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+%global _empty_manifest_terminate_build 0
+Name:		python-scETM
+Version:	0.5.0
+Release:	1
+Summary:	Single cell embedded topic model for integrated scRNA-seq data analysis.
+License:	BSD 3-Clause License
+URL:		https://github.com/hui2000ji/scETM/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/bb/76/7eb2b1d0a50783dd5cf5b189c335608b4ed626cf3ebe6c910c7c88aca424/scETM-0.5.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-torch
+Requires:	python3-numpy
+Requires:	python3-matplotlib
+Requires:	python3-scikit-learn
+Requires:	python3-h5py
+Requires:	python3-pandas
+Requires:	python3-tqdm
+Requires:	python3-anndata
+Requires:	python3-scanpy
+Requires:	python3-scipy
+Requires:	python3-louvain
+Requires:	python3-leidenalg
+Requires:	python3-psutil
+
+%description
+# scETM: single-cell Embedded Topic Model
+A generative topic model that facilitates integrative analysis of large-scale single-cell RNA sequencing data.
+
+The full description of scETM and its application on published single cell RNA-seq datasets are available [here](https://www.biorxiv.org/content/10.1101/2021.01.13.426593v1).
+
+This repository includes detailed instructions for installation and requirements, demos, and scripts used for the benchmarking of 7 other state-of-art methods.
+
+
+## Contents ##
+
+- [scETM: single-cell Embedded Topic Model](#scetm-single-cell-embedded-topic-model)
+  - [Contents](#contents)
+  - [1 Model Overview](#1-model-overview)
+  - [2 Installation](#2-installation)
+  - [3 Usage](#3-usage)
+    - [Data format](#data-format)
+    - [A taste of scETM](#a-taste-of-scetm)
+    - [p-scETM](#p-scetm)
+    - [Transfer learning](#transfer-learning)
+    - [Tensorboard Integration](#tensorboard-integration)
+  - [4 Benchmarking](#4-benchmarking)
+
+## 1 Model Overview
+
+![](doc/scETM.png "scETM model overview")
+**(a)** Probabilistic graphical model of scETM. We model the scRNA-profile read count matrix y<sub>d,g</sub> in cell d and gene g across S subjects or studies by a multinomial distribution with the rate parameterized by cell topic mixture θ, topic embedding α, gene embedding ρ, and batch effects λ. **(b)** Matrix factorization view of scETM. **(c)** Encoder architecture for inferring the cell topic mixture θ.
+
+## 2 Installation
+Python version: 3.7+
+scETM is included in PyPI, so you can install it by
+
+```bash
+pip install scETM
+```
+
+To enable GPU computing (which significantly boosts the performance), please install [PyTorch](https://pytorch.org/) with GPU support **before** installing scETM.
+
+## 3 Usage
+**A step-by-step scETM tutorial can be found in [here](/notebooks/scETM%20introductory%20tutorial.ipynb).**
+
+### Data format
+scETM requires a cells-by-genes matrix `adata` as input, in the format of an AnnData object. Detailed description about AnnData can be found [here](https://anndata.readthedocs.io/en/latest/).
+
+By default, scETM looks for batch information in the 'batch_indices' column of the `adata.obs` DataFrame, and cell type identity in the 'cell_types' column. If your data stores the batch and cell type information in different columns, pass them to the `batch_col` and `cell_type_col` arguments, respectively, when calling scETM functions.
+
+### A taste of scETM
+
+```python
+from scETM import scETM, UnsupervisedTrainer, evaluate
+import anndata
+
+# Prepare the source dataset, Mouse Pancreas
+mp = anndata.read_h5ad("MousePancreas.h5ad")
+# Initialize model
+model = scETM(mp.n_vars, mp.obs.batch_indices.nunique(), enable_batch_bias=True)
+# The trainer object will set up the random seed, optimizer, training and evaluation loop, checkpointing and logging.
+trainer = UnsupervisedTrainer(model, mp, train_instance_name="MP", ckpt_dir="../results")
+# Train the model on adata for 12000 epochs, and evaluate every 1000 epochs. Use 4 threads to sample minibatches.
+trainer.train(n_epochs=12000, eval_every=1000, n_samplers=4)
+# Obtain scETM cell, gene and topic embeddings. Unnormalized cell embeddings will be stored at mp.obsm['delta'], normalized cell embeddings at mp.obsm['theta'], gene embeddings at mp.varm['rho'], topic embeddings at mp.uns['alpha'].
