![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6531908e",
+ "metadata": {
+ "id": "6531908e"
+ },
+ "source": [
+ "# Image Processing & Analysis Assignment #2\n",
+ "\n",
+ "## Summary of tasks (repeated within each section)\n",
+ "### You will need to perform the following Tasks: \n",
+ "\n",
+ "----- \n",
+ "\n",
+ "#### Using `image_cell.tif`\n",
+ "**Task 1.** \n",
+ " \n",
+ " From the downloaded image (`img`; file = `image_cell.tif`), make an image `img1` from just the red channel (channel 0) at time point 23 of the tensor [T,Y,X,C] \n",
+ "\n",
+ "**Task 2.** \n",
+ "\n",
+ " Using `img1`, create a new image `img2` where values less than than the mean are set to the background value (calculated as the minimum intensity value in the image). \n",
+ "\n",
+ "**Task 3.** \n",
+ "\n",
+ " - Set `img2[0:20,0:20] = np.amax(img2)` (Make a white box in the image to more easily visualize the transformation) \n",
+ " - Compress `img2` in half, rotate it 30 degrees clockwise, and translate its `(0,0)` to `(dx,dy)=(200,100)` \n",
+ " - Save resulting image as `img3` \n",
+ " - Plot `img3` side-by-side with `img2` \n",
+ "\n",
+ "**Task 4.** **EXTRA CREDIT** \n",
+ "\n",
+ " - Build a slider function to display different frames of an RGB image over time. \n",
+ " - Consider 3 subplots for: 'RED', 'GREEN', 'BLUE' channels\n",
+ " - Make the interactive slider for time: int with range 0 to the number of frames in video.\n",
+ "\n",
+ "----- \n",
+ "#### Using `FISH_example.tif`\n",
+ "**Task 5.** \n",
+ "\n",
+ " - Make a slice of `img_FISH` and save as `img_FISH_slice` using: `Z=10`, `Y=120:350`, `X=50:250` \n",
+ " - Apply a Gaussian filter to `img_FISH_slice` with `sigma=1` and save it as `img_gaussian_filter_simga_1`\n",
+ " - Make difference of Gaussians filter of `img_FISH_slice` using `low_sigma=1` and `high_sigma=5` and `img_diff_gaussians`\n",
+ " - Plot all 3 images side-by-side (in 3 subplots). \n",
+ " \n",
+ "-----"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8b448ed2-7ca9-4b42-bd44-1ff39a36c994",
+ "metadata": {
+ "id": "8b448ed2-7ca9-4b42-bd44-1ff39a36c994"
+ },
+ "source": [
+ "## Loading libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c8cf520e",
+ "metadata": {
+ "id": "c8cf520e"
+ },
+ "outputs": [],
+ "source": [
+ "# Load libraries\n",
+ "import matplotlib.pyplot as plt # Library used for plotting\n",
+ "from matplotlib.patches import Rectangle # module to plot a rectangle in the image\n",
+ "import numpy as np # library for array manipulation\n",
+ "import seaborn as sn # plotting library\n",
+ "import pandas as pd # data frames library\n",
+ "import tifffile # library to store numpy arrays in TIFF\n",
+ "import skimage # Library for image manipulation\n",
+ "from skimage.io import imread # sublibrary from skimage\n",
+ "from scipy.ndimage import gaussian_filter\n",
+ "from skimage.filters import difference_of_gaussians\n",
+ "from skimage import transform\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a9ddaede",
+ "metadata": {
+ "id": "a9ddaede"
+ },
+ "outputs": [],
+ "source": [
+ "import ipywidgets as widgets # Importing library\n",
+ "from ipywidgets import interact, interactive, HBox, Layout, VBox # importing modules and functions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c8a8caf9",
+ "metadata": {
+ "id": "c8a8caf9"
+ },
+ "outputs": [],
+ "source": [
+ "\n",
+ "import importlib\n",
+ "\n",
+ "def module_exists(module_name):\n",
+ " try:\n",
+ " importlib.import_module(module_name)\n",
+ " return True\n",
+ " except ImportError:\n",
+ " return False\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bcab7cb7-9fe7-4ade-bbd4-df56d3faf694",
