Local False Discovery Rate Research Articles

A novel biomarker selection method using multimodal neuroimaging data.

Identifying biomarkers is essential to obtain the optimal therapeutic benefit while treating patients with late-life depression (LLD). We compare LLD patients with healthy controls (HC) using resting-state functional magnetic resonance and diffusion tensor imaging data to identify neuroimaging biomarkers that may be potentially associated with the underlying pathophysiology of LLD. We implement a Bayesian multimodal local false discovery rate approach for functional connectivity, borrowing strength from structural connectivity to identify disrupted functional connectivity of LLD compared to HC. In the Bayesian framework, we develop an algorithm to control the overall false discovery rate of our findings. We compare our findings with the literature and show that our approach can better detect some regions never discovered before for LLD patients. The Hub of our discovery related to various neurobehavioral disorders can be used to develop behavioral interventions to treat LLD patients who do not respond to antidepressants.

DOP58 Human Leukocyte Antigen drives methylation signatures in primary sclerosing cholangitis but not in IgG4-related sclerosing cholangitis

Abstract Background Primary sclerosing cholangitis (PSC) and IgG4-related sclerosing cholangitis (IgG4-SC) are fibro-inflammatory hepatobiliary conditions, with genetic, environmental, and immunologic risk factors, in which epigenetic alterations may provide insights into pathophysiology and novel biomarkers. Methods Whole blood DNA methylation profiling and genotyping was performed in 70 PSC (57 with PSC and ulcerative colitis (PSC-UC)), 56 IgG4-SC, 66 UC, and 88 healthy controls (HC), with MethylationEPIC and Global Screening arrays (Illumina). UC patients were selected to match the clinical characteristics of UC in the PSC-UC cohort. Methylation data was processed with Minfi and limma, genotyping analysed with Plink, and meQTLs with linear models. Results There were 31, 13 and 8 Holm-significant methylation differences between PSC and controls, IgG4-SC and controls, and UC and controls respectively (8 probes shared). Inflammatory genes (including CEP97, IFNAR1, TXK, and SMAD2) and methylation loci associated with mitochondrial DNA copy number (C5orf36, PYY, and MTRNR2L1) were predominant. Sub-analyses of PSC-UC and PSC without IBD detected 25 and 4 differences with HC respectively (p<0.05). DNA methylation age acceleration was observed in IgG4-SC, but not on PSC, PSC-IBD or UC. MeQTL analyses to identify genetic determinants of methylation revealed a strong HLA signal in PSC but not IgG4-SC, with 19/40 significant findings in 5 clusters (HLA-DRB1, HLA-DPB1, BTNL2, CSNK2B, and SFTA2). There was no overlap with the 36 meQTLs found in IgG4-SC. Using multivariate adaptive shrinkage for meQTLs in PSC, IgG4-SC, and UC yielded 5505 meQTLs with local false discovery rate <0.05 in at least one condition, with 1230 unique to PSC, and 1043 to IgG4-SC. Conclusion We identify characteristic epigenetic alterations in PSC and IgG4-related disease, providing insights into disease pathogenesis; and highlight the potential interaction with germ-line variation.

Empirical Bayes single nucleotide variant-calling for next-generation sequencing data

One of the fundamental computational problems in cancer genomics is the identification of single nucleotide variants (SNVs) from DNA sequencing data. Many statistical models and software implementations for SNV calling have been developed in the literature, yet, they still disagree widely on real datasets. Based on an empirical Bayesian approach, we introduce a local false discovery rate (LFDR) estimator for germline SNV calling. Our approach learns model parameters without prior information, and simultaneously accounts for information across all sites in the genomic regions of interest. We also propose another LFDR-based algorithm that reliably prioritizes a given list of mutations called by any other variant-calling algorithm. We use a suite of gold-standard cell line data to compare our LFDR approach against a collection of widely used, state of the art programs. We find that our LFDR approach approximately matches or exceeds the performance of all of these programs, despite some very large differences among them. Furthermore, when prioritizing other algorithms’ calls by our LFDR score, we find that by manipulating the type I-type II tradeoff we can select subsets of variant calls with minimal loss of sensitivity but dramatic increases in precision.

