Human heart cell-cell interaction analysis using Giotto

[145]:

library(Giotto)
library(data.table)

[168]:

# tissue = HComD2
path_to_matrix = '../output/heart/express.txt'
path_to_locations = '../output/heart/coords.txt'
path_to_celltype = '../output/heart/cell_type.txt'
my_cell_metadata = read.table(path_to_celltype)

[169]:

my_instructions = createGiottoInstructions(python_path='/home/xwanaf/.conda/envs/rbayes/bin/python')
my_giotto_object = createGiottoObject(raw_exprs = path_to_matrix,
                                      spatial_locs = path_to_locations,
                                      cell_metadata = my_cell_metadata,
                                      instructions = my_instructions)
my_giotto_object@cell_metadata$cell_types <- my_giotto_object@cell_metadata$V1


 external python path provided and will be used
Consider to install these (optional) packages to run all possible Giotto commands for spatial analyses:  MAST smfishHmrf trendsceek SPARK multinet RTriangle FactoMiner
 Giotto does not automatically install all these packages as they are not absolutely required and this reduces the number of dependencies

[4]:

# processing
my_giotto_object <- filterGiotto(gobject = my_giotto_object,
                             expression_threshold = 0.5,
                             gene_det_in_min_cells = 20,
                             min_det_genes_per_cell = 0)
my_giotto_object <- normalizeGiotto(gobject = my_giotto_object)

# dimension reduction
my_giotto_object <- calculateHVG(gobject = my_giotto_object)
my_giotto_object <- runPCA(gobject = my_giotto_object)
my_giotto_object <- runUMAP(my_giotto_object, dimensions_to_use = 1:5)
my_giotto_object <- createNearestNetwork(my_giotto_object)

return_plot = TRUE and return_gobject = TRUE

          plot will not be returned to object, but can still be saved with save_plot = TRUE or manually
hvg  was found in the gene metadata information and will be used to select highly variable genes

Warning message in runPCA_prcomp_irlba(x = t_giotto(expr_values), center = center, :
“ncp >= minimum dimension of x, will be set to minimum dimension of x - 1”
Warning message in (function (A, nv = 5, nu = nv, maxit = 1000, work = nv + 7, reorth = TRUE, :
“You're computing too large a percentage of total singular values, use a standard svd instead.”
Warning message in (function (A, nv = 5, nu = nv, maxit = 1000, work = nv + 7, reorth = TRUE, :
“did not converge--results might be invalid!; try increasing work or maxit”
../_images/notebooks_Human-Heart-CCell-interaction_4_2.png 
[5]:

# # annotate
# metadata = pDataDT(my_giotto_object)
# uniq_clusters = length(unique(metadata$leiden_clus))

# clusters_cell_types = paste0('cell ', LETTERS[1:uniq_clusters])
# names(clusters_cell_types) = 1:uniq_clusters

# my_giotto_object = annotateGiotto(gobject = my_giotto_object,
#                               annotation_vector = clusters_cell_types,
#                               cluster_column = 'leiden_clus',
#                               name = 'cell_types')

# create network (required for binSpect methods)
my_giotto_object = createSpatialNetwork(gobject = my_giotto_object, minimum_k = 2)

# identify genes with a spatial coherent expression profile
km_spatialgenes = binSpect(my_giotto_object, bin_method = 'kmeans')


 This is the single parameter version of binSpect
 1. matrix binarization complete

 2. spatial enrichment test completed

 3. (optional) average expression of high expressing cells calculated

 4. (optional) number of high expressing cells calculated

[6]:

set.seed(seed = 2841)
cell_proximities = cellProximityEnrichment(gobject = my_giotto_object,
                                           cluster_column = 'cell_types',
                                           spatial_network_name = 'Delaunay_network',
                                           adjust_method = 'fdr',
                                           number_of_simulations = 1000)
# network with self-edges
cellProximityNetwork(gobject = my_giotto_object, CPscore = cell_proximities,
                     remove_self_edges = F, self_loop_strength = 0.3,
                     only_show_enrichment_edges = F,
                     rescale_edge_weights = T,
                     node_size = 6, node_text_size=8,
                     edge_weight_range_depletion = c(1, 2),
                     edge_weight_range_enrichment = c(2,5))
../_images/notebooks_Human-Heart-CCell-interaction_6_0.png
../_images/notebooks_Human-Heart-CCell-interaction_6_1.png 
[7]:

my_giotto_object <- addGeneStatistics(my_giotto_object)

## select top 25th highest expressing genes
gene_metadata = fDataDT(my_giotto_object)
plot(gene_metadata$nr_cells, gene_metadata$mean_expr)
plot(gene_metadata$nr_cells, gene_metadata$mean_expr_det)

quantile(gene_metadata$mean_expr_det)
high_expressed_genes = gene_metadata[mean_expr_det > 4]$gene_ID

## identify genes that are associated with proximity to other cell types
ICGscoresHighGenes =  findICG(gobject = my_giotto_object,
                              selected_genes = high_expressed_genes,
                              spatial_network_name = 'Delaunay_network',
                              cluster_column = 'cell_types',
                              diff_test = 'permutation',
                              adjust_method = 'fdr',
                              nr_permutations = 500,
                              do_parallel = T, cores = 2)

