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Spatial proteomic analysis in human Alzheimer's disease brains enables identification of microenvironment-dependent microglial cell states.

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3 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 3 matches
  1. [1] § Methods › Pseudotime analysis ↔ scFates/tools/conversion.py, lines 12–75 · score 0.57 · epg_lambda, scFates, tl, pseudotime, rooted, Cells
  2. [2] § Methods › Aβ plaque prediction based on myeloid cell features ↔ bbknn/__init__.py, lines 133–208 · score 0.52 · Regressor model, scikit-learn, dense, cell
  3. [3] § Methods › Myeloid cell clustering based on protein expression ↔ bbknn/__init__.py, lines 16–131 · score 0.51 · neighbor graph, scikit-learn, dimensional, metrics, clustering, cell

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The authors' code

Python · 229 lines · 11 KB · MIT · 2 matches

  1. """Batch balanced KNN"""
  2. __version__ = "1.6.0"
  4. import pandas as pd
  5. import numpy as np
  6. import scipy
  7. import sys
  8. from sklearn.linear_model import Ridge
  9. try:
  10. from scanpy import logging as logg
  11. except ImportError:
  12. pass
  14. from . import matrix
  16. def bbknn(adata, batch_key='batch', use_rep='X_pca', key_added=None, copy=False, **kwargs):
  17. '''
  18. Batch balanced KNN, altering the KNN procedure to identify each cell's top neighbours in
  19. each batch separately instead of the entire cell pool with no accounting for batch.
  20. The nearest neighbours for each batch are then merged to create a final list of
  21. neighbours for the cell.
  22. Aligns batches in a quick and lightweight manner.
  23. For use in the scanpy workflow as an alternative to ``scanpy.pp.neighbors()``.
  25. Input
  26. -----
  27. adata : ``AnnData``
  28. Needs your dimensionality reduction of choice computed and stored in ``.obsm``.
  29. batch_key : ``str``, optional (default: "batch")
  30. ``adata.obs`` column name discriminating between your batches.
  31. neighbors_within_batch : ``int``, optional (default: 3)
  32. How many top neighbours to report for each batch; total number of neighbours in
  33. the initial k-nearest-neighbours computation will be this number times the number
  34. of batches. This then serves as the basis for the construction of a symmetrical
  35. matrix of connectivities.
  36. use_rep : ``str``, optional (default: "X_pca")
  37. The dimensionality reduction in ``.obsm`` to use for neighbour detection. Defaults to PCA.
  38. n_pcs : ``int``, optional (default: 50)
  39. How many dimensions (in case of PCA, principal components) to use in the analysis.
  40. trim : ``int`` or ``None``, optional (default: ``None``)
  41. Trim the neighbours of each cell to these many top connectivities. May help with
  42. population independence and improve the tidiness of clustering. The lower the value the
  43. more independent the individual populations, at the cost of more conserved batch effect.
  44. If ``None``, sets the parameter value automatically to 10 times ``neighbors_within_batch``
  45. times the number of batches. Set to 0 to skip.
  46. computation : ``str``, optional (default: "annoy")
  47. Which KNN algorithm to use. BBKNN supports the approximate neighbour search of "annoy"
  48. and "pynndescent", and the exact neighbour search of "faiss", "cKDTree" and "KDTree".
  49. Available metric choices depend on the package used here.
  50. annoy_n_trees : ``int``, optional (default: 10)
  51. Only used with annoy neighbour identification. The number of trees to construct in the
  52. annoy forest. More trees give higher precision when querying, at the cost of increased
  53. run time and resource intensity.
  54. pynndescent_n_neighbors : ``int``, optional (default: 30)
  55. Only used with pyNNDescent neighbour identification. The number of neighbours to include
  56. in the approximate neighbour graph. More neighbours give higher precision when querying,
  57. at the cost of increased run time and resource intensity.
  58. pynndescent_random_state : ``int``, optional (default: 0)
  59. Only used with pyNNDescent neighbour identification. The RNG seed to use when creating
  60. the graph.
  61. metric : ``str`` or ``sklearn.neighbors.DistanceMetric`` or ``types.FunctionType``, optional (default: "euclidean")
  62. What distance metric to use. The options depend on the choice of neighbour algorithm.
  64. "euclidean", the default, is always available.
