Biology subjects

Geman, D.

Publications and source records attributed to Geman, D..

9 recordsLinked to original sources

bioRxiv2025

Mapping spatial gradients in spatial transcriptomics data with score matching

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 by spatial 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 an inhomogeneous Poisson point process (IPPP) and uses score matching to 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 at https://github.com/chitra-lab/SLOPER.

bioinformatics↗

bioRxiv2024

GeneCover: A Combinatorial Approach for Label-free Marker Gene Selection

The selection of marker gene panels is critical for capturing the cellular and spatial hetero-geneity in the expanding atlases of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data. Most current approaches to marker gene selection operate in a label-based framework, which is inherently limited by its dependency on predefined cell type labels or clustering results. In contrast, existing label-free methods often struggle to identify genes that characterize rare cell types or subtle spatial patterns, and they frequently fail to scale efficiently with large datasets. Here, we introduce geneCover, a label-free combinatorial method that selects an optimal panel of minimally redundant marker genes based on gene-gene correlations. Our method demonstrates excellent scalability to large datasets and identifies marker gene panels that capture distinct correlation structures across the transcriptome. This allows geneCover to distinguish cell states in various tissues of living organisms effectively, including those associated with rare or otherwise difficult-to-identify cell types. We evaluate the performance of geneCover across various scRNA-seq and spatial transcriptomics datasets, comparing it to other label-free algorithms to highlight its utility and potential in diverse biological contexts.

bioinformatics↗

bioRxiv2023

CellCover Defines Conserved Cell Types and Temporal Progression in scRNA-seq Data across Mammalian Neocortical Development

1Definition of cell classes across the tissues of living organisms is central in the analysis of growing atlases of single-cell RNA sequencing (scRNA-seq) data across biomedicine. Marker genes for cell classes are most often defined by differential expression (DE) methods that serially assess individual genes across landscapes of diverse cells. This serial approach has been extremely useful, but is limited because it ignores possible redundancy or complementarity across genes that can only be captured by analyzing multiple genes simultaneously. Interrogating binarized expression data, we aim to identify discriminating panels of genes that are specific to, not only enriched in, individual cell types. To efficiently explore the vast space of possible marker panels, leverage the large number of cells often sequenced, and overcome zero-inflation in scRNA-seq data, we propose viewing marker gene panel selection as a variation of the "minimal set-covering problem" in combinatorial optimization. Using scRNA-seq data from blood and brain tissue, we show that this new method, CellCover, performs as good or better than DE and other methods in defining cell-type discriminating gene panels, while reducing gene redundancy and capturing cell-class-specific signals that are distinct from those defined by DE methods. Transfer learning experiments across mouse, primate, and human data demonstrate that CellCover identifies markers of conserved cell classes in neocortical neurogenesis, as well as developmental progression in both progenitors and neurons. Exploring markers of human outer radial glia (oRG, or basal RG) across mammals, we show that transcriptomic elements of this key cell type in the expansion of the human cortex likely appeared in gliogenic precursors of the rodent before the full program emerged in neurogenic cells of the primate lineage. We have assembled the public datasets we use in this report within the NeMO Analytics multi-omic data exploration environment [1], where the expression of individual genes (NeMO: Individual genes in cortex and NeMO: Individual genes in blood) and marker gene panels (NeMO: Telley 3 CellCover Panels, NeMO: Telley 12 CellCover Panels, NeMO: Sorted Brain Cell CellCover Panels, and NeMO: Blood 34 CellCover Panels) can be freely explored without coding expertise. CellCover is available in CellCover R and CellCover Python. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=67 SRC="FIGDIR/small/535943v6_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@893301org.highwire.dtl.DTLVardef@173a6baorg.highwire.dtl.DTLVardef@1c70e7eorg.highwire.dtl.DTLVardef@1888ab3_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2022

