A Critical Survey of Deconvolution Methods for Separating cell-types in Complex Tissues
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paper_title: On Semi-Blind Source Separation Using Spatial Constraints With Applications in EEG Analysis

paper_content:

Blind source separation (BSS) techniques, such as independent component analysis (ICA), are increasingly being used in biomedical signal processing applications, including the analysis of multichannel electroencephalogram (EEG) and magnetoencephalogram (MEG) signals. These methods estimate a set of sources from the observed data, which reflect the underlying physiological signal generating and mixing processes, noise and artifacts. In practice, BSS methods are often applied in the context of additional information and expectations regarding the spatial or temporal characteristics of some sources of interest, whose identification requires complicated post-hoc analysis or, more commonly, manual selection by human experts. An alternative would be to incorporate any available prior knowledge about the source signals or locations into a semi-blind source separation (SBSS) approach, effectively by imposing temporal or spatial constraints on the underlying source mixture model. This work is concerned with biomedical applications of SBSS using spatial constraints, particularly for artifact removal and source tracking in EEG analysis, and provides definitions of different types of spatial constraint along with general guidelines on how these can be implemented in conjunction with conventional BSS methods
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paper_title: Nonnegative Matrix Factorization for Spectral Data Analysis

paper_content:

Data analysis is pervasive throughout business, engineering and science. Very often the data to be analyzed is nonnegative, and it is often preferable to take this constraint into account in the analysis process. Here we are concerned with the application of analyzing data obtained using astronomical spectrometers, which provide spectral data, which is inherently nonnegative. The identification and classification of space objects that cannot be imaged in the normal way with telescopes is an important but difficult problem for tracking thousands of objects, including satellites, rocket bodies, debris, and asteroids, in orbit around the earth. In this paper, we develop an effective nonnegative matrix factorization algorithm with novel smoothness constraints for unmixing spectral reflectance data for space object identification and classification purposes. Promising numerical results are presented using laboratory and simulated datasets.
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paper_title: Convolutive Blind Source Separation Algorithms Applied to the Electrocardiogram of Atrial Fibrillation: Study of Performance

paper_content:

The analysis of the surface electrocardiogram (ECG) is the most extended noninvasive technique in medical diagnosis of atrial fibrillation (AF). In order to use the ECG as a tool for the analysis of AF, we need to separate the atrial activity (AA) from other cardioelectric signals. In this matter, statistical signal processing techniques, like blind source separation (BSS), are able to perform a multilead statistical analysis with the aim to obtain the AA. Linear BSS techniques can be divided in two groups depending on the mixing model: algorithms where instantaneous mixing of sources is assumed, and convolutive BSS (CBSS) algorithms. In this work, a comparison of performance between one relevant CBSS algorithm, namely Infomax, and one of the most effective independent component analysis (ICA) algorithms, namely FastICA, is developed. To carry out the study, pseudoreal AF ECGs have been synthesized by adding fibrillation activity to normal sinus rhythm. The algorithm performances are expressed by two indexes: the signal to interference ratio (SIRAA) and the cross-correlation (RAA) between the original and the estimated AA. Results empirically prove that the instantaneous mixing model is the one that obtains the best results in the AA extraction, given that the mean SIRAA obtained by the FastICA algorithm (37.6 plusmn 17.0 dB) is higher than the main SIRAA obtained by Infomax (28.5 plusmn 14.2 dB). Also the RAA obtained by FastICA (0.92 plusmn 0.13) is higher than the one obtained by Infomax (0.78 plusmn 0.16).
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paper_title: Blind source separation and analysis of multispectral astronomical images

paper_content:

Celestial sources are studied by means of mul- tispectral analysis, for which a pixel value can be consid- ered as a mixture of dierent sources. We show that the application of blind source separation methods can dis- play interesting features, which can help astronomers to improve their description of celestial sources. Experiments were carried out on Hubble Space Telescope images of the galaxy 3C 120. Dierent separation methods were applied, either based on the source correlation, or on non-Gaussian probability density functions. Each obtained source set was quantied by the mutual information between the sources. Dierent algorithms give quite similar decompo- sitions which mainly correspond to real phenomena.
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paper_title: Source Separation and Higher-Order Causal Analysis of MEG and EEG

paper_content:

Separation of the sources and analysis of their connectivity have been an important topic in EEG/MEG analysis. To solve this problem in an automatic manner, we propose a two-layer model, in which the sources are conditionally uncorrelated from each other, but not independent; the dependence is caused by the causality in their time-varying variances (envelopes). The model is identified in two steps. We first propose a new source separation technique which takes into account the autocorrelations (which may be time-varying) and time-varying variances of the sources. The causality in the envelopes is then discovered by exploiting a special kind of multivariate GARCH (generalized autoregressive conditional heteroscedasticity) model. The resulting causal diagram gives the effective connectivity between the separated sources; in our experimental results on MEG data, sources with similar functions are grouped together, with negative influences between groups, and the groups are connected via some interesting sources.
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paper_title: A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing

paper_content:

Blind hyperspectral unmixing (HU), also known as unsupervised HU, is one of the most prominent research topics in signal processing (SP) for hyperspectral remote sensing [1], [2]. Blind HU aims at identifying materials present in a captured scene, as well as their compositions, by using high spectral resolution of hyperspectral images. It is a blind source separation (BSS) problem from a SP viewpoint. Research on this topic started in the 1990s in geoscience and remote sensing [3]-[7], enabled by technological advances in hyperspectral sensing at the time. In recent years, blind HU has attracted much interest from other fields such as SP, machine learning, and optimization, and the subsequent cross-disciplinary research activities have made blind HU a vibrant topic. The resulting impact is not just on remote sensing - blind HU has provided a unique problem scenario that inspired researchers from different fields to devise novel blind SP methods. In fact, one may say that blind HU has established a new branch of BSS approaches not seen in classical BSS studies. In particular, the convex geometry concepts - discovered by early remote sensing researchers through empirical observations [3]-[7] and refined by later research - are elegant and very different from statistical independence-based BSS approaches established in the SP field. Moreover, the latest research on blind HU is rapidly adopting advanced techniques, such as those in sparse SP and optimization. The present development of blind HU seems to be converging to a point where the lines between remote sensing-originated ideas and advanced SP and optimization concepts are no longer clear, and insights from both sides would be used to establish better methods.
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paper_title: Hyperspectral data deconvolution for galaxy kinematics with MCMC

paper_content:

The development of hyperspectral instruments requires new methods for data processing and analysis. We focus in this work on the estimation of the flux, position and width of spectral lines from astrophysical data, necessary to study the kinematics of galaxies. Classically used estimation methods, such as the methodof moments and the maximum likelihood (ML), neglect the effect of the spatial Point Spread Function of the data acquisition system. The aim of this paper is to propose 3D deconvolution methods: the first is based on the ML estimator; a second introduces weak priors on the parameters and computes the posterior mean estimator with a Monte-Carlo Markov Chain, using a hybrid Gibbs/Metropolis-Hastings algorithm. The methods are compared on simulated hyperspectral data and the latter is shown to give the best results, in particular in the case of a low signal to noise ratio.
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paper_title: Successive Nonnegative Projection Algorithm for Robust Nonnegative Blind Source Separation

paper_content:

In this paper, we propose a new fast and robust recursive algorithm for near-separable nonnegative matrix factorization, a particular nonnegative blind source separation problem. This algorithm, which we refer to as the successive nonnegative projection algorithm (SNPA), is closely related to the popular successive projection algorithm (SPA), but takes advantage of the nonnegativity constraint in the decomposition. We prove that SNPA is more robust than SPA and can be applied to a broader class of nonnegative matrices. This is illustrated on some synthetic data sets, and on a real-world hyperspectral image.
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paper_title: source separation of multichannel neuromagnetic responses

paper_content:

