Multi-distance based spectral embedding fusion for clustering single-cell methylation data

Author(s):  
Qi Tian ◽  
Jianxiao Zou ◽  
Jianxiong Tang ◽  
Shicai Fan
2018 ◽  
Author(s):  
Camila P.E. de Souza ◽  
Mirela Andronescu ◽  
Tehmina Masud ◽  
Farhia Kabeer ◽  
Justina Biele ◽  
...  

AbstractWe present Epiclomal, a probabilistic clustering method arising from a hierarchical mixture model to simultaneously cluster sparse single-cell DNA methylation data and impute missing values. Using synthetic and published single-cell CpG datasets we show that Epiclomal outperforms non-probabilistic methods and is able to handle the inherent missing data feature which dominates single-cell CpG genome sequences. Using a recently published single-cell 5mCpG sequencing method (PBAL), we show that Epiclomal discovers sub-clonal patterns of methylation in aneuploid tumour genomes, thus defining epiclones. We show that epiclones may transcend copy number determined clonal lineages, thus opening this important form of clonal analysis in cancer. Epiclomal is written in R and Python and is available at https://github.com/shahcompbio/Epiclomal.


2021 ◽  
Vol 23 (Supplement_1) ◽  
pp. i22-i22
Author(s):  
John DeSisto ◽  
Andrew Donson ◽  
Rui Fu ◽  
Bridget Sanford ◽  
Kent Riemondy ◽  
...  

Abstract Background Pediatric high-grade glioma (PHGG) is a deadly childhood brain tumor that responds poorly to treatment. PHGG comprises two major subtypes: cortical tumors with wild-type H3K27 and diffuse midline gliomas (DMG) that occur in the midline and have characteristic H3K27M mutations. Cortical PHGG is heterogeneous with multiple molecular subtypes. In order to identify underlying commonalities in cortical PHGG that might lead to better treatment modalities, we performed molecular profiling, including single-cell RNA-Seq (scRNA-Seq), on PHGG samples from Children’s Hospital Colorado. Methods Nineteen cortical PHGG tumor samples, one DMG and one normal margin sample obtained at biopsy were disaggregated to isolate viable cells. Fifteen were glioblastomas (GBM), including five with epithelioid and/or giant cell features and five radiation-induced glioblastomas (RIG). There were also four non-GBM PHGG. We performed scRNA-Seq using 10X Genomics v.3 library preparation to enable capture of infiltrating immune cells. We also performed bulk RNA-Seq and DNA methylation profiling. Results After eliminating patient-specific and cell-cycle effects, RIG, epithelioid GBM, and other GBM each formed identifiable subgroups in bulk RNA-Seq and scRNA-Seq datasets. In the scRNA-Seq data, clusters with cells from multiple tumor samples included a PDGFRA-positive population expressing oligodendrocyte progenitor markers, astrocytic, mesenchymal and stemlike populations, macrophage/monocyte immune cells, and a smaller T-cell population. Analyses of DNA methylation data showed PDGFRA and CDK4 amplification and CDKN2A deletion are common alterations among PHGG. Inferred copy number variation analysis of the single-cell data confirmed that individual tumors include populations that both include and lack the molecular alterations identified in the methylation data. RNA velocity studies to define tumor cells of origin and further analyses of the immune cell populations are underway. Conclusions Single-cell analysis of PHGG confirms a large degree of tumor heterogeneity but also shows that PHGG have stemlike, mesenchymal and immune cell populations with common characteristics.


2021 ◽  
Author(s):  
Xuan C. Li ◽  
Yuelin Liu ◽  
Farid Rashidi ◽  
Salem Malikic ◽  
Stephen M. Mount ◽  
...  

2020 ◽  
Vol 16 (9) ◽  
pp. e1008270
Author(s):  
Camila P. E. de Souza ◽  
Mirela Andronescu ◽  
Tehmina Masud ◽  
Farhia Kabeer ◽  
Justina Biele ◽  
...  

2021 ◽  
Author(s):  
Geert-Jan Huizing ◽  
Gabriel Peyré ◽  
Laura Cantini

AbstractThe recent advent of high-throughput single-cell molecular profiling is revolutionizing biology and medicine by unveiling the diversity of cell types and states contributing to development and disease. The identification and characterization of cellular heterogeneity is typically achieved through unsupervised clustering, which crucially relies on a similarity metric.We here propose the use of Optimal Transport (OT) as a cell-cell similarity metric for single-cell omics data. OT defines distances to compare, in a geometrically faithful way, high-dimensional data represented as probability distributions. It is thus expected to better capture complex relationships between features and produce a performance improvement over state-of-the-art metrics. To speed up computations and cope with the high-dimensionality of single-cell data, we consider the entropic regularization of the classical OT distance. We then extensively benchmark OT against state-of-the-art metrics over thirteen independent datasets, including simulated, scRNA-seq, scATAC-seq and single-cell DNA methylation data. First, we test the ability of the metrics to detect the similarity between cells belonging to the same groups (e.g. cell types, cell lines of origin). Then, we apply unsupervised clustering and test the quality of the resulting clusters.In our in-depth evaluation, OT is found to improve cell-cell similarity inference and cell clustering in all simulated and real scRNA-seq data, while its performances are comparable with Pearson correlation in scATAC-seq and single-cell DNA methylation data. All our analyses are reproducible through the OT-scOmics Jupyter notebook available at https://github.com/ComputationalSystemsBiology/OT-scOmics.


2016 ◽  
Author(s):  
Christof Angermueller ◽  
Heather J. Lee ◽  
Wolf Reik ◽  
Oliver Stegle

AbstractRecent technological advances have enabled assaying DNA methylation at single-cell resolution. Current protocols are limited by incomplete CpG coverage and hence methods to predict missing methylation states are critical to enable genome-wide analyses. Here, we report DeepCpG, a computational approach based on deep neural networks to predict DNA methylation states from DNA sequence and incomplete methylation profiles in single cells. We evaluated DeepCpG on single-cell methylation data from five cell types generated using alternative sequencing protocols, finding that DeepCpG yields substantially more accurate predictions than previous methods. Additionally, we show that the parameters of our model can be interpreted, thereby providing insights into the effect of sequence composition on methylation variability.


Author(s):  
Debby A. Jennings ◽  
Michael J. Morykwas ◽  
Louis C. Argenta

Grafts of cultured allogenic or autogenic keratlnocytes have proven to be an effective treatment of chronic wounds and burns. This study utilized a collagen substrate for keratinocyte and fibroblast attachment. The substrate provided mechanical stability and augmented graft manipulation onto the wound bed. Graft integrity was confirmed by light and transmission electron microscopy.Bovine Type I dermal collagen sheets (100 μm thick) were crosslinked with 254 nm UV light (13.5 Joules/cm2) to improve mechanical properties and reduce degradation. A single cell suspension of third passage neonatal foreskin fibroblasts were plated onto the collagen. Five days later, a single cell suspension of first passage neonatal foreskin keratinocytes were plated on the opposite side of the collagen. The grafts were cultured for one month.The grafts were fixed in phosphate buffered 4% formaldehyde/1% glutaraldehyde for 24 hours. Graft pieces were then washed in 0.13 M phosphate buffer, post-fixed in 1% osmium tetroxide, dehydrated, and embedded in Polybed 812.


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