Sequential Monte Carlo multiple testing

MERIT: Controlling Monte-Carlo error rate in large-scale Monte-Carlo hypothesis testing

The use of Monte-Carlo (MC)p $$ p $$ -values when testing the significance of a large number of hypotheses is now commonplace. In large-scale hypothesis testing, we will typically encounter at least somep $$ p $$ -values near the threshold of significance, which require a larger number of MC replicates thanp $$ p $$ -values that are far from the threshold. As a result, some incorrect conclusions can be reached due to MC error alone; for hypotheses near the threshold, even a very large number (eg,1 0 6 $$ 1{0}^6 $$ ) of MC replicates may not be enough to guarantee conclusions reached using MCp $$ p $$ -values. Gandy and Hahn (GH)6-8 have developed the only method that directly addresses this problem. They defined a Monte-Carlo error rate (MCER) to be the probability that any decisions on accepting or rejecting a hypothesis based on MCp $$ p $$ -values are different from decisions based on idealp $$ p $$ -values; their method then makes decisions by controlling the MCER. Unfortunately, the GH method is frequently very conservative, often making no rejections at all and leaving a large number of hypotheses "undecided". In this article, we propose MERIT, a method for large-scale MC hypothesis testing that also controls the MCER but is more statistically efficient than the GH method. Through extensive simulation studies, we demonstrate that MERIT controls the MCER while making more decisions that agree with the idealp $$ p $$ -values than GH does. We also illustrate our method by an analysis of gene expression data from a prostate cancer study.

Early-life stress and ovarian hormones alter transcriptional regulation in the nucleus accumbens resulting in sex-specific responses to cocaine

Early-life stress and ovarian hormones contribute to increased female vulnerability to cocaine addiction. Here, we reveal molecular substrates in the reward area, the nucleus accumbens, through which these female-specific factors affect immediate and conditioning responses to cocaine. We find shared involvement of X chromosome inactivation-related and estrogen signaling-related gene regulation in enhanced conditioning responses following early-life stress and during the low-estrogenic state in females. Low-estrogenic females respond to acute cocaine by opening neuronal chromatin enriched for the sites of ΔFosB, a transcription factor implicated in chronic cocaine response and addiction. Conversely, high-estrogenic females respond to cocaine by preferential chromatin closing, providing a mechanism for limiting cocaine-driven chromatin and synaptic plasticity. We find that physiological estrogen withdrawal, early-life stress, and absence of one X chromosome all nullify the protective effect of a high-estrogenic state on cocaine conditioning in females. Our findings offer a molecular framework to enable understanding of sex-specific neuronal mechanisms underlying cocaine use disorder.

Skin microbiome alterations in upper extremity secondary lymphedema

Lymphedema is a chronic condition that commonly occur from lymphatic injury following surgical resection of solid malignancies. While many studies have centered on the molecular and immune pathways that perpetuate lymphatic dysfunction, the role of the skin microbiome in lymphedema development remains unclear. In this study, skin swabs collected from normal and lymphedema forearms of 30 patients with unilateral upper extremity lymphedema were analyzed by 16S ribosomal RNA sequencing. Statistical models for microbiome data were utilized to correlate clinical variables with microbial profiles. Overall, 872 bacterial taxa were identified. There were no significant differences in microbial alpha diversity of the colonizing bacteria between normal and lymphedema skin samples (p = 0.25). Notably, for patients without a history of infection, a one-fold change in relative limb volume was significantly associated with a 0.58-unit increase in Bray-Curtis microbial distance between paired limbs (95%CI = 0.11,1.05, p = 0.02). Additionally, several genera, including Propionibacterium and Streptococcus, demonstrated high variability between paired samples. In summary, we demonstrate high compositional heterogeneity in the skin microbiome in upper extremity secondary lymphedema, supporting future studies into the role of host-microbe interactions on lymphedema pathophysiology.

LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control

Compositional analysis is based on the premise that a relatively small proportion of taxa are differentially abundant, while the ratios of the relative abundances of the remaining taxa remain unchanged. Most existing methods use log-transformed data, but log-transformation of data with pervasive zero counts is problematic, and these methods cannot always control the false discovery rate (FDR). Further, high-throughput microbiome data such as 16S amplicon or metagenomic sequencing are subject to experimental biases that are introduced in every step of the experimental workflow. McLaren et al. [eLife 8, e46923 (2019)] have recently proposed a model for how these biases affect relative abundance data. Motivated by this model, we show that the odds ratios in a logistic regression comparing counts in two taxa are invariant to experimental biases. With this motivation, we propose logistic compositional analysis (LOCOM), a robust logistic regression approach to compositional analysis, that does not require pseudocounts. Inference is based on permutation to account for overdispersion and small sample sizes. Traits can be either binary or continuous, and adjustment for confounders is supported. Our simulations indicate that LOCOM always preserved FDR and had much improved sensitivity over existing methods. In contrast, analysis of composition of microbiomes (ANCOM) and ANCOM with bias correction (ANCOM-BC)/ANOVA-Like Differential Expression tool (ALDEx2) had inflated FDR when the effect sizes were small and large, respectively. Only LOCOM was robust to experimental biases in every situation. The flexibility of our method for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. Our R package LOCOM is publicly available.

A new approach to testing mediation of the microbiome at both the community and individual taxon levels

MOTIVATION

Understanding whether and which microbes played a mediating role between an exposure and a disease outcome are essential for researchers to develop clinical interventions to treat the disease by modulating the microbes. Existing methods for mediation analysis of the microbiome are often limited to a global test of community-level mediation or selection of mediating microbes without control of the false discovery rate (FDR). Further, while the null hypothesis of no mediation at each microbe is a composite null that consists of three types of null, most existing methods treat the microbes as if they were all under the same type of null, leading to excessive false positive results.

RESULTS

We propose a new approach based on inverse regression that regresses the microbiome data at each taxon on the exposure and the exposure-adjusted outcome. Then, the P-values for testing the coefficients are used to test mediation at both the community and individual taxon levels. This approach fits nicely into our Linear Decomposition Model (LDM) framework, so our new method LDM-med, implemented in the LDM framework, enjoys all the features of the LDM, e.g. allowing an arbitrary number of taxa to be tested simultaneously, supporting continuous, discrete, or multivariate exposures and outcomes (including survival outcomes), and so on. Using extensive simulations, we showed that LDM-med always preserved the FDR of testing individual taxa and had adequate sensitivity; LDM-med always controlled the type I error of the global test and had compelling power over existing methods. The flexibility of LDM-med for a variety of mediation analyses is illustrated by an application to a murine microbiome dataset, which identified several plausible mediating taxa.

AVAILABILITY AND IMPLEMENTATION

Our new method has been added to our R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Signatures of copy number alterations in human cancer

Gains and losses of DNA are prevalent in cancer and emerge as a consequence of inter-related processes of replication stress, mitotic errors, spindle multipolarity and breakage-fusion-bridge cycles, among others, which may lead to chromosomal instability and aneuploidy1,2. These copy number alterations contribute to cancer initiation, progression and therapeutic resistance3-5. Here we present a conceptual framework to examine the patterns of copy number alterations in human cancer that is widely applicable to diverse data types, including whole-genome sequencing, whole-exome sequencing, reduced representation bisulfite sequencing, single-cell DNA sequencing and SNP6 microarray data. Deploying this framework to 9,873 cancers representing 33 human cancer types from The Cancer Genome Atlas6 revealed a set of 21 copy number signatures that explain the copy number patterns of 97% of samples. Seventeen copy number signatures were attributed to biological phenomena of whole-genome doubling, aneuploidy, loss of heterozygosity, homologous recombination deficiency, chromothripsis and haploidization. The aetiologies of four copy number signatures remain unexplained. Some cancer types harbour amplicon signatures associated with extrachromosomal DNA, disease-specific survival and proto-oncogene gains such as MDM2. In contrast to base-scale mutational signatures, no copy number signature was associated with many known exogenous cancer risk factors. Our results synthesize the global landscape of copy number alterations in human cancer by revealing a diversity of mutational processes that give rise to these alterations.

Integrative analysis of relative abundance data and presence-absence data of the microbiome using the LDM

SUMMARY

We previously developed the LDM for testing hypotheses about the microbiome that performs the test at both the community level and the individual taxon level. The LDM can be applied to relative abundance data and presence-absence data separately, which work well when associated taxa are abundant and rare, respectively. Here, we propose LDM-omni3 that combines LDM analyses at the relative abundance and presence-absence data scales, thereby offering optimal power across scenarios with different association mechanisms. The new LDM-omni3 test is available for the wide range of data types and analyses that are supported by the LDM.

