{ "cells": [ { "cell_type": "markdown", "id": "539f7263-8136-4654-a771-01790adabeea", "metadata": { "tags": [] }, "source": [ "\n", " \"Open" ] }, { "cell_type": "markdown", "id": "0d991acc-8907-420b-8cdc-5536f44856f2", "metadata": { "tags": [] }, "source": [ "Before you start, make sure to set your runtime type to GPU in colab." ] }, { "cell_type": "code", "execution_count": 2, "id": "af1bb4b1-102b-4764-9b1d-711773db9e36", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Install SOFA + dependencies\n", "!pip install --quiet biosofa" ] }, { "cell_type": "code", "execution_count": 1, "id": "96e9ae47-b714-4804-93db-82c7f917dbf2", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2024-11-07 10:03:30-- https://zenodo.org/records/14044221/files/pancan_depmap.h5mu?download=1\n", "Resolving zenodo.org (zenodo.org)... 188.184.98.238, 188.184.103.159, 188.185.79.172, ...\n", "Connecting to zenodo.org (zenodo.org)|188.184.98.238|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 79551896 (76M) [application/octet-stream]\n", "Saving to: ‘pancan_depmap.h5mu?download=1’\n", "\n", "pancan_depmap.h5mu? 100%[===================>] 75.87M 534KB/s in 1m 55s \n", "\n", "2024-11-07 10:05:26 (677 KB/s) - ‘pancan_depmap.h5mu?download=1’ saved [79551896/79551896]\n", "\n" ] } ], "source": [ "!wget https://zenodo.org/records/14044221/files/pancan_depmap.h5mu?download=1\n", "!mv pancan_depmap.h5mu?download=1 pancan_depmap.h5mu" ] }, { "cell_type": "code", "execution_count": 4, "id": "1039b922-7673-4ae5-bd80-0536c5a22b62", "metadata": { "tags": [] }, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings('ignore')\n", "import pandas as pd\n", "import sofa\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import matplotlib\n", "from muon import MuData\n", "import muon as mu\n", "from sklearn.manifold import TSNE\n", "import scanpy as sc\n", "import anndata as ad\n", "from anndata import AnnData\n", "import torch" ] }, { "cell_type": "markdown", "id": "48916839-4f05-4457-ad47-b9f588e704aa", "metadata": { "tags": [] }, "source": [ "# Analysis of DepMap data\n", "\n", "## Introduction\n", "\n", "In this notebook we will explore how `SOFA` can be used to analyze multi-omics data from the DepMap [[1,2,3,4,5]](#1,#2,#3,#4,#5). \n", "Here we give a brief introduction what the SOFA model does and what it can be used for. For a more \n", "detailed description please refer to our preprint: https://doi.org/10.1101/2024.10.10.617527 \n", "\n", "\n", "### The SOFA model\n", "Given a set of real-valued data\n", "matrices containing multi-omic measurements from overlapping samples (also called views),\n", "along with sample-level guiding variables that capture additional properties such as batches\n", "or mutational profiles, SOFA extracts an interpretable lower-dimensional data representation,\n", "consisting of a shared factor matrix and modality-specific loading matrices. The goal of these \n", "factors is to explain the major axes of variation in the data. SOFA explicitly assigns a subset of factors \n", "to explain both the multi-omics data and the guiding\n", "variables (guided factors), while preserving another subset of factors exclusively\n", "for explaining the multi-omics data (unguided factors). Importantly, this feature allows the\n", "analyst to discern variation that is driven by known sources from novel, unexplained sources\n", "of variability.\n", "\n", "#### Interpretation of the factors (Z)\n", "Analogous to the interpretation of factors in PCA, SOFA factors ordinate samples along a\n", "zero-centered axis, where samples with opposing signs exhibit contrasting phenotypes along\n", "the inferred axis of variation, and the absolute value of the factor indicates the strength of the\n", "phenotype. Importantly, SOFA partitions the factors of the low-rank decomposition into\n", "guided and unguided factors: the guided factors are linked to specific guiding variables,\n", "while the unguided factors capture global, yet unexplained, sources of variability in the data. \n", "The factor values can be used in downstream analysis tasks related to the samples, such as clustering \n", "or survival analysis. The factor values are called Z in SOFA.\n", "\n", "#### Interpretation of the loading weights (W)\n", "SOFA’s loading weights indicate the importance of each feature for its respective factor,\n", "thereby enabling the interpretation of SOFA factors. Loading weights close to zero indicate\n", "that a feature has little to no importance for the respective factor, while large magnitudes\n", "suggest strong relevance. The sign of the loading weight aligns with its corresponding factor,\n", "meaning that positive loading weights indicate higher feature levels in samples with positive\n", "factor values, and negative loading weights indicate higher feature levels in samples with\n", "negative factor values. The top loading weights can be simply inspected or used in downstream analysis such as gene set \n", "enrichment analysis. The factor values are called W in SOFA.\n", "\n", "#### Supported data\n", "SOFA expects a set of matrices containing omics measurements with matching and aligned samples and different features. \n", "Currently SOFA only supports Gaussian likelihoods, for the multi-omics data. \n", "Data should therefore be appropriately normalized according to\n", "its omics modality. Additionally, data should be centered and scaled.\n", "\n", "\n", "For the guiding variables SOFA supports Gaussian, Bernoulli and Categorical likelihoods. Guiding variables\n", "can therefore be continuous, binary or categorical. Guiding variables should be vectors with matching samples with \n", "the multi-omics data.\n", "\n", "In SOFA the multi-omics data is denoted as X and the guiding variables as Y.\n", "\n", "\n", "### The DepMap data set\n", "The DepMap project aims to identify cancer vulnerabilities and drug targets across a diverse range of cancer types. The data set includes multi-omics data, encompassing transcriptomics[[4]](#4), proteomics[[1]](#1), and methylation[[5]](#5), as well as drug response profiles for 627 drugs[[5]](#5) and CRISPR-Cas9 gene essentiality scores[[2,3]](#2,#3) for 17485 genes for 949 cancer cell lines across 26 different tissues. We will fit a SOFA model with 20 factors, while accounting for potential/known drivers of variation such as growth rate, microsatellite instability (MSI) status, BRAF, TP53 and PIK3CA mutation and hematopoietic lineage. We will use the essentiality score data test factor associations with essentiality scores.\n", "We will first load the preprocessed data in `MuData` format, fit a SOFA model and perform various downstream analyses. \n", "\n", "\n", "\n", "[1] \n", "Gonçalves, E. et al. Pan-cancer proteomic map of 949 human cell lines. Cancer Cell 40, 835–849.e8 (2022).\\\n", "[2] \n", "Boehm, J. S. et al. Cancer research needs a better map. Nature 589, 514–516 (2021).\\\n", "[3] \n", "Pacini, C. et al. Integrated cross-study datasets of genetic dependencies in cancer. Nat. Commun. 12, 1661 (2021).\\\n", "[4] \n", "Garcia-Alonso, L. et al. Transcription factor activities enhance markers of drug sensitivity in cancer. Cancer Res. 78, 769–780 (2018).\\\n", "[5] \n", "Iorio, F. et al. A landscape of pharmacogenomic interactions in cancer. Cell 166, 740–754 (2016)." ] }, { "cell_type": "markdown", "id": "8eb95739-7f76-4a0b-b806-860152361226", "metadata": { "tags": [] }, "source": [ "## Read data and set hyperparameters" ] }, { "cell_type": "code", "execution_count": 9, "id": "9b3b41ce-966a-4726-a63c-aaf209c58813", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
