{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial 2: Step-by-step scATAC-seq analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we will use scATAC-seq dataset `Forebrain' as an example to illustrate how scAGDE performs scATAC-seq analysis in an step-by-step style." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Read and preprocess data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first read '.h5ad' data file using [Scanpy](https://github.com/scverse/scanpy) package" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import scanpy as sc\n", "adata = sc.read_h5ad(\"data/Forebrain.h5ad\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use Scanpy to further filter data. In our case, we pass this step because the loaded dataset has been preprocessed. Some codes for filtering are copied below for easy reference:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "sc.pp.filter_cells(adata, min_genes=100)\n", "min_cells = int(adata.shape[0] * 0.01)\n", "sc.pp.filter_genes(adata, min_cells=min_cells)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 2088 × 11285\n", " obs: 'celltype', 'n_genes'\n", " var: 'n_cells'\n", " uns: 'neighbors', 'pca', 'tsne', 'umap'\n", " obsm: 'X_pca', 'X_tsne', 'X_umap'\n", " varm: 'PCs'\n", " obsp: 'connectivities', 'distances'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Setup and train scAGDE model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can initialize the trainer with the AnnData object, which will ensure settings for model are in place for training. \n", "\n", "We can specify the `outdir` to the dir path where we want to save the output file (mainly the model weights file).\n", "\n", "`n_centroids` represents the cluster number of dataset. If this information is unknown, we can set `n_centroids=None` and in this case, scAGDE will apply the estimation strategy to estimate the optimal cluster number for the initialization of its cluster layer. Here, we set `n_centroids=None` to allow the estimation strategy.\n", " \n", "We can train scAGDE on specified device by setting `gpu`. For example, train scAGDE on CPUs by `gpu=None` and trian it on GPU #0 by `gpu=\"0\"`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "If you are merely interested in learning cell embeddings without consiering any optimization about cell clustering, you can specify `cluster_opt=False` here. In this case, scAGDE will be run withour any clustering-related module and optimization.\n", "
" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "device used: cuda:3\n", "\n" ] } ], "source": [ "import scAGDE\n", "trainer = scAGDE.Trainer(adata,outdir=\"output\",n_centroids=None,gpu=\"3\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.1 Train the chromatin accessibility-based autoencoder" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can train scAGDE model in step-by-step style. First, we train an chromatin accessibility-based autoencoder to measure the importance of the peaks and select the key peaks. In the meanwhile, the initial cell representations for cell graph construction are stored in `adata.obs[embed_init_key]`, which is `\"latent_init\"` in default." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cell number: 2088\n", "Peak number: 11285\n", "n_centroids: None\n", "\n", "\n", "## Training CountModel ##\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "CountModel: 100%|██████████| 5000/5000 [00:31<00:00, 159.14it/s, loss=1687.1017]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Resolution: 0.1, Clusters: 4, Silhouette Score: 0.2519724369049072\n", "Resolution: 0.2, Clusters: 6, Silhouette Score: 0.2892927825450897\n", "Resolution: 0.30000000000000004, Clusters: 6, Silhouette Score: 0.288756400346756\n", "Resolution: 0.4, Clusters: 7, Silhouette Score: 0.23812147974967957\n", "Resolution: 0.5, Clusters: 8, Silhouette Score: 0.23155203461647034\n", "Resolution: 0.6, Clusters: 8, Silhouette Score: 0.22821781039237976\n", "Resolution: 0.7000000000000001, Clusters: 8, Silhouette Score: 0.2266818881034851\n", "Resolution: 0.8, Clusters: 8, Silhouette Score: 0.22870215773582458\n", "Resolution: 0.9, Clusters: 9, Silhouette Score: 0.21382907032966614\n", "Resolution: 1.0, Clusters: 10, Silhouette Score: 0.2054450511932373\n", "Estimated Optimal Number of Clusters: 6 at Resolution: 0.2\n" ] } ], "source": [ "trainer.CountModel(embed_init_key=\"latent_init\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At this time, scAGDE yields an estimate of 6, which will be used for the dimension initialization of cluster layer's centroids. We can get the estimate value as below:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The estimate of cluster number is: 6\n" ] } ], "source": [ "print(\"The estimate of cluster number is: %d\" % trainer.estimates)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2 Peak selection" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we can filter peaks based on the peak importance scores which are calculated in the last step. The importance scores are stored in `trainer.peakImportance` and can be plotted by function of `trainer.plotPeakImportance()` " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.07481935 0.16716415 0.20202534 ... 