MOFA model on aIg stimulated BJAB cells

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Last updated: 2020-11-24

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Knit directory: QuRIE-seq_manuscript/

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File	Version	Author	Date	Message
Rmd	2290baf	Jessie van Buggenum	2020-11-24	hide figure panels in html
html	f95d1d2	Jessie van Buggenum	2020-11-24	Build site.
Rmd	6b16185	Jessie van Buggenum	2020-11-24	update minor changes to suppl fig mofa
html	df6d45f	Jessie van Buggenum	2020-11-24	Build site.
Rmd	a96d5ba	Jessie van Buggenum	2020-11-24	update minor changes to documentation aIg
html	38267b1	Jessie van Buggenum	2020-11-24	Build site.
Rmd	8b81996	Jessie van Buggenum	2020-11-24	update minor changes to documentation & figure1/2
html	aed3aab	Jessie van Buggenum	2020-11-23	Build site.
Rmd	b1037ae	Jessie van Buggenum	2020-11-23	update suppl fig MOFA aIG
html	8c8e40e	Jessie van Buggenum	2020-11-20	Build site.
Rmd	b8950ba	Jessie van Buggenum	2020-11-20	update mofa plot axis orientation
html	081882d	Jessie van Buggenum	2020-11-19	Build site.
Rmd	0cda3dc	Jessie van Buggenum	2020-11-19	Figure 1 and 2 plus supplementary figures manuscript
html	2ffe38d	Jessie van Buggenum	2020-11-17	Build site.
Rmd	4487750	Jessie van Buggenum	2020-11-17	basis set-up of page display
Rmd	d9372d1	Jessie van Buggenum	2020-11-17	Publish the initial files for this website

---

source("code/load_packages.R")
seu_combined_selectsamples <- readRDS("output/seu_aIG_samples.rds")


panellabels <- c('a', 'b', 'c','d' , 'e', 'f', 'g', 'h', 'i', 'j', 'k')

add.textsize <- theme(axis.text.x = element_text(colour = 'black', size = 7),
          axis.text.y = element_text(colour = 'black',size=7),
          text = element_text(size=7),
          axis.text=element_text(size=7),
          plot.title = element_text(size=7)
          )


colorgradient6_manual <- c("#F7FBFF","#CFE1F2", "#93C4DE", "#4A97C9", "#1F5284", "#0C2236" )
colorgradient6_manual2 <- c("#d4d4d3","#CFE1F2", "#93C4DE", "#4A97C9", "#1F5284", "#0C2236" )
labels <- c("0", "2", "4", "6", "60", "180")

MOFA analysis time-series aIg

Variable features

Determine variable genes and proteins

seu_combined_selectsamples <- FindVariableFeatures(seu_combined_selectsamples, selection.method = "mvp", assay = "SCT.RNA", num.bin =20, mean.cutoff = c(0, 5), dispersion.cutoff = c(0.5,10), nfeatures =500)

genes.variable <- seu_combined_selectsamples@assays$SCT.RNA@var.features[-grep("^MT", seu_combined_selectsamples@assays$SCT.RNA@var.features)] # without the mitochondrial genes 

proteins.all <- row.names(seu_combined_selectsamples[["PROT"]])

meta.allcells <- seu_combined_selectsamples@meta.data %>%
  mutate(sample = rownames(seu_combined_selectsamples@meta.data))

MOFA model

## Note, if mofa object was generated before, it will read the generated rds file. (this will speed-up the process of generating this html document if edits are required)

myfiles <- list.files(path="output/", pattern = ".rds$")

if("MOFA_aIg.rds" %in%  myfiles){mofa <- readRDS("output/MOFA_aIg.rds")} else { #If so, read object, else do:
     
      mofa <- create_mofa(list(
        "RNA" = as.matrix( seu_combined_selectsamples@assays$SCT.RNA@scale.data[genes.variable,] ),
        "PROT" = as.matrix( seu_combined_selectsamples@assays$PROT@scale.data[proteins.all,] ))
        )

      # Default settings used (try 15 factors, excludes all non-informative factors)
      data_opts <- get_default_data_options(mofa)
      model_opts <- get_default_model_options(mofa)
      train_opts <- get_default_training_options(mofa)
      train_opts$seed <- 42 # use same seed for reproducibility
      mofa <- prepare_mofa(
        object = mofa,
        data_options = data_opts,
        model_options = model_opts,
        training_options = train_opts
        )

    mofa <- run_mofa(mofa, outfile = "output/MOFA_aIg.hdf5")
    mofa <- run_umap(mofa, factors = c(1:7))
    samples_metadata(mofa) <- meta.allcells
    saveRDS(mofa, file= "output/MOFA_aIg.rds")
  
}


mofa

Trained MOFA with the following characteristics: 
 Number of views: 2 
 Views names: RNA PROT 
 Number of features (per view): 2159 80 
 Number of groups: 1 
 Groups names: group1 
 Number of samples (per group): 4754 
 Number of factors: 7 

# Export Factor 1 and 3 weights for Cytoscape plot
data_weights_prot <- get_weights(object = mofa, views = "PROT", factors = c(1,3), as.data.frame = TRUE)

data_weights_prot <- as.data.frame(data_weights_prot)  %>%
  spread(factor, value) %>%
  mutate(Factor1_signflip = Factor1 *-1)

write.csv2(data_weights_prot, file = "output/data_weights_prot_fact1and3.csv")

Figure 1

UMAP on 7 MOFA factors

## UMAP
plot.umap.data <-  plot_dimred(mofa, method="UMAP", color_by = "condition",stroke = 0.001, dot_size =1, alpha = 0.2, return_data = T)

plot.umap.all <- ggplot(plot.umap.data, aes(x=x, y = y, fill = color_by))+
  geom_point(size = 0.8, alpha = 0.6, shape = 21, stroke = 0) +
  theme_half_open() +
  scale_fill_manual(values = colorgradient6_manual, labels = c(labels), name = "Time aIg",)+
   theme(legend.position="none")+
  scale_x_reverse()+
  scale_y_reverse()+
  add.textsize +
  labs(title = "UMAP on MOFA+ factors shows minute and \nhour time-scale signal transductions", x = "UMAP 1", y = "UMAP 2")