MuData object with n_obs × n_vars = 778 × 23503\n",
       "  obs:\t'DepMap_ID', 'cell_line_name', 'stripped_cell_line_name', 'CCLE_Name', 'alias', 'COSMICID', 'sex', 'source', 'RRID', 'WTSI_Master_Cell_ID', 'sample_collection_site', 'primary_or_metastasis', 'primary_disease', 'Subtype', 'age', 'Sanger_Model_ID', 'depmap_public_comments', 'lineage', 'lineage_subtype', 'lineage_sub_subtype', 'lineage_molecular_subtype', 'default_growth_pattern', 'model_manipulation', 'model_manipulation_details', 'patient_id', 'parent_depmap_id', 'Cellosaurus_NCIt_disease', 'Cellosaurus_NCIt_id', 'Cellosaurus_issues', 'model_id', 'Project_Identifier', 'Cell_line', 'Source', 'Identifier', 'Gender', 'Tissue_type', 'Cancer_type', 'Cancer_subtype', 'Haem_lineage', 'BROAD_ID', 'CCLE_ID', 'ploidy', 'mutational_burden', 'msi_status', 'growth_properties', 'growth', 'size', 'media', 'replicates_correlation', 'number_of_proteins', 'EMT', 'Proteasome', 'TranslationInitiation', 'CopyNumberInstability', 'GeneExpressionCorrelation', 'CopyNumberAttenuation', 'crispr_source', 'hema/lymph'\n",
       "  12 modalities\n",
       "    RNA:\t778 x 2000\n",
       "      uns:\t'llh', 'log1p'\n",
       "      obsm:\t'mask'\n",
       "    Protein:\t778 x 2000\n",
       "      uns:\t'llh'\n",
       "      obsm:\t'mask'\n",
       "    Methylation:\t778 x 2000\n",
       "      uns:\t'llh', 'log1p'\n",
       "      obsm:\t'mask'\n",
       "    Drug response:\t778 x 627\n",
       "      uns:\t'llh'\n",
       "      obsm:\t'mask'\n",
       "    CRISPR scores:\t778 x 16258\n",
       "      uns:\t'llh'\n",
       "      obsm:\t'mask'\n",
       "    Mutations:\t778 x 612\n",
       "      uns:\t'llh'\n",
       "      obsm:\t'mask'\n",
       "    Growth:\t778 x 1\n",
       "      uns:\t'llh', 'scaling_factor'\n",
       "      obsm:\t'mask'\n",
       "    MSI:\t778 x 1\n",
       "      uns:\t'llh', 'scaling_factor'\n",
       "      obsm:\t'mask'\n",
       "    BRAF:\t778 x 1\n",
       "      uns:\t'llh', 'scaling_factor'\n",
       "      obsm:\t'mask'\n",
       "    TP53:\t778 x 1\n",
       "      uns:\t'llh', 'scaling_factor'\n",
       "      obsm:\t'mask'\n",
       "    PIK3CA:\t778 x 1\n",
       "      uns:\t'llh', 'scaling_factor'\n",
       "      obsm:\t'mask'\n",
       "    Hema:\t778 x 1\n",
       "      uns:\t'llh', 'scaling_factor'\n",
       "      obsm:\t'mask'
" ], "text/plain": [ "MuData object with n_obs × n_vars = 778 × 23503\n", " obs:\t'DepMap_ID', 'cell_line_name', 'stripped_cell_line_name', 'CCLE_Name', 'alias', 'COSMICID', 'sex', 'source', 'RRID', 'WTSI_Master_Cell_ID', 'sample_collection_site', 'primary_or_metastasis', 'primary_disease', 'Subtype', 'age', 'Sanger_Model_ID', 'depmap_public_comments', 'lineage', 'lineage_subtype', 'lineage_sub_subtype', 'lineage_molecular_subtype', 'default_growth_pattern', 'model_manipulation', 'model_manipulation_details', 'patient_id', 'parent_depmap_id', 'Cellosaurus_NCIt_disease', 'Cellosaurus_NCIt_id', 'Cellosaurus_issues', 'model_id', 'Project_Identifier', 'Cell_line', 'Source', 'Identifier', 'Gender', 'Tissue_type', 'Cancer_type', 'Cancer_subtype', 'Haem_lineage', 'BROAD_ID', 'CCLE_ID', 'ploidy', 'mutational_burden', 'msi_status', 'growth_properties', 'growth', 'size', 'media', 'replicates_correlation', 'number_of_proteins', 'EMT', 'Proteasome', 'TranslationInitiation', 'CopyNumberInstability', 'GeneExpressionCorrelation', 'CopyNumberAttenuation', 'crispr_source', 'hema/lymph'\n", " 12 modalities\n", " RNA:\t778 x 2000\n", " uns:\t'llh', 'log1p'\n", " obsm:\t'mask'\n", " Protein:\t778 x 2000\n", " uns:\t'llh'\n", " obsm:\t'mask'\n", " Methylation:\t778 x 2000\n", " uns:\t'llh', 'log1p'\n", " obsm:\t'mask'\n", " Drug response:\t778 x 627\n", " uns:\t'llh'\n", " obsm:\t'mask'\n", " CRISPR scores:\t778 x 16258\n", " uns:\t'llh'\n", " obsm:\t'mask'\n", " Mutations:\t778 x 612\n", " uns:\t'llh'\n", " obsm:\t'mask'\n", " Growth:\t778 x 1\n", " uns:\t'llh', 'scaling_factor'\n", " obsm:\t'mask'\n", " MSI:\t778 x 1\n", " uns:\t'llh', 'scaling_factor'\n", " obsm:\t'mask'\n", " BRAF:\t778 x 1\n", " uns:\t'llh', 'scaling_factor'\n", " obsm:\t'mask'\n", " TP53:\t778 x 1\n", " uns:\t'llh', 'scaling_factor'\n", " obsm:\t'mask'\n", " PIK3CA:\t778 x 1\n", " uns:\t'llh', 'scaling_factor'\n", " obsm:\t'mask'\n", " Hema:\t778 x 1\n", " uns:\t'llh', 'scaling_factor'\n", " obsm:\t'mask'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# First we read the preprocessed data as a single MuData object\n", "mdata = mu.read(\"pancan_depmap.h5mu\")\n", "mdata" ] }, { "cell_type": "markdown", "id": "2d6550fe-d5fc-4a56-8a54-ed3647c5bd2e", "metadata": {}, "source": [ "The mdata object contains 12 data modalities and the obs slot contains metadata of the cell lines. \n", "We will use RNA, Protein, Methylation and Drug response as the multi-omics data input for `SOFA`. \n", "We will use the modalities Growth (the growth rate of the cell lines), MSI (microsatellite instability status), BRAF (whether the cell line is mutated in BRAF), TP53 \n", "(whether the cell line is mutated in TP53), PIK3CA (whether the cell line is mutated in PIK3CA) and Hema (whether the cell line is from the \n", "hematopoietic lineage) as guiding variables. The modalities Mutations and CRISPR scores will be used in the downstream analysis, to test for significant associations with factors." ] }, { "cell_type": "code", "execution_count": null, "id": "84c4b6bb-8317-4dc3-b7e8-49b6b1e33e01", "metadata": { "tags": [] }, "outputs": [], "source": [ "# We create the MuData object Xmdata, which contains the multi-omics data:\n", "Xmdata = MuData({\"RNA\":mdata[\"RNA\"], \"Protein\":mdata[\"Protein\"], \"Methylation\":mdata[\"Methylation\"], \"Drug response\":mdata[\"Drug response\"]})\n", "# We create the MuData objectYmdata, which contains the guiding variables:\n", "Ymdata = MuData({\"Growth\":mdata[\"Growth\"], \"MSI\": mdata[\"MSI\"], \"BRAF\": mdata[\"BRAF\"], \"TP53\":mdata[\"TP53\"], \"PIK3CA\":mdata[\"PIK3CA\"], \"Hema\": mdata[\"Hema\"]})" ] }, { "cell_type": "markdown", "id": "6da7062f-3f08-4206-a6bd-478ab9b2ea45", "metadata": {}, "source": [ "### (Optional for this Tutorial) here we will show how you would prepare the input data for SOFA yourself\n", "We assume that you have a `pandas.DataFrame` for each of the data modalities. " ] }, { "cell_type": "code", "execution_count": 14, "id": "6572b8d9-73de-46be-a1f2-98141a1ac027", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Extract dataframes from the `MuData` object\n", "rna_df = mdata[\"RNA\"].to_df()\n", "prot_df = mdata[\"Protein\"].to_df()\n", "meth_df = mdata[\"Methylation\"].to_df()\n", "drug_df = mdata[\"Drug response\"].to_df()" ] }, { "cell_type": "markdown", "id": "e149922a-f51e-4e72-8240-50c0b506f753", "metadata": {}, "source": [ "Then we can use the sofa.tl.get_ad() function to produce an appropriate `AnnData` object." ] }, { "cell_type": "code", "execution_count": 15, "id": "9f00602d-915f-4e71-9a60-e7a311d509b6", "metadata": { "tags": [] }, "outputs": [], "source": [ "rna_ad = sofa.tl.get_ad(rna_df, llh = \"gaussian\") # currently only the Gaussian likelihood is supported for the omics data\n", "prot_ad = sofa.tl.get_ad(prot_df, llh = \"gaussian\")\n", "meth_ad = sofa.tl.get_ad(meth_df, llh = \"gaussian\")\n", "drug_ad = sofa.tl.get_ad(drug_df, llh = \"gaussian\")\n", "# Finally as before wrap all the `AnnData` objects in a single `MuData` object.