BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference

Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges

Single-cell RNA sequencing (scRNA-Seq) is a recent technology that allows for the measurement of the expression of all genes in each individual cell contained in a sample. Information at the single-cell level has been shown to be extremely useful in many areas. However, performing single-cell experiments is expensive. Although cellular deconvolution cannot provide the same comprehensive information as single-cell experiments, it can extract cell-type information from bulk RNA data, and therefore it allows researchers to conduct studies at cell-type resolution from existing bulk datasets. For these reasons, a great effort has been made to develop such methods for cellular deconvolution. The large number of methods available, the requirement of coding skills, inadequate documentation, and lack of performance assessment all make it extremely difficult for life scientists to choose a suitable method for their experiment. This paper aims to fill this gap by providing a comprehensive review of 53 deconvolution methods regarding their methodology, applications, performance, and outstanding challenges. More importantly, the article presents a benchmarking of all these 53 methods using 283 cell types from 30 tissues of 63 individuals. We also provide an R package named DeconBenchmark that allows readers to execute and benchmark the reviewed methods (https://github.com/tinnlab/DeconBenchmark).

Associations in cell type-specific hydroxymethylation and transcriptional alterations of pediatric central nervous system tumors

Although intratumoral heterogeneity has been established in pediatric central nervous system tumors, epigenomic alterations at the cell type level have largely remained unresolved. To identify cell type-specific alterations to cytosine modifications in pediatric central nervous system tumors, we utilize a multi-omic approach that integrated bulk DNA cytosine modification data (methylation and hydroxymethylation) with both bulk and single-cell RNA-sequencing data. We demonstrate a large reduction in the scope of significantly differentially modified cytosines in tumors when accounting for tumor cell type composition. In the progenitor-like cell types of tumors, we identify a preponderance differential Cytosine-phosphate-Guanine site hydroxymethylation rather than methylation. Genes with differential hydroxymethylation, like histone deacetylase 4 and insulin-like growth factor 1 receptor, are associated with cell type-specific changes in gene expression in tumors. Our results highlight the importance of epigenomic alterations in the progenitor-like cell types and its role in cell type-specific transcriptional regulation in pediatric central nervous system tumors.

Computational deconvolution of DNA methylation data from mixed DNA samples

In this review, we provide a comprehensive overview of the different computational tools that have been published for the deconvolution of bulk DNA methylation (DNAm) data. Here, deconvolution refers to the estimation of cell-type proportions that constitute a mixed sample. The paper reviews and compares 25 deconvolution methods (supervised, unsupervised or hybrid) developed between 2012 and 2023 and compares the strengths and limitations of each approach. Moreover, in this study, we describe the impact of the platform used for the generation of methylation data (including microarrays and sequencing), the applied data pre-processing steps and the used reference dataset on the deconvolution performance. Next to reference-based methods, we also examine methods that require only partial reference datasets or require no reference set at all. In this review, we provide guidelines for the use of specific methods dependent on the DNA methylation data type and data availability.

Challenges and perspectives in computational deconvolution of genomics data

Deciphering cell-type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach for estimating cell-type abundances from a variety of omics data. Despite substantial methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four important challenges related to computational deconvolution: the quality of the reference data, generation of ground truth data, limitations of computational methodologies, and benchmarking design and implementation. Finally, we make recommendations on reference data generation, new directions of computational methodologies, and strategies to promote rigorous benchmarking.

EpiMix is an integrative tool for epigenomic subtyping using DNA methylation

DNA methylation (DNAme) is a major epigenetic factor influencing gene expression with alterations leading to cancer and immunological and cardiovascular diseases. Recent technological advances have enabled genome-wide profiling of DNAme in large human cohorts. There is a need for analytical methods that can more sensitively detect differential methylation profiles present in subsets of individuals from these heterogeneous, population-level datasets. We developed an end-to-end analytical framework named "EpiMix" for population-level analysis of DNAme and gene expression. Compared with existing methods, EpiMix showed higher sensitivity in detecting abnormal DNAme that was present in only small patient subsets. We extended the model-based analyses of EpiMix to cis-regulatory elements within protein-coding genes, distal enhancers, and genes encoding microRNAs and long non-coding RNAs (lncRNAs). Using cell-type-specific data from two separate studies, we discover epigenetic mechanisms underlying childhood food allergy and survival-associated, methylation-driven ncRNAs in non-small cell lung cancer.