Olfactory neuronal cells: biopsied surrogates for neurons in the brain

Biopsy is crucial in clinical medicine to obtain tissues and cells that directly reflect the pathological changes of each disease. However, the brain is an exception due to ethical and practical challenges. Nasal biopsy, which captures the olfactory neuronal epithelium, has been considered as an alternative method of obtaining neuronal cells from living patients. Multiple groups have enriched olfactory neuronal cells (ONCs) from biopsied nasal tissue. ONCs can be obtained from repeated biopsies in a longitudinal study, providing mechanistic insight associated with dynamic changes along the disease trajectory and treatment response. Nevertheless, molecular characterization of biopsied nasal cells/tissue has been insufficient. Taking advantage of recent advances in next-generation sequencing technologies at the single-cell resolution and related rich public databases, we aimed to define the neuronal characteristics, homogeneity, and utility of ONCs. We applied single-cell and bulk RNA sequencing for ONCs, analyzing and comparing the data with multiple public datasets. We observed that the molecular signatures of ONCs are similar to those of neurons, distinct from major glial cells. The signatures of ONCs resemble those of developing neurons and share features of excitatory neurons in the prefrontal and cingulate cortex. The high homogeneity of ONCs is advantageous in pharmacological, functional, and protein studies. Accordingly, we provide two proof-of-concept examples for functional and protein studies, solidifying the utility of ONCs in studying objective biomarkers and molecular mechanisms for brain disorders. The ONCs may also be useful in the studies for the olfactory epithelium impairment and the resultant mental dysfunction elicited by SARS-CoV-2. SIGNIFICANCE STATEMENTTo study dynamic changes and underlying mechanisms along disease trajectory and treatment response in neuropsychiatric disorders, olfactory neuronal cells (ONCs) enriched from biopsied nasal tissue may provide a crucial tool. Because ONCs can be obtained from repeated biopsies in a longitudinal study, this tool has been believed to be useful and complementary to postmortem brains and induced pluripotent stem cell-derived neurons. Nevertheless, molecular characterization of biopsied nasal cells/tissue has been insufficient, which hampers a broader use of this resource. Taking advantage of recent advances in next-generation sequencing technologies at the single-cell resolution and related rich public databases, the present study defines ONCs neuronal characteristics, homogeneity, and unique utility for the first time.

molecular biology↗

bioRxiv2021

Biological Constraints Can Improve Prediction inPrecision Oncology

Many gene signatures have been developed by applying machine learning (ML) on omics profiles, however, their clinical utility is often hindered by limited interpretability and unstable performance in different datasets. Here, we show the importance of embedding prior biological knowledge in the decision rules yielded by ML approaches to build robust classifiers. We tested this by applying different ML algorithms on gene expression data to predict three difficult cancer phenotypes: bladder cancer progression to muscle invasive disease; response to neoadjuvant chemotherapy in triple-negative breast cancer, and prostate cancer metastatic progression. We developed two sets of classifiers: mechanistic, by restricting the training process to features capturing a specific biological mechanism; and agnostic, in which the training didnt use any a priori biological information. Mechanistic models had a similar or better performance to their agnostic counterparts in the testing data, with enhanced stability, robustness, and interpretability. Our findings support the use of biological constraints to develop robust and interpretable gene signatures with high translational potential. MotivationOmics-based gene signatures often suffer from overfitting and reduced performance when tested on independent data. This usually results from the discrepancy between the high number of features compared to the much smaller number of samples used in the training process, which results in the machine learning algorithm perfectly fitting the training data with a subsequent deterioration in performance in independent cohorts. We introduce a mechanistic framework to mitigate overfitting and improve interpretability by constraining the training process to simple rank-based decision rules recapitulating relevant, cancer-related, biological mechanisms. Our approach aims at reducing the number of training variables to a pre-defined set of biologically important features in the form of gene pairs. The classification mechanism depends entirely on the relative ordering of these pairs, making it robust to data preprocessing techniques, improving the overall interpretability of the resulting models with significant translational implications. Most importantly, these pairs are configured in such a way that the decision rules resulting from the genes relative order embed and recapitulate specific biological mechanism, inherently enhancing the classifiers interpretability.

