diff --git a/.github/workflows/continuous_integration.yml b/.github/workflows/continuous_integration.yml index 8109f213..113c905d 100644 --- a/.github/workflows/continuous_integration.yml +++ b/.github/workflows/continuous_integration.yml @@ -14,7 +14,7 @@ jobs: uses: actions/checkout@v4 - name: Install uv - uses: astral-sh/setup-uv@v3 + uses: astral-sh/setup-uv@v4 - name: Create Virtual Environment run: uv venv @@ -26,17 +26,17 @@ jobs: run: uv run jupyter nbconvert --clear-output --inplace "main/COMO.ipynb" - name: Format Python Imports - uses: astral-sh/ruff-action@v1 + uses: astral-sh/ruff-action@v2 with: args: "check --fix --select I" - name: Format code - uses: astral-sh/ruff-action@v1 + uses: astral-sh/ruff-action@v2 with: args: "format" - name: Format Notebook - uses: astral-sh/ruff-action@v1 + uses: astral-sh/ruff-action@v2 with: args: "format main/COMO.ipynb" @@ -54,7 +54,7 @@ jobs: uses: actions/checkout@v4 - name: Check Lint - uses: astral-sh/ruff-action@v1 + uses: astral-sh/ruff-action@v2 with: args: "check --no-fix --verbose" diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 16d682c0..bf67014b 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,8 +1,6 @@ -# See https://pre-commit.com for more information -# See https://pre-commit.com/hooks.html for more hooks repos: -- repo: https://github.com/opensource-nepal/commitlint - rev: v1.2.0 + - repo: https://github.com/commitizen-tools/commitizen + rev: master hooks: - - id: commitlint - name: Commit Lint + - id: commitizen + stages: [ commit-msg ] \ No newline at end of file diff --git a/main/COMO.ipynb b/main/COMO.ipynb index 8178cd67..d361f9b2 100644 --- a/main/COMO.ipynb +++ b/main/COMO.ipynb @@ -235,27 +235,66 @@ ] }, { - "metadata": {}, "cell_type": "code", - "outputs": [], - "execution_count": null, + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-07T03:30:27.253112Z", + "start_time": "2024-12-07T03:30:27.236557Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001B[32m2024-12-06 23:12:10\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m629\u001B[0m - \u001B[1mTEST\u001B[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001B[32m2024-12-06 23:12:11\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m274\u001B[0m - \u001B[32m\u001B[1mWrote gene count matrix for 'total' RNA at '/Users/joshl/Projects/COMO/main/data/results/naiveB/total-rna/totalrna_naiveB.csv'\u001B[0m\n", + "\u001B[32m2024-12-06 23:12:11\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m274\u001B[0m - \u001B[32m\u001B[1mWrote gene count matrix for 'polya' RNA at '/Users/joshl/Projects/COMO/main/data/results/naiveB/polya-rna/polyarna_naiveB.csv'\u001B[0m\n", + "\u001B[32m2024-12-06 23:12:11\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m451\u001B[0m - \u001B[1mFetching gene info (this may take 1-5 minutes)\u001B[0m\n", + "\u001B[32m2024-12-06 23:13:04\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m488\u001B[0m - \u001B[32m\u001B[1mGene Info file written at '/Users/joshl/Projects/COMO/main/data/results/naiveB/gene_info.csv'\u001B[0m\n" + ] + } + ], "source": [ - "context_names = \"naiveB\"\n", - "taxon_id = \"human\" # accepts integer (bioDBnet taxon id) or \"human\" or \"mouse\"\n", - "preprocess_mode = \"create\" # \"create\" or \"provide\"\n", - "\n", - "# fmt: off\n", - "cmd = \" \".join(\n", - " [\n", - " \"python3\", \"como/rnaseq_preprocess.py\",\n", - " \"--context-names\", context_names,\n", - " \"--taxon-id\", taxon_id,\n", - " \"--mode\", preprocess_mode,\n", - " ]\n", - ")\n", - "# fmt: on\n", + "from pathlib import Path\n", "\n", - "!{cmd}" + "from como.rnaseq_preprocess import rnaseq_preprocess\n", + "from como.types import RNAPrepMethod\n", + "\n", + "context_names = [\"naiveB\"]\n", + "output_gene_info_filepaths = [Path(f\"data/results/{context}/gene_info.csv\") for context in context_names]\n", + "como_context_dirs = [Path(f\"data/COMO_input/{context}\") for context in context_names]\n", + "output_trna_filepaths = [Path(f\"data/results/{context}/total-rna/totalrna_{context}.csv\") for context in context_names]\n", + "output_polya_filepaths = [Path(f\"data/results/{context}/polya-rna/polyarna_{context}.csv\") for context in context_names]\n", + "\n", + "\n", + "for i in range(len(context_names)):\n", + " await rnaseq_preprocess(\n", + " context_name=context_names[i],\n", + " taxon=9606,\n", + " output_gene_info_filepath=output_gene_info_filepaths[i],\n", + " como_context_dir=como_context_dirs[i],\n", + " output_trna_config_filepath=Path(\"./data/config_sheets/trna_config.xlsx\"),\n", + " output_trna_count_matrix_filepath=output_trna_filepaths[i],\n", + " output_polya_config_filepath=Path(\"./data/config_sheets/mrna_config.xlsx\"),\n", + " output_polya_count_matrix_filepath=output_polya_filepaths[i],\n", + " cache=True,\n", + " log_level=\"INFO\",\n", + " )" ] }, { @@ -329,10 +368,10 @@ ] }, { - "metadata": {}, "cell_type": "code", - "outputs": [], "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# step 2.2 RNA-seq Analysis for Total RNA-seq library preparation\n", "\n", @@ -385,10 +424,10 @@ ] }, { - "metadata": {}, "cell_type": "code", - "outputs": [], "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "mrnaseq_config_file = \"mrnaseq_data_inputs_auto.xlsx\"\n", "rep_ratio = 0.75\n", @@ -439,10 +478,10 @@ ] }, { - "metadata": {}, "cell_type": "code", - "outputs": [], "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "scrnaseq_config_file = \"scrnaseq_data_inputs_auto.xlsx\"\n", "rep_ratio = 0.75\n", @@ -627,10 +666,10 @@ ] }, { - "metadata": {}, "cell_type": "code", - "outputs": [], "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "expression_requirement = 3\n", "requirement_adjust = \"regressive\"\n", @@ -730,125 +769,16 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/joshl/Projects/COMO/.venv/lib/python3.10/site-packages/cobamp/wrappers/external_wrappers.py:9: UserWarning: \n", - "The wrappers.external_wrappers module will be deprecated in a future release in favour of the wrappers module. \n", - " Available ModelObjectReader classes can still be loaded using cobamp.wrappers.. An appropriate model \n", - " reader can also be created using the get_model_reader function on cobamp.wrappers\n", - " warnings.warn(\n", - "\u001B[32m2024-12-04 10:27:00.656\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36mcreate_context_specific_model\u001B[0m:\u001B[36m654\u001B[0m - \u001B[1mCreating 'naiveB' model using 'IMAT' reconstruction and 'GUROBI' solver\u001B[0m\n", - "\u001B[32m2024-12-04 10:27:00.656\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mcomo.project\u001B[0m:\u001B[36m__init__\u001B[0m:\u001B[36m30\u001B[0m - \u001B[33m\u001B[1m'data_dir' not provided to Config, using /Users/joshl/Projects/COMO/main/data\u001B[0m\n", - "\u001B[32m2024-12-04 10:27:00.656\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mcomo.project\u001B[0m:\u001B[36m__init__\u001B[0m:\u001B[36m36\u001B[0m - \u001B[33m\u001B[1m'config_dir' not provided to Config, using /Users/joshl/Projects/COMO/main/data/config_sheets\u001B[0m\n", - "\u001B[32m2024-12-04 10:27:00.656\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mcomo.project\u001B[0m:\u001B[36m__init__\u001B[0m:\u001B[36m42\u001B[0m - \u001B[33m\u001B[1m'results_dir' not provided to Config, using /Users/joshl/Projects/COMO/main/data/results\u001B[0m\n", - "Set parameter WLSAccessID\n", - "Set parameter WLSSecret\n", - "Set parameter LicenseID to value 898845\n", - "Academic license 898845 - for non-commercial use only - registered to jl___@huskers.unl.edu\n", - "No defined compartments in model GeneralModelUpdatedV2. Compartments will be deduced heuristically using regular expressions.\n", - "Using regular expression found the following compartments:c, e, g, i, l, m, n, r, x\n", - "/Users/joshl/Projects/COMO/main/como/utils.py:154: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['1' '10' '100' ... '9992' '9993' '9997']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", - " expression_data.loc[:, \"entrez_gene_id\"] = expression_data[\"entrez_gene_id\"].astype(str)\n", - "\u001B[32m2024-12-04 10:27:04.957\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36m_map_expression_to_reaction\u001B[0m:\u001B[36m391\u001B[0m - \u001B[1mMapped gene expression to reactions, found 2806 error(s).\u001B[0m\n", - "\u001B[32m2024-12-04 10:27:04.959\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36m_build_model\u001B[0m:\u001B[36m468\u001B[0m - \u001B[33m\u001B[1mThe force reaction 'ICDH_m' was not found in the general reference_model. Check BiGG, or the relevant database for your general reference_model, for synonyms.\u001B[0m\n", - "\u001B[32m2024-12-04 10:27:04.959\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36m_build_model\u001B[0m:\u001B[36m468\u001B[0m - \u001B[33m\u001B[1mThe force reaction 'CATC140_c' was not found in the general reference_model. Check BiGG, or the relevant database for your general reference_model, for synonyms.\u001B[0m\n", - "Read LP format model from file /var/folders/6s/9l9z74v90tn8lprrp7fchb9w0000gn/T/tmprdfbwfb2.lp\n", - "Reading time = 0.02 seconds\n", - ": 5837 rows, 21220 columns, 81018 nonzeros\n", - "/Users/joshl/Projects/COMO/.venv/lib/python3.10/site-packages/cobra/core/group.py:147: UserWarning: need to pass in a list\n", - " warn(\"need to pass in a list\")\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "\u001B[32m2024-12-04 10:27:45.905\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36mcreate_context_specific_model\u001B[0m:\u001B[36m690\u001B[0m - \u001B[32m\u001B[1mSaved output file to /Users/joshl/Projects/COMO/main/data/results/naiveB\u001B[0m\n", - "\u001B[32m2024-12-04 10:27:45.905\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36mcreate_context_specific_model\u001B[0m:\u001B[36m691\u001B[0m - \u001B[1mNumber of Genes: 1,477\u001B[0m\n", - "\u001B[32m2024-12-04 10:27:45.905\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36mcreate_context_specific_model\u001B[0m:\u001B[36m692\u001B[0m - \u001B[1mNumber of Metabolites: 3,204\u001B[0m\n", - "\u001B[32m2024-12-04 10:27:45.905\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36mcreate_context_specific_model\u001B[0m:\u001B[36m693\u001B[0m - \u001B[1mNumber of Reactions: 5,263\u001B[0m\n", - "Warning: environment still referenced so free is deferred (Continue to use WLS)\n" - ] - } - ], + "outputs": [], "source": [ + "import json\n", + "import os\n", + "from pathlib import Path\n", + "\n", + "from como.project import Config\n", + "\n", "# Set your objectives before running!