Magnetoencephalography (MEG) is a functional brain imaging technique with millisecond temporal resolution and millimeter spatial sensitivity. The high temporal resolution of MEG compared to fMRI and PET (milliseconds vs. seconds and tens of seconds) makes it ideal for measuring the precise time of neuronal responses, thereby o!ering a powerful tool for studying temporal dynamics. We applied blind-source separation (BSS) to continuous 122-channel human magnetoencephalographic data from two subjects and "ve tasks. We demonstrate that without using any domain-speci"c knowledge and without making the common assumption of single- or multiple-current dipole sources, BSS is capable of separating non-neuronal noise sources from neuronal responses and also of separating neuronal responses from di!erent sensory modalities, and from di!erent processing stages within a given modality. ( 2000 Elsevier Science B.V. All rights reserved.
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paper_title: Multi-channel audio source separation using multiple deformed references

paper_content:

We present a general multi-channel source separation framework where additional audio references are available for one (or more) source(s) of a given mixture. Each audio reference is another mixture which is supposed to contain at least one source similar to one of the target sources. Deformations between the sources of interest and their references are modeled in a linear manner using a generic formulation. This is done by adding transformation matrices to an excitation-filter model, hence affecting different axes, namely frequency, dictionary component or time. A nonnegative matrix co-factorization algorithm and a generalized expectation-maximization algorithm are used to estimate the parameters of the model. Different model parameterizations and different combinations of algorithms are tested on music plus voice mixtures guided by music and/or voice references and on professionally-produced music recordings guided by cover references. Our algorithms improve the signal-to-distortion ratio (SDR) of the sources with the lowest intensity by 9 to 15 decibels (dB) with respect to original mixtures.
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paper_title: On Semi-Blind Source Separation Using Spatial Constraints With Applications in EEG Analysis

paper_content:

Blind source separation (BSS) techniques, such as independent component analysis (ICA), are increasingly being used in biomedical signal processing applications, including the analysis of multichannel electroencephalogram (EEG) and magnetoencephalogram (MEG) signals. These methods estimate a set of sources from the observed data, which reflect the underlying physiological signal generating and mixing processes, noise and artifacts. In practice, BSS methods are often applied in the context of additional information and expectations regarding the spatial or temporal characteristics of some sources of interest, whose identification requires complicated post-hoc analysis or, more commonly, manual selection by human experts. An alternative would be to incorporate any available prior knowledge about the source signals or locations into a semi-blind source separation (SBSS) approach, effectively by imposing temporal or spatial constraints on the underlying source mixture model. This work is concerned with biomedical applications of SBSS using spatial constraints, particularly for artifact removal and source tracking in EEG analysis, and provides definitions of different types of spatial constraint along with general guidelines on how these can be implemented in conjunction with conventional BSS methods
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paper_title: Computational Deconvolution: Extracting Cell Type-Specific Information from Heterogeneous Samples

paper_content:

The quanta unit of the immune system is the cell, yet analyzed samples are often heterogeneous with respect to cell subsets which can mislead result interpretation. Experimentally, researchers face a difficult choice whether to profile heterogeneous samples with the ensuing confounding effects, or a priori focus on a few cell subsets of interest, potentially limiting new discoveries. An attractive alternative solution is to extract cell subset-specific information directly from heterogeneous samples via computational deconvolution techniques, thereby capturing both cell-centered and whole system level context. Such approaches are capable of unraveling novel biology, undetectable otherwise. Here we review the present state of available deconvolution techniques, their advantages and limitations, with a focus on blood expression data and immunological studies in general.
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paper_title: Robust enumeration of cell subsets from tissue expression profiles

paper_content:

A computational method to identify cell types within a complex tissue, based on analysis of gene expression profiles, is described in this paper.
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paper_title: Cell population-specific expression analysis of human cerebellum

paper_content:

BackgroundInterpreting gene expression profiles obtained from heterogeneous samples can be difficult because bulk gene expression measures are not resolved to individual cell populations. We have recently devised Population-Specific Expression Analysis (PSEA), a statistical method that identifies individual cell types expressing genes of interest and achieves quantitative estimates of cell type-specific expression levels. This procedure makes use of marker gene expression and circumvents the need for additional experimental information like tissue composition.ResultsTo systematically assess the performance of statistical deconvolution, we applied PSEA to gene expression profiles from cerebellum tissue samples and compared with parallel, experimental separation methods. Owing to the particular histological organization of the cerebellum, we could obtain cellular expression data from in situ hybridization and laser-capture microdissection experiments and successfully validated computational predictions made with PSEA. Upon statistical deconvolution of whole tissue samples, we identified a set of transcripts showing age-related expression changes in the astrocyte population.ConclusionsPSEA can predict cell-type specific expression levels from tissues homogenates on a genome-wide scale. It thus represents a computational alternative to experimental separation methods and allowed us to identify age-related expression changes in the astrocytes of the cerebellum. These molecular changes might underlie important physiological modifications previously observed in the aging brain.
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paper_title: Multi-channel audio source separation using multiple deformed references

paper_content:

We present a general multi-channel source separation framework where additional audio references are available for one (or more) source(s) of a given mixture. Each audio reference is another mixture which is supposed to contain at least one source similar to one of the target sources. Deformations between the sources of interest and their references are modeled in a linear manner using a generic formulation. This is done by adding transformation matrices to an excitation-filter model, hence affecting different axes, namely frequency, dictionary component or time. A nonnegative matrix co-factorization algorithm and a generalized expectation-maximization algorithm are used to estimate the parameters of the model. Different model parameterizations and different combinations of algorithms are tested on music plus voice mixtures guided by music and/or voice references and on professionally-produced music recordings guided by cover references. Our algorithms improve the signal-to-distortion ratio (SDR) of the sources with the lowest intensity by 9 to 15 decibels (dB) with respect to original mixtures.
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paper_title: Algorithms for nonnegative matrix and tensor factorizations : a unified view based on block coordinate descent framework

paper_content:

We review algorithms developed for nonnegative matrix factorization (NMF) and nonnegative tensor factorization (NTF) from a unified view based on the block coordinate descent (BCD) framework. NMF and NTF are low-rank approximation methods for matrices and tensors in which the low-rank factors are constrained to have only nonnegative elements. The nonnegativity constraints have been shown to enable natural interpretations and allow better solutions in numerous applications including text analysis, computer vision, and bioinformatics. However, the computation of NMF and NTF remains challenging and expensive due the constraints. Numerous algorithmic approaches have been proposed to efficiently compute NMF and NTF. The BCD framework in constrained non-linear optimization readily explains the theoretical convergence properties of several efficient NMF and NTF algorithms, which are consistent with experimental observations reported in literature. In addition, we discuss algorithms that do not fit in the BCD framework contrasting them from those based on the BCD framework. With insights acquired from the unified perspective, we also propose efficient algorithms for updating NMF when there is a small change in the reduced dimension or in the data. The effectiveness of the proposed updating algorithms are validated experimentally with synthetic and real-world data sets.
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paper_title: Robust Linear and Support Vector Regression

paper_content:

The robust Huber M-estimator, a differentiable cost function that is quadratic for small errors and linear otherwise, is modeled exactly, in the original primal space of the problem, by an easily solvable simple convex quadratic program for both linear and nonlinear support vector estimators. Previous models were significantly more complex or formulated in the dual space and most involved specialized numerical algorithms for solving the robust Huber linear estimator. Numerical test comparisons with these algorithms indicate the computational effectiveness of the new quadratic programming model for both linear and nonlinear support vector problems. Results are shown on problems with as many as 20000 data points, with considerably faster running times on larger problems.
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paper_title: Robust enumeration of cell subsets from tissue expression profiles

paper_content:

A computational method to identify cell types within a complex tissue, based on analysis of gene expression profiles, is described in this paper.
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paper_title: Statistical learning theory

paper_content:

A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
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paper_title: A Self-Directed Method for Cell-Type Identification and Separation of Gene Expression Microarrays

paper_content:

