scholarly journals MOSCATO: A Supervised Approach for Analyzing Multi-Omic Single-Cell Data

2021 ◽  
Author(s):  
Lorin M Towle-Miller ◽  
Jeffrey C Miecznikowski
Keyword(s):  
Single Cell ◽  
Multiple Testing ◽  
Complex Diseases ◽  
Real Data ◽  
Cellular Level ◽  
Sequencing Data ◽  
Network Analytics ◽  
Data Application ◽  
Cell Experiment ◽  
Cell Simulation

Advancements in genomic sequencing continually improve personalized medicine in complex diseases. Recent breakthroughs generate multiple types of signatures (or multi-omics) from each cell, producing different data 'omic' types per single-cell experiment. We introduce MOSCATO, a technique for selecting features across multi-omic single-cell datasets that relate to clinical outcomes. For example, we leverage penalization concepts often used in multi-omic network analytics to accommodate the high-dimensionality where multiple-testing is likely underpowered. We organize the data into multi-dimensional tensors where the dimensions correspond to the different 'omic' types. Using the outcome and the single-cell tensors, we perform regularized tensor regression to return a variable set for each 'omic' type that forms the clinically-associated network. Robustness is assessed over simulations based on available single-cell simulation methods. Real data comparing healthy subjects versus subjects with leukemia is also considered in order to identify genes associated with the disease. The flexibility of our approach enables future extensions on distributional assumptions and covariate adjustments. This algorithm may identify clinically-relevant genetic patterns on a cellular-level that span multiple layers of sequencing data and ultimately inform highly precise therapeutic targets in complex diseases. Code to perform MOSCATO and replicate the real data application is publicly available on GitHub at https://github.com/lorinmil/MOSCATO and https://github.com/lorinmil/MOSCATOLeukemiaExample.

scholarly journals Distinguishing linear and branched evolution given single-cell DNA sequencing data of tumors

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Leah L. Weber ◽  
Mohammed El-Kebir
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Evolutionary Process ◽  
Treatment Decision ◽  
Real Data ◽  
Current Data ◽  
Fast Method ◽  
Sequencing Data ◽  
Evolutionary Trajectory ◽  
Cancer Types

Abstract Background Cancer arises from an evolutionary process where somatic mutations give rise to clonal expansions. Reconstructing this evolutionary process is useful for treatment decision-making as well as understanding evolutionary patterns across patients and cancer types. In particular, classifying a tumor’s evolutionary process as either linear or branched and understanding what cancer types and which patients have each of these trajectories could provide useful insights for both clinicians and researchers. While comprehensive cancer phylogeny inference from single-cell DNA sequencing data is challenging due to limitations with current sequencing technology and the complexity of the resulting problem, current data might provide sufficient signal to accurately classify a tumor’s evolutionary history as either linear or branched. Results We introduce the Linear Perfect Phylogeny Flipping (LPPF) problem as a means of testing two alternative hypotheses for the pattern of evolution, which we prove to be NP-hard. We develop Phyolin, which uses constraint programming to solve the LPPF problem. Through both in silico experiments and real data application, we demonstrate the performance of our method, outperforming a competing machine learning approach. Conclusion Phyolin is an accurate, easy to use and fast method for classifying an evolutionary trajectory as linear or branched given a tumor’s single-cell DNA sequencing data.

scholarly journals Splatter: simulation of single-cell RNA sequencing data

2017 ◽  
Author(s):  
Luke Zappia ◽  
Belinda Phipson ◽  
Alicia Oshlack
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Real Data ◽  
Cell Types ◽  
Rna Seq ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Simulation Based ◽  
Single Cell Rna Sequencing ◽  
Multiple Cell

AbstractAs single-cell RNA sequencing technologies have rapidly developed, so have analysis methods. Many methods have been tested, developed and validated using simulated datasets. Unfortunately, current simulations are often poorly documented, their similarity to real data is not demonstrated, or reproducible code is not available.Here we present the Splatter Bioconductor package for simple, reproducible and well-documented simulation of single-cell RNA-seq data. Splatter provides an interface to multiple simulation methods including Splat, our own simulation, based on a gamma-Poisson distribution. Splat can simulate single populations of cells, populations with multiple cell types or differentiation paths.

scholarly journals Distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data

2018 ◽  
Author(s):  
Aaron T. L. Lun ◽  
Samantha Riesenfeld ◽  
Tallulah Andrews ◽  
Tomas Gomes ◽  
John C. Marioni ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Real Data ◽  
Cell Types ◽  
Sequencing Data ◽  
Minimum Threshold ◽  
False Discovery ◽  
Distinct Cell ◽  
Single Cell Rna Sequencing ◽  
Unique Molecular Identifier

