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
Cites
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · cited 2×
“The generative pre-training used in OpenAI GPT3 [71] and GPT4 [19] employs a unified 585”
MethodRoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515
Integrated analysis of multimodal single-cell data
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“In the paired data integration experiment, we benchmarked scGPT with scGLUE 951 [14] and Seurat v4 [46] on the 10X Multiome PBMC [47] dataset with RNA and ATAC-seq data as 952 the first example.”
MethodInfluentialComprehensive Integration of Single-Cell Data
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Comprehensive integration of single-cell dataPreprint
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Fast, sensitive and accurate integration of single-cell data with Harmony
Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488 · cited 5×
“In our benchmarking experiments, we compared scGPT with three popular integration methods: scVI [41], Seurat [42], and Harmony [43].”
MethodInfluentialScaling Laws for Neural Language Models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., & Amodei, D. (2020) · arXiv · doi:10.48550/arxiv.2001.08361 · Cited by 9,550 · cited 2×
“Notably, our findings also align with the scaling law reported in natural language models 392 [60], highlighting the significant role of data size in model performance.”
MethodResultSCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881 · cited 1×
“We used the SCANPY python library [84] for gene expression pre-processing, including normal1042 ization, log-transformation, and highly variable gene selection.”
MethodUMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101 · cited 1×
“After pre-training, we visualized the scGPT 114 cell embeddings on 10% of the human cells out of the 33 million data using UMAP visualization 115 [29] (Figure 1E).”
MethodSpatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055
From Louvain to Leiden: guaranteeing well-connected communities
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Unsupervised Domain Adaptation by BackpropagationPreprint
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“While several methods have 122 been proposed for cell annotation [30, 31, 32], they often require dimension reduction prior to 123 model input, which can lead to information loss.”
MethodImmunobiology: the immune system in health and disease
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On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676 · cited 1×
“One promising approach to address this challenge is the generative pre-training of founda39 tion models [16, 17].”
BackgroundDeep generative modeling for single-cell transcriptomics
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018) · Nature Methods · doi:10.1038/s41592-018-0229-2 · Cited by 2,530 · cited 2×
“234 In our benchmarking experiments, we compared scGPT with three popular integration methods: 235 scVI [41], Seurat [42], and Harmony [43].”
MethodHierarchical Text-Conditional Image Generation with CLIP Latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022) · arXiv · doi:10.48550/arxiv.2204.06125 · Cited by 2,511
API design for machine learning software: experiences from the scikit-learn projectPreprint
Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., et al. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1309.0238 · Cited by 2,387
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232
Current best practices in single-cell RNA-seq analysis: a tutorial
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Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks
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Foundation models for generalist medical artificial intelligence
Moor, M., Banerjee, O., Abad, Z. S. H., Krumholz, H. M., Leskovec, J., Topol, E. J., & Rajpurkar, P. (2023) · Nature · doi:10.1038/s41586-023-05881-4 · Cited by 2,028 · cited 1×
“One promising approach to address this challenge is the generative pre-training of founda39 tion models [16, 17].”
BackgroundMulti-omics Data Integration, Interpretation, and Its Application
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A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells
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MethodMapping the Mouse Cell Atlas by Microwell-Seq
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Pre-trained models for natural language processing: A survey
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“44 More interestingly, these generative pre-trained models consistently outperform task-specific mod45 els trained from scratch [21, 22, 23].”
MethodSingle-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes
Segerstolpe, Å., Palasantza, A., Eliasson, P., Andersson, E.-M., Andréasson, A.-C., Sun, X., Picelli, S., Sabirsh, A., Clausen, M., Bjursell, M. K., et al. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2016.08.020 · Cited by 1,550
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.12712 · Cited by 1,526 · cited 1×
“This framework offers great flexibility to be utilized in various natural 587 language generation (NLG) applications and demonstrates new capabilities such as contextual 588 awareness in zero-shot and fine-tuned settings [72].”
