The DOI matches its Crossref record; 110 of the 110 references it cites were found in a registry or an index; none of the 12 sources its claims cite is checked for support yet.
scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI
Open accessAbstract
Generative pre-trained models have achieved remarkable success in various domains such as natural language processing and computer vision. Specifically, the combination of large-scale diverse datasets and pre-trained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between linguistic constructs and cellular biology — where texts comprise words, similarly, cells are defined by genes — our study probes the applicability of foundation models to advance cellular biology and genetics research. Utilizing the burgeoning single-cell sequencing data, we have pioneered the construction of a foundation model for single-cell biology, scGPT, which is based on generative pre-trained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT, a generative pre-trained transformer, effectively distills critical biological insights concerning genes and cells. Through the further adaptation of transfer learning, scGPT can be optimized to achieve superior performance across diverse downstream applications. This includes tasks such as cell-type annotation, multi-batch integration, multi-omic integration, genetic perturbation prediction, and gene network inference. The scGPT codebase is publicly available at https://github.com/bowang-lab/scGPT .
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
scGPT is a generative pretrained transformer foundation model for single-cell biology, pretrained on over 33 million human scRNA-seq cells and fine-tuned for cell-type annotation, integration, perturbation prediction, and gene network inference. It reports state-of-the-art or competitive performance across these tasks and identifies a scaling law where larger pretraining data improves downstream fine-tuning.
Key findings
- Pretraining used 33 million normal human scRNA-seq cells from CELLxGENE, spanning 51 organs/tissues and 441 studies; UMAP of a 10% subset showed cell types separated into distinct clusters despite over 400 studies.
- Cell-type annotation: scGPT achieved high precision (>0.8) for most hPancreas cell types except rare types, around 0.85 accuracy on multiple sclerosis, and ≥0.7 precision for most tumor-infiltrating myeloid cell types; it outperformed TOSICA and scBert on Accuracy, Precision, Recall, and MacroF1.
- Perturbation prediction: scGPT achieved the highest correlation on both Adamson and Norman datasets versus GEARS and CPA; in reverse perturbation prediction it identified 71.4% correct perturbations (5/7 test cases) within top 5 predictions, a 95.2% speedup over random tryouts (105.5 average attempts out of 210).
- Integration and GRN: PBMC 10K AvgBIO was 0.821, 5–10% higher than compared methods; ASAP PBMC AvgBATCH was 0.951 versus scMoMaT’s 0.916; scGPT uniquely identified 22 Reactome pathways, 14 immune-related, and attention analysis found all top 20 DDIT3-influenced genes and 19/20 BHLHE40-influenced genes as ChIP-Atlas targets.
Methods
- Design: two-stage generative pretraining and fine-tuning of a transformer that jointly learns cell and gene embeddings; a specialized attention mask enables generative prediction on non-sequential gene expression, with fine-tuning objectives including GEP, GEPC, ECS, DAR, and CLS.
- Data: pretraining on 33 million normal human scRNA-seq cells from CELLxGENE; downstream datasets included hPancreas, multiple sclerosis, myeloid, Adamson, Norman, COVID-19, PBMC 10K, Perirhinal Cortex, 10X Multiome PBMC, BMMC, ASAP PBMC, and Immune Human.
- Analysis: benchmarks against TOSICA, scBert, GEARS, CPA, scVI, Seurat, Harmony, scGLUE, and scMoMaT; metrics included Accuracy, Precision, Recall, MacroF1, Pearson correlation and corr(Δ), AvgBIO, AvgBATCH, Overall, and Reactome pathway enrichment.
Limitations
- Stated: the scaling law appeared to saturate after 3M pretraining cells; the authors hypothesize larger model sizes could improve performance at 33M and beyond but leave larger model sizes to future work.
- Stated: future work plans larger-scale, more diverse pretraining including multi-omic, spatial omics, diseased conditions, perturbation and temporal data, broader biological validation, and in-context instruction learning.
- Evident: this is a bioRxiv preprint not certified by peer review; results are computational benchmarks, and the text does not report prospective wet-lab validation of predictions.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the fine-tuned scGPT consistently showcases performance gain for tasks such as integration376 and cell type annotation.”