## visualize all genes
plotCellProximityGenes(my_giotto_object, cpgObject = ICGscoresHighGenes, method = 'dotplot')

## filter genes
ICGscoresFilt = filterICG(ICGscoresHighGenes,
                          min_cells = 2, min_int_cells = 2, min_fdr = 0.1,
                          min_spat_diff = 0.1, min_log2_fc = 0.1, min_zscore = 0.0)
# ICGscoresFilt = filterICG(ICGscoresHighGenes,
#                           min_cells = 2, min_int_cells = 2, min_fdr = 0.1,
#                           min_spat_diff = 0.1, min_log2_fc = 0.1, min_zscore = 1)
../_images/notebooks_Human-Heart-CCell-interaction_7_0.png
0%
2.26574402750246
25%
3.63427153928577
50%
4.05780871264046
75%
4.64868220483457
100%
11.3255376347626
../_images/notebooks_Human-Heart-CCell-interaction_7_2.png
../_images/notebooks_Human-Heart-CCell-interaction_7_3.png
../_images/notebooks_Human-Heart-CCell-interaction_7_4.png 
[8]:

# write.csv(ICGscoresHighGenes$CPGscores, '/home/share/xwanaf/sour_sep/newCKPT/MerM/giotto_data/ICGscoresHighGenes.csv', row.names = FALSE)
# write.csv(ICGscoresFilt$CPGscores, '/home/share/xwanaf/sour_sep/newCKPT/MerM/giotto_data/ICGscoresFilt_zscore0.csv', row.names = FALSE)

[ ]:



[9]:


LR_data = data.table::fread(system.file("extdata", "mouse_ligand_receptors.txt", package = 'Giotto'))

LR_data$L_upper = toupper(LR_data$mouseLigand)
LR_data$R_upper = toupper(LR_data$mouseReceptor)
LR_data[, ligand_det := ifelse(L_upper %in% my_giotto_object@gene_ID, T, F)]
LR_data[, receptor_det := ifelse(R_upper %in% my_giotto_object@gene_ID, T, F)]
LR_data_det = LR_data[ligand_det == T & receptor_det == T]
select_ligands = LR_data_det$L_upper
select_receptors = LR_data_det$R_upper


## get statistical significance of gene pair expression changes based on expression ##
expr_only_scores = exprCellCellcom(gobject = my_giotto_object,
                                   cluster_column = 'cell_types',
                                   random_iter = 500,
                                   gene_set_1 = select_ligands,
                                   gene_set_2 = select_receptors)

## get statistical significance of gene pair expression changes upon cell-cell interaction
spatial_all_scores = spatCellCellcom(my_giotto_object,
                                     spatial_network_name = 'Delaunay_network',
                                     cluster_column = 'cell_types',
                                     random_iter = 500,
                                     gene_set_1 = select_ligands,
                                     gene_set_2 = select_receptors,
                                     adjust_method = 'fdr',
                                     do_parallel = T,
                                     cores = 4,
                                     verbose = 'none')


## * plot communication scores ####

## select top LR ##
selected_spat = spatial_all_scores[p.adj <= 0.5 & abs(log2fc) > 0.1 & lig_nr >= 10 & rec_nr >= 10]
data.table::setorder(selected_spat, -PI)

top_LR_ints = unique(selected_spat[order(-abs(PI))]$LR_comb)[1:33]
top_LR_cell_ints = unique(selected_spat[order(-abs(PI))]$LR_cell_comb)[1:33]

plotCCcomHeatmap(gobject = my_giotto_object,
                 comScores = spatial_all_scores,
                 selected_LR = top_LR_ints,
                 selected_cell_LR = top_LR_cell_ints,
                 show = 'LR_expr')

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../_images/notebooks_Human-Heart-CCell-interaction_10_1.png
../_images/notebooks_Human-Heart-CCell-interaction_10_2.png 
[ ]:



[ ]:



[10]:

plotCCcomDotplot(gobject = my_giotto_object,
                 comScores = spatial_all_scores,
                 selected_LR = top_LR_ints,
                 selected_cell_LR = top_LR_cell_ints,
                 cluster_on = 'PI')
../_images/notebooks_Human-Heart-CCell-interaction_13_0.png
../_images/notebooks_Human-Heart-CCell-interaction_13_1.png 
[ ]:

selected_spat = spatial_all_scores[p.adj <= 0.25 & abs(log2fc) > 0.1 & lig_nr >= 5 & rec_nr >= 5 & lig_expr > 0.5 & rec_expr > 0.5]
# selected_spat = spatial_all_scores[p.adj <= 0.1 & abs(log2fc) > 0.1 & lig_nr >= 5 & rec_nr >= 5 & LR_comb == 'JAG1-NOTCH2']
data.table::setorder(selected_spat, -PI)

[12]:

spatial_all_scores[(ligand == 'SLIT3') & (receptor == 'ROBO1')]
A data.table: 0 × 17
LR_comblig_cell_typelig_exprligandrec_cell_typerec_exprreceptorLR_exprlig_nrrec_nrrand_exprav_difflog2fcpvalueLR_cell_combp.adjPI
<chr><fct><dbl><chr><fct><dbl><chr><dbl><int><int><dbl><dbl><dbl><dbl><chr><dbl><dbl>
 