\n", "Xmdata = MuData({\"RNA\":rna_ad, \"Protein\":prot_ad, \"Methylation\":meth_ad, \"Drug response\":drug_ad})" ] }, { "cell_type": "markdown", "id": "178662e2-a76f-4f01-bdc3-3fb7fab8f222", "metadata": {}, "source": [ "and analogously for the guiding variables:" ] }, { "cell_type": "code", "execution_count": 16, "id": "cee40548-3f54-48bf-ba35-e75fc0e7616a", "metadata": { "tags": [] }, "outputs": [], "source": [ "growth_df = mdata[\"Growth\"].to_df()\n", "msi_df = mdata[\"MSI\"].to_df()\n", "braf_df = mdata[\"BRAF\"].to_df()\n", "tp53_df = mdata[\"TP53\"].to_df()\n", "pik3ca_df = mdata[\"PIK3CA\"].to_df()\n", "hema_df = mdata[\"Hema\"].to_df()" ] }, { "cell_type": "markdown", "id": "eb408df0-9033-4b28-b3db-905f6a5dbe83", "metadata": {}, "source": [ "Again we can use the sofa.tl.get_ad() function to produce an appropriate `AnnData` object.\n", "We need to specify an appropriate likelihood for each guiding variables. For the continuous growth rate we use \n", "`gaussian` and for the remaining binary variables `bernoulli`. SOFA also supports the `categorical` likelihood.\n", "Additionally, we need to set a scaling factor for each guiding \n", "variables, determining the strength of the supervision in the fitting process. To high values lead to guided factors that do not explain any variance \n", "of the multi-omics data. Too low values would lead to factors that are not associated with their guiding variables.\n", "We recommend the default of 0.1." ] }, { "cell_type": "code", "execution_count": 19, "id": "c0ed3381-9784-4b39-aa40-2cd4c2d16776", "metadata": { "tags": [] }, "outputs": [], "source": [ "growth_ad = sofa.tl.get_ad(growth_df, llh = \"gaussian\", scaling_factor = 0.01) \n", "msi_ad = sofa.tl.get_ad(msi_df, llh = \"bernoulli\", scaling_factor = 0.1)\n", "braf_ad = sofa.tl.get_ad(braf_df, llh = \"bernoulli\", scaling_factor = 0.1)\n", "tp53_ad = sofa.tl.get_ad(tp53_df, llh = \"bernoulli\", scaling_factor = 0.1)\n", "pik3ca_ad = sofa.tl.get_ad(pik3ca_df, llh = \"bernoulli\", scaling_factor = 0.1)\n", "hema_ad = sofa.tl.get_ad(hema_df, llh = \"bernoulli\", scaling_factor = 0.1)\n", "\n", "# Finally as before wrap all the `AnnData` objects in a single `MuData` object.\n", "Ymdata = MuData({\"Growth\":growth_ad, \"MSI\": msi_ad, \"BRAF\": braf_ad, \"TP53\": tp53_ad, \"PIK3CA\": pik3ca_ad, \"Hema\": hema_ad})" ] }, { "cell_type": "code", "execution_count": 20, "id": "3c321a3d-3bd0-4b51-8fd1-b9d2780cc840", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "tensor([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0.],\n", " [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0.],\n", " [0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0.],\n", " [0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0.],\n", " [0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0.],\n", " [0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0.]], dtype=torch.float64)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We set the number of factors to infer\n", "num_factors = 20\n", "# Use obs as metadata of the cell lines\n", "metadata = mdata.obs\n", "# In order to relate factors to guiding variables we need to provide a design matrix (guiding variables x number of factors) \n", "# indicating which factor is guided by which guiding variable.\n", "# Here we just indicate that the first 6 factors are each guided by a different guiding variable:\n", "design = np.zeros((len(Ymdata.mod), num_factors))\n", "for i in range(len(Ymdata.mod)):\n", " design[i,i] = 1\n", " \n", "# convert to torch tensor to make it usable by SOFA\n", "design = torch.tensor(design)\n", "design" ] }, { "cell_type": "markdown", "id": "0f111725-7a24-4b32-94d0-5f133fae5d77", "metadata": { "tags": [] }, "source": [ "## Fit the `SOFA` model" ] }, { "cell_type": "code", "execution_count": 21, "id": "4fdcecf1-88bb-4f61-a235-2fcb581481ce", "metadata": { "tags": [] }, "outputs": [], "source": [ "model = sofa.SOFA(Xmdata = Xmdata, # the input multi-omics data \n", " num_factors=num_factors, # number of factors to infer\n", " Ymdata = Ymdata, # the input guiding variables\n", " design = design, # design matrix relating factors to guiding variables\n", " device='cuda', # set device to \"cuda\" to enable computation on the GPU, if you don't have a GPU available set it to \"cpu\"\n", " seed=42) # set seed to get the same results every time we run it" ] }, { "cell_type": "code", "execution_count": 22, "id": "3b1c2f5c-5a15-4710-865b-adc99181b81e", "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Current Elbo 3.69E+06 | Delta: 494761: 100%|██████████| 3000/3000 [05:01<00:00, 9.94it/s] \n", "Current Elbo 2.61E+06 | Delta: -65210: 100%|██████████| 3000/3000 [04:43<00:00, 10.58it/s] \n" ] } ], "source": [ "# train SOFA with learning rate of 0.01 for 3000 steps\n", "model.fit(n_steps=3000, lr=0.01)\n", "# decrease learning rate to 0.005 and continue training\n", "model.fit(n_steps=3000, lr=0.005)" ] }, { "cell_type": "code", "execution_count": 6, "id": "8eca0ee3-88f3-49d9-a2bd-6563a429410c", "metadata": { "tags": [] }, "outputs": [], "source": [ "# if we would like to save the fitted model we can save it using:\n", "#sofa.tl.save_model(model,\"depmap_example_model\")\n", "\n", "# to load the model use:\n", "model = sofa.tl.load_model(\"depmap_example_model\")" ] }, { "cell_type": "markdown", "id": "9bb9324a-d093-4995-84ff-b9745e18c2a6", "metadata": { "tags": [] }, "source": [ "## Downstream analysis\n", "\n", "\n", "### Convergence\n", "\n", "We will first assess whether the ELBO loss of SOFA has converged by plotting it over training steps" ] }, { "cell_type": "code", "execution_count": 