Package 'liana'

ligand_receptor tibble

resource

name of the entity

Seurat object as input

OmniPath Intercell Resource DN

bool whether to format output

Annotation criteria to be excluded

number of bootstraps to calculate p-value

assay name (RNA by default)

random seed

# bool whether to normalize non-normalized data with

whether to parallelize or not

whether use the raw data or gene expression data projectected to a ppi (should be kept to TRUE)

minimum proportion of gene expression per cell type (0 by default), yet perhaps one should consider setting this to an appropriate value between 0 and 1, as an assumptions of these method is that communication is coordinated at the cluster level.

Obtain CellChatDB for which organism ('mouse' or 'human')

p-value threshold (1 by default)

diff expression of genes p-value

Arguments passed on to CellChat::subsetCommunication

object

CellChat object

net

Alternative input is a data frame with at least with three columns defining the cell-cell communication network ("source","target","interaction_name")

slot.name

the slot name of object: slot.name = "net" when extracting the inferred communications at the level of ligands/receptors; slot.name = "netP" when extracting the inferred communications at the level of signaling pathways

sources.use

a vector giving the index or the name of source cell groups

targets.use

a vector giving the index or the name of target cell groups.

signaling

a character vector giving the name of signaling pathways of interest

pairLR.use

a data frame consisting of one column named either "interaction_name" or "pathway_name", defining the interactions of interest

datasets

select the inferred cell-cell communications from a particular 'datasets' when inputing a data frame 'net'

ligand.pvalues,ligand.logFC,ligand.pct.1,ligand.pct.2

set threshold for ligand genes

ligand.pvalues: threshold for pvalues in the differential expression gene analysis (DEG)

ligand.logFC: threshold for logFoldChange in the DEG analysis; When ligand.logFC > 0, keep upgulated genes; otherwise, kepp downregulated genes

ligand.pct.1: threshold for the percent of expressed genes in the defined 'positive' cell group. keep genes with percent greater than ligand.pct.1

ligand.pct.2: threshold for the percent of expressed genes in the cells except for the defined 'positive' cell group

receptor.pvalues,receptor.logFC,receptor.pct.1,receptor.pct.2

set threshold for receptor genes

Seurat object as input

OmniPath Intercell Resource DN

bool whether to format output

assay name

dot params passed to connectome

Seurat object or SingleCellExperiment as input

OmniPath Intercell Resource DN

assay to use from Seurat object

bool: whether to format output

Parameters passed to Seurat FindMarkers (ref requires import)

Seurat or SingleCellExperiment object

List of OmniPath resources

expression matrix file name

annotations (i.e. clusters) file name

NATMI output directory

name of the resource usually in the format list(name = op_resource)

Seurat assay to be used

number of cores to be used

name of the conda environment via which NATMI is called

logcounts by default, but it's converted back into counts as suggested by the authors

bool whether to format output

bool whether Extract and overwrite csv with data from Seurat Object

random seed

path of NATMI code and dbs (by default set to liana path)

logical whether to delete input and output after run.

SingleCellExperiment or SeuratObject as input

OmniPath Intercell Resource DN

bool whether to format output

Seurat assay data to use

count slot (logcounts by default)

arguments passed to 'SCAomni::cell_signaling'

SingleCellExperiment or Seurat Object as input

Tibble or list of OmniPath resources, typically obtained via select_resource

seed passed to squidpy's ligrec function

python conda environment to run Squidpy. If NULL, will try: 1. Currently active reticulate environment 2. Environment from RETICULATE_PYTHON_ENV environment variable 3. Fall back to "liana_env" for backward compatibility

logical. If TRUE and a Python environment is already active, use it instead of switching. Default: TRUE

assay name

count slot (logcounts by default)

kwargs passed to Squidpy; For more information see: https://squidpy.readthedocs.io/en/latest/api/squidpy.gr.ligrec.html

aggregated 'liana_wrap' results from multiple methods, or alternatively results from running 'liana_wrap' with a single method. Should be filtered by some condition (e.g. preferential consesus ranking, specific interactions, etc).

names of the source (sender) cell types (NULL = no filter)

names of the target cell types (NULL = no filter)

label relative font size

transparency

axis label rotation (check 'circlize::circos.text' for options)

other paramters passed to 'circlize::chordDiagram'

for text.

a ligrec resource

columns to separate and pivot long (e.g. genesymbol or uniprot), 'source_genesymbol' and 'target_genesymbol' by default

Tensor-cell2cell Prebuilt.Tensor class instance

Ranks for the Tensor Factorization (number of factors to deconvolve the original tensor). If NULL, then rank selection is performed using the 'elbow_rank_selection' function.

indicates whether running the analysis in the ''regular'‘ or the '’robust'' way. The regular way means that the tensor decomposition is run 10 times per rank evaluated in the elbow analysis, and 1 time in the final decomposition. Additionally, the optimization algorithm has less number of iterations in the regular than the robust case (100 vs 500) and less precision (tolerance of 1e-7 vs 1e-8). The robust case runs the tensor decomposition 20 times per rank evaluated in the elbow analysis, and 100 times in the final decomposition. Here we could use the ‘tf_optimization=’regular'', which is faster but generates less robust results. We recommend using ‘tf_optimization=’robust', which takes longer to run (more iteractions and more precise too).

Random seed integer

Upper bound of ranks to explore with the elbow analysis.

Initialization method for computing the Tensor Factorization. ‘svd’, ‘random’

whether to return only the factors after factorization

verbosity logical

Dictionary containing keyword arguments for the c2c.compute_tensor_factorization function. The function deals with 'random_state' (seed) and 'rank' internally.

