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build(deps): bump tifffile from 2024.2.12 to 2024.8.30 #464

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78eb36d
build(deps): bump tifffile from 2024.2.12 to 2024.8.30
dependabot[bot] Sep 1, 2024
6a9c6c0
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Sep 1, 2024
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64 changes: 32 additions & 32 deletions README.md
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Expand Up @@ -193,14 +193,14 @@ pip install -e .

| BackBone | Model | Algorithm | Year | CheckIn |
| ------------------- | ------------ | ------------------------------------------------------------------------------------------------------------ | ---- | ------- |
| GNN | GraphSCI | Imputing Single-cell RNA-seq data by combining Graph Convolution and Autoencoder Neural Networks | 2021 | ✅ |
| GNN | GraphSCI | Imputing Single-cell RNA-seq data by combining Graph Convolution and Autoencoder Neural Networks | 2021 | ✅ |
| GNN | scGNN (2020) | SCGNN: scRNA-seq Dropout Imputation via Induced Hierarchical Cell Similarity Graph | 2020 | P1 |
| GNN | scGNN (2021) | scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses | 2021 | ✅ |
| GNN | scGNN (2021) | scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses | 2021 | ✅ |
| GNN | GNNImpute | An efficient scRNA-seq dropout imputation method using graph attention network | 2021 | P1 |
| Graph Diffusion | MAGIC | MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data | 2018 | P1 |
| Probabilistic Model | scImpute | An accurate and robust imputation method scImpute for single-cell RNA-seq data | 2018 | P1 |
| GAN | scGAIN | scGAIN: Single Cell RNA-seq Data Imputation using Generative Adversarial Networks | 2019 | P1 |
| NN | DeepImpute | DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data | 2019 | ✅ |
| NN | DeepImpute | DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data | 2019 | ✅ |
| NN + TF | Saver-X | Transfer learning in single-cell transcriptomics improves data denoising and pattern discovery | 2019 | P1 |

| Model | Evaluation Metric | Mouse Brain (current/reported) | Mouse Embryo (current/reported) | PBMC (current/reported) |
Expand All @@ -215,12 +215,12 @@ pip install -e .

| BackBone | Model | Algorithm | Year | CheckIn |
| ----------------------- | ------------- | ------------------------------------------------------------------------------------------------------------- | ---- | ------- |
| GNN | ScDeepsort | Single-cell transcriptomics with weighted GNN | 2021 | ✅ |
| Logistic Regression | Celltypist | Cross-tissue immune cell analysis reveals tissue-specific features in humans. | 2021 | ✅ |
| Random Forest | singleCellNet | SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species. | 2019 | ✅ |
| Neural Network | ACTINN | ACTINN: automated identification of cell types in single cell RNA sequencing. | 2020 | ✅ |
| GNN | ScDeepsort | Single-cell transcriptomics with weighted GNN | 2021 | ✅ |
| Logistic Regression | Celltypist | Cross-tissue immune cell analysis reveals tissue-specific features in humans. | 2021 | ✅ |
| Random Forest | singleCellNet | SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species. | 2019 | ✅ |
| Neural Network | ACTINN | ACTINN: automated identification of cell types in single cell RNA sequencing. | 2020 | ✅ |
| Hierarchical Clustering | SingleR | Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. | 2019 | P1 |
| SVM | SVM | A comparison of automatic cell identification methods for single-cell RNA sequencing data. | 2018 | ✅ |
| SVM | SVM | A comparison of automatic cell identification methods for single-cell RNA sequencing data. | 2018 | ✅ |

| Model | Evaluation Metric | Mouse Brain 2695 (current/reported) | Mouse Spleen 1759 (current/reported) | Mouse Kidney 203 (current/reported) |
| ------------- | ----------------- | ----------------------------------- | ------------------------------------ | ----------------------------------- |
Expand All @@ -234,12 +234,12 @@ pip install -e .