## UMAP legend
legend.umap <- get_legend( ggplot(plot.umap.data, aes(x=x, y = y, fill = color_by))+
  geom_point(size = 2, alpha = 1, shape = 21, stroke = 0) +
  theme_half_open() +
  scale_fill_manual(values = colorgradient6_manual, labels = c(labels), name = "Time aIg \n(minutes)",)+
  add.textsize +
  labs(title = "UMAP on MOFA+ factors shows minute and \nhour time-scale signal transductions", x = "UMAP 1", "y = UMAP 2"))
legend.umap <- as_ggplot(legend.umap)

plot_fig1_umap <- plot_grid(plot.umap.all,legend.umap, labels = c(""), label_size = 10, ncol = 2, rel_widths = c(1, 0.2))

ggsave(plot_fig1_umap, filename = "output/paper_figures/Fig1_UMAP.pdf", width = 68, height = 62, units = "mm",  dpi = 300, useDingbats = FALSE)
ggsave(plot_fig1_umap, filename = "output/paper_figures/Fig1_UMAP.png", width = 68, height = 62, units = "mm",  dpi = 300)

plot_fig1_umap

Figure 1. UMAP of 7 MOFA factors, integrating phospho-protein and RNA measurements

Suppl PCA

seu_combined_selectsamples <- RunPCA(seu_combined_selectsamples,assay = "SCT.RNA", features = genes.variable, verbose = FALSE, ndims.print = 0, reduction.name = "pca.RNA")
seu_combined_selectsamples <- RunPCA(seu_combined_selectsamples, assay = "PROT", features = proteins.all, verbose = FALSE, ndims.print = 0,reduction.name = "pca.PROT")

## PCA analysis

plot.PCA.RNA <- DimPlot(seu_combined_selectsamples, reduction = "pca.RNA", group.by = "condition", pt.size =0.2) +
  scale_color_manual(values = colorgradient6_manual2, labels = c(labels), name = "Time aIg",)+
  labs(title = "RNA PCA separates cells \nat hour time-scale", x= "RNA PC 1", y = " RNA PC 2") +
  add.textsize +
  theme(legend.position = "none")


plot.PCA.PROT <- DimPlot(seu_combined_selectsamples, reduction = "pca.PROT", group.by = "condition", pt.size = 0.2) +
  scale_color_manual(values = colorgradient6_manual2, labels = c(labels), name = "Time aIg",)+
  labs(title = "(phospho-)protein PCA separates cells\nat minutes time-scale", x= "Protein PC 1", y = "Protein PC 2") +
  add.textsize +
  #  scale_x_reverse()+
  theme(legend.position = "none")

legend <- get_legend(DimPlot(seu_combined_selectsamples, reduction = "pca.RNA", group.by = "condition", pt.size =0.1) +
  scale_color_manual(values = colorgradient6_manual2, labels = c(labels), name = "Time aIg",)+
  add.textsize)
legend <- as_ggplot(legend)


PCA.PROTPC1.data <- data.frame(rank = 1:80, 
                               protein = names(sort(seu_combined_selectsamples@reductions$pca.PROT@feature.loadings[,1])),
                               weight.PC1 = sort(seu_combined_selectsamples@reductions$pca.PROT@feature.loadings[,1]),
                               highlights = c(names(sort(seu_combined_selectsamples@reductions$pca.PROT@feature.loadings[,1]))[1:4],rep("",76))
                               ) 

plot.PCA.PROTweightsPC1 <- ggplot(PCA.PROTPC1.data, aes(x=weight.PC1, y = rank, label = highlights)) +
  geom_point(size=0.1) +
  labs(title = "Top 4 protein loadings \n", x= "Weight Protein PC1") +
  geom_point(color = ifelse(PCA.PROTPC1.data$highlights == "", "grey50", "red")) +
  geom_text_repel(size = 2, segment.size = 0.25)+
  theme_half_open()+
  add.textsize +
  theme(axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank()
        ) +
  scale_x_reverse()+
  scale_y_reverse()


PCA.RNAPC1.data <- data.frame(rank = 1:length(genes.variable), 
                               RNA = names(sort(seu_combined_selectsamples@reductions$pca.RNA@feature.loadings[,1])),
                               weight.PC1 = sort(seu_combined_selectsamples@reductions$pca.RNA@feature.loadings[,1]),
                               highlights = c(rep("",(length(genes.variable)-4)),names(sort(seu_combined_selectsamples@reductions$pca.RNA@feature.loadings[,1]))[(length(genes.variable)-3):length(genes.variable)])
                               ) 
## Loadings RNA
plot.PCA.RNAweightsPC1 <- ggplot(PCA.RNAPC1.data, aes(x=weight.PC1, y = rank, label = highlights)) +
  geom_point(size=0.1) +
  labs(title = "Top 4 RNA loadings \n", x= "Weight RNA PC1") +
  geom_point(color = ifelse(PCA.RNAPC1.data$highlights == "", "grey50", "red")) +
  geom_text_repel(size = 2, segment.size = 0.25)+
  theme_half_open()+
  add.textsize +
  theme(axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank()
        ) 

## ridgeplots
PC1.data <- data.frame(sample = rownames(seu_combined_selectsamples@reductions$pca.PROT@cell.embeddings),
                       PC1_PROT = seu_combined_selectsamples@reductions$pca.PROT@cell.embeddings[,1],
                       PC1_RNA = seu_combined_selectsamples@reductions$pca.RNA@cell.embeddings[,1]) %>%
  left_join(meta.allcells)

plot_ridge_PC1Prot <- ggplot(PC1.data, aes(x = PC1_PROT, y = condition, fill = condition)) + 
  scale_fill_manual(values = colorgradient6_manual, labels = c(labels), name = "Time aIg \n(minutes)")+
  geom_density_ridges2() +
  scale_y_discrete(labels = labels,  name = "Time aIg (minutes)")+
  scale_x_continuous(name = "PC1 Proteins") +
  theme_half_open() +
  add.textsize+
  theme(legend.position = "none")

plot_ridge_PC1RNA <- ggplot(PC1.data, aes(x = PC1_RNA, y = condition, fill = condition)) + 
  scale_fill_manual(values = colorgradient6_manual, labels = c(labels), name = "Time aIg \n(minutes)")+
  geom_density_ridges2() +
  scale_y_discrete(labels = labels,  name = "Time aIg (minutes)")+
  scale_x_continuous(name = "PC1 RNA") +
  theme_half_open() +
  add.textsize+
  theme(legend.position = "none")