SingleCellExperiment Object

column with sample ids

column with cell identity (cell type) ids

minimum cells per identity in each sample

minimum samples per cell identity

minimum proportion of samples in which a cell identity is present (with at least 'min_cells')

cell type quality control summary obtained from 'get_abundance_summary'

connectome object

iTalk results object

bool whether to filter NA

NATMI output path

results for which resources to load

bool whether to format output

Unformatted SCA results

bool whether to filter SCA output

a resource in the format of OmniPath/LIANA

'ncbi_taxid' or 'name' of the target organism. See ‘show_homologene' for available organisms via OmnipathR’s 'HomoloGene'

Determines the max number of homologs to be translated. Certain genes will have multiple homolog matches, with some having also certain isoforms considered. To exclude cases in which the number of matched homologs is too high, one can adjust the homologs parameter. Setting this to '1' would mean that one-to-many homolog matches are discarded

approach to handle missing interactions. By default set to 'NULL' which would mean that any interactions without a homology match will be filtered. This can be set to e.g. 'str_to_title' when working with murine symbols. Then if a gene has no matched homolog, instead of discarding it, the '.missing_fun' will be used to format the name from human. Hence, increasing the number of matches, but likely introducing some mismatches.

'NULL' by default, then 'get_homologene_dict' is called to generate a dictionary from OmniPathR's homologene resource. Alternatively, one can pass their own symbols_dictionary.

logical for verbosity

name of the source (ligand) column

name of the target (receptor) column

SingleCellExperiment object with liana_tensor_c2c computed

resource with 'source', 'target', 'weight' columns

Arguments passed on to generate_orthologs

SingleCellExperiment with factors output from tensor-cell2cell

context descriptor - to be obtained from 'colData(sce)'

context/sample names - obtained from 'colData(sce)'

ligand-receptor DE results and other stats between clusters

Arguments passed on to liana_call

method

name of the method to be called

the curated resources from which we wish to obtain interactions. By default, it includes interactions curated in the context of CCC from CellPhoneDB, CellChat, ICELLNET, connectomeDB, CellTalkDB, and SignaLink.

ligand-receptor DE results and other stats between clusters

Arguments passed on to liana_call

method

name of the method to be called

ligand-receptor DE results and other stats between clusters

Arguments passed on to liana_call

method

name of the method to be called

'ligand' (trans), 'receptor' (rec) or 'both' (both short or long notation can be used)

Name of current resource (taken from get_lr_resources)

Inherit dot params from import_omnipath_intercell

liana_pipe results

SingleCellExperiment Object

number of permutations

number used to set random seed

logical whether to parallelize

Number of workers to be used in parallelization

- the type of data to be used to calculate the means (logcounts by default), available options are: "counts" and "logcounts"

logical for verbosity

ligand-receptor DE results and other stats between clusters

Arguments passed on to liana_call

method

name of the method to be called

aggregated liana results (preferably truncated to some threshold)

Arguments passed on to liana_heatmap

mat

Diagonal celltype-celltype matrix to be plotted. In theory, any metric deemed meaningful between cell pairs can be plotted.

font_size

base font_size - other fontsizes are relative to this one

grid_text

logical whether to display grid text or not

name

name of the heatmap. By default the heatmap name is used as the title of the heatmap legend.

row_title

Row tittle

column_title

Column tittle

LIANA results

way to aggregate, by default (NULL) will aggregate all passed methods with the approach specified in 'liana:::.score_specs'. Alternative options are 'magnitude' and 'specificity'.

If methods are ran with multiple resources, the name of the resource of interest needs to be provided *Note* if a name is not provided, the first results based on the first resource in the list will be returned

Function used to set ranked cap (i.e. the value that is assigned to interactions with NA for scores); By default, this is set to "max", which is the maximum number of interactions obtained by the methods; Some methods return all possible ligand-receptor combinations for each possible source and target cell pair - i.e. the known universe of all possible interactions (based on the CCC resource)

A cap can for all methods can also be manually set, then the top X interactions, based on the 'specificity' scores for each method will be returned and the ranking will be carried out solely on them

boolean, whether to return consensus ranks for methods

boolean, whether to return aggregate rank using the 'RobustRankAggreg' package.

defines the way that the methods would be aggragate. By default, we use the score of each method which reflects specificity (if available), if not e.g. the case of SCA we use it's sole scoring function. This aggregation is by default done on the basis of the list returns by '.score_mode'. Alternatively, one could pass '.score_housekeep' to obtain an aggragate of the housekeeping interactions of each method.

columns by which different method results will be joined. NULL by default, and automatically will handle the columns depending on the methods used.

Additional parameters (currently unused, reserved for future extensions)

name of the cluster column

name of the sample/context column

verbosity logical

logical (TRUE by default) if liana results are to be saved to the SingleCellExperiment object ('sce@metadata$liana_res')

if running multiple methods (default), then one cal also choose to aggregate the CCC results by sample.

Arguments passed on to liana_wrap

sce

'SingleCellExperiment' object or 'SeuratObject'

method

method(s) to be run via liana

resource

resource(s) to be used by the methods ('Consensus' by default), Use 'all' to run all 'human' resources in one go), or 'custom' to run liana_wrap with an appropriately formatted custom resource, passed via 'exernal_resource'

external_resource

external resource in OmniPath tibble format

min_cells

minimum cell per cell identity to be considered for analysis

return_all

whether to return all possible interactions. Any interaction with 'expr_prop' below the specific threshold will be assigned to the *worst* possible score in those that pass the threshold. For example, p-values from CellPhoneDB will be assigned to max(pvalue) - likely 1, and lr_means will be assigned to min(lr_means). Note that this applies only to the internal methods of liana.

supp_columns

any supplementary/additional columns which are to be returned by liana. Possibilities include: c("ligand.expr", "receptor.expr" "ligand.stat", "receptor.stat", "ligand.pval", "receptor.pval", "ligand.FDR", "receptor.FDR", etc)

assay

assay to be used by Seurat, by default set to 'NULL' and will use the DefaultAssay.

