A massively parallel CRISPR-based screening platform for modifiers of neuronal depolarization.

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[1] § Methods › Single-cell analysis › Cluster overrepresentation analysis ↔ cshift/utils.py, lines 74–84 · score 0.54 · multiple hypothesis, Benjamini Hochberg, cshift

Paper

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The authors' code

Python · 84 lines · 2.5 KB · MIT · 1 match

import numpy as np
from scipy.stats import chi2_contingency
def chisquare_test(ref: np.ndarray, obs: np.ndarray) -> np.ndarray:
"""
Multivariate chi-square test for each categorical variable abundance.
Parameters
----------
ref : np.ndarray
Reference abundance of each categorical variable.
obs : np.ndarray
Observed abundance of each categorical variable.
"""
x_max = ref.sum()
y_max = obs.sum()
pvalues = np.zeros(ref.size)
for i in np.arange(pvalues.size):
if ref[i] == 0 and obs[i] == 0:
pvalues[i] = 1.0
continue
m = np.array([[ref[i], x_max - ref[i]], [obs[i], y_max - obs[i]]])
_, pvalues[i], _, _ = chi2_contingency(m)
return pvalues
def aggregate_chisquare_test(ref: np.ndarray, obs: np.ndarray) -> np.ndarray:
"""
Aggregate chi-square test for all categorical variables using multiple
reference distributions. Aggregated using a geometric mean on each
categorical p-value.
Parameters
----------
ref : np.ndarray
Reference abundance of each categorical variable (2D matrix).
obs : np.ndarray
Observed abundance of each categorical variable (1D array).
"""
pvalues = np.stack([chisquare_test(ref[i], obs) for i in np.arange(ref.shape[0])])
return np.exp(np.log(pvalues).mean(axis=0))
def percent_change(ref: np.ndarray, obs: np.ndarray) -> np.ndarray:
"""
calculates the percent change between a reference group
and a test group. Will first normalize the vectors so that
their total will sum to 1
"""
assert ref.size == obs.size
r_norm = ref / ref.sum()
t_norm = obs / obs.sum()
with np.errstate(divide="ignore", invalid="ignore"):
pcc = (t_norm - r_norm) / r_norm
pcc[np.isinf(pcc)] = 1.0
pcc[np.isnan(pcc)] = 0.0
return pcc
def false_discovery_rate(pval: np.ndarray) -> np.ndarray:
"""
converts the pvalues into false discovery rate q-values
"""
dim = pval.shape
qval = p_adjust_bh(pval.ravel())
return qval.reshape(dim)
def p_adjust_bh(p: np.ndarray) -> np.ndarray:
"""
Benjamini-Hochberg p-value correction for multiple hypothesis testing.
https://stackoverflow.com/a/33532498
"""
p = np.asfarray(p)
by_descend = p.argsort()[::-1]
by_orig = by_descend.argsort()
steps = float(len(p)) / np.arange(len(p), 0, -1)
q = np.minimum(1, np.minimum.accumulate(steps * p[by_descend]))
return q[by_orig]

utils.py at commit f232215, under MIT · at the source

Overview

Authors: Steven C Boggess^1,2, Vaidehi Gandhi^1,2, Ming-Chi Tsai³, Emily Marzette^1,2, Noam Teyssier^1,2,4, Joanna Yu-Ying Chou^1,2,5, Xiaoyu Hu⁶, Amber Cramer³, Lin Yadanar^1,2, Kunal Shroff^1,2,7,8, Claire G Jeong³, Celine Eidenschenk⁶, Jesse E Hanson³, Ruilin Tian^1,2, Martin Kampmann^1,2,9

ORCID iDs: Ming-Chi Tsai, Emily Marzette, Joanna Yu-Ying Chou, Lin Yadanar, Kunal Shroff, Jesse E Hanson, Ruilin Tian, Martin Kampmann

Institute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA USA
Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA USA
Department of Neuroscience, Genentech, South San Francisco, CA USA
Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA USA
City College of San Francisco, San Francisco, CA USA
Department of Functional Genomics, Genentech, South San Francisco, CA USA
Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA USA
Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA USA
Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA USA

