wrapper_ABA_RSS: The wrapper function to calculate ABA-RSS with the following...
In maplesword/RefSimSpec:

Description Usage Arguments Value

View source: R/RSS.r

This function integrates the basic ABA-RSS analysis, including RSS calculation, dimension reduction for visualization using tSNE, sample (cell) clustering based on the RSS matrix, progenitor/neuron classification with potential intermediate progenitor clusters identified.

wrapper_ABA_RSS(input, onlyFetal = TRUE, highVar = TRUE,
  tsneMethod = c("pca", "cor_dist"), tsne_pcaDim = 100,
  tsne_perplexity = 50, clustMethod = c("pam", "kmeans", "pca-kmeans"),
  numClust = 20, maxCells = 50000, seedset = NULL,
  progenitorScores = NULL, nameMap = NULL, progenitorModel = NULL,
  forceRetrain = TRUE, returnModel = TRUE, progenitorThres = 0.5,
  threads = 1, verbose = TRUE)

`input`	The input matrix of sample (cell) transcriptome profiles. Should be properly normalized and log-transformed. Its rows representing genes should be named by ENSEMBL IDs, gene symbols or Entrez IDs.
`onlyFetal`	Whether only considering the fetal ABA reference samples.
`highVar`	Whether only considering the highly variable genes across the ABA reference samples.
`tsneMethod`	The method used for tSNE (Rtsne).
`tsne_pcaDim`	When pca method is used for tSNE, it defines the initial dimension used by PCA.
`tsne_perplexity`	The perplexity parameter of tSNE.
`clustMethod`	The method used to do cell clustering.
`numClust`	The expected number of clusters
`maxCells`	The maximum number of cells selected for clustering. NULL or NA for unlimitation.
`progenitorScores`	The pre-calculated progenitor scores. When it is NULL, the score calculation is involved in the pipeline.
`nameMap`	A matrix/data.frame showing the gene name mappings. It is expected to be of multiple columns, one of which is for gene symbols.
`progenitorModel`	The model used for progenitor scores estimation. It is expected to be a list with at least two components: 'gene' for the list of genes to be ranked; 'coefficients' for the model coefficients. When it is NULL, either the default or a re-trained model is used.
`forceRetrain`	When it is TRUE, the progenitor model is always retrained using only genes appears in the input matrix.
`returnModel`	Whether to return the retrained model for progenitor score estimation.
`progenitorThres`	The progenitor score threshold to distinct progenitors and neurons
`threads`	The number of threads to run.
`verbose`	Whether to output the progress information.
`seedIdx`	The indices of samples (cells) used for clustering. The remaining samples will be assigned to clusters based on the SVM-classification model.