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![CCND1 gene methylation and expression analysis. (A) DNA methylation landscape of CpGs and non-CpGs associated with the CCND1 gene. From top to bottom, we provide the following information: location and structure of the gene, chromatin and transcriptional domains, heatmap with DNA methylation levels, presence of hypomethylated (green) or hypermethylated (red) regions, presence of significant correlation between DNA methylation and gene expression levels (black) and TFBs density [37]. (B) Scatterplot with linear trend line of CpG (cg19137748) at CCND1 gene-body representing a negative correlation with gene expression. (C) Scatterplot with linear trend line of CpG (cg00211115) located 200 KB upstream of CCND1 TSS representing a positive correlation with gene expression. (D) Differential gene expression levels between neuroblastoma and reference samples. AG: Adrenal gland; β-value levels: DNA methylation level (value); FB: Fetal brain; hESC: Human embryonic stem cell; NB INSS Stage: Stage according to the International Neuroblastoma Staging System.](profile/Giancarlo-Castellano-2/publication/278040265/figure/fig4/AS:614185176162314@1523444565379/CCND1-gene-methylation-and-expression-analysis-A-DNA-methylation-landscape-of-CpGs-and.png)
CCND1 gene methylation and expression analysis. (A) DNA methylation landscape of CpGs and non-CpGs associated with the CCND1 gene. From top to bottom, we provide the following information: location and structure of the gene, chromatin and transcriptional domains, heatmap with DNA methylation levels, presence of hypomethylated (green) or hypermethylated (red) regions, presence of significant correlation between DNA methylation and gene expression levels (black) and TFBs density [37]. (B) Scatterplot with linear trend line of CpG (cg19137748) at CCND1 gene-body representing a negative correlation with gene expression. (C) Scatterplot with linear trend line of CpG (cg00211115) located 200 KB upstream of CCND1 TSS representing a positive correlation with gene expression. (D) Differential gene expression levels between neuroblastoma and reference samples. AG: Adrenal gland; β-value levels: DNA methylation level (value); FB: Fetal brain; hESC: Human embryonic stem cell; NB INSS Stage: Stage according to the International Neuroblastoma Staging System.
Source publication
To define the DNA methylation landscape of neuroblastoma and its clinicopathological impact.
Microarray DNA methylation data were analyzed and associated with functional/regulatory genome annotation data, transcriptional profiles and clinicobiological parameters.
DNA methylation changes in neuroblastoma affect not only promoters but also intragenic...
Contexts in source publication
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... CpG hypomethylation identified in low- risk tumors (1029 CpG; 453 genes) targeted gene- body and intergenic regions mostly outside CGIs (p < 0.05; Supplementary Figure 5C & D). These DNA methylation changes were clearly enriched for hetero- chromatic/quiescent (47%) and weak repressed poly- comb (23%) regions (p < 0.05). ...
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... DNA methylation changes were clearly enriched for hetero- chromatic/quiescent (47%) and weak repressed poly- comb (23%) regions (p < 0.05). On the other hand, in high-risk tumors, de novo CpG hypomethylation was found to be more limited (87 CpG; 52 genes) and affected mostly regions of polycomb repressed (43%), together with enhancer (32%) regions (p < 0.0001) (Supplementary Figure 5C-E). In general, hypometh- ylation in low-risk tumors significantly (p < 0.05) affected genes related to development (i.e., FOXP1, SOX13, RARRES3) and in high-risk tumors genes related to regulation of gene expression and RNA processing (i.e., FUBP1, DOM3Z and NHP2, and POLR3H; Supplementary Tables 4 & 5). ...
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... we identified 118 methylated non- CpG-associated preferentially with the clinically more favorable tumors whereas high-risk tumors showed very low or absent non-CpG methylation ( Figure 3B). Although the number of non-CpG sites probed by the methylation array was small, we identified non-CpG methylation throughout the genome and more prevalent at chromosome 2 (p < 0.05) (Supplementary Figure 5B). We ana- lyzed the sequence surrounding non-CpG meth- ylation (± 4 bp) to determine whether enrichment of particular nucleotide combinations was evident, as previously reported [12]. ...
