Cell type-agnostic transcriptomic signatures enable uniform comparisons of neural maturation.

Code ↔ Paper

9 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 9 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted

[1] § Materials and methods › MetaNeighbor assessment of cell type replicability ↔ pymn/MetaNeighborUS.py, lines 12–142 · score 0.74 · highly variable genes, fast_version, AnnData, MetaNeighbor, pyMN
[2] § Materials and methods › Developmental age predictions in external datasets ↔ code/ageprediction_Paulsen_ASD_organoids.ipynb, lines 92–103 · score 0.67 · ARID1B, SUV420H1, Paulsen, CHD8, ASD, mutant
[3] § Results › Cell type-agnostic model trained on primary tissue predicts developmental progression in human neural organoids › Detecting disease-related shifts and atlas-scale maturation in neural organo ↔ code/ageprediction_Paulsen_ASD_organoids.ipynb, lines 92–103 · score 0.67 · ARID1B, CHD8 mutants, SUV420H1 mutants, ASD, organoids, ages
[4] § Materials and methods › Aggregate co-expression network and module analysis ↔ vignettes/EGAD.Rmd, lines 26–143 · score 0.65 · co expression networks, networks constructed, EGAD, prediction gene, cross validation, connectivity
[5] § Materials and methods › MetaNeighbor assessment of cell type replicability ↔ pymn/trainModel.py, the whole file · a weak match · score 0.63 · highly variable genes, AnnData, MetaNeighbor, pyMN
[6] § Materials and methods › Cell-autonomous age prediction models ↔ code/ageprediction_newfetaldatasets_modeltraining.ipynb, lines 42–56 · score 0.61 · log normalized gene, AggregateExpression, Seurat, meta, age, predict
[7] § Materials and methods › Cell-autonomous age prediction models ↔ code/fetal_hypothalamus_processing.r, lines 1–42 · score 0.59 · AggregateExpression, assigned cell, Seurat, Herb, meta, age
[8] § Materials and methods › Developmental age predictions in external datasets ↔ code/ageprediction_newfetaldatasets_modeltraining.ipynb, lines 42–56 · score 0.57 · log normalized, aggregated expression, Seurat, days, brain, Age
[9] § Materials and methods › Aggregate co-expression network and module analysis ↔ vignettes/EGAD.Rmd, lines 294–303 · score 0.56 · Spearman correlation coefficient, co expression network, gene pair, ranked, matrix

