bug fixes

    reshape2,

    ggbeeswarm,

    purrr,

    UpSetR,

    ComplexUpset,

    matrixStats,

    DoubletFinder,

    methods,

    reshape2,

    ggbeeswarm,

    purrr,

    UpSetR,

    ComplexUpset,

    matrixStats,

    DoubletFinder,

    methods,

export(complex_dotplot_multiple)

export(complex_dotplot_single)

export(complex_featureplot)

export(complex_heatmap_group)

export(complex_heatmap_unique)

export(complex_upset_plot)

export(complex_vlnplot_multiple)

export(complex_vlnplot_single)

export(convert_geneid)

export(order_gene_up)

export(plot_circlize)

export(plot_qpcr)

export(plot_upset)

export(prepare_circlize_data)

export(run_correlation)

export(transform_coordinates)

#' @return A ComplexHeatmap object or/and a gene list

#' @export

complex_heatmap_group<-function(

complex_heatmap_unique<-function(

  seu_obj,

  celltype,

  group,

cell1<-subset(seu_obj, idents=celltype)

cell1<-SetIdent(cell1, value = group)

group_levels<-levels(seu_obj@meta.data[,group])

seu_obj@meta.data[,group]<-gsub("_","-",seu_obj@meta.data[,group])

if (is.null(group_levels)){

  seu_obj@meta.data[,group] <-factor(seu_obj@meta.data[,group], levels = names(table(seu_obj@meta.data[,group])))

}

#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

# Functions

#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

#' UpSet plot for the cell markers

#' UpSet plot visualize the number of unique and shared DEGs across group 

#'

#' This function takes the marker gene table as input and visualize the genes that 

#' are unique to a particular cell type or shared by multiple cell types. 

#' @param deg_table The dataframe from Seurat::FindAllMarkers function.

#' @param celltypes The cell types of interest

#' @param textscale Scale of the text font size

#' @param pointsize The size of the dots in the matrix

#' @param nintersects The number of intersections showing in the main bar

#' @param alpha The transparency of the bars

#' This function takes Seurat object as input and visualize the genes that 

#' are unique to a particular group or shared by multiple group. 

#' @param seu_obj A complete Seurat object.

#' @param celltype The cell type to analyze.

#' @param group Group factor in meta data.

#' @param logfc Log fold change to select the DEGs

#' @param min_size Minimal number of observations in an intersection for it to be included

#' @return An UpSet plot

#' @export

plot_upset<-function(

  deg_table, 

  celltypes = NULL, 

  nintersects = NULL,

  textscale = 1.5,

  pointsize = 2,

  alpha = 0.3

complex_upset_plot<-function(

  seu_obj, 

  celltype, 

  group,

  logfc = 0.5,

  min_size = 1

){

    fullcelltypes<-as.character(unique(deg_table$cluster))

    if(is.null(celltypes)){

      celltypes = fullcelltypes

  cell1<-subset(seu_obj, idents=celltype)

  cell1<-SetIdent(cell1, value = group)

  group_levels<-levels(seu_obj@meta.data[,group])

  if (is.null(group_levels)){

    seu_obj@meta.data[,group] <-factor(seu_obj@meta.data[,group], levels = names(table(seu_obj@meta.data[,group])))

    group_levels<-levels(seu_obj@meta.data[,group])

  }

    gene_list<-list()

    for(i in 1:length(fullcelltypes)){

      cluster_marker <- deg_table[deg_table$cluster == fullcelltypes[i],]$gene

  levels(cell1)<-group_levels

  all_markers<-FindAllMarkers(cell1, min.pct = 0.1, logfc.threshold = logfc,verbose = F)

  all_markers1<-all_markers[all_markers$avg_log2FC>0,]

  all_markers2<-all_markers[all_markers$avg_log2FC<0,]

  gene_list1<-list()

  for(i in 1:length(group_levels)){

    cluster_marker <- all_markers1[all_markers1$cluster == group_levels[i],]$gene

    cluster_marker <- data.frame("gene" = cluster_marker)

    cluster_marker$cell1 <- 1

      colnames(cluster_marker)[2] <- fullcelltypes[i]

      gene_list[[i]] <- cluster_marker

    colnames(cluster_marker)[2] <- group_levels[i]

    gene_list1[[i]] <- cluster_marker

  }

  combined_data <- reduce(gene_list, full_join)

  combined_data[is.na(combined_data)] <- 0

  group.color<-scales::hue_pal()(length(celltypes))

  names(group.color)<-fullcelltypes

  group.color<-group.color[celltypes]

  metadata<-data.frame(sets=celltypes)

  metadata$group<-metadata$sets

  combined_data<-combined_data[,celltypes]