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... ana- lyzed the sequence surrounding non-CpG meth- ylation (± 4 bp) to determine whether enrichment of particular nucleotide combinations was evident, as previously reported [12]. We observed that non- CpG methylation took place in CpApG (53%) and CpApC trinucleotides (37%), being only the latter enriched as compared with the background (37 vs 5%, p < 0.001) (Supplementary Figure 5H). Non- CpG methylation targeted preponderantly intragenic regions, exclusively at introns (18%), and intergenic regions (66%) associated with heterochromatic/qui- escent domains and weak transcription chromatin marks (Supplementary Figure 5F & G). ...
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... observed that non- CpG methylation took place in CpApG (53%) and CpApC trinucleotides (37%), being only the latter enriched as compared with the background (37 vs 5%, p < 0.001) (Supplementary Figure 5H). Non- CpG methylation targeted preponderantly intragenic regions, exclusively at introns (18%), and intergenic regions (66%) associated with heterochromatic/qui- escent domains and weak transcription chromatin marks (Supplementary Figure 5F & G). Notably, genes showing non-CpG methylation were significantly (p < 0.05) associated with development, cell differ- entiation and the Wnt receptor signaling pathway (Supplementary Table 5). ...
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... CCND1 gene showed extensive loss of CpG methylation (23 CpGs) in gene-body and 3′-UTR regions in the absence of alterations in the promoter region in all neuroblastoma tumors ( Figure 5A & Supplementary Table 2). A large propor- tion of the differentially methylated CpGs target- ing CCND1 (12/23) showed a significant (p < 0.05) correlation, mostly negative, with gene expression in neuroblastoma ( Figure 5B). Differential expres- sion between neuroblastoma and references samples is reported in Figure 5D. ...
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... large propor- tion of the differentially methylated CpGs target- ing CCND1 (12/23) showed a significant (p < 0.05) correlation, mostly negative, with gene expression in neuroblastoma ( Figure 5B). Differential expres- sion between neuroblastoma and references samples is reported in Figure 5D. We found that these CpGs were enriched for binding sites with a high degree of affinity for the POLR2A (Supplementary Table 8). ...
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... Article Gómez, Castellano, Mayol et al. explored methylation of CpG sites in this distal region in neuroblastoma. We identified 14 differentially methylated (both hyper-and hypomethylated) CpGs located 160-190 kb upstream of the TSS of CCND1, which contained several potential enhancer regions that correlated (mostly positive correlation) with gene expression ( Figure 5C). These enhancer-related CpG sites were enriched for binding sites of diverse tran- scription factors, among these BHLHE40, FOXA1, GATA3 and MAX, reported to have high affinity for these binding sites (Supplementary Table 8). ...
Citations
... We set out to define the transcriptional signatures associated to the aetiology of LR-and HR-NBs and to the malignant behavior of HR-NBs. As such, we analyzed the transcriptome of primary tumor samples from 18 NB patients, classified as LR (n = 8) or HR (n = 10) based on their tumor stage (International Neuroblastoma Staging System INSS 1-3, 4s and 4), their MYCN amplification status and their age at diagnosis [23,24]. Human fetal adrenal gland (fAG) samples were used as a normal reference tissue (Fig. 1A, Supplementary Fig. S1A and Supplementary Table S1). ...
... HSJD-NB-012 was established from a 4 year old male and its genetic profile is unknown. HSJD-NB-011 and HSJD-NB-007 have been previously used for publication [24,52]. Experimental manipulation of NB-PDXs was performed in HSJD under the supervision of A.M.C. ...
... The HSJD-NB dataset used in this study has been published previously and is available at GEO repository (GSE54720) [24]. The full human SCP, bridge cell, chromaffin cell and sympathoblast signatures were obtained from a published study [20], and the complementary signatures were obtained from a Venn diagram analysis performed with a freely available software developed by the group of Dr Y. Van de Peer (VIB, Brussels, Belgium, http:// bioinformatics.psb.ugent.be/webtools/Venn/). ...