Hydroxymethylation alterations in progenitor-like cell types of pediatric central nervous system tumors are associated with cell type-specific transcriptional changes

Although intratumoral heterogeneity has been established in pediatric central nervous system tumors, epigenomic alterations at the cell type level have largely remained unresolved. To identify cell type-specific alterations to cytosine modifications in pediatric central nervous system tumors we utilized a multi-omic approach that integrated bulk DNA cytosine modification data (methylation and hydroxymethylation) with both bulk and single-cell RNA-sequencing data. We demonstrate a large reduction in the scope of significantly differentially modified cytosines in tumors when accounting for tumor cell type composition. In the progenitor-like cell types of tumors, we identified a preponderance differential CpG hydroxymethylation rather than methylation. Genes with differential hydroxymethylation, like HDAC4 and IGF1R, were associated with cell type-specific changes in gene expression in tumors. Our results highlight the importance of epigenomic alterations in the progenitor-like cell types and its role in cell type-specific transcriptional regulation in pediatric CNS tumors.

CeDAR: incorporating cell type hierarchy improves cell type-specific differential analyses in bulk omics data

Bulk high-throughput omics data contain signals from a mixture of cell types. Recent developments of deconvolution methods facilitate cell type-specific inferences from bulk data. Our real data exploration suggests that differential expression or methylation status is often correlated among cell types. Based on this observation, we develop a novel statistical method named CeDAR to incorporate the cell type hierarchy in cell type-specific differential analyses of bulk data. Extensive simulation and real data analyses demonstrate that this approach significantly improves the accuracy and power in detecting cell type-specific differential signals compared with existing methods, especially in low-abundance cell types.

EpiMix: an integrative tool for epigenomic subtyping using DNA methylation

DNA methylation (DNAme) is a major epigenetic factor influencing gene expression with alterations leading to cancer, immunological, and cardiovascular diseases. Recent technological advances enable genome-wide quantification of DNAme in large human cohorts. So far, existing methods have not been evaluated to identify differential DNAme present in large and heterogeneous patient cohorts. We developed an end-to-end analytical framework named "EpiMix" for population-level analysis of DNAme and gene expression. Compared to existing methods, EpiMix showed higher sensitivity in detecting abnormal DNAme that was present in only small patient subsets. We extended the model-based analyses of EpiMix to cis-regulatory elements within protein-coding genes, distal enhancers, and genes encoding microRNAs and lncRNAs. Using cell-type specific data from two separate studies, we discovered novel epigenetic mechanisms underlying childhood food allergy and survival-associated, methylation-driven non-coding RNAs in non-small cell lung cancer.

A systematic assessment of cell type deconvolution algorithms for DNA methylation data

We performed systematic assessment of computational deconvolution methods that play an important role in the estimation of cell type proportions from bulk methylation data. The proposed framework methylDeConv (available as an R package) integrates several deconvolution methods for methylation profiles (Illumina HumanMethylation450 and MethylationEPIC arrays) and offers different cell-type-specific CpG selection to construct the extended reference library which incorporates the main immune cell subsets, epithelial cells and cell-free DNAs. We compared the performance of different deconvolution algorithms via simulations and benchmark datasets and further investigated the associations of the estimated cell type proportions to cancer therapy in breast cancer and subtypes in melanoma methylation case studies. Our results indicated that the deconvolution based on the extended reference library is critical to obtain accurate estimates of cell proportions in non-blood tissues.

Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes

DNA methylation analysis by sequencing is becoming increasingly popular, yielding methylomes at single-base pair and single-molecule resolution. It has tremendous potential for cell-type heterogeneity analysis using intrinsic read-level information. Although diverse deconvolution methods were developed to infer cell-type composition based on bulk sequencing-based methylomes, systematic evaluation has not been performed yet. Here, we thoroughly benchmark six previously published methods: Bayesian epiallele detection, DXM, PRISM, csmFinder+coMethy, ClubCpG and MethylPurify, together with two array-based methods, MeDeCom and Houseman, as a comparison group. Sequencing-based deconvolution methods consist of two main steps, informative region selection and cell-type composition estimation, thus each was individually assessed. With this elaborate evaluation, we aimed to establish which method achieves the highest performance in different scenarios of synthetic bulk samples. We found that cell-type deconvolution performance is influenced by different factors depending on the number of cell types within the mixture. Finally, we propose a best-practice deconvolution strategy for sequencing data and point out limitations that need to be handled. Array-based methods—both reference-based and reference-free—generally outperformed sequencing-based methods, despite the absence of read-level information. This implies that the current sequencing-based methods still struggle with correctly identifying cell-type-specific signals and eliminating confounding methylation patterns, which needs to be handled in future studies.

