Data normalization is a crucial step in the gene expression analysis as it ensures the validity of its downstream analyses. Although many metrics have been designed to evaluate the existing normalization methods, different metrics or different datasets by the same metric yield inconsistent results, particularly for the single-cell RNA sequencing (scRNA-seq) data. The worst situations could be that one method evaluated as the best by one metric is evaluated as the poorest by another metric, or one method evaluated as the best using one dataset is evaluated as the poorest using another dataset. Here raises an open question: principles need to be established to guide the evaluation of normalization methods. In this study, we propose a principle that one normalization method evaluated as the best by one metric should also be evaluated as the best by another metric (the consistency of metrics) and one method evaluated as the best using scRNA-seq data should also be evaluated as the best using bulk RNA-seq data or microarray data (the consistency of datasets). Then, we designed a new metric named Area Under normalized CV threshold Curve (AUCVC) and applied it with another metric mSCC to evaluate 14 commonly used normalization methods using both scRNA-seq data and bulk RNA-seq data, satisfying the consistency of metrics and the consistency of datasets. Our findings paved the way to guide future studies in the normalization of gene expression data with its evaluation. The raw gene expression data, normalization methods, and evaluation metrics used in this study have been included in an R package named NormExpression. NormExpression provides a framework and a fast and simple way for researchers to select the best method for the normalization of their gene expression data based on the evaluation of different methods (particularly some data-driven methods or their own methods) in the principle of the consistency of metrics and the consistency of datasets.

1. Microbiome sequencing data often need to be normalized due to differences in read depths, and recommendations for microbiome analyses generally warn against using proportions or rarefying to normalize data and instead advocate alternatives, such as upper quartile, CSS, edgeR-TMM, or DESeq-VS. Those recommendations are, however, based on studies that focused on differential abundance testing and variance standardization, rather than community-level comparisons (i.e., beta diversity), Also, standardizing the within-sample variance across samples may suppress differences in species evenness, potentially distorting community-level patterns. Furthermore, the recommended methods use log transformations, which we expect to exaggerate the importance of differences among rare OTUs, while suppressing the importance of differences among common OTUs. 2. We tested these theoretical predictions via simulations and a real-world data set. 3. Proportions and rarefying produced more accurate compariso...

User guide To generate the reports: prerequisite: Java 8 runtime environment download metadata-qa-marc project as it is described at https://github.com/pkiraly/metadata-qa-marc (e.g. into ~/git/metadata-qa-marc directory) download the .sh and .R files from this project to a subdirectory (e.g. 'scripts') adjust the DIR variable in the [library-name].sh files according to your directory structure run-all.sh creates -details.csv and -summary.csv files into $DIR/_reports directory If you do not want to generate the reports, but would like to use the data files provided, download *.csv.gz files to a '_reports' directory. To generate Table 2. and 3. of the paper: prerequisite: R move normalize-summary.sh, distill-ids.sh, and normalize-ids.sh into $DIR/_reports directory cd $DIR/_reports ./normalize-summary.sh ./distill-ids.sh ./normalize-ids.sh Rscript evaluate-details.R Rscript evaluate-summary.R

Analysis of bulk RNA sequencing (RNA-Seq) data is a valuable tool to understand transcription at the genome scale. Targeted sequencing of RNA has emerged as a practical means of assessing the majority of the transcriptomic space with less reliance on large resources for consumables and bioinformatics. TempO-Seq is a templated, multiplexed RNA-Seq platform that interrogates a panel of sentinel genes representative of genome-wide transcription. Nuances of the technology require proper preprocessing of the data. Various methods have been proposed and compared for normalizing bulk RNA-Seq data, but there has been little to no investigation of how the methods perform on TempO-Seq data. We simulated count data into two groups (treated vs. untreated) at seven-fold change (FC) levels (including no change) using control samples from human HepaRG cells run on TempO-Seq and normalized the data using seven normalization methods. Upper Quartile (UQ) performed the best with regard to maintaining FC levels as detected by a limma contrast between treated vs. untreated groups. For all FC levels, specificity of the UQ normalization was greater than 0.84 and sensitivity greater than 0.90 except for the no change and +1.5 levels. Furthermore, K-means clustering of the simulated genes normalized by UQ agreed the most with the FC assignments [adjusted Rand index (ARI) = 0.67]. Despite having an assumption of the majority of genes being unchanged, the DESeq2 scaling factors normalization method performed reasonably well as did simple normalization procedures counts per million (CPM) and total counts (TCs). These results suggest that for two class comparisons of TempO-Seq data, UQ, CPM, TC, or DESeq2 normalization should provide reasonably reliable results at absolute FC levels ≥2.0. These findings will help guide researchers to normalize TempO-Seq gene expression data for more reliable results.

