Commit fb7b8e7d authored by HaojiaWu's avatar HaojiaWu
Browse files

Add more methods

parent 9d6d6048

NAMESPACE

+2 −0
Original line number Diff line number Diff line
@@ -3,10 +3,12 @@ 
export(add_sliding_windows)

export(add_tract)

export(cell_order)

export(complex_vlnplot_single)

export(convert_geneid)

export(creat_cellphonedb_file)

export(create_pyscenic_file)

export(data_processing)

export(extract_gene_count)

export(get_metadata)

export(get_segment)

export(mk_color_table)

R/modified_seurat_plot.R

+15 −24
Original line number Diff line number Diff line
@@ -29,31 +29,20 @@ modified_dotplot <- function( 
    'radius' = scale_radius,

    stop("'scale.by' must be either 'size' or 'radius'")

  )

  

  if(max(dataplot$Pct.Exp)>=20) {

  dotplot<-ggplot(dataplot, aes(x = features.plot, y = id, fill=Avg.Exp)) + 

    geom_tile(fill="white", color="white") +

    geom_point(aes( size =Pct.Exp), shape=21, color='grey80')  +  

    scale_fill_gradientn(colours  =  col_palette)+

      scale_size(range = c(0, 10))+

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

          axis.line = element_blank(),axis.text.x = element_text(angle = 45, hjust = 1), 

          legend.key = element_rect(colour = NA, fill = NA),

          axis.text = element_text(size = 12),axis.title=element_text(size=8),legend.text=element_text(size=8), 

          legend.title = element_text(size = 10),legend.position="right", legend.margin=margin())+ylab("")+xlab("")+

    guides(size = guide_legend(override.aes = list(color='black')))

  if(max(dataplot$Pct.Exp)>=20) {

    dotplot + scale_size(range = c(0, 10))

  } else {

    dotplot<-ggplot(dataplot, aes(x = features.plot, y = id, fill=Avg.Exp)) +  

      geom_tile(fill="white", color="white") +

      geom_point(aes( size =Pct.Exp), shape=21, color='grey80')  +  

      scale_fill_gradientn(colours  = col_palette)+

      scale.func(range = c(0, 10), limits = c(0, 20)) +

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

            axis.line = element_blank(),axis.text.x = element_text(angle = 45, hjust = 1), 

            legend.key = element_rect(colour = NA, fill = NA),

            axis.text = element_text(size = 12),axis.title=element_text(size=8),legend.text=element_text(size=8), 

            legend.title = element_text(size = 10),legend.position="right", legend.margin=margin())+ylab("")+xlab("")+

      guides(size = guide_legend(override.aes = list(color='black')))

    dotplot + scale.func(range = c(0, 10), limits = c(0, 20))

  }

  dotplot

}
@@ -95,3 +84,5 @@ modified_dimplot <- function( 
  ylab('UMAP_2')+theme(axis.title.y = element_text(hjust = 0.05, angle = 90, size = 12))+

  ggtitle(title)+theme(plot.title = element_text(hjust = 0.5))

}

R/plot_violin.R

0 → 100644
+127 −0
Original line number Diff line number Diff line 
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

# Functions

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

#' Violin plot for a single gene across groups

#'

#' This function generates violin plot(s) to compare the expression of a single gene across 

#' different groups or cell types. It is designed for visualizing a complicated scenario: 

#' Gene expression on multiple cell types and multiple conditions.

#'

#' @param seu A complete Seurat object

#' @param feature Gene name. Only one gene is allowed.

#' @param cell.types Cell types of interest. By default, all cell types are included.

#' @param groups Groups to split the plot. Support multiple groups.

#' @param add.dot Whether or not to add points on the violins.

#' @param font.size Font size for the labels.

