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Summary This article suggests a novel positive false discovery rate (pFDR) controlling method for testing gene-specific hypotheses using a gene-specific covariate variable, such as gene length. We suppose the null probability depends on the covariate variable. In this context, we propose a rejection rule that accounts for heterogeneity among tests by using two distinct types of null probabilities. We establish a pFDR estimator for a given rejection rule by following Storey's q -value framework. A condition on a type 1 error posterior probability is provided that equivalently characterizes our rejection rule. We also present a suitable procedure for selecting a tuning parameter through cross-validation that maximizes the expected number of hypotheses declared significant. A simulation study demonstrates that our method is comparable to or better than existing methods across realistic scenarios. In data analysis, we find support for our method's premise that the null probability varies with a gene-specific covariate variable. Availability and implementation The source code repository is publicly available at https://github.com/hsjeon1217/conditional_method. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) data has been widely used for cell trajectory inference, with the assumption that cells with similar expression profiles share the same differentiation state. However, the inferred trajectory may not reveal clonal differentiation heterogeneity among T cell clones. Single-cell T cell receptor sequencing (scTCR-seq) data provides invaluable insights into the clonal relationship among cells, yet it lacks functional characteristics. Therefore, scRNA-seq and scTCR-seq data complement each other in improving trajectory inference, where a reliable computational tool is still missing. We developed LRT, a computational framework for the integrative analysis of scTCR-seq and scRNA-seq data to explore clonal differentiation trajectory heterogeneity. Specifically, LRT uses the transcriptomics information from scRNA-seq data to construct overall cell trajectories and then utilizes both the TCR sequence information and phenotype information to identify clonotype clusters with distinct differentiation biasedness. LRT provides a comprehensive analysis workflow, including preprocessing, cell trajectory inference, clonotype clustering, trajectory biasedness evaluation, and clonotype cluster characterization. We illustrated its utility using scRNA-seq and scTCR-seq data of CD8+ T cells and CD4+ T cells with acute lymphocytic choriomeningitis virus infection. These analyses identified several clonotype clusters with distinct skewed distribution along the differentiation path, which cannot be revealed solely based on scRNA-seq data. Clones from different clonotype clusters exhibited diverse expansion capability, V-J gene usage pattern and CDR3 motifs. The LRT framework was implemented as an R package 'LRT', and it is now publicly accessible at https://github.com/JuanXie19/LRT. In addition, it provides two Shiny apps 'shinyClone' and 'shinyClust' that allow users to interactively explore distributions of clonotypes, conduct repertoire analysis, implement clustering of clonotypes, trajectory biasedness evaluation, and clonotype cluster characterization. Author summary: Understanding the dynamic changes behind biological processes is important for determining molecular mechanisms underlying normal tissue formulation, developmental disorders and pathologies. Usually, a biological process can be characterized by identifying a trajectory, a path that goes through the various cellular states associated with the process. Since cells in different states may express different sets of genes, researchers often infer cell trajectory via capturing transcriptomics changes. Dozens of methods have been developed for cell trajectory inference, and scRNA-seq data is predominantly utilized. However, cells from different clones may exist distinct differentiation patterns, while methods based only on scRNA-seq data cannot capture this heterogeneity. T cells play a key role in the immune system, and their high antigen recognition specificity is largely determined by their TCR sequences. Thanks to the advent of scTCR-seq technology, we can now identify the group of cells coming from the same clone. This paper describes our novel computational framework, namely LRT, and demonstrates that by complementing scRNA-seq data with the clonal information from scTCR-seq data using LRT, we are able to examine the clonal differentiation heterogeneity that cannot be revealed solely based on scRNA-seq data. [ABSTRACT FROM AUTHOR]

Gene expression profiling technologies have been used in various applications such as cancer biology. The development of gene expression profiling has expanded the scope of target discovery in transcriptomic studies, and each technology produces data with distinct characteristics. In order to guarantee biologically meaningful findings using transcriptomic experiments, it is important to consider various experimental factors in a systematic way through statistical power analysis. In this paper, we review and discuss the power analysis for three types of gene expression profiling technologies from a practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics. Specifically, we describe the existing power analysis tools for each research objective for each of the bulk RNA-seq and scRNA-seq experiments, along with recommendations. On the other hand, since there are no power analysis tools for high-throughput spatial transcriptomics at this point, we instead investigate the factors that can influence power analysis. [ABSTRACT FROM AUTHOR]

