diff --git a/CHANGELOG.md b/CHANGELOG.md index 4ffb5671..df39e291 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [UNRELEASED] - 2024-??-?? +### Added + +* Add `dsb_normalization` function for normalization of marker abundance. + ### Fixed * Fix a bug where `a_pixels_per_b_pixel` summary statistics where equal to the `b_pixels_per_a_pixel` statistics. diff --git a/CITATIONS.md b/CITATIONS.md index fe12af2d..9f77e8f4 100644 --- a/CITATIONS.md +++ b/CITATIONS.md @@ -25,3 +25,7 @@ - [local G](https://doi.org/10.1007/s11749-018-0599-x) > Bivand, R.S., Wong, D.W.S. Comparing implementations of global and local indicators of spatial association. TEST 27, 716–748 (2018). https://doi.org/10.1007/s11749-018-0599-x + +- [dsb](https://doi.org/10.1038/s41467-022-29356-8) + + > Mulè, M.P., Martins, A.J. & Tsang, J.S. Normalizing and denoising protein expression data from droplet-based single cell profiling. Nat Commun 13, 2099 (2022). https://doi.org/10.1038/s41467-022-29356-8 diff --git a/src/pixelator/analysis/normalization/__init__.py b/src/pixelator/analysis/normalization/__init__.py new file mode 100644 index 00000000..c4592373 --- /dev/null +++ b/src/pixelator/analysis/normalization/__init__.py @@ -0,0 +1,66 @@ +"""Functions for the colocalization analysis in pixelator. + +Copyright © 2024 Pixelgen Technologies AB. +""" + +from typing import List, Union + +import numpy as np +import pandas as pd +from sklearn.decomposition import PCA +from sklearn.mixture import GaussianMixture +from sklearn.preprocessing import StandardScaler + + +def _regress_out_confounder(pheno, exprs, rcond=1e-8): + """Linear regression to remove confounding factors from abundance data.""" + design_matrix = np.column_stack((np.ones((len(pheno), 1)), pheno)) + coefficients, res, rank, s = np.linalg.lstsq(design_matrix, exprs, rcond=rcond) + beta = coefficients[1:] # remove intercept term + return exprs - design_matrix[:, 1:].dot(beta) + + +def _get_background_abundance(dataframe: pd.DataFrame, axis=0): + """Fit a double gaussian distribution to the abundance data and return the mean of the first gaussian as an estimation of the background level.""" + background = pd.Series(index=dataframe.index if axis == 0 else dataframe.columns) + scores = pd.Series(index=dataframe.index if axis == 0 else dataframe.columns) + gmm = GaussianMixture(n_components=2, max_iter=1000, random_state=0) + if axis not in {0, 1}: + raise ValueError(f"Axis was {axis}. Must be 0 or 1") + ax_iter = dataframe.index if axis == 0 else dataframe.columns + for i in ax_iter: + current_axis = dataframe.loc[i, :] if axis == 0 else dataframe.loc[:, i] + gmm = gmm.fit(current_axis.to_frame()) + background[i] = np.min(gmm.means_) + scores[i] = np.abs(gmm.means_[1] - gmm.means_[0]) / np.sum(gmm.covariances_) + return background, scores + + +def dsb_normalize( + raw_abundance: pd.DataFrame, isotype_controls: Union[List, None] = None +): + """empty-droplet-free method as implemented in Mulè et. al. dsb package. + + The normalization steps are: 1- log1p transformation, 2- remove background + abundance per marker, 3- regularize abundance per component. + + :param raw_abundance: the raw abundance count data. + :param isotype_controls: list of isotype controls. + :return: normalized abundance data. + """ + log_abundance = np.log1p(raw_abundance) + marker_background, _ = _get_background_abundance(log_abundance, axis=1) + log_abundance = log_abundance - marker_background + component_background, _ = _get_background_abundance(log_abundance, axis=0) + + if isotype_controls is not None: + control_signals = log_abundance.loc[:, isotype_controls] + control_signals["component_background"] = component_background + control_signals = StandardScaler().fit_transform(control_signals) + pheno = PCA(n_components=1).fit_transform(control_signals) + else: + raise ValueError(f"At least one isotype control must be provided.") + + normalized_abundance = _regress_out_confounder(pheno, log_abundance) + + return normalized_abundance diff --git a/tests/analysis/normalization/__init__.py b/tests/analysis/normalization/__init__.py new file mode 100644 index 00000000..f6b4c8e1 --- /dev/null +++ b/tests/analysis/normalization/__init__.py @@ -0,0 +1,5 @@ +""" +Tests for the normalization modules + +Copyright © 2024 Pixelgen Technologies AB. +""" diff --git a/tests/analysis/normalization/test_normalization.py b/tests/analysis/normalization/test_normalization.py new file mode 100644 index 00000000..69f797d9 --- /dev/null +++ b/tests/analysis/normalization/test_normalization.py @@ -0,0 +1,26 @@ +""" +Tests for the normalization modules + +Copyright © 2024 Pixelgen Technologies AB. +""" + +from pathlib import Path + +import pandas as pd +from pandas.testing import assert_frame_equal +from pixelator.analysis.normalization import dsb_normalize + +DATA_ROOT = Path(__file__).parents[2] / "data" + + +def test_dsb_normalize(): + input_data = pd.read_csv( + str(DATA_ROOT / "dsb_normalization_test_input.csv") + ).astype(float) + output_data = pd.read_csv( + str(DATA_ROOT / "dsb_normalization_test_output.csv") + ).astype(float) + output_data = output_data - output_data.iloc[0, :] + result = dsb_normalize(input_data, isotype_controls=["mIgG1", "mIgG2a", "mIgG2b"]) + result = result - result.iloc[0, :] + assert_frame_equal(result, output_data, atol=0.05) diff --git a/tests/data/dsb_normalization_test_input.csv b/tests/data/dsb_normalization_test_input.csv new file mode 100644 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