Paper

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The authors' code

Jupyter notebook · 132 lines · 5.3 KB · MIT · 2 matches

# %% [markdown]
# # Age prediction in Paulsen et al., 2022 human neural organoids with ASD mutations
# > This notebook contains R code to predict developmental stage in cells from Paulsen et al., 2022, a human neural organoid dataset with ASD mutations. Part 2 uses the pre-trained celltype agnostic model to predict developmental age of mutant and wildtype organoid cells from the transcriptome
# First download Paulsen et al 2022 dataset from https://www.synapse.org/Synapse:syn26346581 and run Paulsen_preprocessing.r
# %% [markdown]
# ## Part 1. Load preprocessed data and get metadata
# %%
setwd("/home/sridevi/inkwell03_sridevi//metadevorganoid/werneranalysis/DevTime_gitub_repo/") #change to the working directory
# %%
library(Seurat)
library(caret)
library(dplyr)
library(Matrix)
library(readr)
library(ggplot2)
library(stringr)
library(ggpubr)
library(tidyr)
# %%
final_common_genes<-readRDS("models/commongenes.rds")#list of common genes
# %%
# Load preprocessed data obtained using the R script Paulsen_preprocessing.r
# %%
load("processed_data/Paulsen2022_processed.Rdata")
load("processed_data/Paulsen2022_metadata.Rdata")
# %%
#obtain metadata for each cell type per sample
rep_times <- lapply(Paulsen_pb_celltypes,ncol) # e.g., replicate the first row 3 times, the second row 2 times
# Replicate rows based on specified times
metadata_full <- metadata[rep(1:nrow(metadata), times = rep_times), ]
dim(metadata_full)
# %%
#get the lognormalized counts
merged_paulsen_normalized<-lapply(Paulsen_pb_celltypes,function(x){
data<-GetAssayData(x,layer="data")
})%>%do.call(cbind,.)
# %%
#get additional metadata from the colnames
temp=colnames(merged_paulsen_normalized)%>%str_split(.,"_") #gives the annotations; age and other annotations come from the file name
celltypeinfo<- do.call(rbind, lapply(temp, function(x) { length(x) <- 3; return(x) }))
celltypeinfo <- as.data.frame(celltypeinfo, stringsAsFactors = FALSE)
# Assign column names
colnames(celltypeinfo) <- c("organoid", "celltype", "mutant_wt")
celltypeinfo
# %%
metadata_final<-cbind(metadata_full,celltypeinfo)
head(metadata_final)
# %%
metadata_final$age_months<-parse_number(metadata_final$age)
metadata_final$age_months[metadata_final$age_months>6]<-metadata_final$age_months[metadata_final$age_months>6]/30
table(metadata_final$age_months)
# %%
metadata_final$age_mut<-paste0(round(metadata_final$age_months,2),"_",metadata_final$mutant_wt)
metadata_final$line_age<-paste0(metadata_final$line,"_",round(metadata_final$age_months,2))
# %% [markdown]
# ---
# %% [markdown]
# ## Part 2. Predict age in organoid cell types using celltype agnostic model
# %%
celltypeagnosticmodel<-readRDS("models/original_celltypeagnostic_model.rds")
#check model coefficients
coefs<-as.data.frame(coef(celltypeagnosticmodel$finalModel, celltypeagnosticmodel$finalModel$lambdaOpt))%>%dplyr::filter(s1!=0)
coefs$genes<-rownames(coefs)
coefs
# %%
#Use pretrained model to predict the ages of the organoid cell types in weeks
metadata_final$predicted_ages_Paulsen<-predict(celltypeagnosticmodel,t(merged_paulsen_normalized[final_common_genes,]))
# %%
split_predictions<-split(metadata_final,metadata_final$gene)
# %%
ggplot(split_predictions$ARID1B,aes(y=predicted_ages_Paulsen,col=mutant_wt,x=celltype))+geom_boxplot(outlier.shape = NA,position = position_dodge(width = 1))+
geom_point(size=1,alpha=0.5,aes(col=mutant_wt),position = position_jitterdodge(jitter.width = 0.15, dodge.width = 1))+facet_wrap(~line_age)+xlab("")+
ggtitle("Predicted age distributions by celltype in ARIDB1 mutant")+theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplot(split_predictions$CHD8,aes(y=predicted_ages_Paulsen,x=celltype,col=mutant_wt))+geom_boxplot(outlier.shape = NA,position = position_dodge(width = 1))+
geom_point(size=1,alpha=0.5,aes(col=mutant_wt),position = position_jitterdodge(jitter.width = 0.15, dodge.width = 1))+facet_wrap(~line_age)+xlab("")+
ggtitle("Predicted age distributions by celltype in CHD8 mutant")+theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplot(split_predictions$SUV420H1,aes(y=predicted_ages_Paulsen,x=celltype,col=mutant_wt))+geom_boxplot(outlier.shape = NA)+
geom_point(size=1,alpha=0.5,aes(col=mutant_wt),position = position_jitterdodge(jitter.width = 0.15, dodge.width = 1))+facet_wrap(~line_age)+xlab("")+
ggtitle("Predicted age distributions by celltype in SUV420H1 mutant")+theme(axis.text.x = element_text(angle = 90, hjust = 1))
# %%
datatoplot_gabaergicprog<-metadata_final%>%filter(str_detect(celltype,"GABAergic Progenitors") ==TRUE)
# %%
gabaprog_plt_paulsen<- ggplot(datatoplot_gabaergicprog,aes(y=predicted_ages_Paulsen,x=age_mut))+
geom_boxplot(outlier.shape = NA,position = position_dodge(width = 1))+
geom_jitter(size=2,aes(col=round(age_months,2),shape=line),width=0.2)+scale_color_viridis_c()+
facet_wrap(~gene)+xlab("")+
ggtitle("Predicted age distributions in GABAergic Progenitors")+theme(axis.text.x = element_text(angle = 90, hjust = 1))
gabaprog_plt_paulsen
# %%
png(file="figures/Paulsen2022_predictions.png")
gabaprog_plt_paulsen
dev.off()
# %%
result<-datatoplot_gabaergicprog%>%group_by(gene,age)%>%summarise(
p_value = ifelse(
length(unique(mutant_wt)) == 2,
wilcox.test(predicted_ages_Paulsen ~ mutant_wt)$p.value,
NA
),
.groups = "drop"
)
result$adj_p<-p.adjust(result$p_value,method='fdr')
result

