Biology subjects

Engelhardt, B. E.

Publications and source records attributed to Engelhardt, B. E..

25 records · Page 2Linked to original sources

bioRxiv2021

Brain kernel: a new spatial covariance function for fMRI data

A key problem in functional magnetic resonance imaging (fMRI) is to estimate spatial activity patterns from noisy high-dimensional signals. Spatial smoothing provides one approach to regularizing such estimates. However, standard smoothing methods ignore the fact that correlations in neural activity may fall off at different rates in different brain areas, or exhibit discontinuities across anatomical or functional boundaries. Moreover, such methods do not exploit the fact that widely separated brain regions may exhibit strong correlations due to bilateral symmetry or the network organization of brain regions. To capture this non-stationary spatial correlation structure, we introduce the brain kernel, a continuous covariance function for whole-brain activity patterns. We define the brain kernel in terms of a continuous nonlinear mapping from 3D brain coordinates to a latent embedding space, parametrized with a Gaussian process (GP). The brain kernel specifies the prior covariance between voxels as a function of the distance between their locations in embedding space. The GP mapping warps the brain nonlinearly so that highly correlated voxels are close together in latent space, and uncorrelated voxels are far apart. We estimate the brain kernel using resting-state fMRI data, and we develop an exact, scalable inference method based on block coordinate descent to overcome the challenges of high dimensionality (10-100K voxels). Finally, we illustrate the brain kernels usefulness with applications to brain decoding and factor analysis with multiple task-based fMRI datasets.

neuroscience↗

bioRxiv2020

A self-exciting point process to study multi-cellular spatial signaling patterns

Multi-cellular organisms rely on spatial signaling among cells to drive their organization, development, and response to stimuli. Several models have been proposed to capture the behavior of spatial signaling in multi-cellular systems, but existing approaches fail to capture both the autonomous behavior of single cells and the interactions of a cell with its neighbors simultaneously. We propose a spatiotemporal model of dynamic cell signaling based on Hawkes processes--self-exciting point processes--that model the signaling processes within a cell and spatial couplings between cells. With this cellular point process (CPP) model, we capture both the single-cell protein bursting rate and the magnitude and duration of signaling between cells relative to spatial locations. Furthermore, our model captures tissues composed of heterogeneous cell types with different bursting rates and signaling behaviors across multiple signaling proteins. We apply our model to epithelial cell systems that exhibit a range of autonomous and spatial signaling behaviors basally and under pharmacological exposure. Our model identifies known drug-induced signaling deficits, characterizes differences in signaling across a wound front, and generalizes to multi-channel observations.

cell biology↗

bioRxiv2020

A Bayesian nonparametric semi-supervised model for integration of multiple single-cell experiments

Joint analysis of multiple single cell RNA-sequencing (scRNA-seq) data is confounded by technical batch effects across experiments, biological or environmental variability across cells, and different capture processes across sequencing platforms. Manifold alignment is a principled, effective tool for integrating multiple data sets and controlling for confounding factors. We demonstrate that the semi-supervised t-distributed Gaussian process latent variable model (sstGPLVM), which projects the data onto a mixture of fixed and latent dimensions, can learn a unified low-dimensional embedding for multiple single cell experiments with minimal assumptions. We show the efficacy of the model as compared with state-of-the-art methods for single cell data integration on simulated data, pancreas cells from four sequencing technologies, induced pluripotent stem cells from male and female donors, and mouse brain cells from both spatial seqFISH+ and traditional scRNA-seq. Code and data is available at https://github.com/architverma1/sc-manifold-alignment

bioinformatics↗

bioRxiv2019

Optimal gene selection for cell type discrimination in single cell analyses

Single-cell technologies characterize complex cell populations across multiple data modalities at un-precedented scale and resolution. Multi-omic data for single cell gene expression, in situ hybridization, or single cell chromatin states are increasingly available across diverse tissue types. When isolating specific cell types from a sample of disassociated cells or performing in situ sequencing in collections of heterogeneous cells, one challenging task is to select a small set of informative markers to identify and differentiate specific cell types or cell states as precisely as possible. Given single cell RNA-seq data and a set of cellular labels to discriminate, scGene-Fit selects gene transcript markers that jointly optimize cell label recovery using label-aware compressive classification methods, resulting in a substantially more robust and less redundant set of markers than existing methods. When applied to a data set given a hierarchy of cell type labels, the markers found by our method enable the recovery of the label hierarchy through a computationally efficient and principled optimization.

genomics↗

bioRxiv2019

Causal Network Inference from Gene Transcriptional Time Series Response to Glucocorticoids

Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately paving the way for regulatory network re-engineering. Network inference from transcriptional time series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance but additionally infers whether the causal effects are activating or inhibitory. We apply BETS to transcriptional time series data of 2, 768 differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2, 768 genes and 31, 945 directed edges (FDR [≤] 0.2). We validate inferred causal network edges using two external data sources: overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is freely available as an open source software package at https://github.com/lujonathanh/BETS.

bioinformatics↗

bioRxiv2019

netNMF-sc: Leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis

MotivationSingle-cell RNA-sequencing (scRNA-seq) enables high throughput measurement of RNA expression in individual cells. Due to technical limitations, scRNA-seq data often contain zero counts for many transcripts in individual cells. These zero counts, or dropout events, complicate the analysis of scRNA-seq data using standard analysis methods developed for bulk RNA-seq data. Current scRNA-seq analysis methods typically overcome dropout by combining information across cells, leveraging the observation that cells generally occupy a small number of RNA expression states. ResultsWe introduce netNMF-sc, an algorithm for scRNA-seq analysis that leverages information across both cells and genes. netNMF-sc combines network-regularized non-negative matrix factorization with a procedure for handling zero inflation in transcript count matrices. The matrix factorization results in a low-dimensional representation of the transcript count matrix, which imputes gene abundance for both zero and non-zero entries and can be used to cluster cells. The network regularization leverages prior knowledge of gene-gene interactions, encouraging pairs of genes with known interactions to be close in the low-dimensional representation. We show that netNMF-sc outperforms existing methods on simulated and real scRNA-seq data, with increasing advantage at higher dropout rates (e.g. above 60%). Furthermore, we show that the results from netNMF-sc - including estimation of gene-gene covariance - are robust to choice of network, with more representative networks leading to greater performance gains. AvailabilitynetNMF-sc is available at github.com/raphael-group/netNMF-sc. Contactbraphael@princeton.edu

genomics↗

bioRxiv2018

GT-TS: Experimental design for maximizing cell type discovery in single-cell data

We present the Good-Toulmin like estimator via Thompson sampling, a computational method for iterative experimental design in multi-tissue single-cell RNA-seq (scRNA-seq) data. Given a budget and modeling cell type information across tissues, GT-TS estimates how many cells are required for sampling from each tissue with the goal of maximizing cell type discovery across samples from multiple iterations. In both real and simulated data, we demonstrate the advantages of GT-TS in data collection planning when compared to a random strategy in the absence of experimental design.

genomics↗