BackgroundBaron, M., Veres, A., Wolock, S. L., Faust, A. L., Gaujoux, R., Vetere, A., Ryu, J. H., Wagner, B. K., Shen-Orr, S. S., Klein, A. M., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.08.011 · Cited by 1,508 · cited 2×
“The reference set consists of Braon[79] and Muraro[80] 804 datasets, and the query set consists of Xin[81], Segerstolpe[82], and Lawlor[83] datasets.”
MethodRegev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., & al, E. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-162597 · Cited by 1,457
Benchmarking atlas-level data integration in single-cell genomics
Luecken, M. D., Büttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colomé-Tatché, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01336-8 · Cited by 1,438 · cited 1×
“324 In Figure 5C, we visualized the gene programs extracted by the fine-tuned scGPT model on the 325 highly variable genes in the Immune Human dataset [53], and their expression in different cell types.”
MethodKim, N., Kim, H. K., Lee, K., Hong, Y., Cho, J. H., Choi, J. W., Lee, J.-I., Suh, Y., Ku, B. M., Eum, H. H., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16164-1 · Cited by 1,347
Effective gene expression prediction from sequence by integrating long-range interactions
Avsec, Ž., Agarwal, V., Visentin, D., Ledsam, J. R., Grabska-Barwinska, A., Taylor, K. R., Assael, Y., Jumper, J., Kohli, P., & Kelley, D. R. (2021) · Nature Methods · doi:10.1038/s41592-021-01252-x · Cited by 1,326
A Single-Cell Transcriptome Atlas of the Human Pancreas
Muraro, M. J., Dharmadhikari, G., Grün, D., Gröen, N., Dielen, T., Jansen, E., van Gurp, L., Engelse, M. A., Carlotti, F., de Koning, E. J. P., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.09.002 · Cited by 1,307 · cited 2×
“The reference set consists of Braon[79] and Muraro[80] 804 datasets, and the query set consists of Xin[81], Segerstolpe[82], and Lawlor[83] datasets.”
MethodMultiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing
Cusanovich, D. A., Daza, R., Adey, A., Pliner, H. A., Christiansen, L., Gunderson, K. L., Steemers, F. J., Trapnell, C., & Shendure, J. (2015) · Science · doi:10.1126/science.aab1601 · Cited by 1,248 · cited 7×
“10X Multiome PBMC The 10X Multiome PBMC dataset [47] contains paired single-cell RNA and ATAC data on human PBMC cells sequenced by the 10X Single Cell Multiome protocol.”
MethodInfluentialGSEApy: a comprehensive package for performing gene set enrichment analysis in Python
Fang, Z., Liu, X., & Peltz, G. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac757 · Cited by 1,244 · cited 1×
“The 1048 pathway enrichment analysis was implemented using the GSEApy package [86].”
MethodFlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Dao, T., Fu, D. Y., Ermon, S., Rudra, A., & Ré, C. (2022) · arXiv · doi:10.48550/arxiv.2205.14135 · Cited by 1,214 · cited 3×
“To address this challenge and ensure efficient self-attention mechanisms, we leverage the accelerated self-attention implementation by Flash-Attention [65].”
MethodAdamson, B., Norman, T. M., Jost, M., Cho, M. Y., Nuñez, J. K., Chen, Y., Villalta, J. E., Gilbert, L. A., Horlbeck, M. A., Hein, M. Y., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.048 · Cited by 1,193 · cited 8×
“For the attention-based target gene selection, we fine-tuned the scGPT blood model on the 756 Adamson perturbation dataset that consists of 87 CRISPR interference experiments on a leukemia 757 cell line [36].”
