This package contains two functions a) variable_features() and b) scaling_seurat() which correspond to the third (3rd) and fourth (4th) steps of the standard scRNAseq processing workflow.
The variable_features() function calculated which genes have high cell-to-cell variation in the dataset and will be used to distinguish cell types.
The scaling_seurat() function removes unwanted sources of variation to ensure that the succeeding clusterings (for dimensionality reduction) are due to actual differences in gene expression and not due to unwanted sources of variations.
The package can be installed using
devtools::install_github("BTIP2024/selection-scaling-seurat")
The output of this function would be an rds file which when loaded in R, would be a Seurat object.
# for the selection of variable feature
variable_features("normalized_seurat.rds")
# to scale after variable feature selection
scaling_seurat("variable_features.rds")
The standard scRNAseq processing workflow with the R package Seurat consists of seven (7) steps. The output of this package and function should be used as input for the scRNAseq processing pipeline.
The following are the repositories of the packages for every step of the pipeline:
- QC and filtering: qualitycontrolseurat package
- Normalization: qualitycontrolseurat package
- Identification of highly variable features: selectionscalingseurat package
- Scaling: selectionscalingseurat package
- Linear Dimensionality Reduction (PCA): pcaseurat package
- Clustering: nonlinearreduction package
- Non-linear dimensionality reduction (t-SNE and UMAP): nonlinearreduction package
An overview of the pipeline and its outputs can be observed below:
