{
"cells": [
{
"cell_type": "markdown",
"id": "71cf25ed",
"metadata": {},
"source": [
"# 4 - Distance extraction\n",
"\n",
"**Description:** This notebook contains an example on how to read SRX localization data (in csv format) and extract spatial features like center of mass distances bwetween different chromating segments.\n",
"\n",
"The following index has links to the different sections of the notebook. Some sections may have additional indexes to access the subparts. \n",
"If you are using VS Code to visualize the notebook, the links may not work. This is due to how VC Code render the notebook. To navigate the notebook use the outline panel. To know more about it, check this [link](https://code.visualstudio.com/docs/getstarted/userinterface#_outline-view).\n",
"\n",
"Content:\n",
"\n",
"- [Library imports](#library-imports-and-functions)\n",
"- [Setting some variables](#setting-some-variables)\n",
"- [Exporting the data with CIMA](#exporting-the-data-with-cima)\n",
"- [Spatial features intra-walk](#spatial-feature-intra-walk)\n",
"\n",
"Next notebook to run: \n",
"[Comparison with orthogonal methods](5_Comparison_with_orthogonal_methods.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "08f63401",
"metadata": {},
"source": [
"## Library imports and functions"
]
},
{
"cell_type": "markdown",
"id": "2f9902e1",
"metadata": {},
"source": [
"[Back to the general index](#distance-extraction)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"id": "4e7216b3",
"metadata": {},
"outputs": [],
"source": [
"import re\n",
"from pathlib import Path\n",
"from collections import defaultdict\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.gridspec as gridspec\n",
"from matplotlib.colors import LinearSegmentedColormap\n",
"\n",
"from matplotlib import colormaps\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"from scipy import stats\n",
"from scipy.cluster.hierarchy import linkage, fcluster\n",
"from scipy.spatial.distance import squareform\n",
"\n",
"from cima.parsers.parser_csv import CSVParser\n",
"from cima.utils.walk_features import getSpatialFeaturesIntraWalk, convertToMatrix\n",
"from cima.utils.misc import calculate_rmsd_matrix, get_dtype_dict\n",
"\n",
"# Set the maximum number of rows to display in the output\n",
"pd.options.display.max_rows = 999\n",
"\n",
"# Set the options for saving the plots\n",
"plot_opts = {\"dpi\": 300, \"bbox_inches\": \"tight\", \"transparent\": True}\n",
"\n",
"# Labels for the plots of the features, so they look nice\n",
"plot_labels = {\n",
" \"volume\": \"Volume ($nm^3$)\",\n",
" \"area\": \"Area ($nm^2$)\",\n",
" \"sphericity\": \"Sphericity\",\n",
" \"sphericity_bribiesca\": \"Sphericity Bribiesca\",\n",
" \"density_kb\": \"Density (blink/bp)\",\n",
" \"density\": \"Density (blink/$nm^3$)\",\n",
" \"compaction\": \"Genomic compactness (bp/$nm^3$)\",\n",
" \"solidity\": \"Solidity\",\n",
" \"radius_of_gyration\": \"Radius of gyration\",\n",
" \"point_cloud_ellipticity\": \"Ellipticity\",\n",
"}\n",
"\n",
"# When showing dataframes we want that this are the first columns, so the user can manually search for them (not recomended)\n",
"first_cols = [\"experimentID\", \"nucleusID\", \"chromosome\", \"homolog\", \"timepoint\", \"name\", \"flag\"]\n",
"\n",
"## Regular expressions to get the metadata from the filename\n",
"regex_patterns = {\n",
" \"nucleusID\": re.compile(r\"(?i)nuc(\\d+)\"), # This searches for \"Nuc\" or \"nuc\" followed by digits\n",
" \"cellID\": re.compile(r\"(?i)cell(\\d+)\"), # This searches for \"Cell\" or \"cell\" followed by digits\n",
" \"locationID\": re.compile(r\"(?i)loc-?(\\d+)\"), # This searches for \"Loc\" or \"loc\" followed by optional hyphen and digits\n",
" \"date\": re.compile(r\"(\\d{4}[-\\.]\\d{2}[-\\.]\\d{2})\"), # This searches for dates in the format YYYY-MM-DD or YYYY.MM.DD\n",
" \"homolog\": re.compile(r\"_([ABPM01pm])\"), ## Added just in case the homolog is in the filename.\n",
" \"chromosome\": re.compile(r\"(?i)(chr[\\d+MXY])\"), ## Added just in case the chromosome is in the filename.\n",
"}\n",
"\n",
"# Dictionary to change all the data types of the columns of the different dataframes, so the notebook is mor memory\n",
"# efficient.\n",
"dtype_dict = {\n",
" \"experimentID\": \"string\",\n",
" \"nucleusID\": \"string\",\n",
" \"chromosome\": \"string\",\n",
" \"homolog\": \"string\",\n",
" \"timepoint\": np.int16,\n",
" \"Name\": \"string\",\n",
" \"flag\": \"string\",\n",
" \"segment1\": np.int16,\n",
" \"segment1_timepoint\": np.int16,\n",
" \"segment1_name\": \"string\",\n",
" \"flag_seg1\": \"string\",\n",
" \"segment2\": np.int16,\n",
" \"segment2_timepoint\": np.int16,\n",
" \"segment2_name\": \"string\",\n",
" \"flag_seg2\": \"string\",\n",
" \"coms_distance\": np.float32,\n",
" \"surface_distance\": np.float32,\n",
" \"entanglement\": np.float32,\n",
" \"volume\": np.float32,\n",
" \"area\": np.float32,\n",
" \"sphericity_bribiesca\": np.float32,\n",
" \"sphericity\": np.float32,\n",
" \"radius_of_gyration\": np.float32,\n",
" \"point_cloud_ellipticity\": np.float32,\n",
" \"point_cloud_eccentricity\": np.float32,\n",
" \"mvee_ellipticity\": np.float32,\n",
" \"mvee_eccentricity\": np.float32,\n",
" \"solidity\": np.float32,\n",
" \"holeless_solidity\": np.float32,\n",
" \"numerosity\": np.uint32,\n",
" \"compaction\": np.float32,\n",
" \"density\": np.float32,\n",
" \"density_kb\": np.float32,\n",
"}\n",
"\n",
"# We create a color map that goes from white to red for the HiC plots (put it in CIMA)\n",
"if \"bright_red\" not in colormaps:\n",
" colormaps.register(name=\"bright_red\", cmap=LinearSegmentedColormap.from_list(\"bright_red\", [(1, 1, 1), (1, 0, 0)]))\n",
"\n",
"# Nice names for the distances\n",
"nice_names = {\n",
" \"coms_distance\": \"COMs distance\",\n",
" \"surface_distance\": \"Surface distance\",\n",
" \"entanglement\": \"Entanglement\",\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "f5555970",
"metadata": {},
"outputs": [],
"source": [
"# Function to plot mean vs variance heatmap of a given feature\n",
"def mean_vs_variance_heatmap(mean_matrix, std_matrix, nice_name, timepoint_name_dict, hm_color=\"Reds_r\"):\n",
" \"\"\"\n",
" Plot the heatmaps corresponding to the mean and std matrices for a given feature.\n",
"\n",
" Parameters\n",
" ----------\n",
" mean_matrix : np.ndarray\n",
" 2D array representing the mean values.\n",
" std_matrix : np.ndarray\n",
" 2D array representing the standard deviation values.\n",
" nice_name : str\n",
" Nice name of the feature to be displayed in the plot titles and colorbars.\n",