15, "id": "f95de90d-80dd-44d9-9ce2-28d059cc613c", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'ELBO')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjoAAAGwCAYAAACgi8/jAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAxa0lEQVR4nO3deXiU1d3/8c9ksrEkkT0ge0EhrEJYRUVRKYvK01/7qA/u2v6wuPBoW0FUaMsS2yqtbcSKfcCqLfp7UKsiIsimBWRLFAirLEEghCVkIWSd8/sDZ3BICBPIPSeZeb+uK9c1c+575v5yFPK5zn3OuV3GGCMAAIAQFGG7AAAAAKcQdAAAQMgi6AAAgJBF0AEAACGLoAMAAEIWQQcAAIQsgg4AAAhZkbYLsMnj8ejQoUOKi4uTy+WyXQ4AAAiAMUb5+flq1aqVIiKqHrMJ66Bz6NAhtWnTxnYZAADgIhw4cECtW7eu8pywDjpxcXGSznRUfHy85WoAAEAg8vLy1KZNG9/v8aqEddDx3q6Kj48n6AAAUMcEMu2EycgAACBkEXQAAEDIIugAAICQRdABAAAhi6ADAABCFkEHAACELIIOAAAIWQQdAAAQsgg6AAAgZBF0AABAyCLoAACAkEXQAQAAISusH+rpFI/H6HBekTweo8svq6eIiAs/dAwAANQ8RnQcUFzm0dUpy3TN75arsLTcdjkAAIQtgo4DIr7Xqx5j7BUCAECYI+g4IMJ19laVx0PQAQDAFoKOA9zfCzrlBB0AAKwJy6CTmpqqpKQk9evXz5Hv//7k43JuXQEAYE1YBp3x48crIyND69evd+wa7u/Cjsfj2CUAAMAFhGXQCQbv7SsmIwMAYA9BxyHelVfM0QEAwB6CjkMiGNEBAMA6go5DvLeuGNEBAMAego5DvCuvGNEBAMAego5DfKuuyDkAAFhD0HFIBLeuAACwjqDjEDerrgAAsI6g4xBWXQEAYB9BxyFng47lQgAACGMEHYd4JyNz6woAAHsIOg5xs7wcAADrCDoO8T7AnBEdAADsIeg4xDdHh6ADAIA1BB2HsGEgAAD2EXQc4tswkDk6AABYQ9BxiG9EhyEdAACsIeg4JILl5QAAWEfQcYjbu+qKW1cAAFhD0HGId46OIegAAGANQcchZ29dWS4EAIAwRtBxiJtVVwAAWEfQcUik2zuiw5AOAAC2hGXQSU1NVVJSkvr16+fYNdzcugIAwLqwDDrjx49XRkaG1q9f79g1IiMY0QEAwLawDDrB4B3RKWMfHQAArCHoOCQy4kzXsmEgAAD2EHQc4hvRKSfoAABgC0HHIZE8AgIAAOsIOg5hjg4AAPYRdBzCPjoAANhH0HEIIzoAANhH0HEIq64AALCPoOMQRnQAALCPoOMQVl0BAGAfQcch7KMDAIB9BB2H8KwrAADsI+g4xP3dZGTm6AAAYA9BxyFn99Eh6AAAYAtBxyGsugIAwD6CjkNYdQUAgH0EHYcwogMAgH0EHYew6goAAPsIOg7xrbpiHx0AAKwh6DiEOToAANhH0HEIc3QAALCPoOMQ9tEBAMA+go5Dzo7oMBkZAABbCDoOYY4OAAD2EXQcwrOuAACwj6DjEEZ0AACwj6DjEN8cHfbRAQDAGoKOQxjRAQDAPoKOQ1h1BQCAfQQdh7CPDgAA9oVl0ElNTVVSUpL69evn2DW8q65KmaMDAIA1YRl0xo8fr4yMDK1fv96xa8REnuna4jJuXQEAYEtYBp1gqBflliSdLimzXAkAAOGLoOMQ7xwdNgwEAMAego5DvKuuPIagAwCALQQdh7hdrLoCAMA2go5DInwjOpJhVAcAACsIOg7x7owsMaoDAIAtBB2HRHw/6DCiAwCAFQQdh3jn6EgST4EAAMAOgo5D3IzoAABgHUHHIREu5ugAAGAbQcchTEYGAMA+go5DIgg6AABYR9BxELsjAwBgF0HHQeyODACAXQQdB0V817sEHQAA7CDoOCjyu6RD0AEAwA6CjoO885HZRwcAADsIOg7yTUZmRAcAACsIOg7yBh1GdAAAsIOg4yBv0CkrJ+gAAGADQcdB3snIZdy6AgDACoKOg6Lc3n10eHw5AAA2EHQc5L11VcqtKwAArCDoOCjK/d2tK4IOAABWEHQcFPndrasybl0BAGAFQcdB7ghGdAAAsImg46CoCEZ0AACwiaDjIN8+OiwvBwDACoKOg5iMDACAXQQdB52djEzQAQDABoKOgyJ9j4Bgjg4AADYQdBzkfQREKSM6AABYQdBxkO/WFSM6AABYQdBxEJORAQCwi6DjIO9DPUsY0QEAwAqCjoMiGdEBAMAqgo6D2BkZAAC7CDoO8s7R4dYVAAB2hGXQSU1NVVJSkvr16+fodbh1BQCAXWEZdMaPH6+MjAytX7/e0etEsbwcAACrwjLoBMvZW1eM6AAAYANBx0FsGAgAgF0EHQdFffcICB7qCQCAHQQdB7FhIAAAdhF0HHR21RVBBwAAGwg6DvKO6JQyGRkAACsIOg6KjXJLkopKyy1XAgBAeCLoOKh+dKQkqbCEoAMAgA0EHQf5lpfzrCsAAKwg6DjIt7ycOToAAFhB0HFQpG8yMiM6AADYQNBxUGTEmaBTzoaBAABYQdBxkHcfnX3HCy1XAgBAeCLoOOhgzmnfazYNBAAg+Ag6Dvr+aiuedwUAQPARdBzUuXmc7zXzdAAACD6CjoM6NW/oe80ScwAAgo+g4yDvqiuJTQMBALCBoOOgiAiXvFmHOToAAAQfQcdh3iXmBB0AAIKPoOOwqO+GdFheDgBA8BF0HOb2Bh1GdAAACDqCjsOi3DzYEwAAWwg6Djs7osOtKwAAgo2g4zBGdAAAsIeg4zBGdAAAsIeg47B6UW5JUnEpQQcAgGAj6DisXvSZoHOqpNxyJQAAhB+CjsOiv5ujU8o+OgAABB1Bx2HeOToEHQAAgo+g47Byc2a1VSG3rgAACDqCjsPW7T0hSXr6vc2WKwEAIPwQdILEsI0OAABBR9ABAAAhi6ADAABCFkEHAACELIKOw356TQdJ0k/6trZcCQAA4Yeg47CEelGSzu6nAwAAgoeg47CI7wJOuYdlVwAABBtBx2Fu13dBh/XlAAAEHUHHYd5bVh5GdAAACDqCjsO8QaeMoAMAQNARdBy2/XC+JOmjrw9brgQAgPBD0HHYv746aLsEAADCFkHHYb++tZvtEgAACFsEHYe1bdxAktSpeUPLlQAAEH4IOg6LiTrTxcVl5ZYrAQAg/BB0HBYTeaaLS8o8lisBACD8EHQc5g06xQQdAACCjqDjsJhItySpuJSgAwBAsBF0HHZ2RIc5OgAABBtBx2HeER2PkcrKGdUBACCYqh10ysrK9Pvf/159+vRRw4YNFRcXpz59+ugPf/iDSktLnaixTouOPNvFBcVlFisBACD8VCvonD59WkOHDtXEiRPVrFkzPfTQQ3rggQfUrFkzPfXUUxo2bJiKioqcqrVOio0628WvrNxjsRIAAMJPZHVOnjlzpg4cOKC0tDT17NnT79hXX32lW2+9VSkpKZo6dWpN1ljjUlNTlZqaqvJy5+fNuFwu3+uth3Idvx4AADirWiM68+fP14svvlgh5EhSr1699Ic//EH/+Mc/aqw4p4wfP14ZGRlav359UK87oEPjoF4PAIBwV62gk5mZqf79+5/3+MCBA5WZmXnJRYWazt89/qFDUx4DAQBAMFUr6MTHxys7O/u8x7OyshQfH3/JRYWaZnExkqQyD6uuAAAIpmoFneuvv14zZsw47/GUlBQNHTr0UmsKOe6IM/N0yj3GciUAAISXak1GnjJligYMGKCBAwfqiSeeUJcuXSRJGRkZmjVrljIyMrR27VpHCq3LIr8LOmUEHQAAgqpaQScpKUlLlizRgw8+qDvuuMO3osgYoy5dumjx4sXq1q2bI4XWZe6IMwNnjOgAABBc1Qo60pkJx1u3blV6erp27twpSbriiivUu3fvmq4tZDCiAwCAHdUOOl69e/f2hZuSkhIVFBSoYUNWFVWmsPTMfj1H89hMEQCAYKr2IyDmzp2rRx99VG+99ZYk6emnn1ZcXJwSEhJ000036fjx4zVeZF23audRSdJLy3ZbrgQAgPBSraAzffp0jR8/Xtu2bdNjjz2mhx9+WHPnztVvfvMbpaSkaPv27XrmmWecqhUAAKBaqnXrat68efrb3/6mO++8Uxs2bNCAAQP09ttv68c//rEkqXv37ho3bpwjhQIAAFRXtXdGHjJkiCQpOTlZkZGR6tGjh+94z549dfjw4ZqtEAAA4CJVK+iUlpYqJibG9z46OlpRUVG+95GRkUF5UCYAAEAgqr3qKiMjQ1lZWZLO7J+zfft2FRQUSJKOHTtWs9WFiOR2jbRhf47tMgAACDvVDjrDhg2TMWf3gxk9erQkyeVyyRjj20QQZ90zuL027M/RoI5NbJcCAEBYqVbQ2bt3r1N1hIU1e1h6DwBAMFUr6LRr167K4zk5Ofrwww91zz33XFJRoWbZtiO2SwAAICxVe8PAqmRmZur++++vya8MCXcPOhsQv3/bDwAAOKtGgw4qd/ll9X2vtx7Ks1gJAADhhaATBO6IsxO0S8o9FisBACC8EHSCwOjs7SoPTzAHACBoqjUZ+aWXXqry+MGDBy+pmFAVGXE2TxJzAAAInmoFnVmzZl3wnLZt2150MaGqcYNo3+vYSLfFSgAACC/soxMkbRvXV+aJQuboAAAQRNWaozNy5Ejl5ub63k+fPl0nT570vT9+/LiSkpJqrLhQUlhy5hlgR/OLLFcCAED4qFbQ+eSTT1RcXOx7//zzz+vEiRO+92VlZdqxY0fNVRdCjhWc6bdxb25iLx0AAILkklZd8Qv74pSW028AAAQDy8st8BAQAQAIimoFHZfLVeHp5DytvPrK2UsHAICgqNaqK2OM7rvvPsXExEiSioqKNG7cODVo0ECS/Obv4PzKCDoAAARFtYLOvffe6/f+rrvuqnAOTy6/MEZ0AAAIjmoFnblz5zpVR1gp87CXDgAAwcBkZAsY0QEAIDgIOkEyskei7/Xy7UctVgIAQPgg6ATJ7f3OPgPs6fc2W6wEAIDwQdAJkqt/0MR2CQAAhB2CTpBEuulqAACCjd++AAAgZBF0AABAyCLoAACAkEXQAQAAIYugY8l7ad9q5qJtMjzJHAAAx1TrERCoOf/99leSpGs6NdOQzk0tVwMAQGhiRCeIhnSqGGiOn+KJ7wAAOIWgE0SP3tDJdgkAAIQVgk4QuSNctksAACCsEHSCqGFsxSlRzEUGAMA5BJ0g6pIYX6HNQ9IBAMAxBB3LyDkAADiHoGMZOQcAAOcQdCxjw0AAAJxD0AmycxdeEXMAAHAOQSfIpt7azb+BpAMAgGMIOkEW4fIf0jEkHQAAHEPQCbJzNw1kig4AAM4h6ATZNec8wJOcAwCAcwg6Qda6UX39pG9r22UAABAWCDoWDPneqE7GoTyLlQAAENoIOhYM6tjE9/qNtfstVgIAQGgj6FjQPD7W7/3q3ccsVQIAQGgj6NQC//Xal7ZLAAAgJBF0LPnsyetslwAAQMgj6FjSplF9v/cnC0ssVQIAQOgi6FgSHenf9b1/s8RSJQAAhC6CDgAACFkEHYtuT27j997wPAgAAGoUQceic59k/uQ7X1mqBACA0ETQsahetNvv/btpBxnVAQCgBhF0LPv+LsmS1G/6Z5YqAQAg9BB0LHvroQF+748VFOuxf6bxaAgAAGoAQceyiAiX3vv5YL+2D746pGff36KvDpy0UxQAACGCoFMLXNW2UaXth3OLglwJAAChhaBTS+ydObJC27bDeRYqAQAgdBB0agmXy6WvnrvZr+1Pn+2yVA0AAKGBoFOLJNSPUmyU/3+SHlMWa8vBXEsVAQBQtxF0apltv/mh3/v84jKN/vMXlqoBAKBuI+jUMi6XS0/edEWF9q2HclVS5rFQEQAAdRdBpxb6+fWdKrSNeukLXfHMIj37/hZ5POyeDABAIAg6tZA7wqV/jb+60mNvrN2vVbuOBrkiAADqJoJOLdWrzWXnDTsb9+cEuRoAAOomgk4t1qvNZZXur/PnZbuVuny3svOKdPOslZr07tcWqgMAoPZzmTB+XHZeXp4SEhKUm5ur+Ph42+Wc1/p9J/STV9ZUec6+lFFBqgYAALuq8/ubEZ06oF/7xtr+2x9e+EQAAOCHoFNHxEa5K72NBQAAzo+gU4e4XK7z3qL6fxsOBLkaAABqP4JOHVRZ2Pnl/36tpRlHLFQDAEDtRdCpo/aljFLKj3r4tT309w1KP3DSTkEAANRCBJ067I7+bTXr9l5+bWNS/632ExdaqggAgNqFoFPH/cdVrbV64g0V2ttPXKhTxWUWKgIAoPYg6ISAVpfV04ZnbqzQ3m3KYu05WmChIgAAageCToho2jBGu6aPqNB+wwsrtXr3MQsVAQBgX50POjt27FDv3r19P/Xq1dP7779vuywrotwR2pcyStPGdPdr/6/XvtQ/vsy0VBUAAPaE1CMgCgoK1L59e+3fv18NGjS44Pl15REQFyM7v0j9p3/m1/azazvq6ZFdLVUEAEDNCNtHQHzwwQcaNmxYQCEn1DWPi1Xaszf5tb26ao/aT1yob3MKLVUFAEBwWQ86q1at0i233KJWrVrJ5XJVetvp5ZdfVocOHRQbG6u+ffvq888/r/S73nnnHd1+++0OV1x3NGoQrX0po+Ry+bcPeX65+k9fqhAazAMAoFLWg86pU6fUq1cv/eUvf6n0+Ntvv60JEyZo8uTJSktL0zXXXKMRI0YoM9N/zkleXp7+/e9/a+TI8z8Pqri4WHl5eX4/4WDvzFF67Z5kv7bs/GL95JU1Ol1SbqkqAACcV6vm6LhcLr333nsaM2aMr23AgAHq06ePZs+e7Wvr2rWrxowZo5kzZ/ra3njjDS1evFhvvvnmeb9/6tSp+vWvf12hPRTn6FTmxKkS9fntkgrtv7mtm+4Z1D74BQEAcBFCZo5OSUmJNm7cqJtvvtmv/eabb9bq1av92gK5bTVp0iTl5ub6fg4cCK8HYTZuEK09MyqOeD33r616P+2ghYoAAHBWrQ46x44dU3l5uVq0aOHX3qJFC2VlZfne5+bmat26dRo+fHiV3xcTE6P4+Hi/n3ATEeHS3pkjlRgf69c+4e10tZ+4UGXlHkuVAQBQ82p10PFynTOb1hjj15aQkKAjR44oOjo62KXVSS6XS2ufHlbpbsqdJi9SYUmZsvOLtDs730J1AADUnEjbBVSladOmcrvdfqM3kpSdnV1hlAfV591NufPkRX7tSc8t9r1ePfEGtbqsXrBLAwCgRtTqEZ3o6Gj17dtXS5b4T6BdsmSJBg8ebKmq0OLdTXnOOauyvBZtyaq0HQCAusB60CkoKFB6errS09MlSXv37lV6erpv+fgTTzyh1157Tf/zP/+jbdu26b//+7+VmZmpcePGWaw69NyU1ELbfvPDCu2//ShD//nXNcrOK7JQFQAAl8b68vIVK1bo+uuvr9B+7733at68eZLObBj4u9/9TocPH1b37t01a9YsXXvttZd87VB+BMTFMsborS8z9cz7Wyoc2zlthKIjrWdjAECYq87vb+tBxyaCzvkdKyhW8rSlFdrrR7u16dmbFBvltlAVAAAhtI8O7PFOVO7fvrFfe2FJubo8+4lOFZdZqgwAgMARdHBeUe4IvTNukP498YYKx7pNWczycwBArUfQwQVdflk97UsZpb/e3dev/cYXV1V6ewsAgNqCoIOADe+WWGF051hBsdpPXKgtB3MtVQUAwPkRdFAt3tGdfu0b+bWP/vMXent95nk+BQCAHQQdXJT/N26wfjn8Sr+2pxZsVvuJC3U497SlqgAA8BeWQSc1NVVJSUnq16+f7VLqtPHXd9I3lTwNfdDMZTqaX2yhIgAA/LGPDvvo1Ihb/vyFNp8zTyfCJe2YNkJR7rDM0wAAh7CPDoLuw0eH6OWxffzaPEYVHhgKAEAwEXRQY0b2aKnlvxhaob39xIXa/C2rsgAAwUfQQY3q0LSB9qWM0h9v7+3XfstfvtAba/ZZqQkAEL6Yo8McHccUFJep+5TFFdp3Tx+hSObtAAAuEnN0UCs0jInU3pkVV2V1mrxIYZyvAQBBRNCBo1wul/aljFLn5g392jtM+thSRQCAcELQQVAseeI6tWlcz6+t/cSFKinzWKoIABAOCDoIms9/dYNm/EcPv7ZrfrfMUjUAgHBA0EFQ/deAtpp739kdqY/kFWv4rFUWKwIAhDKCDoLu+i7N/Z6CvuNIvjKPF/reM1EZAFBTCDqw4vLL6qlvu7NPQL/298u1Yd8J7T9+Sn2nLdVflu2yWB0AIFSwjw776Fg1cMZnysorqvTYvpRRQa4GAFAXsI8O6ozV37uFBQBATSPowKqICJd2TR9huwwAQIgKy6CTmpqqpKQk9evX78Inw3FR7ght/fXwCu1P/e/X8njC9s4qAKAGMEeHOTq1SvuJCyu0MVcHAPB9zNFBnVXZs7HaT1yo5duzLVQDAKjrCDqoVbzPxro9uY1f+/3z1qu4rNxSVQCAuoqgg1rp+R/31J392/q1XfnMJ9p6KNdSRQCAuog5OszRqdXSMnP0Hy+vrtD+zYyRcke4LFQEALCNOToIGVe1baTdlSw//8HTH2vaRxkWKgIA1CUEHdR6ke4I7UsZpf97bUe/9te+2Kv2Excqr6jUUmUAgNqOoIM6Y9LIrppzT3KF9p5TP610WToAAAQd1Ck3JbXQvpRRmnV7rwrH2k9cqHfWH7BQFQCgtmIyMpOR67SH39yoRVuyKrTfPbCdfjumu4WKAABOq87vb4IOQafOKyv3qNPkRZUe+7/XddSkEV2DXBEAwEkEnQARdEJL5vFCXfv75ZUeqx/t1tZfD5fLxZJ0AKjrCDoBIuiEpn/vPqaxr3153uMrfzlU7Zo0CGJFAICaRNAJEEEntB3OPa1BM5dVec4nE65Rl0T+2wNAXULQCRBBJzwYYzTsxZXac/TUec8Z1LGJfv+TnmrdqH4QKwMAXAyCToAIOuFn0ebDevitTVWe8+RNV+jRYZ2DVBEAoLoIOgEi6ISv4wXFevD1DUo/cLLK81i1BQC1D0EnQAQdSFK5x2jAjM90rKC4yvPu6NdGU2/tptgod5AqAwBUhqBzAampqUpNTVV5ebl27txJ0IEkyeMx2nEkX7f+5QuVllf91+LhoT/Qj666XJ1bxAWpOgCAF0EnQIzooCoej1HHpz8O6Ny3fzZQAzo2cbgiAIBE0AkYQQeBKiot1+g/f6Hd2QUBnf/gkA6aPLKrIiLYoBAAahpBJ0AEHVysjzcf1s8vsHrLq2+7Rnp5bB+1iI91uCoACA8EnQARdFATSso8+uPSnXp5xTcBf2bZk9epY7OGDlYFAKGLoBMggg6cctdrX+qL3ccCOve+we015ZYknsMFAAEi6ASIoINgKCot1zW/W66j+VUvX/fq0/YyzbknWU0axjhcGQDUTQSdABF0YMO3OYX61f9+rdXfHA/o/N/e1k0jerRUU4IPAEgi6ASMoIPaYPHWLP3t871at+/EBc/90VWXa8ot3ZRQPyoIlQFA7UTQCRBBB7WNMUY7jxRo7GtfXnCnZkl6dnSS7h/cnmXsAMIKQSdABB3UBRv35+j/zF4d0Ll39GujoVc21w+7JzpcFQDYQ9AJEEEHdc2p4jL9fvEOzVu9L6DznxudpAeGdHC2KAAIMoJOgAg6qOuy84t0wx9WqqC4LKDzHxvWWY8P6yw3t7oA1GEEnQARdBBKjDF6ddUezVy0PaDzWzeqpzG9L9eEGzsr0h3hcHUAUHMIOgEi6CCUGWO0bHu2Hnx9Q8Cf+WG3RP3lv64i+ACo1Qg6ASLoIJwYY/TVt7kak/rvan0u/bmbdFn9aIeqAoDqI+gEiKCDcLd8R7bun7u+2p9748H+GtChiaIjGfkBEHwEnQARdAB/R/OLdd/cddp6KK/an31wSAf9oFlD3d6vDZOdATiKoBMggg5QtXKP0eur9+mdDQe0PSv/or9n4ogu+tk1HdnYEECNIOgEiKADXJzTJeWa8fE2vbF2/yV/1/BuLTRpRFe1b9qgBioDEA4IOgEi6AA1x+Mx2nEkX59sydKfPttVI9+ZGB+rB4d0UKnHozG9L1fLhFi5XIwKAeGOoBMggg4QHNn5RVrzzXHNX3dAa/YE9tT2QL34n710RYs4xcdGqdVlsSyNB8IAQecCUlNTlZqaqvLycu3cuZOgA1hijFFpudErK7/RoZOnNX/9AUeu0ywuRq0SYvXTaztq8A+aqnEDlssDdRlBJ0CM6AC1W2m5R1m5RVq67Yi+zTmtv32x1/FrXn5ZPf3+xz21+2iBCkvKNbpnS7VuVN/x6wIIHEEnQAQdoO7LKyrViYISffjVIS3fka1NmSeDXkOES2rcIFpXd2qqB4d0UE5hqZLbNVKDmEjfOSdOlSjS7VJ8bFTQ6wNCDUEnQAQdIHwUFJeprNyjtXtOaMGmb7Uk40jQrh0bFaGiUo/vffsm9XW8oET53z2M9c7+bXX9lc107RXNtO/4Ke08UqCBHRurQXSkX1gCcAZBJ0AEHQCVKSnzaPPBkyorNyr3GL2z4YA2H8zVN0dP2S5N0pmg1LFZQzWMidTeY6c0cUQXlZR5lFdUqg+/OqRberXSyB4tFRnh0u7sAiUmxCqOkSSEEIJOgAg6AGpKuccop7BEa745rpOFJYqOjNC+44V6ffU+3di1hT746pDtEqv0+LDO+uuqb/xGniTpT3f01vBuiYqNcks6M4H8ZGGp6se4FRPptlEqQNAJFEEHgE3ef37LPUaZJwr1wVeHdCSvSC6XS4u3ZKm4zKOC725v1XUpP+qh/KIyTf94myRpdM+Wmjamu2Kj3Ory7CeSpGdGddW0hdt0Y9fm+uvdycopLFH9aLciXC5f0Cr3GBUUlalhbCSPGgljBJ0AEXQAhAKPx6jU49HxghKdKi7T5oO5WrrtiD7enGW7tFrr5qQWyjxRWOmjTSaO6KKURdslSWnP3qQIl0sNYtzaeaRAI1/6XM//nx66vV/bgK6TW1iqorJyvbpqj+4d1F5tm5xdwWeM0b/SD6lT84bqfnlCpZ83xlzSJplbD+Uqyh2hK1rEXfR31EYEnQARdACgou//cs0tLNWh3NPadjhPmzJzlFNYqpbxsVq166iKSj3KPFFoudq66YYuzbVse/Z5j4/q2VI/7tNa989b79f+n8mtNeHGKzR7xTeVPoJl7v39dP2VzVXuMbp51krfvLI7+7fVEzddoWZxMb5z209cKEna+MyNalQ/uspn0Xk8Rku3HVHP1pcpMSG2yj+bMUYrdh7VwA5NVC/amdubBJ0AEXQAIPi8Qep0SbkyDucpyu1So/rRKiguU1ZukU6eLtEfFu/UwZOnfZ/p1ipexwqKdSSv2GLluBi/ua2b7hnUvka/k6ATIIIOAIQnY4y+zTmtiAiX6kW5FR8bKSPp0MnT2p1d8N050tcHc/VSNZ/d5o5wqdwTtr9aK7Xql9f73ba7VASdABF0AAC1UVm5R/uOFyoxIVb1vSveJHmM0ab9Odp8MFfGnBnp6tOukSIjXCrzGB0/VaIXPt2hdzcd9H3XYzd00kvLdkuSbkpqEdQ9pCSd2eZgxsga/U6CToAIOgAA1D3V+f3NY34BAEDIIugAAICQRdABAAAhi6ADAABCFkEHAACELIIOAAAIWQQdAAAQsgg6AAAgZBF0AABAyCLoAACAkEXQAQAAIYugAwAAQlZYBp3U1FQlJSWpX79+tksBAAAO4unlPL0cAIA6pTq/vyODVFOt5M14eXl5lisBAACB8v7eDmSsJqyDTn5+viSpTZs2lisBAADVlZ+fr4SEhCrPCetbVx6PR4cOHVJcXJxcLleNfndeXp7atGmjAwcOcFvsAuirwNFX1UN/BY6+Chx9FTin+soYo/z8fLVq1UoREVVPNw7rEZ2IiAi1bt3a0WvEx8fzFyFA9FXg6Kvqob8CR18Fjr4KnBN9daGRHK+wXHUFAADCA0EHAACELIKOQ2JiYjRlyhTFxMTYLqXWo68CR19VD/0VOPoqcPRV4GpDX4X1ZGQAABDaGNEBAAAhi6ADAABCFkEHAACELIIOAAAIWQQdB7z88svq0KGDYmNj1bdvX33++ee2S3LcqlWrdMstt6hVq1ZyuVx6//33/Y4bYzR16lS1atVK9erV09ChQ7V161a/c4qLi/Xoo4+qadOmatCggW699VZ9++23fufk5OTo7rvvVkJCghISEnT33Xfr5MmTDv/patbMmTPVr18/xcXFqXnz5hozZox27Njhdw79dcbs2bPVs2dP32ZjgwYN0qJFi3zH6afzmzlzplwulyZMmOBro7/OmDp1qlwul99PYmKi7zj95O/gwYO666671KRJE9WvX1+9e/fWxo0bfcdrfX8Z1Kj58+ebqKgoM2fOHJORkWEef/xx06BBA7N//37bpTnq448/NpMnTzYLFiwwksx7773ndzwlJcXExcWZBQsWmM2bN5vbb7/dtGzZ0uTl5fnOGTdunLn88svNkiVLzKZNm8z1119vevXqZcrKynzn/PCHPzTdu3c3q1evNqtXrzbdu3c3o0ePDtYfs0YMHz7czJ0712zZssWkp6ebUaNGmbZt25qCggLfOfTXGR988IFZuHCh2bFjh9mxY4d5+umnTVRUlNmyZYsxhn46n3Xr1pn27dubnj17mscff9zXTn+dMWXKFNOtWzdz+PBh3092drbvOP101okTJ0y7du3MfffdZ7788kuzd+9es3TpUrN7927fObW9vwg6Nax///5m3Lhxfm1dunQxEydOtFRR8J0bdDwej0lMTDQpKSm+tqKiIpOQkGBeeeUVY4wxJ0+eNFFRUWb+/Pm+cw4ePGgiIiLMJ598YowxJiMjw0gya9eu9Z2zZs0aI8ls377d4T+Vc7Kzs40ks3LlSmMM/XUhjRo1Mq+99hr9dB75+fmmc+fOZsmSJea6667zBR3666wpU6aYXr16VXqMfvL31FNPmSFDhpz3eF3oL25d1aCSkhJt3LhRN998s1/7zTffrNWrV1uqyr69e/cqKyvLr19iYmJ03XXX+fpl48aNKi0t9TunVatW6t69u++cNWvWKCEhQQMGDPCdM3DgQCUkJNTp/s3NzZUkNW7cWBL9dT7l5eWaP3++Tp06pUGDBtFP5zF+/HiNGjVKN954o187/eVv165datWqlTp06KA77rhDe/bskUQ/neuDDz5QcnKyfvKTn6h58+a66qqrNGfOHN/xutBfBJ0adOzYMZWXl6tFixZ+7S1atFBWVpalquzz/tmr6pesrCxFR0erUaNGVZ7TvHnzCt/fvHnzOtu/xhg98cQTGjJkiLp37y6J/jrX5s2b1bBhQ8XExGjcuHF67733lJSURD9VYv78+dq0aZNmzpxZ4Rj9ddaAAQP097//XYsXL9acOXOUlZWlwYMH6/jx4/TTOfbs2aPZs2erc+fOWrx4scaNG6fHHntMf//73yXVjf+vwvrp5U5xuVx+740xFdrC0cX0y7nnVHZ+Xe7fRx55RF9//bW++OKLCsforzOuvPJKpaen6+TJk1qwYIHuvfderVy50necfjrjwIEDevzxx/Xpp58qNjb2vOfRX9KIESN8r3v06KFBgwbpBz/4gV5//XUNHDhQEv3k5fF4lJycrBkzZkiSrrrqKm3dulWzZ8/WPffc4zuvNvcXIzo1qGnTpnK73RXSZ3Z2doW0G068qxmq6pfExESVlJQoJyenynOOHDlS4fuPHj1aJ/v30Ucf1QcffKDly5erdevWvnb6y190dLQ6deqk5ORkzZw5U7169dKf/vQn+ukcGzduVHZ2tvr27avIyEhFRkZq5cqVeumllxQZGen7s9BfFTVo0EA9evTQrl27+P/qHC1btlRSUpJfW9euXZWZmSmpbvx7RdCpQdHR0erbt6+WLFni175kyRINHjzYUlX2dejQQYmJiX79UlJSopUrV/r6pW/fvoqKivI75/Dhw9qyZYvvnEGDBik3N1fr1q3znfPll18qNze3TvWvMUaPPPKI3n33XS1btkwdOnTwO05/Vc0Yo+LiYvrpHMOGDdPmzZuVnp7u+0lOTtbYsWOVnp6ujh070l/nUVxcrG3btqlly5b8f3WOq6++usL2Fzt37lS7du0k1ZF/ry5pKjMq8C4v/9vf/mYyMjLMhAkTTIMGDcy+fftsl+ao/Px8k5aWZtLS0owk8+KLL5q0tDTfsvqUlBSTkJBg3n33XbN582Zz5513Vrr8sHXr1mbp0qVm06ZN5oYbbqh0+WHPnj3NmjVrzJo1a0yPHj3q3HLNhx9+2CQkJJgVK1b4LW8tLCz0nUN/nTFp0iSzatUqs3fvXvP111+bp59+2kRERJhPP/3UGEM/Xcj3V10ZQ395Pfnkk2bFihVmz549Zu3atWb06NEmLi7O9+80/XTWunXrTGRkpJk+fbrZtWuXeeutt0z9+vXNm2++6TuntvcXQccBqamppl27diY6Otr06dPHt2w4lC1fvtxIqvBz7733GmPOLEGcMmWKSUxMNDExMebaa681mzdv9vuO06dPm0ceecQ0btzY1KtXz4wePdpkZmb6nXP8+HEzduxYExcXZ+Li4szYsWNNTk5OkP6UNaOyfpJk5s6d6zuH/jrjgQce8P1datasmRk2bJgv5BhDP13IuUGH/jrDu89LVFSUadWqlfnRj35ktm7d6jtOP/n78MMPTffu3U1MTIzp0qWLefXVV/2O1/b+chljzKWNCQEAANROzNEBAAAhi6ADAABCFkEHAACELIIOAAAIWQQdAAAQsgg6AAAgZBF0AABAyCLoAACAkEXQARB0Q4cO1YQJEwI+f9++fXK5XEpPT3esJgChiZ2RAZyXy+Wq8vi9996refPmVft7T5w4oaioKMXFxQV0fnl5uY4ePaqmTZsqMjKy2terCfv27VOHDh2Ulpam3r17W6kBQPXZ+RcDQJ1w+PBh3+u3335bzz33nN+TjOvVq+d3fmlpqaKioi74vY0bN65WHW63W4mJidX6DABI3LoCUIXExETfT0JCglwul+99UVGRLrvsMr3zzjsaOnSoYmNj9eabb+r48eO688471bp1a9WvX189evTQP//5T7/vPffWVfv27TVjxgw98MADiouLU9u2bfXqq6/6jp9762rFihVyuVz67LPPlJycrPr162vw4MF+IUySpk2bpubNmysuLk4PPfSQJk6cWOVoTE5OjsaOHatmzZqpXr166ty5s+bOnStJ6tChgyTpqquuksvl0tChQ32fmzt3rrp27arY2Fh16dJFL7/8coXa58+fr8GDBys2NlbdunXTihUrArougEtD0AFwSZ566ik99thj2rZtm4YPH66ioiL17dtXH330kbZs2aKf/exnuvvuu/Xll19W+T0vvPCCkpOTlZaWpp///Od6+OGHtX379io/M3nyZL3wwgvasGGDIiMj9cADD/iOvfXWW5o+fbqef/55bdy4UW3bttXs2bOr/L5nn31WGRkZWrRokbZt26bZs2eradOmkqR169ZJkpYuXarDhw/r3XfflSTNmTNHkydP1vTp07Vt2zbNmDFDzz77rF5//XW/7/7lL3+pJ598UmlpaRo8eLBuvfVWHT9+/ILXBXCJLvn55wDCwty5c01CQoLv/d69e40k88c//vGCnx05cqR58sknfe+vu+468/jjj/vet2vXztx1112+9x6PxzRv3tzMnj3b71ppaWnGGGOWL19uJJmlS5f6PrNw4UIjyZw+fdoYY8yAAQPM+PHj/eq4+uqrTa9evc5b5y233GLuv//+So+dW4NXmzZtzD/+8Q+/tt/+9rdm0KBBfp9LSUnxHS8tLTWtW7c2zz///AWvC+DSMKID4JIkJyf7vS8vL9f06dPVs2dPNWnSRA0bNtSnn36qzMzMKr+nZ8+evtfeW2TZ2dkBf6Zly5aS5PvMjh071L9/f7/zz31/rocffljz589X79699atf/UqrV6+u8vyjR4/qwIEDevDBB9WwYUPfz7Rp0/TNN9/4nTto0CDf68jISCUnJ2vbtm0XdV0AgSPoALgkDRo08Hv/wgsvaNasWfrVr36lZcuWKT09XcOHD1dJSUmV33PuJGaXyyWPxxPwZ7wrxL7/mXNXjZkLLDIdMWKE9u/frwkTJujQoUMaNmyYfvGLX5z3fO+15syZo/T0dN/Pli1btHbt2iqv9f36qntdAIEj6ACoUZ9//rluu+023XXXXerVq5c6duyoXbt2Bb2OK6+80jevxmvDhg0X/FyzZs1033336c0339Qf//hH36To6OhoSWdGrLxatGihyy+/XHv27FGnTp38fryTl72+H3zKysq0ceNGdenS5YLXBXBpWF4OoEZ16tRJCxYs0OrVq9WoUSO9+OKLysrKUteuXYNax6OPPqqf/vSnSk5O1uDBg/X222/r66+/VseOHc/7meeee059+/ZVt27dVFxcrI8++shXd/PmzVWvXj198sknat26tWJjY5WQkKCpU6fqscceU3x8vEaMGKHi4mJt2LBBOTk5euKJJ3zfnZqaqs6dO6tr166aNWuWcnJyfJOnq7ougEvDiA6AGvXss8+qT58+Gj58uIYOHarExESNGTMm6HWMHTtWkyZN0i9+8Qv16dNHe/fu1X333afY2NjzfiY6OlqTJk1Sz549de2118rtdmv+/PmSzsyreemll/TXv/5VrVq10m233SZJeuihh/Taa69p3rx56tGjh6677jrNmzevwohOSkqKnn/+efXq1Uuff/65/vWvf/lWVlV1XQCXhp2RAYSNm266SYmJiXrjjTeCdk12VAbs4tYVgJBUWFioV155RcOHD5fb7dY///lPLV26VEuWLLFdGoAgIugACEkul0sff/yxpk2bpuLiYl155ZVasGCBbrzxRtulAQgibl0BAICQxWRkAAAQsgg6AAAgZBF0AABAyCLoAACAkEXQAQAAIYugAwAAQhZBBwAAhCyCDgAACFn/HyFnEWVG97VeAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.semilogy(model.history)\n", "plt.xlabel(\"Training steps\")\n", "plt.ylabel(\"ELBO\")" ] }, { "cell_type": "markdown", "id": "3e6be74f-ee8c-4072-bf3f-f11fcc5c6d37", "metadata": { "tags": [] }, "source": [ "### Variance explained\n", "\n", "A good first step in a SOFA analysis is to plot how much variance is explained by each factor for each modality. This gives us an overview \n", "which factors are active across multiple modalities, capturing correlated variation across multiple measurements \n", "and which are private to a single modality, most probably capturing technical effects related to this modality." ] }, { "cell_type": "code", "execution_count": 16, "id": "37632690-3696-4590-9d7f-8b5d5362ea94", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sofa.pl.plot_variance_explained(model)" ] }, { "cell_type": "code", "execution_count": 13, "id": "e0425e1d-9604-4172-a28a-50358d7d9c5d", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# We can also plot how much variance of each view is explained\n", "sofa.pl.plot_variance_explained_view(model)" ] }, { "cell_type": "code", "execution_count": 14, "id": "09914fd8-6a0b-4b30-9199-b58be6694d12", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# or how much variance is explained by each factor in total\n", "sofa.pl.plot_variance_explained_factor(model)" ] }, { "cell_type": "markdown", "id": "11310a7a-5bc8-4c84-ae95-fa3296d9ae47", "metadata": { "tags": [] }, "source": [ "### Check factor guidance" ] }, { "cell_type": "code", "execution_count": 23, "id": "30001a1c-28b4-4dc2-8bf5-01ce030a8ecf", "metadata": { "tags": [] }, "outputs": [], "source": [ "# concatenate metadata with TP53, BRAF and PIK3CA mutation columns\n", "metadata= pd.concat((metadata, mdata.mod[\"Mutations\"].to_df()[[\"TP53\", \"BRAF\", \"PIK3CA\"]]), axis=1)" ] }, { "cell_type": "code", "execution_count": 24, "id": "3f435bd8-8d10-4dc6-8bf1-4e0f2200ab8b", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot correlation of factor values with selected metadata variables [\"growth\", \"msi_status\",\"BRAF\",\"TP53\", \"PIK3CA\", \"hema/lymph\"]\n", "sofa.pl.plot_factor_metadata_cor(model, metadata[[\"growth\", \"msi_status\",\"BRAF\",\"TP53\", \"PIK3CA\", \"hema/lymph\"]])" ] }, { "cell_type": "markdown", "id": "687f318f-d74e-47ee-9764-417269e77675", "metadata": { "tags": [] }, "source": [ "As expected the guided factors a correlated with their respective guiding variables. The unguided factors are not correlated with the guiding variables." ] }, { "cell_type": "markdown", "id": "e462eb11-da2d-409d-ab85-0f9cdff28af1", "metadata": { "tags": [] }, "source": [ "### Downstream analysis of the factor values\n", "\n", "The factor values represent the new coordinates in lower dimensional space of our samples and have dimensions samples x factors. \n", "The factor values called Z in SOFA.\n", "We can use the factor values for all kinds of downstream analyses on the sample level. Here we will cluster the unguided factors.\n", "\n", "We first retrieve the factor values:" ] }, { "cell_type": "code", "execution_count": 17, "id": "e2b6d2bd-dc7d-4489-a645-f3d8479f9c2a", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Factor_0 (Growth)Factor_1 (MSI)Factor_2 (BRAF)Factor_3 (TP53)Factor_4 (PIK3CA)Factor_5 (Hema)Factor_6Factor_7Factor_8Factor_9Factor_10Factor_11Factor_12Factor_13Factor_14Factor_15Factor_16Factor_17Factor_18Factor_19
0-0.9845630.0949031.8679360.991292-0.0055100.174301-0.149569-0.1012850.1183060.228873-0.208032-0.263638-0.600392-0.3822080.1401030.344184-0.8158460.4592980.719330-0.313031
1-0.6746580.031586-0.789824-0.4073471.1240870.458733-0.060520-0.040364-0.503078-0.3992770.166812-0.0329130.652486-0.6141640.1541610.3777860.100994-1.004648-0.4080090.504229
2-0.361459-0.191929-0.3070240.3836020.0184040.8970710.294184-0.3724040.1484750.785472-0.2044830.0985360.0976770.411425-0.3911760.3748270.386192-0.4564840.3460300.550844
3-0.605062-0.200432-0.2990460.2026950.2214960.108696-0.0919750.0262840.1586530.0967150.113269-0.056898-0.1527900.2094360.3540100.3362110.376640-0.477210-0.3790580.468571
40.212342-0.120816-0.2122160.6909390.1867700.8975760.1233700.123256-0.1111560.608043-0.0655530.236904-0.198532-0.240049-0.6494140.3717910.6413090.1059650.2073810.464103
...............................................................