Social Factors, Epigenomics and Lupus in African American Women (SELA) Study: protocol for an observational mechanistic study examining the interplay of multiple individual and social factors on lupus outcomes in a health disparity population

Introduction

Despite the disproportional impact of SLE on historically marginalised communities, the individual and sociocultural factors underlying these health disparities remain elusive. We report the design and methods for a study aimed at identifying epigenetic biomarkers associated with racism and resiliency that affect gene function and thereby influence SLE in a health disparity population.

Methods and analysis

The Social Factors, Epigenomics and Lupus in African American Women (SELA) Study is a cross-sectional, case–control study. A total of 600 self-reported African American women will be invited to participate. All participants will respond to questionnaires that capture detailed sociodemographic and medical history, validated measures of racial discrimination, social support, as well as disease activity and damage for cases. Participants who wish will receive their genetic ancestry estimates and be involved in research. Blood samples are required to provide peripheral blood mononuclear cell counts, DNA and RNA. The primary goals of SELA are to identify variation in DNA methylation (DNAm) associated with self-reported exposure to racial discrimination and social support, to evaluate whether social DNAm sites affect gene expression, to identify the synergistic effects of social factors on DNAm changes on SLE and to develop a social factors-DNAm predictive model for disease outcomes. This study is conducted in cooperation with the Sea Island Families Project Citizen Advisory Committee.

Discussion and dissemination

SELA will respond to the pressing need to clarify the interplay and regulatory mechanism by which various positive and negative social exposures influence SLE. Results will be published and shared with patients and the community. Knowledge of the biological impact of social exposures on SLE, as informed by the results of this study, can be leveraged by advocacy efforts to develop psychosocial interventions that prevent or mitigate risk exposures, and services or interventions that promote positive exposures. Implementation of such interventions is paramount to the closure of the health disparities gap.

A transdisciplinary approach to understand the epigenetic basis of race/ethnicity health disparities

Health disparities correspond to differences in disease burden and mortality among socially defined population groups. Such disparities may emerge according to race/ethnicity, socioeconomic status and a variety of other social contexts, and are documented for a wide range of diseases. Here, we provide a transdisciplinary perspective on the contribution of epigenetics to the understanding of health disparities, with a special emphasis on disparities across socially defined racial/ethnic groups. Scientists in the fields of biological anthropology, bioinformatics and molecular epidemiology provide a summary of theoretical, statistical and practical considerations for conducting epigenetic health disparities research, and provide examples of successful applications from cancer research using this approach.

Low variability in the underlying cellular landscape adversely affects the performance of interaction-based approaches for conducting cell-specific analyses of DNA methylation in bulk samples

Statistical methods that allow for cell type specific DNA methylation (DNAm) analyses based on bulk-tissue methylation data have great potential to improve our understanding of human disease and have created unprecedented opportunities for new insights using the wealth of publicly available bulk-tissue methylation data. These methodologies involve incorporating interaction terms formed between the phenotypes/exposures of interest and proportions of the cell types underlying the bulk-tissue sample used for DNAm profiling. Despite growing interest in such "interaction-based" methods, there has been no comprehensive assessment how variability in the cellular landscape across study samples affects their performance. To answer this question, we used numerous publicly available whole-blood DNAm data sets along with extensive simulation studies and evaluated the performance of interaction-based approaches in detecting cell-specific methylation effects. Our results show that low cell proportion variability results in large estimation error and low statistical power for detecting cell-specific effects of DNAm. Further, we identified that many studies targeting methylation profiling in whole-blood may be at risk to be underpowered due to low variability in the cellular landscape across study samples. Finally, we discuss guidelines for researchers seeking to conduct studies utilizing interaction-based approaches to help ensure that their studies are adequately powered.

Complete deconvolution of DNA methylation signals from complex tissues: a geometric approach

MOTIVATION

It is a common practice in epigenetics research to profile DNA methylation on tissue samples, which is usually a mixture of different cell types. To properly account for the mixture, estimating cell compositions has been recognized as an important first step. Many methods were developed for quantifying cell compositions from DNA methylation data, but they mostly have limited applications due to lack of reference or prior information.