+ "metadata": {
+ "id": "bcab7cb7-9fe7-4ade-bbd4-df56d3faf694"
+ },
+ "source": [
+ "## Download image files\n",
+ "Do NOT push the image files to your GitHub repository! This notebook with the rendered images is okay. \n",
+ "\n",
+ "The `.gitgnore` file in this repository should ignore the image files if you use git to push your changes to your repository back to GitHub."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "40804778",
+ "metadata": {
+ "id": "40804778",
+ "outputId": "d2991501-0c44-4da1-bb52-f3618d9d1fd1",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting wget\n",
+ " Downloading wget-3.2.zip (10 kB)\n",
+ " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ "Building wheels for collected packages: wget\n",
+ " Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9656 sha256=f0b49b8670ad9a4471567ab80c3ad8db7097f95238f1e439da8b00f64118f7f6\n",
+ " Stored in directory: /root/.cache/pip/wheels/8b/f1/7f/5c94f0a7a505ca1c81cd1d9208ae2064675d97582078e6c769\n",
+ "Successfully built wget\n",
+ "Installing collected packages: wget\n",
+ "Successfully installed wget-3.2\n",
+ "Collecting ffmpeg\n",
+ " Downloading ffmpeg-1.4.tar.gz (5.1 kB)\n",
+ " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ "Building wheels for collected packages: ffmpeg\n",
+ " Building wheel for ffmpeg (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for ffmpeg: filename=ffmpeg-1.4-py3-none-any.whl size=6082 sha256=73b36a1e068cc17ff1da89faa24115bc057bf65bc5d3597e0f588697abba5c91\n",
+ " Stored in directory: /root/.cache/pip/wheels/8e/7a/69/cd6aeb83b126a7f04cbe7c9d929028dc52a6e7d525ff56003a\n",
+ "Successfully built ffmpeg\n",
+ "Installing collected packages: ffmpeg\n",
+ "Successfully installed ffmpeg-1.4\n"
+ ]
+ }
+ ],
+ "source": [
+ "if not module_exists('wget'):\n",
+ " !pip install wget\n",
+ "if not module_exists('ffmpeg'):\n",
+ " !pip install ffmpeg"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7433390c-add4-4d48-ac40-89ed3e451188",
+ "metadata": {
+ "id": "7433390c-add4-4d48-ac40-89ed3e451188"
+ },
+ "outputs": [],
+ "source": [
+ "import wget\n",
+ "if not os.path.exists(\"./FISH_example.tif\"):\n",
+ " wget.download(\"https://www.dropbox.com/s/669j6zags6xd7gr/FISH_example.tif?dl=1\", \"FISH_example.tif\")\n",
+ "\n",
+ "if not os.path.exists(\"./image_cell.tif\"):\n",
+ " wget.download(\"https://www.dropbox.com/scl/fi/3y4yzq0qywia4i8oftdir/image_cell.tif?rlkey=c8r31vv7o15kmx1axtvmt53bk&dl=1\",\n",
+ " \"image_cell.tif\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9a4b5224-fc61-4365-9181-f612e0e30a10",
+ "metadata": {
+ "id": "9a4b5224-fc61-4365-9181-f612e0e30a10",
+ "outputId": "7bc91aa8-09ca-4747-cc8a-ab5996a4ecbb",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "(35, 512, 512, 3)"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 6
+ }
+ ],
+ "source": [
+ "# reading the image and saving as variable img\n",
+ "img = imread('./image_cell.tif')\n",
+ "img.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "377961d2",
+ "metadata": {
+ "scrolled": true,
+ "id": "377961d2",
+ "outputId": "1295c96f-99ab-4e67-f292-4e3fda5c009a",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 611
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "03994108-9a10-4b4c-b4bf-0a48f6d1c023",
+ "metadata": {
+ "id": "03994108-9a10-4b4c-b4bf-0a48f6d1c023"
+ },
+ "source": [
+ "# Analysis of Cell Proliferation\n",
+ "## Objectives\n",
+ "This notebook has sections designed to:\n",
+ "1) Provide background on cell proliferation \n",
+ "2) Describe basic mathematical models of population growth \n",
+ "3) Analyze dynamic cell count data \n",