Gene expression analysis is generally performed on heterogeneous tissue samples consisting of multiple cell types. Current methods developed to separate heterogeneous gene expression rely on prior knowledge of the cell-type composition and/or signatures--these are not available in most public datasets. We present a novel method to identify the cell-type composition, signatures and proportions per sample without need for a-priori information. The method was successfully tested on controlled and semi-controlled datasets and performed as accurately as current methods that do require additional information. As such, this method enables the analysis of cell-type specific gene expression using existing large pools of publically available microarray datasets.
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paper_title: Digital sorting of complex tissues for cell type-specific gene expression profiles

paper_content:

BackgroundCellular heterogeneity is present in almost all gene expression profiles. However, transcriptome analysis of tissue specimens often ignores the cellular heterogeneity present in these samples. Standard deconvolution algorithms require prior knowledge of the cell type frequencies within a tissue or their in vitro expression profiles. Furthermore, these algorithms tend to report biased estimations.ResultsHere, we describe a Digital Sorting Algorithm (DSA) for extracting cell-type specific gene expression profiles from mixed tissue samples that is unbiased and does not require prior knowledge of cell type frequencies.ConclusionsThe results suggest that DSA is a specific and sensitivity algorithm in gene expression profile deconvolution and will be useful in studying individual cell types of complex tissues.
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paper_title: Nonnegative Matrix Factorization for Spectral Data Analysis

paper_content:

Data analysis is pervasive throughout business, engineering and science. Very often the data to be analyzed is nonnegative, and it is often preferable to take this constraint into account in the analysis process. Here we are concerned with the application of analyzing data obtained using astronomical spectrometers, which provide spectral data, which is inherently nonnegative. The identification and classification of space objects that cannot be imaged in the normal way with telescopes is an important but difficult problem for tracking thousands of objects, including satellites, rocket bodies, debris, and asteroids, in orbit around the earth. In this paper, we develop an effective nonnegative matrix factorization algorithm with novel smoothness constraints for unmixing spectral reflectance data for space object identification and classification purposes. Promising numerical results are presented using laboratory and simulated datasets.
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paper_title: MMAD: microarray microdissection with analysis of differences is a computational tool for deconvoluting cell type-specific contributions from tissue samples

paper_content:

Background: One of the significant obstacles in the development of clinically relevant microarray-derived biomarkers and classifiers is tissue heterogeneity. Physical cell separation techniques, such as cell sorting and laser-capture microdissection, can enrich samples for cell types of interest, but are costly, labor intensive and can limit investigation of important interactions between different cell types. ::: ::: Results: We developed a new computational approach, called microarray microdissection with analysis of differences (MMAD), which performs microdissection in silico. Notably, MMAD (i) allows for simultaneous estimation of cell fractions and gene expression profiles of contributing cell types, (ii) adjusts for microarray normalization bias, (iii) uses the corrected Akaike information criterion during model optimization to minimize overfitting and (iv) provides mechanisms for comparing gene expression and cell fractions between samples in different classes. Computational microdissection of simulated and experimental tissue mixture datasets showed tight correlations between predicted and measured gene expression of pure tissues as well as tight correlations between reported and estimated cell fraction for each of the individual cell types. In simulation studies, MMAD showed superior ability to detect differentially expressed genes in mixed tissue samples when compared with standard metrics, including both significance analysis of microarrays and cell type-specific significance analysis of microarrays. ::: ::: Conclusions: We have developed a new computational tool called MMAD, which is capable of performing robust tissue microdissection in silico, and which can improve the detection of differentially expressed genes. MMAD software as implemented in MATLAB is publically available for download at http://sourceforge.net/projects/mmad/. ::: ::: Contact: moc.liamg@renbeil.divad ::: ::: Supplementary Information: Supplementary data are available at Bioinformatics online.
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paper_title: Separation of samples into their constituents using gene expression data

paper_content:

Gene expression measurements are a powerful tool in molecular biology, but when applied to heterogeneous samples containing more than one cellular type the results are difficult to interpret. We present here a new approach to this problem allowing to deduce the gene expression profile of the various cellular types contained in a set of samples directly from the measurements taken on the whole sample.
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paper_title: Blind source separation and analysis of multispectral astronomical images

paper_content:

Celestial sources are studied by means of mul- tispectral analysis, for which a pixel value can be consid- ered as a mixture of dierent sources. We show that the application of blind source separation methods can dis- play interesting features, which can help astronomers to improve their description of celestial sources. Experiments were carried out on Hubble Space Telescope images of the galaxy 3C 120. Dierent separation methods were applied, either based on the source correlation, or on non-Gaussian probability density functions. Each obtained source set was quantied by the mutual information between the sources. Dierent algorithms give quite similar decompo- sitions which mainly correspond to real phenomena.
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paper_title: Deconvolution of Blood Microarray Data Identifies Cellular Activation Patterns in Systemic Lupus Erythematosus

paper_content:

Systemic Lupus Erythematosus (SLE) is a systemic autoimmune disease with a complex spectrum of cellular and molecular characteristics including several dramatic changes in the populations of peripheral leukocytes. These changes include general leukopenia, activation of B and T cells, and maturation of granulocytes. The manifestation of SLE in peripheral blood is central to the disease but is incompletely understood. A technique for rigorously characterizing changes in mixed populations of cells, microarray expression deconvolution, has been applied to several areas of biology but not to SLE or to blood. Here we demonstrate that microarray expression deconvolution accurately quantifies the constituents of real blood samples and mixtures of immune-derived cell lines. We characterize a broad spectrum of peripheral leukocyte cell types and states in SLE to uncover novel patterns including: specific activation of NK and T helper lymphocytes, relationships of these patterns to each other, and correlations to clinical variables and measures. The expansion and activation of monocytes, NK cells, and T helper cells in SLE at least partly underlie this disease's prominent interferon signature. These and other patterns of leukocyte dynamics uncovered here correlate with disease severity and treatment, suggest potential new treatments, and extend our understanding of lupus pathology as a complex autoimmune disease involving many arms of the immune system.
---
paper_title: Robust enumeration of cell subsets from tissue expression profiles

paper_content:

A computational method to identify cell types within a complex tissue, based on analysis of gene expression profiles, is described in this paper.
---
paper_title: Algorithms for nonnegative matrix and tensor factorizations : a unified view based on block coordinate descent framework

paper_content:

We review algorithms developed for nonnegative matrix factorization (NMF) and nonnegative tensor factorization (NTF) from a unified view based on the block coordinate descent (BCD) framework. NMF and NTF are low-rank approximation methods for matrices and tensors in which the low-rank factors are constrained to have only nonnegative elements. The nonnegativity constraints have been shown to enable natural interpretations and allow better solutions in numerous applications including text analysis, computer vision, and bioinformatics. However, the computation of NMF and NTF remains challenging and expensive due the constraints. Numerous algorithmic approaches have been proposed to efficiently compute NMF and NTF. The BCD framework in constrained non-linear optimization readily explains the theoretical convergence properties of several efficient NMF and NTF algorithms, which are consistent with experimental observations reported in literature. In addition, we discuss algorithms that do not fit in the BCD framework contrasting them from those based on the BCD framework. With insights acquired from the unified perspective, we also propose efficient algorithms for updating NMF when there is a small change in the reduced dimension or in the data. The effectiveness of the proposed updating algorithms are validated experimentally with synthetic and real-world data sets.
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paper_title: Probabilistic analysis of gene expression measurements from heterogeneous tissues

paper_content:

