scholarly journals scAEspy: a unifying tool based on autoencoders for the analysis of single-cell RNA sequencing data

2019 ◽  
Author(s):  
Andrea Tangherloni ◽  
Federico Ricciuti ◽  
Daniela Besozzi ◽  
Pietro Liò ◽  
Ana Cvejic
Keyword(s):  
Principal Component Analysis ◽  
Single Cell ◽  
Rna Sequencing ◽  
Principal Component ◽  
Loss Functions ◽  
Gene Interactions ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
The Common ◽  
Public Datasets

Autoencoders (AEs) have been effectively used to capture the non-linearities among gene interactions of single-cell RNA sequencing (scRNA-Seq) data. However, their integration with the common scRNA-Seq bioinformatics pipelines still poses a challenge. Here, we introduce scAEspy, a unifying tool that embodies five of the most advanced AEs and different loss functions, including two novel AEs that we developed. scAEspy allows the integration of data generated using different scRNA-Seq platforms. We benchmarked scAEspy against principal component analysis (PCA) on five public datasets, showing that our new AEs outperform the existing solutions, achieving more than 20% increase of the Rand Index in the identification of cell clusters.

scholarly journals scFlow: A Scalable and Reproducible Analysis Pipeline for Single-Cell RNA Sequencing Data

2021 ◽  
Author(s):  
Combiz Khozoie ◽  
Nurun Fancy ◽  
Mahdi Moradi Marjaneh ◽  
Alan E. Murphy ◽  
Paul M. Matthews ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Sparse Matrix ◽  
Data Generation ◽  
Sequencing Data ◽  
Alternative Analysis ◽  
Analysis Pipeline ◽  
Matrix Quality ◽  
Single Cell Rna Sequencing ◽  
Public Datasets

Advances in single-cell RNA-sequencing technology over the last decade have enabled exponential increases in throughput: datasets with over a million cells are becoming commonplace. The burgeoning scale of data generation, combined with the proliferation of alternative analysis methods, led us to develop the scFlow toolkit and the nf-core/scflow pipeline for reproducible, efficient, and scalable analyses of single-cell and single-nuclei RNA-sequencing data. The scFlow toolkit provides a higher level of abstraction on top of popular single-cell packages within an R ecosystem, while the nf-core/scflow Nextflow pipeline is built within the nf-core framework to enable compute infrastructure-independent deployment across all institutions and research facilities. Here we present our flexible pipeline, which leverages the advantages of containerization and the potential of Cloud computing for easy orchestration and scaling of the analysis of large case/control datasets by even non-expert users. We demonstrate the functionality of the analysis pipeline from sparse-matrix quality control through to insight discovery with examples of analysis of four recently published public datasets and describe the extensibility of scFlow as a modular, open-source tool for single-cell and single nuclei bioinformatic analyses.

scholarly journals scFlow: A Scalable and Reproducible Analysis Pipeline for Single-Cell RNA Sequencing Data

2021 ◽  
Author(s):  
Combiz Khozoie ◽  
Nurun Fancy ◽  
Mahdi M. Marjaneh ◽  
Alan E. Murphy ◽  
Paul M. Matthews ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Sparse Matrix ◽  
Data Generation ◽  
Sequencing Data ◽  
Alternative Analysis ◽  
Analysis Pipeline ◽  
Matrix Quality ◽  
Single Cell Rna Sequencing ◽  
Public Datasets

Advances in single-cell RNA-sequencing technology over the last decade have enabled exponential increases in throughput:   datasets with over a million cells are becoming commonplace.   The burgeoning scale of data generation, combined with the proliferation of alternative analysis methods,  led us to develop the scFlow toolkit and the nf-core/scflow pipeline for reproducible, efficient, and scalable analyses of single-cell and single-nuclei RNA-sequencing data.  The scFlow toolkit provides a higher level of abstraction on top of popular single-cell packages within an R ecosystem, while the nf-core/scflow Nextflow pipeline is built within the nf-core framework to enable compute infrastructure-independent deployment across all institutions and research facilities.  Here we present our flexible pipeline, which leverages the advantages of containerization and the potential of Cloud computing for easy orchestration and scaling of the analysis of large case/control datasets by even non-expert users.  We demonstrate the functionality of the analysis pipeline from sparse-matrix quality control through to insight discovery with examples of analysis of four recently published public datasets and describe the extensibility of scFlow as a modular, open-source tool for single-cell and single nuclei bioinformatic analyses.

scholarly journals scFlow: A Scalable and Reproducible Analysis Pipeline for Single-Cell RNA Sequencing Data

2021 ◽  
Author(s):  
Combiz Khozoie ◽  
Nurun Fancy ◽  
Mahdi Moradi Marjaneh ◽  
Alan E. Murphy ◽  
Paul M. Matthews ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Sparse Matrix ◽  
Data Generation ◽  
Sequencing Data ◽  
Alternative Analysis ◽  
Analysis Pipeline ◽  
Matrix Quality ◽  
Single Cell Rna Sequencing ◽  
Public Datasets

Advances in single-cell RNA-sequencing technology over the last decade have enabled exponential increases in throughput:   datasets with over a million cells are becoming commonplace.   The burgeoning scale of data generation, combined with the proliferation of alternative analysis methods,  led us to develop the scFlow toolkit and the nf-core/scflow pipeline for reproducible, efficient, and scalable analyses of single-cell and single-nuclei RNA-sequencing data.  The scFlow toolkit provides a higher level of abstraction on top of popular single-cell packages within an R ecosystem, while the nf-core/scflow Nextflow pipeline is built within the nf-core framework to enable compute infrastructure-independent deployment across all institutions and research facilities.  Here we present our flexible pipeline, which leverages the advantages of containerization and the potential of Cloud computing for easy orchestration and scaling of the analysis of large case/control datasets by even non-expert users.  We demonstrate the functionality of the analysis pipeline from sparse-matrix quality control through to insight discovery with examples of analysis of four recently published public datasets and describe the extensibility of scFlow as a modular, open-source tool for single-cell and single nuclei bioinformatic analyses.

scholarly journals Adapted single-cell consensus clustering (adaSC3)

2020 ◽  
Author(s):  
Cornelia Fuetterer ◽  
Thomas Augustin ◽  
Christiane Fuchs
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Single Cells ◽  
Linear Method ◽  
Principal Component ◽  
Data Sets ◽  
Consensus Clustering ◽  
Sequencing Data ◽  
Reduction Techniques ◽  
Single Cell Rna Sequencing

AbstractThe analysis of single-cell RNA sequencing data is of great importance in health research. It challenges data scientists, but has enormous potential in the context of personalized medicine. The clustering of single cells aims to detect different subgroups of cell populations within a patient in a data-driven manner. Some comparison studies denote single-cell consensus clustering (SC3), proposed by Kiselev et al. (Nat Methods 14(5):483–486, 2017), as the best method for classifying single-cell RNA sequencing data. SC3 includes Laplacian eigenmaps and a principal component analysis (PCA). Our proposal of unsupervised adapted single-cell consensus clustering (adaSC3) suggests to replace the linear PCA by diffusion maps, a non-linear method that takes the transition of single cells into account. We investigate the performance of adaSC3 in terms of accuracy on the data sets of the original source of SC3 as well as in a simulation study. A comparison of adaSC3 with SC3 as well as with related algorithms based on further alternative dimension reduction techniques shows a quite convincing behavior of adaSC3.

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.

Sign in / Sign up

Export Citation Format

Share Document