More methods

export(complex_dotplot_multiple)

export(complex_dotplot_single)

export(complex_featureplot)

export(complex_heatmap_group)

export(complex_vlnplot_single)

export(convert_geneid)

export(creat_cellphonedb_file)

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

# Functions

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

#' Plot gene expression across groups using ComplexHeatmap

#'

#' This function is for identifying the group-specific genes in a selected celltype

#' and plot the expression of those genes in heatmap.

#'

#' @param seu_obj A complete Seurat object

#' @param celltype Cell types selected for gene plot.

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

#' @param gene_highlight Gene names showing on the rows. Default: all genes

#' @param logfc Fold change to select the genes

#' @param return_marker If TRUE, a list of specific gene will be returned.

#' @param col_fun Heatmap color key.

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

#' @export

complex_heatmap_group<-function(

  seu_obj,

  celltype,

  group,

  gene_highlight=NULL,

  logfc=0.5,

  return_marker=FALSE,

  col_fun = colorRamp2(c(-2, -1, 0, 1, 2), rev(c("#BF0080", "#CE6EAE", "#dddddd", "#6EAE6E", "#008000")))

){

cell1<-subset(seu_obj, idents=celltype)

cell1<-SetIdent(cell1, value = group)

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

cell1_avg<-AverageExpression(cell1, verbose = F, return.seurat = F, assays = "RNA")

cell1_avg<-cell1_avg$RNA

cell1_avg<-data.frame(cell1_avg)

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,]

unique_list1<-list()

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

  group1<-all_markers1[all_markers1$cluster==group_levels[i],]

  group2<-all_markers1[all_markers1$cluster!=group_levels[i],]

  group1_unique<-setdiff(group1$gene, intersect(group1$gene, group2$gene))

  unique_list1[[i]]<-group1_unique

}

unique_list2<-list()

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

  group1<-all_markers2[all_markers2$cluster==group_levels[i],]

  group2<-all_markers2[all_markers2$cluster!=group_levels[i],]

  group1_unique<-setdiff(group1$gene, intersect(group1$gene, group2$gene))

  unique_list2[[i]]<-group1_unique

}

unique_list<-c(unique_list1,unique_list2)

unique_genes<-as.character(unlist(unique_list))

data_plot<-cell1_avg[unique_genes,]

gene_num<-c()

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

  gene_num[i]<-length(unique_list[[i]])

}

unique_list<-unique_list[which(gene_num!=0)]

col_split<-group_levels

col_split<-factor(col_split, levels = group_levels)

gene_groups<-c(paste0(group_levels, "_up"),paste0(group_levels, "_down"))

gene_groups<-gene_groups[which(gene_num!=0)]

row_split<-list()

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

  row_split[[i]]<-rep(gene_groups[i], length(unique_list[[i]]))

}

row_split<-as.character(unlist(row_split))

row_split<-factor(row_split, levels = gene_groups)

term = list()

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

  txt1<-as.character(unlist(strsplit(gene_groups[i], split = "_")))

  term[[i]]<-data.frame(txt=txt1, index=c(1,2))

}

names(term) = gene_groups

data_plot<-t(scale(t(data_plot)))

label1<-gene_highlight

if(is.null(label1)){

  label1=rownames(data_plot)

}

ht_opt$message = FALSE

ht<- Heatmap(data_plot,name = "mat", cluster_rows = F, cluster_columns = F, 

             column_title = NULL,col = col_fun, row_title = NULL,

             cluster_row_slices = FALSE, cluster_column_slices = FALSE,

             column_split  = col_split, row_split = row_split,

             column_gap = unit(0, "mm"), row_gap = unit(0, "mm"), 

             heatmap_legend_param = list(direction = "horizontal",title = celltype)