ageprediction_Paulsen_ASD_organoids.ipynb at commit d2f1959, under MIT · at the source

Overview

Authors: Sridevi Venkatesan^1,2,3, Jonathan M Werner³, Yun Li^2,4, Jesse Gillis^1,3,4

ORCID iDs: Sridevi Venkatesan, Jesse Gillis

Institutions: University of Toronto (Canada); Hospital for Sick Children (Canada)

Journal: PLoS biology, volume 24, issue 4, article e3003757

Dates: received 11 July 2025; accepted 31 March 2026; published online 15 April 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1371/journal.pbio.3003757 · PMID 41984978 · PMCID PMC13095120 · OpenAlex W7154508420

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: genetics / omics (modality), human (organism), mouse (organism)

Methods: Statistics, Machine learning, Connectivity, fMRI & imaging

MeSH: Neurogenesis*, Neurons*, Transcriptome*, Animals, Astrocytes, Brain, Gene Expression Profiling, Gene Expression Regulation, Developmental, Humans, Machine Learning, Mice, Neurodevelopment, Single-Cell Analysis, Single-Cell Gene Expression Analysis (* major topic)

Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex

Funding: Schmidt Science Fellows; Institute of Neurosciences, Mental Health and Addiction (PJT-180565); Office of Extramural Research, National Institutes of Health (U24MH130968, R01MH113005, R01MH133181)

Citations: cited by 1 paper (Europe PMC); 93 references in the paper

Research resources: RRID:SCR_026651

Abstract

Understanding where a cell sits along developmental time is as important as identifying its type. While single-cell transcriptomics has catalogued the diversity of neural cell types, aligning them along a shared temporal axis across studies, species, and model systems remains a fundamental challenge. Here, we develop a single-cell transcriptomic ‘clock’ that predicts true developmental age, enabling standardized, cross-context comparisons of neural maturation. Through a meta-analysis of over 2.8 million cells from the developing human brain, we identify robust tissue-level and cell-autonomous predictors of developmental age. We find that bulk tissue composition predicts age within individual studies but lacks generalizability, whereas specific cell type proportions, particularly astrocytes and progenitors, track age reliably across studies. Using machine learning, we develop a cell type-agnostic predictor based on 462 genes that robustly tracks developmental dynamics across diverse cell types and datasets (error = 2.6 weeks). Our model accurately estimates developmental age in human neural organoids and detects disease-associated shifts. Model predictions further generalize across species, revealing 10-fold accelerated neurodevelopment in mice relative to humans. Our approach provides a robust framework to assess neural maturation across contexts, with broad relevance for developmental biology and disease modeling.

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 9 matches between paragraphs and lines of code.

gillislab/pyMN

License: MIT

State: the link answers, verified on 29 September 2026

Evidence: files inventoried

Commit: 6a2ef103b759d8f2a70a4b9fe6db330035ebdbb6, 28 August 2026

Languages: Python (14), Jupyter (4), R (2)

Size: 27 files, 20 scripts

Software Heritage: not archived

Found in: the text, “MetaNeighbor assessment of cell type replicabili”

Holds: README, license file, environment (requirements.txt, setup.py), 3 notebooks