§ 1 3 > 2.6 Analyzing the transfer learning of pretrained knowledge: the scaling370
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“scGPT achieves a higher overall batch correction287 score AvgBATCH of 0.951 compared to scMoMat (AvgBATCH = 0.916).”
§ 1 3 > 2.4 scGPT enables accurate multi-batch and multi-omics integration231
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In the paper
“scGPT334 consistently demonstrates a significantly higher number of enriched pathways across all clustering335 resolutions.”
§ 1 3 > 2.5 scGPT uncovers gene regulatory networks for specific cell states and292
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“scGPT stands264 out as the only method that successfully generates a distinct cluster for CD8 Naive cells, while the265 other two methods fail to do so.”
§ 1 3 > 2.4 scGPT enables accurate multi-batch and multi-omics integration231
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In the paper
“The top performers in data integration tasks were models pretrained415 on whole-human, blood, and lung datasets”
§ 1 3 > 2.6 Analyzing the transfer learning of pretrained knowledge: the scaling370
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“scGPT identified the top 20 genes most influenced by353 DDIT3 transcription factor repression, which are all found to be signaling targets of DDIT3 in354 the Chip-Atlas database”
§ 1 3 > 2.5 scGPT uncovers gene regulatory networks for specific cell states and292
What the sources say
- Not checked yetcites [56] 2022, ChIP-Atlas 2021 update: a data-mining suite for exploring epigenomic landscapes by fully integrating chip-seq, ATAC-seq and Bisulfite-seq data, In: Nucleic acids research
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Notably, scGPT specifically highlighted pathways related to the adaptive immune system, T-cell340 receptor (TCR) signaling, PD-1 signaling, and MHC II presentation, which were not captured by341 the co-expression network.”
§ 1 3 > 2.5 scGPT uncovers gene regulatory networks for specific cell states and292
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: Adaptive immune populations exist in the fine-tuning datasets.
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In the paper
“In the context of the Perirhinal Cortex dataset,246 scGPT remains competitive against all other methodologies”
§ 1 3 > 2.4 scGPT enables accurate multi-batch and multi-omics integration231
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In the paper
“Notably, scGPT also demonstrates a considerable performance244 for integrating PBMC 10K dataset even without fine-tuning”
§ 1 3 > 2.4 scGPT enables accurate multi-batch and multi-omics integration231
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In the paper
“scGPT's biological288 conservation metrics also compare favorably to scMoMat's”
§ 1 3 > 2.4 scGPT enables accurate multi-batch and multi-omics integration231
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- Probably not a substantial claim. 26
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In the paper
“clustering resolution of 0.5, 54 sub-clusters were identified in the UMAP of predicted perturbation”
§ 4 Methods462 > 4.7 Benchmarking Experiment Setup860
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“Both methods identi-337 fied 15 common pathways, including those associated with the cell cycle and the immune system.338 However, scGPT uniquely identified additional 22 pathways, out of which 14 were immune-related.”
§ 1 3 > 2.5 scGPT uncovers gene regulatory networks for specific cell states and292
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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In the paper
“scMoMat produced285 two distinct clusters for each cell type corresponding to the first two and second two batches,286 indicating failure to learn a joint representation.”
§ 1 3 > 2.4 scGPT enables accurate multi-batch and multi-omics integration231
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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In the paper
“This superior integra-240 tion performance of scGPT is further supported by its high biological conservation score, with an241 AvgBIO score of 0.821, which is 5-10% higher than the compared methods.”
§ 1 3 > 2.4 scGPT enables accurate multi-batch and multi-omics integration231
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In the paper
“We observed that a set of HLA class II genes was identified as group 1. Similarly, the CD3 genes327 involved in the T3 complex were identified as group 4, with the highest expression present in T328 cells.”
§ 1 3 > 2.5 scGPT uncovers gene regulatory networks for specific cell states and292
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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In the paper
“scGPT presented more defined cluster structures compared269 to Seurat v4, with a 9% improvement in the AvgBIO score.”
§ 1 3 > 2.4 scGPT enables accurate multi-batch and multi-omics integration231
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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In the paper
“We observed the performance of fine-tuned models improved as the383 size of the pre-training data increased.”
§ 1 3 > 2.6 Analyzing the transfer learning of pretrained knowledge: the scaling370
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe quote only partly states this claim.
