC44 \n\n precision_recall_k precision recall mp_value mp_value_permutations \\\n0 25.0 0.16 0.133333 0.115 1000 \n1 25.0 0.08 0.066667 0.161 1000 \n2 25.0 0.00 0.000000 0.094 1000 \n3 25.0 0.00 0.000000 0.138 1000 \n4 25.0 0.00 0.000000 0.002 1000 \n\n perturbation group grit \n0 AKT1-1 AKT1 0.918037 \n1 AKT1-2 AKT1 0.858680 \n2 ARID1B-1 ARID1B 0.304972 \n3 ARID1B-2 ARID1B 0.096940 \n4 ATF4-1 ATF4 -0.122386 ",
+ "text/html": "\n\n
\n \n \n | \n Metadata_gene_name | \n Metadata_pert_name | \n median_correlation | \n cell_line | \n precision_recall_k | \n precision | \n recall | \n mp_value | \n mp_value_permutations | \n perturbation | \n group | \n grit | \n
\n \n \n \n 0 | \n AKT1 | \n AKT1-1 | \n 0.240928 | \n HCC44 | \n 25.0 | \n 0.16 | \n 0.133333 | \n 0.115 | \n 1000 | \n AKT1-1 | \n AKT1 | \n 0.918037 | \n
\n \n 1 | \n AKT1 | \n AKT1-2 | \n 0.178413 | \n HCC44 | \n 25.0 | \n 0.08 | \n 0.066667 | \n 0.161 | \n 1000 | \n AKT1-2 | \n AKT1 | \n 0.858680 | \n
\n \n 2 | \n ARID1B | \n ARID1B-1 | \n 0.220200 | \n HCC44 | \n 25.0 | \n 0.00 | \n 0.000000 | \n 0.094 | \n 1000 | \n ARID1B-1 | \n ARID1B | \n 0.304972 | \n
\n \n 3 | \n ARID1B | \n ARID1B-2 | \n 0.133993 | \n HCC44 | \n 25.0 | \n 0.00 | \n 0.000000 | \n 0.138 | \n 1000 | \n ARID1B-2 | \n ARID1B | \n 0.096940 | \n
\n \n 4 | \n ATF4 | \n ATF4-1 | \n 0.797046 | \n HCC44 | \n 25.0 | \n 0.00 | \n 0.000000 | \n 0.002 | \n 1000 | \n ATF4-1 | \n ATF4 | \n -0.122386 | \n
\n \n
\n
"
},
- "execution_count": 25,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
@@ -762,7 +784,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 24,
"id": "retained-companion",
"metadata": {},
"outputs": [
@@ -783,9 +805,9 @@
},
{
"data": {
- "text/plain": ""
+ "text/plain": ""
},
- "execution_count": 26,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
@@ -800,7 +822,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 25,
"id": "graphic-actor",
"metadata": {},
"outputs": [
@@ -814,16 +836,16 @@
{
"data": {
"text/plain": "",
- "image/png": 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\n"
+ "image/png": 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\n"
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
- "text/plain": ""
+ "text/plain": ""
},
- "execution_count": 27,
+ "execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
@@ -838,7 +860,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 26,
"id": "ceramic-drunk",
"metadata": {},
"outputs": [
@@ -847,7 +869,7 @@
"text/plain": " Metadata_gene_name Metadata_pert_name median_correlation cell_line \\\n0 MYC MYC-1 0.896095 ES2 \n1 YAP1 YAP1-2 0.855538 ES2 \n2 CCND1 CCND1-2 0.912482 ES2 \n3 EGLN1 EGLN1-9 0.799812 ES2 \n4 CDK2 CDK2-2 0.891436 HCC44 \n5 ITGAV ITGAV-2 0.895882 A549 \n6 CDK4 CDK4-2 0.913841 ES2 \n7 ITGAV ITGAV-2 0.919061 HCC44 \n8 ITGAV ITGAV-1 0.925652 HCC44 \n9 GPX4 GPX4-1 0.844319 ES2 \n\n precision_recall_k precision recall mp_value