An Empirical Bayes Approach to Controlling the False Discovery Exceedance

In large-scale multiple hypothesis testing problems, the false discovery exceedance (FDX) provides a desirable alternative to the widely used false discovery rate (FDR) when the false discovery proportion (FDP) is highly variable. We develop an empirical Bayes approach to control the FDX. We show that, for independent hypotheses from a two-group model and dependent hypotheses from a Gaussian model fulfilling the exchangeability condition, an oracle decision rule based on ranking and thresholding the local false discovery rate (lfdr) is optimal in the sense that the power is maximized subject to the FDX constraint. We propose a data-driven FDX procedure that uses carefully designed computational shortcuts to emulate the oracle rule. We investigate the empirical performance of the proposed method using both simulated and real data and study the merits of FDX control through an application for identifying abnormal stock trading strategies.

Local false discovery rate estimation with competition-based procedures for variable selection.

Multiple hypothesis testing has been widely applied to problems dealing with high-dimensional data, for example, the selection of important variables or features from a large number of candidates while controlling the error rate. The most prevailing measure of error rate used in multiple hypothesis testing is the false discovery rate (FDR). In recent years, the local false discovery rate (fdr) has drawn much attention, due to its advantage of accessing the confidence of individual hypotheses. However, most methods estimate fdr through -values or statistics with known null distributions, which are sometimes unavailable or unreliable. Adopting the innovative methodology of competition-based procedures, for example, the knockoff filter, this paper proposes a new approach, named TDfdr, to fdr estimation, which is free of -values or known null distributions. Extensive simulation studies demonstrate that TDfdr can accurately estimate the fdr with two competition-based procedures. We applied the TDfdr method to two real biomedical tasks. One is to identify significantly differentially expressed proteins related to the COVID-19 disease, and the other is to detect mutations in the genotypes of HIV-1 that are associated with drug resistance. Higher discovery power was observed compared to existing popular methods.

Local and Bayesian Survival FDR Estimations to Identify Reliable Associations in Whole Genome of Bread Wheat.

Estimating the FDR significance threshold in genome-wide association studies remains a major challenge in distinguishing true positive hypotheses from false positive and negative errors. Several comparative methods for multiple testing comparison have been developed to determine the significance threshold; however, these methods may be overly conservative and lead to an increase in false negative results. The local FDR approach is suitable for testing many associations simultaneously based on the empirical Bayes perspective. In the local FDR, the maximum likelihood estimator is sensitive to bias when the GWAS model contains two or more explanatory variables as genetic parameters simultaneously. The main criticism of local FDR is that it focuses only locally on the effects of single nucleotide polymorphism (SNP) in tails of distribution, whereas the signal associations are distributed across the whole genome. The advantage of the Bayesian perspective is that knowledge of prior distribution comes from other genetic parameters included in the GWAS model, such as linkage disequilibrium (LD) analysis, minor allele frequency (MAF) and call rate of significant associations. We also proposed Bayesian survival FDR to solve the multi-collinearity and large-scale problems, respectively, in grain yield (GY) vector in bread wheat with large-scale SNP information. The objective of this study was to obtain a short list of SNPs that are reliably associated with GY under low and high levels of nitrogen (N) in the population. The five top significant SNPs were compared with different Bayesian models. Based on the time to events in the Bayesian survival analysis, the differentiation between minor and major alleles within the association panel can be identified.

