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From raw 10x sequencing matrices to deep generative cell state modeling and spatial tissue deconvolution.
Evaluate, benchmark, and deploy modern single-cell analysis toolkits. Compare scvi-tools, Scanpy, Seurat, scGPT, and cell2location across scalability, batch correction fidelity, spatial mapping accuracy, and foundation model fine-tuning.
The previous fixed-template workflow scorer cannot establish comparative scientific suitability. For a task-specific assessment, enter the biological objective, the actual sample/data modalities and method constraints in the offline Decision Lab. Select the reviewed method deliberately and compare it against a mandatory non-AI baseline.
Open scientific Decision LabHistorical workflow templates are examples, not validated executable pipelines, clinical decisions or ranked model recommendations.
| Strumento e organizzazione | Licenza e termini | GPU / Hardware | Architettura | Formato di input | Livello di evidenza | Catalogo |
|---|---|---|---|---|---|---|
| scvi-tools Yosef Lab / UC Berkeley | BSD 3-Clause (Open) | CPU or GPU for VAE training | Open Source | AnnData (.h5ad) count matrix | Peer-reviewed (Nature Biotechnology) | Visualizza strumento → |
| Scanpy Theis Lab / Helmholtz Munich | BSD 3-Clause (Open) | Multi-core CPU (GPU via rapids-singlecell) | Open Source | AnnData / 10x Genomics Matrix | Peer-reviewed (Genome Biology) | Visualizza strumento → |
| Seurat Satija Lab / New York Genome Center | GPL v3 (Open Source) | Multi-core CPU (R environment) | Open Source | Seurat Object / 10x CellRanger output | Peer-reviewed (Cell 2021) | Visualizza strumento → |
| scGPT Bo Wang Lab / University of Toronto | MIT (Open Source) | NVIDIA A100 (Transformer training & embedding) | Open Source / Open Weights | Pre-processed gene count matrices | Peer-reviewed (Nature Methods 2024) | Visualizza strumento → |
| cell2location Sanger Institute & Bayraktar Lab | Apache 2.0 (Open Source) | GPU Recommended (PyTorch Bayesian model) | Open Source | Spatial Visium counts + scRNA-seq reference | Peer-reviewed (Nature Biotechnology 2022) | Visualizza strumento → |
| CellTypist Teichmann Lab / Wellcome Sanger | MIT (Open Source) | Lightweight CPU / RAM | Open Source | Normalized scRNA-seq gene expression matrix | Peer-reviewed (Science 2022) | Visualizza strumento → |
| GEARS Leskovec Lab / Stanford University | MIT (Open Source) | 1x CUDA GPU | Open Source | Perturb-seq expression matrix + Gene graph | Peer-reviewed (Nature Biotechnology 2023) | Visualizza strumento → |
Scegli l'architettura operativa adatta alla tua infrastruttura di calcolo, ai vincoli di budget e alla flessibilità di licenza.
Standardized reproducible workflow for PhD candidates, postdocs, and core bioinformatics facilities.
Filter low-quality droplets (mitochondrial % > 15%, doublet removal with Scrublet, min genes > 500).
Assign standardized cell ontology labels using pretrained immune and organ-specific reference atlases.
Correct donor and sequencing cohort batch variation using variational autoencoders without distorting biological signal.
Infer cell fate lineage trajectories and identify key driver transcription factors along differentiation pathways.
Enterprise pipeline designed for immuno-oncology startups discovering novel cell surface targets and mechanism-of-action biomarkers.
Generate contextual gene and cell embeddings to uncover hidden disease state signatures across patient cohorts.
Map single-cell identified tumor-infiltrating lymphocyte subsets into 10x Visium / Xenium spatial tumor microenvironment slides.
Simulate combinatorial CRISPR knockouts to predict which dual-target knockouts revert exhausted T cells back to effector phenotypes.
Export prioritized target list to wet-lab screening team for primary human cell assay validation.
Esamina numero di parametri, punteggi benchmark, limiti di input e restrizioni di licenza nella tabella di confronto interattiva di BioAtlas.
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