skip to main content


Search for: All records

Creators/Authors contains: "Hicks, Stephanie C."

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract Feature selection to identify spatially variable genes or other biologically informative genes is a key step during analyses of spatially-resolved transcriptomics data. Here, we propose nnSVG, a scalable approach to identify spatially variable genes based on nearest-neighbor Gaussian processes. Our method (i) identifies genes that vary in expression continuously across the entire tissue or within a priori defined spatial domains, (ii) uses gene-specific estimates of length scale parameters within the Gaussian process models, and (iii) scales linearly with the number of spatial locations. We demonstrate the performance of our method using experimental data from several technological platforms and simulations. A software implementation is available at https://bioconductor.org/packages/nnSVG . 
    more » « less
    Free, publicly-accessible full text available December 1, 2024
  2. Summary

    A standard unsupervised analysis is to cluster observations into discrete groups using a dissimilarity measure, such as Euclidean distance. If there does not exist a ground-truth label for each observation necessary for external validity metrics, then internal validity metrics, such as the tightness or separation of the clusters, are often used. However, the interpretation of these internal metrics can be problematic when using different dissimilarity measures as they have different magnitudes and ranges of values that they span. To address this problem, previous work introduced the “scale-agnostic” $G_{+}$ discordance metric; however, this internal metric is slow to calculate for large data. Furthermore, in the setting of unsupervised clustering with $k$ groups, we show that $G_{+}$ varies as a function of the proportion of observations assigned to each of the groups (or clusters), referred to as the group balance, which is an undesirable property. To address this problem, we propose a modification of $G_{+}$, referred to as $H_{+}$, and demonstrate that $H_{+}$ does not vary as a function of group balance using a simulation study and with public single-cell RNA-sequencing data. Finally, we provide scalable approaches to estimate $H_{+}$, which are available in the $\mathtt{fasthplus}$ R package.

     
    more » « less
  3. Abstract

    The performance of computational methods and software to identify differentially expressed features in single‐cell RNA‐sequencing (scRNA‐seq) has been shown to be influenced by several factors, including the choice of the normalization method used and the choice of the experimental platform (or library preparation protocol) to profile gene expression in individual cells. Currently, it is up to the practitioner to choose the most appropriate differential expression (DE) method out of over 100 DE tools available to date, each relying on their own assumptions to model scRNA‐seq expression features. To model the technological variability in cross‐platform scRNA‐seq data, here we propose to use Tweedie generalized linear models that can flexibly capture a large dynamic range of observed scRNA‐seq expression profiles across experimental platforms induced by platform‐ and gene‐specific statistical properties such as heavy tails, sparsity, and gene expression distributions. We also propose a zero‐inflated Tweedie model that allows zero probability mass to exceed a traditional Tweedie distribution to model zero‐inflated scRNA‐seq data with excessive zero counts. Using both synthetic and published plate‐ and droplet‐based scRNA‐seq datasets, we perform a systematic benchmark evaluation of more than 10 representative DE methods and demonstrate that our method (Tweedieverse) outperforms the state‐of‐the‐art DE approaches across experimental platforms in terms of statistical power and false discovery rate control. Our open‐source software (R/Bioconductor package) is available athttps://github.com/himelmallick/Tweedieverse.

     
    more » « less