RESULTS

We develop Tsisal, a novel complete deconvolution method which accurately estimate cell compositions from DNA methylation data without any prior knowledge of cell types or their proportions. Tsisal is a full pipeline to estimate number of cell types, cell compositions and identify cell-type-specific CpG sites. It can also assign cell type labels when (full or part of) reference panel is available. Extensive simulation studies and analyses of seven real datasets demonstrate the favorable performance of our proposed method compared with existing deconvolution methods serving similar purpose.

AVAILABILITY AND IMPLEMENTATION

The proposed method Tsisal is implemented as part of the R/Bioconductor package TOAST at https://bioconductor.org/packages/TOAST.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comprehensive cell type decomposition of circulating cell-free DNA with CelFiE

Circulating cell-free DNA (cfDNA) in the bloodstream originates from dying cells and is a promising noninvasive biomarker for cell death. Here, we propose an algorithm, CelFiE, to accurately estimate the relative abundances of cell types and tissues contributing to cfDNA from epigenetic cfDNA sequencing. In contrast to previous work, CelFiE accommodates low coverage data, does not require CpG site curation, and estimates contributions from multiple unknown cell types that are not available in external reference data. In simulations, CelFiE accurately estimates known and unknown cell type proportions from low coverage and noisy cfDNA mixtures, including from cell types composing less than 1% of the total mixture. When used in two clinically-relevant situations, CelFiE correctly estimates a large placenta component in pregnant women, and an elevated skeletal muscle component in amyotrophic lateral sclerosis (ALS) patients, consistent with the occurrence of muscle wasting typical in these patients. Together, these results show how CelFiE could be a useful tool for biomarker discovery and monitoring the progression of degenerative disease.

Tissue damage and turnover lead to the release of DNA in the blood and can be used to monitor changes in tissue state. Here, the authors developed a tool to accurately estimate the proportion of cell types contributing to cell-free DNA in the blood, with an application to pregnant women and ALS patients.

EMeth: An EM algorithm for cell type decomposition based on DNA methylation data

We introduce a new computational method named EMeth to estimate cell type proportions using DNA methylation data. EMeth is a reference-based method that requires cell type-specific DNA methylation data from relevant cell types. EMeth improves on the existing reference-based methods by detecting the CpGs whose DNA methylation are inconsistent with the deconvolution model and reducing their contributions to cell type decomposition. Another novel feature of EMeth is that it allows a cell type with known proportions but unknown reference and estimates its methylation. This is motivated by the case of studying methylation in tumor cells while bulk tumor samples include tumor cells as well as other cell types such as infiltrating immune cells, and tumor cell proportion can be estimated by copy number data. We demonstrate that EMeth delivers more accurate estimates of cell type proportions than several other methods using simulated data and in silico mixtures. Applications in cancer studies show that the proportions of T regulatory cells estimated by DNA methylation have expected associations with mutation load and survival time, while the estimates from gene expression miss such associations.

Machine learning for deciphering cell heterogeneity and gene regulation

Epigenetics studies inheritable and reversible modifications of DNA that allow cells to control gene expression throughout their development and in response to environmental conditions. In computational epigenomics, machine learning is applied to study various epigenetic mechanisms genome wide. Its aim is to expand our understanding of cell differentiation, that is their specialization, in health and disease. Thus far, most efforts focus on understanding the functional encoding of the genome and on unraveling cell-type heterogeneity. Here, we provide an overview of state-of-the-art computational methods and their underlying statistical concepts, which range from matrix factorization and regularized linear regression to deep learning methods. We further show how the rise of single-cell technology leads to new computational challenges and creates opportunities to further our understanding of epigenetic regulation.