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In the paper
“source of genetic change within 5 attempts represents a 95.2% speedup.”
§ 1 3 > 2.3 scGPT predicts unseen genetic perturbation responses158
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearIt may state two separate claims as one.
In the paper
“The pre-trained scGPT model successfully identifies the group of315 genes (CD3E, D, and G) encoding the T3 complex for T-cell activation, as well as CD79A and316 CD79B for B-cell signaling, and CD8A and CD8B as co-receptors for HLA class I molecules”
§ 1 3 > 2.5 scGPT uncovers gene regulatory networks for specific cell states and292
What the sources say
- Not checked yetcites [52] 1995, Immunobiology: The immune system in health and disease, In: Journal of Allergy and Clinical Immunology
Strength:boosted· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
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In the paper
“10X Multiome PBMC [47] scGPT (fine-tuned) 0.758 0.807 0.822 0.645 - - - -”
- Also stated in: § Supplementary Notes1264 · plain
“scGLUE [14] 0.747 0.815 0.806 0.619 - - - -”
- Also stated in: § Supplementary Notes1264 · plain
“Seurat v4 [46] 0.722 0.784 0.691 0.691 - - - -”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one. The quote only partly states this claim.
- Also stated in: § Supplementary Notes1264 · plain
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In the paper
“ASAP PBMC [49] scGPT (fine-tuned) 0.587 0.645 0.469 0.648 0.951 0.909 0.992 0.732”
- Also stated in: § Supplementary Notes1264 · plain
“scMoMat [15] 0.546 0.448 0.557 0.633 0.916 0.849 0.983 0.667”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one. The quote only partly states this claim.
- Also stated in: § Supplementary Notes1264 · plain
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In the paper
“scGPT (Zero-shot) Celltype, AvgBIO = 0.728”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe quote only partly states this claim.
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In the paper
“BMMC [48] scGPT (fine-tuned) 0.697 0.783 0.725 0.582 0.871 0.834 0.908 0.766”
- Also stated in: § Supplementary Notes1264 · plain
“Seurat v4 [46] 0.600 0.737 0.470 0.594 0.678 0.685 0.671 0.631”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one. The quote only partly states this claim.
- Also stated in: § Supplementary Notes1264 · plain
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In the paper
“From data1318 presented in table S5, we noticed a significant drop in the training time per epoch as well as in GPU1319 Memory usage throughout the fine-tuning process.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
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In the paper
“Specifically, the removal of zero-expressed genes1320 resulted in a substantial reduction in our model's maximum sequence length to approximately 401321 ∼ 60% of its initial length, effectively halving both the time spent on training epochs and peak1322 GPU Memory utilization.”
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In the paper
“Furthermore, by freezing the embedding layer, we achieved an additional1323 reduction in Peak GPU Memory usage by 1GB, a slight increase in training speed, while preserving1324 a comparable AvgBio score.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
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In the paper
“These findings319 demonstrate scGPT's ability to generalize the knowledge learned during pre-training and extract320 information relevant to specific tissues or cell types after fine-tuning.”
§ 1 3 > 2.5 scGPT uncovers gene regulatory networks for specific cell states and292
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, scGPT demon-273 strates superior cell-type clustering performance in the paired data setting and exhibits robustness274 across diverse biological conservation metrics benchmarked”
§ 1 3 > 2.4 scGPT enables accurate multi-batch and multi-omics integration231
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In the paper
“1,200 highly variable genes, 4,000 highly variable peaks, and all 216 protein features were used as”
§ 4 Methods462 > 4.7 Benchmarking Experiment Setup860
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In the paper
“The evaluation of the integrated cell embeddings was performed using biological conservation”
§ 4 Methods462 > 4.7 Benchmarking Experiment Setup860
What the sources say
- Not checked yetcites Luecken et al. [53] 2022, Benchmarking atlas-level data integration in single-cell genomics, In: Nature methods
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In the paper
“coefficient (corr) between the predicted gene expression and the ground-truth expression values.”
§ 4 Methods462 > 4.7 Benchmarking Experiment Setup860
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In the paper
“making it particularly suitable for capturing interactions between genes.”
§ 4 Methods462 > 4.2 Cell and gene expression modelling by transformers513
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
In the paper
“outlined by Roohani, Huang, and Leskovec [38] in their benchmark.”
