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1. Benchmark Bird Surveys Help Quantify Counting Accuracy in a Citizen-Science Database

   https://doi.org/10.3389/fevo.2021.568278  

   Robinson, W. Douglas ; Hallman, Tyler A. ; Hutchinson, Rebecca A. ( February 2021 , Frontiers in ecology and evolution)

   null (Ed.)

   The growth of biodiversity data sets generated by citizen scientists continues to accelerate. The availability of such data has greatly expanded the scale of questions researchers can address. Yet, error, bias, and noise continue to be serious concerns for analysts, particularly when data being contributed to these giant online data sets are difficult to verify. Counts of birds contributed to eBird, the world’s largest biodiversity online database, present a potentially useful resource for tracking trends over time and space in species’ abundances. We quantified counting accuracy in a sample of 1,406 eBird checklists by comparing numbers contributed by birders (N = 246) who visited a popular birding location in Oregon, USA, with numbers generated by a professional ornithologist engaged in a long-term study creating benchmark (reference) measurements of daily bird counts. We focused on waterbirds, which are easily visible at this site. We evaluated potential predictors of count differences, including characteristics of contributed checklists, of each species, and of time of day and year. Count differences were biased toward undercounts, with more than 75% of counts being below the daily benchmark value. Median count discrepancies were −29.1% (range: 0 to −42.8%; N = 20 species). Model sets revealed an important influence of each species’ reference count, which varied seasonally as waterbird numbers fluctuated, and of percent of species known to be present each day that were included on each checklist. That is, checklists indicating a more thorough survey of the species richness at the site also had, on average, smaller count differences. However, even on checklists with the most thorough species lists, counts were biased low and exceptionally variable in their accuracy. To improve utility of such bird count data, we suggest three strategies to pursue in the future. (1) Assess additional options for analytically determining how to select checklists that include less biased count data, as well as exploring options for correcting bias during the analysis stage. (2) Add options for users to provide additional information that helps analysts choose checklists, such as an option for users to tag checklists where they focused on obtaining accurate counts. (3) Explore opportunities to effectively calibrate citizen-science bird count data by establishing a formalized network of marquis sites where dedicated observers regularly contribute carefully collected benchmark data. 

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2. BG2: Bayesian variable selection in generalized linear mixed models with nonlocal priors for non-Gaussian GWAS data

   https://doi.org/10.1186/s12859-023-05468-w  

   Xu, Shuangshuang ; Williams, Jacob ; Ferreira, Marco A. ( December 2023 , BMC Bioinformatics)

   Abstract Background Genome-wide association studies (GWASes) aim to identify single nucleotide polymorphisms (SNPs) associated with a given phenotype. A common approach for the analysis of GWAS is single marker analysis (SMA) based on linear mixed models (LMMs). However, LMM-based SMA usually yields a large number of false discoveries and cannot be directly applied to non-Gaussian phenotypes such as count data. Results We present a novel Bayesian method to find SNPs associated with non-Gaussian phenotypes. To that end, we use generalized linear mixed models (GLMMs) and, thus, call our method Bayesian GLMMs for GWAS (BG2). To deal with the high dimensionality of GWAS analysis, we propose novel nonlocal priors specifically tailored for GLMMs. In addition, we develop related fast approximate Bayesian computations. BG2 uses a two-step procedure: first, BG2 screens for candidate SNPs; second, BG2 performs model selection that considers all screened candidate SNPs as possible regressors. A simulation study shows favorable performance of BG2 when compared to GLMM-based SMA. We illustrate the usefulness and flexibility of BG2 with three case studies on cocaine dependence (binary data), alcohol consumption (count data), and number of root-like structures in a model plant (count data). 

