OSCR

Single-nucleus multiome analysis in the human prefrontal cortex identifies gene expression and cis-regulatory elements associated with aging.

Code ↔ Paper

11 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 11 matches
  1. [1] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Gene and peak linear regression ↔ scripts/linear_regression_genes.py, lines 52–89 · score 0.80 · brain bank, independent variable, Linear regression, discovery, squared, sex
  2. [2] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Pre-processing expression data ↔ scripts/plot_qc_metrics.py, lines 173–253 · score 0.74 · pct_counts_rb, pct_counts_mt, doublet scores, QC, Scrublet, Scanpy
  3. [3] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Gene and peak linear regression ↔ scripts/linear_regression_genes.py, lines 45–75 · score 0.74 · independent variable, Linear regression, brain bank, discovery, squared, cohort
  4. [4] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Pre-processing expression data ↔ scripts/plot_qc_metrics.py, lines 81–167 · score 0.71 · pct_counts_rb, pct_counts_mt, doublet scores, Scrublet, Scanpy, QC
  5. [5] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Gene expression feature selection ↔ scripts/multiome_feature_selection.py, lines 13–23 · score 0.64 · Seurat v3, highly variable genes, Scanpy, selection
  6. [6] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Gene expression feature selection ↔ scripts/rna_feature_selection.py, lines 12–17 · score 0.64 · Seurat v3, highly variable genes, Scanpy, selection
  7. [7] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Expression dimensionality reduction ↔ scripts/rna_model.py, lines 22–40 · score 0.59 · Gene likelihood, Dispersion, epoches, trained, latent, models
  8. [8] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Cell typing ↔ scripts/SCANVI_annot.py, lines 8–46 · score 0.57 · highly variable genes, AnnData, WNN, UMAP, leiden, neighbors
  9. [9] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Expression dimensionality reduction ↔ scripts/rna_model.py, lines 22–29 · score 0.52 · Gene likelihood, Dispersion, latent, models, scVI, batches
  10. [10] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Correlation analyses ↔ scripts/circe_by_celltype.py, lines 18–28 · score 0.50 · graphical Lasso, Co accessibility, Circe, networks
  11. [11] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Correlation analyses ↔ scripts/circe_by_celltype.py, lines 17–28 · score 0.50 · graphical Lasso, Co accessibility, Circe, networks

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 89 lines · 3.5 KB · CC-BY-4.0 · 1 match

