PIUMA                   PIUMA: Phenotypes Identification Using Mapper
                        from topological data Analysis
TDAobj-class            The object 'TDAobj'
checkNetEntropy         Compute the Network Entropy
checkScaleFreeModel     Assessment of Scale-Free model fitting
dfToDistance            Compute the Distance Matrix from TDAobj
dfToProjection          Data projection using a Dimensionality
                        Reduction Method
df_test_proj            A dataset to test the 'dfToProjection' and
                        'dfToDistance' funtions of 'PIUMA' package.
getComp                 Getter method for the 'comp' slot of a TDAobj
                        object.
getDfMapper             Getter method for the 'dfMapper' slot of a
                        TDAobj object.
getDistMat              Getter method for the 'dist_mat' slot of a
                        TDAobj object.
getJacc                 Getter method for the 'jacc' slot of a TDAobj
                        object.
getNodeDataMat          Getter method for the 'node_data_mat' slot of a
                        TDAobj object.
getOrigData             Getter method for the 'orig_data' slot of a
                        TDAobj object.
getOutcome              Getter method for the 'outcome' slot of a
                        TDAobj object.
getOutcomeFact          Getter method for the 'outcomeFact' slot of a
                        TDAobj object.
getScaledData           Getter method for the 'scaled_data' slot of a
                        TDAobj object.
jaccardMatrix           Compute the Matrix of Jaccard Indexes
makeTDAobj              Import data and generate the TDAobj object
makeTDAobjFromSE        Import SummarizedExperiment data and generate
                        the TDAobj object
mapperCore              Implement the TDA Mapper algorithm on TDAobj
setComp                 Setter method for the 'comp' slot of a TDAobj
                        object.
setDfMapper             Setter method for the 'dfMapper' slot of a
                        TDAobj object.
setDistMat              Setter method for the 'dist_mat' slot of a
                        TDAobj object.
setJacc                 Setter method for the 'jacc' slot of a TDAobj
                        object.
setNodeDataMat          Setter method for the 'node_data_mat' slot of a
                        TDAobj object.
setOrigData             Setter method for the 'orig_data' slot of a
                        TDAobj object.
setOutcome              Setter method for the 'outcome' slot of a
                        TDAobj object.
setOutcomeFact          Setter method for the 'outcomeFact' slot of a
                        TDAobj object.
setScaledData           Setter method for the 'scaled_data' slot of a
                        TDAobj object.
tdaDfEnrichment         Add information to TDAobj
tda_test_data           A TDAobj to test the 'PIUMA' package.
vascEC_meta             Example datasets for PIUMA package
vascEC_norm             We tested PIUMA on a subset of the single-cell
                        RNA Sequencing dataset (GSE:GSE193346 generated
                        and published by Feng et al. (2022) on Nature
                        Communication to demonstrate that distinct
                        transcriptional profiles are present in
                        specific cell types of each heart chambers,
                        which were attributed to have roles in cardiac
                        development. In this tutorial, our aim will be
                        to exploit PIUMA for identifying sub-population
                        of vascular endothelial cells, which can be
                        associated with specific heart developmental
                        stages. The original dataset consisted of three
                        layers of heterogeneity: cell type, stage and
                        zone (i.e., heart chamber). Our testing dataset
                        was obtained by subsetting vascular endothelial
                        cells (cell type) by Seurat object, extracting
                        raw counts and metadata. Thus, we filtered low
                        expressed genes and normalized data by DaMiRseq
