scholarly journals Epigenomic tumor evolution modeling with single-cell methylation data profiling

2021 ◽  
Author(s):  
Xuan Cindy Li ◽  
Yuelin Liu ◽  
Farid Rashidi Mehrabadi ◽  
Salem Malikić ◽  
Stephen M. Mount ◽  
...  
Keyword(s):  
Single Cell ◽  
Sequencing Error ◽  
Whole Genome Sequencing Data ◽  
Tumor Evolution ◽  
Data Sets ◽  
Sequencing Data ◽  
Data Profiling ◽  
Colorectal Cancer Cells ◽  
Methylation Patterns ◽  
Evolution Modeling

AbstractRecent studies on the heritability of methylation patterns in tumor cells, suggest that tumor heterogeneity and progression can be studied through methylation changes. To elucidate methylation-based evolution trajectories in tumors, we introduce a novel computational frame-work for methylation phylogeny reconstruction, leveraging single cell bisulfite treated whole genome sequencing data (scBS-seq), additionally incorporating copy number information inferred independently from matched single cell RNA sequencing (scRNA-seq) data, when available. Our framework consists of three components: (i) noise-minimizing site selection, (ii) likelihood-based sequencing error correction, and (iii) pairwise expected distance calculation for cells, all designed to mitigate the effect of noise and uncertainty due to data sparsity commonly observed in scBS-seq data. We validate our approach with the scBS-seq data of multi-regionally sampled colorectal cancer cells, and demonstrate that the cell lineages constructed by our method strongly correlate with original sampling regions. Additionally, we show that the constructed phylogeny can be used to impute missing entries, which, in turn, may help reduce sparsity issues in scBS-seq data [email protected]

Abstract LB020: Epigenomic tumor evolution modeling with single-cell methylation data profiling

2021 ◽  
Author(s):  
Xuan C. Li ◽  
Yuelin Liu ◽  
Farid Rashidi ◽  
Salem Malikic ◽  
Stephen M. Mount ◽  
...  
Keyword(s):  
Single Cell ◽  
Tumor Evolution ◽  
Methylation Data ◽  
Data Profiling ◽  
Evolution Modeling

scholarly journals Software Benchmark—Classification Tree Algorithms for Cell Atlases Annotation Using Single-Cell RNA-Sequencing Data

2021 ◽  
Vol 12 (2) ◽  
pp. 317-334
Author(s):  
Omar Alaqeeli ◽  
Li Xing ◽  
Xuekui Zhang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Classification Tree ◽  
Area Under The Curve ◽  
Data Sets ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Tree Algorithms ◽  
R Packages

Classification tree is a widely used machine learning method. It has multiple implementations as R packages; rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision; evtree is the best in Recall, F1-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others.

scholarly journals Evaluation of single-cell classifiers for single-cell RNA sequencing data sets

2019 ◽  
Vol 21 (5) ◽  
pp. 1581-1595 ◽  
Author(s):  
Xinlei Zhao ◽  
Shuang Wu ◽  
Nan Fang ◽  
Xiao Sun ◽  
Jue Fan
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Reference Data ◽  
Predictive Accuracy ◽  
Cell Types ◽  
Superior Performance ◽  
Marker Genes ◽  
Data Sets ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Single-cell RNA sequencing (scRNA-seq) has been rapidly developing and widely applied in biological and medical research. Identification of cell types in scRNA-seq data sets is an essential step before in-depth investigations of their functional and pathological roles. However, the conventional workflow based on clustering and marker genes is not scalable for an increasingly large number of scRNA-seq data sets due to complicated procedures and manual annotation. Therefore, a number of tools have been developed recently to predict cell types in new data sets using reference data sets. These methods have not been generally adapted due to a lack of tool benchmarking and user guidance. In this article, we performed a comprehensive and impartial evaluation of nine classification software tools specifically designed for scRNA-seq data sets. Results showed that Seurat based on random forest, SingleR based on correlation analysis and CaSTLe based on XGBoost performed better than others. A simple ensemble voting of all tools can improve the predictive accuracy. Under nonideal situations, such as small-sized and class-imbalanced reference data sets, tools based on cluster-level similarities have superior performance. However, even with the function of assigning ‘unassigned’ labels, it is still challenging to catch novel cell types by solely using any of the single-cell classifiers. This article provides a guideline for researchers to select and apply suitable classification tools in their analysis workflows and sheds some lights on potential direction of future improvement on classification tools.

scholarly journals PaSD-qc: Quality control for single cell whole-genome sequencing data using power spectral density estimation