.simplify

if methods are run with only 1 resource, return a list of tibbles for each method (default), rather than a list of lists with method-resource combinations

base

Default to NULL (i.e. log2-transformation is assumed for SCE, and log-tranformation for Seurat). This is a requred step for the calculation of the logFC method - ensures that any other preprocessing of the counts is preserved. One could also pass 'NaN' if they wish to use the counts stored in the counts assay/slot, or any other number according to the base that was used for log-tranformation.

cell.adj

cell adjacency tibble/dataframe /w weights by which we will 'multiply' the relevant columns. Any cell pairs with a weights of 0 will be filtered out. Note that if working with LIANA's default methods, we suggest weights >= 0 & =< 1. This ensure that all methods' score will be meaningfully weighed without changing the interpretation of their scores, thus allow one to filter SCA, rank NATMI, etc.

name of the method to be called

ligand-receptor DE results and other stats between clusters

Arguments passed on to liana_pipe

sce

SingleCellExperiment Object

op_resource

resource tibble obtained via select_resource

assay

assay to be used ("RNA" by default)

assay.type

- the type of data to be used to calculate the means (logcounts by default), available options are: "counts" and "logcounts"

verbose

logical for verbosity

cell.adj

cell adjacency tibble/dataframe /w weights by which we will 'multiply' the relevant columns. Any cell pairs with a weights of 0 will be filtered out. Note that if working with LIANA's default methods, we suggest weights >= 0 & =< 1. This ensure that all methods' score will be meaningfully weighed without changing the interpretation of their scores, thus allow one to filter SCA, rank NATMI, etc.

test.type

String specifying the type of pairwise test to perform - a t-test with "t", a Wilcoxon rank sum test with "wilcox", or a binomial test with "binom".

pval.type

A string specifying how p-values are to be combined across pairwise comparisons for a given group/cluster.

base

base for conversion from log-tranformed ~CPM back to ~CPM.

Assay name passed to 'call_italk', 'call_sca', 'call_cellchat', and 'call_connectome'

- the type of data to be used to calculate the means (logcounts by default), available options are: "counts" and "logcounts"

minimum proportion of gene expression per cell type (0.1 by default). Note that when working with complexes, the minimum subunit proportion will be used for filtering.

random seed integer

policy how to account for the presence of complexes.

whether to parallelize cellphonedb-like

number of workers to be called

list of parameters passed to permutation methods

list of Parameters passed to NATMI liana_pipe

list of Parameters passed to NATMI liana_call

list of Parameters passed to liana's internal cellphonedb implementation (cellphonedb_score)

list of Parameters passed to liana's internal edge_specificity implementation (natmi_score)

list of Parameters passed to liana's internal LRScore implementation (sca_score)

list of Parameters passed to liana's internal connectome's weight_sc implementation (connectome_score)

list of Parameters passed to liana's internal crosstalk scores implementation (cytotalk_score)

list of Parameters passed to liana's internal logFC implementation (logfc_score)

list of Parameters passed to CellChat call_cellchat

list of Parameters passed to Squidpy call_squidpy

list of Parameters passed to SingleCellSignalR call_sca

list of Parameters passed to NATMI call_natmi

list of Parameters passed to Connectome call_connectome

list of Parameters passed to iTALK call_italk

aggregated 'liana_wrap' results from multiple methods, or alternatively results from running 'liana_wrap' with a single method. Should be filtered by some condition (e.g. preferential consesus ranking, specific interactions, etc).

names of the source (sender) cell types (NULL = no filter)

names of the target cell types (NULL = no filter)

number of interactions to return. Note that this assumes that the tibble is sorted in descending order of interaction importance!

column to represent the dot-size of the interaction (by default 'natmi.edge_specificity')

column to represent interactions expression magnitude (by default 'sca.LRscore')

y label name

size (~specificty) label name

colour (~magnitude) label name

logical whether to show complexes (default - TRUE) or only the subunit with minimum expression.

Diagonal celltype-celltype matrix to be plotted. In theory, any metric deemed meaningful between cell pairs can be plotted.

base font_size - other fontsizes are relative to this one

logical whether to display grid text or not

name of the heatmap. By default the heatmap name is used as the title of the heatmap legend.

Row tittle

Column tittle

parameters passed to 'ComplexHeatmap::Heatmap'

zero or more objects which can be coerced to character (and which are pasted together with no separator) or a single condition object.

type of output - message, warning, or stop

logical for verbosity

SingleCellExperiment Object

resource tibble obtained via select_resource

String specifying the type of pairwise test to perform - a t-test with "t", a Wilcoxon rank sum test with "wilcox", or a binomial test with "binom".

A string specifying how p-values are to be combined across pairwise comparisons for a given group/cluster.

assay to be used ("RNA" by default)

- the type of data to be used to calculate the means (logcounts by default), available options are: "counts" and "logcounts"

logical for verbosity

base for conversion from log-tranformed ~CPM back to ~CPM.

cell adjacency tibble/dataframe /w weights by which we will 'multiply' the relevant columns. Any cell pairs with a weights of 0 will be filtered out. Note that if working with LIANA's default methods, we suggest weights >= 0 & =< 1. This ensure that all methods' score will be meaningfully weighed without changing the interpretation of their scores, thus allow one to filter SCA, rank NATMI, etc.

SingleCellExperiment or Seurat object

dot dot dot bucket - not passed to anything, handles issues with passing non-existing arguments

score_object specific to the test obtained from score_specs

ligand-receptor DE results and other stats between clusters

dot params passed to '*_score' functions

SingleCellExperiment with 'context_df_dict' - i.e. liana results per context (sample) stored at 'sce@metadata$liana_res'

Dictionary (named list) containing a dataframe for each context. The dataframe must contain columns containing sender (source) cells, receiver (target) cells, ligands, receptors, and communication scores, separately. Keys are context names and values are dataframes. NULL by default. If not NULL will be used instead of 'sce@metadata$liana_res'.

Name of the column containing the communication scores in all context dataframes.

Approach to consider cell types and genes present across multiple contexts. - 'inner' : Considers only cell types and LR pairs that are present in all contexts (intersection). - 'outer' : Considers all cell types and LR pairs that are present across contexts (union). - 'outer_lr' : Considers only cell types that are present in all contexts (intersection), while all LR pairs that are present across contexts (union). - 'outer_cells' : Considers only LR pairs that are present in all contexts (intersection), while all cell types that are present across contexts (union).

Value to fill communication scores when a ligand-receptor pair is not present across all contexts. (NaN by default)

Value to fill communication scores when a cell is not present across all ligand-receptor pairs or all contexts. (NaN by default)

Separation character to join ligands and receptors into a LR pair name. ('^' by Default)

List used to sort the contexts when building the tensor. Elements must be all elements in 'names(context_df_dict)'. (NULL by default)

Whether alphabetically sorting elements in the InteractionTensor. The Context Dimension is not sorted if a 'context_order' list is provided. (TRUE by default).