Proteomic investigation of ALS motor cortex identifies known and novel pathogenetic mechanisms

The key pathological feature in ALS is death of motor neurones from the brain and spinal cord, but the molecular mechanisms underlying this degeneration remain unknown. Quantifying the motor cortex proteome in autopsy brain and comparing tissues from ALS cases and non-ALS controls is critical to understanding these mechanisms. We used Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) to characterize the proteomes of the motor cortex from ALS cases (n = 8) and control subjects (n = 8). A total of 1427 proteins were identified at a critical local false discovery rate < 5%; 187 of these exhibited significant expression differences between ALS cases and controls. Of these, 91 proteins were significantly upregulated and 96 proteins were significantly downregulated. Bioinformatics analysis revealed that these proteins are involved in molecular transport, protein trafficking, free radical scavenging, lipid metabolism, cell death and survival, nucleic acid metabolism, inflammatory response or amino acid metabolism and carbohydrate metabolism. Differentially expressed proteins were subjected to pathway analysis. This revealed abnormalities in pathways involving mitochondrial function, sirtuin signaling, oxidative phosphorylation, glycolysis, phagosome maturation, SNARE signaling, redox regulation and several others. Core analysis revealed mitochondrial dysfunction to be the top canonical pathway. The top-enriched networks involved JNK activation and inhibition of AKT signaling, suggesting that disruption of these signaling pathways could lead to demise of motor neurons in the ALS motor cortex.

Mapping interindividual dynamics of innate immune response at single-cell resolution

Common genetic variants across individuals modulate the cellular response to pathogens and are implicated in diverse immune pathologies, yet how they dynamically alter the response upon infection is not well understood. Here, we triggered antiviral responses in human fibroblasts from 68 healthy donors, and profiled tens of thousands of cells using single-cell RNA-sequencing. We developed GASPACHO (GAuSsian Processes for Association mapping leveraging Cell HeterOgeneity), a statistical approach designed to identify nonlinear dynamic genetic effects across transcriptional trajectories of cells. This approach identified 1,275 expression quantitative trait loci (local false discovery rate 10%) that manifested during the responses, many of which were colocalized with susceptibility loci identified by genome-wide association studies of infectious and autoimmune diseases, including the OAS1 splicing quantitative trait locus in a COVID-19 susceptibility locus. In summary, our analytical approach provides a unique framework for delineation of the genetic variants that shape a wide spectrum of transcriptional responses at single-cell resolution.

Feature-specific inference for penalized regression using local false discovery rates.

Penalized regression methods such as the lasso are a popular approach to analyzing high-dimensional data. One attractive property of the lasso is that it naturally performs variable selection. An important area of concern, however, is the reliability of these selections. Motivated by local false discovery rate methodology from the large-scale hypothesis testing literature, we propose a method for calculating a local false discovery rate for each variable under consideration by the lasso model. These rates can be used to assess the reliability of an individual feature, or to estimate the model's overall false discovery rate. The method can be used for any level of regularization. This is particularly useful for models with a few highly significant features but a high overall false discovery rate, a relatively common occurrence when using cross validation to select a model. It is also flexible enough to be applied to many varieties of penalized likelihoods including generalized linear models and Cox regression, and a variety of penalties, including the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalty. We demonstrate the validity of this approach and contrast it with other inferential methods for penalized regression as well as with local false discovery rates for univariate hypothesis tests. Finally, we show the practical utility of our method by applying it to a case study involving gene expression in breast cancer patients.

Bayesian variable selection using Knockoffs with applications to genomics.

Given the costliness of HIV drug therapy research, it is important not only to maximize true positive rate (TPR) by identifying which genetic markers are related to drug resistance, but also to minimize false discovery rate (FDR) by reducing the number of incorrect markers unrelated to drug resistance. In this study, we propose a multiple testing procedure that unifies key concepts in computational statistics, namely Model-free Knockoffs, Bayesian variable selection, and the local false discovery rate. We develop an algorithm that utilizes the augmented data-Knockoff matrix and implement Bayesian Lasso. We then identify signals using test statistics based on Markov Chain Monte Carlo outputs and local false discovery rate. We test our proposed methods against non-bayesian methods such as Benjamini–Hochberg (BHq) and Lasso regression in terms TPR and FDR. Using numerical studies, we show the proposed method yields lower FDR compared to BHq and Lasso for certain cases, such as for low and equi-dimensional cases. We also discuss an application to an HIV-1 data set, which aims to be applied analyzing genetic markers linked to drug resistant HIV in the Philippines in future work.

