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A pipeline that can process single or multiple Single Cell RNAseq samples primarily specializes in Clustering and Dimensionality Reduction. Meanwhile we use common cell type marker genes for T cells, B cells, Myeloid cells, Epithelial cells, and stromal cells (Fiboblast, Endothelial cells, Pericyte, Smooth muscle cells) to visualize the Seurat clusters, to facilitate labeling them by biological names. Once users named each cluster, they can evaluate the quality of them again and find the de novo marker genes also.
| Version: | 0.1.1 |
| Depends: | R (≥ 2.10) |
| Imports: | Seurat, ggplot2, stringr, clustree, magrittr, Matrix, dplyr, patchwork |
| Published: | 2021-09-22 |
| DOI: | 10.32614/CRAN.package.scRNAstat |
| Author: | Jianming Zeng [aut], Yonghe Xia [ctb, cre], Biotrainee group [cph, fnd] |
| Maintainer: | Yonghe Xia <xiayh17 at gmail.com> |
| License: | AGPL (≥ 3) |
| NeedsCompilation: | no |
| CRAN checks: | scRNAstat results |
| Reference manual: | scRNAstat.pdf |
| Package source: | scRNAstat_0.1.1.tar.gz |
| Windows binaries: | r-devel: scRNAstat_0.1.1.zip, r-release: scRNAstat_0.1.1.zip, r-oldrel: scRNAstat_0.1.1.zip |
| macOS binaries: | r-release (arm64): scRNAstat_0.1.1.tgz, r-oldrel (arm64): scRNAstat_0.1.1.tgz, r-release (x86_64): scRNAstat_0.1.1.tgz, r-oldrel (x86_64): scRNAstat_0.1.1.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.