+ "4) Perform linear and nonlinear regression of models fit to cell count data \n",
+ "5) Create functions in Python to perform calcuations\n",
+ "\n",
+ "## Cell proliferation\n",
+ "### Nomenclature\n",
+ "* _Growth_ can mean an increase in mass or volume (at the individual level)\n",
+ "* _Growth_ can also indicate an increase in population size (e.g. the number of cells in a tumor)\n",
+ "* _Proliferation_ generally refers to change in the number of individuals within a group (e.g. exponential population growth)\n",
+ "* _Tumor growth_ generally refers to the size of the tumor, but an increase in the number of tumor cells is implicit\n",
+ "\n",
+ "### Typical assays of proliferation, survival and death\n",
+ "* Usually performed to test effects of perturbation (e.g. a drug) \n",
+ "* Most are static assays (take a snapshot) \n",
+ "* Multiple measurements require multiple samples (since they are destructive) \n",
+ "* Typically reported as fraction of control or percent change (relative, not absolute metrics) \n",
+ "* Biomarkers of the processes that change cell population size \n",
+ "\n",
+ "Images below are representatives of 1) fluorescence microscopy of calcein/propidium iodide stained cells, 2) flow cytometry of cells stained with FxCycle violet and phospho-histone H3, 3) fluorescence microscopy of annexin A5-fluorescein-stained cells and 4) fluorescence microscopy of cells after addition of caspase 3 substrate that becomes fluorogenic upon cleavage by caspase 3 (pink). \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ " ![](\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/calcein-PI.png?raw=1\") \n",
+ " | \n",
+ " \n",
+ " ![](\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/flow-mitosis.png?raw=1\") \n",
+ " | \n",
+ " \n",
+ " ![](\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/Anx5.png?raw=1\") \n",
+ " | \n",
+ " \n",
+ " ![](\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/activeCasp3.png?raw=1\") \n",
+ " | \n",
+ "
![](\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/Math-ugh.png?raw=1\")
\n",
+ "\n",
+ "* Definition of Logarithm: $log_e e^x = x$, where $e > 0$ and $e ≠ 1$ \n",
+ "* Product Rule: $log(xy) = log(x) + log(y)$\n",
+ "* Base change rule: $log_a(n) = log_b(n)/log_a(b)$ \n",
+ "\n",
+ "#### **Exponential growth equation**\n",
+ "$P(t) = P_0e^{at}$, where \n",
+ "$P(t)$ is the population size at time = $t$, \n",
+ "$P_0$ is initial population size, and \n",
+ "$a$ is the growth rate constant.\n",
+ "\n",
+ "_The logarithm of the growth equation is linear_ \n",
+ "$log_e P(t) = log_e P_0e^{at} = log_e P_0 + log_e e^{at} = log_e P_0 + at$ \n",
+ "\n",
+ "_(reorganizing to the form of $y = ax + b$)_ \n",
+ "$log_e P(t) = at + log_e P_0$, where, \n",
+ "growth rate = $a$ = _slope of the line_ and log of initial population size = _y intercept_ \n",
+ "\n",
+ "##### **This means that you can fit exponentially growing population data with linear models**\n",
+ "\n",
+ "This also works in log2 scale using a population doubling function: \n",
+ "$P(t) = P_02^{at}$, \n",
+ "which allows for an easier biological interpretation."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "25b229c6-432b-4d6c-9979-0f67c3246d34",
+ "metadata": {
+ "id": "25b229c6-432b-4d6c-9979-0f67c3246d34"
+ },
+ "source": [
+ "## Plotting and interpreting cell population growth data\n",
+ "These graphs were previously generated and are shown for reference. Data are shown in linear, log2 and normalized log2 scales. Lines shown on log2 and normalized log2 plots represent linear model fits and the slope (proliferation rate) and doubling time (1/proliferation rate) parameters of the optimal model fit are shown. \n",