  sum_col<-colSums(combined_data)

  sum_col<-sum_col[order(sum_col)]

  group.color2<-rev(as.character(group.color[names(sum_col)]))

  if(is.null(nintersects)){

    nintersects = 2^ncol(combined_data)-1

  combined_data1 <- purrr::reduce(gene_list1, full_join)

  combined_data1[is.na(combined_data1)] <- 0

  gene_list2<-list()

  for(i in 1:length(group_levels)){

    cluster_marker <- all_markers2[all_markers2$cluster == group_levels[i],]$gene

    cluster_marker <- data.frame("gene" = cluster_marker)

    cluster_marker$cell1 <- 1

    colnames(cluster_marker)[2] <- group_levels[i]

    gene_list2[[i]] <- cluster_marker

  }

  upset(combined_data,

        set.metadata = list(data = metadata, 

                            plots = list(list(type = "matrix_rows", 

                                              column = "group", 

                                              colors = group.color, 

                                              alpha = alpha))),

        sets.bar.color = group.color2, 

        point.size = pointsize,

        nsets = length(celltypes),

        nintersects = nintersects,

        text.scale = textscale, 

        main.bar.color=colorRampPalette(RColorBrewer::brewer.pal(9, "Set1"))(nintersects),

        matrix.color= "black",

        mainbar.y.label="Unique or shared DEG",

        sets.x.label="Total DEG"

  combined_data2 <- purrr::reduce(gene_list2, full_join)

  combined_data2[is.na(combined_data2)] <- 0

  combined_data1$Direction<-"Upregulated"

  combined_data2$Direction<-"Downregulated"

  combined_data<-rbind(combined_data1, combined_data2)

  all_genes<-combined_data$gene

  gene_count<-data.frame(table(all_markers$gene))

  colnames(gene_count)[1]<-"gene"

  combined_data<-merge(combined_data, gene_count, by='gene')

  combined_data$type<-ifelse(combined_data$Freq==1, "Unique","Shared")

  main_bar_color<-hue_pal()(length(group_levels))

  metadata<-data.frame(set=group_levels)

  metadata$color_col<-metadata$set

  upset(combined_data, group_levels, 

        base_annotations=list(

          "Unique or shared DEG"=intersection_size(

            counts=T,

            mapping=aes(fill=Direction),

            width=0.7,

            alpha=0.4

          ) + scale_fill_manual(values= c("blue", "orange"))+

            theme_void()+

            theme(legend.position = "top", legend.title = element_blank())

        ),

        set_sizes=(

          upset_set_size(

            geom=geom_bar(

              aes(fill=type, x=group),

              width=0.7

            ),

            position='right'

          )+ scale_fill_manual(values= c("hotpink",'green'))+theme_void()+

            theme(axis.line.x = element_line(colour = "black"),

                  axis.ticks.x =element_line(size = 0.5, color="black") ,

                  axis.ticks.length = unit(.05, "cm"),

                  axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 8))

        ),

        width_ratio=0.1,

        stripes=upset_stripes(

          geom = geom_point(size=0.1),

          mapping=aes(color=color_col),

          data=metadata

        ),

        name = celltype,

        min_size = min_size

  )

}

 No newline at end of file

to = c("Ctrl","Hr4","Hr12","Day2", "Day14","Wk6"))

iri.integrated$Group2<-factor(iri.integrated$Group2, levels = c("Ctrl","Hr4","Hr12","Day2", "Day14","Wk6"))

png(filename =  'heatmap_group.png', width = 4, height = 8,units = 'in', res = 100)

complex_heatmap_group(seu_obj = iri.integrated, celltype = "NewPT2", group = "Group2",gene_highlight = c("Slc22a28","Vcam1","Krt20","Havcr1"))

complex_heatmap_unique(seu_obj = iri.integrated, celltype = "NewPT2", group = "Group2",gene_highlight = c("Slc22a28","Vcam1","Krt20","Havcr1"))

dev.off()

```

![alt text](https://github.com/HaojiaWu/Plot1cell/blob/master/data/heatmap_group.png) <br />

### 6. Other ploting functions

### 6. Upset plot to show the unique and shared DEGs across groups.

```

png(filename =  'upset_plot.png', width = 8, height = 4,units = 'in', res = 300)

complex_upset_plot(iri.integrated, celltype = "NewPT2", group = "Group", min_size = 10, logfc=0.5)

dev.off()

```

![alt text](https://github.com/HaojiaWu/Plot1cell/blob/master/data/upset_plot.png) <br />

### 7. Other ploting functions

There are other functions for plotting/data processing in plot1cell. 

```

help(package = plot1cell)