Biology subjects

Blumberg, A. J.

Publications and source records attributed to Blumberg, A. J..

6 recordsLinked to original sources

bioRxiv2024

Automated Cell Type Annotation with Reference Cluster Mapping

Single-cell RNA sequencing has transformed the field of cellular biology by providing unprecedented insights into cellular heterogeneity. However, characterizing scRNA-seq datasets remains a significant challenge. We introduce RefCM, a novel computational method that combines optimal transport and integer programming to enhance the annotation of scRNA clusters using established reference datasets. Our method produces highly accurate cross-technology, cross-tissue, and cross-species mappings while remaining tractable at atlas scale, outperforming existing methods across all these tasks. By providing precise annotations, RefCM can enable the discovery of new cell types, states, and relationships in single-cell transcriptomic data.

bioinformatics↗

bioRxiv2024

Statistical estimation of sparsity and efficiency for molecular codes

AO_SCPLOWBSTRACTC_SCPLOWA fundamental biological question is to understand how cell types and functions are determined by genomic and proteomic coding. A basic form of this question is to ask if small families of genes or proteins code for cell types. For example, it has been shown that the collection of homeodomain proteins can uniquely delineate all 118 neuron classes in the nematode C. elegans. However, unique characterization is neither robust nor rare. Our goal in this paper is to develop a rigorous methodology to characterize molecular codes. We show that in fact for information-theoretic reasons almost any sufficiently large collection of genes is able to disambiguate cell types, and that this property is not robust to noise. To quantify the discriminative properties of a molecular codebook in a more refined way, we develop new statistics - partition cardinality and partition entropy - borrowing ideas from coding theory. We prove these are robust to data perturbations, and then apply these in the C. elegans example and in cancer. In the worm, we show that the homeodomain transcription factor family is distinguished by coding for cell types sparsely and efficiently compared to a control of randomly selected family of genes. Furthermore, the resolution of cell type identities defined using molecular features increases as the worm embryo develops. In cancer, we perform a pan-cancer study where we use our statistics to quantify interpatient tumor heterogeneity and we identify the chromosome containing the HLA family as sparsely and efficiently coding for melanoma.

bioinformatics↗

bioRxiv2023

CellStitch: 3D Cellular Anisotropic Image Segmentation via Optimal Transport

BackgroundSpatial mapping of transcriptional states provides valuable biological insights into cellular functions and interactions in the context of the tissue. Accurate 3D cell segmentation is a critical step in the analysis of this data towards understanding diseases and normal development in situ. Current approaches designed to automate 3D segmentation include stitching masks along one dimension, training a 3D neural network architecture from scratch, and reconstructing a 3D volume from 2D segmentations on all dimensions. However, the applicability of existing methods is hampered by inaccurate segmentations along the non-stitching dimensions, the lack of high-quality diverse 3D training data, and inhomogeneity among different dimensions; as a result, they have not been widely used in practice. MethodsTo address these challenges, we formulate the problem of finding cell correspondence across layers with a novel optimal transport (OT) approach. We propose CellStitch, a flexible pipeline that segments cells from 3D images without requiring large amounts of 3D training data. We further extend our method to interpolate internal slices from highly anisotropic cell images to recover isotropic cell morphology. ResultsWe evaluated the performance of CellStitch through eight 3D plant microscopic datasets with diverse anisotropic levels and cell shapes. CellStitch substantially outperforms the state-of-the art methods on anisotropic images, and achieves comparable segmentation quality against competing methods in isotropic setting. We benchmarked and reported 3D segmentation results of all the methods with instance-level precision, recall and average precision (AP) metrics. ConclusionThe proposed OT-based 3D segmentation pipeline outperformed the existing state-of-the-art methods on different datasets with nonzero anisotropy, providing high fidelity recovery of 3D cell morphology from microscopic images.

bioinformatics↗

bioRxiv2018

Genomic architecture and introgression shape a butterfly radiation

We here pioneer a low-cost assembly strategy for 20 Heliconiini genomes to characterize the evolutionary history of the rapidly radiating genus Heliconius. A bifurcating tree provides a poor fit to the data, and we therefore explore a reticulate phylogeny for Heliconius. We probe the genomic architecture of gene flow, and develop a new method to distinguish incomplete lineage sorting from introgression. We find that most loci with non-canonical histories arose through introgression, and are strongly underrepresented in regions of low recombination and high gene density. This is expected if introgressed alleles are more likely to be purged in such regions due to tighter linkage with incompatibility loci. Finally, we identify a hitherto unrecognized inversion, and show it is a convergent structural rearrangement that captures a known color pattern switch locus within the genus. Our multi-genome assembly approach enables an improved understanding of adaptive radiation.

evolutionary biology↗

bioRxiv2018

Quasi-universality in single-cell sequencing data

The development of single-cell technologies provides the opportunity to identify new cellular states and reconstruct novel cell-to-cell relationships. Applications range from understanding the transcriptional and epigenetic processes involved in metazoan development to characterizing distinct cells types in heterogeneous populations like cancers or immune cells. However, analysis of the data is impeded by its unknown intrinsic biological and technical variability together with its sparseness; these factors complicate the identification of true biological signals amidst artifact and noise. Here we show that, across technologies, roughly 95% of the eigenvalues derived from each single-cell data set can be described by universal distributions predicted by Random Matrix Theory. Interestingly, 5% of the spectrum shows deviations from these distributions and present a phenomenon known as eigenvector localization, where information tightly concentrates in groups of cells. Some of the localized eigenvectors reflect underlying biological signal, and some are simply a consequence of the sparsity of single cell data; roughly 3% is artifactual. Based on the universal distributions and a technique for detecting sparsity induced localization, we present a strategy to identify the residual 2% of directions that encode biological information and thereby denoise single-cell data. We demonstrate the effectiveness of this approach by comparing with standard single-cell data analysis techniques in a variety of examples with marked cell populations.

systems biology↗

bioRxiv2018

Fast Estimation of Recombination Rates Using Topological Data Analysis

Accurate estimation of recombination rates is critical for studying the origins and maintenance of genetic diversity. Because the inference of recombination rates under a full evolutionary model is computationally expensive, an alternative approach using topological data analysis (TDA) has been proposed. Previous TDA methods used information contained solely in the first Betti number ({beta}1)of the cloud of genomes, which relates to the number of loops that can be detected within a genealogy. While these methods are considerably less computationally intensive than current biological model-based methods, these explorations have proven difficult to connect to the theory of the underlying biological process of recombination, and consequently have unpredictable behavior under different perturbations of the data. We introduce a new topological feature with a natural connection to coalescent models, which we call{psi} . We show that{psi} and {beta}1 are differentially affected by changes to the structure of the data and use them in conjunction to provide a robust, efficient, and accurate estimator of recombination rates, TREE. Compared to previous TDA methods, TREE more closely approximates of the results of commonly used model-based methods. These characteristics make TREE well suited as a first-pass estimator of recombination rate heterogeneity or hotspots throughout the genome. In addition, we present novel arguments relating {beta}1 to population genetic models; our work justifies the use of topological statistics as summaries of distributions of genome sequences and describes a new, unintuitive relationship between topological summaries of distance and the footprint of recombination on genome sequences.

evolutionary biology↗