Improved SNV Discovery in Barcode-Stratified scRNA-seq Alignments

Prashant, N M; Liu, Hongyu; Dillard, Christian; Ibeawuchi, Helen; Chan, Hang; Horvath, Anélia

doi:10.3390/genes12101558

Cited by 9 publications

(7 citation statements)

References 36 publications

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“…Each of these components can be adapted to other applications. Recent studies emerged to infer subclone evolution from single-cell RNA sequencing (scRNA-seq) data due to its high input amount [ 46 , 47 , 48 , 49 ]. The sparsity and noise are general for single-cell data (e.g., scRNA-seq and single-cell chromatin accessibility data) and, thus, our extended RDA can be also applied to other areas of single-cell data analysis.…”

Section: Discussionmentioning

confidence: 99%

RDAClone: Deciphering Tumor Heterozygosity through Single-Cell Genomics Data Analysis with Robust Deep Autoencoder

Xia

Wang

Zhang

et al. 2021

Genes

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Rapid advances in single-cell genomics sequencing (SCGS) have allowed researchers to characterize tumor heterozygosity with unprecedented resolution and reveal the phylogenetic relationships between tumor cells or clones. However, high sequencing error rates of current SCGS data, i.e., false positives, false negatives, and missing bases, severely limit its application. Here, we present a deep learning framework, RDAClone, to recover genotype matrices from noisy data with an extended robust deep autoencoder, cluster cells into subclones by the Louvain-Jaccard method, and further infer evolutionary relationships between subclones by the minimum spanning tree. Studies on both simulated and real datasets demonstrate its robustness and superiority in data denoising, cell clustering, and evolutionary tree reconstruction, particularly for large datasets.

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Section: Discussionmentioning

confidence: 99%

RDAClone: Deciphering Tumor Heterozygosity through Single-Cell Genomics Data Analysis with Robust Deep Autoencoder

Xia

Wang

Zhang

et al. 2021

Genes

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“…Many studies have shown that CNVs are associated with complex diseases such as autism, schizophrenia, Alzheimer’s disease, and cancer. In recent years, there have been a large number of studies on SNVs and CNVs ( Jugas et al, 2021 ; Prashant et al, 2021 ; Ladeira et al, 2022 ; Lee et al, 2022 ; Li et al, 2022 ; Zheng, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Enhancing genomic mutation data storage optimization based on the compression of asymmetry of sparsity

Ding

Yuan

et al. 2023

Front. Genet.

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Background: With the rapid development of high-throughput sequencing technology and the explosive growth of genomic data, storing, transmitting and processing massive amounts of data has become a new challenge. How to achieve fast lossless compression and decompression according to the characteristics of the data to speed up data transmission and processing requires research on relevant compression algorithms.Methods: In this paper, a compression algorithm for sparse asymmetric gene mutations (CA_SAGM) based on the characteristics of sparse genomic mutation data was proposed. The data was first sorted on a row-first basis so that neighboring non-zero elements were as close as possible to each other. The data were then renumbered using the reverse Cuthill-Mckee sorting technique. Finally the data were compressed into sparse row format (CSR) and stored. We had analyzed and compared the results of the CA_SAGM, coordinate format (COO) and compressed sparse column format (CSC) algorithms for sparse asymmetric genomic data. Nine types of single-nucleotide variation (SNV) data and six types of copy number variation (CNV) data from the TCGA database were used as the subjects of this study. Compression and decompression time, compression and decompression rate, compression memory and compression ratio were used as evaluation metrics. The correlation between each metric and the basic characteristics of the original data was further investigated.Results: The experimental results showed that the COO method had the shortest compression time, the fastest compression rate and the largest compression ratio, and had the best compression performance. CSC compression performance was the worst, and CA_SAGM compression performance was between the two. When decompressing the data, CA_SAGM performed the best, with the shortest decompression time and the fastest decompression rate. COO decompression performance was the worst. With increasing sparsity, the COO, CSC and CA_SAGM algorithms all exhibited longer compression and decompression times, lower compression and decompression rates, larger compression memory and lower compression ratios. When the sparsity was large, the compression memory and compression ratio of the three algorithms showed no difference characteristics, but the rest of the indexes were still different.Conclusion: CA_SAGM was an efficient compression algorithm that combines compression and decompression performance for sparse genomic mutation data.

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“…However, new research has demonstrated that primate adrenal tissue does not originate from an adrenogenital ridge 165 . Testing whether centripetal migration is the correct model for human adrenal gland development is difficult but becoming feasible through the application of single-cell analyses, which use somatic DNA variants as barcodes for cell ancestry 167 , 168 . Questions about cell ontogeny in human adrenal are raised by the finding of juxta-medullary ZG cells, by the presence of adrenomedullary cells in the ZG 169 , 170 and by our finding that RSPO3-LGR5 signalling inhibits aldosterone production in the human adrenal gland 105 , 169 , 170 .…”

Section: Cacna1dmentioning

confidence: 99%

Primary aldosteronism: molecular medicine meets public health

Azizan,

Drake,

Brown

2023

Nat Rev Nephrol

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Primary aldosteronism is the most common single cause of hypertension and is potentially curable when only one adrenal gland is the culprit. The importance of primary aldosteronism to public health derives from its high prevalence but huge under-diagnosis (estimated to be <1% of all affected individuals), despite the consequences of poor blood pressure control by conventional therapy and enhanced cardiovascular risk. This state of affairs is attributable to the fact that the tools used for diagnosis or treatment are still those that originated in the 1970–1990s. Conversely, molecular discoveries have transformed our understanding of adrenal physiology and pathology. Many molecules and processes associated with constant adrenocortical renewal and interzonal metamorphosis also feature in aldosterone-producing adenomas and aldosterone-producing micronodules. The adrenal gland has one of the most significant rates of non-silent somatic mutations, with frequent selection of those driving autonomous aldosterone production, and distinct clinical presentations and outcomes for most genotypes. The disappearance of aldosterone synthesis and cells from most of the adult human zona glomerulosa is the likely driver of the mutational success that causes aldosterone-producing adenomas, but insights into the pathways that lead to constitutive aldosterone production and cell survival may open up opportunities for novel therapies.

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Improved SNV Discovery in Barcode-Stratified scRNA-seq Alignments

Cited by 9 publications

References 36 publications

RDAClone: Deciphering Tumor Heterozygosity through Single-Cell Genomics Data Analysis with Robust Deep Autoencoder

RDAClone: Deciphering Tumor Heterozygosity through Single-Cell Genomics Data Analysis with Robust Deep Autoencoder

Enhancing genomic mutation data storage optimization based on the compression of asymmetry of sparsity

Primary aldosteronism: molecular medicine meets public health

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