Fig1.pca <- plot_grid(plot.PCA.PROT,plot.PCA.RNA,legend, plot_ridge_PC1Prot, plot_ridge_PC1RNA,NULL,  plot.PCA.PROTweightsPC1, plot.PCA.RNAweightsPC1, labels = c(panellabels[1:2], "",panellabels[3:4],"",panellabels[5:7]), label_size = 10, ncol = 3, rel_widths = c(0.9,0.9,0.2,0.9,0.9), rel_heights = c(1,0.8,1.3))

ggsave(filename = "output/paper_figures/Suppl_PCA_aIg.pdf", width = 143, height = 170, units = "mm",  dpi = 300, useDingbats = FALSE)
ggsave(filename = "output/paper_figures/Suppl_PCA_aIg.png", width = 143, height = 170, units = "mm",  dpi = 300)

Fig1.pca

Supplementary Figure. PCA analysis on RNA ór Protein dataset

Suppl MOFA model properties

## Variance per factor
plot.variance.perfactor.all <- plot_variance_explained(mofa, x="factor", y="view") +
    add.textsize +
    labs(title = "Variance explained by each factor per modality") 

## variance total
plot.variance.total <- plot_variance_explained(mofa, x="view", y="factor", plot_total = T)
plot.variance.total <- plot.variance.total[[2]] +
  add.textsize +
    labs(title = "Total \nvariance") +
  geom_text(aes(label=round(R2,1)), vjust=1.6, color="white", size=2.5)

## Significance correlation covariates
plot.heatmap.pval.covariates <- as.ggplot(correlate_factors_with_covariates(mofa, 
  covariates = c("time"),
  factors = 1:mofa@dimensions$K,
  fontsize = 7, 
  cluster_row = F,
  cluster_col = F
))+ 
  add.textsize +
  theme(axis.text.y=element_blank(),
        axis.text.x=element_blank(),
        plot.title = element_text(size=7, face = "plain"),
        )

## Factor values over time
plot.violin.factorall <- plot_factor(mofa, 
  factor = c(2,4:mofa@dimensions$K),
  color_by = "condition",
  dot_size = 0.2,      # change dot size
  dodge = T,           # dodge points with different colors
  legend = F,          # remove legend
  add_violin = T,      # add violin plots,
  violin_alpha = 0.9  # transparency of violin plots
) +   
  add.textsize +
  scale_color_manual(values=c(colorgradient6_manual2, labels = c(labels), name = "Time aIg")) +
  scale_fill_manual(values=c(colorgradient6_manual2, labels = c(labels), name = "Time aIg"))+
  labs(title = "Factor values per time-point of additional factors not correlating with time." ) +
  theme(axis.text.x=element_blank())

## Loadings factors stable protein

plot.rank.PROT.2.4to7 <- plot_weights(mofa, 
  view = "PROT", 
  factors = c(1:mofa@dimensions$K), 
  nfeatures = 3, 
  text_size = 1.5
) +   
  add.textsize +
  labs(title = "Top 3 Protein loadings per factor") +
  theme(axis.text.y=element_blank(),
        axis.ticks.y=element_blank()
)

## Loadings factors stable protein

plot.rank.RNA.2.4to7 <- plot_weights(mofa, 
  view = "RNA", 
  factors = c(1:mofa@dimensions$K), 
  nfeatures = 3, 
  text_size = 1.5
) +   
  add.textsize +
  labs(title = "Top 3 RNA loadings per factor") +
  theme(axis.text.y=element_blank(),
        axis.ticks.y=element_blank()
)

## correlation time and factors
plot.correlation.covariates <- correlate_factors_with_covariates(mofa, 
  covariates = c("time"),
  factors = mofa@dimensions$K:1,
  plot = "r"
)

plot.correlation.covariates <- ggcorrplot(plot.correlation.covariates, tl.col = "black", method = "square", lab = TRUE, ggtheme = theme_void, colors = c("#11304C", "white", "#11304C"), lab_size = 2.5) +
  add.textsize +
  labs(title = "Correlation of \nfactors with \ntime of treatment", y = "") +
  scale_y_discrete(labels = "") +
  coord_flip() +
  theme(axis.text.x=element_text(angle =0,hjust = 0.5), 
        axis.text.y=element_text(size = 5),
        legend.position="none",
        plot.title = element_text(hjust = 0.5))

Coordinate system already present. Adding new coordinate system, which will replace the existing one.

Fig.1.suppl.mofa.row1 <- plot_grid(plot.variance.perfactor.all, plot.variance.total,NULL, plot.heatmap.pval.covariates, labels = c(panellabels[1:3]), label_size = 10, ncol = 4, rel_widths = c(1.35, 0.3,0.25,0.38))

Fig.1.suppl.mofa.row2 <- plot_grid(plot.violin.factorall,legend.umap, labels = panellabels[4], label_size = 10, ncol = 2, rel_widths = c(1,0.1))

Suppl_mofa <- plot_grid(Fig.1.suppl.mofa.row1, Fig.1.suppl.mofa.row2, plot.rank.PROT.2.4to7, plot.rank.RNA.2.4to7, labels = c("","", panellabels[5:6]),label_size = 10, ncol = 1, rel_heights = c(1.45,1,1.1,1.1))


ggsave(Suppl_mofa, filename = "output/paper_figures/Fig2.Suppl_MOFAaIg.pdf", width = 183, height = 220, units = "mm",  dpi = 300, useDingbats = FALSE)
ggsave(Suppl_mofa, filename = "output/paper_figures/Fig2.Suppl_MOFAaIg.png", width = 183, height = 220, units = "mm",  dpi = 300)