[127]:

## select top LR ##
selected_spat = spatial_all_scores[p.adj <= 0.25 & abs(log2fc) > 0.1 & lig_nr > 10 & rec_nr > 10 & lig_expr > 0.5 & rec_expr > 0.5]
# selected_spat = spatial_all_scores[p.adj <= 0.5 & abs(log2fc) > 0.1 & lig_nr >= 10 & rec_nr >= 10]
data.table::setorder(selected_spat, -PI)

top_LR_ints = unique(selected_spat[order(-abs(PI))]$LR_comb)[1:30]
top_LR_cell_ints = unique(selected_spat[order(-abs(PI))]$LR_cell_comb)[1:30]

plotCCcomHeatmap(gobject = my_giotto_object,
                 comScores = spatial_all_scores,
                 selected_LR = top_LR_ints,
                 selected_cell_LR = top_LR_cell_ints,
                 show = 'LR_expr')
../_images/notebooks_Human-Heart-CCell-interaction_16_0.png
../_images/notebooks_Human-Heart-CCell-interaction_16_1.png 
[148]:

gobject = my_giotto_object
# comScores = spatial_all_scores
comScores = selected_spat
selected_LR = top_LR_ints
selected_cell_LR = top_LR_cell_ints
show_LR_names = TRUE
show_cell_LR_names = TRUE
cluster_on = c('PI', 'LR_expr', 'log2fc')
cor_method = c("pearson", "kendall", "spearman")
aggl_method = c("ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median", "centroid")
show_plot = NA
return_plot = NA
save_plot = NA
save_param =  list()
default_save_name = 'plotCCcomDotplot'

[149]:

dt_to_matrix <- function(x) {
  rownames = as.character(x[[1]])
  mat = methods::as(Matrix::as.matrix(x[,-1]), 'Matrix')
  rownames(mat) = rownames
  return(mat)
}

cor_giotto = function(x, ...) {
  x = as.matrix(x)
  return(stats::cor(x, ...))
}

t_giotto = function(mymatrix) {

  if(methods::is(mymatrix, 'dgCMatrix')) {
    return(Matrix::t(mymatrix)) # replace with sparseMatrixStats
  } else if(methods::is(mymatrix, 'Matrix')) {
    return(Matrix::t(mymatrix))
  } else {
    mymatrix = as.matrix(mymatrix)
    mymatrix = base::t(mymatrix)
    return(mymatrix)
  }
}

[150]:

cor_method = 'pearson'
aggl_method = 'ward.D'
selDT = comScores[LR_comb %in% selected_LR & LR_cell_comb %in% selected_cell_LR]

cluster_on = 'PI'
selDT_d = data.table::dcast.data.table(selDT, LR_cell_comb~LR_comb, value.var = cluster_on, fill = 0)
selDT_m = dt_to_matrix(selDT_d)

# remove zero variance
sd_rows = apply(selDT_m, 1, sd)
sd_rows_zero = names(sd_rows[sd_rows == 0])
if(length(sd_rows_zero) > 0) selDT_m = selDT_m[!rownames(selDT_m) %in% sd_rows_zero, ]

sd_cols = apply(selDT_m, 2, sd)
sd_cols_zero = names(sd_cols[sd_cols == 0])
if(length(sd_cols_zero) > 0) selDT_m = selDT_m[, !colnames(selDT_m) %in% sd_cols_zero]

## cells
corclus_cells_dist = stats::as.dist(1-cor_giotto(x = t_giotto(selDT_m), method = cor_method))
hclusters_cells = stats::hclust(d = corclus_cells_dist, method = aggl_method)
# clus_names = rownames(selDT_m)
# names(clus_names) = 1:length(clus_names)
# # clus_sort_names = clus_names[hclusters_cells$order]
# clus_sort_names = clus_names[order(clus_names)]

##############################################
# reorder cells
clus_names = rownames(selDT_m)
split_clus_names = strsplit(clus_names, split = "--")
clus_names_R = c(split_clus_names[[1]][2])
clus_names_L = c(split_clus_names[[1]][1])
for (i in 2:length(clus_names)){
    clus_names_R = c(clus_names_R, split_clus_names[[i]][2])
    clus_names_L = c(clus_names_L, split_clus_names[[i]][1])
    }

clus_names_L_reorder = clus_names_L[order(clus_names_R)]
clus_names_R_reorder = clus_names_R[order(clus_names_R)]
clus_names_reorderR = clus_names[order(clus_names_R)]


Receptors = unique(clus_names_R[order(clus_names_R)])
clus_names_reorder = c(clus_names_reorderR[clus_names_R[order(clus_names_R)] == Receptors[1]][order(clus_names_L_reorder[clus_names_R[order(clus_names_R)] == Receptors[1]])])
for (i in 2:length(Receptors)){
    clus_names_reorder = c(clus_names_reorder, clus_names_reorderR[clus_names_R[order(clus_names_R)] == Receptors[i]][order(clus_names_L_reorder[clus_names_R[order(clus_names_R)] == Receptors[i]])])
    }
clus_sort_names = clus_names_reorder
##############################################
selDT[, LR_cell_comb := factor(LR_cell_comb, clus_sort_names)]



