Clustering QC for single cells from one subject
SCDC_qc_ONE( sc.eset, ct.varname, sample, scsetname = "Single Cell", ct.sub, iter.max = 1000, nu = 1e-04, epsilon = 0.01, arow = NULL, weight.basis = F, qcthreshold = 0.7, generate.figure = T, ct.cell.size = NULL, cbPalette = c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7"), ... )
| sc.eset | ExpressionSet object for single cells |
|---|---|
| ct.varname | variable name for 'cell type' |
| sample | variable name for subject/sample |
| scsetname | the name for the single cell dataset |
| ct.sub | a subset of cell types that are selected to construct basis matrix |
| iter.max | the maximum number of iteration in WNNLS |
| nu | a small constant to facilitate the calculation of variance |
| epsilon | a small constant number used for convergence criteria |
| arow | annotation of rows for pheatmap |
| qcthreshold | the probability threshold used to filter out questionable cells |
| generate.figure | logical. If generate the heatmap by pheatmap or not. default is TRUE. |
| ct.cell.size | default is NULL, which means the "library size" is calculated based on the data. Users can specify a vector of cell size factors corresponding to the ct.sub according to prior knowledge. The vector should be named: names(ct.cell.size input) should not be NULL. |
a list including: 1) a probability matrix for each single cell input; 2) a clustering QCed ExpressionSet object; 3) a heatmap of QC result.