Code to reproduce analyses and figures from the manuscript: "A Statistical Framework for Analysis of Trial-Level Temporal Dynamics in Fiber Photometry Experiments"
For more information see the fastFMM
R package repo: https://github.com/gloewing/fastFMM
Download the fastFMM
by running the following command within
install.packages("fastFMM", dependencies = TRUE)
For the usage and a tutorial on package functions, please refer to fastFMM's Vignette.
-
$\textbf{Part 1}$ : 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
See the Tutorials
folder above for the datasets and Rmarkdown files used to generate the above guides.
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.