+model.get_all_embeddings_and_nll(mp)
+# Evaluate the model and save the embedding plot
+evaluate(mp, embedding_key="delta", plot_fname="scETM_MP", plot_dir="figures/scETM_MP")
+```
+
+### p-scETM
+p-scETM is a variant of scETM where part or all of the the gene embedding matrix ρ is fixed to a pathways-by-genes matrix, which can be downloaded from the [pathDIP4 pathway database](http://ophid.utoronto.ca/pathDIP/Download.jsp). We only keep pathways that contain more than 5 genes.
+
+If it is desired to fix the gene embedding matrix ρ during training, let trainable_gene_emb_dim be zero. In this case, the gene set used to train the model would be the intersection of the genes in the scRNA-seq data and the genes in the gene-by-pathway matrix. Otherwise, if trainable_gene_emb_dim is set to a positive value, all the genes in the scRNA-seq data would be kept.
+
+### Transfer learning
+
+```python
+from scETM import scETM, UnsupervisedTrainer, prepare_for_transfer
+import anndata
+
+# Prepare the source dataset, Mouse Pancreas
+mp = anndata.read_h5ad("MousePancreas.h5ad")
+# Initialize model
+model = scETM(mp.n_vars, mp.obs.batch_indices.nunique(), enable_batch_bias=True)
+# The trainer object will set up the random seed, optimizer, training and evaluation loop, checkpointing and logging.
+trainer = UnsupervisedTrainer(model, mp, train_instance_name="MP", ckpt_dir="../results")
+# Train the model on adata for 12000 epochs, and evaluate every 1000 epochs. Use 4 threads to sample minibatches.
+trainer.train(n_epochs=12000, eval_every=1000, n_samplers=4)
+
+# Load the target dataset, Human Pancreas
+hp = anndata.read_h5ad('HumanPancreas.h5ad')
+# Align the source dataset's gene names (which are mouse genes) to the target dataset (which are human genes)
+mp_genes = mp.var_names.str.upper()
+mp_genes.drop_duplicates(inplace=True)
+# Generate a new model and a modified dataset from the previously trained model and the mp_genes
+model, hp = prepare_for_transfer(model, hp, mp_genes,
+	  keep_tgt_unique_genes=True,    # Keep target-unique genes in the model and the target dataset
+    fix_shared_genes=True          # Fix parameters related to shared genes in the model
+)
+# Instantiate another trainer to fine-tune the model
+trainer = UnsupervisedTrainer(model, hp, train_instance_name="HP_all_fix", ckpt_dir="../results", init_lr=5e-4)
+trainer.train(n_epochs=800, eval_every=200)
+```
+
+### Tensorboard Integration
+If a Tensorboard SummaryWriter is passed to the `writer` argument of the `UnsupervisedTrainer.train` method, the package will store.
+
+## 4 Benchmarking
+The commands used for running [Harmony](https://github.com/immunogenomics/harmony), [Scanorama](https://github.com/brianhie/scanorama), [Seurat](https://satijalab.org/seurat/), [scVAE-GM](https://github.com/scvae/scvae), [scVI](https://github.com/YosefLab/scvi-tools), [LIGER](https://github.com/welch-lab/liger), [scVI-LD](https://www.biorxiv.org/content/10.1101/737601v1.full.pdf) are available in the [scripts](/scripts) folder.
+
+
+
+
+%package -n python3-scETM
+Summary:	Single cell embedded topic model for integrated scRNA-seq data analysis.
+Provides:	python-scETM
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-scETM
+# scETM: single-cell Embedded Topic Model
+A generative topic model that facilitates integrative analysis of large-scale single-cell RNA sequencing data.
+
+The full description of scETM and its application on published single cell RNA-seq datasets are available [here](https://www.biorxiv.org/content/10.1101/2021.01.13.426593v1).
+
+This repository includes detailed instructions for installation and requirements, demos, and scripts used for the benchmarking of 7 other state-of-art methods.