" timepoint_name_dict : dict\n",
" Dictionary mapping timepoint indices to their names for labeling axes.\n",
" hm_color : str, optional\n",
" Color map to use for the mean heatmap (default is \"Reds_r\").\n",
"\n",
" Returns\n",
" -------\n",
" None\n",
" \"\"\"\n",
"\n",
" _, ax = plt.subplots(nrows=1, ncols=2, figsize=(13, 5))\n",
"\n",
" sns.heatmap(\n",
" data=mean_matrix,\n",
" ax=ax[0],\n",
" cmap=hm_color,\n",
" square=True,\n",
" vmax=np.nanquantile(mean_matrix, 0.9),\n",
" cbar_kws={\"shrink\": 0.9, \"label\": f\"Mean {nice_name}\"},\n",
" xticklabels=list(timepoint_name_dict.values()),\n",
" yticklabels=list(timepoint_name_dict.values()),\n",
" )\n",
" ax[0].set_xticklabels(ax[0].get_xticklabels(), rotation=45, ha=\"right\")\n",
" ax[0].set_title(f\"Mean of {nice_name}\", fontsize=14)\n",
"\n",
" sns.heatmap(\n",
" data=std_matrix,\n",
" ax=ax[1],\n",
" cmap=\"Blues_r\",\n",
" square=True,\n",
" vmax=np.nanquantile(std_matrix, 0.9),\n",
" cbar_kws={\"shrink\": 0.9, \"label\": f\"Mean {nice_name}\"},\n",
" xticklabels=list(timepoint_name_dict.values()),\n",
" yticklabels=list(timepoint_name_dict.values()),\n",
" )\n",
" ax[1].set_xticklabels(ax[1].get_xticklabels(), rotation=45, ha=\"right\")\n",
" ax[1].set_title(f\"Variation of {nice_name}\", fontsize=14)\n",
"\n",
" if save_plots:\n",
" filename = f\"mean_vs_variance_heatmap_{nice_name.replace(' ', '_')}\" + (f\"_{file_suffix}\" if file_suffix != \"\" else \"\") + \".pdf\"\n",
" plot_filename = Path(plots_folder, filename)\n",
" print(f\"Saving plots to: {plot_filename}\")\n",
" plt.savefig(plot_filename, **plot_opts)\n",
" else:\n",
" plt.show()\n",
"\n",
" plt.close()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "7889f8ba",
"metadata": {},
"outputs": [],
"source": [
"def getLogLinearRegression(vals1, vals2):\n",
" arr1 = np.log10(vals1)\n",
" arr2 = np.log10(vals2)\n",
" where_ok = (np.isfinite(arr1) * np.isfinite(arr2)).astype(\"bool\")\n",
" lr = stats.linregress(arr1[where_ok], arr2[where_ok])\n",
" return lr.intercept, lr.slope"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "525ce2b1",
"metadata": {},
"outputs": [],
"source": [
"def getLogCorrAndExp(arr1, arr2):\n",
" import warnings\n",
"\n",
" warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
" arr1_log = np.log10(arr1)\n",
" arr2_log = np.log10(arr2)\n",
" where_ok = (np.isfinite(arr1_log) * np.isfinite(arr2_log)).astype(\"bool\")\n",
" arr1_log = arr1_log[where_ok]\n",
" arr2_log = arr2_log[where_ok]\n",
" _, slope = getLogLinearRegression(arr1, arr2)\n",
" pearson = stats.pearsonr(arr1_log, arr2_log).statistic\n",
" warnings.filterwarnings(\"default\", category=RuntimeWarning)\n",
" return pearson, slope"
]
},
{
"cell_type": "markdown",
"id": "dd945e05",
"metadata": {},
"source": [
"## Setting some variables"
]
},
{
"cell_type": "markdown",
"id": "128d65bc",
"metadata": {},
"source": [
"In the following cell you should set up some variables for the script to run properly. Change the variables at your convenience, and then you can press \"Run all\" in the Jupyter Notebook. \n",
"\n",
"Brief explanation of the different variables:\n",
"* __work_folder__: this variable is where the plots and the tsv files that the script creates will be stored. \n",
"\n",
"* __data_file__: this variable is where the csv files to be assessed are stored. \n",
"* __info_file__: file that contains information about your walks, such as the chromosome, start and end of the steps. You need a columns that matches the 'timepoint' column in the csv files, to be able to show proper information. \n",
"* __column_round__: the column that matches the 'timepoint' column in the csv files.\n",
"* __column_name__: the column with the \"nice\" name for the steps. This will be used instead of the timepoint number. Use \"\" in case the information file does not have this column (in this case the name of the steps will be in the form of 'StepN' where N is an incremental number form 1 to the max number of steps).\n",
"* __column_size__: the column for the size of the segment. Use \"\" in case the information file does not have this column (in this case the notebook will calculate the size automatically of the segments according to the start and end positions in the file).\n",
"* __column_start_pos__: the column of the start position of the timepoint.\n",
"* __column_end_pos__: the column of the start position of the timepoint.\n",
"* __number_of_steps_library__: the number of steps in your library.\n",
"* __starting_step__: the step at which analysis should start.\n",
"* __prec_mean_dataset__: the worst precision of the three axes (usually the Z). This will be used as a blurring factor when creating the 3D maps to calculate the volumetric features.\n",
"* __save_plots__: save the plots into a folder or show them in the notebook.\n",
"* __file_suffix__: add a suffix to the different files created, in case you wish to save the results from different assessments in the same folder. It will be added as \"_suffix\" before the .tsv/.pdf\n",
"\n",
"The notebook will create a folder in the work_folder path called `4_distance_extraction`. Inside this folder 2 subfolders will be created, one for `tsvs` and one for `plots`. Inside each folder, a sub-subfolder with the suffix name will have the plots/tsvs for that experiment.\n",
"\n",
"
\n",
"\n",
"[Back to the general index](#distance-extraction)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "025d3f46",
"metadata": {},
"outputs": [],
"source": [
"work_folder = \"/scratch/CIMA_tutorial/\"\n",
"data_folder = \"/scratch/CIMA_tutorial/data/chr3\"\n",
"\n",
"info_file = \"/scratch/CIMA_tutorial/data/chr3/Info_chr3.txt\"\n",
"\n",
"column_round = \"Round\"\n",
"column_name = \"Name\"\n",
"column_size = \"Size(kb)\"\n",
"column_chr = \"Chr\"\n",
"column_start_pos = \"Start(hg19)\"\n",
"column_end_pos = \"End(hg19)\"\n",
"\n",
"number_of_steps_library = 16\n",
"starting_step = 6\n",
"\n",
"prec_mean_dataset = 30\n",
"\n",
"save_plots = False\n",
"\n",
"file_suffix = \"chr3\""
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "82af39ff",
"metadata": {},
"outputs": [],
"source": [
"work_folder = Path(work_folder)\n",
"data_folder = Path(data_folder)\n",
"pre_assessment_folder = Path(work_folder, \"2_experimental_quality_assessment\")\n",
"reconstruction_tsv_folder = Path(work_folder, \"3_3D_reconstruction_assessment\", \"tsvs\", file_suffix)\n",
"\n",
"info_df = pd.read_table(Path(info_file), sep=\"\\t\")\n",
"info_df.columns = info_df.columns.str.strip() # In case there are leading or trailing spaces in the column names\n",