Institutions: University of California, San Francisco (United States); Genentech; City College of San Francisco (United States)

Journal: Nature communications, volume 17, issue 1, article 9299

Dates: received 5 June 2025; accepted 14 July 2026; published online 1 August 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1038/s41467-026-75882-0 · PMID 42669708 · PMCID PMC13526836 · OpenAlex W7172102303

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: human (organism), cellular / molecular (subfield)

Methods: Statistics, Smoothing, state filtering, decompositions, Single-unit activity, calcium imaging

Keywords: Excitability, Genetics of the nervous system

MeSH: Clustered Regularly Interspaced Short Palindromic Repeats*, CRISPR-Cas Systems*, Neurons*, Calcium, Humans, Induced Pluripotent Stem Cells (* major topic)

Topic: CRISPR and Genetic Engineering (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex

Funding: U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) (U54 NS123746); Alzheimer’s Association (23AARF-1027616); NINDS NIH HHS (U54 NS123746); California Institute for Regenerative Medicine (CIRM) (EDUC2-12730)

Citations: cited by 1 paper (Europe PMC); 103 references in the paper

Research resources: RRID:SCR_013575

Abstract

Understanding the complex interplay between gene expression and neuronal activity is crucial for unraveling the molecular mechanisms underlying cognitive function and neurological disorders. Here, we developed pooled screens using CRISPR interference (CRISPRi) and the fluorescent calcium integrator CaMPARI2 to evaluate genetic modifiers of neuronal depolarization. Using this screening method, we evaluated 1343 genes for their effect on depolarization in a human iPSC-derived neuron model, revealing potential links to neurodegenerative and neurodevelopmental disorders. These genes include known regulators of neuronal excitability, such as TARPs and ion channels, as well as genes associated with autism spectrum disorder and Alzheimer’s disease not previously described to affect neuronal depolarization. This CRISPRi-based screening platform offers a versatile tool to uncover molecular mechanisms controlling neuronal function in health and disease.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

noamteyssier/sgcount

License: MIT

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: c33819359d78986185e12881caaa4a0d96e995d6, 17 June 2026

Languages: Rust (10)

Size: 22 files, 10 scripts

Software Heritage: not archived

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Not found: license file, CITATION.cff, environment file, tests, documentation

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26 September 2026: the link answers

11 files

src/count.rs, Rust, 148 lines
src/counter.rs, Rust, 383 lines
src/genemap.rs, Rust, 157 lines
src/library.rs, Rust, 137 lines
src/main.rs, Rust, 203 lines
src/offsetter.rs, Rust, 363 lines
src/permutes.rs, Rust, 254 lines
src/progress.rs, Rust, 106 lines
src/results.rs, Rust, 178 lines
src/utils.rs, Rust, 105 lines
README.md, Text, 44 lines

At the source: github.com/noamteyssier/sgcount

noamteyssier/crispr_screen

License: MIT

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: 82e282c725dc3883df13e9618b1b00a739815f4b, 22 September 2025

Languages: Rust (34), JavaScript (2)

Size: 68 files, 36 scripts

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Holds: README, license file, continuous integration, documentation

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Availability: 1 check, the latest on 26 September 2026: the link answers