AVAILABILITY AND IMPLEMENTATION

The LDM-omni3 test has been added to the R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Testing hypotheses about the microbiome using the linear decomposition model (LDM)

MOTIVATION

Methods for analyzing microbiome data generally fall into one of two groups: tests of the global hypothesis of any microbiome effect, which do not provide any information on the contribution of individual operational taxonomic units (OTUs); and tests for individual OTUs, which do not typically provide a global test of microbiome effect. Without a unified approach, the findings of a global test may be hard to resolve with the findings at the individual OTU level. Further, many tests of individual OTU effects do not preserve the false discovery rate (FDR).

RESULTS

We introduce the linear decomposition model (LDM), that provides a single analysis path that includes global tests of any effect of the microbiome, tests of the effects of individual OTUs while accounting for multiple testing by controlling the FDR, and a connection to distance-based ordination. The LDM accommodates both continuous and discrete variables (e.g. clinical outcomes, environmental factors) as well as interaction terms to be tested either singly or in combination, allows for adjustment of confounding covariates, and uses permutation-based P-values that can control for sample correlation. The LDM can also be applied to transformed data, and an 'omnibus' test can easily combine results from analyses conducted on different transformation scales. We also provide a new implementation of PERMANOVA based on our approach. For global testing, our simulations indicate the LDM provided correct type I error and can have comparable power to existing distance-based methods. For testing individual OTUs, our simulations indicate the LDM controlled the FDR well. In contrast, DESeq2 often had inflated FDR; MetagenomeSeq generally had the lowest sensitivity. The flexibility of the LDM for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. We also show that our implementation of PERMANOVA can outperform existing implementations.

AVAILABILITY AND IMPLEMENTATION

The R package LDM is available on GitHub at https://github.com/yijuanhu/LDM in formats appropriate for Macintosh or Windows.

CONTACT

yijuan.hu@emory.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Allele-specific control of replication timing and genome organization during development

DNA replication occurs in a defined temporal order known as the replication-timing (RT) program. RT is regulated during development in discrete chromosomal units, coordinated with transcriptional activity and 3D genome organization. Here, we derived distinct cell types from F1 hybrid musculus × castaneus mouse crosses and exploited the high single-nucleotide polymorphism (SNP) density to characterize allelic differences in RT (Repli-seq), genome organization (Hi-C and promoter-capture Hi-C), gene expression (total nuclear RNA-seq), and chromatin accessibility (ATAC-seq). We also present HARP, a new computational tool for sorting SNPs in phased genomes to efficiently measure allele-specific genome-wide data. Analysis of six different hybrid mESC clones with different genomes (C57BL/6, 129/sv, and CAST/Ei), parental configurations, and gender revealed significant RT asynchrony between alleles across ∼12% of the autosomal genome linked to subspecies genomes but not to parental origin, growth conditions, or gender. RT asynchrony in mESCs strongly correlated with changes in Hi-C compartments between alleles but not as strongly with SNP density, gene expression, imprinting, or chromatin accessibility. We then tracked mESC RT asynchronous regions during development by analyzing differentiated cell types, including extraembryonic endoderm stem (XEN) cells, four male and female primary mouse embryonic fibroblasts (MEFs), and neural precursor cells (NPCs) differentiated in vitro from mESCs with opposite parental configurations. We found that RT asynchrony and allelic discordance in Hi-C compartments seen in mESCs were largely lost in all differentiated cell types, accompanied by novel sites of allelic asynchrony at a considerably smaller proportion of the genome, suggesting that genome organization of homologs converges to similar folding patterns during cell fate commitment.

Covariation of Peptide Abundances Accurately Reflects Protein Concentration Differences

Most implementations of mass spectrometry-based proteomics involve enzymatic digestion of proteins, expanding the analysis to multiple proteolytic peptides for each protein. Currently, there is no consensus of how to summarize peptides' abundances to protein concentrations, and such efforts are complicated by the fact that error control normally is applied to the identification process, and do not directly control errors linking peptide abundance measures to protein concentration. Peptides resulting from suboptimal digestion or being partially modified are not representative of the protein concentration. Without a mechanism to remove such unrepresentative peptides, their abundance adversely impacts the estimation of their protein's concentration. Here, we present a relative quantification approach, Diffacto, that applies factor analysis to extract the covariation of peptides' abundances. The method enables a weighted geometrical average summarization and automatic elimination of incoherent peptides. We demonstrate, based on a set of controlled label-free experiments using standard mixtures of proteins, that the covariation structure extracted by the factor analysis accurately reflects protein concentrations. In the 1% peptide-spectrum match-level FDR data set, as many as 11% of the peptides have abundance differences incoherent with the other peptides attributed to the same protein. If not controlled, such contradicting peptide abundance have a severe impact on protein quantifications. When adding the quantities of each protein's three most abundant peptides, we note as many as 14% of the proteins being estimated as having a negative correlation with their actual concentration differences between samples. Diffacto reduced the amount of such obviously incorrectly quantified proteins to 1.6%. Furthermore, by analyzing clinical data sets from two breast cancer studies, our method revealed the persistent proteomic signatures linked to three subtypes of breast cancer. We conclude that Diffacto can facilitate the interpretation and enhance the utility of most types of proteomics data.