"\n",
"info_file_dict = {\n",
" \"column_round\": column_round,\n",
" \"column_name\": column_name,\n",
" \"column_size\": column_size,\n",
" \"column_chr\": column_chr,\n",
" \"column_start_pos\": column_start_pos,\n",
" \"column_end_pos\": column_end_pos,\n",
"}\n",
"\n",
"del column_round, column_name, column_size, column_chr, column_start_pos, column_end_pos, info_file"
]
},
{
"cell_type": "markdown",
"id": "137d1fd0",
"metadata": {},
"source": [
"## Exporting the data with CIMA"
]
},
{
"cell_type": "markdown",
"id": "22012cbd",
"metadata": {},
"source": [
"Content:\n",
"- [Creating the list of CIMA StructuralObject](#creating-the-list-of-cima-structuralobjects)\n",
"- [Checking the information file](#checking-the-information-file)\n",
"- [Creating a color palette and the results folders](#creating-a-color-palette-and-the-results-folders-for-the-tsv-and-plot-files)\n",
"\n",
"[Back to the general index](#distance-extraction)"
]
},
{
"cell_type": "markdown",
"id": "7edca27a",
"metadata": {},
"source": [
"### Creating the list of CIMA StructuralObjects"
]
},
{
"cell_type": "markdown",
"id": "61dc37a1",
"metadata": {},
"source": [
"In this cell we create a list of StructuralObjects (the main CIMA class type). \n",
"This list is the basis to do the assessment through this Jupyter Notebook. \n",
"\n",
"When loading the data, we will also take out those traces or CS that did not pass the assessment in the previous notebooks. \n",
" \n",
"When reading the file, it will also add some metadata gathered from the filename of the csv file:\n",
"
\n",
"
nucleusID: searches for \"nuc\" (with any combination of lower and upper case) followed by digits. If there is not a match, the default is filecount (first file read will be number 1, ...).
\n",
"
cellID: searches for \"cell\" (with any combination of lower and upper case) followed by digits. If there is not a match, the default is filecount (first file read will be number 1, ...).
\n",
"
locationID: searches for \"loc\" (with any combination of lower and upper case) followed by optional hyphen and digits.If there is not a match, the default is filecount (first file read will be number 1, ...).
\n",
"
date: searches for dates in the format YYYY-MM-DD or YYYY.MM.DD. If there is not a match, the default is the file name.
\n",
"
homolog: searches for \"_\" followed by A, B, M, P, m, p, 0 or 1 (only one instance of this). If there is not a match, the default is \"A\".
\n",
"
chromosome: searches for \"chr\" (with any combination of lower and upper case) followed by a number, M, X or Y. If there is not a match, the default is \"chrN\".
\n",
"
\n",
"\n",
"[Back to the section index](#exporting-the-data-with-cima)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "23c69342",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total number of files found: 33\n",
"Loading the experimental quality data from the CSV files\n",
"Loading the bad flags from the CSV files\n",
"\tCreating the 'flag' column for reconstruction-based removal\n",
"Skipping file 2: 2018-07-10_nuc04_A_chr3 because it did not pass the experimental quality assessment.\n",
"Skipping file 5: 2018-07-10_nuc03_A_chr3 because it did not pass the experimental quality assessment.\n",
"\tExcluding timepoint 19 in experimentID: 2018-09-04, nucleusID: 7, homolog: A because it has less than 10 points.\n",
"Skipping file 26: 2018-09-04_nuc01_A_chr3 because it did not pass the experimental quality assessment.\n",
"Processing file 33: 2018-09-04_nuc06_A_chr3\n",
"Total number of objects created: 30\n"
]
}
],
"source": [
"# Get a list of all CSV files in the specified data folder\n",
"csv_files = list(data_folder.glob(\"*.csv\"))\n",
"\n",
"if len(csv_files) == 0:\n",
" print(\"No files found, please input the correct work_folder\")\n",
"else:\n",
" print(f\"Total number of files found: {len(csv_files)}\")\n",
"\n",
"print(\"Loading the experimental quality data from the CSV files\")\n",
"traces_pass = pd.read_csv(Path(pre_assessment_folder, f\"experimental_assessment_pass_{file_suffix}.txt\"), sep=\"\\t\", names=[\"trace\"])\n",
"\n",
"print(\"Loading the bad flags from the CSV files\")\n",
"segments_to_remove_CCC = pd.read_csv(\n",
" Path(reconstruction_tsv_folder, f\"segments2remove_bycccvariation_t2{('_' + file_suffix) if file_suffix != '' else ''}.tsv\"), sep=\"\\t\"\n",
")\n",
"if \"flag\" not in segments_to_remove_CCC.columns and not segments_to_remove_CCC.empty:\n",
" print(\"\\tCreating the 'flag' column for reconstruction-based removal\")\n",
" segments_to_remove_CCC[\"flag\"] = segments_to_remove_CCC[[\"experimentID\", \"nucleusID\", \"homolog\", \"timepoint\"]].astype(str).apply(\"_\".join, axis=1)\n",
" bad_flags = set(segments_to_remove_CCC[\"flag\"].astype(str).values)\n",
"elif not segments_to_remove_CCC.empty:\n",
" bad_flags = set(segments_to_remove_CCC[\"flag\"].astype(str).values)\n",
"else:\n",
" bad_flags = set()\n",
"\n",
"# Get a list of metadata keys to extract from filenames\n",
"metadata_keys = [\"nucleusID\", \"cellID\", \"locationID\", \"date\", \"homolog\", \"chromosome\"]\n",
"\n",
"# Initialize an empty list to store the StructuralObject instances\n",
"obj_list = []\n",
"\n",
"if len(csv_files) > 0:\n",
" # Iterate over each file in the list of file names\n",
" for count, filein in enumerate(csv_files, start=1):\n",
" stem = Path(filein).stem\n",
"\n",
" if stem not in traces_pass[\"trace\"].values:\n",
" print(f\"Skipping file {count}: {stem} because it did not pass the experimental quality assessment.\")\n",
" continue\n",
"\n",
" print(f\"Processing file {count}: {stem}\", end=\"\\r\")\n",
"\n",
" # We mine the metadata from the filename using regex patterns. If not possible, assign default values.\n",
" # In the case of nucleus, cell and location, the default is the filecount (first file read will be number 1, ...)\n",
" # In the case of date, if no date can be found, the filename gets used.\n",
" # If no valid homolog denomination is found, the default value is A.\n",
" # If no valid chromosome denomination is found, the default value is chrN.\n",
" metadata_CIMA = {\n",
" key: (match.group(1) if (match := regex_patterns[key].search(stem)) else default)\n",
" for key, default in zip(metadata_keys, [count, count, count, stem, \"A\", \"chrN\"])\n",
" }\n",
"\n",
" # Adjust the metadata for nucleus and cell (if nucleus is count, but cell is something, use the cell match in nucleus)\n",
" if \"cellID\" in metadata_CIMA and metadata_CIMA[\"nucleusID\"] == count:\n",
" metadata_CIMA[\"nucleusID\"] = metadata_CIMA[\"cellID\"]\n",
"\n",
