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Article ; Online: Tissue-adjusted pathway analysis of cancer (TPAC): A novel approach for quantifying tumor-specific gene set dysregulation relative to normal tissue.

Frost, H Robert

2024 Volume 20, Issue 1, Page(s) e1011717

Abstract: We describe a novel single sample gene set testing method for cancer transcriptomics data named tissue-adjusted pathway analysis of cancer (TPAC). The TPAC method leverages information about the normal tissue-specificity of human genes to compute a ... ...

Abstract	We describe a novel single sample gene set testing method for cancer transcriptomics data named tissue-adjusted pathway analysis of cancer (TPAC). The TPAC method leverages information about the normal tissue-specificity of human genes to compute a robust multivariate distance score that quantifies gene set dysregulation in each profiled tumor. Because the null distribution of the TPAC scores has an accurate gamma approximation, both population and sample-level inference is supported. As we demonstrate through an analysis of gene expression data for 21 solid human cancers from The Cancer Genome Atlas (TCGA) and associated normal tissue expression data from the Human Protein Atlas (HPA), TPAC gene set scores are more strongly associated with patient prognosis than the scores generated by existing single sample gene set testing methods.
MeSH term(s)	Humans ; Neoplasms/genetics ; Gene Expression Profiling/methods
Language	English
Publishing date	2024-01-11
Publishing country	United States
Document type	Journal Article
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1011717
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A generalized eigenvector centrality for multilayer networks with inter-layer constraints on adjacent node importance.

Frost, H Robert

Applied network science

2024 Volume 9, Issue 1, Page(s) 14

Abstract: We present a novel approach for computing a variant of eigenvector centrality for multilayer networks with inter-layer constraints on node importance. Specifically, we consider a multilayer network defined by multiple edge-weighted, potentially directed, ...

Abstract	We present a novel approach for computing a variant of eigenvector centrality for multilayer networks with inter-layer constraints on node importance. Specifically, we consider a multilayer network defined by multiple edge-weighted, potentially directed, graphs over the same set of nodes with each graph representing one layer of the network and no inter-layer edges. As in the standard eigenvector centrality construction, the importance of each node in a given layer is based on the weighted sum of the importance of adjacent nodes in that same layer. Unlike standard eigenvector centrality, we assume that the adjacency relationship and the importance of adjacent nodes may be based on distinct layers. Importantly, this type of centrality constraint is only partially supported by existing frameworks for multilayer eigenvector centrality that use edges between nodes in different layers to capture inter-layer dependencies. For our model, constrained, layer-specific eigenvector centrality values are defined by a system of independent eigenvalue problems and dependent pseudo-eigenvalue problems, whose solution can be efficiently realized using an interleaved power iteration algorithm. We refer to this model, and the associated algorithm, as the Constrained Multilayer Centrality (CMLC) method. The characteristics of this approach, and of standard techniques based on inter-layer edges, are demonstrated on both a simple multilayer network and on a range of random graph models. An R package implementing the CMLC method along with example vignettes is available at https://hrfrost.host.dartmouth.edu/CMLC/.
Language	English
Publishing date	2024-04-30
Publishing country	Switzerland
Document type	Journal Article
ISSN	2364-8228
ISSN (online)	2364-8228
DOI	10.1007/s41109-024-00620-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Reconstruction Set Test (RESET): A computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error.

Frost, H Robert

PLoS computational biology

2024 Volume 20, Issue 4, Page(s) e1012084

Abstract: We have developed a new, and analytically novel, single sample gene set testing method called Reconstruction Set Test (RESET). RESET quantifies gene set importance based on the ability of set genes to reconstruct values for all measured genes. RESET is ... ...

Abstract	We have developed a new, and analytically novel, single sample gene set testing method called Reconstruction Set Test (RESET). RESET quantifies gene set importance based on the ability of set genes to reconstruct values for all measured genes. RESET is realized using a computationally efficient randomized reduced rank reconstruction algorithm (available via the RESET R package on CRAN) that can effectively detect patterns of differential abundance and differential correlation for self-contained and competitive scenarios. As demonstrated using real and simulated scRNA-seq data, RESET provides superior performance at a lower computational cost relative to other single sample approaches.
MeSH term(s)	Algorithms ; Computational Biology/methods ; Humans ; Gene Expression Profiling/methods ; Computer Simulation
Language	English
Publishing date	2024-04-29
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1012084
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Reconstruction Set Test (RESET): a computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error.