Faster permutation inference in brain imaging

Permutation tests are increasingly being used as a reliable method for inference in neuroimaging analysis. However, they are computationally intensive. For small, non-imaging datasets, recomputing a model thousands of times is seldom a problem, but for large, complex models this can be prohibitively slow, even with the availability of inexpensive computing power. Here we exploit properties of statistics used with the general linear model (GLM) and their distributions to obtain accelerations irrespective of generic software or hardware improvements. We compare the following approaches: (i) performing a small number of permutations; (ii) estimating the p-value as a parameter of a negative binomial distribution; (iii) fitting a generalised Pareto distribution to the tail of the permutation distribution; (iv) computing p-values based on the expected moments of the permutation distribution, approximated from a gamma distribution; (v) direct fitting of a gamma distribution to the empirical permutation distribution; and (vi) permuting a reduced number of voxels, with completion of the remainder using low rank matrix theory. Using synthetic data we assessed the different methods in terms of their error rates, power, agreement with a reference result, and the risk of taking a different decision regarding the rejection of the null hypotheses (known as the resampling risk). We also conducted a re-analysis of a voxel-based morphometry study as a real-data example. All methods yielded exact error rates. Likewise, power was similar across methods. Resampling risk was higher for methods (i), (iii) and (v). For comparable resampling risks, the method in which no permutations are done (iv) was the absolute fastest. All methods produced visually similar maps for the real data, with stronger effects being detected in the family-wise error rate corrected maps by (iii) and (v), and generally similar to the results seen in the reference set. Overall, for uncorrected p-values, method (iv) was found the best as long as symmetric errors can be assumed. In all other settings, including for familywise error corrected p-values, we recommend the tail approximation (iii). The methods considered are freely available in the tool PALM - Permutation Analysis of Linear Models.

Speciation with gene flow in equids despite extensive chromosomal plasticity

Horses, asses, and zebras belong to a single genus, Equus, which emerged 4.0-4.5 Mya. Although the equine fossil record represents a textbook example of evolution, the succession of events that gave rise to the diversity of species existing today remains unclear. Here we present six genomes from each living species of asses and zebras. This completes the set of genomes available for all extant species in the genus, which was hitherto represented only by the horse and the domestic donkey. In addition, we used a museum specimen to characterize the genome of the quagga zebra, which was driven to extinction in the early 1900s. We scan the genomes for lineage-specific adaptations and identify 48 genes that have evolved under positive selection and are involved in olfaction, immune response, development, locomotion, and behavior. Our extensive genome dataset reveals a highly dynamic demographic history with synchronous expansions and collapses on different continents during the last 400 ky after major climatic events. We show that the earliest speciation occurred with gene flow in Northern America, and that the ancestor of present-day asses and zebras dispersed into the Old World 2.1-3.4 Mya. Strikingly, we also find evidence for gene flow involving three contemporary equine species despite chromosomal numbers varying from 16 pairs to 31 pairs. These findings challenge the claim that the accumulation of chromosomal rearrangements drive complete reproductive isolation, and promote equids as a fundamental model for understanding the interplay between chromosomal structure, gene flow, and, ultimately, speciation.

