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Minor proposal on line 05 & paragraph 71 #9

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Minor proposal on line 05 & paragraph 71 #9

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4 changes: 2 additions & 2 deletions README.md
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# scAI: a single cell Aggregation and Integration method for analyzing single cell multi-omics data

- scAI is an unsupervised approach for integrative analysis of gene expression and chromatin accessibility or DNA methylation proflies measured in the same individual cells.
- scAI is an unsupervised approach for integrative analysis of gene expression and chromatin accessibility or DNA methylation profiles measured in the same individual cells.
- scAI infers a set of biologically relevant factors, which enable various downstream analyses, including the identification of cell clusters, cluster-specific markers and regulatory relationships.
- scAI provides an intuitive way to visualize features (i.e., genes and loci) alongside the cells in two dimensions.
- scAI aggegrates chromatin profiles of similar cells in an unsupervised and iterative manner, which opens up new avenues for analyzing extremely sparse, binary scATAC-seq data.
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```
- Using informative loci for scATAC-seq or single cell methylation data:

Unlike scRNA-seq data, the largely binary nature of scATAC-seq data makes it challenging to perform ‘variable’ feature selection. One option is to select the nearby chromsome regions of the informative genes.
Unlike scRNA-seq data, the largely binary nature of scATAC-seq data makes it challenging to perform ‘variable’ feature selection. One option is to select the nearby chromosome regions of the informative genes.
```
object <- selectFeatures(object, assay = "RNA")
loci.use <- searchGeneRegions(genes = object@var.features[[1]], species = "mouse")
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