AbstractDroplet-based single-cell RNA sequencing protocols have dramatically increased the throughput and efficiency of single-cell transcriptomics studies. A key computational challenge when processing these data is to distinguish libraries for real cells from empty droplets. Existing methods for cell calling set a minimum threshold on the total unique molecular identifier (UMI) count for each library, which indiscriminately discards cell libraries with low UMI counts. Here, we describe a new statistical method for calling cells from droplet-based data, based on detecting significant deviations from the expression profile of the ambient solution. Using simulations, we demonstrate that our method has greater power than existing approaches for detecting cell libraries with low UMI counts, while controlling the false discovery rate among detected cells. We also apply our method to real data, where we show that the use of our method results in the retention of distinct cell types that would otherwise have been discarded.

scholarly journals scBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustment

2020 ◽  
Vol 36 (10) ◽  
pp. 3115-3123 ◽  
Author(s):  
Teng Fei ◽  
Tianwei Yu
Keyword(s):  
Single Cell ◽  
Differential Expression Analysis ◽  
Distance Matrix ◽  
Real Data ◽  
R Package ◽  
Batch Effect ◽  
Supplementary Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Gene Differential Expression

Abstract Motivation Batch effect is a frequent challenge in deep sequencing data analysis that can lead to misleading conclusions. Existing methods do not correct batch effects satisfactorily, especially with single-cell RNA sequencing (RNA-seq) data. Results We present scBatch, a numerical algorithm for batch-effect correction on bulk and single-cell RNA-seq data with emphasis on improving both clustering and gene differential expression analysis. scBatch is not restricted by assumptions on the mechanism of batch-effect generation. As shown in simulations and real data analyses, scBatch outperforms benchmark batch-effect correction methods. Availability and implementation The R package is available at github.com/tengfei-emory/scBatch. The code to generate results and figures in this article is available at github.com/tengfei-emory/scBatch-paper-scripts. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Comparison of computational methods for imputing single-cell RNA-sequencing data

2017 ◽  
Author(s):  
Lihua Zhang ◽  
Shihua Zhang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Real Data ◽  
Cell Types ◽  
Biological Functions ◽  
Sequencing Data ◽  
Imputation Methods ◽  
Future Studies ◽  
Single Cell Rna Sequencing

AbstractSingle-cell RNA-sequencing (scRNA-seq) is a recent breakthrough technology, which paves the way for measuring RNA levels at single cell resolution to study precise biological functions. One of the main challenges when analyzing scRNA-seq data is the presence of zeros or dropout events, which may mislead downstream analyses. To compensate the dropout effect, several methods have been developed to impute gene expression since the first Bayesian-based method being proposed in 2016. However, these methods have shown very diverse characteristics in terms of model hypothesis and imputation performance. Thus, large-scale comparison and evaluation of these methods is urgently needed now. To this end, we compared eight imputation methods, evaluated their power in recovering original real data, and performed broad analyses to explore their effects on clustering cell types, detecting differentially expressed genes, and reconstructing lineage trajectories in the context of both simulated and real data. Simulated datasets and case studies highlight that there are no one method performs the best in all the situations. Some defects of these methods such as scalability, robustness and unavailability in some situations need to be addressed in future studies.

Goals and approaches for each processing step for single-cell RNA sequencing data

2020 ◽  
Author(s):  
Zilong Zhang ◽  
Feifei Cui ◽  
Chunyu Wang ◽  
Lingling Zhao ◽  
Quan Zou
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cellular Level ◽  
Sequencing Data ◽  
Analysis Tools ◽  
Processing Step ◽  
Study Gene Expression ◽  
Single Cell Rna Sequencing ◽  
Cell Data

Abstract Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at the cellular level. However, due to the extremely low levels of transcripts in a single cell and technical losses during reverse transcription, gene expression at a single-cell resolution is usually noisy and highly dimensional; thus, statistical analyses of single-cell data are a challenge. Although many scRNA-seq data analysis tools are currently available, a gold standard pipeline is not available for all datasets. Therefore, a general understanding of bioinformatics and associated computational issues would facilitate the selection of appropriate tools for a given set of data. In this review, we provide an overview of the goals and most popular computational analysis tools for the quality control, normalization, imputation, feature selection and dimension reduction of scRNA-seq data.