773-0.030411-0.103553-0.3160090.2234810.2448090.5185740.131121-0.026001-0.492095-0.2885410.1675380.1611570.659408-0.4431710.323169-0.498860-0.499177-1.3004910.6628590.273217
7740.561793-0.6628390.209959-0.9776550.2379250.574140-0.0698460.001591-1.8139540.000474-0.0447350.296505-0.0997780.0616580.5673600.424479-0.5120401.120325-0.449671-1.028471
7750.208128-0.1403880.508802-0.937757-0.153248-2.141505-0.0474740.2356400.024192-0.118403-0.061317-0.5304680.032437-0.095084-0.306878-0.3751940.2740540.427416-1.4918170.362090
7761.427114-1.299472-0.234395-0.8942230.2467340.6016280.5433580.153857-1.7821040.3507880.2405570.425656-0.4853190.379114-0.334070-0.8892240.0310680.376890-0.444826-0.905731
777-0.2337960.1989352.7492781.159026-0.6259190.637330-0.6595171.1771390.1775450.033086-0.106065-0.191898-0.288880-1.048721-0.2792300.3335670.6573070.1935872.300986-0.597988
\n", "

778 rows × 20 columns

\n", "
" ], "text/plain": [ " Factor_0 (Growth) Factor_1 (MSI) Factor_2 (BRAF) Factor_3 (TP53) \\\n", "0 -0.984563 0.094903 1.867936 0.991292 \n", "1 -0.674658 0.031586 -0.789824 -0.407347 \n", "2 -0.361459 -0.191929 -0.307024 0.383602 \n", "3 -0.605062 -0.200432 -0.299046 0.202695 \n", "4 0.212342 -0.120816 -0.212216 0.690939 \n", ".. ... ... ... ... \n", "773 -0.030411 -0.103553 -0.316009 0.223481 \n", "774 0.561793 -0.662839 0.209959 -0.977655 \n", "775 0.208128 -0.140388 0.508802 -0.937757 \n", "776 1.427114 -1.299472 -0.234395 -0.894223 \n", "777 -0.233796 0.198935 2.749278 1.159026 \n", "\n", " Factor_4 (PIK3CA) Factor_5 (Hema) Factor_6 Factor_7 Factor_8 \\\n", "0 -0.005510 0.174301 -0.149569 -0.101285 0.118306 \n", "1 1.124087 0.458733 -0.060520 -0.040364 -0.503078 \n", "2 0.018404 0.897071 0.294184 -0.372404 0.148475 \n", "3 0.221496 0.108696 -0.091975 0.026284 0.158653 \n", "4 0.186770 0.897576 0.123370 0.123256 -0.111156 \n", ".. ... ... ... ... ... \n", "773 0.244809 0.518574 0.131121 -0.026001 -0.492095 \n", "774 0.237925 0.574140 -0.069846 0.001591 -1.813954 \n", "775 -0.153248 -2.141505 -0.047474 0.235640 0.024192 \n", "776 0.246734 0.601628 0.543358 0.153857 -1.782104 \n", "777 -0.625919 0.637330 -0.659517 1.177139 0.177545 \n", "\n", " Factor_9 Factor_10 Factor_11 Factor_12 Factor_13 Factor_14 \\\n", "0 0.228873 -0.208032 -0.263638 -0.600392 -0.382208 0.140103 \n", "1 -0.399277 0.166812 -0.032913 0.652486 -0.614164 0.154161 \n", "2 0.785472 -0.204483 0.098536 0.097677 0.411425 -0.391176 \n", "3 0.096715 0.113269 -0.056898 -0.152790 0.209436 0.354010 \n", "4 0.608043 -0.065553 0.236904 -0.198532 -0.240049 -0.649414 \n", ".. ... ... ... ... ... ... \n", "773 -0.288541 0.167538 0.161157 0.659408 -0.443171 0.323169 \n", "774 0.000474 -0.044735 0.296505 -0.099778 0.061658 0.567360 \n", "775 -0.118403 -0.061317 -0.530468 0.032437 -0.095084 -0.306878 \n", "776 0.350788 0.240557 0.425656 -0.485319 0.379114 -0.334070 \n", "777 0.033086 -0.106065 -0.191898 -0.288880 -1.048721 -0.279230 \n", "\n", " Factor_15 Factor_16 Factor_17 Factor_18 Factor_19 \n", "0 0.344184 -0.815846 0.459298 0.719330 -0.313031 \n", "1 0.377786 0.100994 -1.004648 -0.408009 0.504229 \n", "2 0.374827 0.386192 -0.456484 0.346030 0.550844 \n", "3 0.336211 0.376640 -0.477210 -0.379058 0.468571 \n", "4 0.371791 0.641309 0.105965 0.207381 0.464103 \n", ".. ... ... ... ... ... \n", "773 -0.498860 -0.499177 -1.300491 0.662859 0.273217 \n", "774 0.424479 -0.512040 1.120325 -0.449671 -1.028471 \n", "775 -0.375194 0.274054 0.427416 -1.491817 0.362090 \n", "776 -0.889224 0.031068 0.376890 -0.444826 -0.905731 \n", "777 0.333567 0.657307 0.193587 2.300986 -0.597988 \n", "\n", "[778 rows x 20 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Z = sofa.tl.get_factors(model)\n", "Z" ] }, { "cell_type": "markdown", "id": "9696ee05-804e-48cf-bc92-6a7701a46712", "metadata": { "tags": [] }, "source": [ "We will plot a tSNE of all the factors and color them by cancer type:" ] }, { "cell_type": "code", "execution_count": 18, "id": "a7439ae1-987e-452c-8fd6-2d4acd0be177", "metadata": { "tags": [] }, "outputs": [], "source": [ "tsne = TSNE()\n", "tsne_z = tsne.fit_transform(model.Z)" ] }, { "cell_type": "code", "execution_count": 19, "id": "2c39eff4-b0a5-4e99-9149-9bf747ed2f0f", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,ax = plt.subplots(1)\n", "\n", "for i in np.unique(metadata[\"lineage\"].astype(str)):\n", " ax.scatter(tsne_z[metadata[\"lineage\"].astype(str)==i,0],tsne_z[metadata[\"lineage\"].astype(str)==i,1], s =10, alpha=0.8,label=i)\n", "ax.set_aspect(\"equal\")\n", "ax.set_xlabel(\"tSNE 1\")\n", "ax.set_ylabel(\"tSNE 2\")\n", "ax.tick_params(\n", " axis='both', # changes apply to the x-axis\n", " which='both', # both major and minor ticks are affected\n", " bottom=False, # ticks along the bottom edge are off\n", " top=False, \n", " left=False,# ticks along the top edge are off\n", " labelbottom=False,\n", "labelleft=False,)\n", "ax.spines[['top','right','left','bottom']].set_visible(False)\n", "ax.legend(frameon=False, ncols=2, loc=\"center left\",bbox_to_anchor=(1,0.5))\n", " " ] }, { "cell_type": "markdown", "id": "bde35b41-27ef-483d-a8f1-aa4ea80c4ec8", "metadata": {}, "source": [ "### Downstream analysis of loading weights\n", "\n", "The loadings capture the importance of the features in each omics modality for each factor, they have dimensions: factors x features. To retrieve the loadings run:" ] }, { "cell_type": "code", "execution_count": 9, "id": "b63ddfe7-dace-41c3-ae33-e61c6603ba3f", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
symbolA2MAADACL2AADACL3AARDABCB10P1ABI3BPACANACKR3ACP3ACTA2...XBP1XCL2ZNF683ZNF723ZNRF4ZP2ZP4ZPLD1ZSCAN10ZSWIM5P3
00.0152950.0714970.0355680.089748-0.0440690.0732490.0578490.014150-0.007009-0.008777...-0.0297560.0151130.0424060.0263600.010476-0.007051-0.005228-0.0245040.0090370.015518
1-0.034314-0.0000510.023871-0.0977140.070291-0.0470700.091474-0.057706-0.268044-0.026221...-0.0377630.0298780.045231-0.062290-0.131012-0.080212-0.024728-0.0485430.021252-0.004852
2-0.3215720.0455320.004737-0.070684-0.024462-0.011495-0.1508030.258310-0.0236450.054582...0.0710990.1113380.020180-0.049511-0.0599650.063550-0.0193180.1263840.0015820.015054
3-0.009988-0.0036480.0122130.1211170.031789-0.031690-0.0339590.002975-0.057448-0.136400...-0.0689560.0317790.2455070.0279350.038758-0.057646-0.054516-0.055807-0.005945-0.003330
4-0.1045260.0660710.0520310.0070250.0832350.038949-0.0758130.1052650.0762750.093963...-0.0274020.0419360.0136380.0207560.0093670.0037280.0263300.0069120.076745-0.143186
50.1023690.0602840.0383350.013700-0.1114080.2787520.0662900.134587-0.1683860.098990...-0.064056-0.045495-0.201629-0.056866-0.007317-0.084082-0.0517030.098773-0.122135-0.010227
6-0.177834-0.0420880.0004440.0103410.1082040.0059780.027057-0.006823-0.105048-0.166809...0.147754-0.0219450.1992060.040381-0.033136-0.0525120.018241-0.0031540.250065-0.082886
70.064315-0.1799680.041989-0.258878-0.1587740.0524210.052402-0.184161-0.064313-0.016940...0.120286-0.008686-0.2314000.272920-0.0210500.1092550.1187020.021676-0.181603-0.013686
80.0229220.3122080.010867-0.1914990.106837-0.229320-0.008715-0.2107330.0376570.062167...0.0856940.040236-0.080379-0.0423020.103474-0.3191160.086957-0.0021220.176039-0.005275
9-0.3071940.0439820.100535-0.267381-0.050292-0.010615-0.1690090.1122140.375080-0.178900...0.012742-0.063244-0.010274-0.093946-0.0648350.0155740.0669800.022103-0.124436-0.030850
10-0.460474-0.1617750.2486940.0264410.1601280.045803-0.233451-0.0907970.1124070.233218...0.098966-0.276042-0.0146790.2995110.042258-0.020505-0.006881-0.0299530.2510950.169541
11-0.0115330.1128400.052935-0.015447-0.496742-0.0036450.0266470.325467-0.0012020.038805...0.125546-0.109403-0.441826-0.083174-0.185597-0.0141980.024846-0.0647950.0876140.030194
120.2456130.296580-0.038640-0.305640-0.1242000.2710390.2563760.5410470.3425600.444382...0.560416-0.040864-0.018886-0.0350570.0085670.1156180.088392-0.240834-0.047551-0.004341