Suppl_mofa

Supplementary Figure. MOFA model additional information

Figure 2

Prepare main panels

meta.allcells <- seu_combined_selectsamples@meta.data %>%
  mutate(sample = rownames(seu_combined_selectsamples@meta.data))

proteindata <- as.data.frame(t(seu_combined_selectsamples@assays$PROT@scale.data)) %>%
  mutate(sample = rownames(t(seu_combined_selectsamples@assays$PROT@scale.data))) %>%
  left_join(meta.allcells)

MOFAfactors<- as.data.frame(mofa@expectations$Z) %>%
  mutate(sample = rownames(as.data.frame(mofa@expectations$Z)[,1:mofa@dimensions$K])) 

MOFAfactors <- left_join(as.data.frame(MOFAfactors), meta.allcells)

weights.prot <- get_weights(mofa, views = "PROT",as.data.frame = TRUE)

topnegprots.factor1 <- weights.prot %>%
  filter(factor == "Factor1" & value <= 0) %>%
  arrange(value)

topposprots.factor3 <- weights.prot %>%
  filter(factor == "Factor3" & value >= 0) %>%
  arrange(-value)


weights.RNA <- get_weights(mofa, views = "RNA",as.data.frame = TRUE)

## violin plots phospho-proteins highlighted in main

f.violin.fact <- function(data , protein){
  
  ggplot(subset(data)  , aes(x = as.factor(condition), y =get(noquote(protein)))) +
    annotate("rect",
          xmin = 5 - 0.45,
             xmax = 6 + 0.6,
           ymin = -4.5, ymax = 4.5, fill = "lightblue",
           alpha = .4
  )+
    geom_hline(yintercept=0, linetype='dotted', col = 'black')+ 
  geom_violin(alpha = 0.9,aes(fill = condition))+
  geom_jitter(width = 0.05,size = 0.1, fill = "black", shape = 21)+ 
  stat_summary(fun=median, geom="point", shape=95, size=2, inherit.aes = T, position = position_dodge(width = 0.9), color = "red")+
  theme_few()+
  ylab(paste0(protein)) +
  scale_x_discrete(labels = labels, expand = c(0.1,0.1), name = "Time aIg (minutes)") +
    scale_y_continuous(expand = c(0,0), name = strsplit(protein, split = "\\.")[[1]][2]) +
  scale_color_manual(values = colorgradient6_manual2, labels = c(labels), name = "Time aIg ",)+ 
  scale_fill_manual(values = colorgradient6_manual2, labels = c(labels), name = "Time aIg ",) +
  add.textsize +
  theme(#axis.title.x=element_blank(),
        #axis.text.x=element_blank(),
        #axis.ticks.x=element_blank(),
        legend.position="none") 
}

factors_toplot <- c(colnames(MOFAfactors)[c(1,3)])

for(i in factors_toplot) {
assign(paste0("plot.factor.", i), f.violin.fact(data = MOFAfactors,protein = i)) 
}

legend.violinfactor <- as_ggplot( get_legend( ggplot(subset(MOFAfactors)  , aes(x = as.factor(condition), y =get(noquote(factors_toplot[1])))) +
    annotate("rect",
          xmin = 5 - 0.45,
             xmax = 6 + 0.6,
           ymin = -4.5, ymax = 4.5, fill = "lightblue",
           alpha = .4
  )+
    geom_hline(yintercept=0, linetype='dotted', col = 'black')+ 
  geom_violin(alpha = 0.9,aes(fill = condition))+
  geom_jitter(width = 0.05,size = 0.1, aes(col = condition), fill = "black", shape = 21)+ 
  stat_summary(fun=median, geom="point", shape=23, size=2, inherit.aes = T, position = position_dodge(width = 0.9), color = "black")+
  theme_few()+
  ylab(paste0(factors_toplot[1])) +
  scale_x_discrete(labels = labels, expand = c(0.1,0.1), name = "Time aIg  (minutes)") +
    scale_y_continuous(expand = c(0,0), name = strsplit(factors_toplot[1], split = "\\.")[[1]][2]) +
  scale_color_manual(values = colorgradient6_manual2, labels = c(labels), name = "Time aIg \n(minutes)",)+ 
  scale_fill_manual(values = colorgradient6_manual2, labels = c(labels), name = "Time aIg \n(minutes)",) +
  add.textsize ) ) 

plot.violin.factor1 <- plot.factor.group1.Factor1 +
  scale_y_reverse(expand = c(0,0), name = "Factor 1 value")+
  annotate(geom="text", x=1.5, y=-4.3, label="minutes", size = 2,
              color="grey3") +
  annotate(geom="text", x=5.5, y=-4.3, label="hours", size = 2,
              color="grey3") +
  labs(title = "Factor 1 captures \nminute times-scale signal transduction")

plot.violin.factor3 <- plot.factor.group1.Factor3 +
  annotate(geom="text", x=5.5, y=4.3, label="hours", size = 2,
              color="grey3")+
  annotate(geom="text", x=1.5, y=4.3, label="minutes", size = 2,
              color="grey3") +
  scale_y_continuous(expand = c(0,0), name = "Factor 3 value")+
  labs(title = "Factor 3 captures \nhour time-scale signal transduction")