" # Set experimentID as date for consistency\n",
" metadata_CIMA[\"experimentID\"] = metadata_CIMA[\"date\"]\n",
"\n",
" # Read the CSV file and create a StructuralObject instance\n",
" objin = CSVParser.read_CSV_file(filein.as_posix(), metadata=metadata_CIMA, content_type=\"srx\")\n",
"\n",
" # Filter the atomList to include only the specified timepoints in the info file\n",
" objin.atomList = objin.atomList[\n",
" (objin.atomList[\"timepoint\"] >= starting_step - 1) & (objin.atomList[\"timepoint\"] < (starting_step + number_of_steps_library - 1))\n",
" ].copy()\n",
"\n",
" experiment_id = objin.metadata[\"experimentID\"]\n",
" nucleus_id = int(objin.metadata[\"nucleusID\"])\n",
" homolog = objin.metadata[\"homolog\"]\n",
"\n",
" full_flags = experiment_id + \"_\" + str(nucleus_id) + \"_\" + homolog + \"_\" + objin.atomList[\"timepoint\"].astype(str)\n",
" remove_mask = full_flags.isin(bad_flags)\n",
"\n",
" if remove_mask.any():\n",
" removed_tps = objin.atomList.loc[remove_mask, \"timepoint\"].unique()\n",
" # removed_tps = \",\".join([tp_names[tp] for tp in removed_tps])\n",
" # print(f\"Excluding timepoints {removed_tps} in experimentID: {experiment_id}, nucleusID: {nucleus_id}, homolog: {homolog} due to poor assessment.\")\n",
" objin.atomList = objin.atomList.loc[~remove_mask].copy()\n",
"\n",
" for tp in objin.atomList[\"timepoint\"].unique():\n",
" # Take out the timepoint from the atomList if it has less tha 10 points\n",
" tp_mask = objin.atomList[\"timepoint\"] == tp\n",
" if tp_mask.sum() < 10:\n",
" print(\n",
" f\"\\tExcluding timepoint {tp} in experimentID: {experiment_id}, nucleusID: {nucleus_id}, homolog: {homolog} because it has less than 10 points.\"\n",
" )\n",
" objin.atomList = objin.atomList.loc[~tp_mask].copy()\n",
"\n",
" # Append the object to the list\n",
" obj_list.append(objin)\n",
"\n",
" # Nice print to tell the user the number of objects created.\n",
" print(f\"\\nTotal number of objects created: {len(obj_list)}\")\n",
"\n",
" # Clean up memory by deleting some of the variables no longer needed\n",
" del count, filein, stem, metadata_CIMA, match, objin\n",
"\n",
"del csv_files, metadata_keys\n",
"del bad_flags\n",
"del experiment_id, nucleus_id, homolog, full_flags, remove_mask\n",
"if \"segments_to_remove_precision\" in locals():\n",
" del segments_to_remove_CCC"
]
},
{
"cell_type": "markdown",
"id": "c423346f",
"metadata": {},
"source": [
"### Checking the information file"
]
},
{
"cell_type": "markdown",
"id": "d00061de",
"metadata": {},
"source": [
"This cells makes an adjustment in the info_df. \n",
"\n",
"It checks if the column of the info_df that we will use as the link between this data frame and our walks has the same timepoints. This happens because most programs start with timepoint 0, while most information files start at timepoint 1. \n",
"\n",
"This will create a new column in the dataframe that is called 'timepoint'. This column will be modified so it has the same range as the timepoint column from the csv files.\n",
"\n",
"[Back to the section index](#exporting-the-data-with-cima)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "c8d9643c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"List of timepoints found across all objects: [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n",
"List of timepoints in the information file: [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21]\n",
"\n",
"Note: The column for the information file that has the timepoint is offset by 1 compared to the objects, adjusting accordingly.\n"
]
}
],
"source": [
"list_of_timepoints = sorted({int(tp) for obj in obj_list for tp in obj.atomList.timepoint.unique()})\n",
"info_of_timepoints = list(map(int, info_df[info_file_dict[\"column_round\"]].tolist()))\n",
"\n",
"print(f\"List of timepoints found across all objects: {list_of_timepoints}\")\n",
"print(f\"List of timepoints in the information file: {info_of_timepoints}\\n\")\n",
"\n",
"if list_of_timepoints == info_of_timepoints:\n",
" print(\"The timepoints found in the objects match those in the information file.\")\n",
" info_df[\"timepoint\"] = info_df[info_file_dict[\"column_round\"]]\n",
"elif set(list_of_timepoints).issubset(info_of_timepoints):\n",
" print(\"Note: The timepoints found in the objects are a subset of those in the information file. Not all timepoints are present.\")\n",
" info_df[\"timepoint\"] = info_df[info_file_dict[\"column_round\"]]\n",
"elif list_of_timepoints == [x - 1 for x in info_of_timepoints]:\n",
" print(\"Note: The column for the information file that has the timepoint is offset by 1 compared to the objects, adjusting accordingly.\")\n",
" info_df[\"timepoint\"] = info_df[info_file_dict[\"column_round\"]] - 1\n",
"elif set(list_of_timepoints).issubset({x - 1 for x in info_of_timepoints}):\n",
" print(\n",
" \"Note: The timepoints found in the objects are a subset of those in the information file, which are offset by 1 compared to the objects. Adjusting accordingly.\"\n",
" )\n",
" info_df[\"timepoint\"] = info_df[info_file_dict[\"column_round\"]] - 1\n",
"else:\n",
" print(\"Warning: The timepoints found in the objects do not match those in the information file.\")\n",
"\n",
"if info_file_dict[\"column_size\"] == \"\":\n",
" print(\"Calculating size column from start and end positions...\")\n",
" column_size = \"size\"\n",
" info_file_dict[\"column_size\"] = column_size\n",
" info_df[column_size] = info_df[info_file_dict[\"column_end_pos\"]] - info_df[info_file_dict[\"column_start_pos\"]]\n",
"\n",
"if info_file_dict[\"column_name\"] == \"\":\n",
" print(\"Setting name column from round numbers...\")\n",
" info_file_dict[\"column_name\"] = \"name\"\n",
" info_df[\"name\"] = [f\"Step{i}\" for i in range(1, info_df.shape[0] + 1)]\n",
"\n",
"tp_names = info_df.set_index(\"timepoint\")[info_file_dict[\"column_name\"]].to_dict()\n",
"\n",
"del list_of_timepoints, info_of_timepoints"
]
},
{
"cell_type": "markdown",
"id": "9f9ca642",
"metadata": {},
"source": [
"### Creating a color palette and the results folders for the tsv and plot files"
]
},
{
"cell_type": "markdown",
"id": "0ead225c",
"metadata": {},
"source": [
"Here we create a color palette automatically based on the different experiment ID found across the files. Experiment IDs are usually the date of the experiment. This allows to easily check for a possible batch effect.\n",
"\n",
"We also create the folders were to store the results.\n",
"
\n",
"
4_distance_extraction/tsvs: a subfolder using the suffix name will be created, where the different tsv files will be stored.