26 September 2026: the link answers

38 files

doc/theme/book.js, JavaScript, 679 lines
doc/theme/highlight.js, JavaScript, 6 lines
src/aggregation/compute_aggregation.rs, Rust, 381 lines
src/aggregation/mod.rs, Rust, 138 lines
src/aggregation/results.rs, Rust, 184 lines
src/aggregation/utils.rs, Rust, 217 lines
src/cli.rs, Rust, 308 lines
src/differential_expression.rs, Rust, 102 lines
src/enrich/enrichment_testing.rs, Rust, 342 lines
src/enrich/mod.rs, Rust, 22 lines
src/enrich/results.rs, Rust, 183 lines
src/io/gene_frame.rs, Rust, 113 lines
src/io/mod.rs, Rust, 12 lines
src/io/screenviz.rs, Rust, 350 lines
src/io/sgrna_frame.rs, Rust, 45 lines
src/io/utils.rs, Rust, 201 lines
src/main.rs, Rust, 304 lines
src/model/logged_ols.rs, Rust, 199 lines
src/model/math.rs, Rust, 44 lines
src/model/mod.rs, Rust, 28 lines
src/model/model_mean_variance.rs, Rust, 27 lines
src/model/ols.rs, Rust, 106 lines
src/model/sqmean.rs, Rust, 28 lines
src/model/wols.rs, Rust, 102 lines
src/norm/median_ratio_norm.rs, Rust, 83 lines
src/norm/mod.rs, Rust, 8 lines
src/norm/normalize_counts.rs, Rust, 34 lines
src/norm/total_norm.rs, Rust, 43 lines
src/resample.rs, Rust, 136 lines
src/run_aggregation.rs, Rust, 64 lines
src/utils/agg.rs, Rust, 82 lines
src/utils/config.rs, Rust, 108 lines
src/utils/filter.rs, Rust, 134 lines
src/utils/logging.rs, Rust, 364 lines
src/utils/math.rs, Rust, 103 lines
src/utils/mod.rs, Rust, 19 lines
LICENSE.md, License, 7 lines
README.md, Text, 112 lines

At the source: github.com/noamteyssier/crispr_screen

noamteyssier/adpbulk

License: MIT

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: 20ab1f6eaa3a81843cd74905dde705a2e90bd2fb, 17 June 2026

Languages: Python (3)

Size: 8 files, 3 scripts

Software Heritage: not archived

Found in: “Code availability”

Holds: README, license file, environment (pyproject.toml), tests, continuous integration

Not found: CITATION.cff, documentation

Tools: anndata (2 files), NumPy (2 files), pandas (2 files)

Availability: 1 check, the latest on 26 September 2026: the link answers

26 September 2026: the link answers

5 files

adpbulk/__init__.py, Python, 5 lines
adpbulk/adpbulk.py, Python, 285 lines
tests/test_adpbulk.py, Python, 214 lines
LICENSE, License, 21 lines
README.md, Text, 155 lines

At the source: github.com/noamteyssier/adpbulk

noamteyssier/cshift

License: MIT

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Commit: f2322154b73ed907c03215ea5ba898f5a3abdd57, 17 June 2026

Languages: Python (6)

Size: 11 files, 6 scripts

Software Heritage: not archived

Found in: “Code availability”

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Tools: NumPy (4 files), Matplotlib (2 files), pandas (2 files), SciPy (2 files), seaborn (2 files)

Availability: 1 check, the latest on 26 September 2026: the link answers

26 September 2026: the link answers

8 files

cshift/__init__.py, Python, 2 lines
cshift/__version__.py, Python, 1 line
cshift/cshift.py, Python, 205 lines
cshift/utils.py, Python, 84 lines, 1 match
setup.py, Python, 12 lines
tests/test_cshift.py, Python, 97 lines
LICENSE, License, 21 lines
README.md, Text, 93 lines

At the source: github.com/noamteyssier/cshift

noamteyssier/idea

License: MIT

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Languages: Python (6)

Size: 30 files, 6 scripts

Software Heritage: not archived

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Availability: 1 check, the latest on 26 September 2026: the link answers

26 September 2026: the link answers

8 files

docs/conf.py, Python, 41 lines
idea/__init__.py, Python, 10 lines
idea/_constants.py, Python, 14 lines
idea/_go.py, Python, 72 lines
idea/_idea.py, Python, 424 lines
idea/_utils.py, Python, 33 lines
LICENSE, License, 21 lines
README.md, Text, 23 lines

At the source: github.com/noamteyssier/idea

Zenodo 12774353

License: CC-BY-4.0

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26 September 2026: the link answers (HTTP 200)

11 files

src/count.rs, Rust, 144 lines
src/counter.rs, Rust, 383 lines
src/genemap.rs, Rust, 150 lines
src/library.rs, Rust, 137 lines
src/main.rs, Rust, 203 lines
src/offsetter.rs, Rust, 363 lines
src/permutes.rs, Rust, 254 lines
src/progress.rs, Rust, 106 lines
src/results.rs, Rust, 178 lines
src/utils.rs, Rust, 99 lines
README.md, Text, 43 lines

