# Project Structure¶

The source is organized into two main components.

• The cytoflow package. This package contains the actual tools for operating on cytometry data. Key modules and subpackages:
• The cytoflowgui package. Implements the GUI.
• The cytoflowgui.op_plugins subpackage. Contains instances of envisage.Plugin that wrap the operations. See the documentation for adding a new operation GUI plugin.
• The cytoflowgui.op_views subpackage. Contains instances of envisage.Plugin that wrap the views. See the documentation if you want to add a new view GUI plugin.

# Design decisions & justifications¶

• Cytometry analysis as a workflow – an analysis is a set of operations applied sequentially to a dataset. I think this is kind of obvious; it just formalizes the way of doing things that everyone else pretty much already uses.

• Instead of keeping “tubes” or “wells” as first-class objects, represent all the events from all the samples as a big long pandas.DataFrame, distinguishing events from different tubes via their varying experimental conditions. Most of my flow analysis experience is with the R Bioconductor package’s flowCore, which treats tubes as first-class objects akin to separate microarrays. That’s fine if you’ve got just a few tubes (or a few microarrays), but it rapidly gets cumbersome if you’ve got multiple plates of samples, each plate of which has two or three experimental variables; I ended up spending more time and code specifying metadata than I did actually doing analysis.

Cytoflow pushes the metadata down to the event level, doing away entirely with the concept of tubes or wells (after you get your data imported, of course.) This hews much more closely to Hadley Wickham’s concept of Tidy Data, and is also (!) much easier to vectorize computations on using pandas and numpy and numexpr. Now, you can access all the events that are, say Dox-induced, by just saying experiment['Dox'] without having to keep track of which tubes are induced and which weren’t.

Note

If you have tubes that are replicates, just add another experimental condition, perhaps called “replicate”. You can specify that condition to the statistics views to get a standard error.

• Gates don’t actually subset data (delete or copy it); they just add metadata. I struggled for a long time with the question of how to store and manipulate different subsets of data after gating. Again, my own experience is with Bioconductor’s flowCore, which defines a tree structure by data that is included or excluded by gates; if a node is a gate, then its children are the subpopulations produced by that gate. Navigating that tree, though, is really difficult, especially if you want to re-combine data after gating (for plotting, for example.)

Then there was the issue of how to track and manipulate this structure as additional operations were performed. Keep just a single copy and operate on it in-place? Or copy the output of one operation for the input of the next, with the space penalties that implies?

I finally realized I didn’t have to choose; when you copy a pandas.DataFrame, you get a “shallow” copy, with the actual data just linked to by reference. This was perfect; if I needed to transform the data from one copy to another, I could just replace the transformed channels; and “gating” events didn’t have to create new subsets or containers, it could just add another column specifying the gate membership of each event.

• cytoflow discourages wholesale transformation of the underlying data, ie. taking the log of the data set. This is of a part with cytoflow enabling quantitative analysis – if you want a measure of center of data that is log-normal, you should use the geometric mean instead of log-transforming and taking the arithmetic mean. It is frequently useful to transform data before viewing it, or gating it, etc – those transformations can be passed as parameters to the view modules.

The obvious exceptions here, of course, are things like bleedthrough correction and calibration using beads. These operations transform the data, but they don’t cause the same sorts of shift in data structure you see with a log transform. Data that is distributed log-normally before bleedthrough correction, will be distributed log-normally after.

• Easy computation and plotting of summary statistics. The ChannelStatisticOp and FrameStatisticOp operations create new statistics and add them to the Experiment.statistics; and BarChartView, Stats1DView and Stats2DView make it easy to plot them. (A statistic is just a pandas.Series with a hierarchical index that encodes data subsets and the value of a summary statistic for each group.) This may be more useful in the GUI, because pandas.DataFrame.groupby() provides similar functionality in a notebook setting.

• As is made pretty clear in the example Jupyter notebooks, the semantics for views and operations are

1. Instantiate a new operation or view
2. Parameterize the operation or view (possibly by estimating parameters from a provided data set).
3. Apply the operation or view to an Experiment. If applying an operation, apply() returns a new Experiment.

The justification for these semantics is that it makes the state of the interacting objects really obvious. An operation or view’s state doesn’t depend on the data it’s applied to; if its parameters do depend on data, those parameters’ estimation is a separate operation.

It also allows for ready separation of the workflow from the data it’s applied to, allowing for easy sharing of workflows.

• The module attributes have been replaced by Traits. See the Traits documentation for a good overview, but in short they give Python some of the benefits of statically typed languages like Java, without much of the mess that a fully statically typed language incurs. Their power doesn’t see a whole lot of use internal to the cytoflow package, but they make writing the GUI layer a whole lot easier.

• The design of the views are strongly influenced by best-in-class statistics visualization packages from R: lattice and ggplot. If your data is tidy, then each experimental variable you want to plot differently so you can compare them is called a “facet”. For example, a facet might be a timepoint or an inducer level (ie an experimental condition); it might also be some metadata added by an operation (ie gate membership or bin). Then, you plot the dataset broken down in various ways by its facets: for example, each timepoint might be put on its own subplot, while each Dox level might be represented by a different color. (Check out the example Jupyter notebook if this is confusing.