+ "\n",
+ "![](\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/pop_growth_difft_scales.png?raw=1\")
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f5d5336f-8a1c-4beb-adc0-6ba4e0b63804",
+ "metadata": {
+ "id": "f5d5336f-8a1c-4beb-adc0-6ba4e0b63804"
+ },
+ "source": [
+ "### Divergence from exponential growth\n",
+ "There are numerous conditions that result in non-exponential cell population growth, for example, when cells fill in their available space (a.k.a. contact inhibition). \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "095e56de",
+ "metadata": {
+ "id": "095e56de"
+ },
+ "source": [
+ "![](\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/contact_inhibition.png?raw=1\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bcafe3ca",
+ "metadata": {
+ "id": "bcafe3ca"
+ },
+ "source": [
+ "There are some specific models that have been used to model growth inhibition, including the\n",
+ "**Logistic** and **Gompertz** functions. \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fa4c4a18-c3cc-47ea-9e68-5f468497ff94",
+ "metadata": {
+ "id": "fa4c4a18-c3cc-47ea-9e68-5f468497ff94"
+ },
+ "source": [
+ "![](\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/evidence_for_Gompertz_tumors.png?raw=1\")
\n",
+ "![](\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/logistic_growth_model.jpeg?raw=1\")
\n",
+ "![](\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/gompertz_growth_model.jpeg?raw=1\")
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ec305d36-2c3b-4d52-b806-054b615a84f7",
+ "metadata": {
+ "id": "ec305d36-2c3b-4d52-b806-054b615a84f7"
+ },
+ "source": [
+ "_We will do some exploratory data analysis of a small cell count data set to interpret how populations of cells grow in culture._\n",
+ "-----"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "40e0a719-37e3-4a84-8668-769a294dd293",
+ "metadata": {
+ "id": "40e0a719-37e3-4a84-8668-769a294dd293"
+ },
+ "source": [
+ "### First, import necessary Python packages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "549f8f33-de74-4ddd-9645-22a758ea2814",
+ "metadata": {
+ "id": "549f8f33-de74-4ddd-9645-22a758ea2814"
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "import matplotlib.pyplot as plt\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "Sfrp0w8cysS1",
+ "metadata": {
+ "id": "Sfrp0w8cysS1"
+ },
+ "source": [
+ "See whether we are running in Google Colab or not."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "CLSZT8xMyphI",
+ "metadata": {
+ "id": "CLSZT8xMyphI"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " IN_COLAB = True\n",
+ "except:\n",
+ " IN_COLAB = False"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ad299e4b-bd09-40c9-93ce-a8959d76a375",
+ "metadata": {
+ "id": "ad299e4b-bd09-40c9-93ce-a8959d76a375"
+ },
+ "source": [
+ "### Load custom functions\n",
+ "File name `growth_fxns.py` includes functions for: \n",
+ "* flattening a matrix (nested lists): `flatten_matrix` \n",
+ "* stochastic simulation of exponential growth: `gillespie_growth` \n",
+ "* deterministic solution of exponential growth: `exp_growth` \n",
+ " \n",
+ "* deterministic solution of logistic growth: `mylogistic`\n",
+ "* deterministic solution of Gompertzian growth: `gompertz`\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c72e3dc2-f48c-447a-95de-f428c2f6b3da",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "c72e3dc2-f48c-447a-95de-f428c2f6b3da",
+ "outputId": "7f227d27-d465-4385-e257-8288a4e15a57"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "--2024-03-12 00:45:48-- https://raw.githubusercontent.com/VU-CSP/quantbio-assignments/main/growth_fxns.py\n",