\n",
"
4_distance_extraction/plots: a subfolder using the suffix name will be created, where the different plots (in pdf format) will be stored.
\n",
"
\n",
"\n",
"[Back to the section index](#exporting-the-data-with-cima)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "f342977e",
"metadata": {},
"outputs": [],
"source": [
"# Define the color palette for experiments. Use the date or the experimentID (date) as the key.\n",
"# Get one unique experiment ID/date per experiment.\n",
"experiment_ids = sorted({obj.metadata[\"experimentID\"] for obj in obj_list})\n",
"\n",
"# Create one color for each experiment.\n",
"experiment_colors = sns.color_palette(\"tab10\", n_colors=len(experiment_ids))\n",
"\n",
"# Match each experiment ID/date to one color.\n",
"color_palette = dict(zip(experiment_ids, experiment_colors))\n",
"\n",
"del experiment_ids, experiment_colors\n",
"\n",
"# This palette is fixed for the current experiments used in the tutorial, which aim to reproduce the figures in the CIMA paper.\n",
"color_palette = {\"2018-01-11\": \"#a6cee3\", \"2018-06-28\": \"#1f78b4\", \"2018-07-10\": \"#b2df8a\", \"2018-09-04\": \"#33a02c\"}\n",
"\n",
"# Create the folders to save the results. If the folders already exist, do not raise an error.\n",
"tsv_folder = Path(work_folder, \"4_distance_extraction\", \"tsvs\", file_suffix)\n",
"tsv_folder.mkdir(parents=True, exist_ok=True)\n",
"\n",
"plots_folder = Path(work_folder, \"4_distance_extraction\", \"plots\", file_suffix)\n",
"plots_folder.mkdir(parents=True, exist_ok=True)"
]
},
{
"cell_type": "markdown",
"id": "edc97d89",
"metadata": {},
"source": [
"## Spatial feature intra-walk"
]
},
{
"cell_type": "markdown",
"id": "4e70ea93",
"metadata": {},
"source": [
"Content:\n",
"\n",
"- [Spatial feautre intra-walk calculation](#spatial-feature-intra-walk-calculations)\n",
"- [Boxenplots for the different distances calculated](#boxenplots-for-the-coms-distance-surface-distance-and-entanglement-between-the-different-segments-of-the-same-walk)\n",
"- [Mean, variation and RMSD matrices calculation for the different distances](#mean-variation-and-rmsd-matrices-calculation)\n",
"- [Plot the variation matrices for the different distances](#plot-the-variation-matrices-for-each-spatial-feature-analysed)\n",
"\n",
"[Back to the general index](#distance-extraction)"
]
},
{
"cell_type": "markdown",
"id": "ba4c37a4",
"metadata": {},
"source": [
"### Spatial feature intra-walk calculations"
]
},
{
"cell_type": "markdown",
"id": "a1078f8d",
"metadata": {},
"source": [
"Here we will calculate the COM distance, surface distance and entanglement for every pair of segments for every walk. \n",
"\n",
"If this has already been calculated and saved into a file called \"spatial_features_SUFFIX.tsv\", the file will be loaded. \n",
"\n",
"If the file is not located, then the notebook will perform the calculations and store that data in the file.\n",
"\n",
"[Back to the section index](#spatial-feature-intra-walk)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "81837c22",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Trying to read pre-calculated spatial features by pairs from TSV file...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pre-calculated spatial features by pairs loaded from TSV file.\n"
]
},
{
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"name": "homolog",
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"name": "coms_distance",
"rawType": "float32",
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" experimentID nucleusID chromosome homolog flag segment1 \\\n",
"0 2018-09-04 3 chr3 A 2018-09-04_3_A 1 \n",
"1 2018-09-04 3 chr3 A 2018-09-04_3_A 1 \n",
"2 2018-09-04 3 chr3 A 2018-09-04_3_A 1 \n",
"3 2018-09-04 3 chr3 A 2018-09-04_3_A 1 \n",
"4 2018-09-04 3 chr3 A 2018-09-04_3_A 1 \n",
"\n",
" segment1_timepoint segment1_name flag_seg1 segment2 \\\n",
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"3 6 Step2 2018-09-04_3_A_6.0 4 \n",
"4 6 Step2 2018-09-04_3_A_6.0 5 \n",
"\n",
" segment2_timepoint segment2_name flag_seg2 coms_distance \\\n",
"0 6 Step2 2018-09-04_3_A_6.0 0.000000 \n",
"1 7 Step3 2018-09-04_3_A_7.0 648.681274 \n",
"2 8 Step4 2018-09-04_3_A_8.0 893.308655 \n",
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"4 11 Step7 2018-09-04_3_A_11.0 1190.255737 \n",
"\n",
" surface_distance entanglement \n",
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}
],
"source": [
"last_cols = [\n",
" \"segment1\",\n",
" \"segment1_timepoint\",\n",
" \"segment1_name\",\n",
" \"flag_seg1\",\n",
" \"segment2\",\n",
" \"segment2_timepoint\",\n",
" \"segment2_name\",\n",
" \"flag_seg2\",\n",
" \"coms_distance\",\n",
" \"surface_distance\",\n",
" \"entanglement\",\n",
"]\n",
"\n",
"spatial_tsv_filename = f\"spatial_features_{prec_mean_dataset}{('_' + file_suffix if file_suffix != '' else '')}.tsv\"\n",
"\n",
"try:\n",
" print(\"Trying to read pre-calculated spatial features by pairs from TSV file...\")\n",
" dataframe_spatialfeatures = pd.read_csv(Path(tsv_folder, spatial_tsv_filename), sep=\"\\t\")\n",
" dataframe_spatialfeatures[\"flag\"] = dataframe_spatialfeatures[[\"experimentID\", \"nucleusID\", \"homolog\"]].astype(str).apply(\"_\".join, axis=1)\n",
