scholarly journals I-Impute: a self-consistent method to impute single cell RNA sequencing data

BMC Genomics ◽  
2020 ◽  
Vol 21 (S10) ◽  
Author(s):  
Xikang Feng ◽  
Lingxi Chen ◽  
Zishuai Wang ◽  
Shuai Cheng Li
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Embryonic Stem ◽  
The State ◽  
Cell Subpopulation ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Self Consistent ◽  
Consistent Method ◽  
Self Consistency

Abstract Background Single-cell RNA-sequencing (scRNA-seq) is becoming indispensable in the study of cell-specific transcriptomes. However, in scRNA-seq techniques, only a small fraction of the genes are captured due to “dropout” events. These dropout events require intensive treatment when analyzing scRNA-seq data. For example, imputation tools have been proposed to estimate dropout events and de-noise data. The performance of these imputation tools are often evaluated, or fine-tuned, using various clustering criteria based on ground-truth cell subgroup labels. This limits their effectiveness in the cases where we lack cell subgroup knowledge. We consider an alternative strategy which requires the imputation to follow a “self-consistency” principle; that is, the imputation process is to refine its results until there is no internal inconsistency or dropouts from the data. Results We propose the use of “self-consistency” as a main criteria in performing imputation. To demonstrate this principle we devised I-Impute, a “self-consistent” method, to impute scRNA-seq data. I-Impute optimizes continuous similarities and dropout probabilities, in iterative refinements until a self-consistent imputation is reached. On the in silico data sets, I-Impute exhibited the highest Pearson correlations for different dropout rates consistently compared with the state-of-art methods SAVER and scImpute. Furthermore, we collected three wetlab datasets, mouse bladder cells dataset, embryonic stem cells dataset, and aortic leukocyte cells dataset, to evaluate the tools. I-Impute exhibited feasible cell subpopulation discovery efficacy on all the three datasets. It achieves the highest clustering accuracy compared with SAVER and scImpute. Conclusions A strategy based on “self-consistency”, captured through our method, I-Impute, gave imputation results better than the state-of-the-art tools. Source code of I-Impute can be accessed at https://github.com/xikanfeng2/I-Impute.

scholarly journals I-Impute: a self-consistent method to impute single cell RNA sequencing data

2019 ◽  
Author(s):  
Xikang Feng ◽  
Lingxi Chen ◽  
Zishuai Wang ◽  
Shuai Cheng Li
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Embryonic Stem ◽  
Ground Truth ◽  
The Self ◽  
Cell Subpopulation ◽  
Single Cell Rna Sequencing ◽  
Self Consistent ◽  
Consistent Method ◽  
Self Consistency

Single-cell RNA-sequencing (scRNA-seq) is essential for the study of cell-specific transcriptome landscapes. The scRNA-seq techniques capture merely a small fraction of the gene due to “dropout” events. When analyzing with scRNA-seq data, the dropout events receive intensive attentions. Imputation tools are proposed to estimate the values of the dropout events and de-noise the data. To evaluate the imputation tools, researchers have developed different clustering criteria by incorporating the ground-truth cell subgroup labels. There lack measurements without cell subgroup knowledge. A reliable imputation tool should follow the “self-consistency” principle; that is, the tool reports the results only if it finds no further errors or dropouts from the data. Here, we propose “self-consistency” as an explicit evaluation criterion; also, we propose I-Impute, a “self-consistent” method, to impute scRNA-seq data. I-Impute lever-ages continuous similarities and dropout probabilities and refines the data iteratively to make the final output self-consistent. On the in silico data sets, I-Impute exhibited the highest Pearson correlations for different dropout rates consistently compared with the state-of-art methods SAVER and scImpute. On the datasets of 90.87%, 70.98% and 56.65% zero rates, I-Impute exhibited the correlations as 0.78, 0.90, and 0.94, respectively, between ground truth entries and predicted values, while SAVER exhibited the correlations as 0.58, 0.79 and 0.88, respectively and scImpute exhibited correlations as 0.65, 0.86, and 0.93, respectively. Furthermore, we collected three wetlab datasets, mouse bladder cells dataset, embryonic stem cells dataset, and aortic leukocyte cells dataset, to evaluate the tools. I-Impute exhibited feasible cell subpopulation discovery efficacy on all the three datasets. It achieves the highest clustering accuracy compared with SAVER and scImpute; that is, I-Impute displayed the adjusted Rand indices of the three datasets as 0.61, 0.7, 0.52, which improved the indices of SAVER by 0.01 to 0.17, and improved the indices of scImpute by 0.19 to 0.4. Also, I-impute promoted normalized mutual information of the three datasets by 0.01 to 0.09 comparing with SAVER, and by 0.15 to 0.34 comparing with scImpute. I-Impute exhibits robust imputation ability and follows the “self-consistency” principle. It offers perspicacity to uncover the underlying cell subtypes in real scRNA-Seq data. Source code of I-Impute can be accessed at https://github.com/xikanfeng2/I-Impute.

scholarly journals Canonical and early lineage-specific stem cell types identified in planarian SirNeoblasts

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Kaimeng Niu ◽  
Hao Xu ◽  
Yuanyi Zhou Xiong ◽  
Yun Zhao ◽  
Chong Gao ◽  
...  
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Fate ◽  
Pluripotent Stem Cells ◽  
Expression Patterns ◽  
Cell Subpopulation ◽  
Functional Study ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Background The pluripotent stem cells in planarians, a model for tissue and cellular regeneration, remain further identification. We recently developed a method to enrich piwi-1+ cells in Schmidtea mediterranea, by staining cells with SiR-DNA and Cell Tracker Green, named SirNeoblasts that permits their propagation and subsequent functional study in vivo. Since traditional enrichment for planarian neoblasts by Hoechst 33342 staining generates X1 cells, blocking the cell cycle and inducing cytotoxicity, this method by SiR-DNA and Cell Tracker Green represents a complementary technological advance for functional investigation of cell fate and regeneration. However, the similarities in heterogeneity of cell subtypes between SirNeoblasts and X1 remain unknown. Results In this work, we performed single cell RNA sequencing of SirNeoblasts for comparison with differential expression patterns in a publicly available X1 single cell RNA sequencing data. We found first that all of the lineage-specific progenitor cells in X1 were present in comparable proportions in SirNeoblasts. In addition, SirNeoblasts contain an early muscle progenitor that is unreported in X1. Analysis of new markers for putative pluripotent stem cells identified here, with subsequent sub-clustering analysis, revealed earlier lineages of epidermal, muscular, intestinal, and pharyngeal progenitors than have been observed in X1. Using the gcm as a marker, we also identified a cell subpopulation resided in previously identified tgs-1+ neoblasts. Knockdown of gcm impaired the neoblast repopulation, suggesting a function of gcm in neoblasts. Conclusions In summary, the use of SirNeoblasts will enable broad experimental advances in regeneration and cell fate specification, given the possibility for propagation and transplantation of recombinant and mutagenized pluripotent stem cells that are not previously afforded to this rapid and versatile model system.

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.

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