+
+
+## Contents ##
+
+- [scETM: single-cell Embedded Topic Model](#scetm-single-cell-embedded-topic-model)
+  - [Contents](#contents)
+  - [1 Model Overview](#1-model-overview)
+  - [2 Installation](#2-installation)
+  - [3 Usage](#3-usage)
+    - [Data format](#data-format)
+    - [A taste of scETM](#a-taste-of-scetm)
+    - [p-scETM](#p-scetm)
+    - [Transfer learning](#transfer-learning)
+    - [Tensorboard Integration](#tensorboard-integration)
+  - [4 Benchmarking](#4-benchmarking)
+
+## 1 Model Overview
+
+![](doc/scETM.png "scETM model overview")
+**(a)** Probabilistic graphical model of scETM. We model the scRNA-profile read count matrix y<sub>d,g</sub> in cell d and gene g across S subjects or studies by a multinomial distribution with the rate parameterized by cell topic mixture θ, topic embedding α, gene embedding ρ, and batch effects λ. **(b)** Matrix factorization view of scETM. **(c)** Encoder architecture for inferring the cell topic mixture θ.
+
+## 2 Installation
+Python version: 3.7+
+scETM is included in PyPI, so you can install it by
+
+```bash
+pip install scETM
+```
+
+To enable GPU computing (which significantly boosts the performance), please install [PyTorch](https://pytorch.org/) with GPU support **before** installing scETM.
+
+## 3 Usage
+**A step-by-step scETM tutorial can be found in [here](/notebooks/scETM%20introductory%20tutorial.ipynb).**
+
+### Data format
+scETM requires a cells-by-genes matrix `adata` as input, in the format of an AnnData object. Detailed description about AnnData can be found [here](https://anndata.readthedocs.io/en/latest/).
+
+By default, scETM looks for batch information in the 'batch_indices' column of the `adata.obs` DataFrame, and cell type identity in the 'cell_types' column. If your data stores the batch and cell type information in different columns, pass them to the `batch_col` and `cell_type_col` arguments, respectively, when calling scETM functions.
+
+### A taste of scETM
+
+```python
+from scETM import scETM, UnsupervisedTrainer, evaluate
+import anndata
+
+# Prepare the source dataset, Mouse Pancreas
+mp = anndata.read_h5ad("MousePancreas.h5ad")
+# Initialize model
+model = scETM(mp.n_vars, mp.obs.batch_indices.nunique(), enable_batch_bias=True)
+# The trainer object will set up the random seed, optimizer, training and evaluation loop, checkpointing and logging.
+trainer = UnsupervisedTrainer(model, mp, train_instance_name="MP", ckpt_dir="../results")
+# Train the model on adata for 12000 epochs, and evaluate every 1000 epochs. Use 4 threads to sample minibatches.
+trainer.train(n_epochs=12000, eval_every=1000, n_samplers=4)
+# Obtain scETM cell, gene and topic embeddings. Unnormalized cell embeddings will be stored at mp.obsm['delta'], normalized cell embeddings at mp.obsm['theta'], gene embeddings at mp.varm['rho'], topic embeddings at mp.uns['alpha'].
+model.get_all_embeddings_and_nll(mp)
+# Evaluate the model and save the embedding plot
+evaluate(mp, embedding_key="delta", plot_fname="scETM_MP", plot_dir="figures/scETM_MP")
+```
+
+### p-scETM
+p-scETM is a variant of scETM where part or all of the the gene embedding matrix ρ is fixed to a pathways-by-genes matrix, which can be downloaded from the [pathDIP4 pathway database](http://ophid.utoronto.ca/pathDIP/Download.jsp). We only keep pathways that contain more than 5 genes.
+
+If it is desired to fix the gene embedding matrix ρ during training, let trainable_gene_emb_dim be zero. In this case, the gene set used to train the model would be the intersection of the genes in the scRNA-seq data and the genes in the gene-by-pathway matrix. Otherwise, if trainable_gene_emb_dim is set to a positive value, all the genes in the scRNA-seq data would be kept.
+
+### Transfer learning
+
+```python
+from scETM import scETM, UnsupervisedTrainer, prepare_for_transfer
+import anndata
+
+# Prepare the source dataset, Mouse Pancreas
+mp = anndata.read_h5ad("MousePancreas.h5ad")
+# Initialize model
+model = scETM(mp.n_vars, mp.obs.batch_indices.nunique(), enable_batch_bias=True)
+# The trainer object will set up the random seed, optimizer, training and evaluation loop, checkpointing and logging.
+trainer = UnsupervisedTrainer(model, mp, train_instance_name="MP", ckpt_dir="../results")
+# Train the model on adata for 12000 epochs, and evaluate every 1000 epochs. Use 4 threads to sample minibatches.
