| Title: | A Pipeline to Process Single Cell RNAseq Data |
|---|---|
| Description: | 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. |
| Authors: | Jianming Zeng [aut], Yonghe Xia [ctb, cre], Biotrainee group [cph, fnd] |
| Maintainer: | Yonghe Xia <[email protected]> |
| License: | AGPL (>= 3) |
| Version: | 0.1.1 |
| Built: | 2024-10-17 05:17:39 UTC |
| Source: | https://github.com/cran/scRNAstat |
An object of class Seurat
AJ064_small_last_sceAJ064_small_last_sce
An object of class Seurat with 627 rows and 800 columns.
An object of class Seurat
AJ064_small_sceAJ064_small_sce
An object of class Seurat with 713 rows and 1000 columns.
filter the genes which show expression less than 3 cells. filter the cells which percent_mito < 25 & percent_ribo > 3 & percent_hb < 10 filter the cells which nFeature_RNA > 300 & nFeature_RNA < 8000
basic_filter(sce)basic_filter(sce)
sce |
An object of class Seurat |
sce.all.filt An object of class Seurat
basic_filter(AJ064_small_sce)basic_filter(AJ064_small_sce)
To find de 'novo' markers by 'FindAllMarkers' from Seurat with default setting.
basic_find_markers(sce, group = "seurat_clusters", dir = ".")basic_find_markers(sce, group = "seurat_clusters", dir = ".")
sce |
An object of class Seurat |
group |
default:seurat_clusters, you can change it to celltype |
dir |
path for saving results |
sce.markers a data.frame of markers.
basic_find_markers(AJ064_small_last_sce,dir=tempdir())basic_find_markers(AJ064_small_last_sce,dir=tempdir())
Basic Markers
basic_markers(sce, org = "human", group = "orig.ident", dir = ".")basic_markers(sce, org = "human", group = "orig.ident", dir = ".")
sce |
An object of class Seurat |
org |
human or mouse, default: human |
group |
default:'orig.ident', you can change it to 'seurat_clusters' or 'celltype' |
dir |
the path for saving the figures by 'DotPlot' with known famous markers. |
a list of figures by 'DotPlot'
basic_markers(AJ064_small_last_sce,dir=tempdir())basic_markers(AJ064_small_last_sce,dir=tempdir())
add 'percent_mito','percent_ribo','percent_hb' to the Seurat class. And draw 'VlnPlot' for these 'qc' values.
basic_qc(sce, org = "human", group = "orig.ident", dir = ".")basic_qc(sce, org = "human", group = "orig.ident", dir = ".")
sce |
An object of class Seurat |
org |
human or mouse, default: human |
group |
default:'orig.ident',you can change it to 'seurat_clusters' or 'celltype' |
dir |
the path for saving the figures by 'DotPlot' with known famous markers. |
list(p1,p2,p3,sce), the last one in the new 'sce'.
basic_qc(AJ064_small_sce,dir= tempdir())basic_qc(AJ064_small_sce,dir= tempdir())
the workflow from Seurat, including: 'NormalizeData','FindVariableFeatures','ScaleData', 'RunPCA','RunTSNE','RunUMAP','FindNeighbors','FindClusters(sce, resolution = seq(0.1,1,by=0.1))' we use 'clustree' to check the different resolution for 'FindClusters'.
basic_workflow(sce, dir = ".")basic_workflow(sce, dir = ".")
sce |
An object of class Seurat |
dir |
the path for saving the figures by 'DotPlot' with known famous markers. |
list(p1,p2,p3,sce), the last one in the new sce with PCA,tSNE,UMAP information.
## Not run: basic_workflow(AJ064_small_sce,dir=tempdir()) ## End(Not run)## Not run: basic_workflow(AJ064_small_sce,dir=tempdir()) ## End(Not run)