bioinformatics↗

bioRxiv2020

Longitudinal changes in brain metabolites in healthy subjects and patients with first episode psychosis (FEP): a 7-Tesla MRS study

Objective7 Tesla (T) longitudinal magnetic resonance spectroscopy (MRS) offers a precise measurment of metabolic levels in human brain via a non-invasive approach. Studying longitudinal changes in neurometabolites could help identify trait and state markers for diseases and understand inconsistent findings from different researchers due to differences in the age of study participants and duration of illness. This study is the first to report novel longitudinal patterns in young adulthood from both physiological and pathological viewpoints using 7T MRS. MethodsUtilizing a four-year longitudinal cohort with 38 first episode psychosis (FEP) patients (onset within 2 years) and 48 healthy controls (HC), the authors examined the annual percentage changes of 9 neurometabolites in 5 brain regions. ResultsBoth FEP patients and HC subjects were found to have significant longitudinal reductions in glutamate (Glu) in the anterior cingulate cortex (ACC). Only FEP patients were found to have a significant decrease over time in {gamma}-aminobutyric acid (GABA), N-acetyl aspartate (NAA), myo-inositol (mI), and total choline (tCho: phosphocholine plus glycerophosphocholine) in the ACC. Uniquely, glutathione (GSH) was found to have a near zero annual percentage change in both FEP patients and HC subjects in all 5 brain regions over a four-year timespan in young adulthood. ConclusionsGSH could be a trait marker for diagnostic applications at least in young adulthood. Glu, GABA, NAA, mI, and tCho in the ACC are associated with the patients status and could be state markers for mechanistic studies of psychotic disorders, including those for progressive pathological changes and medication effects in young adulthood.

neuroscience↗

bioRxiv2020

Efficient Representations of Tumor Diversity with Paired DNA-RNA Anomalies

Cancer cells display massive dysregulation of key regulatory pathways due to now well-catalogued mutations and other DNA-related aberrations. Moreover, enormous heterogeneity has been commonly observed in the identity, frequency and location of these aberrations across individuals with the same cancer type or subtype, and this variation naturally propagates to the transcriptome, resulting in myriad types of dysregulated gene expression programs. Many have argued that a more integrative and quantitative analysis of heterogeneity of DNA and RNA molecular profiles may be necessary for designing more systematic explorations of alternative therapies and improving predictive accuracy. We introduce a representation of multi-omics profiles which is sufficiently rich to account for observed heterogeneity and support the construction of quantitative, integrated, metrics of variation. Starting from the network of interactions existing in Reactome, we build a library of "paired DNA-RNA aberrations" that represent prototypical and recurrent patterns of dysregulation in cancer; each two-gene "Source-Target Pair" (STP) consists of a "source" regulatory gene and a "target" gene whose expression is plausibly "controlled" by the source gene. The STP is then "aberrant" in a joint DNA-RNA profile if the source gene is DNA-aberrant (e.g., mutated, deleted, or duplicated), and the downstream target gene is "RNA-aberrant", meaning its expression level is outside the normal, baseline range. With M STPs, each sample profile has exactly one of the 2M possible configurations. We concentrate on subsets of STPs, and the corresponding reduced configurations, by selecting tissue-dependent minimal coverings, defined as the smallest family of STPs with the property that every sample in the considered population displays at least one aberrant STP within that family. These minimal coverings can be computed with integer programming. Given such a covering, a natural measure of cross-sample diversity is the extent to which the particular aberrant STPs composing a covering vary from sample to sample; this variability is captured by the entropy of the distribution over configurations. We apply this program to data from TCGA for six distinct tumor types (breast, prostate, lung, colon, liver, and kidney cancer). This enables an efficient simplification of the complex landscape observed in cancer populations, resulting in the identification of novel signatures of molecular alterations which are not detected with frequency-based criteria. Estimates of cancer heterogeneity across tumor phenotypes reveals a stable pattern: entropy increases with disease severity. This framework is then well-suited to accommodate the expanding complexity of cancer genomes and epigenomes emerging from large consortia projects. Author SummaryA large variety of genomic and transcriptomic aberrations are observed in cancer cells, and their identity, location, and frequency can be highly indicative of the particular subtype or molecular phenotype, and thereby inform treatment options. However, elucidating this association between sets of aberrations and subtypes of cancer is severely impeded by considerable diversity in the set of aberrations across samples from the same population. Most attempts at analyzing tumor heterogeneity have dealt with either the genome or transcriptome in isolation. Here we present a novel, multi-omics approach for quantifying heterogeneity by determining a small set of paired DNA-RNA aberrations that incorporates potential downstream effects on gene expression. We apply integer programming to identify a small set of paired aberrations such that at least one among them is present in every sample of a given cancer population. The resulting "coverings" are analyzed for six cancer cohorts from the Cancer Genome Atlas, and facilitate introducing an information-theoretic measure of heterogeneity. Our results identify many known facets of tumorigenesis as well as suggest potential novel genes and interactions of interest. Data Availability StatementRNA-Seq data, somatic mutation data and copy number data for The Cancer Genome Atlas were obtained through the Xena Cancer Genome Browser database (https://xenabrowser.net) from individual cancer type cohorts. Computational functionality for the optimization procedure is provided at https://github.com/wikum/lpcover and the code for the analysis in the manuscript is provided at https://github.com/wikum/CoveringAnalysis. Processed data in the form of TAB delimited files, and selected tissue-level coverings (in excel format) are provided as additional supplementary material and are also available from the Marchionni laboratory website (http://marchionnilab.org/signatures.html)