" dataframe_spatialfeatures[\"flag_seg1\"] = (\n",
" dataframe_spatialfeatures[[\"experimentID\", \"nucleusID\", \"homolog\", \"segment1_timepoint\"]].astype(str).apply(\"_\".join, axis=1)\n",
" )\n",
" dataframe_spatialfeatures[\"flag_seg2\"] = (\n",
" dataframe_spatialfeatures[[\"experimentID\", \"nucleusID\", \"homolog\", \"segment2_timepoint\"]].astype(str).apply(\"_\".join, axis=1)\n",
" )\n",
"\n",
" print(\"Pre-calculated spatial features by pairs loaded from TSV file.\")\n",
"\n",
"except FileNotFoundError:\n",
" print(\"Pre-calculated TSV file not found. Calculating spatial features by pairs from scratch...\")\n",
"\n",
" df_list = []\n",
"\n",
" for file_counter, obj_in in enumerate(obj_list, start=1):\n",
" print(f\"Processing contact matrix for file {file_counter}/{len(obj_list)}: {Path(obj_in.metadata['filename']).stem}\")\n",
"\n",
" list_CS = obj_in.split_into_time(return_list=True)\n",
"\n",
" temp_df = getSpatialFeaturesIntraWalk(list_CS, prec_mean=prec_mean_dataset, n_jobs=8, verbose=False)\n",
"\n",
" temp_df[\"segment1_name\"] = [tp_names[tp] for tp in temp_df[\"segment1_timepoint\"]]\n",
" temp_df[\"segment2_name\"] = [tp_names[tp] for tp in temp_df[\"segment2_timepoint\"]]\n",
" temp_df[\"experimentID\"] = obj_in.metadata[\"experimentID\"]\n",
" temp_df[\"nucleusID\"] = obj_in.metadata[\"nucleusID\"]\n",
" temp_df[\"homolog\"] = obj_in.metadata[\"homolog\"]\n",
" temp_df[\"chromosome\"] = obj_in.metadata[\"chromosome\"]\n",
"\n",
" temp_df[\"flag\"] = temp_df[[\"experimentID\", \"nucleusID\", \"homolog\"]].astype(str).apply(\"_\".join, axis=1)\n",
" temp_df[\"flag_seg1\"] = temp_df[[\"experimentID\", \"nucleusID\", \"homolog\", \"segment1_timepoint\"]].astype(str).apply(\"_\".join, axis=1)\n",
" temp_df[\"flag_seg2\"] = temp_df[[\"experimentID\", \"nucleusID\", \"homolog\", \"segment2_timepoint\"]].astype(str).apply(\"_\".join, axis=1)\n",
"\n",
" df_list.append(temp_df)\n",
"\n",
" mat = convertToMatrix(pairs_features_df=temp_df, value=\"coms_distance\", cluss=np.array(info_df.timepoint.tolist()))\n",
"\n",
" mat = mat.astype(np.float64)\n",
"\n",
" x_labels = info_df.set_index(\"timepoint\").loc[mat.index.tolist()][\"Name\"].tolist()\n",
" y_labels = info_df.set_index(\"timepoint\").loc[mat.columns.tolist()][\"Name\"].tolist()\n",
"\n",
" sns.heatmap(mat, cmap=\"Reds_r\", square=True, vmin=50.0, vmax=1500, xticklabels=x_labels, yticklabels=y_labels)\n",
"\n",
" plt.xlabel(\"Segment name\")\n",
" plt.xticks(rotation=45, ha=\"right\")\n",
" plt.ylabel(\"Segment name\")\n",
" plt.title(f\"COMs distance for {Path(obj_in.metadata['filename']).stem}\")\n",
" if save_plots:\n",
" filename = (\n",
" f\"com_distance_{prec_mean_dataset}_{Path(obj_in.metadata['filename']).stem}\"\n",
" + (f\"_{file_suffix}\" if file_suffix != \"\" else \"\")\n",
" + \".pdf\"\n",
" )\n",
" plot_filename = Path(plots_folder, filename)\n",
" print(f\"\\tSaving plots to: {plot_filename}\")\n",
" plt.savefig(plot_filename, **plot_opts)\n",
" del filename, plot_filename\n",
" else:\n",
" plt.show()\n",
" plt.close()\n",
"\n",
" # Concatenate all the dataframes.\n",
" dataframe_spatialfeatures = pd.concat(df_list, ignore_index=True)\n",
"\n",
" # Reorder the columns, so the table looks nicer and it is easier to manually search for entries.\n",
" cols = dataframe_spatialfeatures.columns.tolist()\n",
" cols_reorder = [col for col in first_cols if col in cols] + last_cols\n",
" dataframe_spatialfeatures = dataframe_spatialfeatures[cols_reorder]\n",
"\n",
" print(\"Calculating spatial features by pairs... Done!\")\n",
"\n",
" # Save the dataframe to a TSV file\n",
" print(f\"Saving spatial features by pairs to TSV file: {spatial_tsv_filename}...\", end=\"\\r\")\n",
" dataframe_spatialfeatures.drop(columns=[\"flag\", \"flag_seg1\", \"flag_seg2\"]).to_csv(Path(tsv_folder, spatial_tsv_filename), sep=\"\\t\", index=None)\n",
" print(f\"Saving spatial features by pairs to TSV file: {spatial_tsv_filename}... Done!\")\n",
"\n",
" del file_counter, obj_in, list_CS, temp_df, mat, x_labels, y_labels, df_list, cols, cols_reorder\n",
"\n",
"# Define the desired dtypes for specific columns.\n",
"dataframe_spatialfeatures = dataframe_spatialfeatures.astype(get_dtype_dict(dataframe_spatialfeatures.columns.to_list(), dtype_dict))\n",
"\n",
"# Reorder the columns, so the table looks nicer and it is easier to manually search for entries.\n",
"cols = dataframe_spatialfeatures.columns.tolist()\n",
"cols_reorder = [col for col in first_cols if col in cols] + last_cols\n",
"dataframe_spatialfeatures = dataframe_spatialfeatures[cols_reorder]\n",
"\n",
"display(dataframe_spatialfeatures.head())\n",
"\n",
"del last_cols, spatial_tsv_filename, cols, cols_reorder"
]
},
{
"cell_type": "markdown",
"id": "e33841f0",
"metadata": {},
"source": [
"### Power law scaling"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "b9a5b146",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Median slope: 0.33\n",
"MAD slope: 0.18\n",
"STD slope: 0.17\n",
"Minimnum slope: 0.12\n",
"Maximum slope: 0.72\n"
]
}
],
"source": [