\n", "objective_dict = {\"naiveB\": \"biomass_maintenance\", \"smB\": \"biomass_maintenance\"}\n", "# -----------------\n", @@ -860,34 +790,28 @@ "recon_algorithms = [\"IMAT\"]\n", "solver = \"GUROBI\"\n", "\n", - "import json\n", - "import os\n", - "from pathlib import Path\n", - "\n", - "from como.project import Config\n", - "\n", "config = Config()\n", "\n", "# Load the output of step 1, which is a dictionary that specifies the merged list of active Gene IDs for each tissue\n", - "step1_results_file = os.path.join(config.data_dir, \"results\", \"step1_results_files.json\")\n", - "with open(step1_results_file) as json_file:\n", + "step1_results_file = config.data_dir / \"results\" \"step1_results_files.json\"\n", + "with step1_results_file.open(\"r\") as json_file:\n", " context_gene_exp = json.load(json_file)\n", "\n", "for recon_algorithm in recon_algorithms:\n", - " for context in context_gene_exp.keys():\n", + " for context in context_gene_exp:\n", " objective = objective_dict[context]\n", "\n", " if recon_algorithm.upper() in [\"IMAT\", \"TINIT\"]:\n", - " active_genes_filepath = os.path.join(config.data_dir, \"results\", context, f\"model_scores_{context}.csv\")\n", + " active_genes_filepath = config.data_dir / \"results\" / context / f\"model_scores_{context}.csv\"\n", " else:\n", " gene_expression_file = context_gene_exp[context]\n", " active_genes_filename = Path(gene_expression_file).name\n", - " active_genes_filepath = os.path.join(config.data_dir, \"results\", context, active_genes_filename)\n", + " active_genes_filepath = config.data_dir / \"results\" / context / active_genes_filename\n", "\n", - " general_model_filepath = os.path.join(config.data_dir, \"GeneralModelUpdatedV2.mat\")\n", - " boundary_reactions_filepath = os.path.join(config.data_dir, \"boundary_rxns\", f\"{context}_boundary_rxns.csv\")\n", - " force_reactions_filepath = os.path.join(config.data_dir, \"force_rxns\", f\"{context}_force_rxns.csv\")\n", - " exclude_reactions_filepath = os.path.join(config.data_dir, \"exclude_rxns\", f\"{context}_exclude_rxns.csv\")\n", + " general_model_filepath = config.data_dir / \"GeneralModelUpdatedV2.mat\"\n", + " boundary_reactions_filepath = config.data_dir / \"boundary_rxns\" / f\"{context}_boundary_rxns.csv\"\n", + " force_reactions_filepath = config.data_dir / \"force_rxns\" / f\"{context}_force_rxns.csv\"\n", + " exclude_reactions_filepath = config.data_dir / \"exclude_rxns\" / f\"{context}_exclude_rxns.csv\"\n", "\n", " # fmt: off\n", " cmd = \" \".join(\n", @@ -941,16 +865,14 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", - "from pathlib import Path\n", - "\n", "import cobra\n", "from como.project import Config\n", "from escher import Builder\n", + "from loguru import logger\n", "\n", "config = Config()\n", "\n", - "user_map_dir = Path(f\"{config.data_dir}/local_files/maps/\")\n", + "user_map_dir = config.data_dir / \"local_files\" / \"maps\"\n", "map_dict = {\n", " \"trypto\": f\"{config.data_dir}/maps/RECON1/RECON1.tryptophan_metabolism.json\",\n", " # \"lipid\": f\"{config.data_dir}/maps/RECON1/RECON1.\", # Not present in COMO by default yet\n", @@ -968,20 +890,18 @@ " index += 1\n", "\n", "# Collect any additional maps under the `{config.data_dir}/maps/` directory\n", - "for file in Path(f\"{config.data_dir}/maps\").glob(\"**/*.json\"):\n", + "for file in (config.data_dir / \"maps\").glob(\"**/*.json\"):\n", " if file not in map_dict.values():\n", " map_dict[file.stem] = file\n", "\n", "for recon_algorithm in recon_algorithms:\n", - " for context in context_gene_exp.keys():\n", - " # for context in [\"naiveB\", \"smB\"]:\n", - " print(f\"Starting {context}\")\n", - " model_json = os.path.join(config.data_dir, \"results\", context, f\"{context}_SpecificModel_{recon_algorithm}.json\")\n", + " for context in context_gene_exp:\n", + " model_json = config.data_dir / \"results\" / context / f\"{context}_SpecificModel_{recon_algorithm}.json\"\n", "\n", - " print(f\"Loading '{context}', this may take some time...\")\n", + " logger.info(f\"Loading '{context}', this may take some time...\")\n", " model = cobra.io.load_json_model(model_json)\n", - " for key in map_dict.keys():\n", - " print(f\"Running with: {key}\")\n", + " for key in map_dict:\n", + " logger.info(f\"Running with: {key}\")\n", " builder = Builder(map_json=str(map_dict[key]))\n", " builder.model = model\n", " solution = cobra.flux_analysis.pfba(model)\n", @@ -995,19 +915,22 @@ " ]\n", " builder.reaction_no_data_color = \"#8e8e8e\"\n", "\n", - " builder.save_html(os.path.join(config.data_dir, \"results\", context, \"figures\", f\"{key}_map_{context}_{recon_algorithm}.html\"))\n", + " builder.save_html(\n", + " config.data_dir / \"results\" / context / \"figures\" / f\"{key}_map_{context}_{recon_algorithm}.html\"\n", + " )\n", "\n", - " out_dir = os.path.join(config.data_dir, \"results\", context)\n", - " # for algorithm in [\"GIMME\", \"IMAT\", \"FASTCORE\", \"tINIT\"]:\n", - " report_file = os.path.join(out_dir, f\"memote_report_{context}_{recon_algorithm}.html\")\n", - " model_file = os.path.join(out_dir, f\"{context}_SpecificModel_{recon_algorithm}.xml\")\n", - " log_dir = os.path.join(out_dir, \"memote\")\n", - " log_file = os.path.join(log_dir, f\"{context}_{recon_algorithm}_memote.log\")\n", + " out_dir = config.data_dir / \"results\" / context\n", + " report_file = out_dir / f\"memote_report_{context}_{recon_algorithm}.html\"\n", + " model_file = out_dir / f\"{context}_SpecificModel_{recon_algorithm}.xml\"\n", + " log_dir = out_dir / \"memote\"\n", + " log_file = log_dir / f\"{context}_{recon_algorithm}_memote.log\"\n", "\n", - " if not os.path.exists(log_dir):\n", - " os.mkdir(log_dir)\n", + " if not log_dir.exists():\n", + " log_dir.mkdir(parents=True, exist_ok=True)\n", "\n", - " cmd = \" \".join([\"memote\", \"report\", \"snapshot\", \"--filename\", f\"{report_file}\", f\"{model_file}\", \">\", f\"{log_file}\"])\n", + " cmd = \" \".join(\n", + " [\"memote\", \"report\", \"snapshot\", \"--filename\", f\"{report_file}\", f\"{model_file}\", \">\", f\"{log_file}\"]\n", + " )\n", "\n", " !{cmd}" ] @@ -1036,11 +959,12 @@ "metadata": {}, "outputs": [], "source": [ + "from como.utils import stringlist_to_list\n", + "\n", "disease_names = [\"arthritis\", \"lupus_a\", \"lupus_b\"]\n", "data_source = \"rnaseq\"\n", "taxon_id = \"human\"\n", "\n", - "from como.utils import stringlist_to_list\n", "\n", "for context_name in stringlist_to_list(context_names):\n", " disease_config_file = f\"disease_data_inputs_{context_name}.xlsx\"\n", @@ -1100,6 +1024,12 @@ "metadata": {}, "outputs": [], "source": [ + "import json\n", + "\n", + "from como.project import Config\n", + "from como.utils import stringlist_to_list\n", + "from loguru import logger\n", + "\n", "# Knock out simulation for the analyzed tissues and diseases\n", "model_files = {\n", " # \"context_name\": \"/path/to/model.mat\"\n", @@ -1107,11 +1037,6 @@ "}\n", "sovler = \"gurobi\"\n", "\n", - "import json\n", - "import os\n", - "\n", - "from como.utils import stringlist_to_list\n", - "from como.project import Config\n", "\n", "config = Config()\n", "\n", @@ -1119,27 +1044,29 @@ "for context in stringlist_to_list(context_names):\n", " for recon_algorithm in recon_algorithms:\n", " for disease in disease_names:\n", - " disease_path = os.path.join(config.data_dir, \"results\", context, disease)\n", - " out_dir = os.path.join(config.data_dir, \"results\", context, disease)\n", - " tissue_gene_folder = os.path.join(config.data_dir, context)\n", - " os.makedirs(tissue_gene_folder, exist_ok=True)\n", + " disease_path = config.data_dir / \"results\" / context / disease\n", + " out_dir = config.data_dir / \"results\" / context / disease\n", + " tissue_gene_folder = config.data_dir / context\n", + " tissue_gene_folder.mkdir(parents=True, exist_ok=True)\n", "\n", - " if not os.path.exists(disease_path):\n", - " print(f\"Disease path doesn't exist! Looking for {disease_path}\")\n", + " if not disease_path.exists():\n", + " logger.warning(f\"Disease path doesn't exist! Looking for {disease_path}\")\n", " continue\n", "\n", " # load the results of step 3 to dictionary \"disease_files\"\n", - " step3_results_file = os.path.join(config.data_dir, \"results\", context, disease, \"step2_results_files.json\")\n", + " step3_results_file = config.data_dir / \"results\" / context / disease / \"step2_results_files.json\"\n", "\n", - " with open(step3_results_file) as json_file:\n", + " with step3_results_file.open(\"r\") as json_file:\n", " disease_files = json.load(json_file)\n", " down_regulated_disease_genes = disease_files[\"down_regulated\"]\n", " up_regulated_disease_genes = disease_files[\"up_regulated\"]\n", "\n", - " if context in model_files.keys():\n", + " if context in model_files:\n", " tissue_specific_model_filepath = model_files[context]\n", " else:\n", - " tissue_specific_model_filepath = os.path.join(config.data_dir, \"results\", context, f\"{context}_SpecificModel_{recon_algorithm}.mat\")\n", + " tissue_specific_model_filepath = (\n", + " config.data_dir / \"results\" / context / f\"{context}_SpecificModel_{recon_algorithm}.mat\"\n", + " )\n", "\n", " # fmt: off\n", " cmd = [\n", @@ -1156,7 +1083,7 @@ " # fmt: on\n", "\n", " if recon_algorithm == \"IMAT\":\n", - " cmd.extend([\"--reference-flux-file\", os.path.join(config.data_dir, \"results\", context, \"IMAT_flux.csv\")])\n", + " cmd.extend([\"--reference-flux-file\", config.data_dir / \"results\" / context / \"IMAT_flux.csv\"])\n", "\n", " cmd = \" \".join(cmd)\n", " !