  1. import scipy
  2. import numpy as np
  3. import pandas as pd
  4. import matplotlib.pyplot as plt
  5. import seaborn as sns
  6. import scanpy as sc
  7. import decoupler as dc
  8. import statsmodels.api as sm
  9. from statsmodels.stats.multitest import multipletests
  10. from patsy import dmatrices
  12. adata = sc.read_h5ad(snakemake.input.merged_rna_anndata)
  14. # RNA pseudobulk
  15. pdata = dc.get_pseudobulk(
  16. adata,
  17. sample_col='SampleID',
  18. groups_col='cell_type',
  19. layer='counts',
  20. mode='sum',
  21. min_cells=1,
  22. min_counts=1
  23. )
  25. pdata.layers['pcounts'] = pdata.X
  26. sc.pp.normalize_total(pdata, target_sum=1e6, max_fraction = 0.001, key_added='cpm', layer='pcounts', copy=False)
  28. # CSV pseudobulk for QTLs
  29. adata_df = pd.DataFrame(pdata.X)
  30. sample_cell = pdata.obs[['SampleID', 'cell_type']]
  31. adata_df.columns = pdata.var_names.to_list()
  32. adata_df.index = sample_cell.index
  33. adata_df = pd.merge(left=sample_cell, right=adata_df, left_index=True, right_index=True)
  35. for cell_type in ['Astro', 'ExN', 'InN', 'MG', 'OPC', 'Oligo', 'VC']:
  36. cell_adata_df = adata_df[adata_df['cell_type'] == cell_type]
  37. cell_adata_df.index = cell_adata_df['SampleID']
  38. del cell_adata_df['SampleID']
  39. del cell_adata_df['cell_type']
  40. cell_adata_df.to_csv(f'/data/CARD_singlecell/PFC_atlas/data/celltypes/{cell_type}/pseudobulk_rna.csv')
  42. # CSV pseudobulk for linear regression (includes covariates)
  43. adata_df = pd.DataFrame(pdata.X)
  44. sample_cell = pdata.obs[['SampleID', 'cell_type', 'PMI', 'Sex', 'Brain_bank', 'Age']]
  45. adata_df.columns = pdata.var_names.to_list()
  46. adata_df.index = sample_cell.index
  47. adata_df = pd.merge(left=sample_cell, right=adata_df, left_index=True, right_index=True)
  49. adata_df['PMI'] = adata_df['PMI'].astype(float)
  50. adata_df['Age'] = adata_df['Age'].astype(float)
  52. # Write out pseudobulk data
  53. adata_df.to_csv(snakemake.output.rna_pseudobulk)
  55. sex2num = dict(zip(adata_df['Sex'].drop_duplicates().to_list(), range(len(adata_df['Sex'].drop_duplicates().to_list()))))
  56. adata_df['Sex_numeric'] = [sex2num[x] for x in adata_df['Sex']]
  58. bbank2num = dict(zip(adata_df['Brain_bank'].drop_duplicates().to_list(), range(len(adata_df['Brain_bank'].drop_duplicates().to_list()))))
  59. adata_df['Brain_bank_numeric'] = [bbank2num[x] for x in adata_df['Brain_bank']]
  61. gene_data = []
  62. for cell_type in adata_df['cell_type'].drop_duplicates():
  63. cohort_df = adata_df[(adata_df['cell_type'] == cell_type)]
  64. for gene in pdata.var_names.to_list():
  65. #print(gene)
  66. # Create independent variable Series object, save as X
  67. _, X = dmatrices(f'Age ~ Age + Sex_numeric + PMI + Brain_bank_numeric', data=cohort_df, return_type='dataframe')
  68. # Create dependent variable Series object, save as y
  69. y = cohort_df[gene].values
  70. # Fit the model
  71. model = sm.OLS(y, X).fit(disp=0)
  72. # Add values to the dataframe
  73. gene_results = {
  74. 'cell_type': cell_type,
  75. 'gene': gene,
  76. 'slope': model.params['Age'],
  77. 'p_value': model.pvalues['Age'],
  78. 'standard_error': model.bse['Age'],
  79. 'r_squared': model.rsquared,
  80. 'adjusted_r_squared': model.rsquared_adj
  81. }
  82. gene_data.append(gene_results)
  84. regression_df = pd.DataFrame(gene_data)
  85. # Adjust p-value
  86. for cell_type in adata_df['cell_type'].drop_duplicates():
  87. regression_df.loc[regression_df['cell_type'] == cell_type, 'p_value_bh'] = scipy.stats.false_discovery_control(regression_df.loc[regression_df['cell_type'] == cell_type, 'p_value'].fillna(1))
  88. regression_df['-log10(p-vlue_bh)'] = -np.log10(regression_df['p_value_bh'])
  89. regression_df.to_csv(snakemake.output.cell_specific_regression)

linear_regression_genes.py, under CC-BY-4.0 · at the source

Overview

Authors: Adam Catching1,2, Cory A. Weller1,2, Fangle Hu1, Sarah Bromberek1, Shahroze Abbas1,2, Kensuke Daida1,3, Laksh Malik1, Breeana Baker1, Pavan K. Auluck4, Laurel A. Screven1, Kate M. Andersh1, Kimberley J. Billingsley1,3, Stefano Marenco4, North American Brain Expression Consortium (NABEC), Mark R. Cookson1,3, Kendall Van Keuren-Jensen1,5, Mike A. Nalls1,2, Andrew B. Singleton1,3, Cornelis Blauwendraat1,3, Xylena Reed1,6
ORCID iDs: Adam Catching, Pavan K. Auluck, Xylena Reed
  1. Center for Alzheimer’s and Related Dementias, National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
  2. DataTecnica, Washington, DC, USA
  3. Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
  4. Human Brain Collection Core, Division of Intramural Research, National Institute of Mental Health, NIH, Bethesda, MD, USA
  5. Present address: Translational Genomics Research Institute, Phoenix, AZ, USA
  6. Lead contact
Institutions: National Institutes of Health (United States); National Institute of Neurological Disorders and Stroke (United States); National Institute on Aging (United States); National Institute of Mental Health (United States)
Journal: Cell reports, volume 45, issue 3, article 117110
Dates: published online 14 March 2026; in print 24 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.celrep.2026.117110 · PMID 41832957 · PMCID PMC13137218 · OpenAlex W7135239431
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity, Machine learning
Keywords: Aging, Gene regulation, Transcription factors, Epigenetics, prefrontal cortex, Cis-regulatory Elements, Single-nucleus Rna-seq, Multi-ancestry, Single-nucleus Atac-seq, Cp: Neuroscience, Single-nucleus Multiome
MeSH: Aging*, Cell Nucleus*, Gene Expression Regulation*, Prefrontal Cortex*, Regulatory Sequences, Nucleic Acid*, Adolescent, Adult, Aged, Aged, 80 and over, Chromatin, Female, Humans, Male, Middle Aged, Multiomics, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Intramural NIH HHS (ZIC MH002903, Z99 AG999999, ZIA AG000545); National Institute of Mental Health; National Institute on Aging
Citations: cited by 5 papers (Europe PMC); 80 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

Its files are read in the Code ↔ Paper reader above, with 11 matches between paragraphs and lines of code.