Bayesian local false discovery rate for sparse count data with application to the discovery of hotspots in protein domains

In cancer research at the molecular level, it is critical to understand which somatic mutations play an important role in the initiation or progression of cancer. Recently, studying cancer somatic variants at the protein domain level is an important area for uncovering functionally related somatic mutations. The main issue is to find the protein domain hotspots which have significantly high frequency of mutations. Multiple testing procedures are commonly used to identify hotspots; however, when data is not large enough, existing methods produce unreliable results with failure in controlling a given type I error rate. We propose multiple testing procedures, based on Bayesian local false discovery rate, for sparse count data and apply it in the identification of clusters of somatic mutations across entire gene families using protein domain models. In multiple testing for count data, it is not clear what kind of the null distribution should be admitted. In our proposed algorithms, we implement the zero assumption in the context of Bayesian methods to identify the null distribution for count data rather than using any theoretical null distribution. Furthermore, we also address different types of modeling of alternative distributions. The proposed fully Bayesian models are efficient when the number of count data is small (50≤N<200) while the local false discovery rate procedures, based on the empirical Bayes, is desirable for a large number of data (N>800). We provide numerical studies to show that the proposed fully Bayesian methods can control a given level of false discovery rate for small number of positions while existing approaches based on nonparametric empirical Bayes fail in controlling a false discovery rate. In addition, we present real data examples of protein domain data to select hotspots in protein domain data.

Model-based identification of conditionally-essential genes from transposon-insertion sequencing data.

The understanding of bacterial gene function has been greatly enhanced by recent advancements in the deep sequencing of microbial genomes. Transposon insertion sequencing methods combines next-generation sequencing techniques with transposon mutagenesis for the exploration of the essentiality of genes under different environmental conditions. We propose a model-based method that uses regularized negative binomial regression to estimate the change in transposon insertions attributable to gene-environment changes in this genetic interaction study without transformations or uniform normalization. An empirical Bayes model for estimating the local false discovery rate combines unique and total count information to test for genes that show a statistically significant change in transposon counts. When applied to RB-TnSeq (randomized barcode transposon sequencing) and Tn-seq (transposon sequencing) libraries made in strains of Caulobacter crescentus using both total and unique count data the model was able to identify a set of conditionally beneficial or conditionally detrimental genes for each target condition that shed light on their functions and roles during various stress conditions.

Psychiatric symptoms of frontotemporal dementia and subcortical (co-)pathology burden: new insights.

