zetapy

Repository containing ZETA functions and dependencies. For an example of how to use the code, check example.py.

Now available as pip package! Simply "pip install zetapy".

• This Python repository and the pip package are maintained by Guido Meijer and Alexander Heimel.
• The original code is written in MATLAB by Jorrit Montijn .
• A Julia port, developed by Pierre Le Merre, is available here: ZetaJu

Our pre-print describing data-stitching, the time-series ZETA-test, and the two-sample tests is available online.

The article describing the original ZETA-test has been published in eLife.

The ZETA-test for spiking data has been extensively tested on real and artificial data, and has been peer-reviewed. The time-series ZETA-test and two-sample ZETA-tests are also thoroughly tested and are described in our pre-print, which we will submit for peer-review soon. We are confident all ZETA-tests, including the two-sample tests, are reliable and statistically sound, but if you find any bugs, do let us know on the Issues page!

This repository contains five main functions:

• zetatest: Calculates the Zenith of Event-based Time-locked Anomalies (ZETA) for spike times of a single neuron. Outputs a p-value.
• zetatstest: Calculates the time-series version of ZETA, for data such as calcium imaging or EEG recordings.
• zetatest2: Same as (1), but for testing whether two neurons respond differently to the same stimulus; or whether one neuron responds differently to two sets of stimuli.
• zetatstest2: Same as (2), but for testing differences between two time-series data arrays.
• ifr: Calculates the instantaneous firing rate (IFR) without running the ZETA-test. Use this as you would a PSTH function.

Rationale for ZETA

Neurophysiological studies depend on a reliable quantification of whether and when a neuron responds to stimulation, be it sensory, optogenetically or otherwise. However, current statistical analysis methods to determine a neuron’s responsiveness require arbitrary parameter choices, such as a binning size. This choice can change the results of the analysis, which invites bad statistical practice and reduces the replicability of analyses. Moreover, many methods, such as bin-wise t-tests, only detect classically mean-rate modulated cells. Especially with advent of techniques that yield increasingly large numbers of cells, such as Neuropixels recordings or two-photon calcium imaging, it is important to use tests for cell-inclusion that require no manual curation. Here, we present a new family of statistical tests for responses in point-event and time-series data for one- and two-sample comparisons: the family of ZETA-tests. As shown in our papers, they outperform approaches such as optimally-binned ANOVAs, t-tests and model-based approaches, in the sense that it includes more cells in real neurophysiological data at a similar false-positive rate.

Latency

For computing the latency of a response, we recommend to use the LatenZy test (https://github.com/Herseninstituut/latenZy), based on the zeta-test.