LRSK: a low-rank self-representation K-means method for clustering single-cell RNA-sequencing data

2020 ◽  
Vol 16 (5) ◽  
pp. 465-473
Author(s):  
Ye-Sen Sun ◽  
Le Ou-Yang ◽  
Dao-Qing Dai
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Quantitative Research ◽  
Cellular Level ◽  
Low Rank ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

The development of single-cell RNA-sequencing (scRNA-seq) technologies brings tremendous opportunities for quantitative research and analyses at the cellular level.

scholarly journals SCSIM: Jointly simulating correlated single-cell and bulk next-generation DNA sequencing data

2020 ◽  
Author(s):  
Collin Giguere ◽  
Harsh Vardhan Dubey ◽  
Vishal Kumar Sarsani ◽  
Hachem Saddiki ◽  
Shai He ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Real Data ◽  
Data Sets ◽  
Next Generation ◽  
Sequencing Data ◽  
Next Generation Dna Sequencing ◽  
Accuracy And Precision ◽  
Downstream Analysis ◽  
Multiple Samples

AbstractBackgroundRecently, it has become possible to collect next-generation DNA sequencing data sets that are composed of multiple samples from multiple biological units where each of these samples may be from a single cell or bulk tissue. Yet, there does not yet exist a tool for simulating DNA sequencing data from such a nested sampling arrangement with single-cell and bulk samples so that developers of analysis methods can assess accuracy and precision.ResultsWe have developed a tool that simulates DNA sequencing data from hierarchically grouped (correlated) samples where each sample is designated bulk or single-cell. Our tool uses a simple configuration file to define the experimental arrangement and can be integrated into software pipelines for testing of variant callers or other genomic tools.ConclusionsThe DNA sequencing data generated by our simulator is representative of real data and integrates seamlessly with standard downstream analysis tools.

scholarly journals SPsimSeq: semi-parametric simulation of bulk and single-cell RNA-sequencing data

2020 ◽  
Vol 36 (10) ◽  
pp. 3276-3278 ◽  
Author(s):  
Alemu Takele Assefa ◽  
Jo Vandesompele ◽  
Olivier Thas
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Real Data ◽  
Simulation Method ◽  
R Package ◽  
Supplementary Information ◽  
Expression Data ◽  
Sequencing Data ◽  
Wide Range ◽  
Single Cell Rna Sequencing

Abstract Summary SPsimSeq is a semi-parametric simulation method to generate bulk and single-cell RNA-sequencing data. It is designed to simulate gene expression data with maximal retention of the characteristics of real data. It is reasonably flexible to accommodate a wide range of experimental scenarios, including different sample sizes, biological signals (differential expression) and confounding batch effects. Availability and implementation The R package and associated documentation is available from https://github.com/CenterForStatistics-UGent/SPsimSeq. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Genotyping Copy Number Alterations from single-cell RNA sequencing

2021 ◽  
Author(s):  
Salvatore Milite ◽  
Riccardo Bergamin ◽  
Giulio Caravagna
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Single Molecule ◽  
Copy Number ◽  
Probabilistic Method ◽  
Real Data ◽  
Copy Number Alterations ◽  
Sequencing Data ◽  
Dna And Rna ◽  
Single Cell Rna Sequencing

AbstractCancers are constituted by heterogeneous populations of cells that show complex genotypes and phenotypes which we can read out by sequencing. Many attempts at deciphering the clonal process that drives these populations are focusing on single-cell technologies to resolve genetic and phenotypic intra-tumour heterogeneity. While the ideal technologies for these investigations are multi-omics assays, unfortunately these types of data are still too expensive and have limited scalability. We can resort to single-molecule assays, which are cheaper and scalable, and statistically emulate a joint assay, only if we can integrate measurements collected from independent cells of the same sample. In this work we follow this intuition and construct a new Bayesian method to genotype copy number alterations on single-cell RNA sequencing data, therefore integrating DNA and RNA measurements. Our method is unsupervised, and leverages on a segmentation of the input DNA to determine the sample subclonal composition at the copy number level, together with clone-specific phenotypes defined from RNA counts. By design our probabilistic method works without a reference RNA expression profile, and therefore can be applied in cases where this information may not be accessible. We implement the method on a probabilistic backend that allows easy running on both CPUs and GPUs, and test it on both simulated and real data. Our analysis shows its ability to determine copy number associated clones and their RNA phenotypes in tumour data from 10x and Smart-Seq assays, as well as in data from the Human Cell Atlas project.

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