Three subtypes of distinct pathological proteins accumulate throughout multiple brain regions and shape the heterogeneous clinical presentation of frontotemporal lobar degeneration (FTLD). Besides the main pathological subtypes, co-occurring pathologies are common in FTLD brain donors. The objective of this study was to investigate how the location and burden of (co-)pathology correlate to early psychiatric and behavioural symptoms of FTLD. Eighty-seven brain donors from The Netherlands Brain Bank cohort (2008-2017) diagnosed with FTLD were included: 46 FTLD-TAR DNA-binding protein 43 (FTLD-TDP), 34 FTLD-tau, and seven FTLD-fused-in-sarcoma (FTLD-FUS). Post-mortem brain tissue was dissected into 20 standard regions and stained for phosphorylated TDP-43, phosphorylated tau, FUS, amyloid-β, and α-synuclein. The burden of each pathological protein in each brain region was assessed with a semi-quantitative score. Clinical records were reviewed for early psychiatric and behavioural symptoms. Whole-brain clinico-pathological partial correlations were calculated (local false discovery rate threshold = 0.01). Elaborating on the results, we validated one finding using a quantitative assessment of TDP-43 pathology in the granular layer of the hippocampus in FTLD-TDP brain donors with (n = 15) and without (n = 15) hallucinations. In subcortical regions, the presence of psychiatric symptoms showed positive correlations with increased hippocampal pathology burden: hallucinations with TDP-43 in the granular layer (R = 0.33), mania with TDP-43 in CA1 (R = 0.35), depression with TDP-43 in CA3 and with parahippocampal tau (R = 0.30 and R = 0.23), and delusions with CA3 tau (R = 0.26) and subicular amyloid-β (R = 0.25). Behavioural disinhibition showed positive correlations with tau burden in the thalamus (R = 0.29) and with both TDP-43 and amyloid-β burden in the subthalamus (R = 0.23 and R = 0.24). In the brainstem, the presence of α-synuclein co-pathology in the substantia nigra correlated with disinhibition (R = 0.24), tau pathology in the substantia nigra correlated with depression (R = 0.25) and in the locus coeruleus with both depression and perseverative/compulsive behaviour (R = 0.26 and R = 0.32). The quantitative assessment of TDP-43 in the granular layer validated the higher burden of TDP-43 pathology in brain donors with hallucinations compared to those without hallucinations (P = 0.007). Our results show that psychiatric symptoms of FTLD are linked to subcortical pathology burden in the hippocampus, and hallucinations are linked to a higher burden of TDP-43 in the granular layer. Co-occurring non-FTLD pathologies in subcortical regions could contribute to configuring the clinical phenotype of FTLD.

Publication Policies for Replicable Research and the Community-Wide False Discovery Rate

Recent literature has shown that statistically significant results are often not replicated because the “p-value < 0.05” publication rule results in a high false positive rate (FPR) or false discovery rate (FDR) in some scientific communities. While recommendations to address the phenomenon vary, many amount to incorporating additional study summary information, such as prior null hypothesis odds and/or effect sizes, in some way. This article demonstrates that a statistic called the local false discovery rate (lfdr), which incorporates this information, is a sufficient summary for addressing false positive rates. Specifically, it is shown that lfdr-values among published results are sufficient for estimating the community-wide FDR for any well-defined publication policy, and that lfdr-values are sufficient for defining policies for community-wide FDR control. It is also demonstrated that, though p-values can be useful for computing an lfdr, they alone are not sufficient for addressing the community-wide FDR. Data from the recent replication study are used to compare publication policies and illustrate the FDR estimator.

Multiple Hypothesis Testing Framework for Spatial Signals

The problem of identifying regions of spatially interesting, different or adversarial behavior is inherent to many practical applications involving distributed multisensor systems. In this work, we develop a general framework stemming from multiple hypothesis testing to identify such regions. A discrete spatial grid is assumed for the monitored environment. The spatial grid points associated with different hypotheses are identified while controlling the false discovery rate at a pre-specified level. Measurements are acquired using a large-scale sensor network. We propose a novel, data-driven method to estimate local false discovery rates based on the spectral method of moments. Our method is agnostic to specific spatial propagation models of the underlying physical phenomenon. It relies on a broadly applicable density model for local summary statistics. In between sensors, locations are assigned to regions associated with different hypotheses based on interpolated local false discovery rates. The benefits of our method are illustrated by applications to spatially propagating radio waves.