§ 4 Methods462 > 4.7 Benchmarking Experiment Setup860
What the sources say
- Not matchedcites Roohani, Huang, and Leskovec [38]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“against the functional groups found in the original publication by Norman et al. [37]”
§ 4 Methods462 > 4.7 Benchmarking Experiment Setup860
What the sources say
- Not checked yetcites Norman et al. [37] 2019, Exploring genetic interaction manifolds constructed from rich single-cell phenotypes, In: Science
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In the paper
“we performed pathway enrich-331 ment analysis against the Reactome database [54] and identified high-confidence”
§ 1 3 > 2.5 scGPT uncovers gene regulatory networks for specific cell states and292
What the sources say
- Not matchedcites [54] Reactome. Reactome Pathway Database: Home. https://reactome.org/. 2022.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [55] Eric W Weisstein . “Bonferroni correction”. In: https://mathworld.wolfram.com/ (2004).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“While keeping the input feature set consistent, we used each method's”
§ 4 Methods462 > 4.7 Benchmarking Experiment Setup860
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In the paper
“comparison since the method was not specifically designed to model protein data.”
§ 4 Methods462 > 4.7 Benchmarking Experiment Setup860
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“Additionally, we reported the batch correction metrics proposed by Luecken et al. [53] to assess”
§ 4 Methods462 > 4.7 Benchmarking Experiment Setup860
What the sources say
- Not checked yetcites Luecken et al. [53] 2022, Benchmarking atlas-level data integration in single-cell genomics, In: Nature methods
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In the paper
“all methods were provided with the same number of 1,200 highly variable genes as input.”
§ 4 Methods462 > 4.7 Benchmarking Experiment Setup860
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In the paper
“all models in the same settings as follows”
§ 4 Methods462 > 4.7 Benchmarking Experiment Setup860
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
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In the paper
“As a sensitivity analysis, we reported the pathway hits for scGPT”
§ 4 Methods462 > 4.7 Benchmarking Experiment Setup860
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In the paper
“the train-test split consists of three scenarios with increasing difficulty”
§ 4 Methods462 > 4.7 Benchmarking Experiment Setup860
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. The quote only partly states this claim.
In the paper
“is a weighted sum of AvgBIO and AvgBATCH, consistent with the approach taken by Luecken”
§ 4 Methods462 > 4.7 Benchmarking Experiment Setup860
What the sources say
- Not checked yetcites Luecken et al. [53] 2022, Benchmarking atlas-level data integration in single-cell genomics, In: Nature methods
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“are split to ensure that test perturbations are not seen in training”
§ 4 Methods462 > 4.7 Benchmarking Experiment Setup860
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“Harmony and scVI are highlighted as the top-performing methods”
§ 4 Methods462 > 4.7 Benchmarking Experiment Setup860
What the sources say
- Not checked yetcites Luecken et al. [53] 2022, Benchmarking atlas-level data integration in single-cell genomics, In: Nature methods
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Bonferroni correction [55] based on the total number of tests performed”
§ 4 Methods462 > 4.7 Benchmarking Experiment Setup860
What the sources say
- Not matchedcites [55] Eric W Weisstein . “Bonferroni correction”. In: https://mathworld.wolfram.com/ (2004).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. The quote only partly states this claim.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Self-attention has been widely used to capture the co-occurrence patterns among tokens.”
§ 4 Methods462 > 4.3 Generative pre-training570
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The same 1,200 highly variable genes and 4,000 highly variable peaks were used”
§ 4 Methods462 > 4.7 Benchmarking Experiment Setup860
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This setting serves as an example of out-of-distribution data.”
§ 4 Methods462 > 4.6 Datasets775
Strength:plain· not yet calibratedn = 9Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The difference between these two attention maps should highlight the”
§ 4 Methods462 > 4.5 Fine-tuning on downstream tasks701
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The data has only one technical batch. Batch correction metrics are not applicable to this setting.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The gene targets selected from the control attention map should reflect the basal”
§ 4 Methods462 > 4.5 Fine-tuning on downstream tasks701
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one. The quote only partly states this claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“that the columns in the attention map indicate how much the gene of interest affects the other”
§ 4 Methods462 > 4.5 Fine-tuning on downstream tasks701
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper relies on this without support.