"dataframe_spatialfeatures[\"gen_dist\"] = info_df[\"Size(kb)\"].unique().tolist()[0] * (\n",
" np.abs(dataframe_spatialfeatures.segment1 - dataframe_spatialfeatures.segment2)\n",
")\n",
"\n",
"exps = {}\n",
"for f, subdf in dataframe_spatialfeatures.groupby(\"flag\"):\n",
" arr1 = subdf[\"gen_dist\"].values\n",
" arr2 = subdf[\"coms_distance\"].values\n",
" _, slope = getLogCorrAndExp(arr1, arr2)\n",
" exps[f] = slope\n",
"\n",
"vals = np.array(list(exps.values()), dtype=float)\n",
"vals = vals[np.isfinite(vals)]\n",
"\n",
"# Stats\n",
"median_val = np.nanmedian(vals)\n",
"mad_val = stats.median_abs_deviation(vals, scale=\"normal\", nan_policy=\"omit\")\n",
"\n",
"print(f\"Median slope: {median_val:.2f}\")\n",
"print(f\"MAD slope: {mad_val:.2f}\")\n",
"print(f\"STD slope: {np.nanstd(vals):.2f}\")\n",
"print(f\"Minimnum slope: {np.nanmin(vals):.2f}\")\n",
"print(f\"Maximum slope: {np.nanmax(vals):.2f}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0e03da5a",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
],
"text/plain": [
" min 25% quantile median 75% quantile max\n",
"feature \n",
"COMs distance 0.0 373.7643 661.1031 1012.8232 2462.811279\n",
"Surface distance 0.0 13.1454 36.2010 333.6948 1896.338623\n",
"Entanglement 0.0 0.0000 0.0000 0.1079 1.000000"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data_table = defaultdict(list)\n",
"\n",
"for feature in [\"coms_distance\", \"surface_distance\", \"entanglement\"]:\n",
" plt.figure(figsize=(4, 6))\n",
" sns.boxenplot(y=dataframe_spatialfeatures[feature])\n",
" plt.title(f\"Distribution of {nice_names[feature]} over all segment pairs\")\n",
" plt.ylabel(nice_names[feature])\n",
" sns.despine()\n",
" if save_plots:\n",
" filename = f\"{feature}_distribution_allsegments_bypairs\" + (f\"_{file_suffix}\" if file_suffix != \"\" else \"\") + \".pdf\"\n",
" plot_filename = Path(plots_folder, filename)\n",
" print(f\"Saving plots to: {plot_filename}\")\n",
" plt.savefig(plot_filename, **plot_opts)\n",
" del filename, plot_filename\n",
" else:\n",
" plt.show()\n",
" plt.close()\n",
"\n",
" data_table[\"feature\"].append(nice_names[feature])\n",
" data_table[\"min\"].append(np.round(dataframe_spatialfeatures[feature].min(), decimals=4))\n",
" data_table[\"25% quantile\"].append(np.round(dataframe_spatialfeatures[feature].quantile(0.25), decimals=4))\n",
" data_table[\"median\"].append(np.round(dataframe_spatialfeatures[feature].quantile(0.5), decimals=4))\n",
" data_table[\"75% quantile\"].append(np.round(dataframe_spatialfeatures[feature].quantile(0.75), decimals=4))\n",
" data_table[\"max\"].append(np.round(dataframe_spatialfeatures[feature].max(), decimals=4))\n",
"\n",
"data_table = pd.DataFrame(data_table).set_index(\"feature\")\n",
"display(data_table)\n",
"\n",
"del feature, data_table"
]
},
{
"cell_type": "markdown",
"id": "81967a1e",
"metadata": {},
"source": [
"### Mean, variation and RMSD matrices calculation"
]
},
{
"cell_type": "markdown",
"id": "378919a0",
"metadata": {},
"source": [
"Calculate mean and variation matrix for the ensemble of walks. \n",
"\n",
"The notebook will also calculate the RMSD between the mean matrices.\n",
"\n",
"[Back to the section index](#spatial-feature-intra-walk)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "585fe22d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Working on feature: coms_distance...\n",
"Working on feature: coms_distance... Done!\n",
"Working on feature: surface_distance...\n",
"Working on feature: surface_distance... Done!\n",
"Working on feature: entanglement...\n",
"Working on feature: entanglement... Done!\n"
]
}
],
"source": [
"mean_combined_mats = {}\n",
"std_combined_mats = {}\n",
"rmsd_combined_mats = {}\n",
"collection_combined_mats = {}\n",
"\n",
"for feat in [\"coms_distance\", \"surface_distance\", \"entanglement\"]:\n",
" print(f\"Working on feature: {feat}...\")\n",
"\n",
" combined_ent_mat0 = []\n",
"\n",
" for f in dataframe_spatialfeatures.flag.unique().tolist():\n",
" # print(f\"\\tProcessing flag: {f}...\")\n",
"\n",
" mat = convertToMatrix(\n",
" pairs_features_df=dataframe_spatialfeatures[dataframe_spatialfeatures.flag == f], value=feat, cluss=np.array(info_df.timepoint.tolist())\n",
" )\n",
"\n",
" mat = mat.astype(np.float64)\n",
" combined_ent_mat0.append(mat)\n",
"\n",
" mean_combined_mats[feat] = np.nanmean(combined_ent_mat0, axis=0)\n",
" std_combined_mats[feat] = np.nanstd(combined_ent_mat0, axis=0)\n",
" collection_combined_mats[feat] = (np.invert(np.isnan(combined_ent_mat0))).sum(axis=0)\n",
"\n",
" # Take out rows/columns that contain 1 or more NaN values\n",
" mean_combined_mats[feat] = mean_combined_mats[feat][~np.isnan(mean_combined_mats[feat]).any(axis=1)][\n",
" :, ~np.isnan(mean_combined_mats[feat]).any(axis=0)\n",
" ]\n",
"\n",
" std_combined_mats[feat] = std_combined_mats[feat][~np.isnan(std_combined_mats[feat]).any(axis=1)][\n",
" :, ~np.isnan(std_combined_mats[feat]).any(axis=0)\n",
" ]\n",
"\n",
" if feat != \"entanglement\":\n",
" rmsd_combined_mats[feat] = calculate_rmsd_matrix(combined_ent_mat0)\n",
"\n",
" list_of_flags = np.array(dataframe_spatialfeatures.flag.unique().tolist())[~np.isnan(rmsd_combined_mats[feat]).any(axis=1)]\n",