Local false discovery rate based methods for multiple testing of one-way classified hypotheses

This paper continues the line of research initiated in Liu, Sarkar and Zhao (2016) on developing a novel framework for multiple testing of hypotheses grouped in a one-way classified form using hypothesis-specific local false discovery rates (Lfdr’s). It is built on an extension of the standard two-class mixture model from single to multiple groups, defining hypothesis-specific Lfdr as a function of the conditional Lfdr for the hypothesis given that it is within an important group and the Lfdr for the group itself and involving a new parameter that measures grouping effect. This definition captures the underlying group structure for the hypotheses belonging to a group more effectively than the standard two-class mixture model. Two new Lfdr based methods, possessing meaningful optimalities, are produced in their oracle forms. One, designed to control false discoveries across the entire collection of hypotheses, is proposed as a powerful alternative to simply pooling all the hypotheses into a single group and using commonly used Lfdr based method under the standard single-group two-class mixture model. The other is proposed as an Lfdr analog of the method of Benjamini and Bogomolov (2014) for selective inference. It controls Lfdr based measure of false discoveries associated with selecting groups concurrently with controlling the average of within-group false discovery proportions across the selected groups. Simulation studies and real-data application show that our proposed methods are often more powerful than their relevant competitors.

Neuroanatomy of FTD: Whole‐brain correlations between symptoms and pathologies

Distinct pathologies accumulate in multiple brain regions (BR) and shape the heterogeneous clinical presentation of frontotemporal dementia (FTD). It is unknown how regional pathological burden links to symptoms, what the role of co-occurring pathologies is, and whether the localization of pathology might be a bigger contributor to symptoms than the type of pathology. Our aim is to investigate how early FTD symptoms correlate to the burden of multiple pathologies throughout the brain.Post-mortem brain tissue of frontotemporal lobar degeneration (FTLD) donors from the Netherlands brain bank was dissected into twenty standard BR and stained for TAR DNA-binding protein 43 (TDP-43), tau, fused-in-sarcoma (FUS), amyloid-beta (Aβ), and alpha-synuclein. The burden of each pathological protein in each BR was quantified. All clinical records were reviewed to assess psychiatric, behavioral, language, and motor symptoms in the first three years from disease onset. Whole-brain clinico-pathological partial correlations were assessed using the heterogeneous correlation function (R 3.6.1). The local false discovery rate threshold was set at 0.01.Eighty-eight FTLD brain donors were studied, including 46 TDP-43, 35 tau, and 7 FUS. Significant positive partial correlations (p < 0.01) were found between hippocampal TDP-43 pathology and hallucinations (R = 0.23), perseverative-compulsive behavior (R = 0.25), depression (R = 0.28), and mania (R = 0.32). Tau pathology in the substantia nigra and locus coeruleus was linked to depression (R = 0.25, R = 0.24). Both TDP-43 and Aβ in the subthalamus were associated with severe disinhibition (R = 0.23, R = 0.25), while apathy correlated with both TDP-43 and tau burden in the parietal lobe (R = 0.27, R = 0.24). Parkinsonism was linked to TDP-43 burden in the substantia nigra (R = 0.33).Neuropsychiatric symptoms of FTD are linked to pathology burden in BR beyond the frontal lobes, including subcortical structures such as the hippocampus, the substantia nigra and locus coeruleus. Co-occurring pathologies are not simple bystanders, but could play a role in configuring FTD clinical phenotype. Different pathologies in the same BR correlate with the same symptoms.

Proteomic analysis of differential expression of lung proteins in response to highly pathogenic avian influenza virus infection in chickens