Not found: CITATION.cff, tests, continuous integration, documentation

Tools: NumPy (14 files), pandas (13 files), Scanpy (8 files), SciPy (7 files), Matplotlib (5 files), seaborn (5 files), anndata (4 files), NetworkX (2 files), h5py (1 file)

Availability: 1 check, the latest on 29 September 2026: the link answers

29 September 2026: the link answers

22 files

data/biccn.py, Python, 221 lines
data/hemberg.R, R, 28 lines
data/hemberg.py, Python, 23 lines
data/tasic.R, R, 8 lines
data/tasic.py, Python, 23 lines
notebooks/.ipynb_checkpoints/protocol1_cluster_replicability-checkpoint.ipynb, Jupyter, 272 lines
notebooks/protocol1_cluster_replicability.ipynb, Jupyter, 268 lines
notebooks/protocol2_pretrained_reference.ipynb, Jupyter, 252 lines
notebooks/protocol3_functional_characterization.ipynb, Jupyter, 176 lines
pymn/MetaNeighbor.py, Python, 245 lines
pymn/MetaNeighborUS.py, Python, 430 lines, 1 match
pymn/__init__.py, Python, 19 lines
pymn/metaClusters.py, Python, 155 lines
pymn/plotting.py, Python, 716 lines
pymn/splitClusters.py, Python, 131 lines
pymn/topHits.py, Python, 97 lines
pymn/trainModel.py, Python, 48 lines, 1 match
pymn/utils.py, Python, 248 lines
pymn/variableGenes.py, Python, 163 lines
setup.py, Python, 17 lines
LICENSE, License, 21 lines
README.md, Text, 56 lines

At the source: github.com/gillislab/pyMN

sarbal/EGAD

License: none: the authors keep all their rights

State: the link answers, verified on 29 September 2026

Evidence: files inventoried

Commit: 6e84074928100542c6f697ef1755cb628875dc8f, 4 May 2021

Languages: R (63), MATLAB (8)

Size: 169 files, 71 scripts

Software Heritage: not archived

Found in: the text, “Aggregate co-expression network and module analy”

Holds: README, environment (DESCRIPTION), tests, documentation, 1 notebook

Not found: license file, CITATION.cff, continuous integration

Tools: Statistics and Machine Learning Toolbox (4 files), igraph (1 file)