#### protein Loadings factor 1

list.bcellpathway.protein <- c("p-CD79a", "p-Syk", "p-Src", "p-Erk1/2", "p-PLC-y2", "p-BLNK","p-PLC-y2Y759","p-PKC-b1", "p-p38", "p-Akt", "p-S6", "p-TOR", "CD79a") # , "p-PLC-y2", "p-PKC-b1", "p-IKKa/b","p-JNK", "p-p38", "p-p65","p-Akt", "p-S6", "p-TOR"
list.top20 <- topnegprots.factor1$feature[1:20]
## protein factor 1
plotdata.rank.PROT.1 <-plot_weights(mofa, 
  view = "PROT", 
  factors = c(1), 
  nfeatures = 15, 
  text_size = 1,
  manual = list(list.top20, list.bcellpathway.protein, "p-JAK1" ), 
  color_manual = list("grey","blue4","red"),
  return_data = TRUE
)

plotdata.rank.PROT.1 <- plotdata.rank.PROT.1 %>%
  mutate(Rank = 1:80,
         Weight = value, 
         colorvalue = ifelse(labelling_group == 2 & value <= -0.2,"grey", ifelse(labelling_group == 3 & value <= -0.2, "blue4", ifelse(labelling_group == 4 & value <= -0.2, "red", "grey"))),
         highlights = ifelse(labelling_group >= 1 & value <= -0.25, as.character(feature), "")
         )

plot.rank.PROT.1 <- ggplot(plotdata.rank.PROT.1, aes(x=Weight, y = Rank, label = highlights)) +
  labs(title = "(Phospho-)Proteins: <p><span style='color:blue4'>BCR </span>&<span style='color:red'> JAK1</span> signaling", #<span style='color:blue4'>BCR signaling</span> and <span style='color:red'>p-JAK1</span>
       x= "Factor 1 loading value",
       y= "Factor 1 loading rank") +
  geom_point(size=0.1, color =plotdata.rank.PROT.1$colorvalue, size =1) +
  geom_text_repel(size = 2, 
                  segment.size = 0.2, 
                  color =plotdata.rank.PROT.1$colorvalue,
                  nudge_x       = 1 - plotdata.rank.PROT.1$Weight,
                  direction     = "y",
                  hjust         = 1,
                  segment.color = "grey50")+
  theme_few()+
  add.textsize  +
  scale_y_continuous(trans = "reverse") +
  add.textsize +
  theme(
    plot.title = element_markdown()
  ) +
  theme(axis.text.y=element_blank(),
        axis.ticks.y=element_blank()
        )+
  xlim(c(-1,1))


## violin plots phospho-proteins highlighted in main

f.violin.prot <- function(data , protein){
  
  ggplot(subset(data)  , aes(x = as.factor(condition), y =get(noquote(protein)))) +
    annotate("rect",
          xmin = 5 - 0.45,
             xmax = 6 + 0.6,
           ymin = -5.5, ymax = 5.5, fill = "lightblue",
           alpha = .4
  )+
  geom_violin(alpha = 0.9,aes(fill = condition))+
  geom_jitter(width = 0.05,size = 0.1, color = "black")+ 
  stat_summary(fun=median, geom="point", shape=95, size=2, inherit.aes = T, position = position_dodge(width = 0.9), color = "red")+
  theme_few()+
  ylab(paste0(protein)) +
  scale_x_discrete(labels = labels, expand = c(0.1,0.1), name = "Time aIg  (minutes)") +
  scale_y_continuous(expand = c(0,0), name = protein) +
  scale_color_manual(values = colorgradient6_manual2, labels = c(labels), name = "Time aIg ",)+ 
  scale_fill_manual(values = colorgradient6_manual2, labels = c(labels), name = "Time aIg ",) +
  add.textsize +
  theme(#axis.title.x=element_blank(),
        #axis.text.x=element_blank(),
        #axis.ticks.x=element_blank(),
        legend.position="none") 
}

proteins_toplot <- c("p-CD79a","p-Syk","p-JAK1", "IgM")

for(i in proteins_toplot) {
assign(paste0("plot.violin.", i), f.violin.prot(data = proteindata ,protein = i)) 

}

## Fig 2 bottom row panels
`plot.violin.p-CD79a` <- `plot.violin.p-CD79a` +
  labs(title = "BCR signaling pathway and \nJAK1 activation")+
  add.textsize+
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),)

`plot.violin.p-Syk` <- `plot.violin.p-Syk` +
  add.textsize+
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),)

`plot.violin.p-JAK1` <-`plot.violin.p-JAK1` +
  add.textsize +
  labs(y = "<span style='color:red'>p-JAK1</span>") +
  theme(axis.title.y = element_markdown()) 

Enrichment analysis of Factor 3 positive loadings

topposrna.factor3 <- weights.RNA %>%
  filter(factor == "Factor3" & value >= 0) %>%
  arrange(-value)
rownames(topposrna.factor3) <- topposrna.factor3$feature

### Convert Gene-names to gene IDs (using 'org.Hs.eg.db' library)

topposrna.factor3 <- topposrna.factor3 %>%
  mutate(ENTREZID = mapIds(org.Hs.eg.db, as.character(topposrna.factor3$feature), 'ENTREZID', 'SYMBOL'))

listgenes.factor3 <- topposrna.factor3$value

names(listgenes.factor3) <- topposrna.factor3$ENTREZID


go.pb.fct3.pos <- enrichGO(gene         = topposrna.factor3$feature,
                OrgDb         = org.Hs.eg.db,
                keyType       = 'SYMBOL',
                ont           = "BP",
                pAdjustMethod = "BH")

go.pb.fct3.pos <- simplify(go.pb.fct3.pos, cutoff=0.6, by="p.adjust", select_fun=min)


go.pb.fct3.pos.dplyr <- mutate(go.pb.fct3.pos, richFactor = Count / as.numeric(sub("/\\d+", "", BgRatio))) 

## plot main panel
plot.enriched.go.bp.factor3 <- ggplot(go.pb.fct3.pos.dplyr, showCategory = 10, 
  aes(-log10(p.adjust), fct_reorder(Description,  -log10(p.adjust)))) +   geom_segment(aes(xend=0, yend = Description)) +
  geom_point(aes(size = Count)) +
  scale_color_viridis_c(guide=guide_colorbar(reverse=TRUE)) +
  scale_size_continuous(range=c(0.5, 3)) +
  theme_minimal() + 
  add.textsize +
  xlab("-log adj pval") +
  ylab(NULL) + 
  ggtitle("Enriched Biological Processes") +
  theme(legend.position="none")

legend.plot.enriched.go.bp.factor3 <- as.ggplot(get_legend(ggplot(go.pb.fct3.pos.dplyr, showCategory = 10, 
  aes(-log10(p.adjust), fct_reorder(Description,  -log10(p.adjust)))) +   geom_segment(aes(xend=0, yend = Description)) +
  geom_point(aes(size = Count)) +
  scale_color_viridis_c(guide=guide_colorbar(reverse=TRUE)) +
  scale_size_continuous(range=c(0.5, 3)) +
  theme_minimal() + 
  add.textsize +
  xlab("-log adj pval") +
  ylab(NULL) + 
  ggtitle("Enriched Biological Processes") +
     theme(legend.position="right")))

