The effects of H22 tumor on the quality of oocytes and the development of early embryos from host mice: A single-cell RNA sequencing approach

ABSTRACTSingle cell RNA sequencing (scRNA-seq) has shown great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant sub-populations of T cells, and notably the identification of the full length T cell receptor (TCRαβ), which defines the specificity against cognate antigens. Several factors, such as RNA library capture, cell quality, and sequencing output have been suggested to affect the quality of scRNA-seq data, but these factors have not been systematically examined.We studied the effect of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCRαβ reconstruction, utilising 1,305 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (<50 bp), but these featured higher technical variability compared to profiles from longer reads. TCRαβ were detected in 1,027 cells (79%), with a success rate between 81% and 100% for datasets with at least 250,000 (PE) reads of length >50 bp.Sufficient read length and sequencing depth can control technical noise to enable accurate identification of TCRαβ and gene expression profiles from scRNA-seq data of T cells.

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Application of HDBSСAN Method for Clustering scRNA-seq Data

Proceedings of the Institute for System Programming of RAS ◽

10.15514/ispras-2020-32(5)-8 ◽

2020 ◽

Vol 32 (5) ◽

pp. 111-120

Author(s):

Maria Andreevna Akimenkova ◽

Anna Anatolyevna Maznina ◽

Anton Yurievich Naumov ◽

Evgeny Andreevich Karpulevich

Keyword(s):

Feature Selection ◽

Dimensionality Reduction ◽

Single Cell ◽

Rna Sequencing ◽

Cell Types ◽

Adjusted Rand Index ◽

Clustering Method ◽

Clustering Problem ◽

Single Cell Rna Sequencing

One of the main tasks in the analysis of single cell RNA sequencing (scRNA-seq) data is the identification of cell types and subtypes, which is usually based on some method of clustering. There is a number of generally accepted approaches to solving the clustering problem, one of which is implemented in the Seurat package. In addition, the quality of clustering is influenced by the use of preprocessing algorithms, such as imputation, dimensionality reduction, feature selection, etc. In the article, the HDBSCAN hierarchical clustering method is used to cluster scRNA-seq data. For a more complete comparison Experiments and comparisons were made on two labeled datasets: Zeisel (3005 cells) and Romanov (2881 cells). To compare the quality of clustering, two external metrics were used: Adjusted Rand index and V-measure. The experiments demonstrated a higher quality of clustering by the HDBSCAN method on the Zeisel dataset and a poorer quality on the Romanov dataset.

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ROGUE: an entropy-based universal metric for assessing the purity of single cell population

10.1101/819581 ◽

2019 ◽

Cited By ~ 2

Author(s):

Baolin Liu ◽

Chenwei Li ◽

Ziyi Li ◽

Xianwen Ren ◽

Zemin Zhang

Keyword(s):

B Cell ◽

Single Cell ◽

Rna Sequencing ◽

Cell Types ◽

Wide Range ◽

Single Cell Rna Sequencing ◽

Pure Cell ◽

Versatile Tool ◽

Cluster Purity

AbstractSingle-cell RNA sequencing (scRNA-seq) is a versatile tool for discovering and annotating cell types and states, but the determination and annotation of cell subtypes is often subjective and arbitrary. Often, it is not even clear whether a given cluster is uniform. Here we present an entropy-based statistic, ROGUE, to accurately quantify the purity of identified cell clusters. We demonstrated that our ROGUE metric is generalizable across datasets, and enables accurate, sensitive and robust assessment of cluster purity on a wide range of simulated and real datasets. Applying this metric to fibroblast and B cell datasets, we identified additional subtypes and demonstrated the application of ROGUE-guided analyses to detect true signals in specific subpopulations. ROGUE can be applied to all tested scRNA-seq datasets, and has important implications for evaluating the quality of putative clusters, discovering pure cell subtypes and constructing comprehensive, detailed and standardized single cell atlas.

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