Ten simple rules for researchers who want to develop web apps

Saia, Sheila M.; Nelson, Natalie; Young, Sierra; Parham, Stanton; Vandegrift, Micah

doi:10.1371/journal.pcbi.1009663

Cited by 11 publications

(2 citation statements)

References 46 publications

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“…Given time and effort, a motivated researcher will manage to find the right combination of package versions and Python version that will be compatible, but this level of difficulty is both a significant barrier to use and common in open-source scientific software generally (8). An increasingly common way to address the challenges of running open-source scientific software is by implementing the methods of the software in a web application (9,10). Here we present a new web application named MANGEM (Multimodal Analysis of Neuronal Gene Expression, Electrophysiology and Morphology), developed to address the challenges researchers may experience in using existing methods of aligning and analyzing multi-modal single-cell data.…”

Section: Introductionmentioning

confidence: 99%

MANGEM: a web app for Multimodal Analysis of Neuronal Gene expression, Electrophysiology and Morphology

Olson

Kalafut

Wang

2023

Preprint

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Single-cell techniques have enabled the acquisition of multi-modal data, particularly for neurons, to characterize cellular functions. Patch-seq, for example, combines patch-clamp recording, cell imaging, and single-cell RNA-seq to obtain electrophysiology, morphology, and gene expression data from a single neuron. While these multi-modal data offer potential insights into neuronal functions, they can be heterogeneous and noisy. To address this, machine-learning methods have been used to align cells from different modalities onto a low-dimensional latent space, revealing multi-modal cell clusters. However, the use of those methods can be challenging for biologists and neuroscientists without computational expertise and also requires suitable computing infrastructure for computationally expensive methods. To address these issues, we developed a cloud-based web application, MANGEM (Multimodal Analysis of Neuronal Gene expression, Electrophysiology, and Morphology) at https://ctc.waisman.wisc.edu/mangem. MANGEM provides a step-by-step accessible and user-friendly interface to machine-learning alignment methods of neuronal multi-modal data while enabling real-time visualization of characteristics of raw and aligned cells. It can be run asynchronously for large-scale data alignment, provides users with various downstream analyses of aligned cells and visualizes the analytic results such as identifying multi-modal cell clusters of cells and detecting correlated genes with electrophysiological and morphological features. We demonstrated the usage of MANGEM by aligning Patch-seq multimodal data of neuronal cells in the mouse visual cortex.

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Section: Introductionmentioning

confidence: 99%

MANGEM: a web app for Multimodal Analysis of Neuronal Gene expression, Electrophysiology and Morphology

Olson

Kalafut

Wang

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…An increasingly common way to address the challenges of running open-source scientific software is by implementing the methods of the software in a web application. 10 , 11 Here, we present a web application named MANGEM, developed to address the challenges researchers may experience in using existing methods of aligning and analyzing multimodal single-cell data. In particular, MANGEM (1) provides an easy-to-use interface to a variety of machine learning alignment methods, (2) requires no coding to use, and (3) does not require installation of software or management of computing infrastructure.…”

Section: Introductionmentioning

confidence: 99%