Frost, H Robert

bioRxiv : the preprint server for biology

2023

Abstract: We have developed a new, and analytically novel, single sample gene set testing method called Reconstruction Set Test (RESET). RESET quantifies gene set importance at both the sample-level and for the entire dataset based on the ability of set genes to ... ...

Abstract	We have developed a new, and analytically novel, single sample gene set testing method called Reconstruction Set Test (RESET). RESET quantifies gene set importance at both the sample-level and for the entire dataset based on the ability of set genes to reconstruct values for all measured genes. RESET addresses four important limitations of current techniques: 1) existing single sample methods are designed to detect mean differences and struggle to identify differential correlation patterns, 2) computationally efficient techniques are self-contained methods and cannot directly detect competitive scenarios where set genes differ from non-set genes in the same sample, 3) the scores generated by current methods can only be accurately compared across samples for a single set and not between sets, and 4) the computational performance of even the fastest existing methods be significant on very large datasets. RESET is realized using a computationally efficient randomized reduced rank reconstruction algorithm (available via the RESET R package on CRAN) that can effectively detect patterns of differential abundance and differential correlation for self-contained and competitive scenarios. As demonstrated using real and simulated scRNA-seq data, RESET provides superior accuracy at a lower computational cost relative to other single sample approaches.
Language	English
Publishing date	2023-04-20
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.04.03.535366
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Analyzing cancer gene expression data through the lens of normal tissue-specificity.

Frost, H Robert

PLoS computational biology

2021 Volume 17, Issue 6, Page(s) e1009085

Abstract: The genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific ... ...

Abstract	The genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific functional impact. This tissue-specificity means that the biology of normal tissues carries important information regarding the pathophysiology of the associated cancers, information that can be leveraged to improve the power and accuracy of cancer genomic analyses. Research exploring the use of normal tissue data for the analysis of cancer genomics has primarily focused on the paired analysis of tumor and adjacent normal samples. Efforts to leverage the general characteristics of normal tissue for cancer analysis has received less attention with most investigations focusing on understanding the tissue-specific factors that lead to individual genomic alterations or dysregulated pathways within a single cancer type. To address this gap and support scenarios where adjacent normal tissue samples are not available, we explored the genome-wide association between the transcriptomes of 21 solid human cancers and their associated normal tissues as profiled in healthy individuals. While the average gene expression profiles of normal and cancerous tissue may appear distinct, with normal tissues more similar to other normal tissues than to the associated cancer types, when transformed into relative expression values, i.e., the ratio of expression in one tissue or cancer relative to the mean in other tissues or cancers, the close association between gene activity in normal tissues and related cancers is revealed. As we demonstrate through an analysis of tumor data from The Cancer Genome Atlas and normal tissue data from the Human Protein Atlas, this association between tissue-specific and cancer-specific expression values can be leveraged to improve the prognostic modeling of cancer, the comparative analysis of different cancer types, and the analysis of cancer and normal tissue pairs.
MeSH term(s)	Computational Biology ; Databases, Genetic/statistics & numerical data ; Female ; Gene Expression ; Gene Expression Profiling/statistics & numerical data ; Humans ; Male ; Neoplasms/genetics ; Organ Specificity/genetics ; Principal Component Analysis ; RNA-Seq ; Reference Values ; Survival Analysis
Language	English
Publishing date	2021-06-18
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1009085
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA).

Frost, H Robert

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America

2021 Volume 31, Issue 2, Page(s) 486–501

Abstract: We present a novel technique for sparse principal component analysis. This method, named Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA), is based on the formula for computing squared eigenvector loadings of a Hermitian matrix ... ...