Neuroblastoma is a pediatric cancer that can present as low- or high-risk tumors (LR-NBs and HR-NBs), the latter group showing poor prognosis due to metastasis and strong resistance to current therapy. Whether LR-NBs and HR-NBs differ in the way they exploit the transcriptional program underlying their neural crest, sympatho-adrenal origin remains unclear. Here, we identified the transcriptional signature distinguishing LR-NBs from HR-NBs, which consists mainly of genes that belong to the core sympatho-adrenal developmental program and are associated with favorable patient prognosis and with diminished disease progression. Gain- and loss-of-function experiments revealed that the top candidate gene of this signature, Neurexophilin-1 (NXPH1), has a dual impact on NB cell behavior in vivo: whereas NXPH1 and its receptor α-NRXN1 promote NB tumor growth by stimulating cell proliferation, they conversely inhibit organotropic colonization and metastasis. As suggested by RNA-seq analyses, these effects might result from the ability of NXPH1/α-NRXN signalling to restrain the conversion of NB cells from an adrenergic state to a mesenchymal one. Our findings thus uncover a transcriptional module of the sympatho-adrenal program that opposes neuroblastoma malignancy by impeding metastasis, and pinpoint NXPH1/α-NRXN signaling as a promising target to treat HR-NBs.
... harboring either MYCN amplification or 11q deletion alone. For comparisons with the DNA methylation landscape of human early embryos, we also included available whole genome bisulfite sequencing data of human oocytes, sperm and blastocysts [25] as well as data from adrenal gland [26]. Processed methylation data were downloaded from https://humandbs.biosciencedbc.jp/en/hum0009-v1#hum0009v1.CpG.v1 and from https://www.ncbi.nlm.nih.gov/ ...
Analysis of the methylome of tumor cell-free deoxyribonucleic acid (DNA; cfDNA) has emerged as a powerful non-invasive technique for cancer subtyping and prognosis. However, its application is frequently hampered by the quality and total cfDNA yield. Here, we demonstrate the feasibility of very low-input cfDNA for whole-methylome and copy-number profiling studies using enzymatic conversion of unmethylated cysteines [enzymatic methyl-seq (EM-seq)] to better preserve DNA integrity. We created a model for predicting genomic subtyping and prognosis with high accuracy. We validated our tool by comparing whole-genome CpG sequencing with in situ cohorts generated with bisulfite conversion and array hybridization, demonstrating that, despite the different techniques and sample origins, information on cfDNA methylation is comparable with in situ cohorts. Our findings support use of liquid biopsy followed by EM-seq to assess methylome of cancer patients, enabling validation in external cohorts. This advance is particularly relevant for rare cancers like neuroblastomas where liquid-biopsy volume is restricted by ethical regulations in pediatric patients.
... Decock et al. [19] selected 43 candidate markers from the methylome data of 5-aza-2′-deoxycytidine (DAC) treatment and MBD-seq analysis, and found a relationship between DNA methylation and risk factors such as age, stage, and MYCN amplification. Comparative DNA methylome analysis of clinical samples showed that variable DNA methylation sites were observed on the gene body and within the intragenic regions rather than the "promoter region" [20,21] of a gene, and some prognosis marker genes, such as CCND, were proposed. Henrich et al. [22] showed that the DNA methylation pattern is related to NB status, specifically MYCN amplification. ...
... Probe annotation groups were defined by EPIC probe annotation (details in Materials and Methods section). We found that Group A was generally accurately classified when "450K_enhancer" probes (probe group 14, Fig. 3A and B) were used, and the enhancer region is known to possess variable β-values in NB [20,21]. Meanwhile, promoter regions with CpG islands had low prediction ability (probe groups 9 and 10, Fig. 3A, B). ...
... Our probe annotation comparison showed that probes in the enhancer region had strong classification power for MYCN-amplified tumors ( Fig. 3; Figs. S4 and S5), confirming results from previously conducted DNA methylation analysis for NB [20,21]. Although DNA methylation around the TSS and CGI regions is generally used as a tumor epigenetic marker, its classification power is still insufficient compared to that of the gene expression signature. ...
Background
Neuroblastoma (NB) is the second most common pediatric solid tumor. Because the number of genetic mutations found in tumors are small, even in some patients with unfavorable NB, epigenetic variation is expected to play an important role in NB progression. DNA methylation is a major epigenetic mechanism, and its relationship with NB prognosis has been a concern. One limitation with the analysis of variation in DNA methylation is the lack of a suitable analytical model. Therefore, in this study, we performed a random forest (RF) analysis of the DNA methylome data of NB from multiple databases.