Effects of sulforaphane and 3,3'-diindolylmethane on genome-wide promoter methylation in normal prostate epithelial cells and prostate cancer cells

Epigenetic changes, including aberrant DNA methylation, result in altered gene expression and play an important role in carcinogenesis. Phytochemicals such as sulforaphane (SFN) and 3,3'-diindolylmethane (DIM) are promising chemopreventive agents for the treatment of prostate cancer. Both have been shown to induce re-expression of genes, including tumor suppressor genes silenced in cancer cells, via modulation of epigenetic marks including DNA methylation. However, it remained unclear the effects SFN and DIM on DNA methylation at a genomic scale. The goal of this study was to determine the genome-wide effects of SFN and DIM on promoter methylation in normal prostate epithelial cells and prostate cancer cells. Both SFN and DIM treatment decreased DNA methyltransferase expression in normal prostate epithelial cells (PrEC), and androgen-dependent (LnCAP) and androgen-independent (PC3) prostate cancer cells. The effects of SFN and DIM on promoter methylation profiles in normal PrEC, LnCAP and PC3 prostate cancer cells were determined using methyl-DNA immunoprecipitation followed by genome-wide DNA methylation array. We showed widespread changes in promoter methylation patterns, including both increased and decreased methylation, in all three prostate cell lines in response to SFN or DIM treatments. In particular, SFN and DIM altered promoter methylation in distinct sets of genes in PrEC, LnCAP, and PC3 cells, but shared similar gene targets within a single cell line. We further showed that SFN and DIM reversed many of the cancer-associated methylation alterations, including aberrantly methylated genes that are dysregulated or are highly involved in cancer progression. Overall, our data suggested that both SFN and DIM are epigenetic modulators that have broad and complex effects on DNA methylation profiles in both normal and cancerous prostate epithelial cells. Results from our study may provide new insights into the epigenetic mechanisms by which SFN and DIM exert their cancer chemopreventive effects.

The Genomic HyperBrowser: an analysis web server for genome-scale data

The immense increase in availability of genomic scale datasets, such as those provided by the ENCODE and Roadmap Epigenomics projects, presents unprecedented opportunities for individual researchers to pose novel falsifiable biological questions. With this opportunity, however, researchers are faced with the challenge of how to best analyze and interpret their genome-scale datasets. A powerful way of representing genome-scale data is as feature-specific coordinates relative to reference genome assemblies, i.e. as genomic tracks. The Genomic HyperBrowser (http://hyperbrowser.uio.no) is an open-ended web server for the analysis of genomic track data. Through the provision of several highly customizable components for processing and statistical analysis of genomic tracks, the HyperBrowser opens for a range of genomic investigations, related to, e.g., gene regulation, disease association or epigenetic modifications of the genome.

Identification, characterization and distribution of transposable elements in the flax (Linum usitatissimum L.) genome

BACKGROUND

Flax (Linum usitatissimum L.) is an important crop for the production of bioproducts derived from its seed and stem fiber. Transposable elements (TEs) are widespread in plant genomes and are a key component of their evolution. The availability of a genome assembly of flax (Linum usitatissimum) affords new opportunities to explore the diversity of TEs and their relationship to genes and gene expression.

RESULTS

Four de novo repeat identification algorithms (PILER, RepeatScout, LTR_finder and LTR_STRUC) were applied to the flax genome assembly. The resulting library of flax repeats was combined with the RepBase Viridiplantae division and used with RepeatMasker to identify TEs coverage in the genome. LTR retrotransposons were the most abundant TEs (17.2% genome coverage), followed by Long Interspersed Nuclear Element (LINE) retrotransposons (2.10%) and Mutator DNA transposons (1.99%). Comparison of putative flax TEs to flax transcript databases indicated that TEs are not highly expressed in flax. However, the presence of recent insertions, defined by 100% intra-element LTR similarity, provided evidence for recent TE activity. Spatial analysis showed TE-rich regions, gene-rich regions as well as regions with similar genes and TE density. Monte Carlo simulations for the 71 largest scaffolds (≥ 1 Mb each) did not show any regional differences in the frequency of TE overlap with gene coding sequences. However, differences between TE superfamilies were found in their proximity to genes. Genes within TE-rich regions also appeared to have lower transcript expression, based on EST abundance. When LTR elements were compared, Copia showed more diversity, recent insertions and conserved domains than the Gypsy, demonstrating their importance in genome evolution.

CONCLUSIONS

The calculated 23.06% TE coverage of the flax WGS assembly is at the low end of the range of TE coverages reported in other eudicots, although this estimate does not include TEs likely found in unassembled repetitive regions of the genome. Since enrichment for TEs in genomic regions was associated with reduced expression of neighbouring genes, and many members of the Copia LTR superfamily are inserted close to coding regions, we suggest Copia elements have a greater influence on recent flax genome evolution while Gypsy elements have become residual and highly mutated.