BackgroundMethodInfluentialTransfer learning enables predictions in network biology
Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., Mantineo, H., Brydon, E. M., Zeng, Z., Liu, X. S., et al. (2023) · Nature · doi:10.1038/s41586-023-06139-9 · Cited by 1,193
A benchmark of batch-effect correction methods for single-cell RNA sequencing data
Tran, H. T. N., Ang, K. S., Chevrier, M., Zhang, X., Lee, N. Y. S., Goh, M., & Chen, J. (2020) · Genome Biology · doi:10.1186/s13059-019-1850-9 · Cited by 1,113 · cited 1×
“introduced by sequencing technologies [74, 75].”
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications
Haque, A., Engel, J., Teichmann, S. A., & Lönnberg, T. (2017) · Genome Medicine · doi:10.1186/s13073-017-0467-4 · Cited by 1,071
Yoshida, H., Okada, T., Haze, K., Yanagi, H., Yura, T., Negishi, M., & Mori, K. (2000) · Molecular and Cellular Biology · doi:10.1128/mcb.20.18.6755-6767.2000 · Cited by 934 · cited 1×
“Notably, the ATF6 pathway identified in DDIT3 -knockout setting is known to mediate 357 unfolded protein response and regulates cell apoptosis [57, 58].”
BackgroundLinformer: Self-Attention with Linear Complexity
Wang, S., Li, B. Z., Khabsa, M., Fang, H., & Ma, H. (2020) · arXiv · doi:10.48550/arxiv.2006.04768 · Cited by 867 · cited 1×
“Although Flash-Attention was adopted, any 529 efficient transformers can be potentially utilized for scGPT as well, such as Transformers with 530 linear complexity (Linformer) [66] and Kernelized Self-Attention (KSA) [67].”
MethodTranscriptomic diversity of cell types across the adult human brain
Siletti, K., Hodge, R., Mossi Albiach, A., Lee, K. W., Ding, S.-L., Hu, L., Lönnerberg, P., Bakken, T., Casper, T., Clark, M., et al. (2023) · Science · doi:10.1126/science.add7046 · Cited by 832 · cited 8×
“[45], which originally contained 606 high-quality samples encompassing 824 ten diverse brain regions.”
BackgroundMethodInfluentialTransformers are RNNs: Fast Autoregressive Transformers with Linear Attention
Katharopoulos, A., Vyas, A., Pappas, N., & Fleuret, F. (2020) · arXiv · doi:10.48550/arxiv.2006.16236 · Cited by 827 · cited 2×
“Although Flash-Attention was adopted, any efficient transformers can be potentially utilized for scGPT as well, such as Transformers with linear complexity (Linformer) [66] and Kernelized Self-Attention (KSA) [67].”
MethodMapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq
Replogle, J. M., Saunders, R. A., Pogson, A. N., Hussmann, J. A., Lenail, A., Guna, A., Mascibroda, L., Wagner, E. J., Adelman, K., Lithwick-Yanai, G., et al. (2022) · Cell · doi:10.1016/j.cell.2022.05.013 · Cited by 795
Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A., & Murali, T. M. (2020) · Nature Methods · doi:10.1038/s41592-019-0690-6 · Cited by 792 · cited 1×
“Existing GRN inference 295 methods often rely on correlation in static gene expression or pseudo-time estimates as a proxy for 296 causal graphs [50].”
MethodA Python library for probabilistic analysis of single-cell omics data
Gayoso, A., Lopez, R., Xing, G., Boyeau, P., Valiollah Pour Amiri, V., Hong, J., Wu, K., Jayasuriya, M., Mehlman, E., Langevin, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01206-w · Cited by 789
RNA Sequencing of Single Human Islet Cells Reveals Type 2 Diabetes Genes
Xin, Y., Kim, J., Okamoto, H., Ni, M., Wei, Y., Adler, C., Murphy, A. J., Yancopoulos, G. D., Lin, C., & Gromada, J. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2016.08.018 · Cited by 684 · cited 2×
“The reference set consists of Braon[79] and Muraro[80] 804 datasets, and the query set consists of Xin[81], Segerstolpe[82], and Lawlor[83] datasets.”