Epigenetic signatures in cancer: proper controls, current challenges and the potential for clinical translation

Epigenetic alterations are associated with normal biological processes such as aging or differentiation. Changes in global epigenetic signatures, together with genetic alterations, are driving events in several diseases including cancer. Comparative studies of cancer and healthy tissues found alterations in patterns of DNA methylation, histone posttranslational modifications, and changes in chromatin accessibility. Driven by sophisticated, next-generation sequencing-based technologies, recent studies discovered cancer epigenomes to be dominated by epigenetic patterns already present in the cell-of-origin, which transformed into a neoplastic cell. Tumor-specific epigenetic changes therefore need to be redefined and factors influencing epigenetic patterns need to be studied to unmask truly disease-specific alterations. The underlying mechanisms inducing cancer-associated epigenetic alterations are poorly understood. Studies of mutated epigenetic modifiers, enzymes that write, read, or edit epigenetic patterns, or mutated chromatin components, for example oncohistones, help to provide functional insights on how cancer epigenomes arise. In this review, we highlight the importance and define challenges of proper control tissues and cell populations to exploit cancer epigenomes. We summarize recent advances describing mechanisms leading to epigenetic changes in tumorigenesis and briefly discuss advances in investigating their translational potential.

Maternal DNA Methylation During Pregnancy: a Review

Multiple environmental, behavioral, and hereditary factors affect pregnancy. Recent studies suggest that epigenetic modifications, such as DNA methylation (DNAm), affect both maternal and fetal health during the period of gestation. Some of the pregnancy-related risk factors can influence maternal DNAm, thus predisposing both the mother and the neonate to clinical adversities with long-lasting consequences. DNAm alterations in the promoter and enhancer regions modulate gene expression changes which play vital physiological role. In this review, we have discussed the recent advances in our understanding of maternal DNA methylation changes during pregnancy and its associated complications such as gestational diabetes and anemia, adverse pregnancy outcomes like preterm birth, and preeclampsia. We have also highlighted some major gaps and limitations in the area which if addressed might improve our understanding of pregnancy and its associated adverse clinical conditions, ultimately leading to healthy pregnancies and reduction of public health burden.

Cell Types in Environmental Epigenetic Studies: Biological and Epidemiological Frameworks

PURPOSE OF REVIEW

This article introduces the roles of perinatal DNA methylation in human health and disease, highlights the challenges of tissue and cellular heterogeneity to studying DNA methylation, summarizes approaches to overcome these challenges, and offers recommendations in conducting research in environmental epigenetics.

RECENT FINDINGS

Epigenetic modifications are essential for human development and are labile to environmental influences, especially during gestation. Epigenetic dysregulation is also a hallmark of multiple diseases. Environmental epigenetic studies routinely measure DNA methylation in readily available tissues. However, tissues and cell types exhibit specific epigenetic patterning and heterogeneity between samples complicates epigenetic studies. Failure to account for cell-type heterogeneity limits identification of biological mechanisms and biases study results. Tissue-level epigenetic measures represent a convolution of epigenetic signals from individual cell types. Tissue-specific epigenetics is an evolving field and the use of disease-affected target, surrogate, or multiple tissues has inherent trade-offs and affects inference. Likewise, experimental and bioinformatic approaches to accommodate cell-type heterogeneity have varying assumptions and inherent trade-offs that affect inference. The relationships between exposure, disease, tissue-level DNA methylation, cell type-specific DNA methylation, and cell-type heterogeneity must be carefully considered in study design and analysis. Causal diagrams can inform study design and analytic strategies. Properly addressing cell-type heterogeneity limits sources of potential bias, avoids misinterpretation of study results, and allows investigators to distinguish shifts in cell-type proportions from direct changes to cellular epigenetic programming, both of which provide insights into environmental disease etiology and aid development of novel methods for prevention and treatment.

Progress and challenge for computational quantification of tissue immune cells

Tissue immune cells have long been recognized as important regulators for the maintenance of balance in the body system. Quantification of the abundance of different immune cells will provide enhanced understanding of the correlation between immune cells and normal or abnormal situations. Currently, computational methods to predict tissue immune cell compositions from bulk transcriptomes have been largely developed. Therefore, summarizing the advantages and disadvantages is appropriate. In addition, an examination of the challenges and possible solutions for these computational models will assist the development of this field. The common hypothesis of these models is that the expression of signature genes for immune cell types might represent the proportion of immune cells that contribute to the tissue transcriptome. In general, we grouped all reported tools into three groups, including reference-free, reference-based scoring and reference-based deconvolution methods. In this review, a summary of all the currently reported computational immune cell quantification tools and their applications, limitations, and perspectives are presented. Furthermore, some critical problems are found that have limited the performance and application of these models, including inadequate immune cell type, the collinearity problem, the impact of the tissue environment on the immune cell expression level, and the deficiency of standard datasets for model validation. To address these issues, tissue specific training datasets that include all known immune cells, a hierarchical computational framework, and benchmark datasets including both tissue expression profiles and the abundances of all the immune cells are proposed to further promote the development of this field.