Results
RF is a popular machine learning model owing to its simplicity, intuitiveness, and computational cost. RF analysis identified novel intermediate-risk patient groups with characteristic DNA methylation patterns within the low-risk group. Feature selection analysis based on probe annotation revealed that enhancer-annotated regions had strong predictive power, particularly for MYCN-amplified NBs. We developed a gene-based analytical model to identify candidate genes related to disease progression, such as PRDM8 and FAM13A-AS1 . RF analysis revealed sufficient predictive power compared to other machine learning models.
Conclusions
RF is a useful tool for DNA methylome analysis in cancer epigenetic studies, and has potential to identify a novel cancer-related genes.
... In terms of the hypermethylated pathways identified in our study, genes identified from the ERBB family, such as EGF, show a negative correlation of reduced mRNA expression with DNA methylation . This is also the case for genes involved in "dendrite morphogenesis", such as SHANK2 (Fu et al., 2020) and ALK (Gomez et al., 2015) and "axogenesis" and "positive regulation of nervous system development" such as the Slit/Robo genes (Zheng et al., 2009). ...
Background: Infection during pregnancy can increase the risk of neurodevelopmental disorders in offspring. The impact of maternal SARS-CoV-2 infection on infant neurodevelopment is poorly understood. The maternal immune response to infection may be mimicked in rodent models of maternal immune activation which recapitulate altered neurodevelopment and behavioural disturbances in the offspring. In these models, epigenetic mechanisms, in particular DNA methylation, are one pathway through which this risk is conferred in utero to offspring. We hypothesised that in utero exposure to SARS-CoV-2 in humans may alter infant DNA methylation, particularly in genes associated with neurodevelopment. We aimed to test this hypothesis in a pilot sample of children in Victoria, Australia, who were exposed in utero to SARS-CoV-2. Methods: DNA was extracted from buccal swab specimens from (n = 4) SARS-CoV-2 in utero exposed and (n = 4) non-exposed infants and methylation status assessed across 850,000 methylation sites using an Illumina EPIC BeadChip. We also conducted an exploratory enrichment analysis using Gene Ontology annotations. Results: 1962 hypermethylated CpG sites were identified with an unadjusted p-value of 0.05, where 1133 CpGs mapped to 959 unique protein coding genes, and 716 hypomethylated CpG sites mapped to 559 unique protein coding genes in SARS-CoV-2 exposed infants compared to non-exposed. One differentially methylated position (cg06758191), located in the gene body of AFAP1 that was hypomethylated in the SARS-CoV-2 exposed cohort was significant after correction for multiple testing (FDR-adjusted p-value
... e interrelationship between these genes has not been studied. Global promoter methylation by CCND1 often displays differentially expressed DNA hypomethylation, especially when compared to normal tissue [64]. Different CpG sites located near promoter regions of more than 14,000 predicted genes were screened. ...
Background:
Parathyroid tumors are common endocrine neoplasias associated with primary hyperparathyroidism. Although numerous studies have studied the subject, the predictive value of gene biomarkers nevertheless remains low.
Methods:
In this study, we performed genomic analysis of abnormal DNA methylation in parathyroid tumors. After data preprocessing, differentially methylated genes were extracted from patients with parathyroid tumors by using t-tests.
Results:
After refinement of the basic differential methylation, 28241 unique CpGs (634 genes) were identified to be methylated. The methylated genes were primarily involved in 7 GO terms, and the top 3 terms were associated with cyst morphogenesis, ion transport, and GTPase signal. Following pathway enrichment analyses, a total of 10 significant pathways were enriched; notably, the top 3 pathways were cholinergic synapses, glutamatergic synapses, and oxytocin signaling pathways. Based on PPIN and ego-net analysis, 67 ego genes were found which could completely separate the diseased group from the normal group. The 10 most prominent genes included POLA1, FAM155 B, AMMECR1, THOC2, CCND1, CLDN11, IDS, TST, RBPJ, and GNA11. SVM analysis confirmed that this grouping approach was precise.