Elucidation of the molecular pathogenesis underlying virus-host interactions is important for the development of new diagnostic and therapeutic strategies against highly pathogenic avian influenza (HPAI) virus infection in chickens. However, the pathogenesis of HPAI virus in chickens is not completely understood. To identify the intracellular signaling pathways and critical host proteins associated with influenza pathogenesis, we analyzed the lung proteome of a chicken infected with HPAI H5N1 virus (A/duck/India/02CA10/2011/Agartala). Mass spectrometry data sets were searched against the chicken UniProt reference database. At the local false discovery rate level of 5%, a total of 3313 proteins with the presence of at least one unique peptide were identified in the chicken lung proteome datasets. Differential expression analysis of these proteins showed that 247 and 1754 proteins were downregulated at 12 h and 48 h postinfection, respectively. We observed expression of proteins of the predominant signaling pathways, including Toll-like receptors (TLRs), retinoic acid-inducible gene I-like receptors (RLRs), NOD-like receptors (NLRs), and JAK-STAT signaling. Activation of these pathways is associated with the cytokine storm effect and thus may be the cause of the severity of HPAI H5N1 infection in chickens. We also observed the expression of myeloid differentiation primary response protein (MyD88), inhibitor of nuclear factor kappa B kinase subunit beta (IKBKB), interleukin 1 receptor associated kinase 4 (IRAK4), RELA proto-oncogene NF-κB subunit (RELA), and mitochondrial antiviral signaling protein (MAVS), which are involved in critical signaling pathways, as well as other, less-commonly identified proteins such as hepatocyte nuclear factor 4 alpha (HNF4A), ELAV-like RNA binding protein 1 (ELAVL1), fibronectin 1 (FN1), COP9 signalosome subunit 5 (COPS5), cullin 1 (CUL1), breast cancer type 1 susceptibility protein (BRCA1), and the FYN proto-oncogene Src family tyrosine kinase (FYN) as main hub proteins that might play important roles in influenza pathogenesis in chickens. In summary, we identified the signaling pathways and the proteomic determinants associated with disease pathogenesis in chickens infected with HPAI H5N1 virus.

Circall: fast and accurate methodology for discovery of circular RNAs from paired-end RNA-sequencing data

BackgroundCircular RNA (circRNA) is an emerging class of RNA molecules attracting researchers due to its potential for serving as markers for diagnosis, prognosis, or therapeutic targets of cancer, cardiovascular, and autoimmune diseases. Current methods for detection of circRNA from RNA sequencing (RNA-seq) focus mostly on improving mapping quality of reads supporting the back-splicing junction (BSJ) of a circRNA to eliminate false positives (FPs). We show that mapping information alone often cannot predict if a BSJ-supporting read is derived from a true circRNA or not, thus increasing the rate of FP circRNAs.ResultsWe have developed Circall, a novel circRNA detection method from RNA-seq. Circall controls the FPs using a robust multidimensional local false discovery rate method based on the length and expression of circRNAs. It is computationally highly efficient by using a quasi-mapping algorithm for fast and accurate RNA read alignments. We applied Circall on two simulated datasets and three experimental datasets of human cell-lines. The results show that Circall achieves high sensitivity and precision in the simulated data. In the experimental datasets it performs well against current leading methods. Circall is also substantially faster than the other methods, particularly for large datasets.ConclusionsWith those better performances in the detection of circRNAs and in computational time, Circall facilitates the analyses of circRNAs in large numbers of samples. Circall is implemented in C++ and R, and available for use at https://www.meb.ki.se/sites/biostatwiki/circall and https://github.com/datngu/Circall.

Adaptive local false discovery rate procedures for highly spiky data and their application RNA sequencing data of yeast SET4 deletion mutants.

Chromatin dynamics are central to the regulation of gene expression and genome stability. In order to improve understanding of the factors regulating chromatin dynamics, the genes encoding these factors are deleted and the differential gene expression profiles are determined using approaches such as RNA sequencing. Here, we analyzed a gene expression dataset aimed at uncovering the function of the relatively uncharacterized chromatin regulator, Set4, in the model system Saccharomyces cerevisiae (budding yeast). The main theme of this paper focuses on identifying the highly differentially expressed genes in cells deleted for Set4 (referred to as Set4 mutant dataset) compared to the wild-type yeast cells. The Set4 mutant data produce a spiky distribution on the log-fold changes of their expressions, and it is reasonably assumed that genes which are not highly differentially expressed come from a mixture of two normal distributions. We propose an adaptive local false discovery rate (FDR) procedure, which estimates the null distribution of the log-fold changes empirically. We numerically show that, unlike existing approaches, our proposed method controls FDR at the aimed level (0.05) and also has competitive power in finding differentially expressed genes. Finally, we apply our procedure to analyzing the Set4 mutantdataset.