Device to use when backend is pytorch. Options are: ['cpu', 'cuda:0', None]. NULL by Default

Ranks for the Tensor Factorization (number of factors to deconvolve the original tensor). If NULL, then rank selection is performed using the 'elbow_rank_selection' function.

Random seed integer

Upper bound of ranks to explore with the elbow analysis.

Number of tensor factorization performed for a given rank. Each factorization varies in the seed of initialization. Consider increasing the number of runs, in order to obtain a more robust rank estimate.

Initialization method for computing the Tensor Factorization. ‘svd’, ‘random’

Whether to only return a tensor instance, without rank selection and no factorization.

whether to return only the factors after factorization

name of existing conda environment

whether to use c2c if available in current env. False by default.

verbosity logical

logical (TRUE by default) if liana results are to be saved to the SingleCellExperiment object ('sce@metadata$liana_res')

Name of the column containing the sender cells in all context dataframes.

Name of the column containing the receiver cells in all context dataframes.

Name of the column containing the ligands in all context dataframes.

Name of the column containing the receptors in all context dataframes.

Dictionary containing keyword arguments for the c2c.compute_tensor_factorization function. The function deals with 'random_state' (seed) and 'rank' internally.

'SingleCellExperiment' object or 'SeuratObject'

method(s) to be run via liana

resource(s) to be used by the methods ('Consensus' by default), Use 'all' to run all 'human' resources in one go), or 'custom' to run liana_wrap with an appropriately formatted custom resource, passed via 'exernal_resource'

the cell identities/labels to be used. By default set to NULL, and will used the active Idents or colLabels from the seurat_object or SCE, respectively.

external resource in OmniPath tibble format

minimum cell per cell identity to be considered for analysis

whether to return all possible interactions. Any interaction with 'expr_prop' below the specific threshold will be assigned to the *worst* possible score in those that pass the threshold. For example, p-values from CellPhoneDB will be assigned to max(pvalue) - likely 1, and lr_means will be assigned to min(lr_means). Note that this applies only to the internal methods of liana.

any supplementary/additional columns which are to be returned by liana. Possibilities include: c("ligand.expr", "receptor.expr" "ligand.stat", "receptor.stat", "ligand.pval", "receptor.pval", "ligand.FDR", "receptor.FDR", etc)

logical whether to output messages and warnings ('TRUE' by default)

assay to be used by Seurat, by default set to 'NULL' and will use the DefaultAssay.

if methods are run with only 1 resource, return a list of tibbles for each method (default), rather than a list of lists with method-resource combinations

cell adjacency tibble/dataframe /w weights by which we will 'multiply' the relevant columns. Any cell pairs with a weights of 0 will be filtered out. Note that if working with LIANA's default methods, we suggest weights >= 0 & =< 1. This ensure that all methods' score will be meaningfully weighed without changing the interpretation of their scores, thus allow one to filter SCA, rank NATMI, etc.

Default to NULL (i.e. log2-transformation is assumed for SCE, and log-tranformation for Seurat). This is a requred step for the calculation of the logFC method - ensures that any other preprocessing of the counts is preserved. One could also pass 'NaN' if they wish to use the counts stored in the counts assay/slot, or any other number according to the base that was used for log-tranformation.

Arguments passed on to liana_defaults

expr_prop

minimum proportion of gene expression per cell type (0.1 by default). Note that when working with complexes, the minimum subunit proportion will be used for filtering.

complex_policy

policy how to account for the presence of complexes.

seed

random seed integer

parallelize

whether to parallelize cellphonedb-like

workers

number of workers to be called

liana_pipe.params

list of Parameters passed to NATMI liana_pipe

liana_call.params

list of Parameters passed to NATMI liana_call

cellchat.params

list of Parameters passed to CellChat call_cellchat

squidpy.params

list of Parameters passed to Squidpy call_squidpy

call_connectome.params

list of Parameters passed to Connectome call_connectome

call_italk.params

list of Parameters passed to iTALK call_italk

call_natmi.params

list of Parameters passed to NATMI call_natmi

call_sca.params

list of Parameters passed to SingleCellSignalR call_sca

cellphonedb.params

list of Parameters passed to liana's internal cellphonedb implementation (cellphonedb_score)

natmi.params

list of Parameters passed to liana's internal edge_specificity implementation (natmi_score)

sca.params

list of Parameters passed to liana's internal LRScore implementation (sca_score)

connectome.params

list of Parameters passed to liana's internal connectome's weight_sc implementation (connectome_score)

cytotalk.params

list of Parameters passed to liana's internal crosstalk scores implementation (cytotalk_score)

logfc.params

list of Parameters passed to liana's internal logFC implementation (logfc_score)

permutation.params

list of parameters passed to permutation methods

assay.type

- the type of data to be used to calculate the means (logcounts by default), available options are: "counts" and "logcounts"

numeric vector

numeric vector

Arguments passed on to base::max

factor of interest e.g. Factor.8

Arguments passed on to ComplexHeatmap::Heatmap

matrix

A matrix. Either numeric or character. If it is a simple vector, it will be converted to a one-column matrix.

col

A vector of colors if the color mapping is discrete or a color mapping function if the matrix is continuous numbers (should be generated by colorRamp2). If the matrix is continuous, the value can also be a vector of colors so that colors can be interpolated. Pass to ColorMapping. For more details and examples, please refer to https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#colors .

name

Name of the heatmap. By default the heatmap name is used as the title of the heatmap legend.

na_col

Color for NA values.

rect_gp

Graphic parameters for drawing rectangles (for heatmap body). The value should be specified by gpar and fill parameter is ignored.

color_space

The color space in which colors are interpolated. Only used if matrix is numeric and col is a vector of colors. Pass to colorRamp2.

border

Whether draw border. The value can be logical or a string of color.

border_gp

Graphic parameters for the borders. If you want to set different parameters for different heatmap slices, please consider to use decorate_heatmap_body.

