photometry_FLMM

Code to reproduce analyses and figures from the manuscript: "A Statistical Framework for Analysis of Trial-Level Temporal Dynamics in Fiber Photometry Experiments"

fastFMM R Package

For more information see the fastFMM R package repo: https://github.com/gloewing/fastFMM

Installation

Download the $\texttt{R}$ Package fastFMM by running the following command within $\texttt{R}$ or $\texttt{RStudio}$:

install.packages("fastFMM", dependencies = TRUE)

Package Usage

For the usage and a tutorial on package functions, please refer to fastFMM's Vignette.

Photometry Analysis Guide

• $\textbf{Part 1}$: Binary Variables
• $\textbf{Part 2}$: Testing changes within a trial between 2 periods (baseline vs. cue period)
• $\textbf{Part 3}$: Associations with continuous variables
• $\textbf{Part 4}$: Testing Factor Variables
• $\textbf{Part 5}$: Testing how signal–covariate associations change across time

Calling fastFMM from Python

See 'python_fastFMM_vignette.py' in the Tutorials folder for a brief example of using fastFMM on Python through the Python package rpy2. We are working on more documentation. The tutorial assumes the fastFMM R package (and all its dependenices), and the rpy2 Python package have already been installed. Even if you intend to use the package purely within Python, it may be helpful to first install fastFMM from within RStudio to ensure all package dependenices are installed automatically.