  66. Annoy supports "angular", "manhattan" and "hamming".
  68. PyNNDescent supports metrics listed in ``pynndescent.distances.named_distances``
  69. and custom functions, including compiled Numba code.
  71. >>> pynndescent.distances.named_distances.keys()
  72. dict_keys(['euclidean', 'l2', 'sqeuclidean', 'manhattan', 'taxicab', 'l1', 'chebyshev', 'linfinity',
  73. 'linfty', 'linf', 'minkowski', 'seuclidean', 'standardised_euclidean', 'wminkowski', 'weighted_minkowski',
  74. 'mahalanobis', 'canberra', 'cosine', 'dot', 'correlation', 'hellinger', 'haversine', 'braycurtis', 'spearmanr',
  75. 'kantorovich', 'wasserstein', 'tsss', 'true_angular', 'hamming', 'jaccard', 'dice', 'matching', 'kulsinski',
  76. 'rogerstanimoto', 'russellrao', 'sokalsneath', 'sokalmichener', 'yule'])
  78. KDTree supports members of the ``sklearn.neighbors.KDTree.valid_metrics()`` list, or parameterised
  79. ``sklearn.metrics.DistanceMetric`` `objects
  80. <https://scikit-learn.org/stable/modules/generated/sklearn.metrics.DistanceMetric.html>`_:
  82. >>> sklearn.neighbors.KDTree.valid_metrics()
  83. ['euclidean', 'l2', 'minkowski', 'p', 'manhattan', 'cityblock', 'l1', 'chebyshev', 'infinity']
  84. set_op_mix_ratio : ``float``, optional (default: 1)
  85. UMAP connectivity computation parameter, float between 0 and 1, controlling the
  86. blend between a connectivity matrix formed exclusively from mutual nearest neighbour
  87. pairs (0) and a union of all observed neighbour relationships with the mutual pairs
  88. emphasised (1)
  89. local_connectivity : ``int``, optional (default: 1)
  90. UMAP connectivity computation parameter, how many nearest neighbors of each cell
  91. are assumed to be fully connected (and given a connectivity value of 1)
  92. copy : ``bool``, optional (default: ``False``)
  93. If ``True``, return a copy instead of writing to the supplied adata.
  94. '''
  95. start = logg.info('computing batch balanced neighbors')
  96. adata = adata.copy() if copy else adata
  97. #basic sanity checks to begin
  98. #is our batch key actually present in the object?
  99. if batch_key not in adata.obs:
  100. raise ValueError("Batch key '"+batch_key+"' not present in `adata.obs`.")
  101. #do we have a computed PCA?
  102. if use_rep not in adata.obsm.keys():
  103. raise ValueError("Did not find "+use_rep+" in `.obsm.keys()`. You need to compute it first.")
  104. #prepare bbknn.matrix.bbknn input
  105. pca = adata.obsm[use_rep]
  106. batch_list = adata.obs[batch_key].values
  107. #call BBKNN proper, telling it to use scanpy logging for its internal things
  108. bbknn_out = matrix.bbknn(pca=pca, batch_list=batch_list, scanpy_logging=True, **kwargs)
  109. #store the parameters, add use_rep and batch_key
  110. #mirror scanpy neighbour key_added logic
  111. if key_added is None:
  112. key_added = 'neighbors'
  113. conns_key = 'connectivities'
  114. dists_key = 'distances'
  115. else:
  116. conns_key = key_added + '_connectivities'
  117. dists_key = key_added + '_distances'
  118. adata.uns[key_added] = {}
  119. adata.uns[key_added]['params'] = bbknn_out[2]
  120. adata.uns[key_added]['params']['use_rep'] = use_rep
  121. adata.uns[key_added]['params']['bbknn']['batch_key'] = batch_key
  122. #store the graphs in an anndata 0.7.0+ compliant manner
  123. adata.obsp[dists_key] = bbknn_out[0]
  124. adata.obsp[conns_key] = bbknn_out[1]
  125. adata.uns[key_added]['distances_key'] = dists_key
  126. adata.uns[key_added]['connectivities_key'] = conns_key
  127. logg.info(' finished', time=start,
  128. deep=(f'added to `.uns[{key_added!r}]`\n'
  129. f' `.obsp[{dists_key!r}]`, distances for each pair of neighbors\n'
  130. f' `.obsp[{conns_key!r}]`, weighted adjacency matrix'))
  131. return adata if copy else None
  133. def ridge_regression(adata, batch_key, confounder_key=[], chunksize=1e8, copy=False, **kwargs):
  134. '''
  135. Perform ridge regression on scaled expression data, accepting both technical and
  136. biological categorical variables. The effect of the technical variables is removed
  137. while the effect of the biological variables is retained. This is a preprocessing
  138. step that can aid BBKNN integration `(Park, 2020) <https://science.sciencemag.org/content/367/6480/eaay3224.abstract>`_.
  140. Alters the object's ``.X`` to be the regression residuals, and creates ``.layers['X_explained']``
  141. with the expression explained by the technical effect.
  143. Input
