In Tong-Chen/YSX: For Yishengxin Training

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Heatmap plot

Transfer a file to heatmap

library(YSX)
library(pheatmap)
library(RColorBrewer)
library(grid)
sp_pheatmap("exprTable.txt", xtics_angle = 45, cluster_rows = T, cluster_cols = T)

sp_pheatmap("exprTable.txt", xtics_angle = 45, cluster_rows = T, cluster_cols = T, saveppt = TRUE, filename = "pheatmap.pdf")

library(vegan)
sp_pheatmap("exprTable.txt", xtics_angle = 45, cluster_rows = T, cluster_cols = T, clustering_distance_cols = "binomial")

sp_pheatmap("exprTable.txt", xtics_angle = 45, cluster_rows = T, cluster_cols = T, clustering_distance_cols = "spearman")

Transfer a matrix to heatmap

exprTable <- read.table("exprTable.txt", sep="\t", row.names=1, header=T)
exprTable
sp_pheatmap(exprTable, manual_color_vector="Set2")

sp_pheatmap(exprTable, manual_color_vector=c("green","red"), scale="row")

sp_pheatmap(exprTable, cluster_rows = T, logv="log2", manual_color_vector="YlOrRd", clustering_distance_rows = "binary")

Heatmap add side color bars

exprTable <- read.table("exprTable.txt", sep="\t", row.names=1, header=T)
# Row annotation
# Can be a dataframe or a file
annotation_row = data.frame(Type=c("TF","TF","TF","TF","Enzyme","Enzyme"), row.names=rownames(exprTable))

# sp_writeTable(annotation_row, "exprTable.annorow.txt")

annotation_col = data.frame(Count=c(1,2,4,8,16,32), row.names=colnames(exprTable))

# sp_writeTable(annotation_col, "exprTable.annocol.txt")

sp_pheatmap(exprTable, xtics_angle = 90, cluster_rows = T, cluster_cols = T,
            annotation_row = annotation_row, annotation_col = annotation_col)

Reorder branches

exprTable <- read.table("exprTable.txt", sep="\t", row.names=1, header=T)
# Row annotation
# Can be a dataframe or a file
annotation_row = data.frame(Type=c("TF","TF","TF","TF","Enzyme","Enzyme"), Weight=c(10,20,30,40,50,60), row.names=rownames(exprTable))

# sp_writeTable(annotation_row, "exprTable.annorow.txt")

annotation_col = data.frame(Count=c(1,2,4,8,16,32), row.names=colnames(exprTable))

# sp_writeTable(annotation_col, "exprTable.annocol.txt")

sp_pheatmap(exprTable, xtics_angle = 90, cluster_rows = T, cluster_cols = T,
            annotation_row = annotation_row, annotation_col = annotation_col,
            cluster_cols_variable = "Count", cluster_rows_variable = "Weight",
            remove_cluster_cols_variable_in_annocol = FALSE,
            remove_cluster_rows_variable_in_annorow = T)

Heatmap changing colors of side color bars

annotation_colors <- list(Type=c(TF="red",Enzyme="green"), Count=c("grey","blue"))
sp_pheatmap(exprTable, xtics_angle = 90, cluster_rows = T, cluster_cols = T,
            annotation_row = annotation_row, annotation_col = annotation_col,
            manual_annotation_colors_sidebar = annotation_colors)

pheatmap(exprTable, cluster_rows = T, cluster_cols = T,
            annotation_row = annotation_row, annotation_col = annotation_col,
            annotation_colors = annotation_colors, clustering_distance_rows = "correlation",
         clustering_distance_cols = "correlation")

Heatmap set breaks

sp_pheatmap(exprTable, breaks="quantile", breaks_mid = 20)

sp_pheatmap(exprTable, breaks=c(0,5,10,20,40), manual_color_vector = "YlGnBu")

Flower plot

library(YSX)
library(plotrix)
library(RColorBrewer)

set_data = "Set.data"

flower_plot(set_data, item_variable = "Gene", set_variable = "Sample",saveplot = "flower.pdf",saveppt = TRUE)
flower_plot(set_data, group_color = c("Set2"), r = 1, item_variable = "Gene", set_variable = "Sample")
flower_plot(set_data, label_total_num_items = T, a = 1, item_variable = "Gene", set_variable = "Sample")
# flower_plot(set_data, saveplot="Set.data.flower.pdf")