Deconvolution of heterogeneous tumor samples using partial reference signals

Deconvolution of heterogeneous bulk tumor samples into distinct cellular populations is an important yet challenging problem, particularly when only partial references are available. A common approach to dealing with this problem is to deconvolve the mixed signals using available references and leverage the remaining signal as a new cell component. However, as indicated in our simulation, such an approach tends to over-estimate the proportions of known cell types and fails to detect novel cell types. Here, we propose PREDE, a partial reference-based deconvolution method using an iterative non-negative matrix factorization algorithm. Our method is verified to be effective in estimating cell proportions and expression profiles of unknown cell types based on simulated datasets at a variety of parameter settings. Applying our method to TCGA tumor samples, we found that proportions of pure cancer cells better indicate different subtypes of tumor samples. We also detected several cell types for each cancer type whose proportions successfully predicted patient survival. Our method makes a significant contribution to deconvolution of heterogeneous tumor samples and could be widely applied to varieties of high throughput bulk data. PREDE is implemented in R and is freely available from GitHub (https://xiaoqizheng.github.io/PREDE).

Tumor tissues are mixtures of different cell types. Identification and quantification of constitutional cell types within tumor tissues are important tasks in cancer research. The problem can be readily solved using regression-based methods if reference signals are available. But in most clinical applications, only partial references are available, which significantly reduces the deconvolution accuracy of the existing regression-based methods. In this paper, we propose a partial-reference based deconvolution model, PREDE, integrating the non-negative matrix factorization framework with an iterative optimization strategy. We conducted comprehensive evaluations for PREDE using both simulation and real data analyses, demonstrating better performance of our method than other existing methods.

Robust partial reference-free cell composition estimation from tissue expression

MOTIVATION

In the analysis of high-throughput omics data from tissue samples, estimating and accounting for cell composition have been recognized as important steps. High cost, intensive labor requirements and technical limitations hinder the cell composition quantification using cell-sorting or single-cell technologies. Computational methods for cell composition estimation are available, but they are either limited by the availability of a reference panel or suffer from low accuracy.

RESULTS

We introduce TOols for the Analysis of heterogeneouS Tissues TOAST/-P and TOAST/+P, two partial reference-free algorithms for estimating cell composition of heterogeneous tissues based on their gene expression profiles. TOAST/-P and TOAST/+P incorporate additional biological information, including cell-type-specific markers and prior knowledge of compositions, in the estimation procedure. Extensive simulation studies and real data analyses demonstrate that the proposed methods provide more accurate and robust cell composition estimation than existing methods.

AVAILABILITY AND IMPLEMENTATION

The proposed methods TOAST/-P and TOAST/+P are implemented as part of the R/Bioconductor package TOAST at https://bioconductor.org/packages/TOAST.

CONTACT

ziyi.li@emory.edu or hao.wu@emory.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Prognostic outcome prediction by semi-supervised least squares classification

Although great progress has been made in prognostic outcome prediction, small sample size remains a challenge in obtaining accurate and robust classifiers. We proposed the Rescaled linear square Regression based Least Squares Learning (RRLSL), a jointly developed semi-supervised feature selection and classifier, for predicting prognostic outcome of cancer patients. RRLSL used the least square regression to identify the scale factors and then rank the features in available multiple types of molecular data. We applied the unlabeled multiple molecular data in conjunction with the labeled data to develop a similarity graph. RRLSL produced the constraint with kernel functions to bridge the gap between label information and geometry information from messenger RNA and microRNA expression profiling. Importantly, this semi-supervised model proposed the least squares learning with L2 regularization to develop a semi-supervised classifier. RRLSL suggested the performance improvement in the prognostic outcome prediction and successfully discriminated between the recurrent patients and non-recurrent ones. We also demonstrated that RRLSL improved the accuracy and Area Under the Precision Recall Curve (AUPRC) as compared to the baseline semi-supervised methods. RRLSL is available for a stand-alone software package (https://github.com/ShiMGLab/RRLSL). A short abstract We proposed the Rescaled linear square Regression based Least Squares Learning (RRLSL), a jointly developed semi-supervised feature selection and classifier, for predicting prognostic outcome of cancer patients. RRLSL used the least square regression to identify the scale factors to rank the features in available multiple types of molecular data. RRLSL produced the constraint with kernel functions to bridge the gap between label information and geometry information from messenger RNA and microRNA expression profiling. Importantly, this semi-supervised model proposed the least squares learning with L2 regularization to develop the semi-supervised classifier. RRLSL suggested the performance improvement in the prognostic outcome prediction and successfully discriminated between the recurrent patients and non-recurrent ones.