+ "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.110.133, ...\n",
+ "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 2307 (2.3K) [text/plain]\n",
+ "Saving to: ‘growth_fxns.py’\n",
+ "\n",
+ "\rgrowth_fxns.py 0%[ ] 0 --.-KB/s \rgrowth_fxns.py 100%[===================>] 2.25K --.-KB/s in 0s \n",
+ "\n",
+ "2024-03-12 00:45:48 (29.2 MB/s) - ‘growth_fxns.py’ saved [2307/2307]\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "if IN_COLAB and not os.path.exists(\"growth_fxns.py\"):\n",
+ " !wget \"https://raw.githubusercontent.com/VU-CSP/quantbio-assignments/main/growth_fxns.py\"\n",
+ "from growth_fxns import *"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "95fd8d3f-61c2-463d-8b01-1e961269af9c",
+ "metadata": {
+ "id": "95fd8d3f-61c2-463d-8b01-1e961269af9c"
+ },
+ "source": [
+ "### Adjust seaborn plot settings\n",
+ "_To make the graphics prettier._ You can learn more about seaborn at the [seaborn website]() "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f28ff5a6-dc57-4fe3-a49f-d448f8bbc226",
+ "metadata": {
+ "id": "f28ff5a6-dc57-4fe3-a49f-d448f8bbc226"
+ },
+ "outputs": [],
+ "source": [
+ "sns.set_context(\"notebook\", font_scale=1.5, rc={\"lines.linewidth\": 2.5})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fb6587a1-2b3d-4aaf-ad96-c50e6eb7547a",
+ "metadata": {
+ "id": "fb6587a1-2b3d-4aaf-ad96-c50e6eb7547a"
+ },
+ "source": [
+ "#### Load a small dataset\n",
+ "Data from multiwell plate of cells, some of which have been treated with a drug."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5ffdbde1-987c-4817-9487-288cce2d3509",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 405
+ },
+ "id": "5ffdbde1-987c-4817-9487-288cce2d3509",
+ "outputId": "f36c43e1-20a1-43eb-c31b-23b3e25a4397"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "--2024-03-12 00:46:03-- https://raw.githubusercontent.com/VU-CSP/quantbio-assignments/main/MCF10A_trametinib.csv\n",
+ "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
+ "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 13490 (13K) [text/plain]\n",
+ "Saving to: ‘MCF10A_trametinib.csv’\n",
+ "\n",
+ "\rMCF10A_trametinib.c 0%[ ] 0 --.-KB/s \rMCF10A_trametinib.c 100%[===================>] 13.17K --.-KB/s in 0s \n",
+ "\n",
+ "2024-03-12 00:46:04 (39.4 MB/s) - ‘MCF10A_trametinib.csv’ saved [13490/13490]\n",
+ "\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " well time cell.count uid drug1 drug1.conc\n",
+ "0 A01 0.8 32 10ACh-28A_H03 trametinib 0.000004\n",
+ "1 A01 5.6 25 10ACh-28A_H03 trametinib 0.000004\n",
+ "2 A01 10.5 31 10ACh-28A_H03 trametinib 0.000004\n",
+ "3 A01 15.3 53 10ACh-28A_H03 trametinib 0.000004\n",
+ "4 A01 20.0 39 10ACh-28A_H03 trametinib 0.000004"
+ ],
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|---|---|---|---|---|---|---|
| 0 | \n", + "A01 | \n", + "0.8 | \n", + "32 | \n", + "10ACh-28A_H03 | \n", + "trametinib | \n", + "0.000004 | \n", + "
| 1 | \n", + "A01 | \n", + "5.6 | \n", + "25 | \n", + "10ACh-28A_H03 | \n", + "trametinib | \n", + "0.000004 | \n", + "
| 2 | \n", + "A01 | \n", + "10.5 | \n", + "31 | \n", + "10ACh-28A_H03 | \n", + "trametinib | \n", + "0.000004 | \n", + "
| 3 | \n", + "A01 | \n", + "15.3 | \n", + "53 | \n", + "10ACh-28A_H03 | \n", + "trametinib | \n", + "0.000004 | \n", + "
| 4 | \n", + "A01 | \n", + "20.0 | \n", + "39 | \n", + "10ACh-28A_H03 | \n", + "trametinib | \n", + "0.000004 | \n", + "
| \n", + " | well | \n", + "time | \n", + "l2 | \n", + "pd | \n", + "cell.count | \n", + "uid | \n", + "drug1 | \n", + "drug1.conc | \n", + "x | \n", + "
