A User-friendly and Powerful R Analysis of Large-scale Datasets

Large datasets are increasingly common in the scientific field. It is important to develop user-friendly tools to allow researchers to analyze these large datasets with ease. Here, we introduce a method involving an R script in the open-source software RStudio to analyze large-scale datasets obtained from time series experiments. This method requires minimal input from a user, allowing a beginner who does not have prior R knowledge or programming experience to use it. The detailed instructions described here and in the R script shall further guide users on how to use the method. The input data and the output results are stored in the same folder of a local computer, making it possible to do the analysis anywhere and anytime. The output results are organized into folders for easy interpretation, and they can be conveniently processed to generate figures for publications. This method has been successfully used to analyze circadian clock data and reactive oxygen species burst data, both containing large-scale datasets from time series experiments in a 96-well-plate format. We believe that this method provides a facile and powerful solution for researchers in analyzing similar large datasets obtained through time series experiments.

Publication Details

Journal
Maryland Shared Open Access Repository (USMAI Consortium)
Published
2025-11-04
DOI
https://doi.org/10.13016/m2vwrm-l0kw
Primary Topic
Circadian rhythm and melatonin
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A User-friendly and Powerful R Analysis of Large-scale Datasets

Maryland Shared Open Access Repository (USMAI Consortium)
Circadian rhythm and melatonin
article

A User-friendly and Powerful R Analysis of Large-scale Datasets

article en

Abstract

Large datasets are increasingly common in the scientific field. It is important to develop user-friendly tools to allow researchers to analyze these large datasets with ease. Here, we introduce a method involving an R script in the open-source software RStudio to analyze large-scale datasets obtained from time series experiments. This method requires minimal input from a user, allowing a beginner who does not have prior R knowledge or programming experience to use it. The detailed instructions described here and in the R script shall further guide users on how to use the method. The input data and the output results are stored in the same folder of a local computer, making it possible to do the analysis anywhere and anytime. The output results are organized into folders for easy interpretation, and they can be conveniently processed to generate figures for publications. This method has been successfully used to analyze circadian clock data and reactive oxygen species burst data, both containing large-scale datasets from time series experiments in a 96-well-plate format. We believe that this method provides a facile and powerful solution for researchers in analyzing similar large datasets obtained through time series experiments.

Maryland Shared Open Access Repository (USMAI Consortium)
Openalex Percentile: Top 39%
Circadian rhythm and melatonin
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A User-friendly and Powerful R Analysis of Large-scale Datasets · Maryland Shared Open Access Repository (USMAI Consortium) (2025) | TGRS Research Map | TGRS