| BackBone | Model | Algorithm | Year | CheckIn |
| ----------- | ------------- | ------------------------------------------------------------------------------------------------------------ | ---- | ------- |
| GNN | graph-sc | GNN-based embedding for clustering scRNA-seq data | 2022 | ✅ |
| GNN | scTAG | ZINB-based Graph Embedding Autoencoder for Single-cell RNA-seq Interpretations | 2022 | ✅ |
| GNN | scDSC | Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network | 2022 | ✅ |
| GNN | graph-sc | GNN-based embedding for clustering scRNA-seq data | 2022 | ✅ |
| GNN | scTAG | ZINB-based Graph Embedding Autoencoder for Single-cell RNA-seq Interpretations | 2022 | ✅ |
| GNN | scDSC | Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network | 2022 | ✅ |
| GNN | scGAC | scGAC: a graph attentional architecture for clustering single-cell RNA-seq data | 2022 | P1 |
| AutoEncoder | scDeepCluster | Clustering single-cell RNA-seq data with a model-based deep learning approach | 2019 | ✅ |
| AutoEncoder | scDCC | Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data | 2021 | ✅ |
| AutoEncoder | scDeepCluster | Clustering single-cell RNA-seq data with a model-based deep learning approach | 2019 | ✅ |
| AutoEncoder | scDCC | Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data | 2021 | ✅ |
| AutoEncoder | scziDesk | Deep soft K-means clustering with self-training for single-cell RNA sequence data | 2020 | P1 |

| Model | Evaluation Metric | 10x PBMC (current/reported) | Mouse ES (current/reported) | Worm Neuron (current/reported) | Mouse Bladder (current/reported) |
Expand All @@ -256,12 +256,12 @@ pip install -e .

| BackBone | Model | Algorithm | Year | CheckIn |
| ---------------- | ------------------------ | -------------------------------------------------------------------------------------------------- | ---- | ------- |
| GNN | ScMoGCN | Graph Neural Networks for Multimodal Single-Cell Data Integration | 2022 | ✅ |
| GNN | ScMoGCN | Graph Neural Networks for Multimodal Single-Cell Data Integration | 2022 | ✅ |
| GNN | ScMoLP | Link Prediction Variant of ScMoGCN | 2022 | P1 |
| GNN | GRAPE | Handling Missing Data with Graph Representation Learning | 2020 | P1 |
| Generative Model | SCMM | SCMM: MIXTURE-OF-EXPERTS MULTIMODAL DEEP GENERATIVE MODEL FOR SINGLE-CELL MULTIOMICS DATA ANALYSIS | 2021 | ✅ |
| Auto-encoder | Cross-modal autoencoders | Multi-domain translation between single-cell imaging and sequencing data using autoencoders | 2021 | ✅ |
| Auto-encoder | BABEL | BABEL enables cross-modality translation between multiomic profiles at single-cell resolution | 2021 | ✅ |
| Generative Model | SCMM | SCMM: MIXTURE-OF-EXPERTS MULTIMODAL DEEP GENERATIVE MODEL FOR SINGLE-CELL MULTIOMICS DATA ANALYSIS | 2021 | ✅ |
| Auto-encoder | Cross-modal autoencoders | Multi-domain translation between single-cell imaging and sequencing data using autoencoders | 2021 | ✅ |
| Auto-encoder | BABEL | BABEL enables cross-modality translation between multiomic profiles at single-cell resolution | 2021 | ✅ |

| Model | Evaluation Metric | GEX2ADT (current/reported) | ADT2GEX (current/reported) | GEX2ATAC (current/reported) | ATAC2GEX (current/reported) |
| ------------------------ | ----------------- | -------------------------- | -------------------------- | --------------------------- | --------------------------- |
Expand All @@ -274,10 +274,10 @@ pip install -e .

| BackBone | Model | Algorithm | Year | CheckIn |
| ---------------- | ------------------------ | -------------------------------------------------------------------------------------------------- | ---- | ------- |
| GNN | ScMoGCN | Graph Neural Networks for Multimodal Single-Cell Data Integration | 2022 | ✅ |
| GNN | ScMoGCN | Graph Neural Networks for Multimodal Single-Cell Data Integration | 2022 | ✅ |
| GNN/Auto-ecnoder | GLUE | Multi-omics single-cell data integration and regulatory inference with graph-linked embedding | 2021 | P1 |
| Generative Model | SCMM | SCMM: MIXTURE-OF-EXPERTS MULTIMODAL DEEP GENERATIVE MODEL FOR SINGLE-CELL MULTIOMICS DATA ANALYSIS | 2021 | ✅ |
| Auto-encoder | Cross-modal autoencoders | Multi-domain translation between single-cell imaging and sequencing data using autoencoders | 2021 | ✅ |
| Generative Model | SCMM | SCMM: MIXTURE-OF-EXPERTS MULTIMODAL DEEP GENERATIVE MODEL FOR SINGLE-CELL MULTIOMICS DATA ANALYSIS | 2021 | ✅ |
| Auto-encoder | Cross-modal autoencoders | Multi-domain translation between single-cell imaging and sequencing data using autoencoders | 2021 | ✅ |

| Model | Evaluation Metric | GEX2ADT (current/reported) | GEX2ATAC (current/reported) |
| ------------------------ | ----------------- | -------------------------- | --------------------------- |
Expand All @@ -289,11 +289,11 @@ pip install -e .