The COVID-19 dataset comprises diverse cellular compositions, making it suitable for evaluating in-context pre-training effects.
“The COVID-19 dataset serves as an ideal task for this investigation, as it comprises diverse412 cellular compositions.”
The paper relies on this without support.
Adaptive immune populations exist in the fine-tuning datasets.
“This is concordant with the fact that adaptive immune populations342 exist in the fine-tuning datasets.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Empirical benchmarking studies have documented substantial performance degradation for biological foundation models under cross-platform transfer, preprocessing variability, and rare-population evaluation [20–24].”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [20]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Compared to tasks such as cell annotation and batch correction, perturbation prediction offers experimentally generated model input/output with well-defined objectives, making it a more robust benchmark for assessing foundation models (15, 20, 21).”
From A systematic comparison of single-cell perturbation response prediction models (2026) · cites this paper as [15]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We chose these models because they offer pretrained weights and have been trained using unsupervised objectives on extensive datasets (ca. 30M single-cell transcriptomes).”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [7]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, scGPT directly learns a cell embedding, which is integrated into its pretraining loss of predicting masked genes: scGPT first predicts a masked gene expression bin and a cell embedding from unmasked genes; then, in a second step, it further iteratively refines masked gene expression using the cell embedding predicted in the first step.”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [7]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Recent advances have incorporated graph-based methods and foundational models that take advantage of prior knowledge of gene expression relationships, resulting in improved generalizability (14–17).”
From A systematic comparison of single-cell perturbation response prediction models (2026) · cites this paper as [15]
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Recent work suggests that architectural choices, such as generative versus contrastive pretraining objectives, introduce distinct trade-offs between representation flexibility, interpretability, and robustness to experimental variability [20–23, 36].”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [20]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“These models are pre-trained on vast amounts (> 10 M examples) of unlabelled scRNA-seq data, with the aim that such large datasets allow the models to capture general principles of gene regulation and signalling.”
From Benchmarking foundation cell models for post-perturbation RNA-seq prediction (2025) · cites this paper as [6]
The cited source says what the claim attributes to it.
In the citing paper
“Next, the binned values and the gene tokens (i.e., a unique index for each gene) are separately embedded and summed in the embedding space—jointly representing the gene and its binned expression.”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [7]
The cited source says what the claim attributes to it.
In the citing paper
“Foundation models for single-cell and multi-omic biology, such as scGPT [20], Nicheformer [21], and EpiAgent [22, 23], produce high-quality continuous representations of biological entities that support cross-tissue transfer, perturbation inference, and regulatory feature learning.”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [20]
The cited source says what the claim attributes to it.
In the citing paper
“We first examine recent foundational models that produce transferable representations across omic modalities, such as scGPT, Nicheformer, and EpiAgent, and discuss their architectural choices, training regimes, and interpretability constraints.”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [20]
In the citing paper
“scGPT is available in several variants, each pretrained on multiple different datasets.”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [7]
In the citing paper
“Finally, compared to Geneformer, scGPT has 3 the number of parameters, using 12 Transformer layers with 8 attention heads.”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [7]
In the citing paper
“scGPT preprocesses each gene expression vector by independently binning values into 50 equidistant bins where the lowest bin is the lowest expression and the highest bin corresponds to the highest expression.”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [7]
In the citing paper
“Like Geneformer, scGPT uses an MLM task.”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [7]
In the citing paper
“To assess this, we employ metrics based on Average Silhouette Width (ASW) [25] and Average Bio (AvgBIO) scores [7].”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [7]
In the citing paper
“To evaluate the performance of scGPT in its pretraining objective, we used the mean squared error (MSE), as used by the original authors for the model’s loss [7].”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [7]
In the citing paper
“scGPT [14] focuses on processing these high-dimensional and sparse RNA data to capture complex relationships between cells, thereby supporting transfer learning across datasets and even across species.”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [14]
In the citing paper
“Empirical benchmarking studies have shown that performance improvements observed in standard evaluation settings can decrease substantially when models are tested across different laboratories, sequencing platforms, or preprocessing pipelines [20–23].”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [20]
In the citing paper
“Single-cell foundation models and perturbation predictors support cellular-response forecasting [48-51].”
