apply_combat            Apply ComBat batch correction to pseudobulk
                        matrices. Generally, this should be done
                        through calling the form_tensor() wrapper
                        function.
calculate_fiber_fstats
                        Calculate F-Statistics for the association
                        between donor scores for each factor donor
                        values of shuffled gene_ctype fibers
check_rec_pres          Helper function to check whether receptor is
                        present in target cell type
clean_data              Clean data to remove genes only expressed in a
                        few cells and donors with very few cells.
                        Generally, this should be done through calling
                        the form_tensor() wrapper function.
colMeanVars             Calculates column mean and variance. Adapted
                        from pagoda2.
                        https://github.com/kharchenkolab/pagoda2/blob/main/src/misc2.cpp
compare_decompositions
                        Plot a pairwise comparison of factors from two
                        separate decompositions
compute_LR_interact     Compute and plot the LR interactions for one
                        factor
compute_associations    Compute associations between donor proportions
                        and factor scores
compute_donor_props     Get donor proportions of each cell type or
                        subtype
convert_gn              Convert gene identifiers to gene symbols
count_word              count_word. From older version of
                        simplifyEnrichment package.
determine_ranks_tucker
                        Run rank determination by svd on the tensor
                        unfolded along each mode
form_tensor             Form the pseudobulk tensor as preparation for
                        running the tensor decomposition.
get_all_lds_factor_plots
                        Generate loadings heatmaps for all factors
get_callouts_annot      Get gene callout annotations for a loadings
                        heatmap
get_ctype_exp_var       Get explained variance of the reconstructed
                        data using one cell type from one factor
get_ctype_prop_associations
                        Compute and plot associations between donor
                        factor scores and donor proportions of major
                        cell types
get_ctype_subc_prop_associations
                        Compute and plot associations between donor
                        factor scores and donor proportions of cell
                        subtypes
get_ctype_vargenes      Partition main gene by cell matrix into per
                        cell type matrices with significantly variable
                        genes only. Generally, this should be done
                        through calling the form_tensor() wrapper
                        function.
get_donor_meta          Get metadata matrix of dimensions donors by
                        variables (not per cell)
get_factor_exp_var      Get the explained variance of the reconstructed
                        data using one factor
get_fstats_pvals        Calculate adjusted p-values for gene_celltype
                        fiber-donor score associations
get_gene_modules        Compute WGCNA gene modules for each cell type
get_gene_set_vectors    Get logical vectors indicating which genes are
                        in which pathways
get_indv_subtype_associations
                        Compute subtype proportion-factor association
                        p-values for all subclusters of a given major
                        cell type
get_intersecting_pathways
                        Extract the intersection of gene sets which are
                        enriched in two or more cell types for a factor
get_leading_edge_genes
                        Get the leading edge genes from GSEA results
get_lm_pvals            Compute gene-factor associations using
                        univariate linear models
get_max_correlations    Computes the max correlation between each
                        factor of the decomposition done using the
                        whole dataset to each factor computed using the
                        subsampled/bootstrapped dataset
get_meta_associations   Get metadata associations with factor donor
                        scores
get_min_sig_genes       Evaluate the minimum number for significant
                        genes in any factor for a given number of
                        factors extracted by the decomposition
get_module_enr          Identify gene sets that are enriched within
                        specified gene co-regulatory modules. Uses a
                        hypergeometric test for over-representation.
                        Used in plot_multi_module_enr().
get_normalized_variance
                        Get normalized variance for each gene, taking
                        into account mean-variance trend
get_num_batch_ranks     Plot factor-batch associations for increasing
                        number of donor factors
get_one_factor          Get the donor scores and loadings matrix for a
                        single-factor
get_one_factor_gene_pvals
                        Get significant genes for a factor
get_pseudobulk          Collapse data from cell-level to donor-level
                        via summing counts. Generally, this should be
                        done through calling the form_tensor() wrapper
                        function.
get_real_fstats         Get F-Statistics for the real (non-shuffled)
                        gene_ctype fibers
get_reconstruct_errors_svd
                        Calculate reconstruction errors using svd
                        approach
get_significance_vectors
                        Get vectors indicating which genes are
                        significant in which cell types for a factor of
                        interest
get_subclust_de_hmaps   Get list of cell subtype differential
                        expression heatmaps
get_subclust_enr_dotplot
                        Get scatter plot for association of a cell
                        subtype proportion with scores for a factor
get_subclust_enr_fig    Get a figure showing cell subtype proportion
                        associations with each factor. Combines this
                        plot with subtype UMAPs and differential
                        expression heatmaps. Note that this function
                        runs better if the number of cores in the conos
                        object in container$embedding has n.cores set
                        to a relatively small value < 10.
get_subclust_enr_hmap   Get heatmap of subtype proportion associations
                        for each celltype/subtype and each factor
get_subclust_umap       Get a figure to display subclusterings at
                        multiple resolutions
get_subclusters         Perform leiden subclustering to get cell
                        subtypes
get_subtype_prop_associations
                        Compute and plot associations between factor
                        scores and cell subtype composition for various
                        clustering resolution parameters
get_sums                Calculates factor-stratified sums for each
                        column. Adapted from pagoda2.
                        https://github.com/kharchenkolab/pagoda2/blob/main/src/misc2.cpp
ht_clusters             Visualize the similarity matrix and the
                        clustering. Adapted from simplifyEnrichment
                        package.