## genes
corclus_genes_dist = stats::as.dist(1-cor_giotto(x = selDT_m, method = cor_method))
hclusters_genes = stats::hclust(d = corclus_genes_dist, method = aggl_method)
clus_names = colnames(selDT_m)
names(clus_names) = 1:length(clus_names)
clus_sort_names = clus_names[hclusters_genes$order]
selDT[, LR_comb := factor(LR_comb, clus_sort_names)]

pl = ggplot2::ggplot()
pl = pl + ggplot2::geom_point(data = selDT, ggplot2::aes_string(x = 'LR_cell_comb',
                                             y = 'LR_comb', size = 'pvalue', color = 'log2fc'))
pl = pl + ggplot2::theme_classic()
if(show_LR_names == TRUE) pl = pl + ggplot2::theme(axis.text.y = ggplot2::element_text(),
                                         axis.ticks.y = ggplot2::element_line())
if(show_cell_LR_names == TRUE) pl = pl + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 1, hjust = 1),
                                              axis.ticks.x = ggplot2::element_line())
pl = pl + ggplot2::scale_fill_gradientn(colours = c('darkblue', 'blue', 'white', 'red', 'darkred'))
pl = pl + ggplot2::scale_size_continuous(range = c(3, 0.5)) + ggplot2::scale_color_gradientn(colours = c('darkblue', 'blue', 'white', 'red', 'darkred'))
pl = pl + ggplot2::labs(x = 'cell-cell', y = 'ligand-receptor')


# # print, return and save parameters
# show_plot = ifelse(is.na(show_plot), readGiottoInstructions(gobject, param = 'show_plot'), show_plot)
# save_plot = ifelse(is.na(save_plot), readGiottoInstructions(gobject, param = 'save_plot'), save_plot)
# return_plot = ifelse(is.na(return_plot), readGiottoInstructions(gobject, param = 'return_plot'), return_plot)
print(pl)
## print plot
# if(show_plot == TRUE) {
# print(pl)
# }
../_images/notebooks_Human-Heart-CCell-interaction_19_0.png 
[151]:

# write.csv(selDT, '/home/share/xwanaf/sour_sep/newCKPT/MerM/giotto_data/selDT.csv', row.names = FALSE)

[152]:

# pl = ggplot2::ggplot()
# pl = pl + ggplot2::geom_point(data = selDT, ggplot2::aes_string(x = 'LR_cell_comb',
#                                              y = 'LR_comb', size = 'pvalue', color = 'log2fc'))
# options(repr.plot.width = 8, repr.plot.height = 10)
# pl = pl + ggplot2::theme_classic()
# if(show_LR_names == TRUE) pl = pl + ggplot2::theme(axis.text.y = ggplot2::element_text(),
#                                          axis.ticks.y = ggplot2::element_line())
# if(show_cell_LR_names == TRUE) pl = pl + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 1, hjust = 1),
#                                               axis.ticks.x = ggplot2::element_line())
# # pl = pl + ggplot2::scale_fill_gradientn(colours = c('darkblue', 'blue', 'white', 'red', 'darkred'))
# pl = pl + ggplot2::scale_fill_gradientn(colours = c('darkblue', 'blue', 'lightblue', 'white', 'red'))
# # pl = pl + ggplot2::scale_size_continuous(range = c(3, 0.5)) + ggplot2::scale_color_gradientn(colours = c('darkblue', 'blue', 'white', 'red', 'darkred'))
# pl = pl + ggplot2::scale_size_continuous(range = c(3, 0.5)) + ggplot2::scale_color_gradientn(colours = c('darkblue', 'blue', 'lightblue', 'white', 'red'))

# pl = pl + ggplot2::labs(x = 'cell-cell', y = 'ligand-receptor')

# print(pl)

[153]:

pl = ggplot2::ggplot()
# pl = pl + ggplot2::geom_point(data = selDT, ggplot2::aes_string(x = 'LR_cell_comb',
#                                              y = 'LR_comb', size = 'pvalue', color = 'log2fc'))
pl = pl + ggplot2::geom_point(data = selDT, ggplot2::aes_string(x = 'LR_cell_comb',
                                             y = 'LR_comb', size = 'p.adj', color = 'log2fc'))
options(repr.plot.width = 4, repr.plot.height = 5)
pl = pl + ggplot2::theme_classic()
if(show_LR_names == TRUE) pl = pl + ggplot2::theme(axis.text.y = ggplot2::element_text(),
                                         axis.ticks.y = ggplot2::element_line())
if(show_cell_LR_names == TRUE) pl = pl + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 1, hjust = 1),
                                              axis.ticks.x = ggplot2::element_line())
# pl = pl + ggplot2::scale_fill_gradientn(colours = c('darkblue', 'blue', 'white', 'red', 'darkred'))
# pl = pl + ggplot2::scale_fill_gradientn(colours = c('darkblue', 'blue', 'lightblue', 'white', 'red'))
# pl = pl + ggplot2::scale_fill_gradientn(colours = c('darkblue', 'blue', 'white', 'red'))
pl = pl + ggplot2::scale_fill_gradientn(colours = c('white', 'lightsalmon', 'red', 'darkred'))
pl = pl + ggplot2::scale_color_gradientn(colours=c('darkblue','blue', 'white', 'red','darkred'))
# pl = pl + ggplot2::scale_size_continuous(range = c(3, 0.5)) + ggplot2::scale_color_gradientn(colours = c('darkblue', 'blue', 'white', 'red', 'darkred'))
# pl = pl + ggplot2::scale_size_continuous(range = c(3, 0.1), limits = c(0, 0.2)) + ggplot2::scale_color_gradientn(colours = c('darkblue', 'blue', 'white', 'red'))
pl = pl + ggplot2::scale_size_continuous(range = c(3, 0.1), limits = c(0, 0.2)) + ggplot2::scale_color_gradientn(colours = c('white', 'lightsalmon', 'red', 'darkred'))

pl = pl + ggplot2::labs(x = 'cell-cell', y = 'ligand-receptor')

print(pl)

Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
../_images/notebooks_Human-Heart-CCell-interaction_22_1.png 
[ ]:



[154]:

pl = ggplot2::ggplot()
# pl = pl + ggplot2::geom_point(data = selDT, ggplot2::aes_string(x = 'LR_cell_comb',
#                                              y = 'LR_comb', size = 'pvalue', color = 'log2fc'))
pl = pl + ggplot2::geom_point(data = selDT, ggplot2::aes_string(x = 'LR_cell_comb',
                                             y = 'LR_comb', size = 'p.adj', color = 'log2fc'))
options(repr.plot.width = 4, repr.plot.height = 5)
pl = pl + ggplot2::theme_classic()
if(show_LR_names == TRUE) pl = pl + ggplot2::theme(axis.text.y = ggplot2::element_text(),
                                         axis.ticks.y = ggplot2::element_line())
if(show_cell_LR_names == TRUE) pl = pl + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 1, hjust = 1),
                                              axis.ticks.x = ggplot2::element_line())
# pl = pl + ggplot2::scale_fill_gradientn(colours = c('darkblue', 'blue', 'white', 'red', 'darkred'))
# pl = pl + ggplot2::scale_fill_gradientn(colours = c('darkblue', 'blue', 'lightblue', 'white', 'red'))
# pl = pl + ggplot2::scale_fill_gradientn(colours = c('darkblue', 'blue', 'white', 'red'))
pl = pl + ggplot2::scale_fill_gradientn(colours = c('white', 'lightsalmon', 'red', 'darkred'))
pl = pl + ggplot2::scale_color_gradientn(colours=c('darkblue','blue', 'white', 'red','darkred'))
# pl = pl + ggplot2::scale_size_continuous(range = c(3, 0.5)) + ggplot2::scale_color_gradientn(colours = c('darkblue', 'blue', 'white', 'red', 'darkred'))
# pl = pl + ggplot2::scale_size_continuous(range = c(3, 0.1), limits = c(0, 0.2)) + ggplot2::scale_color_gradientn(colours = c('darkblue', 'blue', 'white', 'red'))
pl = pl + ggplot2::scale_size_continuous(range = c(3, 0.1), limits = c(0, 0.2)) + ggplot2::scale_color_gradientn(colours = c('white', 'lightsalmon', 'red', 'darkred'))

pl = pl + ggplot2::labs(x = 'cell-cell', y = 'ligand-receptor')

print(pl)

Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
../_images/notebooks_Human-Heart-CCell-interaction_24_1.png 
[155]:

# array(['Endothelial', 'Fibroblast', 'Lymphoid', 'Myeloid', 'Neuronal',
#        'Pericytes', 'Smooth_muscle_cells', 'Ventricular_Cardiomyocyte'],
#       dtype=object)

[156]:

selDT[(selDT$lig_cell_type == 'Myeloid') & (selDT$rec_cell_type == 'Endothelial')]
A data.table: 0 × 17
LR_comblig_cell_typelig_exprligandrec_cell_typerec_exprreceptorLR_exprlig_nrrec_nrrand_exprav_difflog2fcpvalueLR_cell_combp.adjPI
<fct><fct><dbl><chr><fct><dbl><chr><dbl><int><int><dbl><dbl><dbl><dbl><fct><dbl><dbl>
 
[157]:

selDT[(selDT$lig_cell_type == 'Endothelial') & (selDT$rec_cell_type == 'Smooth_muscle_cells')]
A data.table: 6 × 17
LR_comblig_cell_typelig_exprligandrec_cell_typerec_exprreceptorLR_exprlig_nrrec_nrrand_exprav_difflog2fcpvalueLR_cell_combp.adjPI
<fct><fct><dbl><chr><fct><dbl><chr><dbl><int><int><dbl><dbl><dbl><dbl><fct><dbl><dbl>
SERPINE2-LRP1Endothelial0.8821606SERPINE2Smooth_muscle_cells0.7238008LRP1 1.60596145410.96497850.64098290.67976070.000Endothelial--Smooth_muscle_cells0.000000001.3056971
ADAM15-ITGA9 Endothelial0.9791948ADAM15 Smooth_muscle_cells0.5732272ITGA9 1.55242245411.02050550.53191650.56043240.008Endothelial--Smooth_muscle_cells0.036000000.8090949
JAG1-NOTCH3 Endothelial1.1053646JAG1 Smooth_muscle_cells2.2582759NOTCH33.36364045412.65189670.71174370.33186280.008Endothelial--Smooth_muscle_cells0.036000000.4791095
DLL4-NOTCH3 Endothelial0.8002950DLL4 Smooth_muscle_cells2.2582759NOTCH33.05857145412.58628080.47229000.23366180.016Endothelial--Smooth_muscle_cells0.057600000.2896419
A2M-LRP1 Endothelial3.8846267A2M Smooth_muscle_cells0.7238008LRP1 4.60842745414.02099760.58742980.19225170.024Endothelial--Smooth_muscle_cells0.082285710.2085307
PDGFD-PDGFRB Endothelial1.0304364PDGFD Smooth_muscle_cells2.5353614PDGFRB3.56579845413.09240620.47339170.19948300.052Endothelial--Smooth_muscle_cells0.169043480.1540012
 
