Visualizing Single-cell Multi-Omics Data

import matplotlib as mpl
from matplotlib.ticker import FuncFormatter

import marsilea as ma

dataset = ma.load_data("sc_multiomics")

fmt = FuncFormatter(lambda x, _: f'{x:.0%}')
lineage = ['B Cells', 'T Cells', 'Mono/DC', 'Plasma']
lineage_colors = ["#D83F31", "#EE9322", "#E9B824", "#219C90"]
m = dict(zip(lineage, lineage_colors))
cluster_data = dataset['gene_exp_matrix']
interaction = dataset['interaction']
lineage_cells = dataset['lineage_cells']
marker_names = dataset['marker_names']
cells_count = dataset['cells_count']
display_cells = dataset['display_cells']

with mpl.rc_context({'font.size': 14}):
    gene_profile = ma.SizedHeatmap(dataset['gene_pct_matrix'], color=dataset['gene_exp_matrix'], height=6, width=6,
                                   cluster_data=cluster_data, marker="P", cmap="PuRd", sizes=(1, 100),
                                   color_legend_kws={'title': 'Mean Expression\n(RNA)'},
                                   size_legend_kws={'colors': '#e252a4', 'fmt': fmt, 'title': '% expression in group'})
    gene_profile.hsplit(labels=lineage_cells, order=lineage)
    gene_profile.add_left(ma.plotter.Chunk(lineage, lineage_colors, padding=10))
    gene_profile.add_dendrogram("left", method="ward", colors=lineage_colors, meta_color="#451952", linewidth=1.5)
    gene_profile.add_bottom(ma.plotter.Labels(marker_names, color='#392467', align="bottom", padding=10))
    gene_profile.vsplit([13])
    gene_profile.add_bottom(ma.plotter.Chunk(["Cluster of Differentiation", "Other Immune"],
                                             ["#537188", "#CBB279"], padding=10))
    gene_profile.add_right(ma.plotter.Labels(display_cells, text_props={'color': [m[c] for c in lineage_cells]},
                                            align="center", padding=10))
    gene_profile.add_title("Transcriptomics Profile", fontsize=16)

    protein_profile = ma.SizedHeatmap(dataset['protein_pct_matrix'], color=dataset['protein_exp_matrix'],
                                      cluster_data=cluster_data, marker="*", cmap="YlOrBr", height=6, width=6,
                                      color_legend_kws={'title': 'Mean Expression\n(Protein)'},
                                      size_legend_kws={'colors': '#de600c', 'fmt': fmt,
                                                       'title': '% expression in group'})
    protein_profile.hsplit(labels=lineage_cells, order=lineage)
    protein_profile.add_bottom(ma.plotter.Labels(marker_names, color="#E36414", align="bottom", padding=10))
    protein_profile.add_dendrogram("left", method="ward", show=False)
    score = interaction['STRING Score']
    protein_profile.add_bottom(ma.plotter.Arc(marker_names, interaction[['N1', 'N2']].values,
                                              # weights=score,
                                              colors=interaction['Type'].map({'Homo': '#864AF9', 'Hetero': '#FF9BD2'}),
                                              labels=interaction['Type'], width=1,
                                              legend_kws={'title': 'PPI Type'}), size=2)
    protein_profile.add_right(ma.plotter.Numbers(cells_count, color="#B80000"), pad=.1)
    protein_profile.add_title("Proteomics Profile", fontsize=16)

    comb = (gene_profile + protein_profile)
    comb.add_legends("left", stack_size=1, stack_by="column", align_stacks="top")
    comb.render()