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_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_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_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_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_subheats()

PLot a series of heat maps to describe metacell groups

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_new_mc()

Generate a new metacell in scdb

mcell_new_mc2d()

Generate a new metacell 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_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_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_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_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_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

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()

scm_merge_mats: Merge two single cell matrix object. Return the merged matrix, with merged meta data and issues an error if there are overlapping cell names between the two matrices. In case genes sets differs between the matrices, the union is used, with zeros (not NAs!) filling up the missing genes in the respective matrix.

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_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

tgMCCov-class

Meta cell cover

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(<tgMCCov>)

Construct a meta cell object

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.