{cmd}" diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py index 373dab92..efa34332 100644 --- a/main/como/create_context_specific_model.py +++ b/main/como/create_context_specific_model.py @@ -532,7 +532,7 @@ def _build_model( # noqa: C901 ) -def _create_df(path: Path) -> pd.DataFrame: +def _create_df_from_file(path: Path) -> pd.DataFrame: match path.suffix: case ".csv": df = pd.read_csv(path, header=0, sep=",") @@ -547,9 +547,10 @@ def _create_df(path: Path) -> pd.DataFrame: def _collect_boundary_reactions(path: Path) -> _BoundaryReactions: - df = _create_df(path) + df = _create_df_from_file(path) for column in df.columns: if column not in [ + "boundary", "reaction", "abbreviation", "compartment", @@ -642,7 +643,7 @@ def create_context_specific_model( # noqa: C901 exclude_rxns: list[str] = [] if exclude_rxns_filepath: exclude_rxns_filepath: Path = Path(exclude_rxns_filepath) - df = _create_df(exclude_rxns_filepath) + df = _create_df_from_file(exclude_rxns_filepath) if "abbreviation" not in df.columns: raise ValueError("The exclude reactions file should have a single column with a header named Abbreviation") exclude_rxns = df["abbreviation"].tolist() @@ -650,7 +651,7 @@ def create_context_specific_model( # noqa: C901 force_rxns: list[str] = [] if force_rxns_filepath: force_rxns_filepath: Path = Path(force_rxns_filepath) - df = _create_df(force_rxns_filepath) + df = _create_df_from_file(force_rxns_filepath) if "abbreviation" not in df.columns: raise ValueError("The force reactions file should have a single column with a header named Abbreviation") force_rxns = df["abbreviation"].tolist() diff --git a/main/como/custom_types.py b/main/como/custom_types.py deleted file mode 100644 index a4fcd19b..00000000 --- a/main/como/custom_types.py +++ /dev/null @@ -1,23 +0,0 @@ -from enum import Enum - - -class RNASeqPreparationMethod(Enum): - TOTAL = "total" - MRNA = "mrna" - SCRNA = "scrna" - - @staticmethod - def from_string(value: str) -> "RNASeqPreparationMethod": - """Build a preparation method object from a string.""" - match_value = "".join(c for c in value if c.isascii()).lower() - - match match_value: - case "total" | "trna": - return RNASeqPreparationMethod.TOTAL - case "mrna": - return RNASeqPreparationMethod.MRNA - case "scrna": - return RNASeqPreparationMethod.SCRNA - case _: - possible_values = [t.value for t in RNASeqPreparationMethod] - raise ValueError(f"Filtering technique must be one of {possible_values}; got: {value}") diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py index 7079d456..8dc2a6fd 100644 --- a/main/como/merge_xomics.py +++ b/main/como/merge_xomics.py @@ -16,7 +16,7 @@ from como import proteomics_gen, return_placeholder_data from como.combine_distributions import _combine_zscores -from como.custom_types import RNASeqPreparationMethod +from como.custom_types import RNAPrepMethod from como.project import Config from como.utils import split_gene_expression_data @@ -93,7 +93,7 @@ def __post_init__(self): raise ValueError("Adjust method must be either 'progressive', 'regressive', 'flat', or 'custom'") -def _load_rnaseq_tests(filename, context_name, prep_method: RNASeqPreparationMethod) -> tuple[str, pd.DataFrame]: +def _load_rnaseq_tests(filename, context_name, prep_method: RNAPrepMethod) -> tuple[str, pd.DataFrame]: """Load rnaseq results. Returns a dictionary of test (context, context, cell, etc ) names and rnaseq expression data @@ -112,11 +112,11 @@ def load_dummy_dict(): raise FileNotFoundError(f"Error: Config file not found at {inquiry_full_path}") match prep_method: - case RNASeqPreparationMethod.TOTAL: + case RNAPrepMethod.TOTAL: filename = f"rnaseq_total_{context_name}.csv" - case RNASeqPreparationMethod.MRNA: + case RNAPrepMethod.MRNA: filename = f"rnaseq_mrna_{context_name}.csv" - case RNASeqPreparationMethod.SCRNA: + case RNAPrepMethod.SCRNA: filename = f"rnaseq_scrna_{context_name}.csv" case _: raise ValueError( @@ -344,15 +344,9 @@ async def _merge_xomics( config = Config() logger.info(f"Merging data for {context_name}") # load data for each source if it exists. IF not load an empty dummy dataset - trnaseq = _load_rnaseq_tests( - filename=trnaseq_file, context_name=context_name, prep_method=RNASeqPreparationMethod.TOTAL - ) - mrnaseq = _load_rnaseq_tests( - filename=mrnaseq_file, context_name=context_name, prep_method=RNASeqPreparationMethod.MRNA - ) - scrnaseq = _load_rnaseq_tests( - filename=scrnaseq_file, context_name=context_name, prep_method=RNASeqPreparationMethod.SCRNA - ) + trnaseq = _load_rnaseq_tests(filename=trnaseq_file, context_name=context_name, prep_method=RNAPrepMethod.TOTAL) + mrnaseq = _load_rnaseq_tests(filename=mrnaseq_file, context_name=context_name, prep_method=RNAPrepMethod.MRNA) + scrnaseq = _load_rnaseq_tests(filename=scrnaseq_file, context_name=context_name, prep_method=RNAPrepMethod.SCRNA) proteomics = proteomics_gen.load_proteomics_tests(filename=proteomics_file, context_name=context_name) expression_list = [] diff --git a/main/como/rnaseq.py b/main/como/rnaseq.py index 1c504d97..cf45f39c 100644 --- a/main/como/rnaseq.py +++ b/main/como/rnaseq.py @@ -27,9 +27,8 @@ from scipy.signal import find_peaks from sklearn.neighbors import KernelDensity -from como.custom_types import RNASeqPreparationMethod from como.migrations import gene_info_migrations -from como.project import Config +from como.types import RNAPrepMethod from como.utils import convert_gene_data @@ -525,20 +524,17 @@ def calculate_z_score(metrics: NamedMetrics) -> NamedMetrics: def cpm_filter( *, - context_name: str, metrics: NamedMetrics, filtering_options: _FilteringOptions, - prep: RNASeqPreparationMethod, + output_csv_filepath: Path, ) -> NamedMetrics: """Apply Counts Per Million (CPM) filtering to the count matrix for a given sample.""" - config = Config() n_exp = filtering_options.replicate_ratio n_top = filtering_options.high_replicate_ratio cut_off = filtering_options.cut_off - sample: str metric: _StudyMetrics - for sample, metric in metrics.items(): + for metric in metrics.values(): counts: pd.DataFrame = metric.count_matrix entrez_ids: list[str] = metric.entrez_gene_ids library_size: pd.DataFrame = counts.sum(axis=1) @@ -548,12 +544,11 @@ def cpm_filter( # thus, (0 / 1) * 1_000_000 = 0 library_size[library_size == 0] = 1 - output_filepath = config.result_dir / context_name / prep.value / f"CPM_Matrix_{prep.value}_{sample}.csv" - output_filepath.parent.mkdir(parents=True, exist_ok=True) + output_csv_filepath.parent.mkdir(parents=True, exist_ok=True) counts_per_million: pd.DataFrame = (counts / library_size) * 1_000_000 counts_per_million.insert(0, "entrez_gene_ids", pd.Series(entrez_ids)) - logger.debug(f"Writing CPM matrix to {output_filepath}") - counts_per_million.to_csv(output_filepath, index=False) + logger.debug(f"Writing CPM matrix to {output_csv_filepath}") + counts_per_million.to_csv(output_csv_filepath, index=False) # TODO: Counts per million is adding ~61,500 columns (equal to the number of genes) for some reason. # Most likely due to multiplying by 1_000_000, not exactly sure why @@ -656,24 +651,31 @@ def zfpkm_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions, def filter_counts( *, - context_name: str, metrics: NamedMetrics, technique: FilteringTechnique, filtering_options: _FilteringOptions, - prep: RNASeqPreparationMethod, + cpm_output_filepath: Path | None = None, ) -> NamedMetrics: """Filter the count matrix based on the specified technique.""" match technique: case FilteringTechnique.cpm: + if cpm_output_filepath is None: + raise ValueError("CPM output filepath must be provided") return cpm_filter( - context_name=context_name, metrics=metrics, filtering_options=filtering_options, prep=prep + metrics=metrics, + filtering_options=filtering_options, + output_csv_filepath=cpm_output_filepath, ) + case FilteringTechnique.tpm: return tpm_quantile_filter(metrics=metrics, filtering_options=filtering_options) + case FilteringTechnique.zfpkm: return zfpkm_filter(metrics=metrics, filtering_options=filtering_options, calcualte_fpkm=True) + case FilteringTechnique.umi: return zfpkm_filter(metrics=metrics, filtering_options=filtering_options, calcualte_fpkm=False) + case _: raise ValueError(f"Technique must be one of {FilteringTechnique}") @@ -684,7 +686,7 @@ async def save_rnaseq_tests( config_filepath: Path, gene_info_filepath: Path, output_filepath: Path, - prep: RNASeqPreparationMethod, + prep: RNAPrepMethod, taxon_id: Taxon, replicate_ratio: float, batch_ratio: float, @@ -702,7 +704,7 @@ async def save_rnaseq_tests( high_batch_ratio=high_batch_ratio, ) - if prep == RNASeqPreparationMethod.SCRNA: + if prep == RNAPrepMethod.SCRNA: technique = FilteringTechnique.umi logger.warning( "Single cell filtration does not normalize and assumes " @@ -721,11 +723,9 @@ async def save_rnaseq_tests( entrez_gene_ids = read_counts_results.entrez_gene_ids metrics = filter_counts( - context_name=context_name, metrics=metrics, technique=technique, filtering_options=filtering_options, - prep=prep, ) expressed_genes: list[str] = [] @@ -758,6 +758,7 @@ async def save_rnaseq_tests( boolean_matrix.to_csv(output_filepath, index=False) logger.info( - f"{context_name} - Found {expressed_count} expressed and {high_confidence_count} confidently expressed genes" + f"{context_name} - Found {expressed_count} expressed genes, " + f"{high_confidence_count} of which are confidently expressed" ) logger.success(f"Wrote boolean matrix to {output_filepath}") diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py index 8f8ffa34..47aeb8ee 100644 --- a/main/como/rnaseq_gen.py +++ b/main/como/rnaseq_gen.py @@ -9,7 +9,7 @@ from loguru import logger from como import Config -from como.custom_types import RNASeqPreparationMethod +from como.custom_types import RNAPrepMethod from como.rnaseq import FilteringTechnique, save_rnaseq_tests @@ -22,11 +22,11 @@ class _Arguments: high_batch_ratio: float filtering_technique: FilteringTechnique minimum_cutoff: int | str - library_prep: RNASeqPreparationMethod + library_prep: RNAPrepMethod taxon: Taxon def __post_init__(self): - self.library_prep = RNASeqPreparationMethod.from_string(str(self.library_prep)) + self.library_prep = RNAPrepMethod.from_string(str(self.library_prep)) self.filtering_technique = FilteringTechnique.from_string(str(self.filtering_technique)) if self.minimum_cutoff is None: @@ -46,7 +46,7 @@ async def _handle_context_batch( batch_ratio_high: float, technique: FilteringTechnique, cut_off: int | float | str, - prep: RNASeqPreparationMethod, + prep: RNAPrepMethod, taxon: Taxon, ) -> None: """Iterate through each context type and create rnaseq expression file. @@ -81,9 +81,9 @@ async def _handle_context_batch( rnaseq_input_filepath = ( config.data_dir / "data_matrices" / context_name / f"gene_counts_matrix_{prep.value}_{context_name}" ) - if prep == RNASeqPreparationMethod.SCRNA: + if prep == RNAPrepMethod.SCRNA: rnaseq_input_filepath = rnaseq_input_filepath.with_suffix(".h5ad") - elif prep in {RNASeqPreparationMethod.TOTAL, RNASeqPreparationMethod.MRNA}: + elif prep in {RNAPrepMethod.TOTAL, RNAPrepMethod.MRNA}: rnaseq_input_filepath = rnaseq_input_filepath.with_suffix(".csv") if not rnaseq_input_filepath.exists(): @@ -117,7 +117,7 @@ async def _handle_context_batch( async def rnaseq_gen( # config_filepath: Path, config_filename: str, - prep: RNASeqPreparationMethod, + prep: RNAPrepMethod, taxon_id: int | str | Taxon, replicate_ratio: float = 0.5, high_replicate_ratio: float = 1.0, diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py index c522f0f1..ae8bcdea 100644 --- a/main/como/rnaseq_preprocess.py +++ b/main/como/rnaseq_preprocess.py @@ -1,30 +1,25 @@ from __future__ import annotations -import argparse import asyncio +import contextlib import re +import sys from dataclasses import dataclass, field +from io import StringIO, TextIOWrapper +from itertools import chain from pathlib import Path from typing import Literal +import aiofiles import numpy as np import pandas as pd import scanpy as sc -from fast_bioservices import Input, Taxon from fast_bioservices.biothings.mygene import MyGene +from fast_bioservices.pipeline import ensembl_to_gene_id_and_symbol, gene_symbol_to_ensembl_and_gene_id from loguru import logger -from como import Config, stringlist_to_list -from como.utils import convert_gene_data - - -@dataclass -class _Arguments: - context_names: list[str] - taxon_id: Taxon | int | str - mode: Literal["create", "provide"] - input_format: str - provided_matrix_fname: str = None +from como.types import RNAPrepMethod, type_path, type_rna +from como.utils import _listify @dataclass @@ -37,31 +32,34 @@ class _STARinformation: count_matrix: pd.DataFrame @property - def num_genes(self): + def num_genes(self) -> int: return len(self.count_matrix) @classmethod - def build_from_tab(cls, filepath: Path) -> _STARinformation: + async def build_from_tab(cls, filepath: Path) -> _STARinformation: if filepath.suffix != ".tab": raise ValueError(f"Building STAR information requires a '.tab' file; received: '{filepath}'") - with filepath.open("r") as i_stream: - num_unmapped = [int(i) for i in next(i_stream).rstrip("\n").split("\t")[1:]] - num_multimapping = [int(i) for i in next(i_stream).rstrip("\n").split("\t")[1:]] - num_no_feature = [int(i) for i in next(i_stream).rstrip("\n").split("\t")[1:]] - num_ambiguous = [int(i) for i in next(i_stream).rstrip("\n").split("\t")[1:]] - - df = pd.read_csv( - filepath, - sep="\t", - skiprows=4, - names=[ - "ensembl_gene_id", - "unstranded_rna_counts", - "first_read_transcription_strand", - "second_read_transcription_strand", - ], - ) - # Remove NA values + + async with aiofiles.open(filepath) as i_stream: + unmapped = await i_stream.readline() + multimapping = await i_stream.readline() + no_feature = await i_stream.readline() + ambiguous = await i_stream.readline() + + num_unmapped = [int(i) for i in unmapped.rstrip("\n").split("\t")[1:]] + num_multimapping = [int(i) for i in multimapping.rstrip("\n").split("\t")[1:]] + num_no_feature = [int(i) for i in no_feature.rstrip("\n").split("\t")[1:]] + num_ambiguous = [int(i) for i in ambiguous.rstrip("\n").split("\t")[1:]] + remainder = await i_stream.read() + + string_io = StringIO(remainder) + df = pd.read_csv(string_io, sep="\t", header=None) + df.columns = [ + "ensembl_gene_id", + "unstranded_rna_counts", + "first_read_transcription_strand", + "second_read_transcription_strand", + ] df = df[~df["ensembl_gene_id"].isna()] return _STARinformation( num_unmapped=num_unmapped, @@ -77,7 +75,7 @@ def build_from_tab(cls, filepath: Path) -> _STARinformation: class _StudyMetrics: study_name: str count_files: list[Path] - strandedness_files: list[Path] + strand_files: list[Path] __sample_names: list[str] = field(default_factory=list) __num_samples: int = 0 @@ -93,10 +91,10 @@ def __post_init__(self): self.__num_samples = len(self.count_files) self.__sample_names = [f.stem for f in self.count_files] - if len(self.count_files) != len(self.strandedness_files): + if len(self.count_files) != len(self.strand_files): raise ValueError( f"Unequal number of count files and strand files for study '{self.study_name}'. " - f"Found {len(self.count_files)} count files and {len(self.strandedness_files)} strand files." + f"Found {len(self.count_files)} count files and {len(self.strand_files)} strand files." ) if self.num_samples != len(self.count_files): @@ -105,44 +103,36 @@ def __post_init__(self): f"Found {self.num_samples} samples and {len(self.count_files)} count files." ) - if self.num_samples != len(self.strandedness_files): + if self.num_samples != len(self.strand_files): raise ValueError( f"Unequal number of samples and strand files for study '{self.study_name}'. " - f"Found {self.num_samples} samples and {len(self.strandedness_files)} strand files." + f"Found {self.num_samples} samples and {len(self.strand_files)} strand files." ) if self.__num_samples == 1: raise ValueError(f"Only one sample exists for study {self.study_name}. Provide at least two samples") self.count_files.sort() - self.strandedness_files.sort() + self.strand_files.sort() self.__sample_names.sort() -def _context_from_filepath(file: Path) -> str: - return file.stem.split("_S")[0] - - def _sample_name_from_filepath(file: Path) -> str: return re.search(r".+_S\d+R\d+", file.stem).group() def _organize_gene_counts_files(data_dir: Path) -> list[_StudyMetrics]: - root_gene_count_dir = Path(data_dir, "geneCounts") - root_strandedness_dir = Path(data_dir, "strandedness") + gene_count_dir = Path(data_dir, "geneCounts") + strand_dir = Path(data_dir, "strandedness") - gene_counts_directories: list[Path] = sorted( - [p for p in Path(root_gene_count_dir).glob("*") if not p.name.startswith(".")] - ) - strandedness_directories: list[Path] = sorted( - [p for p in Path(root_strandedness_dir).glob("*") if not p.name.startswith(".")] - ) + gene_counts_directories: list[Path] = sorted([p for p in gene_count_dir.glob("*") if not p.name.startswith(".")]) + strandedness_directories: list[Path] = sorted([p for p in strand_dir.glob("*") if not p.name.startswith(".")]) if len(gene_counts_directories) != len(strandedness_directories): raise ValueError( f"Unequal number of gene count directories and strandedness directories. " f"Found {len(gene_counts_directories)} gene count directories and {len(strandedness_directories)} strandedness directories." # noqa: E501 - f"\nGene count directory: {root_gene_count_dir}\nStrandedness directory: {root_strandedness_dir}" + f"\nGene count directory: {gene_count_dir}\nStrandedness directory: {strand_dir}" ) # For each study, collect gene count files, fragment files, insert size files, layouts, and strandedness information @@ -157,16 +147,16 @@ def _organize_gene_counts_files(data_dir: Path) -> list[_StudyMetrics]: _StudyMetrics( study_name=gene_dir.stem, count_files=list(gene_dir.glob("*.tab")), - strandedness_files=list(strand_dir.glob("*.txt")), + strand_files=list(strand_dir.glob("*.txt")), ) ) return study_metrics -def _process_first_multirun_sample(strand_file: Path, all_counts_files: list[Path]): +async def _process_first_multirun_sample(strand_file: Path, all_counts_files: list[Path]): sample_count = pd.DataFrame() for file in all_counts_files: - star_information = _STARinformation.build_from_tab(file) + star_information = await _STARinformation.build_from_tab(file) strand_information = strand_file.read_text().rstrip("\n").lower() if strand_information not in ("none", "first_read_transcription_strand", "second_read_transcription_strand"): @@ -178,13 +168,13 @@ def _process_first_multirun_sample(strand_file: Path, all_counts_files: list[Pat if strand_information == "none": strand_information = "unstranded_rna_counts" - run_counts = star_information.count_matrix[["gene_id", strand_information]] + run_counts = star_information.count_matrix[["ensembl_gene_id", strand_information]] run_counts.columns = pd.Index(["ensembl_gene_id", "counts"]) - if sample_count.empty: - sample_count = run_counts - else: - # Merge to take all items from both data frames - sample_count = sample_count.merge(run_counts, on="ensembl_gene_id", how="outer") + sample_count = ( + run_counts + if sample_count.empty + else sample_count.merge(run_counts, on=["ensembl_gene_id", "counts"], how="outer") + ) # Set na values to 0 sample_count = sample_count.fillna(value="0") @@ -196,8 +186,8 @@ def _process_first_multirun_sample(strand_file: Path, all_counts_files: list[Pat return count_sums -def _process_standard_replicate(counts_file: Path, strand_file: Path, sample_name: str): - star_information = _STARinformation.build_from_tab(counts_file) +async def _process_standard_replicate(counts_file: Path, strand_file: Path, sample_name: str): + star_information = await _STARinformation.build_from_tab(counts_file) strand_information = strand_file.read_text().rstrip("\n").lower() if strand_information not in ("none", "first_read_transcription_strand", "second_read_transcription_strand"): @@ -214,7 +204,7 @@ def _process_standard_replicate(counts_file: Path, strand_file: Path, sample_nam return sample_count -def _prepare_sample_counts( +async def _prepare_sample_counts( sample_name: str, counts_file: Path, strand_file: Path, @@ -222,27 +212,27 @@ def _prepare_sample_counts( ) -> pd.DataFrame | Literal["SKIP"]: # Test if the counts_file is the first run in a multi-run smaple if re.search(r"R\d+r1", counts_file.as_posix()): - return _process_first_multirun_sample(strand_file=strand_file, all_counts_files=all_counts_files) + return await _process_first_multirun_sample(strand_file=strand_file, all_counts_files=all_counts_files) elif re.search(r"R\d+r\d+", counts_file.as_posix()): return "SKIP" else: - return _process_standard_replicate(counts_file, strand_file, sample_name) + return await _process_standard_replicate(counts_file, strand_file, sample_name) async def _create_sample_counts_matrix(metrics: _StudyMetrics) -> pd.DataFrame: adjusted_index = 0 - counts: pd.DataFrame | Literal["SKIP"] = _prepare_sample_counts( + counts: pd.DataFrame | Literal["SKIP"] = await _prepare_sample_counts( sample_name=metrics.sample_names[0], counts_file=metrics.count_files[0], - strand_file=metrics.strandedness_files[0], + strand_file=metrics.strand_files[0], all_counts_files=metrics.count_files, ) for i in range(1, metrics.num_samples): - new_counts = _prepare_sample_counts( + new_counts = await _prepare_sample_counts( sample_name=metrics.sample_names[i], counts_file=metrics.count_files[i], - strand_file=metrics.strandedness_files[i], + strand_file=metrics.strand_files[i], all_counts_files=metrics.count_files, ) if isinstance(new_counts, str) and new_counts == "SKIP": @@ -260,35 +250,38 @@ async def _create_sample_counts_matrix(metrics: _StudyMetrics) -> pd.DataFrame: return counts -async def _create_counts_matrix(context_name: str, config: Config): - """Create a counts matrix by reading gene counts table(s).""" - data_dir = config.data_dir / "COMO_input" / context_name - matrix_output_dir = config.data_dir / "data_matrices" / context_name - - study_metrics = _organize_gene_counts_files(data_dir=data_dir) - final_matrix: pd.DataFrame = pd.DataFrame() +async def _write_counts_matrix( + *, + config_df: pd.DataFrame, + como_context_dir: Path, + output_counts_matrix_filepath: Path, + rna: type_rna, +) -> pd.DataFrame: + """Create a counts matrix file by reading gene counts table(s).""" + study_metrics = _organize_gene_counts_files(data_dir=como_context_dir) + counts: list[pd.DataFrame] = await asyncio.gather( + *[_create_sample_counts_matrix(metric) for metric in study_metrics] + ) + final_matrix = pd.DataFrame() + for count in counts: + final_matrix = count if final_matrix.empty else pd.merge(final_matrix, count, on="ensembl_gene_id", how="outer") - for metric in study_metrics: - counts: pd.DataFrame = await _create_sample_counts_matrix(metric) - final_matrix = ( - counts if final_matrix.empty else pd.merge(final_matrix, counts, on="ensembl_gene_id", how="outer") - ) + rna_specific_sample_names = config_df.loc[config_df["library_prep"] == rna, "sample_name"].tolist() + final_matrix = final_matrix[["ensembl_gene_id", *rna_specific_sample_names]] - output_filename = matrix_output_dir / f"gene_counts_matrix_full_{data_dir.stem}.csv" - output_filename.parent.mkdir(parents=True, exist_ok=True) - final_matrix.to_csv(output_filename, index=False) - logger.success(f"Wrote gene count matrix for '{data_dir.stem}' at '{output_filename}'") + output_counts_matrix_filepath.parent.mkdir(parents=True, exist_ok=True) + final_matrix.to_csv(output_counts_matrix_filepath, index=False) + logger.success(f"Wrote gene count matrix for '{rna}' RNA at '{output_counts_matrix_filepath}'") + return final_matrix -async def _create_config_df(context_name: str) -> pd.DataFrame: # noqa: C901 +async def _create_config_df(context_name: str, /, como_input_dir: Path) -> pd.DataFrame: # noqa: C901 """Create configuration sheet. The configuration file created is based on the gene counts matrix. If using zFPKM normalization technique, mean fragment lengths will be fetched """ - config = Config() - gene_counts_files = list(Path(config.data_dir, "COMO_input", context_name, "geneCounts").rglob("*.tab")) - + gene_counts_files = list(Path(como_input_dir, context_name, "geneCounts").rglob("*.tab")) sample_names: list[str] = [] fragment_lengths: list[int | float] = [] layouts: list[str] = [] @@ -329,19 +322,12 @@ async def _create_config_df(context_name: str) -> pd.DataFrame: # noqa: C901 r_label = re.findall(r"r\d{1,3}", r.as_posix())[0] R_label = re.findall(r"R\d{1,3}", r.as_posix())[0] # noqa: N806 frag_filename = "".join([context_name, "_", study_number, R_label, r_label, "_fragment_size.txt"]) - frag_files.append( - config.data_dir / "COMO_input" / context_name / "fragmentSizes" / study_number / frag_filename - ) - - context_path = config.data_dir / "COMO_input" / context_name + frag_files.append(como_input_dir / context_name / "fragmentSizes" / study_number / frag_filename) + context_path = como_input_dir / context_name layout_files: list[Path] = list((context_path / "layouts").rglob(f"{context_name}_{label}_layout.txt")) - strand_files: list[Path] = list( - (context_path / "strandedness").rglob(f"{context_name}_{label}_strandedness.txt") - ) - frag_files: list[Path] = list( - (context_path / "fragmentSizes").rglob(f"{context_name}_{label}_fragment_size.txt") - ) + strand_files: list[Path] = list((context_path / "strandedness").rglob(f"{context_name}_{label}_strandedness.txt")) # fmt: skip # noqa: E501 + frag_files: list[Path] = list((context_path / "fragmentSizes").rglob(f"{context_name}_{label}_fragment_size.txt")) # fmt: skip # noqa: E501 prep_files: list[Path] = list((context_path / "prepMethods").rglob(f"{context_name}_{label}_prep_method.txt")) layout = "UNKNOWN" @@ -351,7 +337,7 @@ async def _create_config_df(context_name: str) -> pd.DataFrame: # noqa: C901 f"this should be defined by user if using zFPKM or rnaseq_gen.py will not run" ) elif len(layout_files) == 1: - with layout_files[0].open("w") as file: + with layout_files[0].open("r") as file: layout = file.read().strip() elif len(layout_files) > 1: raise ValueError( @@ -367,7 +353,7 @@ async def _create_config_df(context_name: str) -> pd.DataFrame: # noqa: C901 f"infer the strandedness when writing the counts matrix" ) elif len(strand_files) == 1: - with strand_files[0].open("w") as file: + with strand_files[0].open("r") as file: strand = file.read().strip() elif len(strand_files) > 1: raise ValueError( @@ -379,7 +365,7 @@ async def _create_config_df(context_name: str) -> pd.DataFrame: # noqa: C901 if len(prep_files) == 0: logger.warning(f"No prep file found for {label}, assuming 'total' as in Total RNA library preparation") elif len(prep_files) == 1: - with prep_files[0].open("w") as file: + with prep_files[0].open("r") as file: prep = file.read().strip().lower() if prep not in ["total", "mrna"]: raise ValueError(f"Prep method must be either 'total' or 'mrna' for {label}") @@ -441,58 +427,32 @@ async def _create_config_df(context_name: str) -> pd.DataFrame: # noqa: C901 return out_df -def _split_config_df(df): - """Split a config dataframe to two. - - One for Total RNA library prep, one for mRNA - """ - df_t = df[df["library_prep"] == "total"] - df_m = df[df["library_prep"] == "mrna"] - - return df_t, df_m - - -def _split_counts_matrices( - count_matrix_all: Path, df_total: pd.DataFrame, df_mrna: pd.DataFrame -) -> tuple[pd.DataFrame, pd.DataFrame]: - """Split a counts-matrix dataframe to two. - - One for Total RNA library prep, one for mRNA - """ - logger.info(f"Reading gene count matrix file at '{count_matrix_all}'") - matrix_all = pd.read_csv(count_matrix_all) - matrix_total = matrix_all[ - ["ensembl_gene_id"] + [n for n in matrix_all.columns if n in df_total["sample_name"].tolist()] - ] - matrix_mrna = matrix_all[ - ["ensembl_gene_id"] + [n for n in matrix_all.columns if n in df_mrna["sample_name"].tolist()] - ] - - return matrix_total, matrix_mrna - - async def _create_gene_info_file( *, - matrix_files: list[Path], - taxon_id, - config: Config, + counts_matrix_filepaths: list[Path], + output_filepath: Path, + taxon: int, cache: bool, ): - """Create gene info file for specified context by reading first column in its count matrix file.""" - logger.info("Fetching gene info") - genes = set() - for file in matrix_files: - data: pd.DataFrame | sc.AnnData = pd.read_csv(file) if file.suffix == ".csv" else sc.read_h5ad(file) - input_values = data.iloc[:, 0].tolist() if isinstance(data, pd.DataFrame) else data.var_names.tolist() - conversion = await convert_gene_data(input_values, taxon_id) - genes.update(conversion["entrez_gene_id"].astype(str).tolist()) + """Create a gene information file context. + + The gene information file will be created by reading each matrix filepath in the provided list + """ + + async def read_counts(file: Path) -> list[str]: + data = await asyncio.to_thread(pd.read_csv if file.suffix == ".csv" else sc.read_h5ad, file) + conversion = ( + await ensembl_to_gene_id_and_symbol(ids=data["ensembl_gene_id"].tolist(), taxon=taxon) + if isinstance(data, pd.DataFrame) + else await