Abstract	We present a novel technique for sparse principal component analysis. This method, named Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA), is based on the formula for computing squared eigenvector loadings of a Hermitian matrix from the eigenvalues of the full matrix and associated sub-matrices. We explore two versions of the EESPCA method: a version that uses a fixed threshold for inducing sparsity and a version that selects the threshold via cross-validation. Relative to the state-of-the-art sparse PCA methods of Witten et al., Yuan & Zhang and Tan et al., the fixed threshold EESPCA technique offers an order-of-magnitude improvement in computational speed, does not require estimation of tuning parameters via cross-validation, and can more accurately identify true zero principal component loadings across a range of data matrix sizes and covariance structures. Importantly, the EESPCA method achieves these benefits while maintaining out-of-sample reconstruction error and PC estimation error close to the lowest error generated by all evaluated approaches. EESPCA is a practical and effective technique for sparse PCA with particular relevance to computationally demanding statistical problems such as the analysis of high-dimensional data sets or application of statistical techniques like resampling that involve the repeated calculation of sparse PCs.
Language	English
Publishing date	2021-11-12
Publishing country	United States
Document type	Journal Article
ZDB-ID	2014382-5
ISSN	1537-2715 ; 1061-8600
ISSN (online)	1537-2715
ISSN	1061-8600
DOI	10.1080/10618600.2021.1987254
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: SPECK: an unsupervised learning approach for cell surface receptor abundance estimation for single-cell RNA-sequencing data.

Javaid, Azka / Frost, H Robert

Bioinformatics advances

2023 Volume 3, Issue 1, Page(s) vbad073

Abstract: Summary: The rapid development of single-cell transcriptomics has revolutionized the study of complex tissues. Single-cell RNA-sequencing (scRNA-seq) can profile tens-of-thousands of dissociated cells from a tissue sample, enabling researchers to ... ...

Abstract	Summary: The rapid development of single-cell transcriptomics has revolutionized the study of complex tissues. Single-cell RNA-sequencing (scRNA-seq) can profile tens-of-thousands of dissociated cells from a tissue sample, enabling researchers to identify cell types, phenotypes and interactions that control tissue structure and function. A key requirement of these applications is the accurate estimation of cell surface protein abundance. Although technologies to directly quantify surface proteins are available, these data are uncommon and limited to proteins with available antibodies. While supervised methods that are trained on Cellular Indexing of Transcriptomes and Epitopes by Sequencing data can provide the best performance, these training data are limited by available antibodies and may not exist for the tissue under investigation. In the absence of protein measurements, researchers must estimate receptor abundance from scRNA-seq data. Therefore, we developed a new unsupervised method for receptor abundance estimation using scRNA-seq data called SPECK (Surface Protein abundance Estimation using CKmeans-based clustered thresholding) and primarily evaluated its performance against unsupervised approaches for at least 25 human receptors and multiple tissue types. This analysis reveals that techniques based on a thresholded reduced rank reconstruction of scRNA-seq data are effective for receptor abundance estimation, with SPECK providing the best overall performance. Availability and implementation: SPECK is freely available at https://CRAN.R-project.org/package=SPECK. Supplementary information: Supplementary data are available at
Language	English
Publishing date	2023-06-13
Publishing country	England
Document type	Journal Article
ISSN	2635-0041
ISSN (online)	2635-0041
DOI	10.1093/bioadv/vbad073
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Iterative execution of discrete and inverse discrete Fourier transforms with applications for signal denoising via sparsification

Frost, H. Robert

2022

Abstract: We describe a family of iterative algorithms that involve the repeated execution of discrete and inverse discrete Fourier transforms. One interesting member of this family is motivated by the discrete Fourier transform uncertainty principle and involves ... ...

Abstract	We describe a family of iterative algorithms that involve the repeated execution of discrete and inverse discrete Fourier transforms. One interesting member of this family is motivated by the discrete Fourier transform uncertainty principle and involves the application of a sparsification operation to both the time domain and frequency domain data with convergence obtained when time domain sparsity hits a stable pattern. This sparsification variant has practical utility for signal denoising, in particular the recovery of a periodic spike signal in the presence of Gaussian noise. General convergence properties and denoising performance are demonstrated using simulation studies. We are not aware of prior work on such iterative Fourier transformation algorithms and have written this paper in part to solicit feedback from others in the field who may be familiar with similar techniques.
Keywords	Electrical Engineering and Systems Science - Signal Processing ; Mathematics - Numerical Analysis ; Statistics - Methodology
Subject code	518
Publishing date	2022-11-16
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: CAMML with the Integration of Marker Proteins (ChIMP).