Conclusions:
This research provides useful data to further explore novel genes and pathways as therapeutic targets for parathyroid tumors.
... Decock et al. [19] selected 43 candidate markers from the methylome data of 5-aza-2'-deoxycytidine (DAC) treatment and MBD-seq analysis, and found a relationship between DNA methylation and risk factors such as age, stage, and MYCN ampli cation. Comparative DNA methylome analysis of clinical samples showed that variable DNA methylation sites were observed on the gene body and within the intragenic regions rather than the "promoter region" [20,21] of a gene, and some prognosis marker genes, such as CCND, were proposed. Henrich et al. [22] showed that the DNA methylation pattern is related to NB status, speci cally MYCN ampli cation. ...
... Probe annotation groups were de ned by EPIC probe annotation (details in Materials and Methods section). We found that Group A was generally accurately classi ed when "450K_enhancer" probes (probe group 14, Fig. 3A and B) were used, and the enhancer region is known to possess variable β-values in NB [20,21]. Meanwhile, promoter regions with CpG islands had low prediction ability (probe groups 9 and 10, Fig. 3A, B). ...
... Our probe annotation comparison showed that probes in the enhancer region had strong classi cation power for MYCN-ampli ed tumors ( Fig. 3; Figs. S4 and S5), con rming results from previously conducted DNA methylation analysis for NB [20,21]. Although DNA methylation around the TSS and CGI regions is generally used as a tumor epigenetic marker, its classi cation power is still insu cient compared to that of the gene expression signature. ...
Background
Neuroblastoma (NB) is the second most common pediatric solid tumor. Because the number of genetic mutations found in tumors are small, even in some patients with unfavorable NB, epigenetic variation is expected to play an important role in NB progression. DNA methylation is a major epigenetic mechanism, and its relationship with NB prognosis has been a concern. One limitation with the analysis of variation in DNA methylation is the lack of a suitable analytical model. Therefore, in this study, we performed a random forest (RF) analysis of the DNA methylome data of NB from multiple databases.
Results
RF is a popular machine learning model owing to its simplicity, intuitiveness, and computational cost. RF analysis identified novel intermediate-risk patient groups with characteristic DNA methylation patterns within the low-risk group. Feature selection analysis based on probe annotation revealed that enhancer-annotated regions had strong predictive power, particularly for MYCN-amplified NBs. We developed a gene-based analytical model to identify candidate genes related to disease progression, such as PRDM8 and FAM13A-AS1. RF analysis revealed sufficient predictive power compared to other machine learning models.
Conclusions
RF is a useful tool for DNA methylome analysis in cancer epigenetic studies, and has potential to identify a novel cancer-related genes.
... DNA methylation of CpG dinucleotides at gene promoter regions is a major regulatory mechanism involved in cellular processes that does not alter the DNA sequence [8]. DNA methylation reveals the pathogenesis and clinical behavior of neuroblastomas [9]. e most described DNA methylation alterations in neuroblastomas are CASP8 and RASSF1A [10,11], and both are correlated with risk factors, such as age at diagnosis, MYCN amplification, and tumor stage [12][13][14][15]. ...
Background:
Neuroblastomas are the most frequent extracranial pediatric solid tumors. The prognosis of children with high-risk neuroblastomas has remained poor in the past decade. A powerful signature is required to identify factors associated with prognosis and improved treatment selection. Here, we identified a strong methylation signature that favored the earlier diagnosis of neuroblastoma in patients.
Methods:
Gene methylation (GM) data of neuroblastoma patients from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) were analyzed using a multivariate Cox regression analysis (MCRA) and univariate Cox proportional hazards regression analysis (UCPHRA).
Results:
The methylated genes' signature consisting of eight genes (NBEA, DDX28, TMED8, LOC151174, EFNB2, GHRHR, MIMT1, and SLC29A3) was selected. The signature divided patients into low- and high-risk categories, with statistically significant survival rates (median survival time: 25.08 vs. >128.80 months, log-rank test, P < 0.001) in the training group, and the validation of the signature's risk stratification ability was carried out in the test group (log-rank test, P < 0.01, median survival time: 30.48 vs. >120.36 months). The methylated genes' signature was found to be an independent predictive factor for neuroblastoma by MCRA. Functional enrichment analysis suggested that these methylated genes were related to butanoate metabolism, beta-alanine metabolism, and glutamate metabolism, all playing different significant roles in the process of energy metabolism in neuroblastomas.
