comm module

Focused on differential communication

nichesphere.comm.calculate_LR_CT_pair_scores_dir(ccommTable, LRscoresCol)

Get cell communication scores per cell type pair per LR pair by summing that LR pair scores for that cell type pair.

Parameters:

  • ccommTable (pd.DataFrame) – Condition specific cell - cell communication table (output from CrossTalkeR)

  • LRscoresCol (str) – name of the ccommTable column where the LR scores are (usually ‘MeanLR’ for CrossTalkeR)

Returns:

scores – cell communication scores per cell type / LR pair (allpair in CrossTalkeR)

Return type:

pd.Series

nichesphere.comm.catNW(x_chem, colocNW, cell_group, group=None, group_cmap='tab20', ncols=20, color_group=None, plot_title='', clist=None, nodeSize=None, legend_ax=[0.7, 0.05, 0.15, 0.2], layout='neato', thr=0, fsize=(8, 8), alpha=1, lab_spacing=7, edge_scale=1, pos=None)

(Differential) communication network

Parameters:

  • xchem (pd.DataFrame) – cell types x cell types data frame of significant scores for each cell cell interaction (for a specific LR category)

  • colocNW (nx.Graph) – Graph object with (differential) co-localization scores as weights

  • cell_group (dict) – dictionary with niche names as keys and lists of their corresponding cell types as values

  • group (list (default: None)) – list of nodes whose interaction will be highlighted

  • group_cmap (str (default: 'tab20')) – name of the color map from which the niche colors will be taken

  • ncols (int (default: 20)) – number of colors for the group_cmap

  • color_group (list) – list of colors (one color per node)

  • plot_title (str (default: '')) – plot title to be shown at the top of the network plot

  • clist (list (default: None)) – alternatively , one can input a list of niche colors

  • nodeSize (str (default: None)) – value that will define the size of the nodes. Options are ‘betweeness’, ‘pagerank’ (network statistics)

  • legend_ax (list (default: [0.7, 0.05, 0.15, 0.2])) – legend position in the form [x0, y0, width, height]

  • layout (str (default: 'neato')) – name of the layout to be used. Options are ‘neato’, ‘dot’, ‘kamada_kawai’, ‘spring’, ‘spectral’, ‘circular’, ‘force_atlas2’, ‘fruchterman_reingold’ and ‘random’)

  • lab_spacing (int (default: 9)) – spacing between labels and nodes

  • thr (float (default: 0)) – edge weights absolute value must be above this value for the edge to be shown

  • alpha (float (default: 1)) – edge transparency (from 0 to 1)

  • fsize (tuple) – figure size in the form (x,y)

  • pos (dict) – dictionary containing the calculated 2D positions (x, y coordinates) for every node

  • edge_scale (float) – factor to scale the edge width

Returns:

  • G (nx.Graph) – Graph object with (differential) cell communication scores as weights

  • Network plot

nichesphere.comm.cellCatContained(pair, cellCat)

Check if a cell group (niche/type/category) is contained in a cell type pair

Parameters:

  • pair (list) – cell type list (usually cell type pairs in the form [cellTypeA,cellTypeB])

  • cellCat (list) – list of cell types in a cell group (niche/type/category)

Return type:

True or False

nichesphere.comm.diffCcommStats(c1CTpairScores_byCat, c2CTpairScores_byCat, cellCatCol)

Differential cell communication per LR category (eg: biological process)

Parameters:

  • c1CTpairScores_byCat (pd.DataFrame) – exp cell communication table with all interactions

  • c2CTpairScores_byCat (pd.DataFrame) – control cell communication table with all interactions

  • cellCatCol (str) – name of the column in the communication tables containing the cell pair grouping we would like to compare (eg: ‘niche_pairs’, ‘cell_pairs’)

Returns:

diffCommTable – dataframe of Wilcoxon statictics and p-values for each cell pair grouping in each LR category (eg: biological process) columns are named ‘wilcoxStat’, ‘wilcoxPval’, ‘cellCat’ and ‘LRcat’