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3. Acquisition of the counting principles during the subset‐knower stages: Insights from children's errors

   https://doi.org/10.1111/desc.13219  

   Cheung, Pierina ; Toomey, Mary ; Jiang, Yahao Harry ; Stoop, Tawni B. ; Shusterman, Anna ( January 2022 , Developmental Science)

   Studies on children's understanding of counting examine when and how children acquire the cardinal principle: the idea that the last word in a counted set reflects the cardinal value of the set. Using Wynn's (1990) Give‐N Task, researchers classify children who can count to generate large sets as having acquired the cardinal principle (cardinal‐principle‐knowers) and those who cannot as lacking knowledge of it (subset‐knowers). However, recent studies have provided a more nuanced view of number word acquisition. Here, we explore this view by examining the developmental progression of the counting principles with an aim to elucidate the gradual elements that lead to children successfully generating sets and being classified as CP‐knowers on the Give‐N Task. Specifically, we test the claim that subset‐knowers lack cardinal principle knowledge by separating children's understanding of the cardinal principle from their ability to apply and implement counting procedures. We also ask when knowledge of Gelman & Gallistel's (1978) other how‐to‐count principles emerge in development. We analyzed how often children violated the three how‐to‐count principles in a secondary analysis of Give‐N data (N = 86). We found that children already have knowledge of the cardinal principle prior to becoming CP‐knowers, and that understanding of the stable‐order and word‐object correspondence principles likely emerged earlier. These results suggest that gradual development may best characterize children's acquisition of the counting principles and that learning to coordinate all three principles represents an additional step beyond learning them individually.

    

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4. Tree-aggregated predictive modeling of microbiome data

   https://doi.org/10.1038/s41598-021-93645-3  

   Bien, Jacob ; Yan, Xiaohan ; Simpson, Léo ; Müller, Christian L. ( December 2021 , Scientific Reports)

   Abstract Modern high-throughput sequencing technologies provide low-cost microbiome survey data across all habitats of life at unprecedented scale. At the most granular level, the primary data consist of sparse counts of amplicon sequence variants or operational taxonomic units that are associated with taxonomic and phylogenetic group information. In this contribution, we leverage the hierarchical structure of amplicon data and propose a data-driven and scalable tree-guided aggregation framework to associate microbial subcompositions with response variables of interest. The excess number of zero or low count measurements at the read level forces traditional microbiome data analysis workflows to remove rare sequencing variants or group them by a fixed taxonomic rank, such as genus or phylum, or by phylogenetic similarity. By contrast, our framework, which we call  (ee-ggregation of ompositional data), learns data-adaptive taxon aggregation levels for predictive modeling, greatly reducing the need for user-defined aggregation in preprocessing while simultaneously integrating seamlessly into the compositional data analysis framework. We illustrate the versatility of our framework in the context of large-scale regression problems in human gut, soil, and marine microbial ecosystems. We posit that the inferred aggregation levels provide highly interpretable taxon groupings that can help microbiome researchers gain insights into the structure and functioning of the underlying ecosystem of interest. 

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5. Heritability estimation and differential analysis of count data with generalized linear mixed models in genomic sequencing studies

   https://doi.org/10.1093/bioinformatics/bty644  

   Sun, Shiquan ; Zhu, Jiaqiang ; Mozaffari, Sahar ; Ober, Carole ; Chen, Mengjie ; Zhou, Xiang ; Birol, ed., Inanc ( July 2018 , Bioinformatics)

   Genomic sequencing studies, including RNA sequencing and bisulfite sequencing studies, are becoming increasingly common and increasingly large. Large genomic sequencing studies open doors for accurate molecular trait heritability estimation and powerful differential analysis. Heritability estimation and differential analysis in sequencing studies requires the development of statistical methods that can properly account for the count nature of the sequencing data and that are computationally efficient for large datasets.

   Here, we develop such a method, PQLseq (Penalized Quasi-Likelihood for sequencing count data), to enable effective and efficient heritability estimation and differential analysis using the generalized linear mixed model framework. With extensive simulations and comparisons to previous methods, we show that PQLseq is the only method currently available that can produce unbiased heritability estimates for sequencing count data. In addition, we show that PQLseq is well suited for differential analysis in large sequencing studies, providing calibrated type I error control and more power compared to the standard linear mixed model methods. Finally, we apply PQLseq to perform gene expression heritability estimation and differential expression analysis in a large RNA sequencing study in the Hutterites.

   PQLseq is implemented as an R package with source code freely available at www.xzlab.org/software.html and https://cran.r-project.org/web/packages/PQLseq/index.html.

   Supplementary data are available at Bioinformatics online.

    

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