An official website of the United States government
Abstract Spatial transcriptomics (ST) technologies measure gene expression at thousands of locations within a two-dimensional tissue slice, enabling the study of spatial gene expression patterns. Spatial variation in gene expression is characterized byspatial gradients, or the collection of vector fields describing the direction and magnitude in which the expression of each gene increases. However, the few existing methods that learn spatial gradients from ST data either make restrictive and unrealistic assumptions on the structure of the spatial gradients or do not accurately model discrete transcript locations/counts. We introduce SLOPER (for Score-based Learning Of Poisson-modeled Expression Rates), a generative model for learning spatial gradients (vector fields) from ST data. SLOPER models the spatial distribution of mRNA transcripts with aninhomogeneous Poisson point process (IPPP)and usesscore matchingto learn spatial gradients for each gene. SLOPER utilizes the learned spatial gradients in a novel diffusion-based sampling approach to enhance the spatial coherence and specificity of the observed gene expression measurements. We demonstrate that the spatial gradients and enhanced gene expression representations learned by SLOPER leads to more accurate identification of tissue organization, spatially variable gene modules, and continuous axes of spatial variation (isodepth) compared to existing methods. Software availabilitySLOPER is available athttps://github.com/chitra-lab/SLOPER.
more »
« less
STalign: Alignment of spatial transcriptomics data using diffeomorphic metric mapping
Abstract Spatial transcriptomics (ST) technologies enable high throughput gene expression characterization within thin tissue sections. However, comparing spatial observations across sections, samples, and technologies remains challenging. To address this challenge, we develop STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping. We apply STalign to align ST datasets within and across technologies as well as to align ST datasets to a 3D common coordinate framework. We show that STalign achieves high gene expression and cell-type correspondence across matched spatial locations that is significantly improved over landmark-based affine alignments. Applying STalign to align ST datasets of the mouse brain to the 3D common coordinate framework from the Allen Brain Atlas, we highlight how STalign can be used to lift over brain region annotations and enable the interrogation of compositional heterogeneity across anatomical structures. STalign is available as an open-source Python toolkit athttps://github.com/JEFworks-Lab/STalignand as Supplementary Software with additional documentation and tutorials available athttps://jef.works/STalign.
more »
« less
- Award ID(s):
- 2047611
- PAR ID:
- 10512356
- Publisher / Repository:
- Nature Communications
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 14
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract BackgroundComputational cell type deconvolution enables the estimation of cell type abundance from bulk tissues and is important for understanding tissue microenviroment, especially in tumor tissues. With rapid development of deconvolution methods, many benchmarking studies have been published aiming for a comprehensive evaluation for these methods. Benchmarking studies rely on cell-type resolved single-cell RNA-seq data to create simulated pseudobulk datasets by adding individual cells-types in controlled proportions. ResultsIn our work, we show that the standard application of this approach, which uses randomly selected single cells, regardless of the intrinsic difference between them, generates synthetic bulk expression values that lack appropriate biological variance. We demonstrate why and how the current bulk simulation pipeline with random cells is unrealistic and propose a heterogeneous simulation strategy as a solution. The heterogeneously simulated bulk samples match up with the variance observed in real bulk datasets and therefore provide concrete benefits for benchmarking in several ways. We demonstrate that conceptual classes of deconvolution methods differ dramatically in their robustness to heterogeneity with reference-free methods performing particularly poorly. For regression-based methods, the heterogeneous simulation provides an explicit framework to disentangle the contributions of reference construction and regression methods to performance. Finally, we perform an extensive benchmark of diverse methods across eight different datasets and find BayesPrism and a hybrid MuSiC/CIBERSORTx approach to be the top performers. ConclusionsOur heterogeneous bulk simulation method and the entire benchmarking framework is implemented in a user friendly packagehttps://github.com/humengying0907/deconvBenchmarkingandhttps://doi.org/10.5281/zenodo.8206516, enabling further developments in deconvolution methods.more » « less
-
dadi-cli: Automated and distributed population genetic model inference from allele frequency spectraAbstract Summarydadi is a popular software package for inferring models of demographic history and natural selection from population genomic data. But using dadi requires Python scripting and manual parallelization of optimization jobs. We developed dadi-cli to simplify dadi usage and also enable straighforward distributed computing. Availability and Implementationdadi-cli is implemented in Python and released under the Apache License 2.0. The source code is available athttps://github.com/xin-huang/dadi-cli. dadi-cli can be installed via PyPI and conda, and is also available through Cacao on Jetstream2https://cacao.jetstream-cloud.org/.more » « less
-
Abstract Single-cell technologies can measure the expression of thousands of molecular features in individual cells undergoing dynamic biological processes. While examining cells along a computationally-ordered pseudotime trajectory can reveal how changes in gene or protein expression impact cell fate, identifying such dynamic features is challenging due to the inherent noise in single-cell data. Here, we present DELVE, an unsupervised feature selection method for identifying a representative subset of molecular features which robustly recapitulate cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effects of confounding sources of variation, and instead models cell states from dynamic gene or protein modules based on core regulatory complexes. Using simulations, single-cell RNA sequencing, and iterative immunofluorescence imaging data in the context of cell cycle and cellular differentiation, we demonstrate how DELVE selects features that better define cell-types and cell-type transitions. DELVE is available as an open-source python package:https://github.com/jranek/delve.more » « less
-
Abstract The DESI Milky Way Survey (MWS) will observe ≥8 million stars between 16 < r < 19 mag, supplemented by observations of brighter targets under poor observing conditions. The survey will permit an accurate determination of stellar kinematics and population gradients, characterize diffuse substructure in the thick disk and stellar halo, enable the discovery of extremely metal-poor stars and other rare stellar types, and improve constraints on the Galaxy’s 3D dark matter distribution from halo star kinematics. MWS will also enable a detailed characterization of the stellar populations within 100 pc of the Sun, including a complete census of white dwarfs. The target catalog from the preliminary selection described here is public (Available at https://data.desi.lbl.gov/public/ets/target/catalogs/and detailed at https://desidatamodel.readthedocs.io).more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1038/s41467-023-43915-7