13-0.6605920.019101-0.120316-0.134759-0.079046-0.341223-0.456594-0.1039170.044514-0.499875...0.0235340.128351-0.001446-0.2763610.0206400.088788-0.147279-0.0299440.0295810.008861
14-0.085970-0.005224-0.109311-0.1248550.0533980.029236-0.034264-0.2095970.003543-0.030162...0.104430-0.231496-0.059287-0.0011350.0463760.015712-0.034782-0.0055700.020778-0.011761
15-0.010765-0.039279-0.0287200.005197-0.021794-0.0203780.0051750.0069820.017629-0.019821...-0.0112460.0082540.0467500.0606180.022018-0.0947010.021379-0.037993-0.040373-0.096038
16-0.1021860.1180450.0078310.0337100.026295-0.103503-0.186066-0.0387860.027735-0.093989...-0.2338360.1374480.046462-0.017949-0.011706-0.0106050.010610-0.0569850.0044760.000662
17-0.1334060.2009710.1154940.2107920.246593-0.616548-0.2654240.1082770.383489-0.634510...0.0057810.341722-0.0291500.164946-0.0039800.1461800.165554-0.024582-0.000005-0.012273
18-0.0318180.0058470.032746-0.1345670.3279350.030983-0.0194910.0834260.073085-0.001174...-0.0621920.260967-0.0088460.142483-0.016763-0.008566-0.008396-0.0538640.0544780.011180
19-0.6008470.0537160.075986-0.0491030.0484170.555588-0.3325650.418213-0.3720070.248540...-0.0528420.318683-0.0094270.426128-0.065958-0.0972750.2615840.066420-0.0608360.083081
\n", "

20 rows × 2000 columns

\n", "
" ], "text/plain": [ "symbol A2M AADACL2 AADACL3 AARD ABCB10P1 ABI3BP ACAN \\\n", "0 0.015295 0.071497 0.035568 0.089748 -0.044069 0.073249 0.057849 \n", "1 -0.034314 -0.000051 0.023871 -0.097714 0.070291 -0.047070 0.091474 \n", "2 -0.321572 0.045532 0.004737 -0.070684 -0.024462 -0.011495 -0.150803 \n", "3 -0.009988 -0.003648 0.012213 0.121117 0.031789 -0.031690 -0.033959 \n", "4 -0.104526 0.066071 0.052031 0.007025 0.083235 0.038949 -0.075813 \n", "5 0.102369 0.060284 0.038335 0.013700 -0.111408 0.278752 0.066290 \n", "6 -0.177834 -0.042088 0.000444 0.010341 0.108204 0.005978 0.027057 \n", "7 0.064315 -0.179968 0.041989 -0.258878 -0.158774 0.052421 0.052402 \n", "8 0.022922 0.312208 0.010867 -0.191499 0.106837 -0.229320 -0.008715 \n", "9 -0.307194 0.043982 0.100535 -0.267381 -0.050292 -0.010615 -0.169009 \n", "10 -0.460474 -0.161775 0.248694 0.026441 0.160128 0.045803 -0.233451 \n", "11 -0.011533 0.112840 0.052935 -0.015447 -0.496742 -0.003645 0.026647 \n", "12 0.245613 0.296580 -0.038640 -0.305640 -0.124200 0.271039 0.256376 \n", "13 -0.660592 0.019101 -0.120316 -0.134759 -0.079046 -0.341223 -0.456594 \n", "14 -0.085970 -0.005224 -0.109311 -0.124855 0.053398 0.029236 -0.034264 \n", "15 -0.010765 -0.039279 -0.028720 0.005197 -0.021794 -0.020378 0.005175 \n", "16 -0.102186 0.118045 0.007831 0.033710 0.026295 -0.103503 -0.186066 \n", "17 -0.133406 0.200971 0.115494 0.210792 0.246593 -0.616548 -0.265424 \n", "18 -0.031818 0.005847 0.032746 -0.134567 0.327935 0.030983 -0.019491 \n", "19 -0.600847 0.053716 0.075986 -0.049103 0.048417 0.555588 -0.332565 \n", "\n", "symbol ACKR3 ACP3 ACTA2 ... XBP1 XCL2 ZNF683 \\\n", "0 0.014150 -0.007009 -0.008777 ... -0.029756 0.015113 0.042406 \n", "1 -0.057706 -0.268044 -0.026221 ... -0.037763 0.029878 0.045231 \n", "2 0.258310 -0.023645 0.054582 ... 0.071099 0.111338 0.020180 \n", "3 0.002975 -0.057448 -0.136400 ... -0.068956 0.031779 0.245507 \n", "4 0.105265 0.076275 0.093963 ... -0.027402 0.041936 0.013638 \n", "5 0.134587 -0.168386 0.098990 ... -0.064056 -0.045495 -0.201629 \n", "6 -0.006823 -0.105048 -0.166809 ... 0.147754 -0.021945 0.199206 \n", "7 -0.184161 -0.064313 -0.016940 ... 0.120286 -0.008686 -0.231400 \n", "8 -0.210733 0.037657 0.062167 ... 0.085694 0.040236 -0.080379 \n", "9 0.112214 0.375080 -0.178900 ... 0.012742 -0.063244 -0.010274 \n", "10 -0.090797 0.112407 0.233218 ... 0.098966 -0.276042 -0.014679 \n", "11 0.325467 -0.001202 0.038805 ... 0.125546 -0.109403 -0.441826 \n", "12 0.541047 0.342560 0.444382 ... 0.560416 -0.040864 -0.018886 \n", "13 -0.103917 0.044514 -0.499875 ... 0.023534 0.128351 -0.001446 \n", "14 -0.209597 0.003543 -0.030162 ... 0.104430 -0.231496 -0.059287 \n", "15 0.006982 0.017629 -0.019821 ... -0.011246 0.008254 0.046750 \n", "16 -0.038786 0.027735 -0.093989 ... -0.233836 0.137448 0.046462 \n", "17 0.108277 0.383489 -0.634510 ... 0.005781 0.341722 -0.029150 \n", "18 0.083426 0.073085 -0.001174 ... -0.062192 0.260967 -0.008846 \n", "19 0.418213 -0.372007 0.248540 ... -0.052842 0.318683 -0.009427 \n", "\n", "symbol ZNF723 ZNRF4 ZP2 ZP4 ZPLD1 ZSCAN10 ZSWIM5P3 \n", "0 0.026360 0.010476 -0.007051 -0.005228 -0.024504 0.009037 0.015518 \n", "1 -0.062290 -0.131012 -0.080212 -0.024728 -0.048543 0.021252 -0.004852 \n", "2 -0.049511 -0.059965 0.063550 -0.019318 0.126384 0.001582 0.015054 \n", "3 0.027935 0.038758 -0.057646 -0.054516 -0.055807 -0.005945 -0.003330 \n", "4 0.020756 0.009367 0.003728 0.026330 0.006912 0.076745 -0.143186 \n", "5 -0.056866 -0.007317 -0.084082 -0.051703 0.098773 -0.122135 -0.010227 \n", "6 0.040381 -0.033136 -0.052512 0.018241 -0.003154 0.250065 -0.082886 \n", "7 0.272920 -0.021050 0.109255 0.118702 0.021676 -0.181603 -0.013686 \n", "8 -0.042302 0.103474 -0.319116 0.086957 -0.002122 0.176039 -0.005275 \n", "9 -0.093946 -0.064835 0.015574 0.066980 0.022103 -0.124436 -0.030850 \n", "10 0.299511 0.042258 -0.020505 -0.006881 -0.029953 0.251095 0.169541 \n", "11 -0.083174 -0.185597 -0.014198 0.024846 -0.064795 0.087614 0.030194 \n", "12 -0.035057 0.008567 0.115618 0.088392 -0.240834 -0.047551 -0.004341 \n", "13 -0.276361 0.020640 0.088788 -0.147279 -0.029944 0.029581 0.008861 \n", "14 -0.001135 0.046376 0.015712 -0.034782 -0.005570 0.020778 -0.011761 \n", "15 0.060618 0.022018 -0.094701 0.021379 -0.037993 -0.040373 -0.096038 \n", "16 -0.017949 -0.011706 -0.010605 0.010610 -0.056985 0.004476 0.000662 \n", "17 0.164946 -0.003980 0.146180 0.165554 -0.024582 -0.000005 -0.012273 \n", "18 0.142483 -0.016763 -0.008566 -0.008396 -0.053864 0.054478 0.011180 \n", "19 0.426128 -0.065958 -0.097275 0.261584 0.066420 -0.060836 0.083081 \n", "\n", "[20 rows x 2000 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# specify the view of which we want to retrieve the loadings\n", "W = sofa.tl.get_loadings(model, view=\"RNA\")\n", "W" ] }, { "cell_type": "code", "execution_count": null, "id": "0b591988-2db2-4be5-84f1-f4560e140cb8", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# to get the names of the top 100 negative loadings of factor 5 (guided by hematopoietic lineage) of the RNA modality run:\n", "top_loadings = sofa.tl.get_top_loadings(model, view=\"RNA\", factor = 6, top_n=100, sign=\"-\")\n", "# this can be useful to perform gene overrepresentation analysis:\n", "sofa.pl.plot_enrichment(top_loadings, # the top features\n", " background=mdata.mod[\"RNA\"].var, # all genes considered in the analysis, used as background\n", " db=[\"GO_Biological_Process_2023\", \"KEGG_2021_Human\"], # a list of databases for overrepresentation analysis, \n", " top_n=[5,5]) # the number of genesets for each database to plot\n", "# sofa.pl.plot_enrichment uses the enrichr API, please refer to https://maayanlab.cloud/Enrichr/#libraries for a full list of available databases" ] }, { "cell_type": "markdown", "id": "c9767393-3019-4997-8258-301fe1f48006", "metadata": { "tags": [] }, "source": [ "As expected Factor 5 guided by the hematopoietic lineage captured genesets related to the immune system and the hematopoietic lineage.\\\n", "Additionally, we can also plot the top loadings weights:" ] }, { "cell_type": "code", "execution_count": 8, "id": "03b5296b-00cd-4e5d-8cd5-0e55f8cce276", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sofa.pl.plot_top_loadings(model, view=\"RNA\", factor = 5, top_n=10, sign=\"-\")" ] } ], "metadata": { "kernelspec": { "display_name": "scib", "language": "python", "name": "scib" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" }, "toc-autonumbering": true, "toc-showcode": false, "toc-showmarkdowntxt": false, "toc-showtags": false }, "nbformat": 4, "nbformat_minor": 5 }