New CellPhoneDB v5 results

2025-01-13

CellPhoneDB v5 results

From version 5 of CellPhoneDB, there are two new output files - interaction_scores and CellSign.

According to the official repository, this table corresponds to:

interaction_scores: stores the new score generated. This score ranges from 0-100.

To score interactions CellPhoneDB v5 employs the following protocol:

1. Exclude genes not participating in any interaction and those expressed in less than k% of cells within a given cell type.
2. Calculate the mean expression (G) of each gene (i) within each cell type (j).
3. For heteromeric proteins, aggregate the mean gene expression of each subunit (n) employing the geometric mean.
4. Scale mean gene/heteromer expression across cell types between 0 and 100.
5. Calculate the product of the scale mean expression of the interaction proteins as a proxy of the interaction relevance.

cellsign: accepts the new CellSign data.

The aim of the CellSign module is to identify activated receptors and prioritise high-confidence interactions by leveraging the activity of the downstream transcription factors (TFs). CellSign relies on a database of receptors linked to their putative downstream TFs. This is a binary table where 1 means it’s a hit. In ktplots/ktplotspy, we convert all other values to 0.5 adn fit it to the alpha parameter for visualisation.

ktplots will support these output via inclusion into the existing plot_cpdb function. We will gradually enable their functionality across the other functions, as well as with in the python package eventually.

Load packages

library(ktplots)
data(cpdb_output_v5)

plot_cpdb_heatmap(pvals = relevant_interactions_v5, degs_analysis = TRUE, title = "Sum of significant interactions")

plot_cpdb(
    scdata = sce_v5,
    cell_type1 = "PV MYH11|PV STEAP4|PV MMPP11",
    cell_type2 = "EVT_1|EVT_2|GC|iEVT|eEVT|VCT_CCC",
    means = means_v5,
    pvals = relevant_interactions_v5,
    celltype_key = "cell_labels",
    genes = c("TGFB2", "CSF1R"),
    title = "Interactions between PV and trophoblast ",
    max_size = 6,
    highlight_size = 0.75,
    degs_analysis = TRUE,
    standard_scale = TRUE
)

Interaction scores

Let’s start with interaction scores. If a dataframe corresponding to the interaction_scores file is provided, you can toggle the alpha transparency of the interactions by the interaction score (interaction ranking is simply the score/100).

plot_cpdb(
    scdata = sce_v5,
    cell_type1 = "PV MYH11|PV STEAP4|PV MMPP11",
    cell_type2 = "EVT_1|EVT_2|GC|iEVT|eEVT|VCT_CCC",
    means = means_v5,
    pvals = relevant_interactions_v5,
    celltype_key = "cell_labels",
    genes = c("TGFB2", "CSF1R"),
    title = "Interactions between PV and trophoblast ",
    max_size = 6,
    highlight_size = 0.75,
    degs_analysis = TRUE,
    standard_scale = TRUE,
    interaction_scores = interaction_scores_v5,
    scale_alpha_by_interaction_scores = TRUE
)

You can also specify a minimum interaction score to keep, removing all interactions lesser than this value.

plot_cpdb(
    scdata = sce_v5,
    cell_type1 = "PV MYH11|PV STEAP4|PV MMPP11",
    cell_type2 = "EVT_1|EVT_2|GC|iEVT|eEVT|VCT_CCC",
    means = means_v5,
    pvals = relevant_interactions_v5,
    celltype_key = "cell_labels",
    genes = c("TGFB2", "CSF1R"),
    title = "Interactions between\nPV and trophoblast ",
    max_size = 6,
    highlight_size = 0.75,
    degs_analysis = TRUE,
    standard_scale = TRUE,
    interaction_scores = interaction_scores_v5,
    min_interaction_score = 20
)

or specify both to have the alpha transparency shown too.

plot_cpdb(
    scdata = sce_v5,
    cell_type1 = "PV MYH11|PV STEAP4|PV MMPP11",
    cell_type2 = "EVT_1|EVT_2|GC|iEVT|eEVT|VCT_CCC",
    means = means_v5,
    pvals = relevant_interactions_v5,
    celltype_key = "cell_labels",
    genes = c("TGFB2", "CSF1R"),
    title = "Interactions between\nPV and trophoblast ",
    max_size = 6,
    highlight_size = 0.75,
    degs_analysis = TRUE,
    standard_scale = TRUE,
    interaction_scores = interaction_scores_v5,
    scale_alpha_by_interaction_scores = TRUE,
    min_interaction_score = 20
)

CellSign

If a dataframe corresponding to the cellsign file is provided, you can toggle the filter the interactions by the results

plot_cpdb(
    scdata = sce_v5,
    cell_type1 = "PV MYH11",
    cell_type2 = "EVT_1|EVT_2|GC|iEVT|eEVT|VCT_CCC",
    means = means_v5,
    pvals = relevant_interactions_v5,
    celltype_key = "cell_labels",
    title = "Interactions between\nPV and trophoblast with\ndownstream significance",
    max_size = 6,
    highlight_size = 0.75,
    degs_analysis = TRUE,
    standard_scale = TRUE,
    cellsign = cellsign_v5,
    filter_by_cellsign = TRUE
)

and also scale the alpha value (50% for 0 and 100% for 1).

plot_cpdb(
    scdata = sce_v5,
    cell_type1 = "PV MYH11",
    cell_type2 = "EVT_1|EVT_2|GC|iEVT|eEVT|VCT_CCC",
    means = means_v5,
    pvals = relevant_interactions_v5,
    celltype_key = "cell_labels",
    title = "Interactions between\nPV and trophoblast with\ndownstream significance",
    max_size = 6,
    highlight_size = 0.75,
    degs_analysis = TRUE,
    standard_scale = TRUE,
    cellsign = cellsign_v5,
    filter_by_cellsign = TRUE,
    scale_alpha_by_cellsign = TRUE
)

Additional plotting data

From now on, is_integrin, directionality and classification are transferred to final output table in plot_cpdb. This means you will be able to use something like facet_grid/facet_wrap to plot them!

library(ggplot2)
p <- plot_cpdb(
    scdata = sce_v5,
    cell_type1 = "PV MYH11|PV STEAP4|PV MMPP11",
    cell_type2 = "EVT_1|EVT_2|GC|iEVT|eEVT|VCT_CCC",
    means = means_v5,
    pvals = relevant_interactions_v5,
    celltype_key = "cell_labels",
    genes = c("TGFB2", "CSF1R", "COL1A1"),
    max_size = 6,
    highlight_size = 0.75,
    degs_analysis = TRUE,
    standard_scale = TRUE,
    interaction_scores = interaction_scores_v5
    # return_table = TRUE
)
p + facet_wrap(~classification, ncol = 3)

p + facet_wrap(~ classification + is_integrin, ncol = 3)

p + facet_wrap(~directionality, ncol = 2)