Conclusions:
The set of eight methylated genes could be used as a new predictive and prognostic signature for patients with INRG high-risk neuroblastomas, thus assisting in treatment, drug development, and predicting survival.
... A loss of global DNA methylation leads to genome instability and this is commonly observed in neuroblastoma [77,78]. On the other hand, increased genomic methylation is associated with poor outcomes in neuroblastoma [79]. Interestingly, expression of DNMTs in neuroblastoma is equally paradoxical. ...
High-risk neuroblastoma is an aggressive childhood cancer that is characterized by high rates of chemoresistance and frequent metastatic relapse. A number of studies have characterized the genetic and epigenetic landscape of neuroblastoma, but due to a generally low mutational burden and paucity of actionable mutations, there are few options for applying a comprehensive personalized medicine approach through the use of targeted therapies. Therefore, the use of multi-agent chemotherapy remains the current standard of care for neuroblastoma, which also conceptually limits the opportunities for developing an effective and widely applicable personalized medicine approach for this disease. However, in this review we outline potential approaches for tailoring the use of chemotherapy agents to the specific molecular characteristics of individual tumours by performing patient-specific simulations of drug-induced apoptotic signalling. By incorporating multiple layers of information about tumour-specific aberrations, including expression as well as mutation data, these models have the potential to rationalize the selection of chemotherapeutics contained within multi-agent treatment regimens and ensure the optimum response is achieved for each individual patient.
... A study by Gómez et al. reported non-CpG methylation on the gene body of ALK, which was present in favorable NB tumors, but was otherwise absent in aggressive NB tumors. Furthermore, the authors suggest that post-chemotherapy, the non-CpG methylation in unfavorable NB, was restored along with the reduced expression levels of ALK [58]. However, non-CpG methylation was determined using the Illumina 450k methylation array, which covers relatively few non-CpG sites, and that bisulfite sequencing cannot discriminate between the 5mC and 5hmC modifications. ...
Neuroblastoma (NB) is a pediatric cancer of the sympathetic nervous system and one of the most common solid tumors in infancy. Amplification of MYCN, copy number alterations, numerical and segmental chromosomal aberrations, mutations, and rearrangements on a handful of genes, such as ALK, ATRX, TP53, RAS/MAPK pathway genes, and TERT, are attributed as underlying causes that give rise to NB. However, the heterogeneous nature of the disease—along with the relative paucity of recurrent somatic mutations—reinforces the need to understand the interplay of genetic factors and epigenetic alterations in the context of NB. Epigenetic mechanisms tightly control gene expression, embryogenesis, imprinting, chromosomal stability, and tumorigenesis, thereby playing a pivotal role in physio- and pathological settings. The main epigenetic alterations include aberrant DNA methylation, disrupted patterns of posttranslational histone modifications, alterations in chromatin composition and/or architecture, and aberrant expression of non-coding RNAs. DNA methylation and demethylation are mediated by DNA methyltransferases (DNMTs) and ten-eleven translocation (TET) proteins, respectively, while histone modifications are coordinated by histone acetyltransferases and deacetylases (HATs, HDACs), and histone methyltransferases and demethylases (HMTs, HDMs). This article focuses predominately on the crosstalk between the epigenome and NB, and the implications it has on disease diagnosis and treatment.
... The neuroblastomas data are downloaded from GEO (accession GSE54719). This dataset contains the DNA methylation 450K profiles of 35 neuroblastomas tumor samples (Gomez et al., 2015). Tsisal chooses 11 as the optimal cell type number and estimates associated cell type proportions. ...
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 data sets demonstrate the favorable performance of our proposed method compared with existing deconvolution methods serving similar purpose.
Availability
The proposed method Tsisal is implemented as part of the R/Bioconductor package TOAST at https://bioconductor.org/packages/TOAST.
Contact
ziyi.li@emory.edu and hao.wu@emory.edu.
Supplementary information
Supplementary data are available at Bioinformatics online.