+trainer.train(n_epochs=12000, eval_every=1000, n_samplers=4)
+
+# Load the target dataset, Human Pancreas
+hp = anndata.read_h5ad('HumanPancreas.h5ad')
+# Align the source dataset's gene names (which are mouse genes) to the target dataset (which are human genes)
+mp_genes = mp.var_names.str.upper()
+mp_genes.drop_duplicates(inplace=True)
+# Generate a new model and a modified dataset from the previously trained model and the mp_genes
+model, hp = prepare_for_transfer(model, hp, mp_genes,
+	  keep_tgt_unique_genes=True,    # Keep target-unique genes in the model and the target dataset
+    fix_shared_genes=True          # Fix parameters related to shared genes in the model
+)
+# Instantiate another trainer to fine-tune the model
+trainer = UnsupervisedTrainer(model, hp, train_instance_name="HP_all_fix", ckpt_dir="../results", init_lr=5e-4)
+trainer.train(n_epochs=800, eval_every=200)
+```
+
+### Tensorboard Integration
+If a Tensorboard SummaryWriter is passed to the `writer` argument of the `UnsupervisedTrainer.train` method, the package will store.
+
+## 4 Benchmarking
+The commands used for running [Harmony](https://github.com/immunogenomics/harmony), [Scanorama](https://github.com/brianhie/scanorama), [Seurat](https://satijalab.org/seurat/), [scVAE-GM](https://github.com/scvae/scvae), [scVI](https://github.com/YosefLab/scvi-tools), [LIGER](https://github.com/welch-lab/liger), [scVI-LD](https://www.biorxiv.org/content/10.1101/737601v1.full.pdf) are available in the [scripts](/scripts) folder.
+
+
+
+
+%package help
+Summary:	Development documents and examples for scETM
+Provides:	python3-scETM-doc
+%description help
+# scETM: single-cell Embedded Topic Model
+A generative topic model that facilitates integrative analysis of large-scale single-cell RNA sequencing data.
+
+The full description of scETM and its application on published single cell RNA-seq datasets are available [here](https://www.biorxiv.org/content/10.1101/2021.01.13.426593v1).
+
+This repository includes detailed instructions for installation and requirements, demos, and scripts used for the benchmarking of 7 other state-of-art methods.
+
+
+## Contents ##
+
+- [scETM: single-cell Embedded Topic Model](#scetm-single-cell-embedded-topic-model)
+  - [Contents](#contents)
+  - [1 Model Overview](#1-model-overview)
+  - [2 Installation](#2-installation)
+  - [3 Usage](#3-usage)
+    - [Data format](#data-format)
+    - [A taste of scETM](#a-taste-of-scetm)
+    - [p-scETM](#p-scetm)
+    - [Transfer learning](#transfer-learning)
+    - [Tensorboard Integration](#tensorboard-integration)
+  - [4 Benchmarking](#4-benchmarking)
+
+## 1 Model Overview
+
+![](doc/scETM.png "scETM model overview")
+**(a)** Probabilistic graphical model of scETM. We model the scRNA-profile read count matrix y<sub>d,g</sub> in cell d and gene g across S subjects or studies by a multinomial distribution with the rate parameterized by cell topic mixture θ, topic embedding α, gene embedding ρ, and batch effects λ. **(b)** Matrix factorization view of scETM. **(c)** Encoder architecture for inferring the cell topic mixture θ.
+
+## 2 Installation
+Python version: 3.7+
+scETM is included in PyPI, so you can install it by
+
+```bash
+pip install scETM
+```
+
+To enable GPU computing (which significantly boosts the performance), please install [PyTorch](https://pytorch.org/) with GPU support **before** installing scETM.
+
+## 3 Usage
+**A step-by-step scETM tutorial can be found in [here](/notebooks/scETM%20introductory%20tutorial.ipynb).**
+
+### Data format
+scETM requires a cells-by-genes matrix `adata` as input, in the format of an AnnData object. Detailed description about AnnData can be found [here](https://anndata.readthedocs.io/en/latest/).
+
+By default, scETM looks for batch information in the 'batch_indices' column of the `adata.obs` DataFrame, and cell type identity in the 'cell_types' column. If your data stores the batch and cell type information in different columns, pass them to the `batch_col` and `cell_type_col` arguments, respectively, when calling scETM functions.