mp_value_permutations \\\n0 25.0 0.72 0.600000 0.0 1000 \n1 25.0 0.80 0.666667 0.0 1000 \n2 25.0 0.64 0.533333 0.0 1000 \n3 25.0 0.72 0.600000 0.0 1000 \n4 25.0 0.60 0.500000 0.0 1000 \n5 25.0 0.56 0.466667 0.0 1000 \n6 25.0 0.40 0.333333 0.0 1000 \n7 25.0 0.40 0.333333 0.0 1000 \n8 25.0 0.40 0.333333 0.0 1000 \n9 25.0 0.40 0.333333 0.0 1000 \n\n perturbation group grit metric_sum_scaled \n0 MYC-1 MYC 3.087105 3.741246 \n1 YAP1-2 YAP1 1.888577 3.609051 \n2 CCND1-2 CCND1 2.555242 3.419675 \n3 EGLN1-9 EGLN1 1.972640 3.388326 \n4 CDK2-2 CDK2 1.582908 3.059086 \n5 ITGAV-2 ITGAV 1.886879 3.038947 \n6 CDK4-2 CDK4 2.829412 2.889714 \n7 ITGAV-2 ITGAV 2.783719 2.882135 \n8 ITGAV-1 ITGAV 2.689741 2.863432 \n9 GPX4-1 GPX4 2.836964 2.839359 ",
"text/html": "\n\n
\n \n \n | \n Metadata_gene_name | \n Metadata_pert_name | \n median_correlation | \n cell_line | \n precision_recall_k | \n precision | \n recall | \n mp_value | \n mp_value_permutations | \n perturbation | \n group | \n grit | \n metric_sum_scaled | \n
\n \n \n \n 0 | \n MYC | \n MYC-1 | \n 0.896095 | \n ES2 | \n 25.0 | \n 0.72 | \n 0.600000 | \n 0.0 | \n 1000 | \n MYC-1 | \n MYC | \n 3.087105 | \n 3.741246 | \n
\n \n 1 | \n YAP1 | \n YAP1-2 | \n 0.855538 | \n ES2 | \n 25.0 | \n 0.80 | \n 0.666667 | \n 0.0 | \n 1000 | \n YAP1-2 | \n YAP1 | \n 1.888577 | \n 3.609051 | \n
\n \n 2 | \n CCND1 | \n CCND1-2 | \n 0.912482 | \n ES2 | \n 25.0 | \n 0.64 | \n 0.533333 | \n 0.0 | \n 1000 | \n CCND1-2 | \n CCND1 | \n 2.555242 | \n 3.419675 | \n
\n \n 3 | \n EGLN1 | \n EGLN1-9 | \n 0.799812 | \n ES2 | \n 25.0 | \n 0.72 | \n 0.600000 | \n 0.0 | \n 1000 | \n EGLN1-9 | \n EGLN1 | \n 1.972640 | \n 3.388326 | \n
\n \n 4 | \n CDK2 | \n CDK2-2 | \n 0.891436 | \n HCC44 | \n 25.0 | \n 0.60 | \n 0.500000 | \n 0.0 | \n 1000 | \n CDK2-2 | \n CDK2 | \n 1.582908 | \n 3.059086 | \n
\n \n 5 | \n ITGAV | \n ITGAV-2 | \n 0.895882 | \n A549 | \n 25.0 | \n 0.56 | \n 0.466667 | \n 0.0 | \n 1000 | \n ITGAV-2 | \n ITGAV | \n 1.886879 | \n 3.038947 | \n
\n \n 6 | \n CDK4 | \n CDK4-2 | \n 0.913841 | \n ES2 | \n 25.0 | \n 0.40 | \n 0.333333 | \n 0.0 | \n 1000 | \n CDK4-2 | \n CDK4 | \n 2.829412 | \n 2.889714 | \n
\n \n 7 | \n ITGAV | \n ITGAV-2 | \n 0.919061 | \n HCC44 | \n 25.0 | \n 0.40 | \n 0.333333 | \n 0.0 | \n 1000 | \n ITGAV-2 | \n ITGAV | \n 2.783719 | \n 2.882135 | \n
\n \n 8 | \n ITGAV | \n ITGAV-1 | \n 0.925652 | \n HCC44 | \n 25.0 | \n 0.40 | \n 0.333333 | \n 0.0 | \n 1000 | \n ITGAV-1 | \n ITGAV | \n 2.689741 | \n 2.863432 | \n
\n \n 9 | \n GPX4 | \n GPX4-1 | \n 0.844319 | \n ES2 | \n 25.0 | \n 0.40 | \n 0.333333 | \n 0.0 | \n 1000 | \n GPX4-1 | \n GPX4 | \n 2.836964 | \n 2.839359 | \n
\n \n
\n
"
},
- "execution_count": 28,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
@@ -876,23 +898,23 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 27,
"id": "cathedral-bailey",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "