Function reference • metacell

metacell pipeline functions
`mcell_add_cgraph_bknn_norm_mc()`	Build a cell graph using balanced knn after normalizing over a given metacell model
`mcell_add_cgraph_from_distmat()`	Build a cell graph using blanacing of an extrenal distance matrix
`mcell_add_cgraph_from_mat_bknn()`	Build a cell graph using balanced knn graph on given gene features
`mcell_add_cgraph_from_mat_raw_knn()`	Build a cell graph using raw knn graph on given gene features
`mcell_add_gene_names_xref_mars10x()`	Create a gene name xref for heuristc map of mars/10x
`mcell_add_gene_stat()`	mcell_gene_stat
`mcell_add_mars_facs_data()`	Creates a new matrix object from a given one by adding FACS index sorting data to matrix metadata table
`mcell_add_mc_from_graph()`	Compute metacell using a native implementation of a graph cover k-means-like approach
`mcell_batch_stats()`	generate a batch stat table for a given single cell umi matrix
`mcell_calc_one_batch_stats()`	calc batch stats - essentially umi distribution
`mcell_cgraph_norm_gcor()`	Computing gene-gene correlation normaized over a similarity graph
`mcell_coclust_filt_by_k_deg()`	Return a filter (boolean vector) selecting only coclust edges that are nearly as frequent as a user defined K-nn parameter
`mcell_coclust_from_graph_resamp()`	Compute metacell using resampling iterations of graph cover k-means-like approach
`mcell_compare_bulk()`	Utility plot function to compare bulk expression of two batches/metadata factors
`mcell_gen_atlas()`	wrap up an atlas object from mc, mc2d and matrix ids
`mcell_gen_cell_cor_gset()`	Compute a cell cell correlation matrix using features defined by a gene set
`mcell_graph_pred_corr()`	Compute predictive value of MC cover as correlation of MC averages of sc umis
`mcell_gset_add_gene()`	adding genes to a gene set
`mcell_gset_filter_cov()`	gnereate/filter gene features from coverage threshold in gstat table
`mcell_gset_filter_multi()`	Select/filter gene features from using multiple statistics from the gstat table. All genes passing the selected thresholds are included
`mcell_gset_filter_szcor()`	gnereate/filter gene features from statistics on correlation with umi count
`mcell_gset_filter_varmean()`	gnereate/filter gene features from gstat normalized var/mean
`mcell_gset_from_mc_markers()`	Generate a gene set of markers from a metacell cover
`mcell_gset_remove_clusts()`	add new gene set based on an existing one with filtering specific clusters
`mcell_gset_split_by_dsmat()`	split gene set into several modules using clustering of genes by correlation over cells downsampled umi vector
`mcell_import_multi_mars()`	Load a matrix from a MARS-seq multi-batch dataset. The scdb version of mcell_read_multi_scmat_mars
`mcell_import_multi_scmat_10x()`	Load a matrix from a 10x multi-batch dataset. The scdb version of mcell_read_multi_scmat_10x
`mcell_import_scmat_10x()`	Load a matrix from a 10x dataset. The scdb version of mcell_read_multi_scmat_10x
`mcell_import_scmat_tsv()`	Load a matrix from a simple dense table with genes in rows and cells in columns. First column is the gene name
`mcell_mat_ignore_cells()`	Generate a new matrix object with a given ignore cell list
`mcell_mat_ignore_genes()`	Generate a new matrix object with a given ignore gene list
`mcell_mat_ignore_small_cells()`	Generate a new matrix object after removing cells without enough umis
`mcell_mat_rpt_cor_anchors()`	Find and report naive gene gene correlation over umi matrix
`mcell_mc2d_force_knn()`	Metacell layout using force directed projection of a low degree mc graph
`mcell_mc2d_force_knn_on_cells()`	Compute cells 2d coordinates based on the mc graph when the mc coordinates are supplied externally
`mcell_mc2d_plot()`	Plot mc+cell graph using pre-defined mc colorization
`mcell_mc2d_plot_by_factor()`	Plot mc+cells using pre-defined mc colorization, breakdown by given metadata field (e.g. patient)
`mcell_mc2d_plot_gene()`	Plot the (log2) metacell footprint value of the selected gene on the 2d projection
`mcell_mc2d_rotate()`	Rotatae/invert projection
`mcell_mc_add_annot()`	Update metacell annotation
`mcell_mc_add_color()`	Update metacells colors
`mcell_mc_cell_homogeneity()`	Compute cell homogeneity - the fraction of intra mc edges per cell
`mcell_mc_coclust_confusion_mat()`	Compute confusion matrix on metacells using a coclustering object
`mcell_mc_confusion_mat()`	Compute confusion matrix on metacells
`mcell_mc_edge_density()`	for each MC we compute mean and variance of the intra-MC edge density
`mcell_mc_export_tab()`	Output genes cell modules footprint matrix with metadata on the cell modules
`mcell_mc_from_coclust_balanced()`	build a metacell cover from co-clust data through filtering un-balanced edges and running graph cover
`mcell_mc_from_coclust_hc()`	build a metacell cover from a co-clust object using a simple hclust approach
`mcell_mc_from_coclust_louv_sub()`	build a metacell cover from a big co-clust using louvain clustering and metacell coverage within clusters
`mcell_mc_hclust_confu()`	comput mc hierarchucal clustering using the normalized confusion matrix
`mcell_mc_hierarchy()`	identify super structure in an mc cover, based on hcluster of the confusion matrix
`mcell_mc_match_graph()`	TEst if a graph object cover all cells in the mc
`mcell_mc_plot_by_factor()`	plot a heatmap of number of cells per metacell and metadata factor (e.g. patient, condition, sample etc.)
`mcell_mc_plot_confusion()`	plot a metacel confusion matrix
`mcell_mc_plot_gg()`	Utility plot function to compare two genes based on mc_fp
`mcell_mc_plot_hierarchy()`	plot super strucutre: super clust mc footprint, and selected genes
`mcell_mc_plot_marks()`	plot a marker heat map give a metacell object
`mcell_mc_plot_submc_marks()`	plot a marker heat map for a subset of metacells - selecting relevant genes for separation
`mcell_mc_plot_vgels()`	plot a "gel" like diagram showing expression of a gene of interest over metacells that are classified into types
`mcell_mc_pred_corr()`	Compute predictive value of MC cover as correlation of MC averages of sc umis
`mcell_mc_reorder_hc()`	Reorder metacells using hierarchical clustering
`mcell_mc_screen_outliers_1gene_fold()`	Simple screen for outlier cells in a metacell cover, finding genes with overly high expression given their metacell mean
`mcell_mc_split_by_color_group()`	Splits input metacell object into sub-objects by color group, naming the new metacells <mc_id>_submc_<group>
`mcell_mc_split_filt()`	Split and filter metacells using dbscan and outlier gene detection
`mcell_merge_mats()`	ncell_merge_mats: Merge two matrix using their ids in scdb. See scm_merge_mats for details on batch management and policies on missing genes
`mcell_mgraph_knn()`	Compute metacell manifod graph using the confusion matrix of balanced K-nn between individual cells projected on metacells
`mcell_mgraph_logistic()`	Compute metacell manifold graph using logistic distances and balanced K-nn
`mcell_new_mc()`	Generate a new metacell in scdb
`mcell_new_mc2d()`	Generate a new metacell in scdb
`mcell_new_mc_mgraph()`	Generate a new metacell manifold graph object
`mcell_new_mcatlas()`	Generate a new atlas in scdb
`mcell_new_mctnetwork()`	Generate a new network in scdb
`mcell_pipe_mat2mc2d()`	Linear simple pipeline for turning a matrix to a MC and plotting std figs
`mcell_plot_batch_stats()`	plot batches stats
`mcell_plot_cross_mc()`	Plotting a matrix of co-occurences between two metacell covers of the same dataset
`mcell_plot_gset_cor_mats()`	Plot gene set correlation matrices given a an scamt. See version using metacells for potentially more robust behavior. This is used to detemrine initial feature selectio (e.g. filtering biologically irrelevant gene modules)
`mcell_plot_gstats()`	plot gene/feature statistics
`mcell_plot_outlier_heatmap()`	Plot and outlier heat map.
`mcell_plot_umis_per_cell()`	Plot histogram of total number of umis per cell in the umis matrix
`mcell_proj_on_atlas()`	Project a metacell object on a reference "atlas"
`mcell_read_multi_scmat_10x()`	read multiple 10x umi matrices and merge them, based on a table defining the datasets. Field amp_batch_id from the table is added to the cell name to prevent cell names clashes.
`mcell_read_multi_scmat_mars()`	read multiple MARS umi matrices and merge them, based on a table defining the datasets.
`mcell_scmat_plot_cmp_kcor()`	Analyze cell cell cor
`mcell_wgtmc_pred_corr()`	Compute predictive value of MC cover as correlation of weighted MC averages of sc umis
scdb functions
`scdb_add_cgraph()`	scdb_add_cgraph - add cgraph to the DB and cahce
`scdb_add_coclust()`	scdb_add_coclust - add coclust to the DB and cahce
`scdb_add_gene_names_xref()`	scdb_add_gene_names_xref - add a gene name xref tab to the DB - will save it and cache
`scdb_add_gset()`	scdb_add_gset - add gset to the DB and cahce
`scdb_add_gstat()`	scdb_add_gstat - add gstat to the DB and cahce
`scdb_add_mat()`	scdb_add_mat - add amatrix to the DB - will save it and cache
`scdb_add_mc()`	scdb_add_mc - add mc to the DB and cahce
`scdb_add_mc2d()`	scdb_add_mc2d - add mc2d to the DB and cahce
`scdb_add_mcatlas()`	scdb_add_mcatlas - add mcatlas to the DB and cahce
`scdb_add_mctnetwork()`	scdb_add_mctnetwork - add mctnetwork to the DB and cahce
`scdb_add_mgraph()`	scdb_add_mgraph - add mgraph to the DB and cahce
`scdb_cgraph()`	scdb_cgraph - get a cgraph object
`scdb_coclust()`	scdb_coclust - get a coclust object
`scdb_del_cgraph()`	scdb_del_cgraph - del cgraph from the DB and cahce
`scdb_del_coclust()`	scdb_del_coclust - del coclust from the DB and cahce
`scdb_del_gene_names_xref()`	scdb_del_gene_names_xref - remove a gene names xref from the DB (not just the cache!)
`scdb_del_gset()`	scdb_del_gset - del gset from the DB and cahce
`scdb_del_gstat()`	scdb_del_gstat - del gstat from the DB and cahce
`scdb_del_mat()`	scdb_del_mat - remove a matrix from the DB (not just the cache!)
`scdb_del_mc()`	scdb_del_mc - del mc from the DB and cahce
`scdb_del_mc2d()`	scdb_del_mc2d - del mc2d from the DB and cahce
`scdb_del_mcatlas()`	scdb_del_mcatlas - del mcatlas from the DB and cahce
`scdb_del_mctnetwork()`	scdb_del_mctnetwork - del mctnetwork from the DB and cahce
`scdb_del_mgraph()`	scdb_del_mgraph - del mgraph from the DB and cahce
`scdb_gene_names_xref()`	scdb_gene_names_xref - get gene names convertor table from db
`scdb_gset()`	scdb_gset - get a gene set
`scdb_gstat()`	scdb_gstat - get a gstat data frame. If it is missing and the id match an existing matrix, a gstat will be gerated for this matrix and added to scdb
`scdb_init()`	Initializing scdb
`scdb_is_valid()`	Testing is scdb is initialized
`scdb_ls()`	List all object of a given type from the current scdb
`scdb_ls_loaded()`	scdb_ls_loaded - list loaded object of a certain type
`scdb_mat()`	scdb_mat - get matrix from db, load it if needed
`scdb_mc()`	scdb_mc - get a mc object
`scdb_mc2d()`	scdb_mc2d - get a mc2d object
`scdb_mcatlas()`	scdb_mcatlas - get a mcatlas object
`scdb_mctnetwork()`	scdb_mctnetwork - get a mctnetwork object
`scdb_mgraph()`	scdb_mgraph - get a mgraph object
scm functions
`scm_add_mars_facs_data()`	Adds FACS index sorting data to matrix metadata table
`scm_downsamp()`	downsampl
`scm_export_mat_to_sce()`	Export a mat object (umi matrix and metadata table) to a SingleCellExperiment object
`scm_gene_stat()`	Calculate basic statistics on a matrix
`scm_ignore_cells()`	Set ignored (i.e. blacklisted) cells
`scm_ignore_genes()`	Set ignored (i.e. blacklisted) genes
`scm_import_sce_to_mat()`	Import a umi count matrix with metadata per cell from a SingleCellExperiment objectto a scmat object to a SingleCellExperiment object
`scm_merge_mats()`	Merge multiple single cell matrix object.
`scm_new_matrix()`	Constract a tgScMat
`scm_sub_mat()`	Extract sub-matrix. This return a matrix object on a subset of the genes and cells.
`scm_which_downsamp_n()`	Determine recommended downsampling depth.
`scmat_read_scmat_10x()`	Read a matrix from the output of a 10x run. Batches can be stripped from the cell identifier if in BARCODE-LANE format.
