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Optimize FDR calculation #3

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merged 4 commits into from
Jan 24, 2024
Merged

Optimize FDR calculation #3

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15a6b9a
optimize FDR calculation
michabirklbauer Jan 24, 2024
3972e36
fix undeclared var
michabirklbauer Jan 24, 2024
6bff9c3
use correct validator
michabirklbauer Jan 24, 2024
5f5871d
fix cutoff comparison
michabirklbauer Jan 24, 2024
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44 changes: 20 additions & 24 deletions msannika_fdr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,10 +6,11 @@
# [email protected]

# version tracking
__version = "1.0.0"
__date = "2024-01-09"
__version = "1.1.0"
__date = "2024-01-24"

# REQUIREMENTS
# pip install numpy
# pip install pandas
# pip install openpyxl

Expand Down Expand Up @@ -41,6 +42,7 @@
######################

import argparse
import numpy as np
import pandas as pd

from typing import List
Expand Down Expand Up @@ -138,20 +140,17 @@ class MSAnnika_CSM_Validator:
def get_class(row: pd.Series) -> str:
return "Decoy" if "D" in row["Alpha T/D"] or "D" in row["Beta T/D"] else "Target"

@staticmethod
def get_fdr(data: pd.DataFrame, score: float) -> float:

df = data[data["Combined Score"] > score].copy()
df["Class"] = df.apply(lambda row: MSAnnika_CSM_Validator.get_class(row), axis = 1)

return df[df["Class"] == "Decoy"].shape[0] / df[df["Class"] == "Target"].shape[0]

@staticmethod
def get_cutoff(data: pd.DataFrame, fdr: float) -> float:

data["Class"] = data.apply(lambda row: MSAnnika_CSM_Validator.get_class(row), axis = 1)
data["Class_label"] = data.apply(lambda row: 0 if row["Class"] == "Target" else 1, axis = 1)
labels = data["Class_label"].to_numpy()
labels_sorted = labels[data["Combined Score"].to_numpy().argsort()]

scores = sorted(data["Combined Score"].tolist())
for score in scores:
if MSAnnika_CSM_Validator.get_fdr(data, score) < fdr:
for i, score in enumerate(scores):
if labels_sorted[i:].sum() / (labels_sorted[i:].shape[0] - labels_sorted[i:].sum()) < fdr:
return score

return scores[0]
Expand All @@ -160,7 +159,7 @@ def get_cutoff(data: pd.DataFrame, fdr: float) -> float:
def validate(data: pd.DataFrame, fdr: float) -> pd.DataFrame:

cutoff = MSAnnika_CSM_Validator.get_cutoff(data, fdr)
df = data[data["Combined Score"] > cutoff].copy()
df = data[data["Combined Score"] >= cutoff].copy()

if "Confidence" not in df.columns:
return df
Expand All @@ -175,20 +174,17 @@ class MSAnnika_Crosslink_Validator:
def get_class(row: pd.Series) -> str:
return "Decoy" if row["Decoy"] else "Target"

@staticmethod
def get_fdr(data: pd.DataFrame, score: float) -> float:

df = data[data["Best CSM Score"] > score].copy()
df["Class"] = df.apply(lambda row: MSAnnika_Crosslink_Validator.get_class(row), axis = 1)

return df[df["Class"] == "Decoy"].shape[0] / df[df["Class"] == "Target"].shape[0]

@staticmethod
def get_cutoff(data: pd.DataFrame, fdr: float) -> float:

data["Class"] = data.apply(lambda row: MSAnnika_Crosslink_Validator.get_class(row), axis = 1)
data["Class_label"] = data.apply(lambda row: 0 if row["Class"] == "Target" else 1, axis = 1)
labels = data["Class_label"].to_numpy()
labels_sorted = labels[data["Best CSM Score"].to_numpy().argsort()]

scores = sorted(data["Best CSM Score"].tolist())
for score in scores:
if MSAnnika_Crosslink_Validator.get_fdr(data, score) < fdr:
for i, score in enumerate(scores):
if labels_sorted[i:].sum() / (labels_sorted[i:].shape[0] - labels_sorted[i:].sum()) < fdr:
return score

return scores[0]
Expand All @@ -197,7 +193,7 @@ def get_cutoff(data: pd.DataFrame, fdr: float) -> float:
def validate(data: pd.DataFrame, fdr: float) -> pd.DataFrame:

cutoff = MSAnnika_Crosslink_Validator.get_cutoff(data, fdr)
df = data[data["Best CSM Score"] > cutoff].copy()
df = data[data["Best CSM Score"] >= cutoff].copy()
df["Confidence"] = df.apply(lambda row: "High", axis = 1)

return df
Expand Down