gene_symbol_to_ensembl_and_gene_id(symbols=data.var_names.tolist(), taxon=taxon) + ) + return conversion["entrez_gene_id"].tolist() + + logger.info("Fetching gene info (this may take 1-5 minutes)") + genes = set(chain.from_iterable(await asyncio.gather(*[read_counts(f) for f in counts_matrix_filepaths]))) mygene = MyGene(cache=cache) - gene_data = await mygene.query( - items=list(genes), - taxon=taxon_id, - scopes="entrezgene", - ) + gene_data = await mygene.query(items=list(genes), taxon=taxon, scopes="entrezgene") gene_info: pd.DataFrame = pd.DataFrame( data=None, columns=pd.Index(data=["ensembl_gene_id", "gene_symbol", "entrez_gene_id", "start_position", "end_position"]), @@ -504,11 +464,9 @@ async def _create_gene_info_file( ensembl_ids = ensembl_ids[0] start_pos = data.get("genomic_pos.start", 0) + start_pos = sum(start_pos) / len(start_pos) if isinstance(start_pos, list) else start_pos end_pos = data.get("genomic_pos.end", 0) - if isinstance(start_pos, list): - start_pos = sum(start_pos) / len(start_pos) - if isinstance(end_pos, list): - end_pos = sum(end_pos) / len(end_pos) + end_pos = sum(end_pos) / len(end_pos) if isinstance(end_pos, list) else end_pos gene_info.at[i, "gene_symbol"] = data.get("symbol", "-") gene_info.at[i, "entrez_gene_id"] = data.get("entrezgene", "-") @@ -517,225 +475,166 @@ async def _create_gene_info_file( gene_info.at[i, "end_position"] = end_pos gene_info = gene_info[ - (gene_info["entrez_gene_id"] != "-") & (gene_info["ensembl_gene_id"] != "-") & (gene_info["gene_symbol"] != "-") + ( + (gene_info["entrez_gene_id"] != "-") + & (gene_info["ensembl_gene_id"] != "-") + & (gene_info["gene_symbol"] != "-") + ) ] gene_info["size"] = gene_info["end_position"].astype(int) - gene_info["start_position"].astype(int) gene_info.drop(columns=["start_position", "end_position"], inplace=True) - output_filepath = config.data_dir / "gene_info.csv" + gene_info.sort_values(by="ensembl_gene_id", inplace=True) gene_info.to_csv(output_filepath, index=False) logger.success(f"Gene Info file written at '{output_filepath}'") -async def _handle_context_batch( # noqa: C901 - context_names: list[str], - mode: Literal["create", "provide"], - taxon_id, - provided_matrix_file, - config: Config, +async def _create_matrix_file( + context_name: str, + output_config_filepath: Path, + como_context_dir: type_path, + output_counts_matrix_filepath: Path, + rna: type_rna, +) -> None: + config_df = await _create_config_df(context_name, como_input_dir=como_context_dir) + await _write_counts_matrix( + config_df=config_df, + como_context_dir=como_context_dir, + output_counts_matrix_filepath=output_counts_matrix_filepath, + rna=rna, + ) + with pd.ExcelWriter(output_config_filepath) as writer: + subset_config = config_df[config_df["library_prep"] == rna] + subset_config.to_excel(writer, sheet_name=context_name, header=True, index=False) + + +async def _process( + context_name: str, + taxon: int, + output_gene_info_filepath: Path, + como_context_dir: Path | None, + input_matrix_filepath: list[Path] | None, + output_trna_config_filepath: Path | None, + output_mrna_config_filepath: Path | None, + output_trna_matrix_filepath: Path | None, + output_mrna_matrix_filepath: Path | None, cache: bool, ): - """Handle iteration through each context type and create appropriate files.""" - trnaseq_config_filename = config.config_dir / "trnaseq_data_inputs_auto.xlsx" - mrnaseq_config_filename = config.config_dir / "mrnaseq_data_inputs_auto.xlsx" - - using_trna = False # turn on when any total set is found to prevent writer from being init multiple times or empty - using_mrna = False # turn on when any mrna set is found to prevent writer from being init multiple times or empty - - logger.success(f"Found {len(context_names)} contexts to process: {', '.join(context_names)}") - - tmatrix_files: list[Path] = [] - mmatrix_files: list[Path] = [] - match mode: - case "create": - for context_name in context_names: - context_name = context_name.strip(" ") - logger.info(f"Processing {context_name}") - gene_output_dir = config.result_dir / context_name - matrix_output_dir = config.data_dir / "data_matrices" / context_name - - gene_output_dir.parent.mkdir(parents=True, exist_ok=True) - matrix_output_dir.parent.mkdir(parents=True, exist_ok=True) - - logger.info(f"Gene info output directory is '{gene_output_dir}'") - - await _create_counts_matrix(context_name, config=config) - # TODO: warn user or remove samples that are all 0 to prevent density plot error in zFPKM - config_df = await _create_config_df(context_name) - trna_df, mrna_df = _split_config_df(config_df) - - matrix_path_total = matrix_output_dir / f"gene_counts_matrix_total_{context_name}.csv" - if not trna_df.empty: - if not using_trna: - using_trna = True - twriter = pd.ExcelWriter(trnaseq_config_filename) - - tmatrix_files.append(matrix_path_total) - trna_df.to_excel(twriter, sheet_name=context_name, header=True, index=False) - - matrix_path_mrna = matrix_output_dir / f"gene_counts_matrix_mrna_{context_name}.csv" - if not mrna_df.empty: - if not using_mrna: - using_mrna = True - mwriter = pd.ExcelWriter(mrnaseq_config_filename) - - mmatrix_files.append(matrix_path_mrna) - mrna_df.to_excel(mwriter, sheet_name=context_name, header=True, index=False) - - matrix_path_all = matrix_output_dir / f"gene_counts_matrix_full_{context_name}.csv" - trna_matrix, mrna_matrix = _split_counts_matrices(matrix_path_all, trna_df, mrna_df) - if len(trna_matrix.columns) >= 1: - trna_matrix.to_csv(matrix_path_total, header=True, index=False) - if len(mrna_matrix.columns) >= 1: - mrna_matrix.to_csv(matrix_path_mrna, header=True, index=False) - - if using_trna: - twriter.close() - if using_mrna: - mwriter.close() - - await _create_gene_info_file( - matrix_files=tmatrix_files + mmatrix_files, - taxon_id=taxon_id, - config=config, - cache=cache, + rna_types: list[tuple[type_rna, Path, Path]] = [] + if output_trna_config_filepath: + rna_types.append(("total", output_trna_config_filepath, output_trna_matrix_filepath)) + if output_mrna_config_filepath: + rna_types.append(("polya", output_mrna_config_filepath, output_mrna_matrix_filepath)) + + # if provided, iterate through como-input specific directories + tasks = [] + for rna, output_config_filepath, output_matrix_filepath in rna_types: + tasks.append( + asyncio.create_task( + _create_matrix_file( + context_name=context_name, + output_config_filepath=output_config_filepath, + como_context_dir=como_context_dir, + output_counts_matrix_filepath=output_matrix_filepath, + rna=rna, + ) ) - case "provide": - matrix_files: list[Path] = [Path(p) for p in stringlist_to_list(provided_matrix_file)] - await _create_gene_info_file( - matrix_files=matrix_files, - taxon_id=taxon_id, - config=config, - cache=cache, - ) - case _: - raise ValueError("'--mode' must be either 'create' or 'provide'") + ) + + await asyncio.gather(*tasks) + + # create the gene info filepath based on provided data + await _create_gene_info_file( + counts_matrix_filepaths=[ + f + for f in [*input_matrix_filepath, output_trna_matrix_filepath, output_mrna_matrix_filepath] + if f is not None + ], + output_filepath=output_gene_info_filepath, + taxon=taxon, + cache=cache, + ) async def rnaseq_preprocess( - context_names: list[str], - mode: Literal["create", "provide"], - taxon_id: int | str, - input_format: Input | str, - matrix_file: str | Path | None = None, - config: Config = None, + context_name: str, + taxon: int, + output_gene_info_filepath: Path, + como_context_dir: Path | None = None, + input_matrix_filepath: Path | list[Path] | None = None, + preparation_method: RNAPrepMethod | list[RNAPrepMethod] | None = None, + output_trna_config_filepath: Path | None = None, + output_polya_config_filepath: Path | None = None, + output_trna_count_matrix_filepath: Path | None = None, + output_polya_count_matrix_filepath: Path | None = None, cache: bool = True, + log_level: Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] = "INFO", + log_location: str | TextIOWrapper = sys.stderr, ) -> None: """Preprocesses RNA-seq data for downstream analysis. Fetches additional gene information from a provided matrix or gene counts, or optionally creates this matrix using gene count files obtained using STAR aligner - """ - config = Config() if config is None else config - if mode not in {"create", "provide"}: - raise ValueError("mode must be either 'create' or 'provide'") - - if not isinstance(taxon_id, int) and taxon_id not in ["human", "mouse"]: - raise ValueError("taxon_id must be either an integer, or accepted string ('mouse', 'human')") - - await _handle_context_batch( - context_names=context_names, - mode=mode, - taxon_id=taxon_id, - provided_matrix_file=Path(matrix_file if matrix_file is not None else "").as_posix(), - config=config, - cache=cache, - ) + :param context_name: The context/cell type being processed + :param taxon: The NCBI taxonomy ID + :param output_gene_info_filepath: Path to the output gene information CSV file + :param output_trna_config_filepath: Path to the output tRNA config file (if in "create" mode) + :param output_polya_config_filepath: Path to the output mRNA config file (if in "create" mode) + :param output_trna_count_matrix_filepath: The path to write total RNA count matrices + :param output_polya_count_matrix_filepath: The path to write messenger RNA count matrices + :param como_context_dir: If in "create" mode, the input path(s) to the COMO_input directory of the current context + i.e., the directory containing "fragmentSizes", "geneCounts", "insertSizeMetrics", etc. directories + :param input_matrix_filepath: If in "provide" mode, the path(s) to the count matrices to be processed + :param preparation_method: The preparation method + :param cache: Should HTTP requests be cached + :param log_level: The logging level + :param log_location: The logging location + """ + with