cell_fun

Self-defined function to add graphics on each cell. Seven parameters will be passed into this function: j, i, x, y, width, height, fill which are column index, row index in matrix, coordinate of the cell, the width and height of the cell and the filled color. x, y, width and height are all unit objects.

layer_fun

Similar as cell_fun, but is vectorized. Check https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#customize-the-heatmap-body .

jitter

Random shifts added to the matrix. The value can be logical or a single numeric value. It it is TRUE, random values from uniform distribution between 0 and 1e-10 are generated. If it is a numeric value, the range for the uniform distribution is (0, jitter). It is mainly to solve the problem of "Error: node stack overflow" when there are too many identical rows/columns for plotting the dendrograms. ADD: From version 2.5.6, the error of node stack overflow has been fixed, now this argument is ignored.

row_title

Title on the row.

row_title_side

Will the title be put on the left or right of the heatmap?

row_title_gp

Graphic parameters for row title.

row_title_rot

Rotation of row title.

column_title

Title on the column.

column_title_side

Will the title be put on the top or bottom of the heatmap?

column_title_gp

Graphic parameters for column title.

column_title_rot

Rotation of column titles.

cluster_rows

If the value is a logical, it controls whether to make cluster on rows. The value can also be a hclust or a dendrogram which already contains clustering. Check https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#clustering .

cluster_row_slices

If rows are split into slices, whether perform clustering on the slice means?

clustering_distance_rows

It can be a pre-defined character which is in ("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski", "pearson", "spearman", "kendall"). It can also be a function. If the function has one argument, the input argument should be a matrix and the returned value should be a dist object. If the function has two arguments, the input arguments are two vectors and the function calculates distance between these two vectors.

clustering_method_rows

Method to perform hierarchical clustering, pass to hclust.

row_dend_side

Should the row dendrogram be put on the left or right of the heatmap?

row_dend_width

Width of the row dendrogram, should be a unit object.

show_row_dend

Whether show row dendrogram?

row_dend_gp

Graphic parameters for the dendrogram segments. If users already provide a dendrogram object with edges rendered, this argument will be ignored.

row_dend_reorder

Apply reordering on row dendrograms. The value can be a logical value or a vector which contains weight which is used to reorder rows. The reordering is applied by reorder.dendrogram.

cluster_columns

Whether make cluster on columns? Same settings as cluster_rows.

cluster_column_slices

If columns are split into slices, whether perform clustering on the slice means?

clustering_distance_columns

Same setting as clustering_distance_rows.

clustering_method_columns

Method to perform hierarchical clustering, pass to hclust.

column_dend_side

Should the column dendrogram be put on the top or bottom of the heatmap?

column_dend_height

height of the column cluster, should be a unit object.

show_column_dend

Whether show column dendrogram?

column_dend_gp

Graphic parameters for dendrogram segments. Same settings as row_dend_gp.

column_dend_reorder

Apply reordering on column dendrograms. Same settings as row_dend_reorder.

row_order

Order of rows. Manually setting row order turns off clustering.

column_order

Order of column.

row_labels

Optional row labels which are put as row names in the heatmap.

row_names_side

Should the row names be put on the left or right of the heatmap?

show_row_names

Whether show row names.

row_names_max_width

Maximum width of row names viewport.

row_names_gp

Graphic parameters for row names.

row_names_rot

Rotation of row names.

row_names_centered

Should row names put centered?

column_labels

Optional column labels which are put as column names in the heatmap.

column_names_side

Should the column names be put on the top or bottom of the heatmap?

column_names_max_height

Maximum height of column names viewport.

show_column_names

Whether show column names.

column_names_gp

Graphic parameters for drawing text.

column_names_rot

Rotation of column names.

column_names_centered

Should column names put centered?

top_annotation

A HeatmapAnnotation object.

bottom_annotation

A HeatmapAnnotation object.

left_annotation

It should be specified by rowAnnotation.

right_annotation

it should be specified by rowAnnotation.

km

Apply k-means clustering on rows. If the value is larger than 1, the heatmap will be split by rows according to the k-means clustering. For each row slice, hierarchical clustering is still applied with parameters above.

split

A vector or a data frame by which the rows are split. But if cluster_rows is a clustering object, split can be a single number indicating to split the dendrogram by cutree.

row_km

Same as km.

row_km_repeats

Number of k-means runs to get a consensus k-means clustering. Note if row_km_repeats is set to more than one, the final number of groups might be smaller than row_km, but this might means the original row_km is not a good choice.

row_split

Same as split.

column_km

K-means clustering on columns.

column_km_repeats

Number of k-means runs to get a consensus k-means clustering. Similar as row_km_repeats.

column_split

Split on columns. For heatmap splitting, please refer to https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#heatmap-split .

gap

Gap between row slices if the heatmap is split by rows. The value should be a unit object.

row_gap

Same as gap.

column_gap

Gap between column slices.

show_parent_dend_line

When heatmap is split, whether to add a dashed line to mark parent dendrogram and children dendrograms?

width

Width of the heatmap body.

height

Height of the heatmap body.

heatmap_width

Width of the whole heatmap (including heatmap components)

heatmap_height

Height of the whole heatmap (including heatmap components). Check https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#size-of-the-heatmap .

show_heatmap_legend

Whether show heatmap legend?

heatmap_legend_param

A list contains parameters for the heatmap legends. See color_mapping_legend,ColorMapping-method for all available parameters.

use_raster

Whether render the heatmap body as a raster image. It helps to reduce file size when the matrix is huge. If number of rows or columns is more than 2000, it is by default turned on. Note if cell_fun is set, use_raster is enforced to be FALSE.

raster_device

Graphic device which is used to generate the raster image.

raster_quality

A value larger than 1.

raster_device_param

A list of further parameters for the selected graphic device. For raster image support, please check https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#heatmap-as-raster-image .

raster_resize_mat

Whether resize the matrix to let the dimension of the matrix the same as the dimension of the raster image? The value can be logical. If it is TRUE, mean is used to summarize the sub matrix which corresponds to a single pixel. The value can also be a summary function, e.g. max.

raster_by_magick

Whether to use image_resize to scale the image.

raster_magick_filter

Pass to filter argument of image_resize. A character scalar and all possible values are in filter_types. The default is "Lanczos".

post_fun

A function which will be executed after the heatmap list is drawn.

factors as formatted by 'format_c2c_factors'

SingleCellExperiment with factors output from tensor-cell2cell

context descriptor - to be obtained from 'colData(sce)'

context/sample names - obtained from 'colData(sce)'

SingleCellExperiment with factors output from tensor-cell2cell

context/sample names - obtained from 'colData(sce)'

context descriptor - to be obtained from 'colData(sce)'

arguments passed to the test used.