Return type:

pd.DataFrame

nichesphere.comm.equalizeScoresTables(ctrlTbl, expTbl, ctrlCondition, expCondition)

Makes communication score tables contain the same interactions through adding 0s to be compared

Parameters:

  • ctrlTbl (pd.DataFrame) – Condition specific (control) processed cell - cell communication table

  • expTbl (pd.DataFrame) – Condition specific (exp) processed cell - cell communication table

  • ctrlCondition (str) – name of the control condition (a column with this string will be added to the resulting table)

  • expCondition (str) – name of the exp condition (a column with this string will be added to the resulting table)

Returns:

  • ctrlTbl,expTbl

  • ctrlTbl (pd.DataFrame) – control cell communication table with all interactions

  • expTbl (pd.DataFrame) – exp cell communication table with all interactions

nichesphere.comm.getDiffComm(diffCommTbl, pairCatDF, ncells, cat)

get the differential communication scores for a specific LR category

Parameters:

  • diffCommTbl (pd.DataFrame) – differential communication scores dataframe of cell pairs (or groupings) x LR categories (obtained with plotDiffCcommStatsHM function)

  • pairCatDF (pd.DataFrame)

  • ncells (int) – number of cells or groups

  • cat (str) – LR category to be tested

Returns:

x_chem – cells x cells or groups x groups dataframe of differential communication scores for a specific LR category

Return type:

pd.DataFrame

nichesphere.comm.lr_ctPairScores_perCat_dir(ccommTable, db, dbCatCol, dbMatchCol, ccommMatchCol, ccommLRscoresCol, oneCTinteractions, condition, pairCatDF)

Calculate cell communication scores per ligand category from a database

Parameters:

  • ccommTable (pd.DataFrame) – Condition specific cell - cell communication table (output from CrossTalkeR)

  • db (pd.DataFrame) – database table with information about element (ligand or receptor or both) category (eg: biological processes)

  • dbCatCol (str) – column in the database table containing the category (eg: biological process) with which the ligands/receptors/LR pairs are associated

  • dbMatchCol (str) – column in the database table containing the elements (ligands/receptors/LR pairs) associated to the categories

  • ccommMatchCol (str) – column in the ccommTable table containing the elements (ligands/receptors/LR pairs) associated to the categories

  • ccommLRscoresCol (str) – name of the ccommTable column where the LR scores are (usually ‘MeanLR’ for CrossTalkeR)

  • oneCTinteractions (list) – list of single cell interactions (celltype@celltype)

  • condition (str) – name of the analyzed condition (a column with this string will be added to the resulting table)

  • pairCatDF (pd.DataFrame) – dataframe of cell pairs and corresponding co-localization niche pairs

Returns:

CTpairScores_byCat – cell communication table with new columns ‘cell_pairs’, ‘niche_pairs’, ‘LRcat’ and ‘condition’

Return type:

pd.DataFrame

nichesphere.comm.plotDiffCcommStatsHM(diffCommTable, min_pval, vmin=None, vmax=None)

Plot heatmap of differential cell communication statistics

Parameters:

  • diffCommTable (pd.DataFrame) – dataframe of Wilcoxon statictics and p-values for each cell pair grouping in each LR category (obtained with diffCcommStats function)

  • min_pval (float) – scores with p-values higher than this won’t be shown

  • vmin (float (default: None)) – minimum value for the color map

  • vmax (float (default: None)) – maximum value for the color map

Returns:

  • x_hm (pd.DataFrame) – differential communication scores dataframe of cell pair groupings x LR categories

  • plot (sns.matrix.ClusterGrid) – seaborn clustermap as an object

nichesphere.comm.unique(array)

get unique elements in array without re-sorting

Parameters:

array (np.array)

Returns:

uniq[index.argsort()]

Return type:

np.array of unique elements of the original array in original order