{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic example cytometry workflow\n",
    "\n",
    "Welcome to `cytoflow`!  I'm glad you're here.  The following is a heavily commented workflow for importing a few tubes of cytometry data and doing some (very) basic analysis.  The goal is to give you not only a taste of using the library for interactive work, but also some insight into the rationale for the way it's designed the way it is and the way it differs from existing projects.\n",
    "\n",
    "`cytoflow`'s goal is to enable *quantitative, reproducible* cytometry analysis.  Reproducibility between cytometry experiments is poor, due in part to differences in analysis between operators; but if all your analysis is in a `Jupyter` notebook (like this one!), then sharing and reuse of workflows is much easier.\n",
    "\n",
    "Let's look at a very basic experiment, containing only two tubes.  These two tubes contain cells that are expressing fluorescent proteins as the read-out of some cell state.  We'll assume that these two tubes are identical, except that one has been induced with 1 µM of the small molecule inducer Doxycycline (aka 'Dox') and the other tube has been induced with 10 µM Dox.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start by setting up Jupyter's plotting interface, then import the `cytoflow` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import cytoflow as flow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The central data structure in `cytoflow` is the `Experiment`, which is basically a `pandas.DataFrame` containing the events; its associated metadata (experimental conditions and the like); and some methods to manipulate them.\n",
    "\n",
    "You usually create an `Experiment` using an instance of the `ImportOp` class.  Start by defining two tubes, including their experimental conditions (ie, how much Dox is in each); then give those tubes, and a `dict` specifying the experimental conditions' names and types, to `ImportOp`.  Call the `apply()` function to get back the `Experiment` with all the data in it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "tube1 = flow.Tube(file = 'data/RFP_Well_A3.fcs',\n",
    "                  conditions = {'Dox' : 10.0})\n",
    "tube2 = flow.Tube(file='data/CFP_Well_A4.fcs',\n",
    "                  conditions = {'Dox' : 1.0})\n",
    "\n",
    "import_op = flow.ImportOp(conditions = {'Dox' : 'float'},\n",
    "                          tubes = [tube1, tube2])\n",
    "\n",
    "ex = import_op.apply()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have an `Experiment` instance, this is the last time you should ever think about tubes or wells.  Rather, think of your experiment as a very large set of single-cell measurements, each of which has some metadata associated with it; in this case, how much Dox the cell was exposed to.  `cytoflow` helps you focus your analysis on how those measurements change as the experimental conditions change, without worrying about what cells were in what tube.\n",
    "\n",
    "---\n",
    "Let's have a quick look at one of the fluorescence channels, `Y2-A`.  Instantiate a `HistogramView` and tell it which channel we're looking at, then call `plot` and pass it the experiment containing the data.  Remember, this is not a single tube, but rather all the data in the `Experiment`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f4d76961e50>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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thhtuyLhx4/LEE0+MGjt06FBaWlrS3t6eWbNmpaOjY1RNR0dHFi5cmOTM6lst6wAAAK4E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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d76961d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = flow.HistogramView()\n",
    "hist.channel = 'Y2-A'\n",
    "hist.plot(ex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmmm.  This plot is hard to interpret because most of the data is clustered around 0, in the linear range of the detector's response.  Let's re-plot using a different scale.  My favorite is `logicle`, which has a linear response around 0 and a log range elsewhere.  We specify the plot scale by setting the `scale` attribute of `HistogramView` to `logicle`; other options are `log` and `linear`.\n",
    "\n",
    "The cell below also demonstrates a different way to parameterize the `HistogramView` instance, by passing parameters to the constructor instead of setting the values of the instance's attributes after we make it.  Either way is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f4d90030b50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fn7Vr1+b222/PHXfckTFjxuTqq6/O8uXLM2rUqL5tFc1RXSNGjMjo0XVDPQYA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\ngGQUwtNVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d75eed250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = flow.HistogramView(channel = 'Y2-A',\n",
    "                          scale = 'logicle')\n",
    "hist.plot(ex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ah, much better.  There is clearly a population of cells around 0 and a population of cells around about 5000.  But!  This is the entire `Experiment` -- is is one of the populations from the low-Dox tube and the other population from the high-Dox tube?\n",
    "\n",
    "Let's see if the histogram is different for the two different concentrations of inducer. `CytoFlow`'s plotting takes inspiration from Trellis plots (eg the lattice package in R): to split the data into subsets, you tell the plotting module which metadata \"facet\" you want to plot in the X, Y and color (hue) axes.\n",
    "\n",
