Open AccessBook
Experimental Design for Laboratory Biologists: Maximising Information and Improving Reproducibility
TLDR
In this paper, the authors present a practical guide for lab-based biomedical researchers to design experiments that are reproducible, with low bias, high precision, and widely applicable results.Abstract:
Specifically intended for lab-based biomedical researchers, this practical guide shows how to design experiments that are reproducible, with low bias, high precision, and widely applicable results. With specific examples from research using both cell cultures and model organisms, it explores key ideas in experimental design, assesses common designs, and shows how to plan a successful experiment. It demonstrates how to control biological and technical factors that can introduce bias or add noise, and covers rarely discussed topics such as graphical data exploration, choosing outcome variables, data quality control checks, and data pre-processing. It also shows how to use R for analysis, and is designed for those with no prior experience. An accompanying website (https://stanlazic.github.io/EDLB.html) includes all R code, data sets, and the labstats R package. This is an ideal guide for anyone conducting lab-based biological research, from students to principle investigators working in either academia or industry.read more
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The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research
Nathalie Percie du Sert,Viki Hurst,Amrita Ahluwalia,Sabina Alam,Marc T. Avey,Monya Baker,William J Browne,Alejandra Clark,Innes C. Cuthill,Ulrich Dirnagl,Michael Emerson,Paul Garner,Stephen T. Holgate,David W. Howells,Natasha A. Karp,Stanley E. Lazic,Katie Lidster,Catriona J. MacCallum,Malcolm R. Macleod,Esther J. Pearl,Ole H. Petersen,Frances Rawle,Penny S. Reynolds,Kieron Rooney,Emily S. Sena,Shai D. Silberberg,Thomas Steckler,Hanno Würbel +27 more
TL;DR: The ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments) have been updated and information reorganised to facilitate their use in practice to help ensure that researchers, reviewers, and journal editors are better equipped to improve the rigour and transparency of the scientific process and thus reproducibility.
Journal ArticleDOI
What exactly is ‘N’ in cell culture and animal experiments?
TL;DR: It is argued that distinguishing between biological units, experimental units, and observational units clarifies where replication should occur, describe the criteria for genuine replication, and provide concrete examples of in vitro, ex vivo, and in vivo experimental designs.
Journal ArticleDOI
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis
Koen Van den Berge,Katharina M. Hembach,Charlotte Soneson,Charlotte Soneson,Simone Tiberi,Lieven Clement,Michael I. Love,Rob Patro,Mark D. Robinson +8 more
TL;DR: The measurement of transcriptome-wide gene expression has convincingly shown the ability to describe the activity of individual genes at different levels ofexpression in the genome.
Journal ArticleDOI
Designing a rigorous microscopy experiment: Validating methods and avoiding bias.
TL;DR: In this article, the authors present a rigorous light microscopy experiment to validate the methods used to prepare samples and of imaging system performance, identification and correction of errors, and strategies for avoiding bias in the acquisition and analysis of images.
Extending the linear model with R : generalized linear, mixedeffects and nonparametric regression models
TL;DR: In this paper, the authors presented a comparison of methods for estimating Equations using multinomial regression models and generalized linear mixed models for non-normal responses in the context of disaster scenarios.
References
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Book
Statistical Power Analysis for the Behavioral Sciences
TL;DR: The concepts of power analysis are discussed in this paper, where Chi-square Tests for Goodness of Fit and Contingency Tables, t-Test for Means, and Sign Test are used.
Book
ggplot2: Elegant Graphics for Data Analysis
TL;DR: This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkisons Grammar of Graphics to create a powerful and flexible system for creating data graphics.
Book
Mixed Effects Models and Extensions in Ecology with R
TL;DR: In this paper, the authors apply additive mixed modelling on phyoplankton time series data and show that the additive model can be used to estimate the age distribution of small cetaceans.
Book
Mixed-Effects Models in S and S-PLUS
TL;DR: Linear Mixed-Effects and Nonlinear Mixed-effects (NLME) models have been studied in the literature as mentioned in this paper, where the structure of grouped data has been used for fitting LME models.
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