Availability: 1 check, the latest on 29 September 2026: the link answers

29 September 2026: the link answers

72 files

R/GO.human.R, R, 22 lines
R/GO.mouse.R, R, 21 lines
R/GO.voc.R, R, 20 lines
R/assortativity.R, R, 47 lines
R/attr.human.R, R, 29 lines
R/attr.mouse.R, R, 28 lines
R/auc_multifunc.R, R, 30 lines
R/auprc.R, R, 27 lines
R/auroc_analytic.R, R, 39 lines
R/biogrid.R, R, 19 lines
R/build_binary_network.R, R, 47 lines
R/build_coexp_GEOID.R, R, 47 lines
R/build_coexp_expressionSet.R, R, 56 lines
R/build_coexp_network.R, R, 56 lines
R/build_semantic_similarity_network.R, R, 40 lines
R/build_weighted_network.R, R, 44 lines
R/calculate_multifunc.R, R, 41 lines
R/conv_smoother.R, R, 59 lines
R/example_annotations.R, R, 12 lines
R/example_binary_network.R, R, 12 lines
R/example_coexpression.R, R, 12 lines
R/example_neighbor_voting.R, R, 20 lines
R/extend_network.R, R, 33 lines
R/filter_network.R, R, 33 lines
R/filter_network_cols.R, R, 44 lines
R/filter_network_rows.R, R, 46 lines
R/filter_orthologs.R, R, 33 lines
R/fmeasure.R, R, 24 lines
R/genes.R, R, 16 lines
R/get_auc.R, R, 26 lines
R/get_biogrid.R, R, 57 lines
R/get_counts.R, R, 23 lines
R/get_density.R, R, 24 lines
R/get_expression_data_gemma.R, R, 31 lines
R/get_expression_matrix_from_GEO.R, R, 85 lines
R/get_phenocarta.R, R, 24 lines
R/get_prc.R, R, 40 lines
R/get_roc.R, R, 38 lines
R/make_annotations.R, R, 48 lines
R/make_gene_network.R, R, 45 lines
R/make_genelist.R, R, 28 lines
R/make_transparent.R, R, 17 lines
R/neighbor_voting.R, R, 204 lines
R/node_degree.R, R, 23 lines
R/ortho.R, R, 22 lines
R/pheno.R, R, 20 lines
R/plot_densities.R, R, 50 lines
R/plot_density_compare.R, R, 55 lines
R/plot_distribution.R, R, 72 lines
R/plot_network_heatmap.R, R, 30 lines
R/plot_prc.R, R, 34 lines
R/plot_roc.R, R, 33 lines
R/plot_roc_overlay.R, R, 62 lines
R/plot_value_compare.R, R, 30 lines
R/predictions.R, R, 59 lines
R/repmat.R, R, 25 lines
R/run_GBA.R, R, 50 lines
inst/matlab/opt_ROC_scores.m, MATLAB, 10 lines
inst/matlab/prec_rec.m, MATLAB, 21 lines
inst/matlab/voter.m, MATLAB, 48 lines
inst/matlab/voter_ROC.m, MATLAB, 33 lines
matlab/opt_ROC_scores.m, MATLAB, 10 lines
matlab/prec_rec.m, MATLAB, 21 lines
matlab/voter.m, MATLAB, 48 lines
matlab/voter_ROC.m, MATLAB, 33 lines
tests/testthat.R, R, 4 lines
tests/testthat/test.auroc.r, R, 27 lines
tests/testthat/test.mf.r, R, 15 lines
tests/testthat/test.network.r, R, 31 lines
tests/testthat/test.nv.r, R, 19 lines
vignettes/EGAD.Rmd, R, 799 lines, 2 matches
README.md, Text, 65 lines

At the source: github.com/sarbal/EGAD

sridevi96/NeuroDevTime

License: MIT

State: the link answers, verified on 29 September 2026

Evidence: files inventoried

Commit: d2f19595d465e73247b9e01d0b1851970e44337e, 30 March 2026

Languages: Jupyter (9), R (3)

Size: 21 files, 12 scripts

Software Heritage: not archived

Found in: “Data Availability”

Holds: README, license file, 4 notebooks

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

Tools: ggplot2 (12 files), ggpubr (12 files), Seurat (12 files), caret (11 files), tidyverse (11 files), reticulate (2 files), circlize (1 file), ComplexHeatmap (1 file)

Availability: 1 check, the latest on 29 September 2026: the link answers

29 September 2026: the link answers

14 files

code/.ipynb_checkpoints/Niceplotting_Fig3_celltypeinv_celltypespecific_models-checkpoint.ipynb, Jupyter, 897 lines
code/.ipynb_checkpoints/ageprediction_HNOCA_organoidatlas-checkpoint.ipynb, Jupyter, 149 lines
code/.ipynb_checkpoints/ageprediction_Paulsen_ASD_organoids-checkpoint.ipynb, Jupyter, 171 lines
code/.ipynb_checkpoints/ageprediction_fetal_hypothalamus-checkpoint.ipynb, Jupyter, 159 lines
code/.ipynb_checkpoints/ageprediction_newfetaldatasets_modeltraining-checkpoint.ipynb, Jupyter, 285 lines
code/.ipynb_checkpoints/agepredictions-checkpoint.Rmd, R, 31 lines
code/Paulsen_preprocessing.r, R, 46 lines
code/ageprediction_HNOCA_organoidatlas.ipynb, Jupyter, 149 lines
code/ageprediction_Paulsen_ASD_organoids.ipynb, Jupyter, 132 lines, 2 matches
code/ageprediction_fetal_hypothalamus.ipynb, Jupyter, 58 lines
code/ageprediction_newfetaldatasets_modeltraining.ipynb, Jupyter, 285 lines, 2 matches
code/fetal_hypothalamus_processing.r, R, 89 lines, 1 match
LICENSE, License, 21 lines
README.md, Text, 4 lines