[158]:

min(selDT$log2fc)
-0.232504898981321
[165]:

selDT[log2fc == min(selDT$log2fc)]$log2fc = -0.3

[167]:

selDT
A data.table: 18 × 17
LR_comblig_cell_typelig_exprligandrec_cell_typerec_exprreceptorLR_exprlig_nrrec_nrrand_exprav_difflog2fcpvalueLR_cell_combp.adjPI
<fct><fct><dbl><chr><fct><dbl><chr><dbl><int><int><dbl><dbl><dbl><dbl><fct><dbl><dbl>
RTN4-LINGO1 Smooth_muscle_cells0.8793521RTN4 Endothelial 1.5048799LINGO12.38423241 451.1699542 1.2142778 0.96802340.000Smooth_muscle_cells--Endothelial 0.00000000 1.8593976
SERPINE2-LRP1Endothelial 0.8821606SERPINE2Smooth_muscle_cells 0.7238008LRP1 1.60596145 410.9649785 0.6409829 0.67976070.000Endothelial--Smooth_muscle_cells 0.00000000 1.3056971
RTN4-LINGO1 Smooth_muscle_cells1.0054810RTN4 Fibroblast 2.7735005LINGO13.77898128 361.9467534 1.8322281 0.92234060.004Smooth_muscle_cells--Fibroblast 0.07200000 1.0539286
JAG1-NOTCH2 Fibroblast 1.0529821JAG1 Smooth_muscle_cells 0.5056152NOTCH21.55859736 280.8531829 0.7054145 0.79913880.004Fibroblast--Smooth_muscle_cells 0.05760000 0.9905944
ADAM15-ITGA9 Endothelial 0.9791948ADAM15 Smooth_muscle_cells 0.5732272ITGA9 1.55242245 411.0205055 0.5319165 0.56043240.008Endothelial--Smooth_muscle_cells 0.03600000 0.8090949
JAG1-NOTCH3 Fibroblast 1.0529821JAG1 Smooth_muscle_cells 2.5971751NOTCH33.65015736 282.6666231 0.9835341 0.43882500.000Fibroblast--Smooth_muscle_cells 0.00000000 0.5787043
COL1A1-DDR2 Smooth_muscle_cells0.5387743COL1A1 Fibroblast 1.6158163DDR2 2.15459128 361.3582296 0.7963610 0.62864760.016Smooth_muscle_cells--Fibroblast 0.16457143 0.4926369
JAG1-NOTCH3 Endothelial 1.1053646JAG1 Smooth_muscle_cells 2.2582759NOTCH33.36364045 412.6518967 0.7117437 0.33186280.008Endothelial--Smooth_muscle_cells 0.03600000 0.4791095
JAG1-NOTCH4 Smooth_muscle_cells2.1747544JAG1 Fibroblast 0.8322134NOTCH43.00696828 362.2420046 0.7649631 0.40776330.004Smooth_muscle_cells--Fibroblast 0.07200000 0.4659379
DLL4-NOTCH3 Endothelial 0.8002950DLL4 Smooth_muscle_cells 2.2582759NOTCH33.05857145 412.5862808 0.4722900 0.23366180.016Endothelial--Smooth_muscle_cells 0.05760000 0.2896419
PDGFD-PDGFRB Fibroblast 1.4379348PDGFD Smooth_muscle_cells 2.8907387PDGFRB4.32867336 283.4314257 0.8972478 0.32662390.020Fibroblast--Smooth_muscle_cells 0.13090909 0.2884188
TGFB1-TGFBR3 Fibroblast 0.5620250TGFB1 Fibroblast 1.0270834TGFBR31.58910853 530.9698985 0.6192100 0.65878810.000Fibroblast--Fibroblast 0.00000000 0.2738376
JAG1-NOTCH4 Smooth_muscle_cells1.7198625JAG1 Endothelial 1.5427746NOTCH43.26263741 452.6032133 0.6594238 0.31491770.020Smooth_muscle_cells--Endothelial 0.16000000 0.2506367
CXCL12-ACKR3 Fibroblast 0.6903565CXCL12 Smooth_muscle_cells 1.7590382ACKR3 2.44939536 281.9232764 0.5261183 0.33346130.032Fibroblast--Smooth_muscle_cells 0.17723077 0.2505831
FN1-ITGA8 Fibroblast 2.8233985FN1 Smooth_muscle_cells 1.9694344ITGA8 4.79283336 283.8838746 0.9089583 0.29649780.040Fibroblast--Smooth_muscle_cells 0.19200000 0.2124996
A2M-LRP1 Endothelial 3.8846267A2M Smooth_muscle_cells 0.7238008LRP1 4.60842745 414.0209976 0.5874298 0.19225170.024Endothelial--Smooth_muscle_cells 0.08228571 0.2085307
PDGFD-PDGFRB Endothelial 1.0304364PDGFD Smooth_muscle_cells 2.5353614PDGFRB3.56579845 413.0924062 0.4733917 0.19948300.052Endothelial--Smooth_muscle_cells 0.16904348 0.1540012
SERPINE2-LRP1Fibroblast 0.6770152SERPINE2Ventricular_Cardiomyocyte0.6589103LRP1 1.335925831781.5870302-0.2511047-0.30000000.008Fibroblast--Ventricular_Cardiomyocyte0.11520000-0.2182169
 