| BackBone | Model | Algorithm | Year | CheckIn |
| ---------------- | ------- | ----------------------------------------------------------------------------------------------------- | ---- | ------- |
| GNN | ScMoGCN | Graph Neural Networks for Multimodal Single-Cell Data Integration | 2022 | ✅ |
| Auto-encoder | scMVAE | Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data | 2020 | ✅ |
| Auto-encoder | scDEC | Simultaneous deep generative modelling and clustering of single-cell genomic data | 2021 | ✅ |
| GNN | ScMoGCN | Graph Neural Networks for Multimodal Single-Cell Data Integration | 2022 | ✅ |
| Auto-encoder | scMVAE | Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data | 2020 | ✅ |
| Auto-encoder | scDEC | Simultaneous deep generative modelling and clustering of single-cell genomic data | 2021 | ✅ |
| GNN/Auto-ecnoder | GLUE | Multi-omics single-cell data integration and regulatory inference with graph-linked embedding | 2021 | P1 |
| Auto-encoder | DCCA | Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data | 2021 | ✅ |
| Auto-encoder | DCCA | Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data | 2021 | ✅ |

| Model | Evaluation Metric | GEX2ADT (current/reported) | GEX2ATAC (current/reported) |
| ---------- | ----------------- | -------------------------- | --------------------------- |
Expand Down Expand Up @@ -329,11 +329,11 @@ pip install -e .

| BackBone | Model | Algorithm | Year | CheckIn |
| -------------------------------- | ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---- | ------- |
| GNN | SpaGCN | SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network | 2021 | ✅ |
| GNN | STAGATE | Deciphering spatial domains from spatially resolved transcriptomics with adaptive graph attention auto-encoder | 2021 | ✅ |
| GNN | SpaGCN | SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network | 2021 | ✅ |
| GNN | STAGATE | Deciphering spatial domains from spatially resolved transcriptomics with adaptive graph attention auto-encoder | 2021 | ✅ |
| Bayesian | BayesSpace | Spatial transcriptomics at subspot resolution with BayesSpace | 2021 | P1 |
| Pseudo-space-time (PST) Distance | stLearn | stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues | 2020 | ✅ |
| Heuristic | Louvain | Fast unfolding of community hierarchies in large networks | 2008 | ✅ |
| Pseudo-space-time (PST) Distance | stLearn | stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues | 2020 | ✅ |
| Heuristic | Louvain | Fast unfolding of community hierarchies in large networks | 2008 | ✅ |

| Model | Evaluation Metric | 151673 (current/reported) | 151676 (current/reported) | 151507 (current/reported) |
| ------- | ----------------- | ------------------------- | ------------------------- | ------------------------- |
Expand All @@ -346,10 +346,10 @@ pip install -e .

| BackBone | Model | Algorithm | Year | CheckIn |
| -------------------------- | ------------ | ------------------------------------------------------------------------------------------------------------- | ---- | ------- |
| GNN | DSTG | DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence | 2021 | ✅ |
| logNormReg | SpatialDecon | Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data | 2022 | ✅ |
| NNMFreg | SPOTlight | SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes | 2021 | ✅ |
| NN Linear + CAR assumption | CARD | Spatially informed cell-type deconvolution for spatial transcriptomics | 2022 | ✅ |
| GNN | DSTG | DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence | 2021 | ✅ |
| logNormReg | SpatialDecon | Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data | 2022 | ✅ |
| NNMFreg | SPOTlight | SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes | 2021 | ✅ |
| NN Linear + CAR assumption | CARD | Spatially informed cell-type deconvolution for spatial transcriptomics | 2022 | ✅ |

| Model | Evaluation Metric | GSE174746 (current/reported) | CARD Synthetic (current/reported) | SPOTlight Synthetic (current/reported) |
| ------------ | ----------------- | ---------------------------- | --------------------------------- | -------------------------------------- |
Expand Down
2 changes: 1 addition & 1 deletion requirements.txt
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Expand Up @@ -23,6 +23,6 @@ scipy==1.13.0
statsmodels==0.14.2
tables==3.9.2
threadpoolctl==3.5.0
tifffile==2024.2.12
tifffile==2024.8.30
torchnmf==0.3.5
tqdm==4.66.2