MethodscGen predicts single-cell perturbation responses
Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2019) · Nature Methods · doi:10.1038/s41592-019-0494-8 · Cited by 661
Neuronal vulnerability and multilineage diversity in multiple sclerosis
Schirmer, L., Velmeshev, D., Holmqvist, S., Kaufmann, M., Werneburg, S., Jung, D., Vistnes, S., Stockley, J. H., Young, A., Steindel, M., et al. (2019) · Nature · doi:10.1038/s41586-019-1404-z · Cited by 638 · cited 1×
“785 [78], is accessed from EMBL-EBI (https://www.”
Lineage tracing meets single-cell omics: opportunities and challenges
Wagner, D. E., & Klein, A. M. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-0223-2 · Cited by 617
Yang, F., Wang, W., Wang, F., Fang, Y., Tang, D., Huang, J., Lu, H., & Yao, J. (2021) · doi:10.1101/2021.12.05.471261 · Cited by 617
Multi-omics single-cell data integration and regulatory inference with graph-linked embedding
Cao, Z.-J., & Gao, G. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01284-4 · Cited by 616 · cited 7×
“The processed data by Cao and Gao [14] contains 9,631 844 cells with read counts from 29,095 genes and 107,194 regions.”
BackgroundMethodInfluentialCell-free gene expression: an expanded repertoire of applications
Silverman, A. D., Karim, A. S., & Jewett, M. C. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0186-3 · Cited by 602
Mapping single-cell data to reference atlases by transfer learning
Lotfollahi, M., Naghipourfar, M., Luecken, M. D., Khajavi, M., Büttner, M., Wagenstetter, M., Avsec, Ž., Gayoso, A., Yosef, N., Interlandi, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01001-7 · Cited by 602
Lawlor, N. T., George, J., Bolisetty, M. T., Kursawe, R., Sun, L., Vijayakumar, S., Kycia, I., Robson, P., & Stitzel, M. L. (2016) · Genome Research · doi:10.1101/gr.212720.116 · Cited by 543 · cited 2×
“The reference set consists of Braon[79] and Muraro[80] 804 datasets, and the query set consists of Xin[81], Segerstolpe[82], and Lawlor[83] datasets.”
MethodMimitou, E. P., Lareau, C. A., Chen, K. Y., Zorzetto-Fernandes, A. L., Hao, Y., Takeshima, Y., Luo, W., Huang, T.-S., Yeung, B. Z., Papalexi, E., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00927-2 · Cited by 529 · cited 8×
“In the mosaic data inte957 gration experiment, we benchmarked scGPT with scMoMat [15] on the ASAP PBMC [49] dataset.”
BackgroundMethodInfluentialThe HLA system: Genetics, immunology, clinical testing, and clinical implications
Choo, S. Y. (2007) · Yonsei Medical Journal · doi:10.3349/ymj.2007.48.1.11 · Cited by 446
Immunobiology: The immune system in health and disease
Norman, P. S. (1995) · Journal of Allergy and Clinical Immunology · doi:10.1016/s0091-6749(95)70025-0 · Cited by 432 · cited 1×
“The pre-trained scGPT model successfully identifies the group of 315 genes (CD3E, D, and G) encoding the T3 complex for T-cell activation, as well as CD79A and 316 CD79B for B-cell signaling, and CD8A and CD8B as co-receptors for HLA class I molecules [52] 317 (Figure 5B).”
MethodExploring genetic interaction manifolds constructed from rich single-cell phenotypes
Norman, T. M., Horlbeck, M. A., Replogle, J. M., Ge, A. Y., Xu, A., Jost, M., Gilbert, L. A., & Weissman, J. S. (2019) · Science · doi:10.1126/science.aax4438 · Cited by 392 · cited 9×
“For the perturbation prediction task, we evaluated our model using two perturbation datasets of 171 K562 leukemia cell lines: the Adamson Pertub-seq dataset [36] consisting of 87 one-gene perturba172 tions and the Norman Perturb-Seq dataset [37] consisting of 131 two-gene perturbations and 105 173 one-gene perturbatio…”
BackgroundMethodInfluentialProbabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling
Zhang, A. W., O’Flanagan, C. H., Chavez, E. A., Lim, J. L., Ceglia, N., McPherson, A., Wiens, M., Walters, P., Chan, T., Hewitson, B., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0529-1 · Cited by 385 · cited 1×
“While several methods have 122 been proposed for cell annotation [30, 31, 32], they often require dimension reduction prior to 123 model input, which can lead to information loss.”