SingleCellExperiment with factors output from tensor-cell2cell

context/sample names - obtained from 'colData(sce)'

context descriptor - to be obtained from 'colData(sce)'

Arguments passed on to ComplexHeatmap::Heatmap

matrix

A matrix. Either numeric or character. If it is a simple vector, it will be converted to a one-column matrix.

col

A vector of colors if the color mapping is discrete or a color mapping function if the matrix is continuous numbers (should be generated by colorRamp2). If the matrix is continuous, the value can also be a vector of colors so that colors can be interpolated. Pass to ColorMapping. For more details and examples, please refer to https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#colors .

name

Name of the heatmap. By default the heatmap name is used as the title of the heatmap legend.

na_col

Color for NA values.

rect_gp

Graphic parameters for drawing rectangles (for heatmap body). The value should be specified by gpar and fill parameter is ignored.

color_space

The color space in which colors are interpolated. Only used if matrix is numeric and col is a vector of colors. Pass to colorRamp2.

border

Whether draw border. The value can be logical or a string of color.

border_gp

Graphic parameters for the borders. If you want to set different parameters for different heatmap slices, please consider to use decorate_heatmap_body.

cell_fun

Self-defined function to add graphics on each cell. Seven parameters will be passed into this function: j, i, x, y, width, height, fill which are column index, row index in matrix, coordinate of the cell, the width and height of the cell and the filled color. x, y, width and height are all unit objects.

layer_fun

Similar as cell_fun, but is vectorized. Check https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#customize-the-heatmap-body .

jitter

Random shifts added to the matrix. The value can be logical or a single numeric value. It it is TRUE, random values from uniform distribution between 0 and 1e-10 are generated. If it is a numeric value, the range for the uniform distribution is (0, jitter). It is mainly to solve the problem of "Error: node stack overflow" when there are too many identical rows/columns for plotting the dendrograms. ADD: From version 2.5.6, the error of node stack overflow has been fixed, now this argument is ignored.

row_title

Title on the row.

row_title_side

Will the title be put on the left or right of the heatmap?

row_title_gp

Graphic parameters for row title.

row_title_rot

Rotation of row title.

column_title

Title on the column.

column_title_side

Will the title be put on the top or bottom of the heatmap?

column_title_gp

Graphic parameters for column title.

column_title_rot

Rotation of column titles.

cluster_rows

If the value is a logical, it controls whether to make cluster on rows. The value can also be a hclust or a dendrogram which already contains clustering. Check https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#clustering .

cluster_row_slices

If rows are split into slices, whether perform clustering on the slice means?

clustering_distance_rows

It can be a pre-defined character which is in ("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski", "pearson", "spearman", "kendall"). It can also be a function. If the function has one argument, the input argument should be a matrix and the returned value should be a dist object. If the function has two arguments, the input arguments are two vectors and the function calculates distance between these two vectors.

clustering_method_rows

Method to perform hierarchical clustering, pass to hclust.

row_dend_side

Should the row dendrogram be put on the left or right of the heatmap?

row_dend_width

Width of the row dendrogram, should be a unit object.

show_row_dend

Whether show row dendrogram?

row_dend_gp

Graphic parameters for the dendrogram segments. If users already provide a dendrogram object with edges rendered, this argument will be ignored.

row_dend_reorder

Apply reordering on row dendrograms. The value can be a logical value or a vector which contains weight which is used to reorder rows. The reordering is applied by reorder.dendrogram.

cluster_columns

Whether make cluster on columns? Same settings as cluster_rows.

cluster_column_slices

If columns are split into slices, whether perform clustering on the slice means?

clustering_distance_columns

Same setting as clustering_distance_rows.

clustering_method_columns

Method to perform hierarchical clustering, pass to hclust.

column_dend_side

Should the column dendrogram be put on the top or bottom of the heatmap?

column_dend_height

height of the column cluster, should be a unit object.

show_column_dend

Whether show column dendrogram?

column_dend_gp

Graphic parameters for dendrogram segments. Same settings as row_dend_gp.

column_dend_reorder

Apply reordering on column dendrograms. Same settings as row_dend_reorder.

row_order

Order of rows. Manually setting row order turns off clustering.

column_order

Order of column.

row_labels

Optional row labels which are put as row names in the heatmap.

row_names_side

Should the row names be put on the left or right of the heatmap?

show_row_names

Whether show row names.

row_names_max_width

Maximum width of row names viewport.

row_names_gp

Graphic parameters for row names.

row_names_rot

Rotation of row names.

row_names_centered

Should row names put centered?

column_labels

Optional column labels which are put as column names in the heatmap.

column_names_side

Should the column names be put on the top or bottom of the heatmap?

column_names_max_height

Maximum height of column names viewport.

show_column_names

Whether show column names.

column_names_gp

Graphic parameters for drawing text.

column_names_rot

Rotation of column names.

column_names_centered

Should column names put centered?

top_annotation

A HeatmapAnnotation object.

bottom_annotation

A HeatmapAnnotation object.

left_annotation

It should be specified by rowAnnotation.

right_annotation

it should be specified by rowAnnotation.

km

Apply k-means clustering on rows. If the value is larger than 1, the heatmap will be split by rows according to the k-means clustering. For each row slice, hierarchical clustering is still applied with parameters above.

split

A vector or a data frame by which the rows are split. But if cluster_rows is a clustering object, split can be a single number indicating to split the dendrogram by cutree.

row_km

Same as km.

row_km_repeats

Number of k-means runs to get a consensus k-means clustering. Note if row_km_repeats is set to more than one, the final number of groups might be smaller than row_km, but this might means the original row_km is not a good choice.

