TY - JOUR
T1 - NeuroWRAP
T2 - integrating, validating, and sharing neurodata analysis workflows
AU - Bowen, Zac
AU - Magnusson, Gudjon
AU - Diep, Madeline
AU - Ayyangar, Ujjwal
AU - Smirnov, Aleksandr
AU - Kanold, Patrick O.
AU - Losert, Wolfgang
N1 - Funding Information:
This work was supported by the NIH U19 NS107464.
Publisher Copyright:
Copyright © 2023 Bowen, Magnusson, Diep, Ayyangar, Smirnov, Kanold and Losert.
PY - 2023
Y1 - 2023
N2 - Multiphoton calcium imaging is one of the most powerful tools in modern neuroscience. However, multiphoton data require significant pre-processing of images and post-processing of extracted signals. As a result, many algorithms and pipelines have been developed for the analysis of multiphoton data, particularly two-photon imaging data. Most current studies use one of several algorithms and pipelines that are published and publicly available, and add customized upstream and downstream analysis elements to fit the needs of individual researchers. The vast differences in algorithm choices, parameter settings, pipeline composition, and data sources combine to make collaboration difficult, and raise questions about the reproducibility and robustness of experimental results. We present our solution, called NeuroWRAP (www.neurowrap.org), which is a tool that wraps multiple published algorithms together, and enables integration of custom algorithms. It enables development of collaborative, shareable custom workflows and reproducible data analysis for multiphoton calcium imaging data enabling easy collaboration between researchers. NeuroWRAP implements an approach to evaluate the sensitivity and robustness of the configured pipelines. When this sensitivity analysis is applied to a crucial step of image analysis, cell segmentation, we find a substantial difference between two popular workflows, CaImAn and Suite2p. NeuroWRAP harnesses this difference by introducing consensus analysis, utilizing two workflows in conjunction to significantly increase the trustworthiness and robustness of cell segmentation results.
AB - Multiphoton calcium imaging is one of the most powerful tools in modern neuroscience. However, multiphoton data require significant pre-processing of images and post-processing of extracted signals. As a result, many algorithms and pipelines have been developed for the analysis of multiphoton data, particularly two-photon imaging data. Most current studies use one of several algorithms and pipelines that are published and publicly available, and add customized upstream and downstream analysis elements to fit the needs of individual researchers. The vast differences in algorithm choices, parameter settings, pipeline composition, and data sources combine to make collaboration difficult, and raise questions about the reproducibility and robustness of experimental results. We present our solution, called NeuroWRAP (www.neurowrap.org), which is a tool that wraps multiple published algorithms together, and enables integration of custom algorithms. It enables development of collaborative, shareable custom workflows and reproducible data analysis for multiphoton calcium imaging data enabling easy collaboration between researchers. NeuroWRAP implements an approach to evaluate the sensitivity and robustness of the configured pipelines. When this sensitivity analysis is applied to a crucial step of image analysis, cell segmentation, we find a substantial difference between two popular workflows, CaImAn and Suite2p. NeuroWRAP harnesses this difference by introducing consensus analysis, utilizing two workflows in conjunction to significantly increase the trustworthiness and robustness of cell segmentation results.
KW - consensus
KW - image analysis
KW - reproducibility
KW - two-photon calcium imaging
KW - workflow management
UR - http://www.scopus.com/inward/record.url?scp=85158898189&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85158898189&partnerID=8YFLogxK
U2 - 10.3389/fninf.2023.1082111
DO - 10.3389/fninf.2023.1082111
M3 - Article
C2 - 37181735
AN - SCOPUS:85158898189
SN - 1662-5196
VL - 17
JO - Frontiers in Neuroinformatics
JF - Frontiers in Neuroinformatics
M1 - 1082111
ER -