THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

animals used (i.e., species/strain, sex, and age/weight). Most of the papers surveyed did not report using randomisation (87%) or blinding (86%) to reduce bias in animal selection and outcome assessment. Only 70% of the publications that used statistical methods fully described them and presented the results with a measure of precision or variability [5]. These findings are a cause for concern and are consistent with reviews of many research areas, including clinical studies, published in recent years [2–22].

https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.1000412&type=printable

Improving Bioscience Research Reporting: The ARRIVE Guidelines for Reporting Animal Research

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Gut commensal microbes shape the mucosal immune system by regulating the differentiation and expansion of several types of T cell. Clostridia, a dominant class of commensal microbe, can induce colonic regulatory T (Treg) cells, which have a central role in the suppression of inflammatory and allergic responses. However, the molecular mechanisms by which commensal microbes induce colonic Treg cells have been unclear. Here we show that a large bowel microbial fermentation product, butyrate, induces the differentiation of colonic Treg cells in mice. A comparative NMR-based metabolome analysis suggests that the luminal concentrations of short-chain fatty acids positively correlates with the number of Treg cells in the colon. Among short-chain fatty acids, butyrate induced the differentiation of Treg cells in vitro and in vivo, and ameliorated the development of colitis induced by adoptive transfer of CD4(+) CD45RB(hi) T cells in Rag1(-/-) mice. Treatment of naive T cells under the Treg-cell-polarizing conditions with butyrate enhanced histone H3 acetylation in the promoter and conserved non-coding sequence regions of the Foxp3 locus, suggesting a possible mechanism for how microbial-derived butyrate regulates the differentiation of Treg cells. Our findings provide new insight into the mechanisms by which host-microbe interactions establish immunological homeostasis in the gut.

Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells

There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, facilitates experimental replication, and enables the re-interrogation and comparison of data by others. Accordingly, the Metabolomics Standards Initiative is building a general consensus concerning the minimum reporting standards for metabolomics experiments of which the Chemical Analysis Working Group (CAWG) is a member of this community effort. This article proposes the minimum reporting standards related to the chemical analysis aspects of metabolomics experiments including: sample preparation, experimental analysis, quality control, metabolite identification, and data pre-processing. These minimum standards currently focus mostly upon mass spectrometry and nuclear magnetic resonance spectroscopy due to the popularity of these techniques in metabolomics. However, additional input concerning other techniques is welcomed and can be provided via the CAWG on-line discussion forum at http://msi-workgroups.sourceforge.net/ or http://Msi-workgroups-feedback@lists.sourceforge.net. Further, community input related to this document can also be provided via this electronic forum.

/pdf/proposed-minimum-reporting-standards-for-chemical-analysis-4ohwb35hkf.pdf

Proposed minimum reporting standards for chemical analysis

The number of national reference populations that are whole-genome sequenced are rapidly increasing. Partly driving this development is the fact that genetic disease studies benefit from knowing the genetic variation typical for the geographical area of interest. A whole-genome sequenced Swedish national reference population (n=1000) has been recently published but with few samples from northern Sweden. In the present study we have whole-genome sequenced a control population (n=300) (ACpop) from Vasterbotten County, a sparsely populated region in northern Sweden previously shown to be genetically different from southern Sweden. The aggregated variant frequencies within ACpop are publicly available (DOI 10.17044/NBIS/G000005) to function as a basic resource in clinical genetics and for genetic studies. Our analysis of ACpop, representing approximately 0.11% of the population in Vasterbotten, indicates the presence of a genetic substructure within the county. Furthermore, a demographic analysis showed that the population from which samples were drawn was to a large extent geographically stationary, a finding that was corroborated in the genetic analysis down to the level of municipalities. Including ACpop in the reference population when imputing unknown variants in a Vasterbotten cohort resulted in a strong increase in the number of high-confidence imputed variants (up to 81% for variants with minor allele frequency

/pdf/a-whole-genome-sequenced-control-population-in-northern-29pfglkp3b.pdf

A whole-genome sequenced control population in northern Sweden reveals subregional genetic differences

Cell Painting is an established community-based, microscopy-assay platform that provides high-throughput, high-content data for biological readouts. In November 2022, the JUMP-Cell Painting Consortium released the largest annotated, publicly available dataset, comprising more than 2 billion cell images. This dataset is designed for predicting the activity and toxicity of 100k drug compounds, with the aim to make cell images as computable as genomes and transcriptomes. In this paper, we have developed a data analytics workflow that is both scalable and computationally efficient, while providing significant, biologically relevant insights for biologists estimating and comparing the effects of different drug treatments. The two main objectives proposed include: 1) a simple, yet sophisticated, scalable data analytics metric that utilizes negative controls for comparing morphological cell profiles. We call this metric the equivalence score (Eq. score). 2) A workflow to identify and amplify subtle morphological image profile changes caused by drug treatments, compared to the negative controls. In summary, we provide a data analytics workflow to assist biologists in interpreting high-dimensional image features, not necessarily limited to morphological ones. This enhances the efficiency of drug candidate screening, thereby streamlining the drug development process. By increasing our understanding of using complex image-based data, we can decrease the cost and time to develop new, life-saving treatments. Author summary Microscopy-assays are often used to study cell responses to treatments in the search for new drugs. In this paper, we present a method that simplifies the understanding of the data generated from such assays. The data in this study consists of 750 morphological features, which describe the traits and characteristics of the cells, extracted from the images. By using untreated cells as a biological baseline, we’re able to detect subtle changes that occur in the treated cells. These changes are then transformed into an equivalence score (Eq. score), a metric that lets us compare the similarities among different treatments relative to our baseline of untreated cells. Our Eq. score approach transforms complex, high-dimensional data about cell morphology into something more interpretable and understandable. It reduces the “noise” in the features and highlights important changes, the “signal”. Our method can be integrated into existing workflows, aiding researchers in understanding and interpreting complex morphological data derived from cell images more easily. Understanding cell morphology is crucial to deepening our knowledge of biological systems. Ultimately, this could contribute to the faster and more cost-effective development of new, life-saving treatments.

/pdf/a-scalable-data-analytics-workflow-for-image-based-1h665flt.pdf

Johan Trygg

Papers

A whole-genome sequenced control population in northern Sweden reveals subregional genetic differences

A scalable, data analytics workflow for image-based morphological profiles