    "This time, we tell `HistogramView` to make a separate plot for each different value of Dox and stack them on top of eachother by saying `yfacet = 'Dox'`; if we wanted the plots side-by-side, we would have said `xfacet = 'Dox'`.  \n",
    "\n",
    "Also note that this time, we don't keep around the `HistogramView` object; if we don't need to re-use it, we can just call `plot` right after the constructor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f4d75cc7150>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XGtDAgQMzZ86cTuN/+Zd/mX379uVLX/pSBg8enNtvvz2NjY1Zs2ZNxo4dmySZPHlyFixY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\n2j1RdSJ+DWgRERGKj49XaWmpx77S0lK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IS9WxMYM0buwFPu5TYCCg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\nAMAiCGgAAAAAYBH/DRrooLkSRkl6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d75cc7290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flow.HistogramView(channel = 'Y2-A',\n",
    "                   scale = 'logicle',\n",
    "                   yfacet = 'Dox').plot(ex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed, the two tubes have dramatically different histograms.  We could also plot them on top of eachother with different colors, by using `huefacet` instead of `yfacet`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f4d75919f10>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TJHV1dcnr9VrKuVwuO34UAAAAAJhwbClyPT09kqSOjg69+OKLwZJ1yy23aOnS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d75919f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flow.HistogramView(channel = \"Y2-A\",\n",
    "                   scale = \"logicle\",\n",
    "                   huefacet = 'Dox').plot(ex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, there's a clear difference between these tubes: one has a substantial population above about ~200 in the Y2-A, and the other doesn't.  What's the proportion of \"high\" cells?  Let's gate out the \"high\" population using a `ThresholdOp`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh = flow.ThresholdOp(name = \"T\",\n",
    "                          channel = \"Y2-A\",\n",
    "                          threshold = 200)\n",
    "\n",
    "ex2 = thresh.apply(ex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two important things to note here: first, an `Operation` (such as `ThresholdOp`) *does not operate in place*; instead, you create a `ThresholdOp` object, then call `apply()` to run it on an `Experiment`.  The `apply()` function returns a new `Experiment`.\n",
    "\n",
    "Second, a gate *does not remove events*; it simply *adds additional metadata* to the events already there.  You can see this if we look at the underlying `pandas.DataFrame` for `ex` and `ex2`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         B1-A        B1-H          B1-W   Dox       FSC-A       FSC-H  \\\n",
      "0 -127.094002  257.718353 -63040.300781  10.0  459.962982  437.354553   \n",
      "1  -70.234840  255.798340 -34034.042969  10.0 -267.174652  365.354553   \n",
      "2  -96.471756  313.398346 -41931.687500  10.0 -201.582336  501.354553   \n",
      "3   18.831570  277.669250   8514.489258  10.0  291.259888  447.029755   \n",
      "4  100.882095  255.756256  51291.074219  10.0 -397.168579  354.565308   \n",
      "\n",
      "           FSC-W      HDR-T        SSC-A        SSC-H          SSC-W  \\\n",
      "0  137847.578125   2.018511   840.091370   747.917847  147225.328125   \n",
      "1  -95849.679688  27.451754  3476.902344  3163.917969  144038.046875   \n",
      "2  -52700.828125  32.043865   480.270691   507.917877  123937.437500   \n",
      "3   85399.273438  79.327492  8026.275879  6741.838867  156043.484375   \n",
      "4 -146821.125000  79.731194  7453.750488   609.884277  262143.968750   \n",
      "\n",
      "         V2-A        V2-H           V2-W         Y2-A         Y2-H  \\\n",
      "0   41.593452  240.153854   22701.017578   109.946274   153.630051   \n",
      "1  103.437637  336.153870   40332.058594  5554.108398  4273.629883   \n",
      "2 -271.375580  256.153870 -138860.828125    81.835281   121.630051   \n",
      "3  -26.212378  207.677841  -16543.453125   -54.120304    98.122017   \n",
      "4   44.559933  216.036865   27035.013672   -10.542595   127.326027   \n",
      "\n",
      "            Y2-W  \n",
      "0   93802.468750  \n",
      "1  170344.203125  \n",
      "2   88188.023438  \n",
      "3  -72294.242188  \n",
      "4  -10852.761719  \n",
      "----\n",
      "         B1-A        B1-H          B1-W   Dox       FSC-A       FSC-H  \\\n",
      "0 -127.094002  257.718353 -63040.300781  10.0  459.962982  437.354553   \n",
      "1  -70.234840  255.798340 -34034.042969  10.0 -267.174652  365.354553   \n",
      "2  -96.471756  313.398346 -41931.687500  10.0 -201.582336  501.354553   \n",
      "3   18.831570  277.669250   8514.489258  10.0  291.259888  447.029755   \n",
      "4  100.882095  255.756256  51291.074219  10.0 -397.168579  354.565308   \n",
      "\n",
      "           FSC-W      HDR-T        SSC-A        SSC-H          SSC-W  \\\n",
      "0  137847.578125   2.018511   840.091370   747.917847  147225.328125   \n",
      "1  -95849.679688  27.451754  3476.902344  3163.917969  144038.046875   \n",
      "2  -52700.828125  32.043865   480.270691   507.917877  123937.437500   \n",
      "3   85399.273438  79.327492  8026.275879  6741.838867  156043.484375   \n",
      "4 -146821.125000  79.731194  7453.750488   609.884277  262143.968750   \n",
      "\n",
      "         V2-A        V2-H           V2-W         Y2-A         Y2-H  \\\n",
      "0   41.593452  240.153854   22701.017578   109.946274   153.630051   \n",