NIH-CARD/scMAVERICS

License: other
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: dce3d79def4e08190aca964183e009b1e90a9041, 22 September 2026
Languages: Python (64), Shell (11)
Size: 98 files, 75 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, license file, documentation
Not found: CITATION.cff, environment file, tests, continuous integration
Tools: Scanpy (54 files), pandas (53 files), NumPy (44 files), SciPy (18 files), anndata (16 files), Matplotlib (10 files), seaborn (9 files), PyTorch (5 files), statsmodels (5 files), BEDTools (1 file), pysam (1 file), Snakemake (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
77 files
At the source: github.com/NIH-CARD/scMAVERICS

Zenodo 18135365

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Scanpy (34 files), pandas (29 files), NumPy (24 files), anndata (12 files), SciPy (12 files), Matplotlib (7 files), seaborn (7 files), PyTorch (3 files), statsmodels (2 files), BCFtools (1 file), SAMtools (1 file), Snakemake (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
42 files
At the source: doi.org/10.5281/zenodo.18135365

The paper's code and data availability statement is in the Data section.

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  • 11 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Code and data availability statement

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Read it in the paper: doi.org/10.1016/j.celrep.2026.117110.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 20 authors, 11 keywords, 16 MeSH terms, 3 funders, 80 references.

Cite

This paper

Catching, A., Weller, C. A., Hu, F., Bromberek, S., Abbas, S., Daida, K., Malik, L., Baker, B., Auluck, P. K., Screven, L. A., Andersh, K. M., Billingsley, K. J., Marenco, S., North American Brain Expression Consortium (NABEC), Cookson, M. R., Van Keuren-Jensen, K., Nalls, M. A., Singleton, A. B., Blauwendraat, C., & Reed, X. (2026). Single-nucleus multiome analysis in the human prefrontal cortex identifies gene expression and cis-regulatory elements associated with aging. Cell reports, 45(3), 117110. https://doi.org/10.1016/j.celrep.2026.117110

BibTeX

@article{catching2026single,
author = {Catching, Adam and Weller, Cory A. and Hu, Fangle and Bromberek, Sarah and Abbas, Shahroze and Daida, Kensuke and Malik, Laksh and Baker, Breeana and Auluck, Pavan K. and Screven, Laurel A. and Andersh, Kate M. and Billingsley, Kimberley J. and Marenco, Stefano and {North American Brain Expression Consortium (NABEC)} and Cookson, Mark R. and Van Keuren-Jensen, Kendall and Nalls, Mike A. and Singleton, Andrew B. and Blauwendraat, Cornelis and Reed, Xylena},
title = {{Single-nucleus multiome analysis in the human prefrontal cortex identifies gene expression and cis-regulatory elements associated with aging}},
journal = {Cell reports},
year = {2026},
month = mar,
volume = {45},
number = {3},
pages = {117110},
publisher = {Cell Press},
issn = {2211-1247},
doi = {10.1016/j.celrep.2026.117110},
url = {https://doi.org/10.1016/j.celrep.2026.117110},
pmid = {41832957},
pmcid = {PMC13137218}
}

RIS

TY - JOUR
AU - Catching, Adam
AU - Weller, Cory A.
AU - Hu, Fangle
AU - Bromberek, Sarah
AU - Abbas, Shahroze
AU - Daida, Kensuke
AU - Malik, Laksh
AU - Baker, Breeana
AU - Auluck, Pavan K.
AU - Screven, Laurel A.
AU - Andersh, Kate M.
AU - Billingsley, Kimberley J.
AU - Marenco, Stefano
AU - North American Brain Expression Consortium (NABEC)
AU - Cookson, Mark R.
AU - Van Keuren-Jensen, Kendall
AU - Nalls, Mike A.
AU - Singleton, Andrew B.
AU - Blauwendraat, Cornelis
AU - Reed, Xylena
TI - Single-nucleus multiome analysis in the human prefrontal cortex identifies gene expression and cis-regulatory elements associated with aging
T2 - Cell reports
J2 - Cell Rep
PY - 2026
DA - 2026/03/14
VL - 45
IS - 3
SP - 117110
SN - 2211-1247
PB - Cell Press
DO - 10.1016/j.celrep.2026.117110
UR - https://doi.org/10.1016/j.celrep.2026.117110
LA - en
ER -

CSL-JSON

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