scholarly journals netSmooth: Network-smoothing based imputation for single cell RNA-seq

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 8 ◽  
Author(s):  
Jonathan Ronen ◽  
Altuna Akalin
Keyword(s):  
Single Cell ◽  
Time Course ◽  
Missing Values ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Covariance Structure ◽  
R Package ◽  
Rna Seq ◽  
Distinct Cell

Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. We provide an R package for our method, available at: https://github.com/BIMSBbioinfo/netSmooth.

scholarly journals netSmooth: Network-smoothing based imputation for single cell RNA-seq

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 8 ◽  
Author(s):  
Jonathan Ronen ◽  
Altuna Akalin
Keyword(s):  
Single Cell ◽  
Time Course ◽  
Missing Values ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Covariance Structure ◽  
R Package ◽  
Rna Seq ◽  
Distinct Cell

Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. We provide an R package for our method, available at: https://github.com/BIMSBbioinfo/netSmooth.

scholarly journals netSmooth: Network-smoothing based imputation for single cell RNA-seq

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 8 ◽  
Author(s):  
Jonathan Ronen ◽  
Altuna Akalin
Keyword(s):  
Single Cell ◽  
Time Course ◽  
Missing Values ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Covariance Structure ◽  
R Package ◽  
Rna Seq ◽  
Distinct Cell

Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. We provide an R package for our method, available at: https://github.com/BIMSBbioinfo/netSmooth.

scholarly journals netSmooth: Network-smoothing based imputation for single cell RNA-seq

2017 ◽  
Author(s):  
Jonathan Ronen ◽  
Altuna Akalin
Keyword(s):  
Single Cell ◽  
Time Course ◽  
Missing Values ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Covariance Structure ◽  
R Package ◽  
Rna Seq ◽  
Distinct Cell

AbstractSingle cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. We provide an R package for our method, available at: https://github.com/BIMSBbioinfo/netSmooth.

scholarly journals SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references

2020 ◽  
Author(s):  
Meichen Dong ◽  
Aatish Thennavan ◽  
Eugene Urrutia ◽  
Yun Li ◽  
Charles M Perou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Mixed Cell ◽  
Single Cell Rna Sequencing

Abstract Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

scholarly journals SCPattern: A statistical approach to identify and classify expression changes in single cell RNA-seq experiments with ordered conditions

2016 ◽  
Author(s):  
Ning Leng ◽  
Li-Fang Chu ◽  
Jeea Choi ◽  
Christina Kendziorski ◽  
James A. Thomson ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Empirical Bayes ◽  
Time Course ◽  
Expression Patterns ◽  
R Package ◽  
Embryonic Cell ◽  
Rna Seq ◽  
Cell Fate Decisions ◽  
Probability Estimates

AbstractMotivationWith the development of single cell RNA-seq (scRNA-seq) technology, scRNA-seq experiments with ordered conditions (e.g. time-course) are becoming common. Methods developed for analyzing ordered bulk RNA-seq experiments are not applicable to scRNA-seq, since their distributional assumptions are often violated by additional heterogeneities prevalent in scRNA-seq. Here we present SC-Pattern - an empirical Bayes model to characterize genes with expression changes in ordered scRNA-seq experiments. SCPattern utilizes the non-parametrical Kolmogorov-Smirnov statistic, thus it has the flexibility to identify genes with a wide variety of types of changes. Additionally, the Bayes framework allows SCPattern to classify genes into expression patterns with probability estimates.ResultsSimulation results show that SCPattern is well powered for identifying genes with expression changes while the false discovery rate is well controlled. SCPattern is also able to accurately classify these dynamic genes into directional expression patterns. Applied to a scRNA-seq time course dataset studying human embryonic cell differentiation, SCPattern detected a group of important genes that are involved in mesendoderm and definitive endoderm cell fate decisions, positional patterning, and cell cycle.Availability and ImplementationThe SCPattern is implemented as an R package along with a user-friendly graphical interface, which are available at:https://github.com/lengning/SCPatternContact:[email protected]

scholarly journals Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data

2016 ◽  
Author(s):  
Aaron T. L. Lun ◽  
John C. Marioni
Keyword(s):  
Single Cell ◽  
Error Control ◽  
Type I Error ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Simulated Data ◽  
Real Data ◽  
Type I ◽  
Rna Seq ◽  
Cell Counts

