{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Multimodal Single-Cell Integration Competition: Data Exploration and Visualization","metadata":{"execution":{"iopub.status.busy":"2022-08-11T21:21:49.972077Z","iopub.execute_input":"2022-08-11T21:21:49.972474Z","iopub.status.idle":"2022-08-11T21:21:49.977181Z","shell.execute_reply.started":"2022-08-11T21:21:49.972443Z","shell.execute_reply":"2022-08-11T21:21:49.975948Z"}}},{"cell_type":"markdown","source":"## 1. Setup Notebook","metadata":{"execution":{"iopub.status.busy":"2022-08-11T21:25:31.091566Z","iopub.execute_input":"2022-08-11T21:25:31.092024Z","iopub.status.idle":"2022-08-11T21:25:31.097555Z","shell.execute_reply.started":"2022-08-11T21:25:31.091958Z","shell.execute_reply":"2022-08-11T21:25:31.096274Z"}}},{"cell_type":"markdown","source":"### 1.1. Import packages\n\n","metadata":{}},{"cell_type":"code","source":"#If you see a urllib warning running this cell, go to \"Settings\" on the right hand side, \n#and turn on internet. Note, you need to be phone verified.\n!pip install --quiet tables","metadata":{"execution":{"iopub.status.busy":"2022-08-15T23:46:39.790643Z","iopub.execute_input":"2022-08-15T23:46:39.791551Z","iopub.status.idle":"2022-08-15T23:46:51.623497Z","shell.execute_reply.started":"2022-08-15T23:46:39.791482Z","shell.execute_reply":"2022-08-15T23:46:51.621971Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns","metadata":{"execution":{"iopub.status.busy":"2022-08-15T23:46:51.631535Z","iopub.execute_input":"2022-08-15T23:46:51.631991Z","iopub.status.idle":"2022-08-15T23:46:52.636409Z","shell.execute_reply.started":"2022-08-15T23:46:51.631933Z","shell.execute_reply":"2022-08-15T23:46:52.635362Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 1.2. Set filepaths","metadata":{"execution":{"iopub.status.busy":"2022-08-11T21:22:04.888845Z","iopub.execute_input":"2022-08-11T21:22:04.889239Z","iopub.status.idle":"2022-08-11T21:22:04.893408Z","shell.execute_reply.started":"2022-08-11T21:22:04.889209Z","shell.execute_reply":"2022-08-11T21:22:04.892357Z"}}},{"cell_type":"code","source":"os.listdir(\"/kaggle/input/open-problems-multimodal/\")","metadata":{"execution":{"iopub.status.busy":"2022-08-15T23:46:52.637863Z","iopub.execute_input":"2022-08-15T23:46:52.638512Z","iopub.status.idle":"2022-08-15T23:46:52.651998Z","shell.execute_reply.started":"2022-08-15T23:46:52.638464Z","shell.execute_reply":"2022-08-15T23:46:52.650647Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"DATA_DIR = \"/kaggle/input/open-problems-multimodal/\"\nFP_CELL_METADATA = os.path.join(DATA_DIR,\"metadata.csv\")\n\nFP_CITE_TRAIN_INPUTS = os.path.join(DATA_DIR,\"train_cite_inputs.h5\")\nFP_CITE_TRAIN_TARGETS = os.path.join(DATA_DIR,\"train_cite_targets.h5\")\nFP_CITE_TEST_INPUTS = os.path.join(DATA_DIR,\"test_cite_inputs.h5\")\n\nFP_MULTIOME_TRAIN_INPUTS = os.path.join(DATA_DIR,\"train_multi_inputs.h5\")\nFP_MULTIOME_TRAIN_TARGETS = os.path.join(DATA_DIR,\"train_multi_targets.h5\")\nFP_MULTIOME_TEST_INPUTS = os.path.join(DATA_DIR,\"test_multi_inputs.h5\")\n\nFP_SUBMISSION = os.path.join(DATA_DIR,\"sample_submission.csv\")\nFP_EVALUATION_IDS = os.path.join(DATA_DIR,\"evaluation_ids.csv\")","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-08-15T23:46:52.655393Z","iopub.execute_input":"2022-08-15T23:46:52.656239Z","iopub.status.idle":"2022-08-15T23:46:52.663207Z","shell.execute_reply.started":"2022-08-15T23:46:52.656207Z","shell.execute_reply":"2022-08-15T23:46:52.662180Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 