contextlib.suppress(ValueError): + logger.remove(0) + logger.add( + sink=log_location, + level=log_level, + format="{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {name}:{line} - {message}", # noqa: E501 + ) -def _parse_args(): - parser = argparse.ArgumentParser( - prog="rnaseq_preprocess.py", - description=""" - Fetches additional gene information from a provided matrix or gene counts, or optionally creates this - matrix using gene count files obtained using STAR aligner. Creation of counts matrix from STAR aligner - output requires that the 'COMO_input' folder exists and is correctly structured according to the - normalization technique being used. A correctly structured folder can be made using our Snakemake-based - alignment pipeline at: - https://github.com/HelikarLab/FastqToGeneCounts""", - epilog=""" - For additional help, please post questions/issues in the MADRID GitHub repo at - https://github.com/HelikarLab/MADRID or email babessell@gmail.com""", - ) - parser.add_argument( - "-n", - "--context-names", - type=str, - nargs="+", - required=True, - dest="context_names", - help="""Tissue/cell name of models to generate. These names should correspond to the folders - in 'COMO_input/' if creating count matrix files, or to - 'work/data/data_matrices//gene_counts_matrix_.csv' if supplying - the count matrix as an imported .csv file. If making multiple models in a batch, then - use the format: "context1 context2 context3". """, - ) - parser.add_argument( - "-i", - "--taxon-id", - required=False, - default=9606, - dest="taxon_id", - help="BioDbNet taxon ID number, also accepts 'human', or 'mouse'", + output_gene_info_filepath = output_gene_info_filepath.resolve() + como_context_dir = como_context_dir.resolve() + input_matrix_filepath = [i.resolve() for i in _listify(input_matrix_filepath)] if input_matrix_filepath else None + output_trna_config_filepath = ( + output_trna_config_filepath.resolve() if output_trna_config_filepath else output_trna_config_filepath ) - parser.add_argument( - "--input-format", - required=True, - dest="input_format", - help="The data input format, such as Ensembl, Entrez, etc.", + output_polya_config_filepath = ( + output_polya_config_filepath.resolve() if output_polya_config_filepath else output_polya_config_filepath ) - parser.add_argument( - "--mode", - type=str, - required=True, - dest="mode", - choices={"create", "provide"}, - help="Mode of rnaseq_preprocess.py, either 'make' or 'provide'", + output_trna_count_matrix_filepath = ( + output_trna_count_matrix_filepath.resolve() + if output_trna_count_matrix_filepath + else output_trna_count_matrix_filepath ) - parser.add_argument( - "--matrix", - required=False, - dest="provided_matrix_fname", - default=None, - help="Name of provided counts matrix in /work/data/data_matrices//.csv", + output_polya_count_matrix_filepath = ( + output_polya_count_matrix_filepath.resolve() + if output_polya_count_matrix_filepath + else output_polya_count_matrix_filepath ) - parsed = parser.parse_args() - parsed.context_names = stringlist_to_list(parsed.context_names) + input_matrix_filepath = _listify(input_matrix_filepath) + preparation_method = _listify(preparation_method) - if parsed.mode == "provide" and parsed.provided_matrix_fname is None: - raise ValueError("If provide_matrix is True, then provided_matrix_fname must be provided") - - # handle species alternative ids - taxon_id = str(parsed.taxon_id) - if taxon_id.isdigit(): - try: - parsed.taxon_id = Taxon.from_int(int(taxon_id)) - except ValueError: - parsed.taxon_id = int(taxon_id) - else: - if taxon_id.upper() in {"HUMAN", "HOMO SAPIENS"}: - parsed.taxon_id = Taxon.HOMO_SAPIENS - elif taxon_id.upper() in {"MOUSE", "MUS MUSCULUS"}: - parsed.taxon_id = Taxon.MUS_MUSCULUS - else: - raise ValueError( - f"Taxon id (--taxon-id) is invalid; accepts 'human', 'mouse', or an integer value; provided: {taxon_id}" - ) - - args = _Arguments(**vars(parsed)) - return args - - -if __name__ == "__main__": - args: _Arguments = _parse_args() - taxon_id_value = args.taxon_id.value if isinstance(args.taxon_id, Taxon) else args.taxon_id - - asyncio.run( - rnaseq_preprocess( - context_names=args.context_names, - mode=args.mode, - taxon_id=taxon_id_value, - matrix_file=args.provided_matrix_fname, - input_format=args.input_format, + if len(input_matrix_filepath) != len(preparation_method): + raise ValueError( + "input_matrix_filepath (--input-matrix-filepath) and " + "preparation_method (--preparation-method) must be the same length." ) + await _process( + context_name=context_name, + taxon=taxon, + como_context_dir=como_context_dir, + input_matrix_filepath=input_matrix_filepath, + output_gene_info_filepath=output_gene_info_filepath, + output_trna_config_filepath=output_trna_config_filepath, + output_mrna_config_filepath=output_polya_config_filepath, + output_trna_matrix_filepath=output_trna_count_matrix_filepath, + output_mrna_matrix_filepath=output_polya_count_matrix_filepath, + cache=cache, ) diff --git a/main/como/types.py b/main/como/types.py new file mode 100644 index 00000000..ebe44ecf --- /dev/null +++ b/main/como/types.py @@ -0,0 +1,50 @@ +from __future__ import annotations + +from abc import ABC +from enum import Enum +from pathlib import Path +from typing import Literal + +from pydantic import BaseModel, ConfigDict +from pydantic_settings import BaseSettings, SettingsConfigDict + + +class _BaseModel(BaseModel, ABC): + model_config = ConfigDict() + + +class _BaseArguments(BaseSettings, ABC): + model_config = SettingsConfigDict( + cli_parse_args=True, + cli_kebab_case=True, + nested_model_default_partial_update=True, + case_sensitive=True, + cli_avoid_json=True, + cli_enforce_required=True, + ) + + +class RNAPrepMethod(Enum): + TOTAL = "total" + MRNA = "mrna" + SCRNA = "scrna" + + @staticmethod + def from_string(value: str) -> RNAPrepMethod: + """Build a preparation method object from a string.""" + match_value = "".join(c for c in value if c.isascii()).lower() + + match match_value: + case "total" | "trna": + return RNAPrepMethod.TOTAL + case "mrna": + return RNAPrepMethod.MRNA + case "scrna": + return RNAPrepMethod.SCRNA + case _: + possible_values = [t.value for t in RNAPrepMethod] + raise ValueError(f"Filtering technique must be one of {possible_values}; got: {value}") + + +type_path = str | Path +type_rna = Literal["total", "polya"] diff --git a/main/como/utils.py b/main/como/utils.py index 9668a34d..a359de0f 100644 --- a/main/como/utils.py +++ b/main/como/utils.py @@ -115,32 +115,32 @@ def stringlist_to_list(stringlist: str | list[str]) -> list[str]: :param stringlist: The "string list" gathered from the command line. Example input: "['mat', 'xml', 'json']" """ - if isinstance(stringlist, str): - if stringlist.startswith("[") and stringlist.endswith("]"): - # Remove any brackets from the first and last items; replace quotation marks and commas with nothing - new_list: list[str] = stringlist.strip("[]").replace("'", "").replace(" ", "").split(",") - - # Show a warning if more than one item is present in the list (this means we are using the old method) - logger.critical( - "DeprecationWarning: Please use the new method of providing context names, " - "i.e. --output-filetypes 'type1 type2 type3'." - ) - logger.critical( - "If you are using COMO, this can be done by setting the 'context_names' variable to a " - "simple string separated by spaces. Here are a few examples!" - ) - logger.critical("context_names = 'cellType1 cellType2 cellType3'") - logger.critical("output_filetypes = 'output1 output2 output3'") - logger.critical( - "\nYour current method of passing context names will be removed in the future. " - "Update your variables above accordingly!\n\n" - ) - - else: - new_list: list[str] = stringlist.split(" ") - - return new_list - return stringlist + if isinstance(stringlist, list): + return stringlist + + if not (stringlist.startswith("[") and stringlist.endswith("]")): + return stringlist.split(" ") + + # Remove any brackets from the first and last items; replace quotation marks and commas with nothing + new_list: list[str] = stringlist.strip("[]").replace("'", "").replace(" ", "").split(",") + + # Show a warning if more than one item is present in the list (this means we are using the old method) + logger.critical( + "DeprecationWarning: Please use the new method of providing context names, " + "i.e. --output-filetypes 'type1 type2 type3'." + ) + logger.critical( + "If you are using COMO, this can be done by setting the 'context_names' variable to a " + "simple string separated by spaces. Here are a few examples!" + ) + logger.critical("context_names = 'cellType1 cellType2 cellType3'") + logger.critical("output_filetypes = 'output1 output2 output3'") + logger.critical( + "\nYour current method of passing context names will be removed in the future. " + "Update your variables above accordingly!