AbstractAn increasing number of studies are using single-cell RNA-sequencing (scRNA-seq) to characterize the gene expression profiles of individual cells. One common analysis applied to scRNA-seq data involves detecting differentially expressed (DE) genes between cells in different biological groups. However, many experiments are designed such that the cells to be compared are processed in separate plates or chips, meaning that the groupings are confounded with systematic plate effects. This confounding aspect is frequently ignored in DE analyses of scRNA-seq data. In this article, we demonstrate that failing to consider plate effects in the statistical model results in loss of type I error control. A solution is proposed whereby counts are summed from all cells in each plate and the count sums for all plates are used in the DE analysis. This restores type I error control in the presence of plate effects without compromising detection power in simulated data. Summation is also robust to varying numbers and library sizes of cells on each plate. Similar results are observed in DE analyses of real data where the use of count sums instead of single-cell counts improves specificity and the ranking of relevant genes. This suggests that summation can assist in maintaining statistical rigour in DE analyses of scRNA-seq data with plate effects.

scholarly journals Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Sophia Clara Mädler ◽  
Alice Julien-Laferriere ◽  
Luis Wyss ◽  
Miroslav Phan ◽  
Anthony Sonrel ◽  
...  
Keyword(s):  
Best Practices ◽  
Translational Research ◽  
Single Cell ◽  
Tumor Tissue ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Supervised Machine Learning ◽  
Rna Seq ◽  
Disease Biology ◽  
Transcriptomics Data

Abstract Single-cell RNA sequencing (scRNA-seq) revolutionized our understanding of disease biology. The promise it presents to also transform translational research requires highly standardized and robust software workflows. Here, we present the toolkit Besca, which streamlines scRNA-seq analyses and their use to deconvolute bulk RNA-seq data according to current best practices. Beyond a standard workflow covering quality control, filtering, and clustering, two complementary Besca modules, utilizing hierarchical cell signatures and supervised machine learning, automate cell annotation and provide harmonized nomenclatures. Subsequently, the gene expression profiles can be employed to estimate cell type proportions in bulk transcriptomics data. Using multiple, diverse scRNA-seq datasets, some stemming from highly heterogeneous tumor tissue, we show how Besca aids acceleration, interoperability, reusability and interpretability of scRNA-seq data analyses, meeting crucial demands in translational research and beyond.

scholarly journals SCDC: Bulk Gene Expression Deconvolution by Multiple Single-Cell RNA Sequencing References

2019 ◽  
Author(s):  
Meichen Dong ◽  
Aatish Thennavan ◽  
Eugene Urrutia ◽  
Yun Li ◽  
Charles M. Perou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Mixed Cell ◽  
Single Cell Rna Sequencing

AbstractRecent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

scholarly journals Computational cell cycle analysis of single cell RNA-Seq data

2020 ◽  
Author(s):  
Marmar Moussa ◽  
Ion I. Măndoiu
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Analysis Tool ◽  
Rna Seq ◽  
Cell Type ◽  
Computational Cell ◽  
Analysis Pipeline ◽  
Web Based

AbstractThe variation in gene expression profiles of cells captured in different phases of the cell cycle can interfere with cell type identification and functional analysis of single cell RNA-Seq (scRNA-Seq) data. In this paper, we introduce SC1CC (SC1 Cell Cycle analysis tool), a computational approach for clustering and ordering single cell transcriptional profiles according to their progression along cell cycle phases. We also introduce a new robust metric, Gene Smoothness Score (GSS) for assessing the cell cycle based order of the cells. SC1CC is available as part of the SC1 web-based scRNA-Seq analysis pipeline, publicly accessible at https://sc1.engr.uconn.edu/.

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