Schiebout, Courtney / Frost, H Robert

Bioinformatics (Oxford, England)

2022 Volume 38, Issue 23, Page(s) 5206–5213

Abstract: Motivation: Cell typing is a critical task in the analysis of single-cell data, particularly when studying complex diseased tissues. Unfortunately, the sparsity and noise of single-cell data make accurate cell typing of individual cells difficult. To ... ...

Abstract	Motivation: Cell typing is a critical task in the analysis of single-cell data, particularly when studying complex diseased tissues. Unfortunately, the sparsity and noise of single-cell data make accurate cell typing of individual cells difficult. To address these challenges, we previously developed the CAMML method for multi-label cell typing of single-cell RNA-sequencing (scRNA-seq) data. CAMML uses weighted gene sets to score each profiled cell for multiple potential cell types. While CAMML outperforms other scRNA-seq cell typing techniques, it only leverages transcriptomic data so cannot take advantage of newer multi-omic single-cell assays that jointly profile gene expression and protein abundance (e.g. joint scRNA-seq/CITE-seq). Results: We developed the CAMML with the Integration of Marker Proteins (ChIMP) method to support multi-label cell typing of individual cells jointly profiled via scRNA-seq and CITE-seq. ChIMP combines cell type scores computed on scRNA-seq data via the CAMML approach with discretized CITE-seq measurements for cell type marker proteins. The multi-omic cell type scores generated by ChIMP allow researchers to more precisely and conservatively cell type joint scRNA-seq/CITE-seq data. Availability and implementation: An implementation of this work is available on CRAN at https://cran.r-project.org/web/packages/CAMML/. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Sequence Analysis, RNA/methods ; Single-Cell Analysis/methods ; Gene Expression Profiling/methods ; Software ; Transcriptome
Language	English
Publishing date	2022-10-07
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btac674
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Article ; Online: Single cell transcriptomics-level Cytokine Activity Prediction and Estimation (SCAPE)

Javaid, Azka / Frost, H. Robert

bioRxiv

Abstract: Cytokine interaction activity modeling is a pressing problem since uncontrolled cytokine influx is at fault in a variety of medical conditions, including viral infections like COVID19, and cancer. Accurate knowledge of cytokine activity levels can be ... ...

Abstract	Cytokine interaction activity modeling is a pressing problem since uncontrolled cytokine influx is at fault in a variety of medical conditions, including viral infections like COVID19, and cancer. Accurate knowledge of cytokine activity levels can be leveraged to provide tailored treatment recommendations based on individual patients9 transcriptomics data. Here, we describe a novel method named Single cell transcriptomics-level Cytokine Activity Prediction and Estimation (SCAPE) that can predict cell-level cytokine activity from scRNA-seq data. SCAPE generates activity estimates using cytokine-specific gene sets constructed using information from the CytoSig and Reactome databases and scored with a modified version of the Variance-adjusted Mahalanobis (VAM) method adjusted for negative weights. We validate SCAPE using both simulated and real single cell RNA-sequencing (scRNA-seq) data. For the simulation study, we perturb real scRNA-seq data to reflect the expected stimulation signature of up to 41 cytokines, including chemokines, interleukins and growth factors. For the real data evaluation, we use publicly accessible scRNA-seq data that captures cytokine stimulation and blockade experiment conditions and a COVID19 transcriptomics data. As demonstrated by these evaluations, our approach can accurately estimate cell-level cytokine activity from scRNA-seq data. Our model has the potential to be incorporated in clinical settings as a way to estimate cytokine signaling for different cell populations within an impacted tissue sample.
Keywords	covid19
Language	English
Publishing date	2023-10-17
Publisher	Cold Spring Harbor Laboratory
Document type	Article ; Online
DOI	10.1101/2023.10.17.562739
Database	COVID19

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