2017 ◽  
Author(s):  
Maxwell A. Sherman ◽  
Alison R. Barton ◽  
Michael Lodato ◽  
Carl Vitzthum ◽  
Michael E. Coulter ◽  
...  
Keyword(s):  
Spectral Density ◽  
Whole Genome Sequencing ◽  
Single Cell ◽  
Power Spectral Density ◽  
Genome Sequencing ◽  
Dna Amplification ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Power Spectral

AbstractSingle cell whole-genome sequencing (scWGS) is providing novel insights into the nature of genetic heterogeneity in normal and diseased cells. However, scWGS introduces DNA amplification-related biases that can confound downstream analysis. Here we present a statistical method, with an accompanying package PaSD-qc (Power Spectral Density-qc), that evaluates the quality of single cell libraries. It uses a modified power spectral density to assess amplification uniformity, amplicon size distribution, autocovariance, and inter-sample consistency as well as identifies aberrantly amplified chromosomes. We demonstrate the usefulness of this tool in evaluating scWGS protocols and in selecting high-quality libraries from low-coverage data for deep sequencing.

scholarly journals DrivAER: Identification of driving transcriptional programs in single-cell RNA sequencing data

2019 ◽  
Author(s):  
Lukas M. Simon ◽  
Fangfang Yan ◽  
Zhongming Zhao
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Disease Status ◽  
Data Sets ◽  
Sequencing Data ◽  
Functional Interpretation ◽  
Recent Success ◽  
Gene Sets ◽  
Single Cell Rna Sequencing ◽  
Cellular Maps

AbstractSingle cell RNA sequencing (scRNA-seq) unfolds complex transcriptomic data sets into detailed cellular maps. Despite recent success, there is a pressing need for specialized methods tailored towards the functional interpretation of these cellular maps. Here, we present DrivAER, a machine learning approach that scores annotated gene sets based on their relevance to user-specified outcomes such as pseudotemporal ordering or disease status. We demonstrate that DrivAER extracts the key driving pathways and transcription factors that regulate complex biological processes from scRNA-seq data.

scholarly journals A statistical test on single-cell data reveals widespread recurrent mutations in tumor evolution

2016 ◽  
Author(s):  
Jack Kuipers ◽  
Katharina Jahn ◽  
Benjamin J. Raphael ◽  
Niko Beerenwinkel
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
General Validity ◽  
Genomic Deletions ◽  
Single Cell Sequencing ◽  
Statistical Framework ◽  
Recurrent Mutations ◽  
Complex Models

The infinite sites assumption, which states that every genomic position mutates at most once over the lifetime of a tumor, is central to current approaches for reconstructing mutation histories of tumors, but has never been tested explicitly. We developed a rigorous statistical framework to test the assumption with single-cell sequencing data. The framework accounts for the high noise and contamination present in such data. We found strong evidence for recurrent mutations at the same site in 8 out of 9 single-cell sequencing datasets from human tumors. Six cases involved the loss of earlier mutations, five of which occurred at sites unaffected by large scale genomic deletions. Two cases exhibited parallel mutation, including the dataset with the strongest evidence of recurrence. Our results refute the general validity of the infinite sites assumption and indicate that more complex models are needed to adequately quantify intra-tumor heterogeneity.

scholarly journals A Multi-center Cross-platform Single-cell RNA Sequencing Reference Dataset

2020 ◽  
Author(s):  
Xin Chen ◽  
Zhaowei Yang ◽  
Wanqiu Chen ◽  
Yongmei Zhao ◽  
Andrew Farmer ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Whole Genome Sequencing Data ◽  
Differential Analysis ◽  
Sequencing Data ◽  
Reference Dataset ◽  
Batch Correction ◽  
Distinct Cell ◽  
Single Cell Rna Sequencing ◽  
Benchmark Datasets

AbstractSingle-cell RNA sequencing (scRNA-seq) is developing rapidly, and investigators seeking to use this technology are left with a variety of options for both experimental platform and bioinformatics methods. There is an urgent need for scRNA-seq reference datasets for benchmarking of different scRNA-seq platforms and bioinformatics methods. To be broadly applicable, these should be generated from renewable, well characterized reference samples and processed in multiple centers across different platforms. Here we present a benchmarking scRNA-seq dataset that includes 20 scRNA-seq datasets acquired either as a mixtures or as individual samples from two biologically distinct cell lines for which a large amount of multi-platform whole genome sequencing data are also available. These scRNA-seq datasets were generated from multiple popular platforms across four sequencing centers. Our benchmark datasets provide a resource that we believe will have great value for the single-cell community by serving as a reference dataset for evaluating various bioinformatics methods for scRNA-seq analyses, including but not limited to data preprocessing, imputation, normalization, clustering, batch correction, and differential analysis.