#' @param pt.size Point size for the data points on the violin

#' @return A ggplot object

#' @export



complex_vlnplot_single <- function(

  seu,

  feature,

  cell.types=NULL,

  groups,

  add.dot = T,

  font.size=14,

  pt.size=0.1

  ){

  if(is.null(cell.types)){

    cell.types = levels(seu)

  }

  gene_count<-extract_gene_count(seu=seu, features = feature, cell.types = cell.types, meta.groups = groups)

  set.seed(seed = 42)

  noise <- rnorm(n = length(x = gene_count[,feature])) / 100000

  gene_count[, feature]<-gene_count[,feature]+noise

  if (length(groups)==1) {

      if(length(cell.types)==1){

      p<-ggplot(gene_count, aes_string(x=groups, y=feature, fill=groups))+

      geom_violin(scale = 'width', adjust = 1, trim = TRUE, size=0.3, alpha=0.5, color="pink")+

      xlab("") + ylab("") + ggtitle(feature) +

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

            axis.title = element_text(size = font.size),

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

            axis.text.y = element_text(size=(font.size-2)),

            legend.title = element_blank(),

            legend.position = 'none',

            plot.title = element_text(size=(font.size+2), hjust = 0.5))

      if(add.dot){

        p = p + geom_quasirandom(size=pt.size, alpha=0.2)

      }

      p

    } else {

      plot_list<-list()

      for(i in 1:length(cell.types)){

        cell_count <- gene_count[gene_count$celltype==cell.types[i],]

        p<-ggplot(cell_count, aes_string(x=groups, y=feature, fill=groups))+

          geom_violin(scale = 'width', adjust = 1, trim = TRUE, size=0.3, alpha=0.5, color="pink")+

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

                legend.position = "none",

                axis.text.x = element_blank(),

                axis.ticks.x = element_blank(),

                axis.title.y = element_text(size = font.size, angle = 0),

                axis.text.y = element_text(size = (font.size-2)),

                plot.margin = unit(c(-0.5, 0, -0.5, 0), "cm") )

        if(add.dot){

          p = p + geom_quasirandom(size=pt.size, alpha=0.2)

        }

        plot_list[[i]]<-p

      }

      plot_list[[length(plot_list)]]<- plot_list[[length(plot_list)]] +

        theme(axis.text.x=element_text(angle = 45, hjust = 1, vjust = 1, size = font.size), axis.ticks.x = element_line())

      p<- patchwork::wrap_plots(plotlist = plot_list, ncol = 1)  + patchwork::plot_annotation(title = feature) & theme(plot.title = element_text(hjust = 0.5, size = (font.size +2)))

    }

    p

  } else {

    if(length(cell.types)==1){

      gene_count<-melt(gene_count[,c(feature, groups)], measure.vars  = groups)

      p<-ggplot(gene_count, aes_string(x="value", y=feature, fill="value"))+

        geom_violin(scale = 'width', adjust = 1, trim = TRUE, size=0.3, alpha=0.5, color="pink")+

        xlab("") + ylab("") + ggtitle(feature) +

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

              axis.title = element_text(size = font.size),

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

              axis.text.y = element_text(size=(font.size-2)),

              legend.title = element_blank(),

              legend.position = 'none',

              plot.title = element_text(size=(font.size+2), hjust = 0.5))+

        facet_wrap(~variable, scales = 'free_x')

      if(add.dot){

        p = p + geom_quasirandom(size=pt.size, alpha=0.2)

      }

      p

    } else {

      plot_list1<-list()

      plot_list2<-list()

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

        group=groups[i]

        cell_count<-gene_count[,c(feature, group, "celltype")]

        for(j in 1:length(cell.types)){

          cell_count2 <- cell_count[cell_count$celltype==cell.types[j],]

          p<-ggplot(cell_count2, aes_string(x=group, y=feature, fill=group))+

            geom_violin(scale = 'width', adjust = 1, trim = TRUE, size=0.3, alpha=0.5, color="pink")+

            xlab("") + ylab(cell.types[j]) +

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

                  legend.position = "none",

                  axis.text.x = element_blank(),

                  axis.ticks.x = element_blank(),

                  axis.title.y = element_text(size = font.size, angle = 0),

                  axis.text.y = element_text(size = (font.size-2)),

                  plot.margin = unit(c(-0.5, 0, -0.5, 0), "cm") )

          if(add.dot){

            p = p + geom_quasirandom(size=pt.size, alpha=0.2)