MethodZou, Z., Ohta, T., Miura, F., & Oki, S. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac199 · Cited by 358 · cited 2×
“We validated scGPT’s attention-based most influenced gene selection method in the ChIP994 Atlas database [56] which contains experiment-validated gene targets for known transcription fac995 tors.”
BackgroundMethodPredicting transcriptional outcomes of novel multigene perturbations with GEARS
Roohani, Y., Huang, K., & Leskovec, J. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01905-6 · Cited by 355
Predicting cellular responses to complex perturbations in high‐throughput screens
Lotfollahi, M., Klimovskaia Susmelj, A., De Donno, C., Hetzel, L., Ji, Y., Ibarra, I. L., Srivatsan, S. R., Naghipourfar, M., Daza, R. M., Martin, B., et al. (2023) · Molecular Systems Biology · doi:10.15252/msb.202211517 · Cited by 307 · cited 4×
“We conducted a performance comparison between scGPT and two other recent methods, GEARS [38] and CPA [39].”
MethodInfluentialCHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing
de Kanter, J. K., Lijnzaad, P., Candelli, T., Margaritis, T., & Holstege, F. C. P. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz543 · Cited by 283 · cited 1×
“While several methods have 122 been proposed for cell annotation [30, 31, 32], they often require dimension reduction prior to 123 model input, which can lead to information loss.”
MethodMulti-omics integration in the age of million single-cell data
Miao, Z., Humphreys, B. D., McMahon, A. P., & Kim, J. (2021) · Nature Reviews Nephrology · doi:10.1038/s41581-021-00463-x · Cited by 253 · cited 1×
“Single-cell multi-omic (scMultiomic) data, which combines multiple views of genetic regulation such 258 as epigenetic, transcriptomic, and translation activities, presents a unique challenge in aggregating 259 cell representations while preserving biological signals [9, 10].”
BackgroundCharacterizing cis-regulatory elements using single-cell epigenomics
Preissl, S., Gaulton, K. J., & Ren, B. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-022-00509-1 · Cited by 251
Sarkar, A., & Stephens, M. (2021) · Nature Genetics · doi:10.1038/s41588-021-00873-4 · Cited by 224 · cited 1×
“A fundamental challenge in gene expression modelling is the vari483 ability in absolute magnitudes across different sequencing protocols [61].”
BackgroundSingle cells make big data: New challenges and opportunities in transcriptomics
Angerer, P., Simon, L. M., Tritschler, S., Wolf, F., Fischer, D. S., & Theis, F. J. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.07.004 · Cited by 218
Jointly defining cell types from multiple single-cell datasets using LIGER
Liu, J., Gao, C., Sodicoff, J., Kozareva, V., Macosko, E. Z., & Welch, J. D. (2020) · Nature Protocols · doi:10.1038/s41596-020-0391-8 · Cited by 203
Temporal modelling using single-cell transcriptomics
Ding, J., Sharon, N., & Bar-Joseph, Z. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00444-7 · Cited by 195
Transformer for one stop interpretable cell type annotation
Chen, J., Xu, H., Tao, W., Chen, Z., Zhao, Y., & Han, J.-D. J. (2023) · Nature Communications · doi:10.1038/s41467-023-35923-4 · Cited by 189 · cited 5×
“scRNA-seq cell type annotation We benchmarked scGPT against two recent transformer861 based cell type annotation methods, scBert [35] and TOSICA [34] on the Mye.”