Motivation: Tissue heterogeneity, arising from multiple cell types, is a major confounding factor in experiments that focus on studying cell types, e.g. their expression profiles, in isolation. Although sample heterogeneity can be addressed by manual microdissection, prior to conducting experiments, computational treatment on heterogeneous measurements have become a reliable alternative to perform this microdissection in silico. Favoring computation over manual purification has its advantages, such as time consumption, measuring responses of multiple cell types simultaneously, keeping samples intact of external perturbations and unaltered yield of molecular content. ::: ::: Results: We formalize a probabilistic model, DSection, and show with simulations as well as with real microarray data that DSection attains increased modeling accuracy in terms of (i) estimating cell-type proportions of heterogeneous tissue samples, (ii) estimating replication variance and (iii) identifying differential expression across cell types under various experimental conditions. As our reference we use the corresponding linear regression model, which mirrors the performance of the majority of current non-probabilistic modeling approaches. ::: ::: Availability and Software: All codes are written in Matlab, and are freely available upon request as well as at the project web page http://www.cs.tut.fi/~erkkila2/. Furthermore, a web-application for DSection exists at http://informatics.systemsbiology.net/DSection. ::: ::: Contact:timo.p.erkkila@tut.fi; harri.lahdesmaki@tut.fi
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paper_title: Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach

paper_content:

BackgroundFor heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or in-silico deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues.ResultsExperimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach.Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available.ConclusionsThe deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in realistically noisy conditions and with moderate sample sizes.
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paper_title: Computational purification of individual tumor gene expression profiles leads to significant improvements in prognostic prediction

paper_content:

Tumor heterogeneity is a limiting factor in cancer treatment and in the discovery of biomarkers to personalize it. We describe a computational purification tool, ISOpure, which directly addresses the effects of variable contamination by normal tissue in clinical tumor specimens. ISOpure uses a set of tumor expression profiles and a panel of healthy tissue expression profiles to generate a purified cancer profile for each tumor sample, and an estimate of the proportion of RNA originating from cancerous cells. Applying ISOpure before identifying gene signatures leads to significant improvements in the prediction of prognosis and other clinical variables in lung and prostate cancer.
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paper_title: PERT: A Method for Expression Deconvolution of Human Blood Samples from Varied Microenvironmental and Developmental Conditions

paper_content:

The cellular composition of heterogeneous samples can be predicted using an expression deconvolution algorithm to decompose their gene expression profiles based on pre-defined, reference gene expression profiles of the constituent populations in these samples. However, the expression profiles of the actual constituent populations are often perturbed from those of the reference profiles due to gene expression changes in cells associated with microenvironmental or developmental effects. Existing deconvolution algorithms do not account for these changes and give incorrect results when benchmarked against those measured by well-established flow cytometry, even after batch correction was applied. We introduce PERT, a new probabilistic expression deconvolution method that detects and accounts for a shared, multiplicative perturbation in the reference profiles when performing expression deconvolution. We applied PERT and three other state-of-the-art expression deconvolution methods to predict cell frequencies within heterogeneous human blood samples that were collected under several conditions (uncultured mono-nucleated and lineage-depleted cells, and culture-derived lineage-depleted cells). Only PERT's predicted proportions of the constituent populations matched those assigned by flow cytometry. Genes associated with cell cycle processes were highly enriched among those with the largest predicted expression changes between the cultured and uncultured conditions. We anticipate that PERT will be widely applicable to expression deconvolution strategies that use profiles from reference populations that vary from the corresponding constituent populations in cellular state but not cellular phenotypic identity.
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paper_title: Defining cell-type specificity at the transcriptional level in human disease.

paper_content:

Cell-lineage-specific transcripts are essential for differentiated tissue function, implicated in hereditary organ failure, and mediate acquired chronic diseases. However, experimental identification of cell-lineage-specific genes in a genome-scale manner is infeasible for most solid human tissues. We developed the first genome-scale method to identify genes with cell-lineage-specific expression, even in lineages not separable by experimental microdissection. Our machine-learning-based approach leverages high-throughput data from tissue homogenates in a novel iterative statistical framework. We applied this method to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary and most acquired glomerular kidney disease. In a systematic evaluation of our predictions by immunohistochemistry, our in silico approach was significantly more accurate (65% accuracy in human) than predictions based on direct measurement of in vivo fluorescence-tagged murine podocytes (23%). Our method identified genes implicated as causal in hereditary glomerular disease and involved in molecular pathways of acquired and chronic renal diseases. Furthermore, based on expression analysis of human kidney disease biopsies, we demonstrated that expression of the podocyte genes identified by our approach is significantly related to the degree of renal impairment in patients. Our approach is broadly applicable to define lineage specificity in both cell physiology and human disease contexts. We provide a user-friendly website that enables researchers to apply this method to any cell-lineage or tissue of interest. Identified cell-lineage-specific transcripts are expected to play essential tissue-specific roles in organogenesis and disease and can provide starting points for the development of organ-specific diagnostics and therapies.
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paper_title: Optimal Deconvolution of Transcriptional Profiling Data Using Quadratic Programming with Application to Complex Clinical Blood Samples

paper_content:

Large-scale molecular profiling technologies have assisted the identification of disease biomarkers and facilitated the basic understanding of cellular processes. However, samples collected from human subjects in clinical trials possess a level of complexity, arising from multiple cell types, that can obfuscate the analysis of data derived from them. Failure to identify, quantify, and incorporate sources of heterogeneity into an analysis can have widespread and detrimental effects on subsequent statistical studies.We describe an approach that builds upon a linear latent variable model, in which expression levels from mixed cell populations are modeled as the weighted average of expression from different cell types. We solve these equations using quadratic programming, which efficiently identifies the globally optimal solution while preserving non-negativity of the fraction of the cells. We applied our method to various existing platforms to estimate proportions of different pure cell or tissue types and gene expression profilings of distinct phenotypes, with a focus on complex samples collected in clinical trials. We tested our methods on several well controlled benchmark data sets with known mixing fractions of pure cell or tissue types and mRNA expression profiling data from samples collected in a clinical trial. Accurate agreement between predicted and actual mixing fractions was observed. In addition, our method was able to predict mixing fractions for more than ten species of circulating cells and to provide accurate estimates for relatively rare cell types (<10% total population). Furthermore, accurate changes in leukocyte trafficking associated with Fingolomid (FTY720) treatment were identified that were consistent with previous results generated by both cell counts and flow cytometry. These data suggest that our method can solve one of the open questions regarding the analysis of complex transcriptional data: namely, how to identify the optimal mixing fractions in a given experiment.
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paper_title: Population-specific expression analysis (PSEA) reveals molecular changes in diseased brain

paper_content:

Human diseases are often accompanied by histological changes that confound interpretation of molecular analyses and identification of disease-related effects. We developed population-specific expression analysis (PSEA), a computational method of analyzing gene expression in samples of varying composition that can improve analyses of quantitative molecular data in many biological contexts. PSEA of brains from individuals with Huntington's disease revealed myelin-related abnormalities that were undetected using standard differential expression analysis.
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paper_title: Deconvolution of Blood Microarray Data Identifies Cellular Activation Patterns in Systemic Lupus Erythematosus

paper_content:

Systemic Lupus Erythematosus (SLE) is a systemic autoimmune disease with a complex spectrum of cellular and molecular characteristics including several dramatic changes in the populations of peripheral leukocytes. These changes include general leukopenia, activation of B and T cells, and maturation of granulocytes. The manifestation of SLE in peripheral blood is central to the disease but is incompletely understood. A technique for rigorously characterizing changes in mixed populations of cells, microarray expression deconvolution, has been applied to several areas of biology but not to SLE or to blood. Here we demonstrate that microarray expression deconvolution accurately quantifies the constituents of real blood samples and mixtures of immune-derived cell lines. We characterize a broad spectrum of peripheral leukocyte cell types and states in SLE to uncover novel patterns including: specific activation of NK and T helper lymphocytes, relationships of these patterns to each other, and correlations to clinical variables and measures. The expansion and activation of monocytes, NK cells, and T helper cells in SLE at least partly underlie this disease's prominent interferon signature. These and other patterns of leukocyte dynamics uncovered here correlate with disease severity and treatment, suggest potential new treatments, and extend our understanding of lupus pathology as a complex autoimmune disease involving many arms of the immune system.
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paper_title: CellMix: a comprehensive toolbox for gene expression deconvolution

paper_content:

Summary: Gene expression data are typically generated from heterogeneous biological samples that are composed of multiple cell or tissue types, in varying proportions, each contributing to global gene expression. This heterogeneity is a major confounder in standard analysis such as differential expression analysis, where differences in the relative proportions of the constituent cells may prevent or bias the detection of cell-specific differences. Computational deconvolution of global gene expression is an appealing alternative to costly physical sample separation techniques, and enables a more detailed analysis of the underlying biological processes, at the cell type level. To facilitate and popularise the application of such methods, we developed CellMix, an R package that incorporates most state of the art deconvolution methods, into an intuitive and extendible framework, providing a single entry point to explore, assess and disentangle gene expression data from heterogeneous samples. Availability and Implementation: The CellMix package builds upon R/BioConductor and is available from http://web.cbio.uct.ac. za/ renaud/CRAN/web/CellMix. It is currently being submitted to BioConductor. The package’s vignettes notably contain additional information, examples and references. Contact: renaud@cbio.uct.ac.za 1 GENE EXPRESSION DECONVOLUTION The vast majority of gene expression data are generated from biological samples that are composed of multiple cell or tissue types that contribute to different extents to the global gene expression, according to their relative proportions. Heterogeneity in sample composition is commonly acknowledged as a major confounder in classical gene expression analysis like differential expression analysis, specially in clinical studies (Zhao and Simon, 2010). In this context, being able to disentangle the effects due to cell-specific expression and/or varying proportions provides finer insights into the biological processes of interest, by enabling the data to be explored at the cell type level. Gene expression deconvolution receives constant interest in bioinformatics research, with new methodologies published regularly (Zhao and Simon, 2010). While all methods apply to ∗to whom correspondence should be addressed global expression data, they differ in the type of auxiliary data they required, such as cell proportion measurements/estimates, cellspecific signatures or sets of marker genes. Having a standardised and unified interface for running a variety of deconvolution methods that can adapt to most common data settings, would therefore be very useful, and help popularise computational deconvolution. In order to facilitate the application and development of gene expression deconvolution methods, we developed an R package called CellMix, whose principal objectives are to provide a) implementations of some common methods; b) easy access to real auxiliary and benchmark data, and especially marker gene lists; c) utilities for assessing results and developing new methods. This paper briefly describes the main features of the CellMix package, and illustrates its capability with some concrete examples. More examples, as well as thorough documentation, references and implementation details are available in the package’s vignettes. 2 THE CELLMIX PACKAGE: OVERVIEW The CellMix package builds upon the Bioconductor project (Gentleman et al., 2004) and the NMF package (Gaujoux and Seoighe, 2010), to provide a flexible general framework for gene expression deconvolution methods. It defines a rich programming interface around three internal extendible registries dedicated to deconvolution methods, marker gene lists and benchmark datasets, respectively. 2.1 Deconvolution methods CellMix provides access to a range of 7 gene expression deconvolution methods, in such a way that they can easily be applied to commonly available data, via a unique interface function called ged. In particular, we implemented a default method selection scheme, which chooses a sensible deconvolution method based on the type of input and auxiliary data that are provided (See section Algorithms on the package’s webpage for details on each available method). 2.2 Cell signatures and marker gene sets In the context of gene expression deconvolution and sample heterogeneity in general, marker genes constitute a critical asset. For example, they can provide cell-specific signals that can be used to estimate cell-specific signatures and/or cell proportions, or detect cell type-related differential expression (Gaujoux and Seoighe, 2011; Kuhn et al., 2011; Bolen et al., 2011). The CellMix package includes a set of 8 marker gene lists, compiled from previous studies and public databases, and provides many convenient filtering or plotting functions for such type of data. Moreover, it implements 1 Associate Editor: Dr. Ziv Bar-Joseph © The Author (2013). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com Bioinformatics Advance Access published July 3, 2013 at N aonal U niersity of Irand, G lw ay on July 4, 2013 httpioinform atics.oxjournals.org/ D ow nladed from a very flexible general pipeline to convert gene identifiers, including across platform or species, which greatly simplifies the use of both marker genes and datasets from one study in another. 2.3 Benchmark datasets CellMix ships with a curated repository of 11 public datasets compiled from a variety of published studies on cell/tissue specific gene expression or deconvolution methods. These datasets were chosen because they contain not only global gene expression from mixed samples, but also data such as cell type specific signatures and/or measured mixture proportions for each sample, making them ideal for developing and validating deconvolution approaches. Each dataset can be loaded in a single call, which applies a pre-processing pipeline to the original – normalised – data, downloaded from public repositories. In particular, data relevant for deconvolution are extracted from sample annotations and processed into a single data objects, from which mixed/pure sample expression profiles and/or cell proportions can be easily retrieved. 3 EXAMPLE: BLOOD SAMPLE DECONVOLUTION The dataset GSE20300 contains gene expression data (on Affymetrix HGU133Plus2) of whole blood samples from stable and acute rejection pediatric kidney transplant, for which Complete Blood Count (CBC) data are available (Shen-Orr et al., 2010). The following code estimates these proportions using an optimised set of immune cell type signatures (on Affymetrix HGU133A/B) (Abbas et al., 2009) and produces the scatter plot in Figure 1. Both these data are available in the CellMix package. Importantly, sensible probeset mapping or joint data transformation and normalisation are transparently handled via a – customisable – pre-processing pipeline. To our knowledge, this is the first time these data have been used in this way. The ease with which the results are generated highlights the usefulness of the CellMix package. # load benchmark data acr <ExpressionMix("GSE20300") # compute proportions res <gedBlood(acr) # plot against actual CBC profplot(acr, asCBC(res)) 4 EXAMPLE: WORKING WITH MARKER GENES In this example, we illustrate how CellMix simplifies working with marker genes lists. The consistency of expression profiles from 4 transformed immune cell lines contained in dataset GSE11058 (on Affymetrix HGU133Plus2) (Abbas et al., 2009) is graphically assessed using the marker gene list from HaemAtlas, which contains markers for 8 immune cell types derived by Watkins et al. (2009) in an independent study (on Illumina Human V2). The following code generates the heatmap in Figure 1. This shows the expression profiles of the HaemAtlas marker genes that are most highly expressed in the cell lines on the columns (50 marker genes per cell type). Rows are scaled into relative expression separately (i.e. sum up to one). The row annotation columns on the right hand side highlight the cell line in which each marker is expressed at the highest level. They show that some markers are highly expressed by cell types other than their own, which suggests either an altered expression profile of these cell lines, or an inadequacy of these markers for this particular dataset. # load/extract expression data pure <pureSamples(ExpressionMix("GSE11058")) # load/convert HaemAtlas markers m <convertIDs(cellMarkers("HaemAtlas"), pure) # reorder/plot markers based on maximum average expression avg <rowMeansBy(pure, pure$LType) m <reorder(m, avg, fun = max) basismarkermap(m[, 1:50], avg) 0.0 0.2 0.4 0.6 0.8 1.0 0 .0 0 .2 0 .4 0 .6 0 .8 1 .0 Mixture coefficient profile correlations Measured CBC proportions E s ti m a te d p ro p o rt io n s Neutrophils (0.39 | 0.82 | 0.68 +/0.18) Lymphocytes (0.62 | 0.85 | 0.72 +/0.17) Monocytes (0.52 | 0.68 | 0.46 +/0.25) α = 0.83 | ρ = 0.92 | R 2
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paper_title: PERT: A Method for Expression Deconvolution of Human Blood Samples from Varied Microenvironmental and Developmental Conditions

paper_content:

