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DNM: Detrend optimally combined data before running PCA #1090

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DNM: Detrend optimally combined data before running PCA #1090

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a379230
Detrend data before running PCA.
tsalo Apr 27, 2024
55e161d
Update pca.py
tsalo Apr 27, 2024
9874ede
Simplify code.
tsalo Apr 28, 2024
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8 changes: 4 additions & 4 deletions tedana/decomposition/pca.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
import numpy as np
import pandas as pd
from mapca import MovingAveragePCA
from nilearn.signal import standardize_signal
from scipy import stats
from sklearn.decomposition import PCA

Expand Down Expand Up @@ -204,12 +205,11 @@ def tedpca(
f"Computing PCA of optimally combined multi-echo data with selection criteria: {algorithm}"
)
data = data_oc[mask, :]

data_z = ((data.T - data.T.mean(axis=0)) / data.T.std(axis=0)).T # var normalize ts
data_z = (data_z - data_z.mean()) / data_z.std() # var normalize everything
data_z = standardize_signal(data.T, detrend=True, standardize="zscore_sample").T
# data_z = (data_z - data_z.mean()) / data_z.std() # var normalize everything
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if algorithm in ["mdl", "aic", "kic"]:
data_img = io.new_nii_like(io_generator.reference_img, utils.unmask(data, mask))
data_img = io.new_nii_like(io_generator.reference_img, utils.unmask(data_z, mask))
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mask_img = io.new_nii_like(io_generator.reference_img, mask.astype(int))
ma_pca = MovingAveragePCA(criterion=algorithm, normalize=True)
_ = ma_pca.fit_transform(data_img, mask_img)
Expand Down