ce                      cell embeddings for pbmc3k data
compute.cell.label      4.2. binarize the label propagation probability
                        in the cell population; result in a binarized
                        vector of cells with 'nagative' and 'positive'
                        labels; 'positive' means that the cells are
                        relevant to the gene set
compute.cell.label.df   similar to compute.cell.label; used when
                        working with multiple gene sets
compute.db              this function is called by 'compute.kld' to
                        aggregate the density contribution of each gene
                        to each grid point, and then normalize the
                        densities of grid points to 1.
compute.grid.coords     2. compute density of gene sets of interest 2.1
                        compute grid point coordinates
compute.jsd             5. compute the specificity of gene set when
                        cell partition information is available; the
                        information could be clustering, sample
                        origins, or other conditions inspired by
                        https://github.com/FloWuenne/scFunctions/blob/0d9ea609fa72210a151f7270e61bdee008e8fc88/R/calculate_rrs.R
compute.kld             2.2 compute KL-divergence (some are adapted
                        from
                        https://github.com/alexisvdb/singleCellHaystack/)
compute.mca             1. compute MCA embeddings
compute.nn.edges        3. compute nearest neighbor graph for genes and
                        cells This graph will be used for fetching the
                        most relevant cells of a gene set
compute.spatial.kld     6. find gene sets with spatial relevance
compute.spatial.kld.df
                        This function is to calculate how likely the
                        cells relevant to multiple gene sets are
                        randomly distributed spatially
compute.spec            This is to calculate the similarity between: 1.
                        the label propagation probability of cells for
                        gene sets and 2. the identify of cells in
                        partitions
compute.spec.single     This is to calculate the similarity between: 1.
                        the label propagation probability of cells for
                        gene sets and 2. the identify of cells in a
                        certain partition This is called by
                        'compute.spec'; can also run by itself
coords.df               mouse brain coords
el_nn_search            this function is called by 'compute.nn.edges'
                        to convert nearest neighbor identity matrix to
                        edge list
gene.set.list           A gene set list containing multiple human GO
                        gene sets
kde2d.weighted          based on
                        https://stat.ethz.ch/pipermail/r-help/2006-June/107405.html
                        this is called by compute.spatial.kld to
                        calculate the kernel density estimation in 2d
                        space with each data point weighted.
pbmc.meta               pbmc3k meta
pbmc.mtx                pbmc3k matrix
run.rwr                 4.1 To calculate the label propagation
                        probability for a gene set among cells; result
                        in a vector (length = number of cells)
                        reflecting the probability each cell is labeled
                        during the propagation (relevance to the gene
                        set)
run.rwr.list            result in a matrix (number of rows = number of
                        cells; number of columns = number of gene sets)
                        reflecting the probability each cell is labeled
                        during the propagation (relevance to the gene
                        set); same idea as run.rwr but with multiple
                        gene sets
sample.kld              this function is called by 'compute.kld' to
                        calculate the kl-divergence between sampled
                        (background) gene set and the ref (all) gene
                        set
sample.spatial.kld      this function is called by
                        'compute.spatial.kld' to calculate the
                        kl-divergence between cell-weighted with
                        shuffled weight vector and the ref (all cells,
                        unweighted)
seed.mat                4. compute label propagation from gene set to
                        cells this function is to form a 'seed matrix'
                        used by the dRWR function (dnet R package); the
                        seed matrix is specifying which nodes are the
                        sources for label propagation
seed.mat.list           this function is used when more than one 'seed
                        sets' will be used (when there are multiple
                        gene sets of interest)
vectorized_pdist        from an excellent post:
                        https://www.r-bloggers.com/2013/05/pairwise-distances-in-r/
                        enhanced the speed this function is called by
                        'compute.kld' to quickly compute the distance
                        between genes to grid points
weight_df               mouse brain gene set activities