                        https://github.com/jokergoo/simplifyEnrichment/blob/master/R/ht_clusters.R
identify_sex_metadata   Extract metadata for sex information if not
                        provided already
initialize_params       Initialize parameters to be used throughout
                        scITD in various functions
instantiate_scMinimal   Create an scMinimal object. Generally, this
                        should be done through calling the
                        make_new_container() wrapper function.
is_GO_id                Check if a character is a go ID
make_new_container      Create a container to store all data and
                        results for the project. You must provide a
                        params list as generated by
                        initialize_params(). You also need to provide
                        either a Seurat object or both a count_data
                        matrix and a meta_data matrix.
merge_small_clusts      Merge small subclusters into larger ones
nmf_unfolded            Computes non-negative matrix factorization on
                        the tensor unfolded along the donor dimension
norm_var_helper         Calculates the normalized variance for each
                        gene. This is adapted from pagoda2.
                        https://github.com/kharchenkolab/pagoda2/blob/main/R/Pagoda2.R
                        Generally, this should be done through calling
                        the form_tensor() wrapper function.
normalize_counts        Helper function to normalize and log-transform
                        count data
normalize_pseudobulk    Normalize the pseudobulked counts matrices.
                        Generally, this should be done through calling
                        the form_tensor() wrapper function.
parse_data_by_ctypes    Parse main counts matrix into
                        per-celltype-matrices. Generally, this should
                        be done through calling the form_tensor()
                        wrapper function.
pca_unfolded            Computes singular-value decomposition on the
                        tensor unfolded along the donor dimension
plotDEheatmap_conos     Plot a heatmap of differential genes. Code is
                        adapted from Conos package.
                        https://github.com/kharchenkolab/conos/blob/master/R/plot.R
plot_donor_matrix       Plot matrix of donor scores extracted from
                        Tucker decomposition
plot_donor_props        Plot donor celltype/subtype proportions against
                        each factor
plot_donor_sig_genes    Generate a gene by donor heatmap showing scaled
                        expression of top loading genes for a given
                        factor
plot_dscore_enr         Compute enrichment of donor metadata
                        categorical variables at high/low factor scores
plot_gsea_hmap          Plot enriched gene sets from all cell types in
                        a heatmap
plot_gsea_hmap_w_similarity
                        Plot already computed enriched gene sets to
                        show semantic similarity between sets
plot_gsea_sub           Look at enriched gene sets from a cluster of
                        semantically similar gene sets. Uses the
                        results from previous run of
                        plot_gsea_hmap_w_similarity()
plot_loadings_annot     Plot the gene by celltype loadings for a factor
plot_mod_and_lig        Plot trio of associations between ligand
                        expression, module eigengenes, and factor
                        scores
plot_multi_module_enr   Generate gene set x ct_module heatmap showing
                        co-expression module gene set enrichment
                        results
plot_rec_errors_bar_svd
                        Plot reconstruction errors as bar plot for svd
                        method
plot_rec_errors_line_svd
                        Plot reconstruction errors as line plot for svd
                        method
plot_scores_by_meta     Plot dotplots for each factor to compare donor
                        scores between metadata groups
plot_select_sets        Plot enrichment results for hand picked gene
                        sets
plot_stability_results
                        Generate a plot for either the donor scores or
                        loadings stability test
plot_subclust_associations
                        Plot association significances for varying
                        clustering resolutions
prep_LR_interact        Prepare data for LR analysis and get soft
                        thresholds to use for gene modules
project_new_data        Project multicellular patterns to get scores on
                        new data
reduce_dimensions       Gets a conos object of the data, aligning
                        datasets across a specified variable such as
                        batch or donors. This can be run independently
                        or through get_subtype_prop_associations().
reduce_to_vargenes      Reduce each cell type's expression matrix to
                        just the significantly variable genes.
                        Generally, this should be done through calling
                        the form_tensor() wrapper function.
render_multi_plots      Create a figure of all loadings plots arranged
reshape_loadings        Reshape loadings for a factor from linearized
                        to matrix form
run_fgsea               Run fgsea for one cell type of one factor
run_gsea_one_factor     Run gsea separately for all cell types of one
                        specified factor and plot results
run_hypergeometric_gsea
                        Compute enriched gene sets among significant
                        genes in a cell type for a factor using
                        hypergeometric test
run_jackstraw           Run jackstraw to get genes that are
                        significantly associated with donor scores for
                        factors extracted by Tucker decomposition
run_stability_analysis
                        Test stability of a decomposition by
                        subsampling or bootstrapping donors. Note that
                        running this function will replace the
                        decomposition in the project container with one
                        resulting from the tucker parameters entered
                        here.
run_tucker_ica          Run the Tucker decomposition and rotate the
                        factors
sample_fibers           Get a list of tensor fibers to shuffle
scale_fontsize          Scale font size. From simplifyEnrichment
                        package.
                        https://github.com/jokergoo/simplifyEnrichment/blob/master/R/ht_clusters.R
scale_variance          Scale variance across donors for each gene
                        within each cell type. Generally, this should
                        be done through calling the form_tensor()
                        wrapper function.
seurat_to_scMinimal     Convert Seurat object to scMinimal object.
                        Generally, this should be done through calling
                        the make_new_container() wrapper function.
shuffle_fibers          Shuffle elements within the selected fibers
stack_tensor            Create the tensor object by stacking each
                        pseudobulk cell type matrix. Generally, this
                        should be done through calling the
                        form_tensor() wrapper function.
stop_wrap               Helper function from simplifyEnrichment
                        package.
                        https://github.com/jokergoo/simplifyEnrichment/blob/master/R/utils.R
subset_scMinimal        Subset an scMinimal object by specified genes,
                        donors, cells, or cell types
test_container          Data container for testing tensor formation
                        steps
tucker_ica_helper       Helper function for running the decomposition.
                        Use the run_tucker_ica() wrapper function
                        instead.
update_params           Update any of the experiment-wide parameters
vargenes_anova          Compute significantly variable genes via anova.
                        Generally, this should be done through calling
                        the form_tensor() wrapper function.