Output files from the 8. Metadata Analysis Workflow page of the SWELTR high-temp study. In this workflow, we compared environmental metadata with microbial communities. The workflow is split into two parts.

metadata_ssu18_wf.rdata : Part 1 contains all variables and objects for the 16S rRNA analysis. To see the Objects, in R run _load("metadata_ssu18_wf.rdata", verbose=TRUE)_

metadata_its18_wf.rdata : Part 2 contains all variables and objects for the ITS analysis. To see the Objects, in R run _load("metadata_its18_wf.rdata", verbose=TRUE)_
Additional files:

In both workflows, we run the following steps:

1) Metadata Normality Tests: Shapiro-Wilk Normality Test to test whether each matadata parameter is normally distributed.
2) Normalize Parameters: R package bestNormalize to find and execute the best normalizing transformation.
3) Split Metadata parameters into groups: a) Environmental and edaphic properties, b) Microbial functional responses, and c) Temperature adaptation properties.
4) Autocorrelation Tests: Test all possible pair-wise comparisons, on both normalized and non-normalized data sets, for each group.
5) Remove autocorrelated parameters from each group.
6) Dissimilarity Correlation Tests: Use Mantel Tests to see if any on the metadata groups are significantly correlated with the community data.
7) Best Subset of Variables: Determine which of the metadata parameters from each group are the most strongly correlated with the community data. For this we use the bioenv function from the vegan package.
8) Distance-based Redundancy Analysis: Ordination analysis of samples and metadata vector overlays using capscale, also from the vegan package.

Source code for the workflow can be found here:
https://github.com/sweltr/high-temp/blob/master/metadata.Rmd

The reported parameters were used to normalize the raw scores according to Eq. (8).

The goal of metabolomics is to measure the entire range of small organic molecules in biological samples. In liquid chromatography–mass spectrometry-based metabolomics, formidable analytical challenges remain in removing the nonbiological factors that affect chromatographic peak areas. These factors include sample matrix-induced ion suppression, chromatographic quality, and analytical drift. The combination of these factors is referred to as obscuring variation. Some metabolomics samples can exhibit intense obscuring variation due to matrix-induced ion suppression, rendering large amounts of data unreliable and difficult to interpret. Existing normalization techniques have limited applicability to these sample types. Here we present a data normalization method to minimize the effects of obscuring variation. We normalize peak areas using a batch-specific normalization process, which matches measured metabolites with isotope-labeled internal standards that behave similarly during the analysis. This method, called best-matched internal standard (B-MIS) normalization, can be applied to targeted or untargeted metabolomics data sets and yields relative concentrations. We evaluate and demonstrate the utility of B-MIS normalization using marine environmental samples and laboratory grown cultures of phytoplankton. In untargeted analyses, B-MIS normalization allowed for inclusion of mass features in downstream analyses that would have been considered unreliable without normalization due to obscuring variation. B-MIS normalization for targeted or untargeted metabolomics is freely available at https://github.com/IngallsLabUW/B-MIS-normalization.

A common problem in confocal microscopy is the decrease in intensity of excitation light and emission signal from fluorophores as they travel through 3D specimens, resulting in decreased signal detected as a function of depth. Here, we report a visualization program compatible with widely used fluorophores in cell biology to facilitate image interpretation of differential protein disposition in 3D specimens. Glioblastoma cell clusters were fluorescently labeled for mitochondrial complex I (COXI), P2X7 receptor (P2X7R), β-Actin, Ki-67, and DAPI. Each cell cluster was imaged using a laser scanning confocal microscope. We observed up to ∼70% loss in fluorescence signal across the depth in Z-stacks. This progressive underrepresentation of fluorescence intensity as the focal plane deepens hinders an accurate representation of signal location within a 3D structure. To address these challenges, we developed ProDiVis: a program that adjusts apparent fluorescent signals by normalizing one fluorescent signal to a reference signal at each focal plane. ProDiVis serves as a free and accessible, unbiased visualization tool to use in conjunction with fluorescence microscopy images and imaging software.