Estimating and accounting for hidden variables is widely practiced as an important step in molecular quantitative trait locus (molecular QTL, henceforth “QTL”) analysis for improving the power of QTL identification. However, few benchmark studies have been performed to evaluate the efficacy of the various methods developed for this purpose.
Here we benchmark popular hidden variable inference methods including surrogate variable analysis (SVA), probabilistic estimation of expression residuals (PEER), and hidden covariates with prior (HCP) against principal component analysis (PCA)—a well-established dimension reduction and factor discovery method—via 362 synthetic and 110 real data sets. We show that PCA not only underlies the statistical methodology behind the popular methods but is also orders of magnitude faster, better-performing, and much easier to interpret and use.
To help researchers use PCA in their QTL analysis, we provide an R package along with a detailed guide, both of which are freely available at
Morphometric analysis is a common approach for comparing and categorizing botanical samples; however, completing a suite of analyses using existing tools may require a multi‐stage, multi‐program process. To facilitate streamlined analysis within a single program, Morphological Analysis of Size and Shape (
Exemplar samples were obtained from across a single tree (
Using
Genetic barcoding provides a high-throughput way to simultaneously track the frequencies of large numbers of competing and evolving microbial lineages. However making inferences about the nature of the evolution that is taking place remains a difficult task.
Here we describe an algorithm for the inference of fitness effects and establishment times of beneficial mutations from barcode sequencing data, which builds upon a Bayesian inference method by enforcing self-consistency between the population mean fitness and the individual effects of mutations within lineages. By testing our inference method on a simulation of 40,000 barcoded lineages evolving in serial batch culture, we find that this new method outperforms its predecessor, identifying more adaptive mutations and more accurately inferring their mutational parameters.
Our new algorithm is particularly suited to inference of mutational parameters when read depth is low. We have made Python code for our serial dilution evolution simulations, as well as both the old and new inference methods, available on GitHub (
Differential correlation networks are increasingly used to delineate changes in interactions among biomolecules. They characterize differences between omics networks under two different conditions, and can be used to delineate mechanisms of disease initiation and progression.
We present a new R package, , that facilitates the estimation and visualization of differential correlation networks using multiple correlation measures and inference methods. The software is implemented in , and , and is available at
Our software offers considerable flexibility by allowing the user to interact with the visualization and choose from different estimation methods and visualizations. It also allows the user to easily toggle between correlation networks for samples under one condition and differential correlations between samples under two conditions. Moreover, the software facilitates integrative analysis of cross-correlation networks between two omics data sets.
Developing appropriate computational tools to distill biological insights from large‐scale gene expression data has been an important part of systems biology. Considering that gene relationships may change or only exist in a subset of collected samples, biclustering that involves clustering both genes and samples has become in‐creasingly important, especially when the samples are pooled from a wide range of experimental conditions.
In this paper, we introduce a new biclustering algorithm to find subsets of genomic expression features (EFs) (e.g., genes, isoforms, exon inclusion) that show strong “group interactions” under certain subsets of samples. Group interactions are defined by strong partial correlations, or equivalently, conditional dependencies between EFs after removing the influences of a set of other functionally related EFs. Our new biclustering method, named SCCA‐BC, extends an existing method for group interaction inference, which is based on sparse canonical correlation analysis (SCCA) coupled with repeated random partitioning of the gene expression data set.
SCCA‐BC gives sensible results on real data sets and outperforms most existing methods in simulations. Software is available at
SCCA‐BC seems to work in numerous conditions and the results seem promising for future extensions. SCCA‐BC has the ability to find different types of bicluster patterns, and it is especially advantageous in identifying a bicluster whose elements share the same progressive and multivariate normal distribution with a dense covariance matrix.
We introduce the Weak-form Estimation of Nonlinear Dynamics (WENDy) method for estimating model parameters for non-linear systems of ODEs. Without relying on any numerical differential equation solvers, WENDy computes accurate estimates and is robust to large (biologically relevant) levels of measurement noise. For low dimensional systems with modest amounts of data, WENDy is competitive with conventional forward solver-based nonlinear least squares methods in terms of speed and accuracy. For both higher dimensional systems and stiff systems, WENDy is typically both faster (often by orders of magnitude) and more accurate than forward solver-based approaches. The core mathematical idea involves an efficient conversion of the strong form representation of a model to its weak form, and then solving a regression problem to perform parameter inference. The core statistical idea rests on the Errors-In-Variables framework, which necessitates the use of the iteratively reweighted least squares algorithm. Further improvements are obtained by using orthonormal test functions, created from a set of