## Plot supplementary
plot.enriched.go.bp.factor3_50 <- ggplot(go.pb.fct3.pos.dplyr, showCategory = 50, 
  aes(-log10(p.adjust), fct_reorder(Description,  -log10(p.adjust)))) +   geom_segment(aes(xend=0, yend = Description)) +
  geom_point(aes(size = Count)) +
  scale_color_viridis_c(guide=guide_colorbar(reverse=TRUE)) +
  scale_size_continuous(range=c(0.5, 3)) +
  theme_minimal() + 
  add.textsize +
  xlab("-log adj pval") +
  ylab(NULL) + 
  ggtitle("Enriched Biological Processes") 

message(c("Significance protein folding GO term: \n",go.pb.fct3.pos.dplyr@result[which(go.pb.fct3.pos.dplyr@result$Description == "protein folding"),"p.adjust"]))

Significance protein folding GO term: 
0.0457794707358827

##For RNA loadings panels
topgeneset.fct3<- unlist(str_split(go.pb.fct3.pos.dplyr@result[1:10,"geneID"], pattern = "/"))

topgeneset.fct3 = bitr(topgeneset.fct3, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")

## RNA factor 1 loadings
plotdata.rank.RNA.3 <-plot_weights(mofa, 
  view = "RNA", 
  factors = c(3), 
  nfeatures = 5, 
  text_size = 1,
  manual = list(topposrna.factor3$feature[c(1:5)] ,topgeneset.fct3$SYMBOL), 
  color_manual = list("grey","blue4"),
  return_data = TRUE
)

plotdata.rank.RNA.3 <- plotdata.rank.RNA.3 %>%
  mutate(Rank = 1:nrow(plotdata.rank.RNA.3),
         Weight = value, 
         colorvalue = ifelse(labelling_group == 3 &value >= 0.25,"blue4", ifelse(labelling_group == 2&value >= 0.25, "grey2", "grey")),
         highlights = ifelse(labelling_group >= 1&value >= 0.25, as.character(feature), "")
         )

plot.rank.RNA.3 <- ggplot(plotdata.rank.RNA.3, aes(x=Weight, y = Rank, label = highlights)) +
  labs(title = "Contributing genes <p><span style='color:blue4'><span style='color:blue4'>GO-term vesicle related </span> ",  #<span style='color:blue4'>BCR signaling regulation (find GO term+list todo)</span>
       x= "Factor 3 loading value",
       y= "Factor 3 loading rank") +
  geom_point(size=0.1, color =plotdata.rank.RNA.3$colorvalue) +
  geom_text_repel(size = 2, 
                  segment.size = 0.2, 
                  color =plotdata.rank.RNA.3$colorvalue,
                  nudge_x       = -1 - plotdata.rank.RNA.3$Weight,
                  direction     = "y",
                  hjust         = 0,
                  segment.color = "grey50")+
  theme_few()+
  add.textsize +
  scale_y_continuous() +
  add.textsize +
  theme(
    plot.title = element_markdown()
  )  +
  theme(axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
        axis.title.y = element_blank())+
  xlim(c(-1,1))


#### Protein loadings factor 3

list.highlight.prot.fct3 <- c("p-p38", "p-Akt", "p-S6", "p-TOR", "XBP1_PROT", "p-STAT5")
list.top20 <- topnegprots.factor1$feature[1:20]

plotdata.rank.PROT.3 <-plot_weights(mofa, 
  view = "PROT", 
  factors = c(3), 
  nfeatures = 30, 
  text_size = 1,
  manual = list(topposprots.factor3$feature[c(1:8)] ,list.highlight.prot.fct3), 
  color_manual = list("grey","blue4"),
  return_data = TRUE
)

plotdata.rank.PROT.3 <- plotdata.rank.PROT.3 %>%
  mutate(Rank = 1:80,
         Weight = value, 
         colorvalue = ifelse(labelling_group == 3 &value >= 0.25,"blue4", ifelse(labelling_group == 2&value >= 0.4, "grey", "grey")),
         highlights = ifelse(labelling_group >= 1&value >= 0.25, as.character(feature), "")
         )

plot.rank.PROT.3 <- ggplot(plotdata.rank.PROT.3, aes(x=Weight, y = Rank, label = highlights)) +
  labs(title = "Signaling implicated in <p><span style='color:blue4'>Unfolded protein response</span>", 
       x= "Factor 3 loading value",
       y= "Factor 3 loading rank") +
  geom_point(size=0.1, color =plotdata.rank.PROT.3$colorvalue, size =1) +
  geom_text_repel(size = 2, 
                  segment.size = 0.2, 
                  color =plotdata.rank.PROT.3$colorvalue,
                  nudge_x       = -1 - plotdata.rank.PROT.3$Weight,
                  direction     = "y",
                  hjust         = 0,
                  segment.color = "grey50")+
  theme_few()+
  add.textsize  +
  scale_y_continuous() +
  add.textsize +
  theme(
    plot.title = element_markdown()
  ) +
  theme(axis.text.y=element_blank(),
        axis.ticks.y=element_blank()
        )+
  xlim(c(-1,1))