At the source:

Zenodo 12774305

License: CC-BY-4.0

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

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Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

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26 September 2026: the link answers (HTTP 200)

38 files

doc/theme/book.js, JavaScript, 679 lines
doc/theme/highlight.js, JavaScript, 6 lines
src/aggregation/compute_aggregation.rs, Rust, 280 lines
src/aggregation/mod.rs, Rust, 118 lines
src/aggregation/results.rs, Rust, 185 lines
src/aggregation/utils.rs, Rust, 217 lines
src/cli.rs, Rust, 207 lines
src/differential_expression.rs, Rust, 93 lines
src/enrich/enrichment_testing.rs, Rust, 218 lines
src/enrich/mod.rs, Rust, 4 lines
src/enrich/results.rs, Rust, 183 lines
src/io/gene_frame.rs, Rust, 109 lines
src/io/hit_list.rs, Rust, 205 lines
src/io/mod.rs, Rust, 11 lines
src/io/screenviz.rs, Rust, 351 lines
src/io/sgrna_frame.rs, Rust, 106 lines
src/io/simple_frame.rs, Rust, 578 lines
src/main.rs, Rust, 205 lines
src/model/logged_ols.rs, Rust, 199 lines
src/model/math.rs, Rust, 44 lines
src/model/mod.rs, Rust, 28 lines
src/model/model_mean_variance.rs, Rust, 27 lines
src/model/ols.rs, Rust, 106 lines
src/model/sqmean.rs, Rust, 28 lines
src/model/wols.rs, Rust, 102 lines
src/norm/median_ratio_norm.rs, Rust, 83 lines
src/norm/mod.rs, Rust, 8 lines
src/norm/normalize_counts.rs, Rust, 34 lines
src/norm/total_norm.rs, Rust, 43 lines
src/run_aggregation.rs, Rust, 60 lines
src/utils/agg.rs, Rust, 82 lines
src/utils/config.rs, Rust, 150 lines
src/utils/filter.rs, Rust, 74 lines
src/utils/logging.rs, Rust, 277 lines
src/utils/math.rs, Rust, 27 lines
src/utils/mod.rs, Rust, 19 lines
LICENSE.md, License, 7 lines
README.md, Text, 107 lines

At the source:

Zenodo 20736458

License: MIT

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: the references

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: anndata (2 files), NumPy (2 files), pandas (2 files)

Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)

26 September 2026: the link answers (HTTP 200)

5 files

adpbulk/__init__.py, Python, 5 lines
adpbulk/adpbulk.py, Python, 285 lines
tests/test_adpbulk.py, Python, 214 lines
LICENSE, License, 21 lines
README.md, Text, 153 lines

At the source:

Zenodo 20736463

License: MIT

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26 September 2026: the link answers (HTTP 200)

8 files

cshift/__init__.py, Python, 2 lines
cshift/__version__.py, Python, 1 line
cshift/cshift.py, Python, 205 lines
cshift/utils.py, Python, 84 lines
setup.py, Python, 12 lines
tests/test_cshift.py, Python, 97 lines
LICENSE, License, 21 lines
README.md, Text, 91 lines

At the source:

Zenodo 20736467

License: MIT

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: the references

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: NumPy (3 files), pandas (2 files), Matplotlib (1 file), NetworkX (1 file)

Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)

26 September 2026: the link answers (HTTP 200)

8 files

docs/conf.py, Python, 41 lines
idea/__init__.py, Python, 10 lines
idea/_constants.py, Python, 14 lines
idea/_go.py, Python, 72 lines
idea/_idea.py, Python, 424 lines
idea/_utils.py, Python, 33 lines
LICENSE, License, 21 lines
README.md, Text, 21 lines

At the source:

Code availability

The sgcount94 and crispr_screen95 bioinformatics pipelines for analysis of pooled screens are available on the Kampmann Lab website (https://kampmannlab.ucsf.edu/sgcount, https://kampmannlab.ucsf.edu/crisprscreen) and on GitHub (https://github.com/noamteyssier/sgcount, https://github.com/noamteyssier/crispr_screen). Analysis pipelines used to analyze CROP-seq data are open source and available on GitHub (adpbulk98-https://github.com/noamteyssier/adpbulk; cshift100-https://github.com/noamteyssier/cshift; IDEA103-https://github.com/noamteyssier/idea). The CellProfiler pipelines will be made available on request to the corresponding authors (M.K.) and will also be submitted to the CellProfiler depository of published pipelines (https://cellprofiler.org/examples/ published_pipelines.html) upon publication.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

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What the map holds:

10 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
122 scripts, each with its path and the digest of its content;
1 match between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

geo:GSE289235, at NCBI GEO; found in “Data availability”

Data Availability Statement

All screen datasets will be shared on the CRISPRbrain data commons (http://crisprbrain.org/) and will be shared upon request (associated with Figs. 2 and 4 and Supplementary Fig. 2). Single-cell RNA sequencing data is available through NCBI GEO (GSE289235 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE289235)). The CaMPARI2 screen counts data generated in this study will deposited in the CRISPRbrain data commons (http://crisprbrain.org/) and will also be shared upon request (associated with Figs. 2 and 4 and Supplementary Fig. 2). The CROP-seq data generated in this study have been deposited in the NCBI GEO under accession code GSE289235 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE289235) https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE289235. There are no restrictions on data availability. Source data are provided with this paper.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 2 keywords, 6 MeSH terms, 4 funders, 102 references, 1 RRID.

Cite

This paper

Boggess, S. C., Gandhi, V., Tsai, M.-C., Marzette, E., Teyssier, N., Chou, J. Y.-Y., Hu, X., Cramer, A., Yadanar, L., Shroff, K., Jeong, C. G., Eidenschenk, C., Hanson, J. E., Tian, R., & Kampmann, M. (2026). A massively parallel CRISPR-based screening platform for modifiers of neuronal depolarization. Nature communications, 17(1), 9299. https://doi.org/10.1038/s41467-026-75882-0

BibTeX

@article{boggess2026massively, author = {Boggess, Steven C and Gandhi, Vaidehi and Tsai, Ming-Chi and Marzette, Emily and Teyssier, Noam and Chou, Joanna Yu-Ying and Hu, Xiaoyu and Cramer, Amber and Yadanar, Lin and Shroff, Kunal and Jeong, Claire G and Eidenschenk, Celine and Hanson, Jesse E and Tian, Ruilin and Kampmann, Martin}, title = {{A massively parallel CRISPR-based screening platform for modifiers of neuronal depolarization}}, journal = {Nature communications}, year = {2026}, month = aug, volume = {17}, number = {1}, pages = {9299}, publisher = {Nature Publishing Group}, issn = {2041-1723}, doi = {10.1038/s41467-026-75882-0}, url = {https://doi.org/10.1038/s41467-026-75882-0}, pmid = {42669708}, pmcid = {PMC13526836} }

RIS

TY - JOUR AU - Boggess, Steven C AU - Gandhi, Vaidehi AU - Tsai, Ming-Chi AU - Marzette, Emily AU - Teyssier, Noam AU - Chou, Joanna Yu-Ying AU - Hu, Xiaoyu AU - Cramer, Amber AU - Yadanar, Lin AU - Shroff, Kunal AU - Jeong, Claire G AU - Eidenschenk, Celine AU - Hanson, Jesse E AU - Tian, Ruilin AU - Kampmann, Martin TI - A massively parallel CRISPR-based screening platform for modifiers of neuronal depolarization T2 - Nature communications J2 - Nat Commun PY - 2026 DA - 2026/08/01 VL - 17 IS - 1 SP - 9299 SN - 2041-1723 PB - Nature Publishing Group DO - 10.1038/s41467-026-75882-0 UR - https://doi.org/10.1038/s41467-026-75882-0 LA - en ER -

CSL-JSON

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A massively parallel CRISPR-based screening platform for modifiers of neuronal depolarization.

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