From RSI-Anything: Unlocking Recursive Self-Improvement with World Models (2026) · cites this paper as [48]
In the citing paper
“We developed xTrimoGene, a scalable transformer-based model with both algorithmically efficient and engineering acceleration strategies 14 .”
From Large-scale foundation model on single-cell transcriptomics (2024) · cites this paper as [14]
In the citing paper
“With the help of self-attention mechanisms over the gene dimension, scGPT can encode complex interactions between perturbed and other genes.”
From A mini-review on perturbation modelling across single-cell omic modalities (2024) · cites this paper as [69]
In the citing paper
“This enables the model to learn from existing experimental data (i.e., Perturb-seq data) through few-shot learning and make accurate predictions of gene expressions following perturbation for unseen experiments (OOD).”
From A mini-review on perturbation modelling across single-cell omic modalities (2024) · cites this paper as [69]
In the citing paper
“In oncology, transformer-based methods support clinical text processing, whole-slide and radiologic image interpretation (vision transformers), and genomic sequence modeling [24,25,26,27].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [27]
In the citing paper
“Both scGPT and Geneformer fine-tune their cell embeddings for cell type classification.”
From Assessing the limits of zero-shot foundation models in single-cell biology (2023) · cites this paper as [6]
In the citing paper
“To evaluate their models, most previous works-including scGPT and Geneformer-rely on fine-tuning to specialize task-specific models.”
From Assessing the limits of zero-shot foundation models in single-cell biology (2023) · cites this paper as [6]
In the citing paper
“We chose these models because they offer pretrained weights (whereas several other possible models did not have public weights at time of evaluation) and have been trained using unsupervised objectives on extensive datasets (ca. 30M single-cell transcriptomes).”
From Assessing the limits of zero-shot foundation models in single-cell biology (2023) · cites this paper as [6]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Overall-score equation is labeled AvgBATCH
MinorThe metric definitions first define AvgBATCH as the mean of two batch metrics. The subsequent equation for the Overall score instead labels its weighted combination of AvgBIO and AvgBATCH as AvgBATCH.
“AvgBATCH = 0.6 ∗AvgBIO + 0.4 ∗AvgBATCH”
“AvgBATCH = (ASWbatch +GraphConn)/2”
The batch-integration benchmark counts three datasets as four
MinorThe benchmarking setup says there are four integration datasets but names only three; the results section explicitly calls the same list three datasets.
“batch correction and cell type clustering on four integration datasets: COVID-19 [13], PBMC 10K930 [44], and Perirhinal Cortex [45].”
“The evaluation was conducted on three integration236 datasets, namely the COVID-19 (18 batches) [13], PBMC 10K (2 batches) [44], and Perirhinal237 Cortex (2 batches) [45].”
MHC class II cells are assigned to the wrong hPancreas partition
MinorThe results say that fewer than 50 MHC class II cells are in the reference set. The dataset description lists MHC class II only among query-set groups, not reference-set groups.
“fewer than 50 cells belong to the mast and MHC class II cell types out of the 106,000134 cells in the reference set.”
“The reference set contains 10,600 cells of 13 cell groups (alpha, beta, ductal,807 acinar, delta, PSC, PP, endothelial, macrophage, mast, epsilon, schwann, and t cell). The query808 set contains 4,218 cells of 11 cell groups (alpha, beta, ductal, PP, acinar, delta, PSC, endothelial,809 epsilon, mast, MHC class II).”
hPancreas reference-set size differs tenfold
MinorThe results describe 106,000 cells in the hPancreas reference set, whereas the dataset description gives 10,600.
“out of the 106,000134 cells in the reference set.”
“The reference set contains 10,600 cells of 13 cell groups”
BMMC cell-type count is reported as both 48 and 45
MinorThe results describe 48 BMMC cell types, while the dataset description says its annotations contain 45 subtypes.
“subgroup annotations (48 cell types).”
“annotations consist of 45 detailed immune cell subtypes.”
Experimental perturbations cover about 4.24%, not 5%, of the stated space
MinorThe paper states that 236 experiments represent 5% of 5,565 possible perturbations. Their quotient is approximately 4.24%, which rounds to 4%, not 5%, at whole-percent precision.