MethodInfluentialSingle-cell biological network inference using a heterogeneous graph transformer
Ma, A., Wang, X., Li, J., Wang, C., Xiao, T., Liu, Y., Cheng, H., Wang, J., Li, Y., Chang, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36559-0 · Cited by 184
Self-citationFast, sensitive, and accurate integration of single cell data with HarmonyPreprint
Korsunsky, I., Fan, J., Slowikowski, K., et al. (2018) · bioRxiv · doi:10.1101/461954 · Cited by 177
ATF6 Is a Critical Determinant of CHOP Dynamics during the Unfolded Protein Response
Yang, H., Niemeijer, M., van de Water, B., & Beltman, J. B. (2020) · iScience · doi:10.1016/j.isci.2020.100860 · Cited by 139 · cited 1×
“Notably, the ATF6 pathway identified in DDIT3 -knockout setting is known to mediate 357 unfolded protein response and regulates cell apoptosis [57, 58].”
BackgroundEpiScanpy: integrated single-cell epigenomic analysis
Danese, A., Richter, M. L., Chaichoompu, K., Fischer, D. S., Theis, F. J., & Colomé‐Tatché, M. (2021) · Nature Communications · doi:10.1038/s41467-021-25131-3 · Cited by 138
Machine learning for perturbational single-cell omics
Ji, Y., Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.016 · Cited by 137 · cited 1×
“However, current 47 machine-learning-based methods in single-cell research are rather scattered, with specific models 48 dedicated to distinct analysis tasks [24, 25, 26].”
Benchmarking atlas-level data integration in single-cell genomicsPreprint
Luecken, M. D., Büttner, M., Chaichoompu, K., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.22.111161 · Cited by 122
The effect of eviction moratoria on the transmission of SARS-CoV-2
Nande, A., Sheen, J. K., Walters, E. L., Klein, B., Chinazzi, M., Gheorghe, A. H., Adlam, B., Shinnick, J. E., Tejeda, M. F., Scarpino, S. V., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22521-5 · Cited by 105
Transcriptomic diversity of cell types across the adult human brainPreprint
Siletti, K., Hodge, R. D., Albiach, A. M., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.12.511898 · Cited by 97
EpiScanpy: integrated single-cell epigenomic analysisPreprint
Danese, A., Richter, M., Fischer, D. S., Theis, F. J., & Colomé-Tatché, M. (2019) · bioRxiv · doi:10.1101/648097 · Cited by 88 · cited 1×
“We used the EpiScanpy python 1043 library [85] on chromatin accessibility data for highly variable peak selection.”
MethodMachine Intelligence in Single-Cell Data Analysis: Advances and New Challenges
Liu, J., Fan, Z., Zhao, W., & Zhou, X. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.655536 · Cited by 86 · cited 1×
“However, current 47 machine-learning-based methods in single-cell research are rather scattered, with specific models 48 dedicated to distinct analysis tasks [24, 25, 26].”
A sandbox for prediction and integration of DNA, RNA, and proteins in single cells
Luecken, M. D., Burkhardt, D. B., Cannoodt, R., et al. (2021) · Ghent University Academic Bibliography (Ghent University) · Cited by 84
Generative pretraining from large-scale transcriptomes for single-cell deciphering
Shen, H., Liu, J., Hu, J., Shen, X., Zhang, C., Wu, D., Feng, M., Yang, M., Li, Y., Yang, Y., et al. (2023) · iScience · doi:10.1016/j.isci.2023.106536 · Cited by 81
scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection
Zhang, Z., Sun, H., Mariappan, R., Chen, X. X., Chen, X., Jain, M. S., Efremova, M., Teichmann, S. A., Rajan, V., & Zhang, X. (2023) · Nature Communications · doi:10.1038/s41467-023-36066-2 · Cited by 60 · cited 5×
“In the mosaic data inte957 gration experiment, we benchmarked scGPT with scMoMat [15] on the ASAP PBMC [49] dataset.”