      "1  103.437637  336.153870   40332.058594  5554.108398  4273.629883   \n",
      "2 -271.375580  256.153870 -138860.828125    81.835281   121.630051   \n",
      "3  -26.212378  207.677841  -16543.453125   -54.120304    98.122017   \n",
      "4   44.559933  216.036865   27035.013672   -10.542595   127.326027   \n",
      "\n",
      "            Y2-W      T  \n",
      "0   93802.468750  False  \n",
      "1  170344.203125   True  \n",
      "2   88188.023438  False  \n",
      "3  -72294.242188  False  \n",
      "4  -10852.761719  False  \n"
     ]
    }
   ],
   "source": [
    "print(ex.data.head())\n",
    "print(\"----\")\n",
    "print(ex2.data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In ex2, the `ThresholdOp` added a new column, named `T`, which has the same name as the `ThresholdOp`'s `name` attribute.  It is `True` if the `Y2-A` value is greater than 200, and `False` otherwise.  The `T` column is now a piece metadata *just like the Dox concentration* that we can use to facet the plots.  For example, the following plots different `Dox` concentrations in separate plots stacked on top of eachother (`yfacet = \"Dox\"`), and on each of these plots uses separate colors for the \"low\" and \"high\" populations (`huefacet = 'T'`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f4d75884090>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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sCcA+mpqatPv//k1NTc2m8iWlR9XvG1dZPCsAAADA+1DkYFpdXZ3e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\nBDkAAAAAsBiCHAAAAABYzP8H2rFNqUm4OEQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d75e7f550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flow.HistogramView(channel = \"Y2-A\",\n",
    "                   scale = \"logicle\",\n",
    "                   yfacet = \"Dox\",\n",
    "                   huefacet = 'T').plot(ex2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's also important to note that we can still access the underlying `pandas.Dataframe`: either by looking at the `Experiment.data` attribute, or by referencing a column directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    False\n",
      "1     True\n",
      "2    False\n",
      "3    False\n",
      "4    False\n",
      "5    False\n",
      "6     True\n",
      "7    False\n",
      "8     True\n",
      "9    False\n",
      "Name: T, dtype: bool\n"
     ]
    }
   ],
   "source": [
    "ex2[\"T\"].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the data is all stored in a single `pandas.Dataframe`, we can use the `pandas` API, and indeed the rest of the SciPy stack, to ask sophisticated questions of the underlying data.  For example, \"how many events of each Dox concentration were above the threshold?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dox   T    \n",
       "1.0   False    9946\n",
       "      True       54\n",
       "10.0  False    5561\n",
       "      True     4439\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex2.data.groupby(['Dox', 'T']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`cytoflow` can answer the same question for us using one of its statistics views.  In `cytoflow`, a *statistic* is a number that summarizes a population; one of the key features\n",
    "of `cytoflow` is that it makes it easy to see how these summary statistics change as your experimental conditions change.\n",
    "\n",
    "Several operations add statistics to an experiment; one of the most straightforward ones is `ChannelStatisticOp`.  It groups the experiment's data by the conditions specified in `by`, then applies `function` to `channel` for each group.\n",
    "\n",
    "In this case, we'll divide up the data into the subgroups `T == True & Dox == 1`; `T == True & Dox == 10`; `T == False & Dox == 1`; and `T == False & Dox == 10`.  Then, we'll apply the function `len` to the `Y2-A` channel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex3 = flow.ChannelStatisticOp(name = \"ByDox\",\n",
    "                             channel = \"Y2-A\",\n",
    "                             by = ['T', 'Dox'],\n",
    "                             function = len).apply(ex2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can look at the new statistic: `Experiment.statistics` is a dictionary whose keys are tuples and whose values are the computed statistics (stored as `MultiIndex`ed `pandas.Series`).  The first element in the tuple is the name of the operation that added it, and the second is defined by that operation.  In this case, it's `len`, the name of the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T      Dox \n",
       "False  1.0     9946\n",
       "       10.0    5561\n",
       "True   1.0       54\n",
       "       10.0    4439\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex3.statistics[(\"ByDox\", \"len\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot statistics using one of the various statistics views, such as `BarChartView`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d757bdc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flow.BarChartView(statistic = (\"ByDox\", \"len\"),\n",
    "                  variable = \"Dox\",\n",
    "                  huefacet = 'T').plot(ex3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Statistics are important enough that they get an entire notebook of examples; please see `Statistics.ipynb` for a more in-depth exploration.\n",
    "\n",
    "I hope this makes the semantics of the `cytoflow` package clear.  This was a pretty simplistic toy analysis; for more sophisticated examples, see the other accompanying Jupyter notebooks."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