) +

  rowAnnotation(link = anno_mark(at = match(label1,unique_genes), which = 'row',

                                 labels = label1, 

                                 labels_gp = gpar(fontsize = 10), padding = unit(1, "mm"))) +

  rowAnnotation(wc = anno_word_cloud(row_split, term, add_new_line = TRUE,

                                     value_range = c(1, 2), fontsize_range = c(12, 12)) ) 

ht=draw(ht,heatmap_legend_side = "top")

down_groups<-sum(grepl("down", gene_groups))

gaps<-c(1:length(group_levels), 1:length(group_levels))

gaps<-gaps[which(gene_num!=0)]

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

  if(i<=down_groups){

    decorate_heatmap_body("mat", row_slice = i, column_slice = 1, code = {

      grid.rect(unit(gaps[i]-1, "npc"), unit(1, "npc"), 

                width = 1 * unit(1, "npc"),

                height = 1 * unit(1, "npc"),

                gp = gpar(lwd = 2, lty = 1, fill=NA, col='black'), just = c("left", "top") 

      )

    }

    )

  } else {

    decorate_heatmap_body("mat", row_slice = i, column_slice = 1, code = {

      grid.rect(unit(gaps[i]-1, "npc"), unit(1, "npc"), 

                width = 1 * unit(1, "npc"),

                height = 1 * unit(1, "npc"),

                gp = gpar(lwd = 2, lty = 1, fill=NA, col='blue'), just = c("left", "top") 

      )

    }

    )

  }

}

if(return_marker){

  names(unique_list)<-gene_groups

  return(unique_list)

}

}

plot1cell R package can be easily installed from Github using devtools. Please make sure you have installed Seurat 4.0, circlize and ComplexHeatmap packages.

```

devtools::install_github("TheHumphreysLab/plot1cell")

## or devtools::install_github("HaojiaWu/plot1cell")

## or the development version, devtools::install_github("HaojiaWu/plot1cell")

```

## Usage

```

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

#### One genes/multiple groups violin plot:

#### One gene/multiple groups violin plot:

```

png(filename =  'vlnplot_multiple.png', width = 8, height = 6,units = 'in', res = 300)

complex_vlnplot_single(iri.integrated, feature = "Havcr1", groups = c("Group","Replicates"),celltypes   = c("PTS1" ,   "PTS2"  ,  "PTS3"  ,  "NewPT1" , "NewPT2"))

```

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

### 5. ComplexHeatmap to show unique genes across groups

plot1cell can directly identify the condition specific genes in a selected cell type and plot those genes using ComplexHeatmap. An example is shown below:

```

iri.integrated$Group2<-mapvalues(iri.integrated$Group, from = c("Control", "4hours",  "12hours", "2days",   "14days" , "6weeks" ),

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"))

dev.off()

```

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

### 6. Other ploting functions

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

% Generated by roxygen2: do not edit by hand

% Please edit documentation in R/plot_heatmap.R

\name{complex_heatmap_group}

\alias{complex_heatmap_group}

\title{Plot gene expression across groups using ComplexHeatmap}

\usage{

complex_heatmap_group(

  seu_obj,

  celltype,

  group,

  gene_highlight = NULL,

  logfc = 0.5,

  return_marker = FALSE,

  col_fun = colorRamp2(c(-2, -1, 0, 1, 2), rev(c("#BF0080", "#CE6EAE", "#dddddd",

    "#6EAE6E", "#008000")))

)

}

\arguments{

\item{seu_obj}{A complete Seurat object}

\item{celltype}{Cell types selected for gene plot.}

\item{group}{The group to show on x axis. One of the column names in meta.data.}

\item{gene_highlight}{Gene names showing on the rows. Default: all genes}

\item{logfc}{Fold change to select the genes}

\item{return_marker}{If TRUE, a list of specific gene will be returned.}

\item{col_fun}{Heatmap color key.}

}

\value{

A ComplexHeatmap object or/and a gene list

}

\description{

This function is for identifying the group-specific genes in a selected celltype

and plot the expression of those genes in heatmap.

}