2. Load and Visualize Data","metadata":{"execution":{"iopub.status.busy":"2022-08-11T21:26:56.381326Z","iopub.execute_input":"2022-08-11T21:26:56.381727Z","iopub.status.idle":"2022-08-11T21:26:56.386286Z","shell.execute_reply.started":"2022-08-11T21:26:56.381695Z","shell.execute_reply":"2022-08-11T21:26:56.385262Z"}}},{"cell_type":"markdown","source":"### 2.1. Cell Metadata","metadata":{}},{"cell_type":"markdown","source":"The metadata of our dataset comes is data about the cells. To understand the different groups of cells, let's first review how the experiment was conducted (see figure below):\n1. On the first day (*day 1*), hemapoetic stem cells are cultured in a dish with liquids that trigger the differentation of these cells into blood cells.\n2. On subsequent *days 2,3,4,7,10* some of the cells are removed and split into two subgroups `CITE` and `MULTIOME`.\n3. Each of these assays (technologies) gives us two readouts per single cell: \n    1. CITEseq measures gene expression (RNA) and surface protein levels.\n    2. Multiome measures gene expression (RNA) and chromatin accessibility (via ATACseq).\n\nThis experiment was repeated for 4 different donors of hemapoetic stem cells. The metadata gives information about day, donor, cell type,\nand technology. `cell_id` is a unique cell identifier and has no meaning beyond its purpose as a cell 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= pd.read_csv(FP_CELL_METADATA)\ndf_cell","metadata":{"execution":{"iopub.status.busy":"2022-08-15T23:46:52.664798Z","iopub.execute_input":"2022-08-15T23:46:52.665908Z","iopub.status.idle":"2022-08-15T23:46:53.042116Z","shell.execute_reply.started":"2022-08-15T23:46:52.665873Z","shell.execute_reply":"2022-08-15T23:46:53.040721Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**NOTE:** the cell type is hidden for the test set of the multiome as this can reveal information about the RNA.","metadata":{}},{"cell_type":"markdown","source":"**Let's split the cells by technology**","metadata":{}},{"cell_type":"code","source":"df_cell_cite = df_cell[df_cell.technology==\"citeseq\"]\ndf_cell_multi = df_cell[df_cell.technology==\"multiome\"]","metadata":{"execution":{"iopub.status.busy":"2022-08-15T23:46:53.044190Z","iopub.execute_input":"2022-08-15T23:46:53.044700Z","iopub.status.idle":"2022-08-15T23:46:53.112705Z","shell.execute_reply.started":"2022-08-15T23:46:53.044652Z","shell.execute_reply":"2022-08-15T23:46:53.111758Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Number of cells per group:**\n\nThe number of cells in each group is relatively constant, around 7500 cells per donor and day.","metadata":{}},{"cell_type":"code","source":"fig, axs = plt.subplots(1,2,figsize=(12,6))\ndf_cite_cell_dist = df_cell_cite.set_index(\"cell_id\")[[\"day\",\"donor\"]].value_counts().to_frame()\\\n                .sort_values(\"day\").reset_index()\\\n                .rename(columns={0:\"# cells\"})\nsns.barplot(data=df_cite_cell_dist, x=\"day\",hue=\"donor\",y=\"# cells\", ax=axs[0])\naxs[0].set_title(\"Number of cells measured with CITEseq\")\n\ndf_multi_cell_dist = df_cell_multi.set_index(\"cell_id\")[[\"day\",\"donor\"]].value_counts().to_frame()\\\n                .sort_values(\"day\").reset_index()\\\n                .rename(columns={0:\"# cells\"})\nsns.barplot(data=df_multi_cell_dist, x=\"day\",hue=\"donor\",y=\"# cells\", ax=axs[1])\naxs[1].set_title(\"Number