1. Coexpression analysis (correlation) and network construction of time series single cell data
2. Creating a package (pipeline) for the entire analysis process
3. Evaluating the pipeline with the existing packages in this paper (see: https://doi.org/10.1016/j.compbiomed.2020.103975). (WGCNA, CEMiTool, and coseq)
4. Show why our pipeline outperforms these tools in analyzing time series single cell data.
1. Seurat (https://satijalab.org/seurat/index.html) for data prepocessing and other related packages such scran or scater in R software
2. Differential gene expression analysis
3. Kl divergence (Brownlee, 2019)
4. Functional PCA (Wu et al., 2014)
5. Spline smoothing (Storey et al., 2005)
6. Differential coexpression (Tesson et al., 2010)
7. Mutual information (concepts of information theory).
8. Constructing a Gene Co-Expression Network and Detecting Modules
9. Co-Expression Network Analysis
10. Functional enrichment analysis
Please use these data:
Covid19 data source: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE166766
refer to: https://pubmed.ncbi.nlm.nih.gov/33730024/
or any disease time series single cell data (cancer or diabetes, etc)
R programming or Python as second option
Potential journals to be published in: please check their paper structure or guidelines
1. Computers in Biology and Medicine
1. Please provide all of the images, table and results of the pipeline code.
2. The code and data outcomaqwes will be publicly accessed after publication.
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