Differential detection workflows for multi-sample single-cell RNA-seq data.

In single-cell transcriptomics, differential gene expression (DE) analyses typically focus on testing differences in the average expression of genes between cell types or conditions of interest. Single-cell transcriptomics, however, also has the promise to prioritise genes for which the expression differ in other aspects of the distribution. Here we develop a workflow for assessing differential detection (DD), which tests for differences in the average fraction of samples or cells in which a gene is detected. After benchmarking eight different DD data analysis strategies, we provide a unified workflow for jointly assessing DE and DD. Using simulations and two case studies, we show that DE and DD analysis provide complementary information, both in terms of the individual genes they report and in the functional interpretation of those genes.

A New Electrochemical Method to Determine Tryptophan in Fruit Juices: Development and Validation.

Tryptophan (Trp) is an essential amino acid usually found in fruit juices. Its determination is necessary for food companies because of its relation to human health. In this work, a new electrochemical method based on sonogel-carbon electrodes (SNGCEs) was developed and validated using an ultra performance liquid chromatography (UPLC) method as a reference method for the determination of Trp in fruit juices. Cyclic voltammetry (CV), chronoamperometry, and differential pulse voltammetry (DPV) techniques were applied to investigate the oxidation of Trp on a previously polarized SNGCE surface in a Britton-Robinson (BR) buffer solution at pH 3.6. The operating conditions for electroanalysis were optimized using a Box-Behnken design (BBD), obtaining an oxidation peak for Trp at 0.749 V. The linear range for this method was from 0.1 to 5 mg/L. The intraday and interday precision, expressed as a relative standard deviation (RSD), were 3.1% and 2.7%, respectively. The average recovery was 99.01%, and the limit of detection and quantitation were 0.33 and 1.09 mg/L, respectively. Therefore, from the quality analytical parameters obtained, it can be concluded that the new electrochemical method can be successfully used for the routine analysis of Trp in fruit juices. As far as we are concerned, this is the first time that a methodology for Trp determination was performed in this kind of real food matrices.

On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments.

BACKGROUND: In gene expression studies, RNA sample pooling is sometimes considered because of budget constraints or lack of sufficient input material. Using microarray technology, RNA sample pooling strategies have been reported to optimize both the cost of data generation as well as the statistical power for differential gene expression (DGE) analysis. For RNA sequencing, with its different quantitative output in terms of counts and tunable dynamic range, the adequacy and empirical validation of RNA sample pooling strategies have not yet been evaluated. In this study, we comprehensively assessed the utility of pooling strategies in RNA-seq experiments using empirical and simulated RNA-seq datasets. RESULT: The data generating model in pooled experiments is defined mathematically to evaluate the mean and variability of gene expression estimates. The model is further used to examine the trade-off between the statistical power of testing for DGE and the data generating costs. Empirical assessment of pooling strategies is done through analysis of RNA-seq datasets under various pooling and non-pooling experimental settings. Simulation study is also used to rank experimental scenarios with respect to the rate of false and true discoveries in DGE analysis. The results demonstrate that pooling strategies in RNA-seq studies can be both cost-effective and powerful when the number of pools, pool size and sequencing depth are optimally defined. CONCLUSION: For high within-group gene expression variability, small RNA sample pools are effective to reduce the variability and compensate for the loss of the number of replicates. Unlike the typical cost-saving strategies, such as reducing sequencing depth or number of RNA samples (replicates), an adequate pooling strategy is effective in maintaining the power of testing DGE for genes with low to medium abundance levels, along with a substantial reduction of the total cost of the experiment. In general, pooling RNA samples or pooling RNA samples in conjunction with moderate reduction of the sequencing depth can be good options to optimize the cost and maintain the power.