11 papers · ranked by Valyu relevance
Matthew R. Whiteway, Daniel A. Butts
The activity of sensory cortical neurons is not only driven by external stimuli, but is also shaped by other sources of input to the cortex. Unlike external stimuli these other sources of input are challenging to experimentally control or even observe, and as a result contribute to variability of neuronal responses to…
Matthew Farrell, Stefano Recanatesi, R. Clay Reid, Stefan Mihalas + 1 more
Spectacular advances in imaging and data processing techniques are revealing a wealth of information about brain connectomes. This raises an exciting scientific opportunity: to infer the underlying circuit function from the structure of its connectivity. A potential roadblock, however, is that – even with well…
Conor Goold, Ruth C. Newberry
Studies of animal personality attempt to uncover underlying or ‘latent’ personality traits that explain broad patterns of behaviour, often by applying latent variable statistical models (e.g. factor analysis) to multivariate data sets. Two integral, but infrequently confirmed, assumptions of latent variable models in…
Basile Jumentier, Kevin Caye, Barbara Heude, Johanna Lepeule + 1 more
Association of phenotypes or exposures with genomic and epigenomic data faces important statistical challenges. One of these challenges is to remove variation due to unobserved confounding factors, such as individual ancestry or cell-type composition in tissues. This issue can be addressed with penalized latent factor…
Muhammad Ammar Malik, Tom Michoel
Random effect models are popular statistical models for detecting and correcting spurious sample correlations due to hidden confounders in genome-wide gene expression data. In applications where some confounding factors are known, estimating simultaneously the contribution of known and latent variance components in…
Bert van der Veen, Francis K.C. Hui, Knut A. Hovstad, Robert B. O’Hara
In community ecology, unconstrained ordination can be used to predict latent variables from a multivariate dataset, which generated the observed species composition. Latent variables can be understood as ecological gradients, which are represented as a function of measured predictors in constrained ordination, so that…
Pär Jonsson, Benny Björkblom, Elin Chorell, Tommy Olsson + 1 more
Multivariate projection methods are unique in being both multivariable by combining many variables into stronger predictive features (latent variables), and multivariate for being able to model systematic variation both related and orthogonal to an observed response. Orthogonal partial least squares (OPLS) is a…
Daniel Lazarev, Grant Chau, Alex Bloemendal, Claire Churchhouse + 1 more
Genome-wide association studies have revealed that the genetic architecture of most complex traits is characterized by a large number of distinct effects scattered across the genome. Functional enrichment analyses help provide some biological interpretation of associated variants but more work is needed to further…
Sutirtha Chakraborty
RNAseq technology has revolutionized the face of gene expression profiling by generating read count data measuring the transcript abundances for each queried gene. But on the other side, the underlying technical artefacts generate a wide variety of hidden effects that may potentially distort the primary signals of…
Changmin Yu, Maneesh Sahani, Máté Lengyel
Gaussian Process Factor Analysis (GPFA) is a powerful latent variable model for extracting low-dimensional manifolds underlying population neural activities. However, one limitation of standard GPFA models is that the number of latent factors needs to be pre-specified or selected through heuristic-based processes, and…
Ruofan Wang, Lei Fang, Yue Wang, Jin Jin
Leveraging observational data to understand the associations between risk factors and disease outcomes and conduct disease risk prediction is a common task in epidemiology. While traditional linear regression and other machine learning models have been extensively implemented for this task, the associations between…