BackgroundMethodInfluentialBenchmarking algorithms for gene regulatory network inference from single-cell transcriptomic dataPreprint
Pratapa, A., Jalihal, A. P., Law, J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/642926 · Cited by 55
Learning interpretable cellular responses to complex perturbations in high-throughput screensPreprint
Lotfollahi, M., Klimovskaia, A., Donno, C. D., Ji, Y., Ibarra, I. L., Wolf, F. A., Yakubova, N., Theis, F. J., & López-Paz, D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.14.439903 · Cited by 49 · cited 5×
“We conducted a performance comparison between scGPT and two other recent methods, GEARS [38] and CPA [39].”
BackgroundMethodInfluentialMajor histocompatibility complex: Antigen processing and presentation
Cruz‐Tapias, P., Castiblanco, J., & Anaya, J. (2013) · Cited by 49
GEARS: Predicting transcriptional outcomes of novel multi-gene perturbationsPreprint
Roohani, Y., Huang, K., & Leskovec, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.12.499735 · Cited by 40 · cited 7×
“We conducted a performance comparison between scGPT and two 182 other recent methods, GEARS [38] and CPA [39].”
MethodInfluentialCHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencingPreprint
de Kanter, J. K., Lijnzaad, P., Candelli, T., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/558908 · Cited by 36
Guided Similarity Separation for Image Retrieval
Liu, C., Yu, G. W., Volkovs, M., et al. (2019) · neural information processing systems · Cited by 36
Single-cell RNA-seq analysis software providers scramble to offer solutions
Eisenstein, M. S. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0449-8 · Cited by 35
Algorithmic advances in machine learning for single-cell expression analysis
Moreno, S. O., Kloiber, K., Machart, P., & Bonn, S. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.02.002 · Cited by 35 · cited 1×
“However, current 47 machine-learning-based methods in single-cell research are rather scattered, with specific models 48 dedicated to distinct analysis tasks [24, 25, 26].”
Sarkar, A., & Stephens, M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.07.030007 · Cited by 35
Jointly Defining Cell Types from Multiple Single-Cell Datasets Using LIGERPreprint
Liu, J., Gao, C., Sodicoff, J., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.07.029546 · Cited by 21
MultiCPA: Multimodal Compositional Perturbation Autoencoder
Inecik, K., Uhlmann, A., Lotfollahi, M., & Theis, F. (2022) · doi:10.1101/2022.07.08.499049 · Cited by 16
DeepMAPS: Single-cell biological network inference using heterogeneous graph transformerPreprint
Ma, A., Wang, X., Wang, C., Li, J., Xiao, T., Wang, J., Li, Y., Liu, Y.-T., Chang, Y., Wang, D., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.31.466658 · Cited by 14
Self-citationOne Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data
Wang, C. X., Zhang, L., & Wang, B. (2022) · Genome biology · doi:10.1186/s13059-022-02659-1 · Cited by 14
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Shen, H., Shen, X., Hu, J., Liu, J., Zhang, C., Wu, D., Feng, M., Yang, M., Li, Y., Yang, Y., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.31.478596 · Cited by 8
Ceglia, N., Sethna, Z. M., Freeman, S. S., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.22.487554 · Cited by 5 · cited 1×
“[77]’s pipelines, we further perform Leiden [40] clustering on the 754 similarity graph and extracted gene programs from gene clusters that consist of five or more genes.”
MethodA single-cell gene expression language modelPreprint
Connell, W., Khan, U., & Keiser, M. J. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2210.14330 · Cited by 3
Processed datasets used in the scGPT foundation model
Wang, C. (2024) · Figshare · doi:10.6084/m9.figshare.24954519 · Cited by 1
Self-citationscGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI
Cui, H., Wang, C., & Pang, K. (2024) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.10466117 · Cited by 1
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Background
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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