#### RNA loadings factor 1

plotdata.rank.RNA.1 <-plot_weights(mofa, 
  view = "RNA", 
  factors = c(1), 
  nfeatures = 2, 
  text_size = 1,
  return_data = TRUE
)

plotdata.rank.RNA.1 <- plotdata.rank.RNA.1 %>%
  mutate(Rank = 1:nrow(plotdata.rank.RNA.1),
         Weight = value, 
         colorvalue = ifelse(labelling_group == 1,"blue4", ifelse(labelling_group == 1, "blue4", "grey")),
         highlights = ifelse(labelling_group >= 1, as.character(feature), "")
         )

plot.rank.RNA.1 <- ggplot(plotdata.rank.RNA.1, aes(x=Weight, y = Rank, label = highlights)) +
  labs(title = " Contributing genes<p><span style='color:blue4'>BCR activation</span>",  
       x= "Factor 1 loading value",
       y= "Factor 1 loading rank") +
  geom_point(size=0.1, color =plotdata.rank.RNA.1$colorvalue) +
  geom_text_repel(size = 2, 
                  segment.size = 0.2, 
                  color =plotdata.rank.RNA.1$colorvalue,
                  nudge_x       = 1 - plotdata.rank.RNA.1$Weight,
                  direction     = "y",
                  hjust         = 1,
                  segment.color = "grey50") +
  theme_few()+
  add.textsize +
  scale_y_continuous(trans = "reverse") +
  add.textsize +
  theme(
    plot.title = element_markdown()
  )  +
  theme(axis.text.y=element_blank(),
        axis.ticks.y=element_blank(), 
        axis.title.y=element_blank()
        )+
  xlim(c(-1,1))


#### Violins genes


f.violin.rna <- function(data , protein){
  
  ggplot(subset(data)  , aes(x = as.factor(condition), y =get(noquote(protein)))) +
    annotate("rect",
          xmin = 5 - 0.45,
             xmax = 6 + 0.6,
           ymin = -3, ymax = 15, fill = "lightblue",
           alpha = .4
  )+
  geom_violin(alpha = 0.9,aes(fill = condition))+
  geom_jitter(width = 0.05,size = 0.1, color = "black")+ 
  stat_summary(fun=median, geom="point", shape=95, size=2, inherit.aes = T, position = position_dodge(width = 0.9), color = "red")+
  theme_few()+
  ylab(paste0(protein)) +
  scale_x_discrete(labels = labels, expand = c(0.1,0.1), name = "Time aIg  (minutes)") +
  scale_y_continuous(expand = c(0,0), name = protein) +
  scale_color_manual(values = colorgradient6_manual2, labels = c(labels), name = "Time aIg ",)+ 
  scale_fill_manual(values = colorgradient6_manual2, labels = c(labels), name = "Time aIg ",) +
  add.textsize +
  theme(#axis.title.x=element_blank(),
        #axis.text.x=element_blank(),
        #axis.ticks.x=element_blank(),
        legend.position="none") 
}
rnadata <- as.data.frame(t(seu_combined_selectsamples@assays$SCT.RNA@scale.data)) %>%
  mutate(sample = rownames(t(seu_combined_selectsamples@assays$SCT.RNA@scale.data))) %>%
  left_join(meta.allcells)

enriched.geneset.posregBcell.fact1 <- c("NPM1", "NEAT1", "BTF3", "IGHM", "IGKC")

##Violin genes
for(i in enriched.geneset.posregBcell.fact1) {
assign(paste0("plot.violin.rna.", i), f.violin.rna(data = rnadata ,protein = i)) 
}

plot.violin.rna.NEAT1 <- plot.violin.rna.NEAT1 +
  labs(title = "Expression upregulated genes\n")+
  add.textsize+
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),)

plot.violin.rna.NPM1 <- plot.violin.rna.NPM1 +
  add.textsize+
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),)

Main figure 2

Fig2.row1.violinsprot <- plot_grid(`plot.violin.p-CD79a`, `plot.violin.p-Syk`, `plot.violin.p-JAK1`,labels = c(panellabels[4]), label_size = 10, ncol = 1, rel_heights = c(1.2,0.95,1.2))
Fig2.row1 <- plot_grid(plot.violin.factor1,plot.rank.PROT.1, NULL, Fig2.row1.violinsprot, labels = c(panellabels[1:3], ""),  label_size = 10, ncol =4, rel_widths = c(0.8,0.55,0.5,0.8))

Fig2.row2.violinsrna <- plot_grid(plot.violin.rna.NEAT1,plot.violin.rna.NPM1, plot.violin.rna.BTF3,labels = "", label_size = 10, ncol = 1, rel_heights = c(1.2,0.95,1.2))

Fig2.row2 <- plot_grid(plot.violin.factor3,plot.rank.PROT.3,plot.rank.RNA.3,Fig2.row2.violinsrna, labels = c(panellabels[5:6], "", panellabels[8]), label_size = 10, ncol =4, rel_widths = c(0.8,0.55,0.5,0.8))

Fig2.row3 <- plot_grid(plot.enriched.go.bp.factor3,legend.plot.enriched.go.bp.factor3,NULL, labels = panellabels[7], label_size = 10, ncol =3, rel_widths = c(1.8,0.2,0.6))

plot_fig2 <- plot_grid(Fig2.row1, Fig2.row2,Fig2.row3, labels = c("", "", ""), label_size = 10, ncol = 1, rel_heights = c(1,1,0.55))

ggsave(plot_fig2, filename = "output/paper_figures/Fig2.pdf", width = 183, height = 183, units = "mm",  dpi = 300, useDingbats = FALSE)
ggsave(plot_fig2, filename = "output/paper_figures/Fig2.png", width = 183, height = 183, units = "mm",  dpi = 300)

plot_fig2

Figure 2. Factor 1 and 3 exploration.

Suppl Enriched Fact 3

ggsave(plot.enriched.go.bp.factor3_50, filename = "output/paper_figures/Suppl_Enrichm_Fct3.pdf", width = 183, height = 183, units = "mm",  dpi = 300, useDingbats = FALSE)
ggsave(plot.enriched.go.bp.factor3_50, filename = "output/paper_figures/Suppl_Enrichm_Fct3.png", width = 183, height = 183, units = "mm",  dpi = 300)
plot.enriched.go.bp.factor3_50

Supplementary Figure. Top 50 enriched gene-sets in postive loadings factor 3.