Reference-free deconvolution, visualization and interpretation of complex DNA methylation data using DecompPipeline, MeDeCom and FactorViz

DNA methylation profiling offers unique insights into human development and diseases. Often the analysis of complex tissues and cell mixtures is the only feasible option to study methylation changes across large patient cohorts. Since DNA methylomes are highly cell type specific, deconvolution methods can be used to recover cell type-specific information in the form of latent methylation components (LMCs) from such 'bulk' samples. Reference-free deconvolution methods retrieve these components without the need for DNA methylation profiles of purified cell types. Currently no integrated and guided procedure is available for data preparation and subsequent interpretation of deconvolution results. Here, we describe a three-stage protocol for reference-free deconvolution of DNA methylation data comprising: (i) data preprocessing, confounder adjustment using independent component analysis (ICA) and feature selection using DecompPipeline, (ii) deconvolution with multiple parameters using MeDeCom, RefFreeCellMix or EDec and (iii) guided biological inference and validation of deconvolution results with the R/Shiny graphical user interface FactorViz. Our protocol simplifies the analysis and guides the initial interpretation of DNA methylation data derived from complex samples. The harmonized approach is particularly useful to dissect and evaluate cell heterogeneity in complex systems such as tumors. We apply the protocol to lung cancer methylomes from The Cancer Genome Atlas (TCGA) and show that our approach identifies the proportions of stromal cells and tumor-infiltrating immune cells, as well as associations of the detected components with clinical parameters. The protocol takes slightly >3 d to complete and requires basic R skills.

Rapid preliminary purity evaluation of tumor biopsies using deep learning approach

Tumor biopsy is one of the most widely used materials in cancer diagnoses and molecular studies, where the purity of the biopsies (i.e., proportion of cells that are cancerous) is crucial for both applications. However, conventional approaches for tumor biopsy purity evaluation require experienced pathologists and/or various materials/experiments therefore were time-consuming and error prone. Rapid, easy-to-perform and cost-effective methods are thus still of demand. Recent studies had demonstrated that molecular signatures were informative to this task. Previously, we had developed GeneCT, a deep learning-based cancerous status and tissue-of-origin classifier for pan-tumor/tissue biopsies. In the current work, we applied GeneCT on datasets collected from various groups, where the experimental protocols and cancer types differed from each other. We found that GeneCT showed high accuracies on most datasets; for samples with unexpected results, in-depth investigations suggested that they might suffer from imperfect purity. In silico mixture experiments further showed that GeneCT classification was highly indicative in predicting the purity of the tumor biopsies. Considering that transcriptome profiling is a common and inexpensive experiment in molecular cancer studies, our deep learning-based GeneCT could thus serve as a valuable tool for rapid, preliminary tumor biopsy purity assessment.

methylCC: technology-independent estimation of cell type composition using differentially methylated regions

A major challenge in the analysis of DNA methylation (DNAm) data is variability introduced from intra-sample cellular heterogeneity, such as whole blood which is a convolution of DNAm profiles across a unique cell type. When this source of variability is confounded with an outcome of interest, if unaccounted for, false positives ensue. Current methods to estimate the cell type proportions in whole blood DNAm samples are only appropriate for one technology and lead to technology-specific biases if applied to data generated from other technologies. Here, we propose the technology-independent alternative: methylCC, which is available at https://github.com/stephaniehicks/methylCC.

Virtual methylome dissection facilitated by single-cell analyses

Background

Numerous cell types can be identified within plant tissues and animal organs, and the epigenetic modifications underlying such enormous cellular heterogeneity are just beginning to be understood. It remains a challenge to infer cellular composition using DNA methylomes generated for mixed cell populations. Here, we propose a semi-reference-free procedure to perform virtual methylome dissection using the nonnegative matrix factorization (NMF) algorithm.

