scholarly journals Regulatory network-based imputation of dropouts in single-cell RNA sequencing data

2019 ◽  
Author(s):  
Ana Carolina Leote ◽  
Xiaohui Wu ◽  
Andreas Beyer
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Regulatory Network ◽  
Missing Values ◽  
Transcriptional Regulatory Network ◽  
Imputation Method ◽  
Sequencing Data ◽  
Expression Levels ◽  
Single Cell Rna Sequencing ◽  
Baseline Approach

AbstractSingle-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information.Here, we show that a transcriptional regulatory network learned from external, independent gene expression data improves dropout imputation. Using a variety of human scRNA-seq datasets we demonstrate that our network-based approach outperforms published state-of-the-art methods. The network-based approach performs particularly well for lowly expressed genes, including cell-type-specific transcriptional regulators. Additionally, we tested a baseline approach, where we imputed missing values using the sample-wide average expression of a gene. Unexpectedly, up to 48% of the genes were better predicted using this baseline approach, suggesting negligible cell-to-cell variation of expression levels for many genes. Our work shows that there is no single best imputation method; rather, the best method depends on gene-specific features, such as expression level and expression variation across cells. We thus implemented an R-package called ADImpute (available from https://github.com/anacarolinaleote/ADImpute) that automatically determines the best imputation method for each gene in a dataset.

scholarly journals bayNorm: Bayesian gene expression recovery, imputation and normalisation for single cell RNA-sequencing data

2018 ◽  
Author(s):  
Wenhao Tang ◽  
François Bertaux ◽  
Philipp Thomas ◽  
Claire Stefanelli ◽  
Malika Saint ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Single Molecule ◽  
Empirical Bayes ◽  
Missing Values ◽  
Likelihood Function ◽  
Differential Expression Analysis ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Normalisation of single cell RNA sequencing (scRNA-seq) data is a prerequisite to their interpretation. The marked technical variability and high amounts of missing observations typical of scRNA-seq datasets make this task particularly challenging. Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq counts. The method’s likelihood function follows a binomial model of mRNA capture, while priors are estimated from expression values across cells using an empirical Bayes approach. We demonstrate using publicly-available scRNA-seq datasets and simulated expression data that bayNorm allows robust imputation of missing values generating realistic transcript distributions that match single molecule FISH measurements. Moreover, by using priors informed by dataset structures, bayNorm improves accuracy and sensitivity of differential expression analysis and reduces batch effect compared to other existing methods. Altogether, bayNorm provides an efficient, integrated solution for global scaling normalisation, imputation and true count recovery of gene expression measurements from scRNA-seq data.

scholarly journals SCMarker: ab initio marker selection for single cell transcriptome profiling

2018 ◽  
Author(s):  
Fang Wang ◽  
Shaoheng Liang ◽  
Tapsi Kumar ◽  
Nicholas Navin ◽  
Ken Chen
Keyword(s):  
Ab Initio ◽  
Single Cell ◽  
Rna Sequencing ◽  
Mrna Transcript ◽  
Cell Type ◽  
Sequencing Data ◽  
Transcript Levels ◽  
Expression Levels ◽  
Marker Selection ◽  
Single Cell Rna Sequencing

AbstractSingle-cell RNA-sequencing data generated by a variety of technologies, such as Drop-seq and SMART-seq, can reveal simultaneously the mRNA transcript levels of thousands of genes in thousands of cells. It is often important to identify informative genes or cell-type-discriminative markers to reduce dimensionality and achieve informative cell typing results. We present an ab initio method that performs unsupervised marker selection by identifying genes that have subpopulation-discriminative expression levels and are co- or mutually-exclusively expressed with other genes. Consistent improvements in cell-type classification and biologically meaningful marker selection are achieved by applying SCMarker on various datasets in multiple tissue types, followed by a variety of clustering algorithms. The source code of SCMarker is publicly available at https://github.com/KChen-lab/SCMarker.Author SummarySingle cell RNA-sequencing technology simultaneously provides the mRNA transcript levels of thousands of genes in thousands of cells. A frequent requirement of single cell expression analysis is the identification of markers which may explain complex cellular states or tissue composition. We propose a new marker selection strategy (SCMarker) to accurately delineate cell types in single cell RNA-sequencing data by identifying genes that have bi/multi-modally distributed expression levels and are co- or mutually-exclusively expressed with some other genes. Our method can determine the cell-type-discriminative markers without referencing to any known transcriptomic profiles or cell ontologies, and consistently achieves accurate cell-type-discriminative marker identification in a variety of scRNA-seq datasets.

scholarly journals PRIME: a probabilistic imputation method to reduce dropout effects in single cell RNA sequencing

2020 ◽  
Author(s):  
Hyundoo Jeong ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Local Community ◽  
Expression Patterns ◽  
Imputation Method ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Depth Analysis ◽  
Single Cell Rna Sequencing

AbstractSingle-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data therefore need to be carefully processed before in-depth analysis. Here we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local community of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single cell sequencing), on six datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise.