  144. -----
  145. adata : ``AnnData``
  146. Needs scaled data in ``.X``.
  147. batch_key : ``list``
  148. A list of categorical ``.obs`` columns to regress out as technical effects.
  149. confounder_key : ``list``, optional (default: ``[]``)
  150. A list of categorical ``.obs`` columns to retain as biological effects.
  151. chunksize : ``int``, optional (default: 1e8)
  152. How many elements of the expression matrix to process at a time. Potentially useful
  153. to manage memory use for larger datasets.
  154. copy : ``bool``, optional (default: ``False``)
  155. If ``True``, return a copy instead of writing to the supplied adata.
  156. kwargs
  157. Any arguments to pass to `Ridge <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html>`_.
  158. '''
  159. start = logg.info('computing ridge regression')
  160. adata = adata.copy() if copy else adata
  161. #just in case the arguments are not provided as lists, convert them to such
  162. #as they need to be lists for downstream application
  163. if not isinstance(batch_key, list):
  164. batch_key = [batch_key]
  165. if not isinstance(confounder_key, list):
  166. confounder_key = [confounder_key]
  168. #construct a helper representation of the batch and biological variables
  169. #as a data frame with one row per cell, with columns specifying the various batch/biological categories
  170. #with values of 1 where the cell is of the category and 0 otherwise (dummy)
  171. #and subsequently identify which of the data frame columns are batch rather than biology (batch_index)
  172. #and subset the data frame to just those columns, in np.array form (dm)
  173. dummy = pd.get_dummies(adata.obs[batch_key+confounder_key],drop_first=False)
  174. if len(batch_key)>1:
  175. batch_index = np.logical_or.reduce(np.vstack([dummy.columns.str.startswith(x) for x in batch_key]))
  176. else:
  177. batch_index = np.vstack([dummy.columns.str.startswith(x) for x in batch_key])[0]
  178. dm = np.array(dummy)[:,batch_index]
  180. #compute how many genes at a time will be processed - aiming for chunksize total elements per
  181. chunkcount = np.ceil(chunksize/adata.shape[0])
  183. #make a Ridge with all the **kwargs passed if need be, and fit_intercept set to False
  184. #(as the data is centered). create holders for results
  185. LR = Ridge(fit_intercept=False, **kwargs)
  186. X_explained = []
  187. X_remain = []
  188. #loop over the gene space in chunkcount-sized chunks
  189. for ind in np.arange(0,adata.shape[1],chunkcount):
  190. #extract the expression and turn to dense if need be
  191. X_exp = adata.X[:,int(ind):int(ind+chunkcount)] # scaled data
  192. if scipy.sparse.issparse(X_exp):
  193. X_exp = np.asarray(X_exp.todense())
  194. #fit the ridge regression model, compute the expression explained by the technical
  195. #effect, and the remaining residual
  196. LR.fit(dummy,X_exp)
  197. X_explained.append(dm.dot(LR.coef_[:,batch_index].T))
  198. X_remain.append(X_exp - X_explained[-1])
  200. #collapse the chunked outputs and store them in the object
  201. X_explained = np.hstack(X_explained)
  202. X_remain = np.hstack(X_remain)
  203. adata.X = X_remain
  204. adata.layers['X_explained'] = X_explained
  205. logg.info(' finished', time=start,
  206. deep=('`.X` now features regression residuals\n'
  207. ' `.layers[\'X_explained\']` stores the expression explained by the technical effect'))
  208. return adata if copy else None
  210. def extract_cell_connectivity(adata, cell, key='extracted_cell_connectivity'):
  211. '''
  212. Helper post-processing function that extracts a single cell's connectivity and stores
  213. it in ``adata.obs``, ready for plotting. Connectivities range from 0 to 1, the higher
  214. the connectivity the closer the cells are in the neighbour graph. Cells with a
  215. connectivity of 0 are unconnected in the graph.
  217. Input
  218. -----
  219. adata : ``AnnData``
  220. After having BBKNN ran on it.
  221. cell : ``str``
  222. The name of the cell to extract the connectivities for.
  223. key : ``str``, optional (default "extracted_cell_connectivity")
  224. What name to store the connectivities under in ``adata.obs``.
  225. '''
  226. if cell not in adata.obs_names:
  227. ValueError('The specified cell is not present in the object.')
  228. index = np.arange(len(adata.obs_names))[adata.obs_names==cell][0]
  229. adata.obs[key] = np.asarray(adata.uns['neighbors']['connectivities'][index,:].todense())[0]