Results

In the pipeline that we implemented to predict cell-subtype percentages, putative cell-type-specific methylated (pCSM) loci were first determined according to their DNA methylation patterns in bulk methylomes and clustered into groups based on their correlations in methylation profiles. A representative set of pCSM loci was then chosen to decompose target methylomes into multiple latent DNA methylation components (LMCs). To test the performance of this pipeline, we made use of single-cell brain methylomes to create synthetic methylomes of known cell composition. Compared with highly variable CpG sites, pCSM loci achieved a higher prediction accuracy in the virtual methylome dissection of synthetic methylomes. In addition, pCSM loci were shown to be good predictors of the cell type of the sorted brain cells. The software package developed in this study is available in the GitHub repository (https://github.com/Gavin-Yinld).

Conclusions

We anticipate that the pipeline implemented in this study will be an innovative and valuable tool for the decoding of cellular heterogeneity.

TOAST: improving reference-free cell composition estimation by cross-cell type differential analysis

In the analysis of high-throughput data from complex samples, cell composition is an important factor that needs to be accounted for. Except for a limited number of tissues with known pure cell type profiles, a majority of genomics and epigenetics data relies on the "reference-free deconvolution" methods to estimate cell composition. We develop a novel computational method to improve reference-free deconvolution, which iteratively searches for cell type-specific features and performs composition estimation. Simulation studies and applications to six real datasets including both DNA methylation and gene expression data demonstrate favorable performance of the proposed method. TOAST is available at https://bioconductor.org/packages/TOAST .

Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology

High costs and technical limitations of cell sorting and single-cell techniques currently restrict the collection of large-scale, cell-type-specific DNA methylation data. This, in turn, impedes our ability to tackle key biological questions that pertain to variation within a population, such as identification of disease-associated genes at a cell-type-specific resolution. Here, we show mathematically and empirically that cell-type-specific methylation levels of an individual can be learned from its tissue-level bulk data, conceptually emulating the case where the individual has been profiled with a single-cell resolution and then signals were aggregated in each cell population separately. Provided with this unprecedented way to perform powerful large-scale epigenetic studies with cell-type-specific resolution, we revisit previous studies with tissue-level bulk methylation and reveal novel associations with leukocyte composition in blood and with rheumatoid arthritis. For the latter, we further show consistency with validation data collected from sorted leukocyte sub-types.

Compared to bulk data, cell-type-specific DNA methylation data provide higher resolution of epigenetic variation. Here, the authors introduce Tensor Composition Analysis, a novel computational approach for learning cell-type-specific DNA methylation from tissue-level bulk data, and show its application in epigenome-wide association studies.

Detection of cell-type-specific risk-CpG sites in epigenome-wide association studies

In epigenome-wide association studies, the measured signals for each sample are a mixture of methylation profiles from different cell types. Current approaches to the association detection claim whether a cytosine-phosphate-guanine (CpG) site is associated with the phenotype or not at aggregate level and can suffer from low statistical power. Here, we propose a statistical method, HIgh REsolution (HIRE), which not only improves the power of association detection at aggregate level as compared to the existing methods but also enables the detection of risk-CpG sites for individual cell types.

Cellular heterogeneity is one of the major confounding factors in EWAS studies. Here the authors present a statistical method, HIgh REsolution (HIRE), which enables the detection of risk-CpG sites for individual cell types.

CONFINED: distinguishing biological from technical sources of variation by leveraging multiple methylation datasets

Methylation datasets are affected by innumerable sources of variability, both biological (cell-type composition, genetics) and technical (batch effects). Here, we propose a reference-free method based on sparse canonical correlation analysis to separate the biological from technical sources of variability. We show through simulations and real data that our method, CONFINED, is not only more accurate than the state-of-the-art reference-free methods for capturing known, replicable biological variability, but it is also considerably more robust to dataset-specific technical variability than previous approaches. CONFINED is available as an R package as detailed at https://github.com/cozygene/CONFINED.

BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference

We introduce a Bayesian semi-supervised method for estimating cell counts from DNA methylation by leveraging an easily obtainable prior knowledge on the cell-type composition distribution of the studied tissue. We show mathematically and empirically that alternative methods which attempt to infer cell counts without methylation reference only capture linear combinations of cell counts rather than provide one component per cell type. Our approach allows the construction of components such that each component corresponds to a single cell type, and provides a new opportunity to investigate cell compositions in genomic studies of tissues for which it was not possible before.