row_split

Same as split.

column_km

K-means clustering on columns.

column_km_repeats

Number of k-means runs to get a consensus k-means clustering. Similar as row_km_repeats.

column_split

Split on columns. For heatmap splitting, please refer to https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#heatmap-split .

gap

Gap between row slices if the heatmap is split by rows. The value should be a unit object.

row_gap

Same as gap.

column_gap

Gap between column slices.

show_parent_dend_line

When heatmap is split, whether to add a dashed line to mark parent dendrogram and children dendrograms?

width

Width of the heatmap body.

height

Height of the heatmap body.

heatmap_width

Width of the whole heatmap (including heatmap components)

heatmap_height

Height of the whole heatmap (including heatmap components). Check https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#size-of-the-heatmap .

show_heatmap_legend

Whether show heatmap legend?

heatmap_legend_param

A list contains parameters for the heatmap legends. See color_mapping_legend,ColorMapping-method for all available parameters.

use_raster

Whether render the heatmap body as a raster image. It helps to reduce file size when the matrix is huge. If number of rows or columns is more than 2000, it is by default turned on. Note if cell_fun is set, use_raster is enforced to be FALSE.

raster_device

Graphic device which is used to generate the raster image.

raster_quality

A value larger than 1.

raster_device_param

A list of further parameters for the selected graphic device. For raster image support, please check https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#heatmap-as-raster-image .

raster_resize_mat

Whether resize the matrix to let the dimension of the matrix the same as the dimension of the raster image? The value can be logical. If it is TRUE, mean is used to summarize the sub matrix which corresponds to a single pixel. The value can also be a summary function, e.g. max.

raster_by_magick

Whether to use image_resize to scale the image.

raster_magick_filter

Pass to filter argument of image_resize. A character scalar and all possible values are in filter_types. The default is "Lanczos".

post_fun

A function which will be executed after the heatmap list is drawn.

SingleCellExperiment with factors output from tensor-cell2cell

Number of rows to return for top_n(), fraction of rows to return for top_frac(). If n is positive, selects the top rows. If negative, selects the bottom rows. If x is grouped, this is the number (or fraction) of rows per group. Will include more rows if there are ties.

Arguments passed on to ComplexHeatmap::Heatmap

matrix

A matrix. Either numeric or character. If it is a simple vector, it will be converted to a one-column matrix.

col

A vector of colors if the color mapping is discrete or a color mapping function if the matrix is continuous numbers (should be generated by colorRamp2). If the matrix is continuous, the value can also be a vector of colors so that colors can be interpolated. Pass to ColorMapping. For more details and examples, please refer to https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#colors .

name

Name of the heatmap. By default the heatmap name is used as the title of the heatmap legend.

na_col

Color for NA values.

rect_gp

Graphic parameters for drawing rectangles (for heatmap body). The value should be specified by gpar and fill parameter is ignored.

color_space

The color space in which colors are interpolated. Only used if matrix is numeric and col is a vector of colors. Pass to colorRamp2.

border

Whether draw border. The value can be logical or a string of color.

border_gp

Graphic parameters for the borders. If you want to set different parameters for different heatmap slices, please consider to use decorate_heatmap_body.

cell_fun

Self-defined function to add graphics on each cell. Seven parameters will be passed into this function: j, i, x, y, width, height, fill which are column index, row index in matrix, coordinate of the cell, the width and height of the cell and the filled color. x, y, width and height are all unit objects.

layer_fun

Similar as cell_fun, but is vectorized. Check https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#customize-the-heatmap-body .

jitter

Random shifts added to the matrix. The value can be logical or a single numeric value. It it is TRUE, random values from uniform distribution between 0 and 1e-10 are generated. If it is a numeric value, the range for the uniform distribution is (0, jitter). It is mainly to solve the problem of "Error: node stack overflow" when there are too many identical rows/columns for plotting the dendrograms. ADD: From version 2.5.6, the error of node stack overflow has been fixed, now this argument is ignored.

row_title

Title on the row.

row_title_side

Will the title be put on the left or right of the heatmap?

row_title_gp

Graphic parameters for row title.

row_title_rot

Rotation of row title.

column_title

Title on the column.

column_title_side

Will the title be put on the top or bottom of the heatmap?

column_title_gp

Graphic parameters for column title.

column_title_rot

Rotation of column titles.

cluster_rows

If the value is a logical, it controls whether to make cluster on rows. The value can also be a hclust or a dendrogram which already contains clustering. Check https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#clustering .

cluster_row_slices

If rows are split into slices, whether perform clustering on the slice means?

clustering_distance_rows

It can be a pre-defined character which is in ("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski", "pearson", "spearman", "kendall"). It can also be a function. If the function has one argument, the input argument should be a matrix and the returned value should be a dist object. If the function has two arguments, the input arguments are two vectors and the function calculates distance between these two vectors.

clustering_method_rows

Method to perform hierarchical clustering, pass to hclust.

row_dend_side

Should the row dendrogram be put on the left or right of the heatmap?

row_dend_width

Width of the row dendrogram, should be a unit object.

show_row_dend

Whether show row dendrogram?

row_dend_gp

Graphic parameters for the dendrogram segments. If users already provide a dendrogram object with edges rendered, this argument will be ignored.

row_dend_reorder

Apply reordering on row dendrograms. The value can be a logical value or a vector which contains weight which is used to reorder rows. The reordering is applied by reorder.dendrogram.

cluster_columns

Whether make cluster on columns? Same settings as cluster_rows.

cluster_column_slices

If columns are split into slices, whether perform clustering on the slice means?

clustering_distance_columns

Same setting as clustering_distance_rows.

clustering_method_columns

Method to perform hierarchical clustering, pass to hclust.

column_dend_side

Should the column dendrogram be put on the top or bottom of the heatmap?

column_dend_height

height of the column cluster, should be a unit object.

show_column_dend

Whether show column dendrogram?

column_dend_gp

Graphic parameters for dendrogram segments. Same settings as row_dend_gp.

column_dend_reorder

Apply reordering on column dendrograms. Same settings as row_dend_reorder.

