Update methods

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

# Functions

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

#' Plot cell fractions across groups

#'

#' This function to show the cell fraction changes across groups.

#'

#' @param seu_obj A complete Seurat object

#' @param celltypes Cell types to be included in the dot plot. Default: all cell types.

#' @param groupby The group to show on x axis. One of the column names in meta.data.

#' @param show_replicate Whether or not to show the individual replicates from the graph. If TRUE, the replicate column names need to specify.

#' @param rep_colname The column name for biological replicates in the meta data.

#' @param strip.color Colors for the strip background

#' @return A ggplot object

#' @export

plot_cell_fraction<-function(

  seu_obj, 

  celltypes=NULL, 

  groupby, 

  show_replicate = FALSE, 

  rep_colname = NULL,

  strip.color = NULL

  ){

  meta_data<-seu_obj@meta.data

  meta_data$celltype<-as.character(Idents(seu_obj))

  groupby_level<-levels(seu_obj@meta.data[,groupby])

  if (is.null(groupby_level)){

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

    groupby_level<-levels(seu_obj@meta.data[,groupby])

  } 

  if(!show_replicate){

    freq_df<-prop.table(table(meta_data[,"celltype"], meta_data[, groupby]), margin = 2)

    freq_df<-data.frame(freq_df)

    colnames(freq_df)[1:2]<-c("celltype","group")

    freq_df$Freq<-freq_df$Freq*100

  } else {

    if(is.null(rep_colname)){

      stop("Please specify the replicate colname in your meta data!")

    } else {

      meta_data$new_group<-paste(meta_data[,"celltype"], meta_data[,groupby], meta_data[, rep_colname], sep = "___")

      freq_df<-data.frame(table(meta_data$new_group))

      freq_df$Var1<-as.character(freq_df$Var1)

      freq_df$celltype <- as.character(lapply(X=strsplit(freq_df$Var1, split = "___"),FUN = function(x){x[[1]]}))

      freq_df$group <- as.character(lapply(X=strsplit(freq_df$Var1, split = "___"),FUN = function(x){x[[2]]}))

      freq_df$replicate <- as.character(lapply(X=strsplit(freq_df$Var1, split = "___"),FUN = function(x){x[[3]]}))

      total_cell<-aggregate(Freq ~ replicate, freq_df, sum)

      colnames(total_cell)[2]<-"Total"

      freq_df <- merge(freq_df, total_cell, by="replicate")

      freq_df$Freq<-freq_df$Freq*100/freq_df$Total

    }

  }

  freq_df$group <- factor(freq_df$group, levels = groupby_level)

  if(is.null(celltypes)){

    celltypes <- levels(seu_obj)

  }

  freq_df <- freq_df[freq_df$celltype %in% celltypes, ]

  freq_df$celltype <- factor(freq_df$celltype, levels = celltypes)

 p <- ggplot(freq_df, aes(group, Freq, fill=group))+

    geom_bar(position = "dodge",  stat = "summary", fun='mean', width = 0.6)+

    stat_summary(fun.data = mean_se, geom = "errorbar", width=0.2, size=1, color='midnightblue', alpha=0.8)+

    ylab('Percentage of cells')+xlab('')+

    scale_fill_manual(values = brewer.pal(8, 'Set2'))+

    theme(panel.background = element_rect(fill = "white", colour = "grey50"),

          strip.text = element_text(size = 12),axis.title = element_text(size = 14),

          axis.text.x = element_text(angle = 45, vjust = 1, hjust=1, size = 12),

          legend.position = "none", axis.text.y = element_text(size = 12),

          legend.text = element_text(size = 12), legend.title = element_text(size = 12, face = 'bold'), 

          plot.title = element_text(hjust =0.5))

 if(show_replicate){

   p <- p +   geom_quasirandom(size=1,width = 0.2, color='midnightblue', alpha=0.8, groupOnX = F)+facet_wrap(~celltype, scales = 'free_y', ncol = length(celltypes))

   g <- change_strip_background(p, type = 'top', n.color = length(celltypes), strip.color = strip.color)

   print(grid.draw(g))

 } else {

   p

 }

}

```

### 1. Circlize plot to visualize cell clustering and meta data

This circlize plot was inspired by the data visualization in a published paper (Figure1, https://www.nature.com/articles/s41586-021-03775-x) from Linnarsson's lab.

```

###Check and see the meta data info on your Seurat object

colnames(iri.integrated@meta.data)  

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

### 7. Cell proportion change across groups

```

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

plot_cell_fraction(iri.integrated,  celltypes = c("PTS1" ,   "PTS2"  ,  "PTS3"  ,  "NewPT1" , "NewPT2"), groupby = "Group", show_replicate = T, rep_colname = "orig.ident")

dev.off()

```

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

### 8. Other ploting functions

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

```

help(package = plot1cell)

```

Many more functions will be added in the future package development. For questions, please raise an issue in this github page or contact <a href="https://humphreyslab.com">TheHumphreysLab</a>. 

### 8. Attributions

### 9. Attributions

This package uses many methods from Seurat (https://github.com/satijalab/seurat) to process the data for ploting. The circlize and heatmap plots were generated by the circlize (https://github.com/jokergoo/circlize) and ComplexHeatmap (https://github.com/jokergoo/ComplexHeatmap) packages. The Upset plot was generated by the ComplexUpset package (https://github.com/krassowski/complex-upset). The violin plot to show multiple genes across groups was inspired by the codes provided here (https://github.com/ycl6/StackedVlnPlot). Most of the graphs were generated by ggplot2 (https://github.com/tidyverse/ggplot2). The package benefits from the following dependencies.

```