genomics↗

bioRxiv2019

Divergence: An R Package for Divergence Analysis of Omics Data

Given the ever-increasing amount of high-dimensional and complex omics data becoming available, it is increasingly important to discover simple but effective methods of analysis. Divergence analysis transforms each entry of a high-dimensional omics profile into a digitized (binary or ternary) code based on the deviation of the entry from a given baseline population. This is a novel framework that is significantly different from existing omics data analysis methods: it allows digitization of continuous omics data at the univariate or multivariate level, facilitates sample level analysis, and is applicable on many different omics platforms. The divergence package, available on the R platform through the Bioconductor repository collection, provides easy-to-use functions for carrying out this transformation. Here we demonstrate how to use the package with data from the Cancer Genome Atlas.

bioinformatics↗

bioRxiv2016

Splice Expression Variation Analysis (SEVA) for Differential Gene Isoform Usage in Cancer

MotivationCurrent bioinformatics methods to detect changes in gene isoform usage in distinct phenotypes compare the relative expected isoform usage in phenotypes. These statistics model differences in isoform usage in normal tissues, which have stable regulation of gene splicing. Pathological conditions, such as cancer, can have broken regulation of splicing that increases the heterogeneity of the expression of splice variants. Inferring events with such differential heterogeneity in gene isoform usage requires new statistical approaches.\n\nResultsWe introduce Splice Expression Variability Analysis (SEVA) to model increased heterogeneity of splice variant usage between conditions (e.g., tumor and normal samples). SEVA uses a rank-based multivariate statistic that compares the variability of junction expression profiles within one condition to the variability within another. Simulated data show that SEVA is unique in modeling heterogeneity of gene isoform usage, and benchmark SEVAs performance against EBSeq, DiffSplice, and rMATS that model differential isoform usage instead of heterogeneity. We confirm the accuracy of SEVAin identifying known splice variants in head and neck cancer and perform cross-study validation of novel splice variants. A novel comparison of splice variant heterogeneity between subtypes of head and neck cancer demonstrated unanticipated similarity between the heterogeneity of gene isoform usage in HPV-positive and HPV-negative subtypes and anticipated increased heterogeneity among HPV-negative samples with mutations in genes that regulate the splice variant machinery.\n\nConclusionThese results show that SEVA accurately models differential heterogeneity of gene isoform usage from RNA-seq data.\n\nAvailabilitySEVA is implemented in the R/Bioconductor package GSReg.\n\nContactbahman@jhu.edu, favorov@sensi.org, ejfertig@jhmi.edu

genomics↗