Session information

sessionInfo()

R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17763)

Matrix products: default

locale:
[1] LC_COLLATE=English_Netherlands.1252  LC_CTYPE=English_Netherlands.1252   
[3] LC_MONETARY=English_Netherlands.1252 LC_NUMERIC=C                        
[5] LC_TIME=English_Netherlands.1252    

attached base packages:
 [1] parallel  stats4    grid      stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] png_0.1-7                   forcats_0.5.0              
 [3] clusterProfiler_3.14.3      clusterProfiler.dplyr_0.0.2
 [5] enrichplot_1.6.1            org.Hs.eg.db_3.10.0        
 [7] AnnotationDbi_1.48.0        IRanges_2.20.2             
 [9] S4Vectors_0.24.4            Biobase_2.46.0             
[11] BiocGenerics_0.32.0         ggridges_0.5.2             
[13] cowplot_1.1.0               ggtext_0.1.0               
[15] ggplotify_0.0.5             ggcorrplot_0.1.3           
[17] ggrepel_0.8.2               ggpubr_0.4.0               
[19] scico_1.2.0                 MOFA2_1.1                  
[21] extrafont_0.17              patchwork_1.0.1            
[23] RColorBrewer_1.1-2          viridis_0.5.1              
[25] viridisLite_0.3.0           ggsci_2.9                  
[27] sctransform_0.3.1           ggthemes_4.2.0             
[29] matrixStats_0.57.0          kableExtra_1.2.1           
[31] gridExtra_2.3               Seurat_3.2.2               
[33] ggplot2_3.3.2               scales_1.1.1               
[35] tidyr_1.1.2                 dplyr_1.0.2                
[37] stringr_1.4.0               workflowr_1.6.1            

loaded via a namespace (and not attached):
  [1] reticulate_1.16       tidyselect_1.1.0      RSQLite_2.2.1        
  [4] htmlwidgets_1.5.2     BiocParallel_1.20.1   Rtsne_0.15           
  [7] munsell_0.5.0         codetools_0.2-16      ica_1.0-2            
 [10] future_1.19.1         miniUI_0.1.1.1        withr_2.3.0          
 [13] GOSemSim_2.12.1       colorspace_1.4-1      knitr_1.30           
 [16] rstudioapi_0.11       ROCR_1.0-11           ggsignif_0.6.0       
 [19] tensor_1.5            DOSE_3.12.0           Rttf2pt1_1.3.8       
 [22] listenv_0.8.0         labeling_0.4.2        git2r_0.27.1         
 [25] urltools_1.7.3        mnormt_2.0.2          polyclip_1.10-0      
 [28] farver_2.0.3          bit64_4.0.5           pheatmap_1.0.12      
 [31] rhdf5_2.30.1          rprojroot_1.3-2       vctrs_0.3.4          
 [34] generics_0.0.2        xfun_0.18             markdown_1.1         
 [37] R6_2.4.1              graphlayouts_0.7.0    rsvd_1.0.3           
 [40] fgsea_1.12.0          spatstat.utils_1.17-0 gridGraphics_0.5-0   
 [43] DelayedArray_0.12.3   promises_1.1.0        ggraph_2.0.3         
 [46] gtable_0.3.0          globals_0.13.1        goftest_1.2-2        
 [49] tidygraph_1.2.0       rlang_0.4.8           splines_3.6.3        
 [52] rstatix_0.6.0         extrafontdb_1.0       lazyeval_0.2.2       
 [55] europepmc_0.4         broom_0.7.1           BiocManager_1.30.10  
 [58] yaml_2.2.1            reshape2_1.4.4        abind_1.4-5          
 [61] backports_1.1.10      httpuv_1.5.2          qvalue_2.18.0        
 [64] gridtext_0.1.1        tools_3.6.3           psych_2.0.9          
 [67] ellipsis_0.3.1        Rcpp_1.0.4.6          plyr_1.8.6           
 [70] progress_1.2.2        purrr_0.3.4           prettyunits_1.1.1    
 [73] rpart_4.1-15          deldir_0.1-29         pbapply_1.4-3        
 [76] zoo_1.8-8             haven_2.3.1           cluster_2.1.0        
 [79] fs_1.4.1              magrittr_1.5          data.table_1.13.0    
 [82] DO.db_2.9             openxlsx_4.2.2        triebeard_0.3.0      
 [85] lmtest_0.9-38         RANN_2.6.1            tmvnsim_1.0-2        
 [88] whisker_0.4           fitdistrplus_1.1-1    hms_0.5.3            
 [91] mime_0.9              evaluate_0.14         xtable_1.8-4         
 [94] rio_0.5.16            readxl_1.3.1          compiler_3.6.3       
 [97] tibble_3.0.4          KernSmooth_2.23-16    crayon_1.3.4         
[100] htmltools_0.5.0       mgcv_1.8-31           later_1.0.0          
[103] DBI_1.1.0             tweenr_1.0.1          corrplot_0.84        
[106] MASS_7.3-53           rappdirs_0.3.1        Matrix_1.2-18        
[109] car_3.0-10            igraph_1.2.6          pkgconfig_2.0.3      
[112] rvcheck_0.1.8         foreign_0.8-75        plotly_4.9.2.1       
[115] xml2_1.3.2            webshot_0.5.2         rvest_0.3.6          
[118] digest_0.6.26         RcppAnnoy_0.0.16      spatstat.data_1.4-3  
[121] fastmatch_1.1-0       rmarkdown_2.4         cellranger_1.1.0     
[124] leiden_0.3.3          uwot_0.1.8            curl_4.3             
[127] shiny_1.5.0           lifecycle_0.2.0       nlme_3.1-144         
[130] jsonlite_1.7.1        Rhdf5lib_1.8.0        carData_3.0-4        
[133] pillar_1.4.6          lattice_0.20-38       GO.db_3.10.0         
[136] fastmap_1.0.1         httr_1.4.2            survival_3.1-8       
[139] glue_1.4.2            zip_2.1.1             spatstat_1.64-1      
[142] bit_4.0.4             ggforce_0.3.2         stringi_1.4.6        
[145] HDF5Array_1.14.4      blob_1.2.1            memoise_1.1.0        
[148] irlba_2.3.3           future.apply_1.6.0