diff --git a/README.md b/README.md index 1ce6cdc..151d883 100644 --- a/README.md +++ b/README.md @@ -12,11 +12,11 @@ conda >= v22 through [Anaconda](https://docs.anaconda.com/free/anaconda/install/ ## Installing -```python +```shell ## Installation # Create and activate virtual environment. This is recommended to avoid conflict in dependencies. -conda create -y -n dissect python=3.8 +conda create -y -n dissect python=3.9 conda activate dissect # Clone DISSECT @@ -37,11 +37,30 @@ cd DISSECT/tutorials ## Launch jupyter lab jupyter notebook +``` +## GPU usage +By default, tensorflow-gpu which is installed while installed DISSECT works as long as appropriate CUDA driver is installed. DISSECT uses tensorflow-gpu version 2.7.0 with CUDA 11.2 and cuDNN 8.1. The available devices to tensorflow can be checked as below. + +```python +import tensorflow as tf +gpus = tf.config.list_physical_devices("GPU") +print(gpus) + +``` +This will output a list of the available GPU devices as the output below where we have 1 GPU available. +``` +[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')] +``` +In case there are multiple GPUs available, a particular GPU can be set by, + +```python +gpu_number = 0 # Using only the first GPU +tf.config.experimental.set_visible_devices(gpus[gpu_number], 'GPU') ``` ## Tutorials Interactive tutorials including required data are available as part of this repository at [Tutorials](https://github.com/imsb-uke/DISSECT/tree/main/tutorials). -1. expanded_tutorial.ipynb: Step by step deconvolution -2. minimal_tutorial.ipynb: Complete deconvolution from a single configuration file using minimal steps of code +1. expanded_tutorial.ipynb: Step by step deconvolution for bulk +2. expanded_tutorial_spatial.ipynb: Step by step deconvolution of spatial transcriptomics data (10x Visium) To get answers quickly for a problem or feature request, please open an issue. diff --git a/dissect/PropsSimulator/simulator.c b/dissect/PropsSimulator/simulator.c index cf30135..05ab7d1 100644 --- a/dissect/PropsSimulator/simulator.c +++ b/dissect/PropsSimulator/simulator.c @@ -1472,6 +1472,7 @@ static const char __pyx_k_MT[] = "MT-"; static const char __pyx_k__4[] = "*"; static const char __pyx_k_ad[] = "ad"; static const char __pyx_k_ax[] = "ax"; +static const char __pyx_k_ha[] = "ha"; static const char __pyx_k_mt[] = "mt"; static const char __pyx_k_np[] = "np"; static const char __pyx_k_os[] = "os"; @@ -1522,7 +1523,6 @@ static const char __pyx_k_test[] = "__test__"; static const char __pyx_k_tmp1[] = "tmp1"; static const char __pyx_k_tqdm[] = "tqdm"; static const char __pyx_k_type[] = "type"; -static const char __pyx_k_Count[] = "Count"; static const char __pyx_k_adata[] = "adata"; static const char __pyx_k_array[] = "array"; static const char __pyx_k_batch[] = "batch"; @@ -1542,6 +1542,7 @@ static const char __pyx_k_numpy[] = "numpy"; static const char __pyx_k_print[] = "print"; static const char __pyx_k_props[] = "props"; static const char __pyx_k_range[] = "range"; +static const char __pyx_k_right[] = "right"; static const char __pyx_k_round[] = "round"; static const char __pyx_k_shape[] = "shape"; static const char __pyx_k_tight[] = "tight"; @@ -1571,6 +1572,7 @@ static const char __pyx_k_scdata[] = "scdata"; static const char __pyx_k_shutil[] = "shutil"; static const char __pyx_k_sparse[] = "sparse"; static const char __pyx_k_unique[] = "unique"; +static const char __pyx_k_xticks[] = "xticks"; static const char __pyx_k_ylabel[] = "ylabel"; static const char __pyx_k_AnnData[] = "AnnData"; static const char __pyx_k_anndata[] = "anndata"; @@ -1598,6 +1600,7 @@ static const char __pyx_k_min_expr[] = "min_expr"; static const char __pyx_k_n_sparse[] = "n_sparse"; static const char __pyx_k_no_keeps[] = "no_keeps"; static const char __pyx_k_qualname[] = "__qualname__"; +static const char __pyx_k_rotation[] = "rotation"; static const char __pyx_k_sc_adata[] = "sc_adata"; static const char __pyx_k_simulate[] = "simulate"; static const char __pyx_k_strftime[] = "strftime"; @@ -1679,15 +1682,12 @@ static const char __pyx_k_Simulate_st_initialize[] = "Simulate_st.initialize"; static const char __pyx_k_Simulate_st_preprocess[] = "Simulate_st.preprocess"; static const char __pyx_k_Simulate_generate_props[] = "Simulate.generate_props"; static const char __pyx_k_boxplot_props_sparse_pdf[] = "boxplot_props_sparse.pdf"; -static const char __pyx_k_boxplot_ncells_sparse_pdf[] = "boxplot_ncells_sparse.pdf"; static const char __pyx_k_Simulate_st_generate_props[] = "Simulate_st.generate_props"; static const char __pyx_k_boxplot_props_complete_pdf[] = "boxplot_props_complete.pdf"; static const char __pyx_k_generate_component_figures[] = "generate_component_figures"; static const char __pyx_k_Simulate_simulate_per_batch[] = "Simulate.simulate_per_batch"; -static const char __pyx_k_boxplot_ncells_complete_pdf[] = "boxplot_ncells_complete.pdf"; static const char __pyx_k_scatterplot_pca_simulated_pdf[] = "scatterplot_pca_simulated.pdf"; static const char __pyx_k_Simulate_st_simulate_per_batch[] = "Simulate_st.simulate_per_batch"; -static const char __pyx_k_Counts_of_cell_types_in_generate[] = "Counts of cell-types in generated samples"; static const char __pyx_k_Number_of_batches_in_single_cell[] = "Number of batches in single-cell data is 1. 