At the source: github.com/sridevi96/NeuroDevTime

Zenodo 14908185

License: CC-BY-4.0

State: the link answers, verified on 29 September 2026

Evidence: files inventoried

Size: 5 files

Software Heritage: not checked

Found in: the text, “R and R packages”

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

Tools: ggplot2 (6 files), ggpubr (6 files), Seurat (6 files), tidyverse (6 files), caret (5 files), reticulate (1 file)

Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)

29 September 2026: the link answers (HTTP 200)

6 files

code.zip/Paulsen_preprocessing.r, R, 46 lines
code.zip/ageprediction_HNOCA_organoidatlas.ipynb, Jupyter, 149 lines
code.zip/ageprediction_Paulsen_ASD_organoids.ipynb, Jupyter, 132 lines
code.zip/ageprediction_fetal_hypothalamus.ipynb, Jupyter, 58 lines
code.zip/ageprediction_newfetaldatasets_modeltraining.ipynb, Jupyter, 285 lines
code.zip/fetal_hypothalamus_processing.r, R, 89 lines

At the source: doi.org/10.5281/zenodo.14908185

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

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4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
109 scripts, each with its path and the digest of its content;
9 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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No dataset and no data link were found in the paper.

Data Availability

This paper analyzes existing, publicly available data, accessible at links provided in S1 Table. All original code and data used to generate figures are available at https://github.com/sridevi96/NeuroDevTime and https://doi.org/10.5281/zenodo.14908185.

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

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 14 MeSH terms, 3 funders, 93 references, 1 RRID.

Cite

This paper

Venkatesan, S., Werner, J. M., Li, Y., & Gillis, J. (2026). Cell type-agnostic transcriptomic signatures enable uniform comparisons of neural maturation. PLoS biology, 24(4), e3003757. https://doi.org/10.1371/journal.pbio.3003757

BibTeX

@article{venkatesan2026cell, author = {Venkatesan, Sridevi and Werner, Jonathan M and Li, Yun and Gillis, Jesse}, title = {{Cell type-agnostic transcriptomic signatures enable uniform comparisons of neural maturation}}, journal = {PLoS biology}, year = {2026}, month = apr, volume = {24}, number = {4}, pages = {e3003757}, publisher = {PLOS}, issn = {1544-9173}, doi = {10.1371/journal.pbio.3003757}, url = {https://doi.org/10.1371/journal.pbio.3003757}, pmid = {41984978}, pmcid = {PMC13095120} }

RIS

TY - JOUR AU - Venkatesan, Sridevi AU - Werner, Jonathan M AU - Li, Yun AU - Gillis, Jesse TI - Cell type-agnostic transcriptomic signatures enable uniform comparisons of neural maturation T2 - PLoS biology J2 - PLoS Biol PY - 2026 DA - 2026/04/15 VL - 24 IS - 4 SP - e3003757 SN - 1544-9173 PB - PLOS DO - 10.1371/journal.pbio.3003757 UR - https://doi.org/10.1371/journal.pbio.3003757 LA - en ER -

CSL-JSON

{ "id": "10.1371/journal.pbio.3003757", "type": "article-journal", "title": "Cell type-agnostic transcriptomic signatures enable uniform comparisons of neural maturation", "container-title": "PLoS biology", "author": [ { "family": "Venkatesan", "given": "Sridevi" }, { "family": "Werner", "given": "Jonathan M" }, { "family": "Li", "given": "Yun" }, { "family": "Gillis", "given": "Jesse" } ], "container-title-short": "PLoS Biol", "volume": "24", "issue": "4", "page": "e3003757", "DOI": "10.1371/journal.pbio.3003757", "PMID": "41984978", "PMCID": "PMC13095120", "ISSN": "1544-9173", "publisher": "PLOS", "URL": "https://doi.org/10.1371/journal.pbio.3003757", "language": "en", "issued": { "date-parts": [ [ 2026, 4, 15 ] ] } }

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Cell type-agnostic transcriptomic signatures enable uniform comparisons of neural maturation.

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