[166]:

pl = ggplot2::ggplot()
pl = pl + ggplot2::geom_point(data = selDT, ggplot2::aes_string(x = 'LR_cell_comb',
                                             y = 'LR_comb', size = 'pvalue', color = 'log2fc'))
options(repr.plot.width = 4, repr.plot.height = 6)
pl = pl + ggplot2::theme_light()
if(show_LR_names == TRUE) pl = pl + ggplot2::theme(axis.text.y = ggplot2::element_text(),
                                         axis.ticks.y = ggplot2::element_line())
if(show_cell_LR_names == TRUE) pl = pl + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 1, hjust = 1),
                                              axis.ticks.x = ggplot2::element_line())
#pl = pl + ggplot2::scale_color_gradient2(low="blue", mid="white", high="red")
pl = pl + ggplot2::scale_color_gradientn(colours=c('blue', 'white', 'tomato', 'orangered', 'red3', 'darkred'))
pl = pl + ggplot2::scale_size_continuous(range = c(3.5, 0.5), limits = c(0, 0.05))
pl = pl + ggplot2::labs(x = 'cell-cell', y = 'ligand-receptor')
print(pl)

Warning message:
“Removed 1 rows containing missing values (geom_point).”
../_images/notebooks_Human-Heart-CCell-interaction_31_1.png 
[184]:

pl = ggplot2::ggplot()
pl = pl + ggplot2::geom_point(data = selDT, ggplot2::aes_string(x = 'LR_cell_comb',
                                             y = 'LR_comb', size = 'pvalue', color = 'log2fc'))
options(repr.plot.width = 4, repr.plot.height = 5)
pl = pl + ggplot2::theme_light()
if(show_LR_names == TRUE) pl = pl + ggplot2::theme(axis.text.y = ggplot2::element_text(face="italic",angle=40),
                                         axis.ticks.y = ggplot2::element_line())
if(show_cell_LR_names == TRUE) pl = pl + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 1, hjust = 1),
                                              axis.ticks.x = ggplot2::element_line())
#pl = pl + ggplot2::scale_color_gradient2(low="blue", mid="white", high="red")
pl = pl + ggplot2::scale_color_gradientn(colours=c('blue', 'white', 'tomato', 'orangered', 'red3', 'darkred'))
pl = pl + ggplot2::scale_size_continuous(range = c(3.5, 0.5), limits = c(0, 0.05))
pl = pl + ggplot2::labs(x = 'cell-cell', y = 'ligand-receptor')
pl = pl + ggplot2::scale_x_discrete(labels=c("Smooth_muscle_cells--Endothelial" = "SMC--EC",
                                            "Endothelial--Smooth_muscle_cells" = "EC--SMC",
                                            "Smooth_muscle_cells--Fibroblast" = "SMC--FB",
                                            "Fibroblast--Smooth_muscle_cells" = "FB--SMC",
                                            "Fibroblast--Fibroblast" = "FB--FB",
                                            "Fibroblast--Ventricular_Cardiomyocyte" = "FB--VC"))
print(pl)

Warning message:
“Removed 1 rows containing missing values (geom_point).”
../_images/notebooks_Human-Heart-CCell-interaction_32_1.png 
[176]:

selDT
A data.table: 18 × 17
LR_comblig_cell_typelig_exprligandrec_cell_typerec_exprreceptorLR_exprlig_nrrec_nrrand_exprav_difflog2fcpvalueLR_cell_combp.adjPI
<fct><fct><dbl><chr><fct><dbl><chr><dbl><int><int><dbl><dbl><dbl><dbl><fct><dbl><dbl>
RTN4-LINGO1 Smooth_muscle_cells0.8793521RTN4 Endothelial 1.5048799LINGO12.38423241 451.1699542 1.2142778 0.96802340.000Smooth_muscle_cells--Endothelial 0.00000000 1.8593976
SERPINE2-LRP1Endothelial 0.8821606SERPINE2Smooth_muscle_cells 0.7238008LRP1 1.60596145 410.9649785 0.6409829 0.67976070.000Endothelial--Smooth_muscle_cells 0.00000000 1.3056971
RTN4-LINGO1 Smooth_muscle_cells1.0054810RTN4 Fibroblast 2.7735005LINGO13.77898128 361.9467534 1.8322281 0.92234060.004Smooth_muscle_cells--Fibroblast 0.07200000 1.0539286
JAG1-NOTCH2 Fibroblast 1.0529821JAG1 Smooth_muscle_cells 0.5056152NOTCH21.55859736 280.8531829 0.7054145 0.79913880.004Fibroblast--Smooth_muscle_cells 0.05760000 0.9905944
ADAM15-ITGA9 Endothelial 0.9791948ADAM15 Smooth_muscle_cells 0.5732272ITGA9 1.55242245 411.0205055 0.5319165 0.56043240.008Endothelial--Smooth_muscle_cells 0.03600000 0.8090949
JAG1-NOTCH3 Fibroblast 1.0529821JAG1 Smooth_muscle_cells 2.5971751NOTCH33.65015736 282.6666231 0.9835341 0.43882500.000Fibroblast--Smooth_muscle_cells 0.00000000 0.5787043
COL1A1-DDR2 Smooth_muscle_cells0.5387743COL1A1 Fibroblast 1.6158163DDR2 2.15459128 361.3582296 0.7963610 0.62864760.016Smooth_muscle_cells--Fibroblast 0.16457143 0.4926369
JAG1-NOTCH3 Endothelial 1.1053646JAG1 Smooth_muscle_cells 2.2582759NOTCH33.36364045 412.6518967 0.7117437 0.33186280.008Endothelial--Smooth_muscle_cells 0.03600000 0.4791095
JAG1-NOTCH4 Smooth_muscle_cells2.1747544JAG1 Fibroblast 0.8322134NOTCH43.00696828 362.2420046 0.7649631 0.40776330.004Smooth_muscle_cells--Fibroblast 0.07200000 0.4659379
DLL4-NOTCH3 Endothelial 0.8002950DLL4 Smooth_muscle_cells 2.2582759NOTCH33.05857145 412.5862808 0.4722900 0.23366180.016Endothelial--Smooth_muscle_cells 0.05760000 0.2896419
PDGFD-PDGFRB Fibroblast 1.4379348PDGFD Smooth_muscle_cells 2.8907387PDGFRB4.32867336 283.4314257 0.8972478 0.32662390.020Fibroblast--Smooth_muscle_cells 0.13090909 0.2884188
TGFB1-TGFBR3 Fibroblast 0.5620250TGFB1 Fibroblast 1.0270834TGFBR31.58910853 530.9698985 0.6192100 0.65878810.000Fibroblast--Fibroblast 0.00000000 0.2738376
JAG1-NOTCH4 Smooth_muscle_cells1.7198625JAG1 Endothelial 1.5427746NOTCH43.26263741 452.6032133 0.6594238 0.31491770.020Smooth_muscle_cells--Endothelial 0.16000000 0.2506367
CXCL12-ACKR3 Fibroblast 0.6903565CXCL12 Smooth_muscle_cells 1.7590382ACKR3 2.44939536 281.9232764 0.5261183 0.33346130.032Fibroblast--Smooth_muscle_cells 0.17723077 0.2505831
FN1-ITGA8 Fibroblast 2.8233985FN1 Smooth_muscle_cells 1.9694344ITGA8 4.79283336 283.8838746 0.9089583 0.29649780.040Fibroblast--Smooth_muscle_cells 0.19200000 0.2124996
A2M-LRP1 Endothelial 3.8846267A2M Smooth_muscle_cells 0.7238008LRP1 4.60842745 414.0209976 0.5874298 0.19225170.024Endothelial--Smooth_muscle_cells 0.08228571 0.2085307
PDGFD-PDGFRB Endothelial 1.0304364PDGFD Smooth_muscle_cells 2.5353614PDGFRB3.56579845 413.0924062 0.4733917 0.19948300.052Endothelial--Smooth_muscle_cells 0.16904348 0.1540012
SERPINE2-LRP1Fibroblast 0.6770152SERPINE2Ventricular_Cardiomyocyte0.6589103LRP1 1.335925831781.5870302-0.2511047-0.30000000.008Fibroblast--Ventricular_Cardiomyocyte0.11520000-0.2182169
 
[ ]:



[144]:

pl = ggplot2::ggplot()
pl = pl + ggplot2::geom_point(data = selDT, ggplot2::aes_string(x = 'LR_cell_comb',
                                             y = 'LR_comb', size = 'p.adj', color = 'log2fc'))
options(repr.plot.width = 4, repr.plot.height = 6)
pl = pl + ggplot2::theme_light()
if(show_LR_names == TRUE) pl = pl + ggplot2::theme(axis.text.y = ggplot2::element_text(),
                                         axis.ticks.y = ggplot2::element_line())
if(show_cell_LR_names == TRUE) pl = pl + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 1, hjust = 1),
                                              axis.ticks.x = ggplot2::element_line())
#pl = pl + ggplot2::scale_color_gradient2(low="blue", mid="white", high="red")
pl = pl + ggplot2::scale_color_gradientn(colours=c('blue', 'white', 'tomato', 'orangered', 'red3', 'darkred'))
pl = pl + ggplot2::scale_size_continuous(range = c(3.5, 0.9), limits = c(0, 0.2))
pl = pl + ggplot2::labs(x = 'cell-cell', y = 'ligand-receptor')
print(pl)
../_images/notebooks_Human-Heart-CCell-interaction_35_0.png 
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