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if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 564, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); - if (PyDict_SetItem(__pyx_d, __pyx_n_s_simulate, __pyx_t_2) < 0) __PYX_ERR(0, 575, __pyx_L1_error) + if (PyDict_SetItem(__pyx_d, __pyx_n_s_simulate, __pyx_t_2) < 0) __PYX_ERR(0, 564, __pyx_L1_error) __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; /* "dissect/PropsSimulator/simulator.pyx":1 diff --git a/dissect/PropsSimulator/simulator.py b/dissect/PropsSimulator/simulator.py index 3a5beb1..af755c7 100644 --- a/dissect/PropsSimulator/simulator.py +++ b/dissect/PropsSimulator/simulator.py @@ -106,6 +106,8 @@ def generate_props(self): fig = plt.figure() ax = plt.boxplot(self.props_complete, labels=self.celltypes) # + if self.n_celltypes>10: + plt.xticks(rotation=45, ha="right") plt.ylabel("Proportion") plt.title("Proportions of cell-types in generated samples") plt.savefig( @@ -319,7 +321,7 @@ def __init__(self): pass def initialize(self, config): - self.config["simulation_params"] = config + self.config = config self.sc_adata = sc.read(config["simulation_params"]["scdata"]) if "sparse" in str(type(self.sc_adata.X)): self.sc_adata.X = np.array(self.sc_adata.X.todense()) @@ -385,18 +387,22 @@ def generate_props(self): fig = plt.figure() ax = plt.boxplot(self.props_sparse, labels=self.celltypes) # + if self.n_celltypes>10: + plt.xticks(rotation=45, ha="right") plt.ylabel("Proportion") plt.title("Proportions of cell-types in generated samples") plt.savefig( - os.path.join(self.config.simulation_folder, "boxplot_props_sparse.pdf") - ) - fig = plt.figure() - ax = plt.boxplot(self.cells_sparse, labels=self.celltypes) - plt.ylabel("Count") - plt.title("Counts of cell-types in generated samples") - plt.savefig( - os.path.join(self.config.simulation_folder, "boxplot_ncells_sparse.pdf") + os.path.join(self.simulation_folder, "boxplot_props_sparse.pdf") ) + # fig = plt.figure() + # ax = plt.boxplot(self.cells_sparse, labels=self.celltypes) + # if self.n_celltypes>10: + # plt.xticks(rotation=45, ha="right") + # plt.ylabel("Count") + # plt.title("Counts of cell-types in generated samples") + # plt.savefig( + # os.path.join(self.simulation_folder, "boxplot_ncells_sparse.pdf") + # ) self.props = self.props_sparse self.cells = self.cells_sparse @@ -477,7 +483,7 @@ def simulate(self, save=True): ) ) - if self.config["generate_component_figures"]: + if self.config["simulation_params"]["generate_component_figures"]: idxs = {} celltypes_col = [] for j in range(self.n_celltypes): @@ -536,19 +542,6 @@ def simulate_per_batch(self, save=True): ) ) - adata.write(os.path.join(self.simulation_folder, "simulated.h5ad")) - sc.set_figure_params(dpi=200) - tmp = adata.copy() - sc.pp.normalize_total(tmp, target_sum=1e6) - sc.pp.log1p(tmp) - sc.tl.pca(tmp) - sc.pl.pca(tmp, color=adata.obs.columns, show=False) - plt.savefig( - os.path.join( - self.simulation_folder, "scatterplot_pca_simulated.pdf" - ) - ) - if self.config["simulation_params"]["generate_component_figures"]: idxs = {} celltypes_col = [] @@ -613,7 +606,8 @@ def simulate(config): print("Number of batches in single-cell data is 1. If this is incorrect, please specify name of the batch column as in the single-cell data object (.obs)") sim.simulate(save=True) sim.config["simulation_params"]["simulation_folder"] = os.path.join(sim.config["experiment_folder"], "simulation") - sim.config["simulation_params"]["concentration"] = list(sim.config["simulation_params"]["concentration"]) + if config["simulation_params"]["type"]=="bulk": + sim.config["simulation_params"]["concentration"] = list(sim.config["simulation_params"]["concentration"]) sim.config["deconv_params"]["reference"] = os.path.join(sim.config["simulation_params"]["simulation_folder"], "simulated.h5ad") save_dict_to_file(sim.config) diff --git a/dissect/PropsSimulator/simulator.pyx b/dissect/PropsSimulator/simulator.pyx index 7ad6927..75d9713 100644 --- a/dissect/PropsSimulator/simulator.pyx +++ b/dissect/PropsSimulator/simulator.pyx @@ -108,16 +108,11 @@ class Simulate(object): ax = plt.boxplot(self.props_complete, labels=self.celltypes) # plt.ylabel("Proportion") plt.title("Proportions of cell-types in generated samples") + if self.n_celltypes>10: + plt.xticks(rotation=45, ha="right") plt.savefig( os.path.join(self.simulation_folder, "boxplot_props_complete.pdf") ) - fig = plt.figure() - ax = plt.boxplot(self.cells_complete, labels=self.celltypes) - plt.ylabel("Count") - plt.title("Counts of cell-types in generated samples") - plt.savefig( - os.path.join(self.simulation_folder, "boxplot_ncells_complete.pdf") - ) self.props = np.concatenate([self.props_complete, self.props_sparse], axis=0) self.cells = np.concatenate([self.cells_complete, self.cells_sparse], axis=0) @@ -304,7 +299,7 @@ class Simulate_st(object): pass def