__init__.py at commit 95ce34b, under MIT · at the source

Overview

Authors: Paula Sanchez-Molina1,2, Dennis-Dominik Rosmus3,4,5, Dillon Brownell1,2, Mert Meral6, Cavanagh Gohlich1,2, Aditya Pratapa7, Yaser Peymanfar7, Alyssa Whitley7, Yue Hou7, Nadezhda Nikulina7, Alina Bogachuk1,2, Ellen Lara Bouchard1,2, Aude Chiot1,2, Heidrun Kuhrt3, Peter Wieghofer3,4, Randall Woltjer8, Fabian Svara6, Oliver Braubach7, Bahareh Ajami1,2,9
ORCID iDs: Paula Sanchez-Molina, Dennis-Dominik Rosmus, Yaser Peymanfar, Peter Wieghofer, Randall Woltjer, Oliver Braubach, Bahareh Ajami
  1. Department of Molecular Microbiology and Immunology, Oregon Health and Science University,Portland, OR USA
  2. Department of Behavioral and Systems Neuroscience, Oregon Health and Science University,Portland, OR USA
  3. Institute of Anatomy, Leipzig University,Leipzig, Germany
  4. Cellular Neuroanatomy, Chair of Anatomy and Cell Biology, Institute of Theoretical Medicine, University of Augsburg,Augsburg, Germany
  5. Department of Neurology, Leipzig University Medical Center,Leipzig, Germany
  6. ariadne.ai AG, Lucerne, Switzerland
  7. Akoya Biosciences,Marlborough, MA USA
  8. Department of Pathology, Oregon Health and Science University,Portland, OR USA
  9. Present Address: Department of Neurology and Regenerative Medicine Institute, Cedars Sinai,Los Angeles, CA USA
Institutions: Oregon Health & Science University (United States); Leipzig University (Germany); University of Augsburg (Germany); Akoya Biosciences (United States) (United States); Cedars-Sinai Medical Center (United States)
Journal: Nature neuroscience, volume 29, issue 7, pages 1599-1614
Dates: received 27 April 2023; accepted 13 March 2026; published online 18 May 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41593-026-02267-3 · PMID 42151483 · PMCID PMC13337490 · OpenAlex W7161592252
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity
Keywords: Neuroimmunology, Microglia, Fluorescence imaging, Alzheimer's disease
MeSH: Alzheimer Disease*, Brain*, Microglia*, Proteomics*, Aged, Aged, 80 and over, Aging, Female, Humans, Male, Plaque, Amyloid (* major topic)
Topic: Neuroinflammation and Neurodegeneration Mechanisms (Neurology, Neuroscience), according to OpenAlex
Funding: Alzheimer’s Association; Alzheimer's Association (AARGD-22-928829) and Collins Medical Trust, Oregon citizens through the Alzheimer’s Disease Research Fund of the Oregon Charitable Checkoff Program; Supported by the Medical Faculty of the University of Augsburg; Akoya Biosciences; P30AG008017 grant
Citations: cited by 4 papers (Europe PMC); 75 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

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Teichlab/bbknn

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 95ce34b8905cbde307704a77436c354938ba0367, 11 October 2023
Languages: Jupyter (7), Python (3)
Size: 32 files, 10 scripts
Software Heritage: not archived
Found in: the text, “Myeloid cell clustering based on protein express”
Holds: README, license file, environment (pyproject.toml, docs/requirements.txt), documentation, 7 notebooks
Not found: CITATION.cff, tests, continuous integration
Tools: Scanpy (5 files), NumPy (4 files), pandas (4 files), SciPy (3 files), Harmony (2 files), Matplotlib (2 files), scikit-learn (2 files), Seurat (2 files), reticulate (1 file), UMAP (1 file)
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At the source: github.com/Teichlab/bbknn