Reported 5 · recomputed 4.240791
“The original Perturb-seq study covered 236 one-gene or two-gene190 perturbations targeting 105 genes. However, considering all possible combinations of these tar-191 get genes, there are a total of 5,565 potential perturbations. This means that the experimental192 Perturb-seq only represents 5% of the entire perturbation space.”
Citations
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MethodInfluentialComprehensive Integration of Single-Cell Data
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“In our benchmarking experiments, we compared scGPT with three popular integration methods: scVI [41], Seurat [42], and Harmony [43].”
MethodInfluential
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Generalist biological artificial intelligence in modeling the language of life
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Self-citationJust how transformative will AI/ML be for immuno-oncology?
Bottomly, D., & McWeeney, S. K. (2024) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2023-007841 · Cited by 9
“…been devised to capture network dynamics such as Geneformer 93 as well as begin to build more foundational generative models such as scGPT 94 and tGPT. 95 The tGPT model in particular showed promise for differentiating immunotherapeutic treatment outcomes in urothelial carcinoma leveraging…”
An efficient full-size convolutional computing method based on memristor crossbar
Tan, J., Shen, S., Duan, S., & Wang, L. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10787-2 · Cited by 10
“Extended author information available on the last page of the article 158 Page 2 of 25 diverse datasets and pre-trained transformers has emerged as a promising approach for developing Single-Cell Multi-omics (Cui et al. 2023).”
Transfer learning of multicellular organization via single-cell and spatial transcriptomics
Tan, Y., Wang, A., Wang, Z., Lin, W., Yan, Y., Nie, Q., & Shi, J. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012991 · Cited by 10
Toward mastering the cell language by learning to generatePreprint
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Towards a universal spatial molecular atlas of the mouse brainPreprint
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Theus, A., Barkmann, F., Wissel, D., & Boeva, V. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.01.621087 · Cited by 10
“…dedicated to extending foundation models beyond text and image data to encompass a wide range of data modalities, including audio (Yang et al., 2023), brain signals (Caro et al., 2024), and, of particular relevance to our work, scRNA-seq (Cui et al., 2023; Rosen et al., 2023; Hao et al., 2023).”
BackgroundMethodInfluentialSelf-Normalizing Foundation Model for Enhanced Multi-Omics Data Analysis in OncologyPreprint
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A perturbation proteomics-based foundation model for virtual cell constructionPreprint
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Virtual Cells Need Context, Not Just ScalePreprint
Dibaeinia, P., Babu, S., Knudson, M. K., ElSheikh, A., Wen, Y., Liu, H., Perera, J. F., & Khan, A. A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.04.703804 · Cited by 22
Foundation Models Improve Perturbation Response PredictionPreprint
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“One key limitation is that, to save computational cost, these models typically perform self-attention on a small subset of genes (e.g., 2048 in Geneformer 8 and scFoundation 10 , 2000 in scGPT 9 , and 1536 in UCE 16 ), often selected based on high expression levels 8 .”
Martinez, G. S., Garduno, A., Ostadgavahi, A. T., et al. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25115920 · Cited by 8
Artificial intelligence in cell annotation for high-resolution RNA sequencing data
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Cytometry masked autoencoder: An accurate and interpretable automated immunophenotyper
Kim, J., Ionita, M., Lee, M., McKeague, M. L., Pattekar, A., Painter, M. M., Wagenaar, J., Truong, V., Norton, D. T., Mathew, D., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101808 · Cited by 13
“This approach allows the pre-trained model to be fine-tuned for specific downstream tasks or to function as generative AI.”