row_order

Order of rows. Manually setting row order turns off clustering.

column_order

Order of column.

row_labels

Optional row labels which are put as row names in the heatmap.

row_names_side

Should the row names be put on the left or right of the heatmap?

show_row_names

Whether show row names.

row_names_max_width

Maximum width of row names viewport.

row_names_gp

Graphic parameters for row names.

row_names_rot

Rotation of row names.

row_names_centered

Should row names put centered?

column_labels

Optional column labels which are put as column names in the heatmap.

column_names_side

Should the column names be put on the top or bottom of the heatmap?

column_names_max_height

Maximum height of column names viewport.

show_column_names

Whether show column names.

column_names_gp

Graphic parameters for drawing text.

column_names_rot

Rotation of column names.

column_names_centered

Should column names put centered?

top_annotation

A HeatmapAnnotation object.

bottom_annotation

A HeatmapAnnotation object.

left_annotation

It should be specified by rowAnnotation.

right_annotation

it should be specified by rowAnnotation.

km

Apply k-means clustering on rows. If the value is larger than 1, the heatmap will be split by rows according to the k-means clustering. For each row slice, hierarchical clustering is still applied with parameters above.

split

A vector or a data frame by which the rows are split. But if cluster_rows is a clustering object, split can be a single number indicating to split the dendrogram by cutree.

row_km

Same as km.

row_km_repeats

Number of k-means runs to get a consensus k-means clustering. Note if row_km_repeats is set to more than one, the final number of groups might be smaller than row_km, but this might means the original row_km is not a good choice.

row_split

Same as split.

column_km

K-means clustering on columns.

column_km_repeats

Number of k-means runs to get a consensus k-means clustering. Similar as row_km_repeats.

column_split

Split on columns. For heatmap splitting, please refer to https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#heatmap-split .

gap

Gap between row slices if the heatmap is split by rows. The value should be a unit object.

row_gap

Same as gap.

column_gap

Gap between column slices.

show_parent_dend_line

When heatmap is split, whether to add a dashed line to mark parent dendrogram and children dendrograms?

width

Width of the heatmap body.

height

Height of the heatmap body.

heatmap_width

Width of the whole heatmap (including heatmap components)

heatmap_height

Height of the whole heatmap (including heatmap components). Check https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#size-of-the-heatmap .

show_heatmap_legend

Whether show heatmap legend?

heatmap_legend_param

A list contains parameters for the heatmap legends. See color_mapping_legend,ColorMapping-method for all available parameters.

use_raster

Whether render the heatmap body as a raster image. It helps to reduce file size when the matrix is huge. If number of rows or columns is more than 2000, it is by default turned on. Note if cell_fun is set, use_raster is enforced to be FALSE.

raster_device

Graphic device which is used to generate the raster image.

raster_quality

A value larger than 1.

raster_device_param

A list of further parameters for the selected graphic device. For raster image support, please check https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#heatmap-as-raster-image .

raster_resize_mat

Whether resize the matrix to let the dimension of the matrix the same as the dimension of the raster image? The value can be logical. If it is TRUE, mean is used to summarize the sub matrix which corresponds to a single pixel. The value can also be a summary function, e.g. max.

raster_by_magick

Whether to use image_resize to scale the image.

raster_magick_filter

Pass to filter argument of image_resize. A character scalar and all possible values are in filter_types. The default is "Lanczos".

post_fun

A function which will be executed after the heatmap list is drawn.

Dictionary (named list) containing a dataframe for each context. The dataframe must contain columns containing sender (source) cells, receiver (target) cells, ligands, receptors, and communication scores, separately. Keys are context names and values are dataframes. NULL by default. If not NULL will be used instead of 'sce@metadata$liana_res'.

Name of the column containing the communication scores in all context dataframes.

Name of the column containing the sender cells in all context dataframes.

Name of the column containing the receiver cells in all context dataframes.

Name of the column containing the ligands in all context dataframes.

Name of the column containing the receptors in all context dataframes.

controls the elements to include in the union scenario of the 'how' options. Only elements that are present at least in this fraction of samples/contexts will be included. When this value is 0, considers all elements across the samples. When this value is 1, it acts as using ‘how=’inner''

boolean wheter to invert the score (TRUE by defeault)

function used to invert scores

whether to set negative scores to 0

the value to be used to fill negative values

ligand-receptor separator; '^' by default.

verbosity logical

LIANA results

Arguments passed on to liana_aggregate

aggregate_how

way to aggregate, by default (NULL) will aggregate all passed methods with the approach specified in 'liana:::.score_specs'. Alternative options are 'magnitude' and 'specificity'.

set_cap

Function used to set ranked cap (i.e. the value that is assigned to interactions with NA for scores); By default, this is set to "max", which is the maximum number of interactions obtained by the methods; Some methods return all possible ligand-receptor combinations for each possible source and target cell pair - i.e. the known universe of all possible interactions (based on the CCC resource)

resource

If methods are ran with multiple resources, the name of the resource of interest needs to be provided *Note* if a name is not provided, the first results based on the first resource in the list will be returned

cap

A cap can for all methods can also be manually set, then the top X interactions, based on the 'specificity' scores for each method will be returned and the ranking will be carried out solely on them

get_ranks

boolean, whether to return consensus ranks for methods

get_agrank

boolean, whether to return aggregate rank using the 'RobustRankAggreg' package.

.score_mode

defines the way that the methods would be aggragate. By default, we use the score of each method which reflects specificity (if available), if not e.g. the case of SCA we use it's sole scoring function. This aggregation is by default done on the basis of the list returns by '.score_mode'. Alternatively, one could pass '.score_housekeep' to obtain an aggragate of the housekeeping interactions of each method.

join_cols

columns by which different method results will be joined. NULL by default, and automatically will handle the columns depending on the methods used.

liana_results for a single method

name of the method

ranking to be carried out. Accepted modes are 'specificity' and 'magnitude'. The first is meant to reflect the specificity of interactions across all cell types, while the latter typically reflects how highly expressed is a given interaction.

names of the resources. Passing 'all' will return all human resources (i.e. all resources, except MouseConsensus)