The cellular composition of heterogeneous samples can be predicted using an expression deconvolution algorithm to decompose their gene expression profiles based on pre-defined, reference gene expression profiles of the constituent populations in these samples. However, the expression profiles of the actual constituent populations are often perturbed from those of the reference profiles due to gene expression changes in cells associated with microenvironmental or developmental effects. Existing deconvolution algorithms do not account for these changes and give incorrect results when benchmarked against those measured by well-established flow cytometry, even after batch correction was applied. We introduce PERT, a new probabilistic expression deconvolution method that detects and accounts for a shared, multiplicative perturbation in the reference profiles when performing expression deconvolution. We applied PERT and three other state-of-the-art expression deconvolution methods to predict cell frequencies within heterogeneous human blood samples that were collected under several conditions (uncultured mono-nucleated and lineage-depleted cells, and culture-derived lineage-depleted cells). Only PERT's predicted proportions of the constituent populations matched those assigned by flow cytometry. Genes associated with cell cycle processes were highly enriched among those with the largest predicted expression changes between the cultured and uncultured conditions. We anticipate that PERT will be widely applicable to expression deconvolution strategies that use profiles from reference populations that vary from the corresponding constituent populations in cellular state but not cellular phenotypic identity.
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paper_title: Optimal Deconvolution of Transcriptional Profiling Data Using Quadratic Programming with Application to Complex Clinical Blood Samples

paper_content:

Large-scale molecular profiling technologies have assisted the identification of disease biomarkers and facilitated the basic understanding of cellular processes. However, samples collected from human subjects in clinical trials possess a level of complexity, arising from multiple cell types, that can obfuscate the analysis of data derived from them. Failure to identify, quantify, and incorporate sources of heterogeneity into an analysis can have widespread and detrimental effects on subsequent statistical studies.We describe an approach that builds upon a linear latent variable model, in which expression levels from mixed cell populations are modeled as the weighted average of expression from different cell types. We solve these equations using quadratic programming, which efficiently identifies the globally optimal solution while preserving non-negativity of the fraction of the cells. We applied our method to various existing platforms to estimate proportions of different pure cell or tissue types and gene expression profilings of distinct phenotypes, with a focus on complex samples collected in clinical trials. We tested our methods on several well controlled benchmark data sets with known mixing fractions of pure cell or tissue types and mRNA expression profiling data from samples collected in a clinical trial. Accurate agreement between predicted and actual mixing fractions was observed. In addition, our method was able to predict mixing fractions for more than ten species of circulating cells and to provide accurate estimates for relatively rare cell types (<10% total population). Furthermore, accurate changes in leukocyte trafficking associated with Fingolomid (FTY720) treatment were identified that were consistent with previous results generated by both cell counts and flow cytometry. These data suggest that our method can solve one of the open questions regarding the analysis of complex transcriptional data: namely, how to identify the optimal mixing fractions in a given experiment.
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paper_title: Graph Implementations for Nonsmooth Convex Programs

paper_content:

We describe graph implementations, a generic method for representing a convex function via its epigraph, described in a disciplined convex programming framework. This simple and natural idea allows a very wide variety of smooth and nonsmooth convex programs to be easily specified and efficiently solved, using interiorpoint methods for smooth or cone convex programs.
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paper_title: Using Control Genes to Correct for Unwanted Variation in Microarray Data

paper_content:

Microarray expression studies suffer from the problem of batch effects and other unwanted variation. Many methods have been proposed to adjust microarray data to mitigate the problems of unwanted variation. Several of these methods rely on factor analysis to infer the unwanted variation from the data. A central problem with this approach is the difficulty in discerning the unwanted variation from the biological variation that is of interest to the researcher. We present a new method, intended for use in differential expression studies, that attempts to overcome this problem by restricting the factor analysis to negative control genes. Negative control genes are genes known a priori not to be differentially expressed with respect to the biological factor of interest. Variation in the expression levels of these genes can therefore be assumed to be unwanted variation. We name this method "Remove Unwanted Variation, 2-step" (RUV-2). We discuss various techniques for assessing the performance of an adjustment method and compare the performance of RUV-2 with that of other commonly used adjustment methods such as Combat and Surrogate Variable Analysis (SVA). We present several example studies, each concerning genes differentially expressed with respect to gender in the brain and find that RUV-2 performs as well or better than other methods. Finally, we discuss the possibility of adapting RUV-2 for use in studies not concerned with differential expression and conclude that there may be promise but substantial challenges remain.
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paper_title: Human housekeeping genes, revisited.

paper_content:

Housekeeping genes are involved in basic cell maintenance and, therefore, are expected to maintain constant expression levels in all cells and conditions. Identification of these genes facilitates exposure of the underlying cellular infrastructure and increases understanding of various structural genomic features. In addition, housekeeping genes are instrumental for calibration in many biotechnological applications and genomic studies. Advances in our ability to measure RNA expression have resulted in a gradual increase in the number of identified housekeeping genes. Here, we describe housekeeping gene detection in the era of massive parallel sequencing and RNA-seq. We emphasize the importance of expression at a constant level and provide a list of 3804 human genes that are expressed uniformly across a panel of tissues. Several exceptionally uniform genes are singled out for future experimental use, such as RT-PCR control genes. Finally, we discuss both ways in which current technology can meet some of past obstacles encountered, and several as yet unmet challenges.
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paper_title: Computational Deconvolution: Extracting Cell Type-Specific Information from Heterogeneous Samples

paper_content:

The quanta unit of the immune system is the cell, yet analyzed samples are often heterogeneous with respect to cell subsets which can mislead result interpretation. Experimentally, researchers face a difficult choice whether to profile heterogeneous samples with the ensuing confounding effects, or a priori focus on a few cell subsets of interest, potentially limiting new discoveries. An attractive alternative solution is to extract cell subset-specific information directly from heterogeneous samples via computational deconvolution techniques, thereby capturing both cell-centered and whole system level context. Such approaches are capable of unraveling novel biology, undetectable otherwise. Here we review the present state of available deconvolution techniques, their advantages and limitations, with a focus on blood expression data and immunological studies in general.
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paper_title: Quantitative noise analysis for gene expression microarray experiments

paper_content:

A major challenge in DNA microarray analysis is to effectively dissociate actual gene expression values from experimental noise. We report here a detailed noise analysis for oligonuleotide-based microarray experiments involving reverse transcription, generation of labeled cRNA (target) through in vitro transcription, and hybridization of the target to the probe immobilized on the substrate. By designing sets of replicate experiments that bifurcate at different steps of the assay, we are able to separate the noise caused by sample preparation and the hybridization processes. We quantitatively characterize the strength of these different sources of noise and their respective dependence on the gene expression level. We find that the sample preparation noise is small, implying that the amplification process during the sample preparation is relatively accurate. The hybridization noise is found to have very strong dependence on the expression level, with different characteristics for the low and high expression values. The hybridization noise characteristics at the high expression regime are mostly Poisson-like, whereas its characteristics for the small expression levels are more complex, probably due to cross-hybridization. A method to evaluate the significance of gene expression fold changes based on noise characteristics is proposed.
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paper_title: DeMix: deconvolution for mixed cancer transcriptomes using raw measured data

paper_content:

Motivation: Tissue samples of tumor cells mixed with stromal cells cause underdetection of gene expression signatures associated with cancer prognosis or response to treatment. In silico dissection of mixed cell samples is essential for analyzing expression data generated in cancer studies. Currently, a systematic approach is lacking to address three challenges in computational deconvolution: (i) violation of linear addition of expression levels from multiple tissues when log-transformed microarray data are used; (ii) estimation of both tumor proportion and tumor-specific expression, when neither is known a priori; and (iii) estimation of expression profiles for individual patients. ::: ::: Results: We have developed a statistical method for deconvolving mixed cancer transcriptomes, DeMix, which addresses the aforementioned issues in array-based expression data. We demonstrate the performance of our model in synthetic and real, publicly available, datasets. DeMix can be applied to ongoing biomarker-based clinical studies and to the vast expression datasets previously generated from mixed tumor and stromal cell samples. ::: ::: Availability: All codes are written in C and integrated into an R function, which is available at http://odin.mdacc.tmc.edu/~wwang7/DeMix.html. ::: ::: Contact: gro.nosrednadm@7gnaww ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online.
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paper_title: PERT: A Method for Expression Deconvolution of Human Blood Samples from Varied Microenvironmental and Developmental Conditions

paper_content:

The cellular composition of heterogeneous samples can be predicted using an expression deconvolution algorithm to decompose their gene expression profiles based on pre-defined, reference gene expression profiles of the constituent populations in these samples. However, the expression profiles of the actual constituent populations are often perturbed from those of the reference profiles due to gene expression changes in cells associated with microenvironmental or developmental effects. Existing deconvolution algorithms do not account for these changes and give incorrect results when benchmarked against those measured by well-established flow cytometry, even after batch correction was applied. We introduce PERT, a new probabilistic expression deconvolution method that detects and accounts for a shared, multiplicative perturbation in the reference profiles when performing expression deconvolution. We applied PERT and three other state-of-the-art expression deconvolution methods to predict cell frequencies within heterogeneous human blood samples that were collected under several conditions (uncultured mono-nucleated and lineage-depleted cells, and culture-derived lineage-depleted cells). Only PERT's predicted proportions of the constituent populations matched those assigned by flow cytometry. Genes associated with cell cycle processes were highly enriched among those with the largest predicted expression changes between the cultured and uncultured conditions. We anticipate that PERT will be widely applicable to expression deconvolution strategies that use profiles from reference populations that vary from the corresponding constituent populations in cellular state but not cellular phenotypic identity.
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paper_title: Optimal Deconvolution of Transcriptional Profiling Data Using Quadratic Programming with Application to Complex Clinical Blood Samples

paper_content:

Large-scale molecular profiling technologies have assisted the identification of disease biomarkers and facilitated the basic understanding of cellular processes. However, samples collected from human subjects in clinical trials possess a level of complexity, arising from multiple cell types, that can obfuscate the analysis of data derived from them. Failure to identify, quantify, and incorporate sources of heterogeneity into an analysis can have widespread and detrimental effects on subsequent statistical studies.We describe an approach that builds upon a linear latent variable model, in which expression levels from mixed cell populations are modeled as the weighted average of expression from different cell types. We solve these equations using quadratic programming, which efficiently identifies the globally optimal solution while preserving non-negativity of the fraction of the cells. We applied our method to various existing platforms to estimate proportions of different pure cell or tissue types and gene expression profilings of distinct phenotypes, with a focus on complex samples collected in clinical trials. We tested our methods on several well controlled benchmark data sets with known mixing fractions of pure cell or tissue types and mRNA expression profiling data from samples collected in a clinical trial. Accurate agreement between predicted and actual mixing fractions was observed. In addition, our method was able to predict mixing fractions for more than ten species of circulating cells and to provide accurate estimates for relatively rare cell types (<10% total population). Furthermore, accurate changes in leukocyte trafficking associated with Fingolomid (FTY720) treatment were identified that were consistent with previous results generated by both cell counts and flow cytometry. These data suggest that our method can solve one of the open questions regarding the analysis of complex transcriptional data: namely, how to identify the optimal mixing fractions in a given experiment.
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paper_title: Comparison of CAGE and RNA-seq transcriptome profiling using clonally amplified and single-molecule next-generation sequencing.

paper_content:

CAGE (cap analysis gene expression) and RNA-seq are two major technologies used to identify transcript abundances as well as structures. They measure expression by sequencing from either the 5' end of capped molecules (CAGE) or tags randomly distributed along the length of a transcript (RNA-seq). Library protocols for clonally amplified (Illumina, SOLiD, 454 Life Sciences [Roche], Ion Torrent), second-generation sequencing platforms typically employ PCR preamplification prior to clonal amplification, while third-generation, single-molecule sequencers can sequence unamplified libraries. Although these transcriptome profiling platforms have been demonstrated to be individually reproducible, no systematic comparison has been carried out between them. Here we compare CAGE, using both second- and third-generation sequencers, and RNA-seq, using a second-generation sequencer based on a panel of RNA mixtures from two human cell lines to examine power in the discrimination of biological states, detection of differentially expressed genes, linearity of measurements, and quantification reproducibility. We found that the quantified levels of gene expression are largely comparable across platforms and conclude that CAGE and RNA-seq are complementary technologies that can be used to improve incomplete gene models. We also found systematic bias in the second- and third-generation platforms, which is likely due to steps such as linker ligation, cleavage by restriction enzymes, and PCR amplification. This study provides a perspective on the performance of these platforms, which will be a baseline in the design of further experiments to tackle complex transcriptomes uncovered in a wide range of cell types.
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paper_title: ROKU: a novel method for identification of tissue-specific genes

paper_content:

BackgroundOne of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. We would like to have ways of identifying such tissue-specific genes.ResultsWe describe a method, ROKU, which selects tissue-specific patterns from gene expression data for many tissues and thousands of genes. ROKU ranks genes according to their overall tissue specificity using Shannon entropy and detects tissues specific to each gene if any exist using an outlier detection method. We evaluated the capacity for the detection of various specific expression patterns using synthetic and real data. We observed that ROKU was superior to a conventional entropy-based method in its ability to rank genes according to overall tissue specificity and to detect genes whose expression pattern are specific only to objective tissues.ConclusionROKU is useful for the detection of various tissue-specific expression patterns. The framework is also directly applicable to the selection of diagnostic markers for molecular classification of multiple classes.
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paper_title: Deconvolution of Blood Microarray Data Identifies Cellular Activation Patterns in Systemic Lupus Erythematosus

paper_content:

Systemic Lupus Erythematosus (SLE) is a systemic autoimmune disease with a complex spectrum of cellular and molecular characteristics including several dramatic changes in the populations of peripheral leukocytes. These changes include general leukopenia, activation of B and T cells, and maturation of granulocytes. The manifestation of SLE in peripheral blood is central to the disease but is incompletely understood. A technique for rigorously characterizing changes in mixed populations of cells, microarray expression deconvolution, has been applied to several areas of biology but not to SLE or to blood. Here we demonstrate that microarray expression deconvolution accurately quantifies the constituents of real blood samples and mixtures of immune-derived cell lines. We characterize a broad spectrum of peripheral leukocyte cell types and states in SLE to uncover novel patterns including: specific activation of NK and T helper lymphocytes, relationships of these patterns to each other, and correlations to clinical variables and measures. The expansion and activation of monocytes, NK cells, and T helper cells in SLE at least partly underlie this disease's prominent interferon signature. These and other patterns of leukocyte dynamics uncovered here correlate with disease severity and treatment, suggest potential new treatments, and extend our understanding of lupus pathology as a complex autoimmune disease involving many arms of the immune system.
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paper_title: Robust enumeration of cell subsets from tissue expression profiles

paper_content:

A computational method to identify cell types within a complex tissue, based on analysis of gene expression profiles, is described in this paper.
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paper_title: The characteristic direction: a geometrical approach to identify differentially expressed genes

paper_content:

Background ::: Identifying differentially expressed genes (DEG) is a fundamental step in studies that perform genome wide expression profiling. Typically, DEG are identified by univariate approaches such as Significance Analysis of Microarrays (SAM) or Linear Models for Microarray Data (LIMMA) for processing cDNA microarrays, and differential gene expression analysis based on the negative binomial distribution (DESeq) or Empirical analysis of Digital Gene Expression data in R (edgeR) for RNA-seq profiling.
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paper_title: Testing the hypothesis of tissue selectivity: the intersection–union test and a Bayesian approach

paper_content:

Motivation: Finding genes that are preferentially expressed in a particular tissue or condition is a problem that cannot be solved by standard statistical testing procedures. A relatively unknown procedure that can be used is the intersection–union test (IUT). However, two disadvantages of the IUT are that it is conservative and it conveys only the information of the least differing target tissue–other tissue pair. ::: ::: Results: We propose a Bayesian procedure that quantifies how much evidence there is in the overall expression profile for selective over-expression. In a small simulation study, it is shown that the proposed method outperforms the IUT when it comes to finding selectively expressed genes. An application to publicly available data consisting of 22 tissues shows that the Bayesian method indeed selects genes with functions that reflect the specific tissue functions. The proposed method can also be used to find genes that are underexpressed in a particular tissue. ::: ::: Availability: Both MATLAB and R code that implement the IUT and the Bayesian procedure in an efficient way, can be downloaded at http://ppw.kuleuven.be/okp/software/BayesianIUT/. ::: ::: Contact: katrijn.vandeun@psy.kuleuven.be
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paper_title: A novel method to enhance the sensitivity of marker detection using a refined hierarchical prior of tissue similarities

paper_content:

Motivation Identification of biochemical processes that drive the transformation of a totipotent cell into various cell types is essential to our understanding of living systems. This complex machinery determines how tissues differ in terms of their anatomy, physiology, morphology, and, more importantly, how various cellular control mechanisms contribute to the observed similarities/ differences. Tissue-selective genes orchestrate various aspects of cellular machinery in different tissues, and are known to be implicated in a number of tissue-specific pathologies. Results We propose a novel statistical approach that identifies and removes the effect of universally expressed genes in groups of tissues. This allows us to better characterize tissue similarities, as well as to identify tissue-selective genes. We use our method to construct a reliable hierarchy of tissue similarities. The groupings of tissues in this hierarchy are used to specify successively refined priors for identifying tissue-selective functions and their corresponding genes in the reduced subspace. We show that our refinement process enhances the signal-to-noise ratio in the identification of markers. Using case studies of immune cells and brain tissues, we show that our approach significantly outperforms the state-of-the-art methods, both in terms of coverage and reliability of the predicted tissue-selective genes. Conclusions Our statistical approach provides a general framework for enhancing the sensitivity of marker detection methods, which can be used in conjunction with other techniques. Even in cases where the number of available expression datasets is limited, we show that our marker detection method outperforms existing techniques. We present detailed validation on immune cells and brain tissues in this paper. Our approach can be applied to construct similar datasets of other human tissues as well, for identifying tissue-specific genes. We demonstrate how these tissue-selective genes enhance our understanding of differentiating biochemical features of brain tissues, shed light on how tissue-selective pathologies progress, and help us identify specific biomarkers and targets for future interventions.
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paper_title: Should We Abandon the t-Test in the Analysis of Gene Expression Microarray Data: A Comparison of Variance Modeling Strategies

paper_content:

High-throughput post-genomic studies are now routinely and promisingly investigated in biological and biomedical research. The main statistical approach to select genes differentially expressed between two groups is to apply a t-test, which is subject of criticism in the literature. Numerous alternatives have been developed based on different and innovative variance modeling strategies. However, a critical issue is that selecting a different test usually leads to a different gene list. In this context and given the current tendency to apply the t-test, identifying the most efficient approach in practice remains crucial. To provide elements to answer, we conduct a comparison of eight tests representative of variance modeling strategies in gene expression data: Welch's t-test, ANOVA [1], Wilcoxon's test, SAM [2], RVM [3], limma [4], VarMixt [5] and SMVar [6]. Our comparison process relies on four steps (gene list analysis, simulations, spike-in data and re-sampling) to formulate comprehensive and robust conclusions about test performance, in terms of statistical power, false-positive rate, execution time and ease of use. Our results raise concerns about the ability of some methods to control the expected number of false positives at a desirable level. Besides, two tests (limma and VarMixt) show significant improvement compared to the t-test, in particular to deal with small sample sizes. In addition limma presents several practical advantages, so we advocate its application to analyze gene expression data.
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paper_title: SpeCond: a method to detect condition-specific gene expression

paper_content:

Transcriptomic studies routinely measure expression levels across numerous conditions. These datasets allow identification of genes that are specifically expressed in a small number of conditions. However, there are currently no statistically robust methods for identifying such genes. Here we present SpeCond, a method to detect condition-specific genes that outperforms alternative approaches. We apply the method to a dataset of 32 human tissues to determine 2,673 specifically expressed genes. An implementation of SpeCond is freely available as a Bioconductor package at http://www.bioconductor.org/packages/release/bioc/html/SpeCond.html.
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Title: A Critical Survey of Deconvolution Methods for Separating Cell-Types in Complex Tissues

Section 1: INTRODUCTION
Description 1: Introduce the problem of source separation or deconvolution and its applications, focusing on the context of molecular biology and tissue samples.

Section 2: Biological Underpinnings
Description 2: Describe the basics of genetic information flow and gene expression measurement techniques in cells.

Section 3: Deconvolution: Formal Definition
Description 3: Introduce formalisms and notation used in in silico deconvolution of biological signals, focusing on models assuming linearity.

Section 4: Choice of Objective Function
Description 4: Discuss different objective functions used in linear regression related to deconvolution and their suitability.

Section 5: Choice of Loss Functions
Description 5: Summarize the most commonly used set of loss functions in deconvolution methods.

Section 6: Choice of Regularizers
Description 6: Describe commonly used regularizers in deconvolution, focusing on norm-1 and norm-2 regularizers and their combinations.

Section 7: Ordinary Least Squares (OLS)
Description 7: Explain the formulation of ordinary least squares (OLS) regression and its application in deconvolution.

Section 8: Ridge Regression
Description 8: Discuss ridge regression, a method incorporating norm-2 regularizer to address the multicollinearity problem in OLS.

Section 9: Least Absolute Selection and Shrinkage Operator (LASSO) Regression
Description 9: Describe LASSO regression, combining OLS with norm-1 regularizer for producing sparse solutions.

Section 10: Robust Regression
Description 10: Discuss robust regression methods that use alternative loss functions to handle outliers in the data.

Section 11: Support Vector Regression
Description 11: Explain the application of support vector regression (SVR) in deconvolution.

Section 12: Overview of Prior in silico Deconvolution Methods
Description 12: Provide an overview of existing deconvolution methods, categorizing them into regression-based frameworks and probabilistic models.

Section 13: RESULTS AND DISCUSSION
Description 13: Present a comprehensive evaluation of various formulations for solving deconvolution problems and discuss findings.

Section 14: Evaluation Measures
Description 14: Describe the evaluation measures used to assess the performance of deconvolution methods.

Section 15: Implementation
Description 15: Provide details on the implementation of different formulations and the computational aspects.

Section 16: Effect of Loss Function and Constraint Enforcement on Deconvolution Performance
Description 16: Evaluate the impact of different loss functions and constraint enforcement strategies on deconvolution performance.

Section 17: Agreement of Gene Expressions with Sum-to-One (STO) Constraint
Description 17: Investigate the impact of enforcing sum-to-one constraints on gene expression data and performance implications.

Section 18: Range Filtering-Finding an Optimal Threshold
Description 18: Discuss the identification of optimal thresholds for range filtering of gene expression values and its effect on deconvolution performance.

Section 19: Selection of Marker Genes -The Good, Bad, and Ugly
Description 19: Analyze the process of selecting marker genes for improving deconvolution accuracy and stability.

Section 20: To Regularize or Not to Regularize
Description 20: Evaluate the role of regularization in enhancing the performance of deconvolution methods, focusing on R1 and R2 regularizers.

Section 21: Summary
Description 21: Summarize the findings and propose guidelines for conducting deconvolution of expression datasets effectively.

Section 22: CONCLUDING REMARKS
Description 22: Provide final thoughts, summarizing the survey's contributions and identifying open problems for future research.