\n\n" + ) + + return new_list def split_gene_expression_data(expression_data: pd.DataFrame, recon_algorithm: Algorithm | None = None): @@ -258,3 +258,8 @@ async def convert_gene_data(values: list[str], taxon_id: int | str | Taxon) -> p return await gene_id_to_ensembl_and_gene_symbol(ids=values, taxon=taxon_id) else: raise ValueError("Gene data must be of the same type (i.e., all Ensembl, Entrez, or Gene Symbols)") + + +def _listify(value): + """Convert items into a list.""" + return [value] if not isinstance(value, list) else value diff --git a/pyproject.toml b/pyproject.toml index 2c6d42c5..85a1308f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,22 +3,30 @@ name = "COMO" dynamic = ["version"] requires-python = ">=3.10,<3.13" dependencies = [ + "aiofiles>=24.1.0", + "aioftp>=0.23.1", + "cobamp", "cobra>=0.28.0", "fast-bioservices>=0.3.9", "gurobipy>=11.0", "kaleido==0.2.1", "loguru>=0.7.2", + "openpyxl>=3.1.5", "pandas>=1.3.5", "plotly>=5.24.1", + "pydantic-settings", + "pydantic>=2.10.3", "scanpy>=1.9.8", - "scipy>=1.7.3", "scikit-learn>=1.5.2", + "scipy>=1.7.3", "setuptools<60.0", - "openpyxl>=3.1.5", - "aiofiles>=24.1.0", - "aioftp>=0.23.1", "troppo", - "cobamp", +] + +[project.optional-dependencies] +interactive = [ + "ipython>=8.0.0", + "jupyterlab>=4.3.2", ] [build-system] @@ -45,8 +53,18 @@ dev-dependencies = [ "ruff>=0.8.0", "hypothesis>=6.122.1", "pytest-cov>=6.0.0", + "commitizen>=4.1.0", + "cz-conventional-gitmoji>=0.6.1", ] [tool.uv.sources] troppo = { git = "https://github.com/JoshLoecker/troppo", rev = "update_dependencies" } cobamp = { git = "https://github.com/JoshLoecker/cobamp", rev = "update_packages" } +pydantic-settings = { git = "https://github.com/pydantic/pydantic-settings" } + +[tool.commitizen] +name = "cz_conventional_commits" +tag_format = "$version" +version_scheme = "semver2" +version_provider = "pep621" +update_changelog_on_bump = true diff --git a/ruff.toml b/ruff.toml index 9591bda3..3691008f 100644 --- a/ruff.toml +++ b/ruff.toml @@ -1,6 +1,6 @@ line-length = 120 -extend-include = ["docs/**/*.py", "tests/**/*.py"] -exclude = ["__init__.py", "main/COMO.ipynb"] +extend-include = ["docs/**/*.py", "tests/**/*.py", "**/*.ipynb"] +exclude = ["__init__.py"] [format] quote-style = "double" @@ -46,5 +46,9 @@ ignore = [ "D101", # allow undocumented public class "D102", # allow undocumented class method "D103", # allow undocumented public method definitions + "F811", # allow redefinition of variables, required for pytest fixtures "S101", # allow use of `assert` in test files ] +"main/COMO.ipynb" = [ + "E501", # allow long lines +] diff --git a/tests/fixtures/collect_files.py b/tests/fixtures/collect_files.py new file mode 100644 index 00000000..93d59e70 --- /dev/null +++ b/tests/fixtures/collect_files.py @@ -0,0 +1,93 @@ +from pathlib import Path +from typing import NamedTuple + +import pytest +from _pytest.fixtures import SubRequest + +_fragment_size_filepaths = list(Path("main/data/COMO_input").absolute().rglob("*fragment_size*.txt")) +_gene_count_filepaths = list(Path("main/data/COMO_input").absolute().rglob("*.tab")) +_insert_size_filepaths = list(Path("main/data/COMO_input").absolute().rglob("*_insert_size.txt")) +_layout_filepaths = list(Path("main/data/COMO_input").absolute().rglob("*_layout.txt")) +_preparation_method_filepaths = list(Path("main/data/COMO_input").absolute().rglob("*_prep_method.txt")) +_strandedness_filepaths = list(Path("main/data/COMO_input").absolute().rglob("*_strandedness.txt")) + + +class PackedFilepaths(NamedTuple): + sample_name: str + fragment_size: Path + gene_count: Path + insert_size: Path + layout: Path + preparation_method: Path + strandedness: Path + + +@pytest.fixture(params=_fragment_size_filepaths) +def fragment_size_filepath(request: SubRequest) -> Path: + return request.param + + +@pytest.fixture(params=_gene_count_filepaths) +def gene_count_filepath(request: SubRequest) -> Path: + return request.param + + +@pytest.fixture +def all_gene_count_filepaths() -> list[Path]: + return _gene_count_filepaths + + +@pytest.fixture(params=_insert_size_filepaths) +def insert_size_filepath(request: SubRequest) -> Path: + return request.param + + +@pytest.fixture(params=_layout_filepaths) +def layout_filepath(request: SubRequest) -> Path: + return request.param + + +@pytest.fixture(params=_preparation_method_filepaths) +def prep_method_filepath(request: SubRequest) -> Path: + return request.param + + +@pytest.fixture(params=_strandedness_filepaths) +def strand_filepath(request: SubRequest) -> Path: + return request.param + + +@pytest.fixture( + params=[ + file + for filepaths in [ + _fragment_size_filepaths, + _gene_count_filepaths, + _insert_size_filepaths, + _layout_filepaths, + _preparation_method_filepaths, + _strandedness_filepaths, + ] + for file in filepaths + ] +) +def any_como_input_filepath(request: SubRequest) -> Path: + return request.param + + +@pytest.fixture(params=[Path("main/data/COMO_input/naiveB").absolute(), Path("main/data/COMO_input/smB").absolute()]) +def como_input_data_directory(request: SubRequest) -> Path: + return request.param + + +@pytest.fixture(params=["naiveB", "smB"]) +def packed_filepaths(request: SubRequest) -> PackedFilepaths: + return PackedFilepaths( + sample_name=request.param, + fragment_size=Path(f"main/data/COMO_input/{request.param}/fragmentSizes/{request.param}_fragment_size.txt"), + gene_count=Path(f"main/data/COMO_input/{request.param}/geneCounts/{request.param}.tab"), + insert_size=Path(f"main/data/COMO_input/{request.param}/insertSizes/{request.param}_insert_size.txt"), + layout=Path(f"main/data/COMO_input/{request.param}/layouts/{request.param}_layout.txt"), + preparation_method=Path(f"main/data/COMO_input/{request.param}/prepMethods/{request.param}_prep_method.txt"), + strandedness=Path(f"main/data/COMO_input/{request.param}/strandedness/{request.param}_strandedness.txt"), + ) diff --git a/tests/test_proteomics.py b/tests/test_proteomics.py index d3c6ad66..0f38be27 100644 --- a/tests/test_proteomics.py +++ b/tests/test_proteomics.py @@ -77,7 +77,7 @@ async def test_ftp_client(self): assert await client.quit() is None @pytest.mark.skip(reason="pyftpdlib is broken, no way to test this") - def test_reader(self, ftpserver, fixture_ftp_server, ftp_file_names): # noqa: F811 + def test_reader(self, ftpserver, fixture_ftp_server, ftp_file_names): # Use pytest_localftpserver and fixtures.fixture_ftp_server.fix # Now we can get login information for our local FTP server file_extensions: list[str] = ["raw"] diff --git a/tests/test_rnaseq_preprocess.py b/tests/test_rnaseq_preprocess.py deleted file mode 100644 index 752ec333..00000000 --- a/tests/test_rnaseq_preprocess.py +++ /dev/null @@ -1,47 +0,0 @@ -from __future__ import annotations - -import sys - -import pytest -from como import rnaseq_preprocess - - -# Define a list of arguments to test -@pytest.mark.skip("Not testing args because the input 'argv' has been removed") -@pytest.mark.parametrize( - "args", - [ - # Test using data in COMO_input data - ["--context-names", "naiveB immNK", "--gene-format", "Ensembl", "--taxon-id", "9606", "--create-matrix"], - [ - "--context-names", - "dimNK brightNK", - "--gene-format", - "SYMBOL", - "--taxon-id", - "human", - "--provide-matrix", - "--matrix", - "COMO_input/counts_matrix.tsv", - ], - ], -) -def test_arg_input(args: list[str]): - """Asserts that the arguments passed into the function are correct.""" - context_names = args[1] - gene_format = args[3] - taxon_id = args[5] - matrix_mode = args[6] - - sys.argv = args - parsed = rnaseq_preprocess._parse_args() - - assert [context_name in parsed.context_names for context_name in context_names.split()] - assert parsed.gene_format == gene_format - assert parsed.taxon_id == taxon_id - - if matrix_mode == "--create-matrix": - assert parsed.make_matrix is True - elif matrix_mode == "--provide-matrix": - assert parsed.make_matrix is False - assert parsed.provided_matrix_fname == args[8] diff --git a/tests/unit/test_rnaseq_preprocess.py b/tests/unit/test_rnaseq_preprocess.py new file mode 100644 index 00000000..c0d49c2e --- /dev/null +++ b/tests/unit/test_rnaseq_preprocess.py @@ -0,0 +1,82 @@ +from __future__ import annotations + +from pathlib import Path + +import pandas as pd +import pytest +from como.rnaseq_preprocess import ( + _organize_gene_counts_files, + _process_first_multirun_sample, + _process_standard_replicate, + _sample_name_from_filepath, + _STARinformation, + _StudyMetrics, +) + +from tests.fixtures.collect_files import ( + PackedFilepaths, + all_gene_count_filepaths, + any_como_input_filepath, + como_input_data_directory, + packed_filepaths, + strand_filepath, +) + + +class TestSTARInformation: + valid_data = Path("main/data/COMO_input/naiveB/geneCounts/S1/naiveB_S1R1.tab").resolve() + invalid_data = Path("main/data/COMO_input/naiveB/fragmentSizes/S1/naiveB_S1R1_fragment_size.txt").resolve() + + @pytest.mark.asyncio + async def test_build_from_tab_valid_file(self): + """Validate building STAR information object.""" + star: _STARinformation = await _STARinformation.build_from_tab(TestSTARInformation.valid_data) + assert len(star.gene_names) == len(star.count_matrix) == 61541 + assert len(star.num_unmapped) == 3 + assert len(star.num_multimapping) == 3 + assert len(star.num_no_feature) == 3 + assert len(star.num_ambiguous) == 3 + + @pytest.mark.asyncio + async def test_build_from_tab_invalid_file(self): + """Validate error on invalid file.""" + with pytest.raises(ValueError, match="Building STAR information requires a '.tab' file"): + await _STARinformation.build_from_tab(TestSTARInformation.invalid_data) + + +def test_sample_name_from_filepath(any_como_input_filepath: Path): + expected = "_".join(any_como_input_filepath.stem.split("_")[:2]) + assert _sample_name_from_filepath(any_como_input_filepath) == expected + + +def test_organize_gene_counts_files(como_input_data_directory: Path): + metrics: list[_StudyMetrics] = _organize_gene_counts_files(como_input_data_directory) + for metric in metrics: + assert len(metric.sample_names) == metric.num_samples == len(metric.count_files) == len(metric.strand_files) + + for file in metric.count_files: + assert f"/{metric.study_name}/" in file.as_posix() + assert "geneCounts" in file.as_posix() + assert file.suffix == ".tab" + + for file in metric.strand_files: + assert f"/{metric.study_name}/" in file.as_posix() + assert "strandedness" in file.as_posix() + assert file.suffix == ".txt" + + +@pytest.mark.asyncio +async def test_process_first_multirun_sample(strand_filepath: Path, all_gene_count_filepaths: list[Path]): + result: pd.DataFrame = await _process_first_multirun_sample(strand_filepath, all_gene_count_filepaths) + assert result.columns[0] == "ensembl_gene_id" + assert 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