of cells measured with Multiome\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-08-15T23:46:53.113951Z","iopub.execute_input":"2022-08-15T23:46:53.114299Z","iopub.status.idle":"2022-08-15T23:46:53.692625Z","shell.execute_reply.started":"2022-08-15T23:46:53.114267Z","shell.execute_reply":"2022-08-15T23:46:53.691396Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 2.2. Citeseq","metadata":{"execution":{"iopub.status.busy":"2022-08-11T21:31:40.539251Z","iopub.execute_input":"2022-08-11T21:31:40.540195Z","iopub.status.idle":"2022-08-11T21:31:40.544877Z","shell.execute_reply.started":"2022-08-11T21:31:40.540149Z","shell.execute_reply":"2022-08-11T21:31:40.543637Z"}}},{"cell_type":"markdown","source":"For CITEseq, the task is to predict surface protein levels (\"targets\") from RNA expression levels (\"inputs\" of the model).","metadata":{"execution":{"iopub.status.busy":"2022-08-12T19:04:11.897950Z","iopub.execute_input":"2022-08-12T19:04:11.899414Z","iopub.status.idle":"2022-08-12T19:04:11.911882Z","shell.execute_reply.started":"2022-08-12T19:04:11.899363Z","shell.execute_reply":"2022-08-12T19:04:11.910607Z"}}},{"cell_type":"markdown","source":"**Inputs:** For the RNA counts, each row corresponds to a cell and each column to a gene. The column format for a gene is given by `{EnsemblID}_{GeneName}` where `EnsemblID` refers to the [Ensembl Gene ID](https://www.ebi.ac.uk/training/online/courses/ensembl-browsing-genomes/navigating-ensembl/investigating-a-gene/#:~:text=Ensembl%20gene%20IDs%20begin%20with,of%20species%20other%20than%20human) and `GeneName` to the gene name.","metadata":{"execution":{"iopub.status.busy":"2022-08-11T21:34:17.940560Z","iopub.execute_input":"2022-08-11T21:34:17.941289Z","iopub.status.idle":"2022-08-11T21:34:17.948656Z","shell.execute_reply.started":"2022-08-11T21:34:17.941248Z","shell.execute_reply":"2022-08-11T21:34:17.946808Z"}}},{"cell_type":"code","source":"df_cite_train_x = pd.read_hdf(FP_CITE_TRAIN_INPUTS)\ndf_cite_test_x = pd.read_hdf(FP_CITE_TEST_INPUTS)\ndf_cite_train_x.head()","metadata":{"execution":{"iopub.status.busy":"2022-08-15T23:46:53.694234Z","iopub.execute_input":"2022-08-15T23:46:53.695397Z","iopub.status.idle":"2022-08-15T23:48:25.061701Z","shell.execute_reply.started":"2022-08-15T23:46:53.695349Z","shell.execute_reply":"2022-08-15T23:48:25.060492Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Targets:** For the surface protein levels, each row corresponds to a cell and each column to a protein:","metadata":{"execution":{"iopub.status.busy":"2022-08-12T20:10:22.275013Z","iopub.execute_input":"2022-08-12T20:10:22.276110Z","iopub.status.idle":"2022-08-12T20:10:22.282283Z","shell.execute_reply.started":"2022-08-12T20:10:22.276047Z","shell.execute_reply":"2022-08-12T20:10:22.280977Z"}}},{"cell_type":"code","source":"df_cite_train_y = pd.read_hdf(FP_CITE_TRAIN_TARGETS)\ndf_cite_train_y.head()","metadata":{"execution":{"iopub.status.busy":"2022-08-15T23:48:25.065281Z","iopub.execute_input":"2022-08-15T23:48:25.065669Z","iopub.status.idle":"2022-08-15T23:48:25.712963Z","shell.execute_reply.started":"2022-08-15T23:48:25.065635Z","shell.execute_reply":"2022-08-15T23:48:25.711766Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Donor and cell types:** The train data consists of both gene expression (RNA) and surface protein data for days 2,3,4 for donors 1-3 (donor IDs: `32606`,`13176`, and `31800`), the public