A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data
Abstract Background Recent development of single cell sequencing technologies has made it possible to identify genes with different expression (DE) levels at the cell type level between different groups of samples. In this article, we propose to borrow information through known biological networks t...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2021-10-01
|
| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-021-04412-0 |
| _version_ | 1818693647151923200 |
|---|---|
| author | Hongyu Li Biqing Zhu Zhichao Xu Taylor Adams Naftali Kaminski Hongyu Zhao |
| author_facet | Hongyu Li Biqing Zhu Zhichao Xu Taylor Adams Naftali Kaminski Hongyu Zhao |
| author_sort | Hongyu Li |
| collection | DOAJ |
| description | Abstract Background Recent development of single cell sequencing technologies has made it possible to identify genes with different expression (DE) levels at the cell type level between different groups of samples. In this article, we propose to borrow information through known biological networks to increase statistical power to identify differentially expressed genes (DEGs). Results We develop MRFscRNAseq, which is based on a Markov random field (MRF) model to appropriately accommodate gene network information as well as dependencies among cell types to identify cell-type specific DEGs. We implement an Expectation-Maximization (EM) algorithm with mean field-like approximation to estimate model parameters and a Gibbs sampler to infer DE status. Simulation study shows that our method has better power to detect cell-type specific DEGs than conventional methods while appropriately controlling type I error rate. The usefulness of our method is demonstrated through its application to study the pathogenesis and biological processes of idiopathic pulmonary fibrosis (IPF) using a single-cell RNA-sequencing (scRNA-seq) data set, which contains 18,150 protein-coding genes across 38 cell types on lung tissues from 32 IPF patients and 28 normal controls. Conclusions The proposed MRF model is implemented in the R package MRFscRNAseq available on GitHub. By utilizing gene-gene and cell-cell networks, our method increases statistical power to detect differentially expressed genes from scRNA-seq data. |
| first_indexed | 2024-12-17T13:17:00Z |
| format | Article |
| id | doaj.art-73f8d1f15edc4208ad885fb4bf2897d7 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-17T13:17:00Z |
| publishDate | 2021-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-73f8d1f15edc4208ad885fb4bf2897d72022-12-21T21:46:59ZengBMCBMC Bioinformatics1471-21052021-10-0122111610.1186/s12859-021-04412-0A Markov random field model for network-based differential expression analysis of single-cell RNA-seq dataHongyu Li0Biqing Zhu1Zhichao Xu2Taylor Adams3Naftali Kaminski4Hongyu Zhao5Department of Biostatistics, School of Public Health, Yale UniversityProgram of Computational Biology and Bioinformatics, Yale UniversityDepartment of Biostatistics, School of Public Health, Yale UniversitySection of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, Yale School of MedicineSection of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, Yale School of MedicineDepartment of Biostatistics, School of Public Health, Yale UniversityAbstract Background Recent development of single cell sequencing technologies has made it possible to identify genes with different expression (DE) levels at the cell type level between different groups of samples. In this article, we propose to borrow information through known biological networks to increase statistical power to identify differentially expressed genes (DEGs). Results We develop MRFscRNAseq, which is based on a Markov random field (MRF) model to appropriately accommodate gene network information as well as dependencies among cell types to identify cell-type specific DEGs. We implement an Expectation-Maximization (EM) algorithm with mean field-like approximation to estimate model parameters and a Gibbs sampler to infer DE status. Simulation study shows that our method has better power to detect cell-type specific DEGs than conventional methods while appropriately controlling type I error rate. The usefulness of our method is demonstrated through its application to study the pathogenesis and biological processes of idiopathic pulmonary fibrosis (IPF) using a single-cell RNA-sequencing (scRNA-seq) data set, which contains 18,150 protein-coding genes across 38 cell types on lung tissues from 32 IPF patients and 28 normal controls. Conclusions The proposed MRF model is implemented in the R package MRFscRNAseq available on GitHub. By utilizing gene-gene and cell-cell networks, our method increases statistical power to detect differentially expressed genes from scRNA-seq data.https://doi.org/10.1186/s12859-021-04412-0Markov random fieldDifferential expressionscRNA-seq |
| spellingShingle | Hongyu Li Biqing Zhu Zhichao Xu Taylor Adams Naftali Kaminski Hongyu Zhao A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data BMC Bioinformatics Markov random field Differential expression scRNA-seq |
| title | A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data |
| title_full | A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data |
| title_fullStr | A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data |
| title_full_unstemmed | A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data |
| title_short | A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data |
| title_sort | markov random field model for network based differential expression analysis of single cell rna seq data |
| topic | Markov random field Differential expression scRNA-seq |
| url | https://doi.org/10.1186/s12859-021-04412-0 |
| work_keys_str_mv | AT hongyuli amarkovrandomfieldmodelfornetworkbaseddifferentialexpressionanalysisofsinglecellrnaseqdata AT biqingzhu amarkovrandomfieldmodelfornetworkbaseddifferentialexpressionanalysisofsinglecellrnaseqdata AT zhichaoxu amarkovrandomfieldmodelfornetworkbaseddifferentialexpressionanalysisofsinglecellrnaseqdata AT tayloradams amarkovrandomfieldmodelfornetworkbaseddifferentialexpressionanalysisofsinglecellrnaseqdata AT naftalikaminski amarkovrandomfieldmodelfornetworkbaseddifferentialexpressionanalysisofsinglecellrnaseqdata AT hongyuzhao amarkovrandomfieldmodelfornetworkbaseddifferentialexpressionanalysisofsinglecellrnaseqdata AT hongyuli markovrandomfieldmodelfornetworkbaseddifferentialexpressionanalysisofsinglecellrnaseqdata AT biqingzhu markovrandomfieldmodelfornetworkbaseddifferentialexpressionanalysisofsinglecellrnaseqdata AT zhichaoxu markovrandomfieldmodelfornetworkbaseddifferentialexpressionanalysisofsinglecellrnaseqdata AT tayloradams markovrandomfieldmodelfornetworkbaseddifferentialexpressionanalysisofsinglecellrnaseqdata AT naftalikaminski markovrandomfieldmodelfornetworkbaseddifferentialexpressionanalysisofsinglecellrnaseqdata AT hongyuzhao markovrandomfieldmodelfornetworkbaseddifferentialexpressionanalysisofsinglecellrnaseqdata |