scholarly journals MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data

2017 ◽  
Author(s):  
David van Dijk ◽  
Juozas Nainys ◽  
Roshan Sharma ◽  
Pooja Kaithail ◽  
Ambrose J. Carr ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Regulatory Networks ◽  
Missing Values ◽  
Cluster Structure ◽  
Gene Interactions ◽  
Specific Gene ◽  
Sequencing Data ◽  
Mesenchymal Transition ◽  
Single Cell Rna Sequencing

ABSTRACTSingle-cell RNA-sequencing is fast becoming a major technology that is revolutionizing biological discovery in fields such as development, immunology and cancer. The ability to simultaneously measure thousands of genes at single cell resolution allows, among other prospects, for the possibility of learning gene regulatory networks at large scales. However, scRNA-seq technologies suffer from many sources of significant technical noise, the most prominent of which is ‘dropout’ due to inefficient mRNA capture. This results in data that has a high degree of sparsity, with typically only ~10% non-zero values. To address this, we developed MAGIC (Markov Affinity-based Graph Imputation of Cells), a method for imputing missing values, and restoring the structure of the data. After MAGIC, we find that two- and three-dimensional gene interactions are restored and that MAGIC is able to impute complex and non-linear shapes of interactions. MAGIC also retains cluster structure, enhances cluster-specific gene interactions and restores trajectories, as demonstrated in mouse retinal bipolar cells, hematopoiesis, and our newly generated epithelial-to-mesenchymal transition dataset.

scholarly journals IMMU-27. SINGLE CELL RNA-SEQUENCING IDENTIFIES NOVEL BONE MARROW DERIVED MYELOID CELLS IN GLIOBLASTOMA ASSOCIATED WITH TUMOR AGGRESSION

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii110-ii110
Author(s):  
Christina Jackson ◽  
Christopher Cherry ◽  
Sadhana Bom ◽  
Hao Zhang ◽  
John Choi ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Single Cell ◽  
Tumor Cells ◽  
Rna Sequencing ◽  
Metabolic Pathways ◽  
Myeloid Cells ◽  
Tumor Grade ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Two Populations

Abstract BACKGROUND Glioma associated myeloid cells (GAMs) can be induced to adopt an immunosuppressive phenotype that can lead to inhibition of anti-tumor responses in glioblastoma (GBM). Understanding the composition and phenotypes of GAMs is essential to modulating the myeloid compartment as a therapeutic adjunct to improve anti-tumor immune response. METHODS We performed single-cell RNA-sequencing (sc-RNAseq) of 435,400 myeloid and tumor cells to identify transcriptomic and phenotypic differences in GAMs across glioma grades. We further correlated the heterogeneity of the GAM landscape with tumor cell transcriptomics to investigate interactions between GAMs and tumor cells. RESULTS sc-RNAseq revealed a diverse landscape of myeloid-lineage cells in gliomas with an increase in preponderance of bone marrow derived myeloid cells (BMDMs) with increasing tumor grade. We identified two populations of BMDMs unique to GBMs; Mac-1and Mac-2. Mac-1 demonstrates upregulation of immature myeloid gene signature and altered metabolic pathways. Mac-2 is characterized by expression of scavenger receptor MARCO. Pseudotime and RNA velocity analysis revealed the ability of Mac-1 to transition and differentiate to Mac-2 and other GAM subtypes. We further found that the presence of these two populations of BMDMs are associated with the presence of tumor cells with stem cell and mesenchymal features. Bulk RNA-sequencing data demonstrates that gene signatures of these populations are associated with worse survival in GBM. CONCLUSION We used sc-RNAseq to identify a novel population of immature BMDMs that is associated with higher glioma grades. This population exhibited altered metabolic pathways and stem-like potentials to differentiate into other GAM populations including GAMs with upregulation of immunosuppressive pathways. Our results elucidate unique interactions between BMDMs and GBM tumor cells that potentially drives GBM progression and the more aggressive mesenchymal subtype. Our discovery of these novel BMDMs have implications in new therapeutic targets in improving the efficacy of immune-based therapies in GBM.

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 Single-cell data clustering based on sparse optimization and low-rank matrix factorization

2021 ◽  
Author(s):  
Yinlei Hu ◽  
Bin Li ◽  
Falai Chen ◽  
Kun Qu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Matrix Factorization ◽  
Data Clustering ◽  
Cell Types ◽  
Low Rank ◽  
Sequencing Data ◽  
Rank Matrix ◽  
Single Cell Rna Sequencing ◽  
Low Rank Matrix

Abstract Unsupervised clustering is a fundamental step of single-cell RNA sequencing data analysis. This issue has inspired several clustering methods to classify cells in single-cell RNA sequencing data. However, accurate prediction of the cell clusters remains a substantial challenge. In this study, we propose a new algorithm for single-cell RNA sequencing data clustering based on Sparse Optimization and low-rank matrix factorization (scSO). We applied our scSO algorithm to analyze multiple benchmark datasets and showed that the cluster number predicted by scSO was close to the number of reference cell types and that most cells were correctly classified. Our scSO algorithm is available at https://github.com/QuKunLab/scSO. Overall, this study demonstrates a potent cell clustering approach that can help researchers distinguish cell types in single-cell RNA sequencing data.

Sign in / Sign up

Export Citation Format

Share Document