scholarly journals Cellsnp-lite: an efficient tool for genotyping single cells

2021 ◽  
Author(s):  
Xianjie Huang ◽  
Yuanhua Huang
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Basic Research ◽  
Substantial Improvement ◽  
Data Sets ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Memory Efficiency ◽  
Computational Speed ◽  
Cell Data

AbstractSummarySingle-cell sequencing is an increasingly used technology and has promising applications in basic research and clinical translations. However, genotyping methods developed for bulk sequencing data have not been well adapted for single-cell data, in terms of both computational parallelization and simplified user interface. Here we introduce a software, cellsnp-lite, implemented in C/C++ and based on well supported package htslib, for genotyping in single-cell sequencing data for both droplet and well based platforms. On various experimental data sets, it shows substantial improvement in computational speed and memory efficiency with retaining highly concordant results compared to existing methods. Cellsnp-lite therefore lightens the genetic analysis for increasingly large single-cell data.AvailabilityThe source code is freely available at https://github.com/single-cell-genetics/[email protected]

scholarly journals Integrated Single-Cell Atlases Reveal an Oral SARS-CoV-2 Infection and Transmission Axis

2020 ◽  
Author(s):  
Ni Huang ◽  
Paola Perez ◽  
Takafumi Kato ◽  
Yu Mikami ◽  
Kenichi Okuda ◽  
...  
Keyword(s):  
Oral Cavity ◽  
Single Cell ◽  
Salivary Glands ◽  
Viral Entry ◽  
Immune Cell ◽  
Data Sets ◽  
Sequencing Data ◽  
Viral Shedding ◽  
Autopsy Tissues

ABSTRACTDespite signs of infection, the involvement of the oral cavity in COVID-19 is poorly understood. To address this, single-cell RNA sequencing data-sets were integrated from human minor salivary glands and gingiva to identify 11 epithelial, 7 mesenchymal, and 15 immune cell clusters. Analysis of SARS-CoV-2 viral entry factor expression showed enrichment in epithelia including the ducts and acini of the salivary glands and the suprabasal cells of the mucosae. COVID-19 autopsy tissues confirmed in vivo SARS-CoV-2 infection in the salivary glands and mucosa. Saliva from SARS-CoV-2-infected individuals harbored epithelial cells exhibiting ACE2 expression and SARS-CoV-2 RNA. Matched nasopharyngeal and saliva samples found distinct viral shedding dynamics and viral burden in saliva correlated with COVID-19 symptoms including taste loss. Upon recovery, this cohort exhibited salivary antibodies against SARS-CoV-2 proteins. Collectively, the oral cavity represents a robust site for COVID-19 infection and implicates saliva in viral transmission.

Frequent extrachromosomal oncogene amplification drives aggressive tumors

2019 ◽  
Author(s):  
Hoon Kim ◽  
Nam Nguyen ◽  
Kristen Turner ◽  
Sihan Wu ◽  
Jihe Liu ◽  
...  
Keyword(s):  
Copy Number ◽  
Computational Analysis ◽  
Chromatin Accessibility ◽  
Tissue Type ◽  
Whole Genome Sequencing Data ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
Oncogene Amplification ◽  
Significant Enrichment ◽  
Aggressive Tumors

Extrachromosomal DNA (ecDNA) amplification promotes high oncogene copy number, intratumoral genetic heterogeneity, and accelerated tumor evolution1–3, but its frequency and clinical impact are not well understood. Here we show, using computational analysis of whole-genome sequencing data from 1,979 cancer patients, that ecDNA amplification occurs in at least 26% of human cancers, of a wide variety of histological types, but not in whole blood or normal tissue. We demonstrate a highly significant enrichment for oncogenes on amplified ecDNA and that the most common recurrent oncogene amplifications arise on ecDNA. EcDNA amplifications resulted in higher levels of oncogene transcription compared to copy number matched linear DNA, coupled with enhanced chromatin accessibility. Patients whose tumors have ecDNA-based oncogene amplification showed increase of cell proliferation signature activity, greater likelihood of lymph node spread at initial diagnosis, and significantly shorter survival, even when controlled for tissue type, than do patients whose cancers are not driven by ecDNA-based oncogene amplification. The results presented here demonstrate that ecDNA-based oncogene amplification plays a central role in driving the poor outcome for patients with some of the most aggressive forms of cancers.

Sign in / Sign up

Export Citation Format

Share Document