+
+### A taste of scETM
+
+```python
+from scETM import scETM, UnsupervisedTrainer, evaluate
+import anndata
+
+# Prepare the source dataset, Mouse Pancreas
+mp = anndata.read_h5ad("MousePancreas.h5ad")
+# Initialize model
+model = scETM(mp.n_vars, mp.obs.batch_indices.nunique(), enable_batch_bias=True)
+# The trainer object will set up the random seed, optimizer, training and evaluation loop, checkpointing and logging.
+trainer = UnsupervisedTrainer(model, mp, train_instance_name="MP", ckpt_dir="../results")
+# Train the model on adata for 12000 epochs, and evaluate every 1000 epochs. Use 4 threads to sample minibatches.
+trainer.train(n_epochs=12000, eval_every=1000, n_samplers=4)
+# Obtain scETM cell, gene and topic embeddings. Unnormalized cell embeddings will be stored at mp.obsm['delta'], normalized cell embeddings at mp.obsm['theta'], gene embeddings at mp.varm['rho'], topic embeddings at mp.uns['alpha'].
+model.get_all_embeddings_and_nll(mp)
+# Evaluate the model and save the embedding plot
+evaluate(mp, embedding_key="delta", plot_fname="scETM_MP", plot_dir="figures/scETM_MP")
+```
+
+### p-scETM
+p-scETM is a variant of scETM where part or all of the the gene embedding matrix ρ is fixed to a pathways-by-genes matrix, which can be downloaded from the [pathDIP4 pathway database](http://ophid.utoronto.ca/pathDIP/Download.jsp). We only keep pathways that contain more than 5 genes.
+
+If it is desired to fix the gene embedding matrix ρ during training, let trainable_gene_emb_dim be zero. In this case, the gene set used to train the model would be the intersection of the genes in the scRNA-seq data and the genes in the gene-by-pathway matrix. Otherwise, if trainable_gene_emb_dim is set to a positive value, all the genes in the scRNA-seq data would be kept.
+
+### Transfer learning
+
+```python
+from scETM import scETM, UnsupervisedTrainer, prepare_for_transfer
+import anndata
+
+# Prepare the source dataset, Mouse Pancreas
+mp = anndata.read_h5ad("MousePancreas.h5ad")
+# Initialize model
+model = scETM(mp.n_vars, mp.obs.batch_indices.nunique(), enable_batch_bias=True)
+# The trainer object will set up the random seed, optimizer, training and evaluation loop, checkpointing and logging.
+trainer = UnsupervisedTrainer(model, mp, train_instance_name="MP", ckpt_dir="../results")
+# Train the model on adata for 12000 epochs, and evaluate every 1000 epochs. Use 4 threads to sample minibatches.
+trainer.train(n_epochs=12000, eval_every=1000, n_samplers=4)
+
+# Load the target dataset, Human Pancreas
+hp = anndata.read_h5ad('HumanPancreas.h5ad')
+# Align the source dataset's gene names (which are mouse genes) to the target dataset (which are human genes)
+mp_genes = mp.var_names.str.upper()
+mp_genes.drop_duplicates(inplace=True)
+# Generate a new model and a modified dataset from the previously trained model and the mp_genes
+model, hp = prepare_for_transfer(model, hp, mp_genes,
+	  keep_tgt_unique_genes=True,    # Keep target-unique genes in the model and the target dataset
+    fix_shared_genes=True          # Fix parameters related to shared genes in the model
+)
+# Instantiate another trainer to fine-tune the model
+trainer = UnsupervisedTrainer(model, hp, train_instance_name="HP_all_fix", ckpt_dir="../results", init_lr=5e-4)
+trainer.train(n_epochs=800, eval_every=200)
+```
+
+### Tensorboard Integration
+If a Tensorboard SummaryWriter is passed to the `writer` argument of the `UnsupervisedTrainer.train` method, the package will store.
+
+## 4 Benchmarking
+The commands used for running [Harmony](https://github.com/immunogenomics/harmony), [Scanorama](https://github.com/brianhie/scanorama), [Seurat](https://satijalab.org/seurat/), [scVAE-GM](https://github.com/scvae/scvae), [scVI](https://github.com/YosefLab/scvi-tools), [LIGER](https://github.com/welch-lab/liger), [scVI-LD](https://www.biorxiv.org/content/10.1101/737601v1.full.pdf) are available in the [scripts](/scripts) folder.
+
+
+
+
+%prep
+%autosetup -n scETM-0.5.0
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-scETM -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 0.5.0-1
+- Package Spec generated