initialize(self, config): - self.config["simulation_params"] = config + self.config = config self.sc_adata = sc.read(config["simulation_params"]["scdata"]) if "sparse" in str(type(self.sc_adata.X)): self.sc_adata.X = np.array(self.sc_adata.X.todense()) @@ -370,19 +365,13 @@ class Simulate_st(object): fig = plt.figure() ax = plt.boxplot(self.props_sparse, labels=self.celltypes) # + if self.n_celltypes>10: + plt.xticks(rotation=45, ha="right") plt.ylabel("Proportion") plt.title("Proportions of cell-types in generated samples") plt.savefig( - os.path.join(self.config.simulation_folder, "boxplot_props_sparse.pdf") + os.path.join(self.simulation_folder, "boxplot_props_sparse.pdf") ) - fig = plt.figure() - ax = plt.boxplot(self.cells_sparse, labels=self.celltypes) - plt.ylabel("Count") - plt.title("Counts of cell-types in generated samples") - plt.savefig( - os.path.join(self.config.simulation_folder, "boxplot_ncells_sparse.pdf") - ) - self.props = self.props_sparse self.cells = self.cells_sparse @@ -462,7 +451,7 @@ class Simulate_st(object): ) ) - if self.config["generate_component_figures"]: + if self.config["simulation_params"]["generate_component_figures"]: idxs = {} celltypes_col = [] for j in range(self.n_celltypes): @@ -598,7 +587,8 @@ def simulate(config): print("Number of batches in single-cell data is 1. If this is incorrect, please specify name of the batch column as in the single-cell data object (.obs)") sim.simulate(save=True) sim.config["simulation_params"]["simulation_folder"] = os.path.join(sim.config["experiment_folder"], "simulation") - sim.config["simulation_params"]["concentration"] = list(sim.config["simulation_params"]["concentration"]) + if config["simulation_params"]["type"]=="bulk": + sim.config["simulation_params"]["concentration"] = list(sim.config["simulation_params"]["concentration"]) sim.config["deconv_params"]["reference"] = os.path.join(sim.config["simulation_params"]["simulation_folder"], "simulated.h5ad") save_dict_to_file(sim.config) - \ No newline at end of file + diff --git a/dissect/configs/config.py b/dissect/configs/config.py index df9661e..552d5c7 100644 --- a/dissect/configs/config.py +++ b/dissect/configs/config.py @@ -4,7 +4,7 @@ "simulation_params": { "scdata": "/home/user/experiment/data.h5ad", # Path to sc/snRNA-seq data, should be anndata "n_samples": None, # Number of samples to generate. Default: 1000 times the number of celltypes, - "type": "bulk", + "type": "bulk", # bulk or st to simulate bulk and spatial transcriptomics respectively "celltype_col": "celltype", # Name of the column corresponding to cell-type labels in adata.obs "batch_col": None, # If more than one batches are present, name of the column corrsponding to batch labels in adata.obs "cells_per_sample": None, # Number of cells to sample to generate one sample. @@ -31,7 +31,7 @@ "deconv_params": { "test_dataset": "../bulk.txt", "test_dataset_format": "txt", # Either tab-delimited txt file with genes in rows or h5ad file compatible with Scanpy. - "test_dataset_type": "bulk", # bulk, microarray or spatial + "test_dataset_type": "bulk", # bulk or microarray. For spatial, set it to bulk as similar training procedure is used. "duplicated": "first", # In case, there are duplicated genes in the test_dataset. To use the first occuring gene, write first. To sum the duplicated genes, write sum. To take average, write mean "normalize_simulated": "cpm", # "cpm", # Only CPM and None is supported. Write CPM if not already TPM/CPM. "normalize_test": "cpm", # Write CPM if not already TPM/CPM @@ -39,7 +39,7 @@ "test_in_mix": None, # Number of test samples to use in the generation of online mixtures. None uses all samples. "simulated": True, # True if dataset is already simulated. False, if it is a single-cell dataset. "sig_matrix": False, - "mix": "srm", + "mix": "srm", # srm for bulk and spatial data, rrm for microarray data "save_config": True, "network_params": { "n_hidden_layers": 4, # Number of hidden layers diff --git a/dissect/prepare_data.py b/dissect/prepare_data.py index 63d2320..f12907a 100644 --- a/dissect/prepare_data.py +++ b/dissect/prepare_data.py @@ -39,6 +39,8 @@ def dataset(config): X_real = pd.read_table(config["deconv_params"]["test_dataset"], index_col=0) elif config["deconv_params"]["test_dataset_format"] == "h5ad": X_real = sc.read(config["deconv_params"]["test_dataset"]) + if "parse" in str(type(X_real.X)): + X_real.X = np.array(X_real.X.todense()) X_real = pd.DataFrame( X_real.X, index=X_real.obs.index.tolist(), columns=X_real.var_names.tolist() ).T @@ -127,9 +129,11 @@ def dataset(config): # Simulated if not simulated if config["deconv_params"]["simulated"]: X_sim = X_sc + else: X_sim = simulate(X_sc, config["deconv_params"]["simulation_params"]) - + if "parse" in str(type(X_sim.X)): + X_sim = np.array(X_sim.X.todense()) # Normalization if config["deconv_params"]["normalize_simulated"] == "cpm": sc.pp.normalize_total(X_sim, target_sum=1e6) diff --git a/docs/index.html b/docs/index.html index 701a92d..75194bf 100644 --- a/docs/index.html +++ b/docs/index.html @@ -50,7 +50,7 @@

