scRNASEQPip: Run a post-quantification 10X technology-format scRNASEQ...
In eudoraleer/Ursa: An Automated Multi-omics Package for Single-cell Analysis

View source: R/Alioth.R

scRNASEQPip

R Documentation

Run a post-quantification 10X technology-format scRNASEQ pipeline

Description

This function will run a bioinformatics analysis of post-quantification scRNASEQ pipeline. Supports multiple samples analysis.

Usage

scRNASEQPip(
  project_name = "Ursa_scRNASEQ",
  input_dir = "./",
  output_dir = "./",
  pheno_file = "study_meta.csv",
  num_genes_lower_bound = 200,
  num_genes_upper_bound = 25000,
  mito_cutoff = 5,
  integration_method = "Seurat",
  cc_regression = 0,
  pc_selection_method = "none",
  num_pcs = 30,
  to_impute = "NO",
  find_doublet = "NO",
  run_unbias_vis = "NO"
)

Arguments

`project_name`	Project name. 'Ursa_scRNASEQ' by default.
`input_dir`	Directory to all input files. Default directory is the current working directory.
`output_dir`	Output directory. Default directory is the current working directory. A new folder with the given project name with time stamp as suffix will be created under the specified output directory.
`pheno_file`	Meta data file directory. Accept only .csv/.txt format files.
`num_genes_lower_bound`	Lower bound to select for cells with number of genes more than this value. Default is 200.
`num_genes_upper_bound`	Upper bound to select for cells with number of genes less than or equals to this value Default is 25000.
`mito_cutoff`	Threshold percentage to select for cells with percentage of mitochondria genes less than this value. This is remove cells which are debris or artefacts and doublets. Default is 5.
`integration_method`	Integration method. Accepts 'Seurat' or "Harmony' integration. Default is Seurat.
`cc_regression`	Cell cycle regression method. Accepts 0 for no regression, 1 for regression with two-phases, 2 for regression with phase difference (See Seurat). Default is 0.
`pc_selection_method`	Select a method to run an automatic selection of the number of principal components (PCs) or Harmonys (if harmony was chosen for integration). Methods include 'none', 'all', 'piecewise linear model', 'first derivative', 'second derivative', 'preceding residual', 'perpendicular line', and 'k-means clustering'. Default is set to 'none', which will ignore this option and use the stated number of PCs in the num_pcs option. If 'all' is used, all methods will be assessed and the final PC number will be determined via the mean of the output of all methods. For more information, please visit: https://github.com/haotian-zhuang/findPC (Zhuang et al., Bioinformatics, 2022)
`num_pcs`	Number of PCs or Harmonys (if harmony was chosen for integration) to be used for the analysis. Default to 30. Please state a reasonable number which is no more than the total number of cells submitted to avoid running into errors. This option will only be considered if option pc_selection_method is set to 'none'.
`to_impute`	Pass 'YES' to perform imputation on individual samples or 'NO' to skip imputation process. Default is set to 'NO'.
`find_doublet`	Pass 'YES' to perform doublets removal using DoubletFinder (Christopher S. M., Cell Systems, 2019). Default is set to 'NO'. If set to 'YES', doublets will be removed using the true doublet rate (TDR).
`run_unbias_vis`	Plot additional unbias visualizations for top features using SCUBI (Wenpin H. & Zhicheng J., Cell Reports Methods, 2021) for dimensionally reduced plots such as UMAP. Pass 'YES' to plot, default to 'NO'.