scaDA: A novel statistical method for differential analysis of single-cell chromatin accessibility sequencing data.
Single-cell ATAC-seq sequencing data (scATAC-seq) has been widely used to investigate chromatin accessibility on the single-cell level. One important application of scATAC-seq data analysis is differential chromatin accessibility (DA) analysis. However, the data characteristics of scATAC-seq such as...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-08-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1011854 |
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| author | Fengdi Zhao Xin Ma Bing Yao Qing Lu Li Chen |
| author_facet | Fengdi Zhao Xin Ma Bing Yao Qing Lu Li Chen |
| author_sort | Fengdi Zhao |
| collection | DOAJ |
| description | Single-cell ATAC-seq sequencing data (scATAC-seq) has been widely used to investigate chromatin accessibility on the single-cell level. One important application of scATAC-seq data analysis is differential chromatin accessibility (DA) analysis. However, the data characteristics of scATAC-seq such as excessive zeros and large variability of chromatin accessibility across cells impose a unique challenge for DA analysis. Existing statistical methods focus on detecting the mean difference of the chromatin accessible regions while overlooking the distribution difference. Motivated by real data exploration that distribution difference exists among cell types, we introduce a novel composite statistical test named "scaDA", which is based on zero-inflated negative binomial model (ZINB), for performing differential distribution analysis of chromatin accessibility by jointly testing the abundance, prevalence and dispersion simultaneously. Benefiting from both dispersion shrinkage and iterative refinement of mean and prevalence parameter estimates, scaDA demonstrates its superiority to both ZINB-based likelihood ratio tests and published methods by achieving the highest power and best FDR control in a comprehensive simulation study. In addition to demonstrating the highest power in three real sc-multiome data analyses, scaDA successfully identifies differentially accessible regions in microglia from sc-multiome data for an Alzheimer's disease (AD) study that are most enriched in GO terms related to neurogenesis and the clinical phenotype of AD, and AD-associated GWAS SNPs. |
| format | Article |
| id | doaj-art-32e3b07f858c4ecd97cf3cc7f2b61316 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-32e3b07f858c4ecd97cf3cc7f2b613162025-01-16T05:30:45ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-08-01208e101185410.1371/journal.pcbi.1011854scaDA: A novel statistical method for differential analysis of single-cell chromatin accessibility sequencing data.Fengdi ZhaoXin MaBing YaoQing LuLi ChenSingle-cell ATAC-seq sequencing data (scATAC-seq) has been widely used to investigate chromatin accessibility on the single-cell level. One important application of scATAC-seq data analysis is differential chromatin accessibility (DA) analysis. However, the data characteristics of scATAC-seq such as excessive zeros and large variability of chromatin accessibility across cells impose a unique challenge for DA analysis. Existing statistical methods focus on detecting the mean difference of the chromatin accessible regions while overlooking the distribution difference. Motivated by real data exploration that distribution difference exists among cell types, we introduce a novel composite statistical test named "scaDA", which is based on zero-inflated negative binomial model (ZINB), for performing differential distribution analysis of chromatin accessibility by jointly testing the abundance, prevalence and dispersion simultaneously. Benefiting from both dispersion shrinkage and iterative refinement of mean and prevalence parameter estimates, scaDA demonstrates its superiority to both ZINB-based likelihood ratio tests and published methods by achieving the highest power and best FDR control in a comprehensive simulation study. In addition to demonstrating the highest power in three real sc-multiome data analyses, scaDA successfully identifies differentially accessible regions in microglia from sc-multiome data for an Alzheimer's disease (AD) study that are most enriched in GO terms related to neurogenesis and the clinical phenotype of AD, and AD-associated GWAS SNPs.https://doi.org/10.1371/journal.pcbi.1011854 |
| spellingShingle | Fengdi Zhao Xin Ma Bing Yao Qing Lu Li Chen scaDA: A novel statistical method for differential analysis of single-cell chromatin accessibility sequencing data. PLoS Computational Biology |
| title | scaDA: A novel statistical method for differential analysis of single-cell chromatin accessibility sequencing data. |
| title_full | scaDA: A novel statistical method for differential analysis of single-cell chromatin accessibility sequencing data. |
| title_fullStr | scaDA: A novel statistical method for differential analysis of single-cell chromatin accessibility sequencing data. |
| title_full_unstemmed | scaDA: A novel statistical method for differential analysis of single-cell chromatin accessibility sequencing data. |
| title_short | scaDA: A novel statistical method for differential analysis of single-cell chromatin accessibility sequencing data. |
| title_sort | scada a novel statistical method for differential analysis of single cell chromatin accessibility sequencing data |
| url | https://doi.org/10.1371/journal.pcbi.1011854 |
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