Installing

## Installation
 
 # Create and activate virtual environment
-conda create -y -n dissect python=3.8
+conda create -y -n dissect python=3.9
 conda activate dissect
 
 # Clone DISSECT
@@ -68,12 +68,34 @@ 
Installing

 # Go to tutorials directory within DISSECT
 cd DISSECT/tutorials
+

GPU usage

+ +

By default, tensorflow-gpu which is installed while installed DISSECT works as long as appropriate CUDA driver is installed. DISSECT uses tensorflow-gpu version 2.7.0 with CUDA 11.2 and cuDNN 8.1. The available devices to tensorflow can be checked as below.

+ +

+import tensorflow as tf
+gpus = tf.config.list_physical_devices("GPU")
+print(gpus)
+
+

This will output a list of the available GPU devices as the output below where we have 1 GPU available.

+

+[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')] 
+
+

In case there are multiple GPUs available, a particular GPU can be set by,

+ +

+gpu_number = 0 # Using only the first GPU
+tf.config.experimental.set_visible_devices(gpus[gpu_number], 'GPU')
+
+ + +

Tutorials

Interactive tutorials including required data are available as part of this repository at Tutorials.

Below are the static versions of these tutorials.

To get answers quickly for a problem or feature request, please open an issue on GitHub.

diff --git a/docs/minimal_tutorial.html b/docs/minimal_tutorial.html deleted file mode 100644 index c839e53..0000000 --- a/docs/minimal_tutorial.html +++ /dev/null @@ -1,8030 +0,0 @@ - - - - - -minimal_tutorial - - - - - - - - - - - - -
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cell_type
cell_7406CD4Tcells
cell_4119CD4Tcells
cell_3032CD4Tcells
cell_3795NK
cell_318Monocytes
......
cell_6912CD4Tcells
cell_5269CD8Tcells
cell_7541Monocytes
cell_4880Monocytes
cell_3135Bcells
+

1000 rows × 1 columns

+ + + + + + +
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@@ -7758,7 +7902,7 @@

Import DISSECT module

- +