          }

          plot_list1[[j]]<-p

        }

        plot_list1[[length(plot_list1)]]<- plot_list1[[length(plot_list1)]] +

          theme(axis.text.x=element_text(angle = 45, hjust = 1, vjust = 1, size = font.size), axis.ticks.x = element_line(), axis.title.x = element_text(size=font.size))+ xlab(group)

        p2<-patchwork::wrap_plots(plotlist = plot_list1, ncol = 1)

        plot_list2[[i]]<-p2

        plot_list1<-list()

      }

      p<-patchwork::wrap_plots(plotlist = plot_list2) + patchwork::plot_annotation(title = feature) & theme(plot.title = element_text(hjust = 0.5, size = (font.size+2)))

      p

    }

  }

}

R/utils.R

+36 −0
Original line number Diff line number Diff line
@@ -327,3 +327,39 @@ data_processing <- function( 
  Sys.time()

}





#' A function to extract gene counts for ploting

#'

#' This function is a modified Seurat::FetchData function to extract gene 

#' counts and the associated meta data for ploting. It returns a dataframe

#' with the requested information from the Seurat object.

#'

#' @param seu A finished Seurat Object with cell type annotation in the active.ident slot

#' @param features Gene names to extract expression data

#' @param cell.types The cell types to be inspected. By default, it will incorporate all cell types.

#' @param data.type The data slot to be accessed. By default, the "data" slot will be used.

#' @param meta.groups The colnames in the meta.data slot you want to include.

#' @return A data frame with the requested info.

#' @export

#' 

extract_gene_count <- function(

  seu, 

  features, 

  cell.types=NULL, 

  data.type="data", 

  meta.groups=NULL

 ){

  if(is.null(cell.types)){

    cell.types=levels(seu)

  }

  seu@meta.data$celltype<-as.character(seu@active.ident)

  if(is.null(meta.groups)){

    meta.groups=colnames(seu@meta.data)

  }

  new_seu<-subset(seu, idents=cell.types)

  feature_count<-Seurat::FetchData(new_seu, slot = data.type, vars = c(features,meta.groups,"celltype"))

  umap_data<-data.frame(new_seu[["umap"]]@cell.embeddings)

  feature_count$UMAP1<-umap_data$UMAP_1

  feature_count$UMAP2<-umap_data$UMAP_2

  feature_count

}

man/complex_vlnplot_single.Rd

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+39 −0
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% Generated by roxygen2: do not edit by hand

% Please edit documentation in R/plot_violin.R

\name{complex_vlnplot_single}

\alias{complex_vlnplot_single}

\title{Violin plot for a single gene across groups}

\usage{

complex_vlnplot_single(

  seu,

  feature,

  cell.types = NULL,

  groups,

  add.dot = T,

  font.size = 14,

  pt.size = 0.1

)

}

\arguments{

\item{seu}{A complete Seurat object}



\item{feature}{Gene name. Only one gene is allowed.}



\item{cell.types}{Cell types of interest. By default, all cell types are included.}



\item{groups}{Groups to split the plot. Support multiple groups.}



\item{add.dot}{Whether or not to add points on the violins.}



\item{font.size}{Font size for the labels.}



\item{pt.size}{Point size for the data points on the violin}

}

\value{

A ggplot object

}

\description{

This function generates violin plot(s) to compare the expression of a single gene across

different groups or cell types. It is designed for visualizing a complicated scenario:

Gene expression on multiple cell types and multiple conditions.

}

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