"\n",
" rmsd_combined_mats[feat] = rmsd_combined_mats[feat][~np.isnan(rmsd_combined_mats[feat]).any(axis=1)][\n",
" :, ~np.isnan(rmsd_combined_mats[feat]).any(axis=0)\n",
" ]\n",
"\n",
" rmsd_combined_mats[feat] = pd.DataFrame(rmsd_combined_mats[feat], index=list_of_flags, columns=list_of_flags)\n",
"\n",
" print(f\"Working on feature: {feat}... Done!\")\n",
"\n",
"del feat, combined_ent_mat0, f, mat, list_of_flags"
]
},
{
"cell_type": "markdown",
"id": "72e1bc6f",
"metadata": {},
"source": [
"#### clustermap and density distibution for RMSD values of COM distances and surface distances"
]
},
{
"cell_type": "code",
"execution_count": 72,
"id": "9891af5c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating clustermap for feature: coms_distance...\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating clustermap for feature: surface_distance...\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Add eficiency of the file, put it in green\n",
"\n",
"for feature in [\"coms_distance\", \"surface_distance\"]:\n",
" print(f\"Creating clustermap for feature: {feature}...\")\n",
"\n",
" lnk = linkage(squareform(rmsd_combined_mats[feature]), method=\"ward\")\n",
"\n",
" clusters = fcluster(lnk, t=3, criterion=\"maxclust\")\n",
"\n",
" cluster_df = pd.DataFrame({\"trace_id\": rmsd_combined_mats[feature].index, \"cluster\": clusters})\n",
"\n",
" trace_cluster_dict = dict(zip(cluster_df[\"trace_id\"], cluster_df[\"cluster\"]))\n",
"\n",
" dataframe_spatialfeatures[\"cluster\"] = dataframe_spatialfeatures[\"flag\"].map(trace_cluster_dict)\n",
"\n",
" labels = [f\"Cell {n.split('_')[1]} allelle {n.split('_')[2]}\" for n in dataframe_spatialfeatures.flag.unique().tolist()]\n",
"\n",
" row_metadata = dataframe_spatialfeatures.groupby(\"flag\").first().reindex(rmsd_combined_mats[feature].index)\n",
"\n",
" cluster_values = sorted(row_metadata[\"cluster\"].dropna().unique())\n",
" cluster_palette = dict(zip(cluster_values, sns.color_palette(\"rainbow\", n_colors=len(cluster_values))))\n",
"\n",
" row_colors = pd.DataFrame(\n",
" {\n",
" \"experiment\": row_metadata[\"experimentID\"].map(color_palette),\n",
" \"cluster\": row_metadata[\"cluster\"].map(cluster_palette),\n",
" },\n",
" index=row_metadata.index,\n",
" )\n",
"\n",
" g = sns.clustermap(\n",
" data=rmsd_combined_mats[feature], # Matrix of RMSD between the different experiments\n",
" cmap=\"Greens_r\", # Color used\n",
" xticklabels=labels, # Labels (basically the file where they come from)\n",
" vmax=np.nanquantile(rmsd_combined_mats[feature], 0.95), # Maximum number to do the colors\n",
" row_linkage=lnk, # Pre-calculated linkage for the rows\n",
" col_linkage=lnk, # Pre-calculated linkage for the columns\n",
" row_colors=row_colors, # Color the rows by the experiment they come from\n",
" figsize=(10, 12),\n",
" )\n",
"\n",
" g.ax_heatmap.set_xticklabels(\n",
" g.ax_heatmap.get_xticklabels(),\n",
" rotation=45,\n",
" ha=\"right\",\n",
" )\n",
" g.ax_heatmap.set_ylabel(\"\")\n",
" g.ax_heatmap.set_yticklabels([])\n",
" g.ax_row_colors.set_xlabel(\"\")\n",
" g.ax_row_colors.set_xticks([])\n",
"\n",
" g.gs.update(bottom=0.05, top=0.7)\n",
"\n",
" g.cax.set_position((0.02, 0.58, 0.02, 0.1))\n",
" g.cax.set_ylabel(\"RMSD\", rotation=90, labelpad=4)\n",
"\n",
" gs2 = gridspec.GridSpec(1, 1, bottom=0.785)\n",
" ax2 = g.fig.add_subplot(gs2[0])\n",
" ax2.set_position((g.ax_heatmap.get_position().x0, 0.75, g.ax_heatmap.get_position().width, 0.15))\n",
"\n",
" sns.kdeplot(data=rmsd_combined_mats[feature].replace(0, np.nan).to_numpy().flatten(), fill=True, alpha=0.5, ax=ax2)\n",
" ax2.set_title(f\"Distribution of RMSD values of {feature.replace('_', ' ')}\")\n",
" sns.despine()\n",
"\n",
" if save_plots:\n",
" filename = f\"{feature}_rmsd_clustermap\" + (f\"_{file_suffix}\" if file_suffix != \"\" else \"\") + \".pdf\"\n",
" plot_filename = Path(plots_folder, filename)\n",
" print(f\"Saving plots to: {plot_filename}\")\n",
" plt.savefig(plot_filename, **plot_opts)\n",
" del filename, plot_filename\n",
" else:\n",
" plt.show()\n",
"\n",
" plt.close()\n",
"\n",
"del feature, lnk, g, gs2, ax2, labels"
]
},
{
"cell_type": "markdown",
"id": "7d2b592b",
"metadata": {},
"source": [
"#### heatmap for COMs distances, surface distance, entanglement and collection"
]
},
{
"cell_type": "markdown",
"id": "0f479485",
"metadata": {},
"source": [
"Plot mean ensemble matrices for varied spatial measuraments available in CIMA, namely: \n",
"
coms_distance: The distance score (DS) between two chromosomal segments is calculated as the spatial distance between the centers of mass of each chromosomal segment of the OligoSTORM density maps.\n",
"
surface_distance: The surface distance score (SDS) between two chromosomal segments is calculated as the minimal spatial distance between the chromosomal segment of the OligoSTORM density maps. \n",
"
entanglement: The entanglement score (ES) between two chromosomal segments is calculated as the fraction of overlapping voxels within the optimal iso-contour threshold with respect to the smaller of the two density maps. \n",
"