`scmat_read_scmat_10x_custom()`	Read a custom count matrix from the output of a 10x run. Batches can be stripped from the cell identifier if in BARCODE-LANE format.
metacell functions
`mc_colorize()`	colorize metacell using a set of prefered markers and their colors
`mc_colorize_default()`	colorize metacell using an ugly default color spectrum, or a user supplied one
`mc_colorize_from_ref_mc()`	colorize metacell by projecting colors from another metacell on a similar (not identical) set of cells
`mc_colorize_sup_hierarchy()`	colorize metacells using a set of super MCs derived by hclust, colored according to a user defined table
`mc_compute_cov_gc()`	Compute fraction of non zero expressing cells per gene and mc
`mc_compute_e_gc()`	Compute metacell absolute mean umi per cell
`mc_compute_fp()`	Compute metacell gene footprint
`mc_compute_n_bc()`	Compute distribution of cells over batches and metacell
`mc_compute_outlier_fc()`	Compute log fold change expression of each gene given its regularized metacell expression
`mc_reorder()`	Reorder metacell data given defined order
`mc_restrict()`	Create a metacell object on a subset of the MCs, with all other cells becoming outliers. There is no re-normalization of mc_fp.
`mc_set_outlier_mc()`	Move all cels from specific metacells to the outliers
`mc_update_stats()`	Compute stats over metacell and update the object
gene set functions
`gset_add_genes()`	Add specific genes to the set
`gset_get_feat_mat()`	extract umi matrix for the genes in the set, possibly donwsampling
`gset_get_genes()`	Get genes of one set
`gset_import_table()`	Import gene set for a text table
`gset_new_gset()`	Generating a new gset
`gset_new_restrict_gset()`	Generate a new gene set from an existing one, filtered by a list of genes
`gset_new_restrict_nms()`	Generate a new gene set from an existing one, filtered by a list of genes
`gset_write_table()`	Exprt gene set to a table
classes
`tgCellGraph-class`	Cell graph
`tgCoClust-class`	Metacell colustering object
`tgGeneSets-class`	Gene sets interface
`tgMC2D-class`	Meta cell 2d projection
`tgMCAtlas-class`	wrap up an atlas object from mc, mc2d and matrix ids
`tgMCCov-class`	Meta cell cover
`tgMCManifGraph-class`	manifold graph structure over a metacell object
`tgMCTNetwork-class`	temporal netwrok over metacells
`tgScMat-class`	Single cell RNA-seq matrix
`initialize(<tgCellGraph>)`	This constructs graph object from a data frame on columns col1,col2, weight, (the format returened by tgs_cor_graph). Note this is not intended to serve natively complex graph algs etc, but is just a container for use by scdb and operaitons on scmats (creating a graph) and mcell (using the graph to compute graph covers)
`initialize(<tgCoClust>)`	Construct a coclust object
`initialize(<tgGeneSets>)`	tgGeneSets public constructor
`initialize(<tgMC2D>)`	Construct a meta cell 2D embedding
`initialize(<tgMCAtlas>)`	Construct a meta cell reference atlas
`initialize(<tgMCCov>)`	Construct a meta cell object
`initialize(<tgMCManifGraph>)`	Construct a meta cell manifold graph
`initialize(<tgMCTNetwork>)`	Construct a meta cell time network
utils
`plot_color_bar()`	Plot a color bar with values
`port_cor()`	port_cor matrix correlation wrap. Parameter selects which function to use, cor or tgs_cor. Tgs_cor can be more efficien if available
`allrow_cor()`	computing correlations between all rows in two matrices
`rescale_sparse_mat_cols()`	Efficient version for scaling all columns in a sparse matrix
`tgs_cor_graph()`	wrapping tgs functions to compute balanced graph from a matrix
`scfigs_dir()`	Generate a standard figure dir name igven and object and figure type
`scfigs_fn()`	Generate a standard figure name igven and object and figure type
`scfigs_init()`	scgfig_set_base - set base directory for metacell figures, creates it if it doesn't exist.

Reference

metacell pipeline functions

scdb functions

scm functions

metacell functions

gene set functions

classes

utils