|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "A01 | \n", + "0.8 | \n", + "5.000 | \n", + "0.000 | \n", + "32 | \n", + "10ACh-28A_H03 | \n", + "trametinib | \n", + "0.000004 | \n", + "137615995405040 | \n", + "
| 1 | \n", + "A01 | \n", + "5.6 | \n", + "4.644 | \n", + "-0.356 | \n", + "25 | \n", + "10ACh-28A_H03 | \n", + "trametinib | \n", + "0.000004 | \n", + "93945591407952 | \n", + "
| 2 | \n", + "A01 | \n", + "10.5 | \n", + "4.954 | \n", + "-0.046 | \n", + "31 | \n", + "10ACh-28A_H03 | \n", + "trametinib | \n", + "0.000004 | \n", + "93945591407952 | \n", + "
| 3 | \n", + "A01 | \n", + "15.3 | \n", + "5.728 | \n", + "0.728 | \n", + "53 | \n", + "10ACh-28A_H03 | \n", + "trametinib | \n", + "0.000004 | \n", + "93945591407952 | \n", + "
| 4 | \n", + "A01 | \n", + "20.0 | \n", + "5.285 | \n", + "0.285 | \n", + "39 | \n", + "10ACh-28A_H03 | \n", + "trametinib | \n", + "0.000004 | \n", + "0 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 95 | \n", + "A04 | \n", + "96.7 | \n", + "11.628 | \n", + "5.651 | \n", + "3164 | \n", + "10ACh-28B_C23 | \n", + "control | \n", + "0.000000 | \n", + "8387200179420031329 | \n", + "
| 96 | \n", + "A04 | \n", + "101.8 | \n", + "11.930 | \n", + "5.953 | \n", + "3903 | \n", + "10ACh-28B_C23 | \n", + "control | \n", + "0.000000 | \n", + "7307218078133023349 | \n", + "
| 97 | \n", + "A04 | \n", + "106.9 | \n", + "12.134 | \n", + "6.157 | \n", + "4494 | \n", + "10ACh-28B_C23 | \n", + "control | \n", + "0.000000 | \n", + "7953392626460355616 | \n", + "
| 98 | \n", + "A04 | \n", + "112.0 | \n", + "12.219 | \n", + "6.242 | \n", + "4767 | \n", + "10ACh-28B_C23 | \n", + "control | \n", + "0.000000 | \n", + "733650746116022368 | \n", + "
| 99 | \n", + "A04 | \n", + "117.2 | \n", + "12.305 | \n", + "6.328 | \n", + "5060 | \n", + "10ACh-28B_C23 | \n", + "control | \n", + "0.000000 | \n", + "2314885530818453514 | \n", + "
100 rows × 9 columns
\n", + "| \n", + " | well | \n", + "time | \n", + "l2 | \n", + "pd | \n", + "cell.count | \n", + "uid | \n", + "drug1 | \n", + "drug1.conc | \n", + "x | \n", + "
|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "A01 | \n", + "0.8 | \n", + "5.000 | \n", + "0.000 | \n", + "32 | \n", + "10ACh-28A_H03 | \n", + "trametinib | \n", + "0.000004 | \n", + "45.00 | \n", + "
| 1 | \n", + "A01 | \n", + "5.6 | \n", + "4.644 | \n", + "-0.356 | \n", + "25 | \n", + "10ACh-28A_H03 | \n", + "trametinib | \n", + "0.000004 | \n", + "41.44 | \n", + "
| 2 | \n", + "A01 | \n", + "10.5 | \n", + "4.954 | \n", + "-0.046 | \n", + "31 | \n", + "10ACh-28A_H03 | \n", + "trametinib | \n", + "0.000004 | \n", + "44.54 | \n", + "
| 3 | \n", + "A01 | \n", + "15.3 | \n", + "5.728 | \n", + "0.728 | \n", + "53 | \n", + "10ACh-28A_H03 | \n", + "trametinib | \n", + "0.000004 | \n", + "52.28 | \n", + "
| 4 | \n", + "A01 | \n", + "20.0 | \n", + "5.285 | \n", + "0.285 | \n", + "39 | \n", + "10ACh-28A_H03 | \n", + "trametinib | \n", + "0.000004 | \n", + "47.85 | \n", + "
| \n", + " | time | \n", + "cell.count | \n", + "pd | \n", + "
|---|---|---|---|
| 0 | \n", + "0 | \n", + "108.452629 | \n", + "0.000000 | \n", + "
| 1 | \n", + "3 | \n", + "114.671082 | \n", + "0.080437 | \n", + "
| 2 | \n", + "6 | \n", + "137.605309 | \n", + "0.343471 | \n", + "
| 3 | \n", + "9 | \n", + "163.397796 | \n", + "0.591324 | \n", + "
| 4 | \n", + "12 | \n", + "178.516048 | \n", + "0.718989 | \n", + "
\n",
+ " ![](\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/cell_division.png?raw=1\") \n",
+ " | \n", + " $\\Longrightarrow$ $\\Longrightarrow$\n", + " | \n",
+ " ![](\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/tumor.png?raw=1\") \n",
+ " |