LouisFaure/scFates

License: BSD-3-Clause
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Commit: 8d8c0e629d7197fcf9801cc595bd23bdbf9f8118, 9 July 2026
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Size: 86 files, 52 scripts
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Found in: the text, “Pseudotime analysis”
Holds: README, license file, environment (Dockerfile, environment.yml, pyproject.toml, docs/requirements.txt), tests, continuous integration, documentation
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Tools: NumPy (35 files), anndata (29 files), pandas (28 files), Matplotlib (24 files), igraph (21 files), Scanpy (19 files), SciPy (11 files), rpy2 (8 files), statsmodels (7 files), scikit-learn (6 files), CuPy (4 files), Numba (2 files), seaborn (2 files), Monocle 3 (1 file), NetworkX (1 file), Plotly (1 file)
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At the source: github.com/LouisFaure/scFates

BaharehAjami/CODEX-CNS

License: none: the authors keep all their rights
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Commit: b46eb3c69cd34cbe5a7cda60555544cd91d02b54, 21 July 2026
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Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: anndata (1 file), h5py (1 file), Matplotlib (1 file), NumPy (1 file), pandas (1 file), Scanpy (1 file), scikit-learn (1 file), SciPy (1 file), seaborn (1 file), tifffile (1 file)
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At the source: github.com/BaharehAjami/CODEX-CNS

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Data

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Recorded: type, language, journal, volume, issue, pages, dates, 19 authors, 4 keywords, 11 MeSH terms, 5 funders, 75 references.

Cite

This paper

Sanchez-Molina, P., Rosmus, D.-D., Brownell, D., Meral, M., Gohlich, C., Pratapa, A., Peymanfar, Y., Whitley, A., Hou, Y., Nikulina, N., Bogachuk, A., Bouchard, E. L., Chiot, A., Kuhrt, H., Wieghofer, P., Woltjer, R., Svara, F., Braubach, O., & Ajami, B. (2026). Spatial proteomic analysis in human Alzheimer's disease brains enables identification of microenvironment-dependent microglial cell states. Nature neuroscience, 29(7), 1599-1614. https://doi.org/10.1038/s41593-026-02267-3

BibTeX

@article{sanchezmolina2026spatial,
author = {Sanchez-Molina, Paula and Rosmus, Dennis-Dominik and Brownell, Dillon and Meral, Mert and Gohlich, Cavanagh and Pratapa, Aditya and Peymanfar, Yaser and Whitley, Alyssa and Hou, Yue and Nikulina, Nadezhda and Bogachuk, Alina and Bouchard, Ellen Lara and Chiot, Aude and Kuhrt, Heidrun and Wieghofer, Peter and Woltjer, Randall and Svara, Fabian and Braubach, Oliver and Ajami, Bahareh},
title = {{Spatial proteomic analysis in human Alzheimer's disease brains enables identification of microenvironment-dependent microglial cell states}},
journal = {Nature neuroscience},
year = {2026},
month = may,
volume = {29},
number = {7},
pages = {1599--1614},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02267-3},
url = {https://doi.org/10.1038/s41593-026-02267-3},
pmid = {42151483},
pmcid = {PMC13337490}
}

RIS

TY - JOUR
AU - Sanchez-Molina, Paula
AU - Rosmus, Dennis-Dominik
AU - Brownell, Dillon
AU - Meral, Mert
AU - Gohlich, Cavanagh
AU - Pratapa, Aditya
AU - Peymanfar, Yaser
AU - Whitley, Alyssa
AU - Hou, Yue
AU - Nikulina, Nadezhda
AU - Bogachuk, Alina
AU - Bouchard, Ellen Lara
AU - Chiot, Aude
AU - Kuhrt, Heidrun
AU - Wieghofer, Peter
AU - Woltjer, Randall
AU - Svara, Fabian
AU - Braubach, Oliver
AU - Ajami, Bahareh
TI - Spatial proteomic analysis in human Alzheimer's disease brains enables identification of microenvironment-dependent microglial cell states
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/05/18
VL - 29
IS - 7
SP - 1599
EP - 1614
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02267-3
UR - https://doi.org/10.1038/s41593-026-02267-3
LA - en
ER -

CSL-JSON

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{
"family": "Ajami",
"given": "Bahareh"
}
],
"container-title-short": "Nat Neurosci",
"volume": "29",
"issue": "7",
"page": "1599-1614",
"DOI": "10.1038/s41593-026-02267-3",
"PMID": "42151483",
"PMCID": "PMC13337490",
"ISSN": "1097-6256",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s41593-026-02267-3",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
18
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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