Introduction Although the greater popularity of electronic cigarettes (EC) among asthmatics is alarming, there is limited knowledge of the long-term consequences of EC exposure in asthmatics. Aims and Methods Mild asthmatic C57/BL6J adult male and female mice were established by intranasal insufflation with three combined allergens. The asthmatic and age and sex-matched' naïve mice were exposed to air, nicotine-free (propylene glycol [PG]/vegetable glycerin [VG]-only), or PG/VG+Nicotine, 4 hours daily for 3 months. The effects of EC exposure were accessed by measuring cytokines in bronchoalveolar lavage, periodic acid-schiff (PAS) staining, mitochondrial DNA copy numbers (mtCN), and the transcriptome in the lung. Significance was false discovery rate <0.2 for transcriptome and 0.05 for the others. Results In asthmatic mice, PG/VG+Nicotine increased PAS-positive cells and IL-13 compared to mice exposed to air and PG/VG-only. In naïve mice exposed to PG/VG+Nicotine and PG/VG-only, higher INF-γ was observed compared to mice exposed only to air. PG/VG-only and PG/VG+Nicotine had significantly higher mtCN compared to air exposure in asthmatic mice, while the opposite pattern was observed in non-asthmatic naïve mice. Different gene expression patterns were profoundly found for asthmatic mice exposed to PG/VG+Nicotine compared to PG/VG-only, including genes involved in mitochondrial dysfunction, oxidative phosphorylation, and p21-activated kinase (PAK) signaling. Conclusions This study provides experimental evidence of the potential impact of nicotine enhancement on the long-term effects of EC in asthmatics compared to non-asthmatics. Implications The findings from this study indicate the potential impact of EC in asthmatics by addressing multiple biological markers. The long-term health outcomes of EC in the susceptible group can be instrumental in supporting policymaking and educational campaigns and informing the public, healthcare providers, and EC users about the underlying risks of EC use. [ABSTRACT FROM AUTHOR]

Delineating functionally normal variants from functionally abnormal variants in tumor suppressor proteins is critical for cancer surveillance, prognosis, and treatment options. BRCA1 is a protein that has many variants of uncertain significance which are not yet classified as functionally normal or abnormal. In vitro functional assays can be used to identify the functional impact of a variant when the variant has not yet been categorized through clinical observation. Here we employ a homology-directed repair (HDR) reporter assay to evaluate over 300 missense and nonsense BRCA1 variants between amino acid residues 1280 and 1576, which encompasses the coiled-coil and serine cluster domains. Functionally abnormal variants tended to cluster in residues known to interact with PALB2, which is critical for homology-directed repair. Multiplexed results were confirmed by singleton assay and by ClinVar database variant interpretations. Comparison of multiplexed results to designated benign or likely benign or pathogenic or likely pathogenic variants in the ClinVar database yielded 100% specificity and 100% sensitivity of the multiplexed assay. Clinicians can reference the results of this functional assay for help in guiding cancer treatment and surveillance options. These results are the first to evaluate this domain of BRCA1 using a multiplexed approach and indicate the importance of this domain in the DNA repair process. [ABSTRACT FROM AUTHOR]

In dairy cows, the period from the end of lactation through the dry period and into the transition period, requires vast physiological and immunological changes critical to mammary health. The dry period is important to the success of the next lactation and intramammary infections during the dry period will adversely alter mammary function, health and milk production for the subsequent lactation. MicroRNAs (miRNAs) are small non-coding RNAs that can post transcriptionally regulate gene expression. We sought to characterize the miRNA profile in dry secretions from the last day of lactation to 3, 10, and 21 days post dry-off. We identified 816 known and 80 novel miRNAs. We found 46 miRNAs whose expression significantly changed (q-value < 0.05) over the first three weeks of dry-off. Additionally, we examined the slopes of random regression models of log transformed normalized counts and cross analyzed the 46 significantly upregulated and downregulated miRNAs. These miRNAs were found to be associated with important components of pregnancy, lactation, as well as inflammation and disease. Detailing the miRNA profile of dry secretions through the dry-off period provides insight into the biology at work, possible means of regulation, components of resistance and/or susceptibility, and outlets for targeted therapy development. [ABSTRACT FROM AUTHOR]

• Precipitation δ 18O predicted using geostatistical and machine learning algorithms. • Prediction performance evaluated using ordinary and hybrid error metrics. • Machine learning algorithms performed better, particularly using scarcer datasets. • Two spatiotemporally different subcontinental datasets of precipitation δ 18O were used. Stable isotopes of precipitation are important natural tracers in hydrology, ecology, and forensics. The spatially explicit predictions of oxygen and hydrogen isotopes in precipitation are obtained through different interpolation techniques. In the present study we aim to examine the performance of various interpolation techniques when predicting the spatial distribution of precipitation stable isotopes. The efficiency of combined geostatistical tools (i.e. regression kriging; RK) and various machine learning methods (including regression enhanced random forest methods: MRRF, RERF) are compared in interpolating the spatial variability of precipitation stable oxygen isotope values from two different sampling networks in Europe. To assess the performance of the models, mean squared error (MSE), nonparametric Kling Gupta efficiency (KGE), absolute differences and relative mean absolute error metrics were employed. It was found that the combination of the different regression techniques with Random Forest can produce estimations with comparable accuracy in terms of descending order of overall average MSE, MRRF: 2.61, RK: 2.77, RERF: 2.99, RF: 3.08. The best performing combined random forest model variant (MRRF) outperformed regression kriging in terms of a hybrid error metric (KGE) by 7.5%. Sequential random rarefying the station networks showed that machine-learning methods are more capable of maintaining high prediction accuracy even with fewer input data. This can be a great advantage when a suitable method is needed to predict the stable isotope composition of precipitation for large spatial domains where the spatial density of data stations shows large differences. [ABSTRACT FROM AUTHOR]