0.2636149 0.19474538 0.18491967]\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(trainer.peakImportance)\n", "trainer.plotPeakImportance()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Accordding to plot, there is a elbow inflection point (after 6k) to identify the top importance peaks using the elbow method. In practice, a conservative choice of this parameter will not significantly impact the results. Here, we can select 8,000 or more peaks for further model training. \n", "\n", "Next, we can excute the code below to select `topn` important peaks. By setting `replace=False`, the AnnData object will not be replaced with subset dataset but the features will be marked as 1 if selected, which is stored in `adata.var[selected_key]`." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "trainer.peakSelect(topn=8000, replace=False, selected_key=\"is_selected\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 Train GCN-based embedded model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lastly, we train the GCN-based embedded model by `trainer.GraphModel()`. scAGDE finally yiels robust and discriminative cell embeddings which are stored in `adata.obsm[embed_key]`, which is `\"latent\"` in default. Also, scAGDE enables imputation task if `impute_key` is not None and the imputed data will be stored in `adata.obsm[impute_key]`, which is `\"impute\"` in default. \n", "\n", "scAGDE performs clustering on final embeddings if `cluster_key` is not None, and the cluster assignments will be in `adata.obs[cluster_key]`, which is `\"cluster\"` in default. Because we have enabled the estimation strategy, the cluster number will be set to the value of estimates." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "## Constructing Cell Graph ##\n", "Cell number: 2088\n", "Peak number: 8000\n", "n_centroids: 6\n", "\n", "\n", "## Training GraphModel ##\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "GraphModel-preTrain: 100%|██████████| 1500/1500 [00:18<00:00, 79.73it/s, loss=1480.6422]\n", "GraphModel: 100%|██████████| 4000/4000 [01:06<00:00, 59.93it/s, loss=3476.6477]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "AnnData object with n_obs × n_vars = 2088 × 11285\n", " obs: 'celltype', 'n_genes', 'cluster'\n", " var: 'n_cells', 'is_selected'\n", " uns: 'neighbors', 'pca', 'tsne', 'umap', 'celltype_colors', 'cluster_colors'\n", " obsm: 'X_pca', 'X_tsne', 'X_umap', 'latent_init', 'impute', 'latent'\n", " varm: 'PCs'\n", " obsp: 'connectivities', 'distances'\n" ] } ], "source": [ "adata = trainer.GraphModel(impute_key=\"impute\",cluster_key=\"cluster\",embed_key=\"latent\")\n", "print(adata)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get more reliable cluster results, we can re-estimate the cluster number on final cell embeddings and re-perform cluster." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Resolution: 0.1, Clusters: 5, Silhouette Score: 0.2183424472808838\n", "Resolution: 0.2, Clusters: 6, Silhouette Score: 0.2397676348686218\n", "Resolution: 0.3, Clusters: 8, Silhouette Score: 0.3397676348686218\n", "Resolution: 0.4, Clusters: 8, Silhouette Score: 0.33547508478164673\n", "Resolution: 0.5, Clusters: 8, Silhouette Score: 0.3355008637905121\n", "Resolution: 0.6, Clusters: 8, Silhouette Score: 0.33356576919555664\n", "Resolution: 0.7000000000000001, Clusters: 9, Silhouette Score: 0.2483772188425064\n", "Resolution: 0.8, Clusters: 10, Silhouette Score: 0.24761515855789185\n", "Resolution: 0.9, Clusters: 13, Silhouette Score: 0.1840001940727234\n", "Resolution: 1.0, Clusters: 13, Silhouette Score: 0.18204689025878906\n", "Estimated Optimal Number of Clusters: 8 at Resolution: 0.3\n" ] } ], "source": [ "# re-estimate\n", "best_k, best_resolution = scAGDE.utils.estimate_optimal_k_louvain(adata.obsm[\"latent\"])\n", "cluster = scAGDE.mclust.mclust_R(adata.obsm[\"latent\"],best_k)\n", "adata.obs[\"cluster\"] = cluster.astype(int).astype(str)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Visualizing and evaluation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now use Scanpy to visualize our latent space." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/haogaoyang/anaconda3/envs/torch/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:394: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n", " cax = scatter(\n", "/home/haogaoyang/anaconda3/envs/torch/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:394: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n", " cax = scatter(\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sc.pp.neighbors(adata, use_rep=\"latent\")\n", "sc.tl.umap(adata, min_dist=0.2)\n", "sc.pl.umap(adata,color=[\"celltype\",\"cluster\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can evaluate the clustering performance with multiple metrics as below:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "## Clustering Evaluation Report ##\n", "# Confusion matrix: #\n", "[[115 0 0 0 0 3 0 2]\n", " [ 0 170 6 13 0 0 0 1]\n", " [ 1 8 315 34 5 0 0 3]\n", " [ 1 28 77 403 1 2 0 7]\n", " [ 2 3 3 2 171 7 1 6]\n", " [ 37 0 10 3 2 267 0 1]\n", " [ 4 1 4 3 1 2 108 3]\n", " [ 0 0 0 0 0 1 0 251]]\n", "# Metric values: #\n", "Adjusted Rand Index: 0.6907\n", "Normalized Mutual Info: 0.7406\n", "F1 score: 0.8621\n" ] } ], "source": [ "y = adata.obs[\"celltype\"].astype(\"category\").cat.codes.values\n", "res = scAGDE.utils.cluster_report(y, adata.obs[\"cluster\"].astype(int))" ] } ], "metadata": { "kernelspec": { "display_name": "torch", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.19" } }, "nbformat": 4, "nbformat_minor": 2 }