Artificial Intelligence in Cancer Immunotherapy: Navigating Challenges and Unlocking Opportunities
Xiang, W., Yu, L., Chen, X. S., & Herold, M. J. (2024) · Engineering · doi:10.1016/j.eng.2024.12.014 · Cited by 8
Unleashing the potential of artificial intelligence in infectious diseases
Zhou, H.-Y., Li, Y., Li, J., Meng, J., & Wu, A. (2025) · National Science Review · doi:10.1093/nsr/nwaf004 · Cited by 8
Omics strategies for plant natural product biosynthesis
Wan, S., Schaap, P. J., Diez, M. S., et al. (2025) · Genomics Communications · doi:10.48130/gcomm-0025-0010 · Cited by 8
Advancing precision diagnosis in autism: Insights from large-scale genomic studies
Kim, S.-W., & An, J. (2025) · Molecules and Cells · doi:10.1016/j.mocell.2025.100248 · Cited by 10
SCassist: an AI based workflow assistant for single-cell analysis
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Chen, Y., Hu, Z., Chen, W., & Huang, H. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf253 · Cited by 11
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Human-scATAC-Corpus: a comprehensive database of scATAC-seq data
Chen, X., Gao, Z., Li, K., Wang, Z., Jiang, Q., Cui, X., Li, Z., & Jiang, R. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1216 · Cited by 11
Computational strategies for cross-species knowledge transfer
Yuan, H., Mancuso, C. A., Johnson, K. A., Braasch, I., & Krishnan, A. (2025) · Nature Methods · doi:10.1038/s41592-025-02931-9 · Cited by 11
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Mapping biology in space: from spatial transcriptomics platforms to analytical tools and databases
Guo, Z., Wu, R., Li, W., Yang, K., Ying, X., Alinejad-Rokny, H., & Ye, Y. (2026) · 中国科学通报：英文版 · doi:10.1016/j.scib.2026.01.034 · Cited by 12
B-cell epitope prediction in the age of machine learning: advancements and challenges
Gabellieri, F., Singh, A., Gupta, S., Bensmail, H., Castiglione, F., & Mall, R. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-025-07673-y · Cited by 8
Artificial Intelligence agents for biological research: a survey
Qi, C., Wang, W., Jiang, S., Liu, Q., Song, X., Fang, H., & Wei, Z. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag075 · Cited by 11
3d-OT: a deep geometry-aware framework for heterogeneous slices alignment of spatial multi-omics
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AI-Driven Chemometrics for Multi-omics Data Integration: Advances, Challenges, and Future Directions
Polu, P. R. (2026) · Critical Reviews in Analytical Chemistry · doi:10.1080/10408347.2026.2657553 · Cited by 9
Kendiukhov, I. (2026) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2026.105056 · Cited by 17
Genome language modeling (GLM): a beginner’s cheat sheet
Tyagi, N., Vahab, N., & Tyagi, S. (2025) · Biology Methods and Protocols · doi:10.1093/biomethods/bpaf022 · Cited by 8
uniLIVER: a human liver cell atlas for data-driven cellular state mapping
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stFormer: a foundation model for spatial transcriptomicsPreprint
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EpiFoundation: A Foundation Model for Single-Cell ATAC-seq via Peak-to-Gene AlignmentPreprint
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geneRNIB: a living benchmark for gene regulatory network inferencePreprint
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“This technique was also performed by another study (24).”
Characterizing chromosomal instability-driven cancer evolution and cell fitness at a glance
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NicheTrans: spatial-aware cross-omics translation
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RegFormer: a single-cell foundation model powered by gene regulatory hierarchies
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Drug Metabolism and Toxicological Mechanisms
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Ovarian aging at single-cell resolution: Current paradigms and perspectives
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GPerturb: Gaussian process modelling of single-cell perturbation data
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Key Technologies of Synthetic Biology in Industrial Microbiology
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Metadata
- Authors
- Cui, H., Wang, C., Maan, H., Pang, K., Luo, F., & Wang, B.
- Affiliations
- University Health Network (CA)
- University of Toronto (CA)
- Corresponding author
- Wang, Bo
- Venue
- Nature Methods
- Publisher
- openRxiv
- Published
- 2023-05-01
- Volume
- 21
- Issue
- 8
- Pages
- 1470-1480
- ISSN
- 1548-7091 (ISSN-L)
- OpenAlex
- W4392168151
- Cited by
- 1,694
- References
- 98
- Citations by year
- 2026: 918 · 2025: 521 · 2024: 234 · 2023: 19
- FWCI
- 186.38
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (submittedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- University Health Network
- Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (DGECR-2020-00294, RGPIN-2020-06189)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- scGPT, single-cell multi-omics, foundation models, generative pre-trained transformer, single-cell RNA sequencing, cell type annotation, multi-omics integration, perturbation response prediction, gene network inference, transfer learning, single-cell biology
- MeSH terms
- Multiomics, Algorithms, Artificial Intelligence, Humans, Computational Biology, Gene Regulatory Networks, Single-Cell Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-11-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-11-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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