test data consists of RNA for days 2,3,4 for donor 4 (donor ID: `27678`) and the private test data consists data from day 7 from all donors.","metadata":{}},{"cell_type":"code","source":"train_cells = df_cite_train_x.index.to_list()    \ntest_cells = df_cite_test_x.index.to_list()                                                     \ndf_cell_cite[\"split\"] = \"\"\ndf_cell_cite.loc[df_cell_cite.cell_id.isin(train_cells),\"split\"] = \"train\"\ndf_cell_cite.loc[df_cell_cite.cell_id.isin(test_cells),\"split\"] = \"test\"\n\ndf_cell_cite[[\"split\",\"day\",\"donor\"]].value_counts().to_frame().sort_values([\"split\",\"day\",\"donor\"]).rename(columns={0: \"n cells\"})","metadata":{"execution":{"iopub.status.busy":"2022-08-15T23:48:25.714352Z","iopub.execute_input":"2022-08-15T23:48:25.714684Z","iopub.status.idle":"2022-08-15T23:48:25.836993Z","shell.execute_reply.started":"2022-08-15T23:48:25.714654Z","shell.execute_reply":"2022-08-15T23:48:25.835719Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 2.3. Multiome\n\nFor the Multiome data set, the task is to predict RNA levels (\"targets\") from ATACseq.","metadata":{}},{"cell_type":"markdown","source":"**Inputs:** for the ATACseq data, each row corresponds to a cell and each column to a fragment of a gene.\n\n<font fontsize=20 color=\"red\"> **NOTE**: to save memory, we only read an excerpt from the ATACseq data!","metadata":{}},{"cell_type":"code","source":"START = int(1e5)\nSTOP = START+1000\n\ndf_multi_train_x = pd.read_hdf(FP_MULTIOME_TRAIN_INPUTS,start=START,stop=STOP)\ndf_multi_test_x = pd.read_hdf(FP_MULTIOME_TEST_INPUTS,start=START,stop=STOP)\ndf_multi_train_x.head()","metadata":{"execution":{"iopub.status.busy":"2022-08-15T23:48:25.838989Z","iopub.execute_input":"2022-08-15T23:48:25.839814Z","iopub.status.idle":"2022-08-15T23:48:30.302909Z","shell.execute_reply.started":"2022-08-15T23:48:25.839770Z","shell.execute_reply":"2022-08-15T23:48:30.300413Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Targets:** the RNA count data is in similar shape as the RNA count data from CITEseq:","metadata":{}},{"cell_type":"code","source":"df_multi_train_y = pd.read_hdf(FP_MULTIOME_TRAIN_TARGETS, start=START, stop=STOP)\ndf_multi_train_y.head()","metadata":{"execution":{"iopub.status.busy":"2022-08-15T23:48:30.304123Z","iopub.execute_input":"2022-08-15T23:48:30.304480Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Donor and cell types:** The train data consists of both gene expression (RNA) and ATACseq data for days 2,3,4,7 for donors 1-3 (donor IDs: `32606`,`13176`, and `31800`), the public test data consists of RNA for days 2,3,4,7 for donor 4 (donor ID: `27678`) and the private test data consists data from day 7 from all donors.\n\n<font fontsize=20 color=\"red\"> **NOTE**: Uncomment the below cell if you have loaded the full ATACseq data!","metadata":{}},{"cell_type":"code","source":"# train_cells = df_multi_train_y.index.to_list()    \n# test_cells = df_multi_test_y.index.to_list()                                                     \n# df_cell_multi[\"split\"] = \"\"\n# df_cell_multi.loc[df_cell_multi.cell_id.isin(train_cells),\"split\"] = \"train\"\n# df_cell_multi.loc[df_cell_multi.cell_id.isin(test_cells),\"split\"] = \"test\"\n\n# df_cell_multi[[\"split\",\"day\",\"donor\"]].value_counts().to_frame().sort_values([\"split\",\"day\",\"donor\"]).rename(columns={0: \"n cells\"})","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}