Volcano plot

library(YSX)
library(ggplot2)
library(ggrepel)

set.seed(1)

res_output <- data.frame(log2FoldChange=rnorm(3000), row.names=paste0("YSX",1:3000))
res_output$padj <- 20 ^ (-1*(res_output$log2FoldChange^2))

padj = 0.05
log2FC = 1
res_output$level <- ifelse(res_output$padj<=padj,
                           ifelse(res_output$log2FoldChange>=log2FC,
                                  paste("groupA","UP"),
                                  ifelse(res_output$log2FoldChange<=(-1)*(log2FC),
                                         paste("groupB","UP"), "NoDiff")) , "NoDiff")
# sp_writeTable(res_output, file="volcano.txt", keep_rownames = T)

head(res_output)



sp_volcano_plot(data=res_output,
           log2fc_var="log2FoldChange",
           fdr_var="padj",
           status_col_var = 'level',
           filename = "1.pdf"
           )

sp_volcano_plot(data=res_output,
           log2fc_var="log2FoldChange",
           fdr_var="padj",
           significance_threshold = c(0.05, 1),
           point_color_vector = c("red","blue","black"),
           max_allowed_log10p = 10
           )

sp_volcano_plot(data=res_output,
           log2fc_var="log2FoldChange",
           fdr_var="padj",
           significance_threshold = c(0.05, 1),
           point_color_vector = c("red","blue","black"),
           coordinate_flip = TRUE
           )

# Generate one column containing genes to be labels with their symbol.
# One can also create this column easily using Excel.

label = c("Pou5f1","Gata2")
names(label) = c("YSX14","YSX4")
res_output$Symbol <- label[match(rownames(res_output), names(label))]
head(res_output)

sp_writeTable(res_output, file="volcano.txt", keep_rownames = T)


sp_volcano_plot(data=res_output,
           log2fc_var="log2FoldChange",
           fdr_var="padj",
           status_col_var = 'level',
           point_label_var = "Symbol"
           )

Venn diagram

library(YSX)
library(VennDiagram)
vennDiagram_data = "vennDiagram.data"
sp_vennDiagram(data = vennDiagram_data, header=T)

sp_vennDiagram(data = vennDiagram_data, label1 = "Set1",label2 = "Set2", label3="Set3", manual_color_vector = "Dark2")

sp_vennDiagram( supplyNumbers = TRUE,  numVector=c (120, 110, 50), labelVector=c('a','b'))

sp_vennDiagram(data = vennDiagram_data, label1 = "Set1",label2 = "Set2", manual_color_vector = c('red', 'green'))

sp_vennDiagram(data = vennDiagram_data, label1 = "Set1",label2 = "Set2", label3="Set3", label4="Set4")

library(YSX)
library(VennDiagram)
vennDiagram_data = "vennDiagram.data"

sp_vennDiagram2(data = vennDiagram_data, header=T, item_variable = "Gene", set_variable = "Sample")

sp_vennDiagram2(data = vennDiagram_data, header=T, item_variable = "Gene", set_variable = "Sample",saveppt = TRUE,filename = "venn2.pdf")

sp_vennDiagram2(data = vennDiagram_data, header=T, item_variable = "Gene", set_variable = "Sample",
               set_variable_order = c("Set1","Set2", "Set3"), manual_color_vector = "Dark2")

sp_vennDiagram( supplyNumbers = TRUE,  numVector=c (120, 110, 50), labelVector=c('a','b'))

sp_vennDiagram2(data = vennDiagram_data, item_variable = "Gene", set_variable = "Sample",
               set_variable_order = c("Set1","Set2"),
               manual_color_vector = c('red', 'green'))

sp_vennDiagram2(data = vennDiagram_data, 
                item_variable = "Gene", set_variable = "Sample",
               set_variable_order = c("Set1","Set2","Set3", "Set4"))

library(Vennerable)
vennDiagram_data = "vennDiagram.data"
sp_vennDiagram3(data=vennDiagram_data,
                             header = TRUE,
                             item_variable = "Gene",
                             set_variable = "Sample",
                             select_set_to_show = c("Set1","Set2","Set3"),
                             doWeights = TRUE,
                             type = "AWFE")
sp_vennDiagram3(data=vennDiagram_data,
                             header = TRUE,
                             item_variable = "Gene",
                             set_variable = "Sample",
                             doWeights = TRUE,
                             type = "squares")
sp_vennDiagram3(data=vennDiagram_data,
                             header = TRUE,
                             item_variable = "Gene",
                             set_variable = "Sample",
                             doWeights = TRUE,
                             type = "squares",
                             saveplot = "venn.pdf",
                             saveppt = TRUE)

UpSet View Plot

library(UpSetR)
library(reshape2)
library(YSX)
vennDiagram_data = "vennDiagram.data"
sp_upsetview(vennDiagram_data, vennFormat = 2)
sp_upsetview(vennDiagram_data, vennFormat = 2, saveppt = TRUE, saveplot = "upsetview.pdf")

sp_upsetview("upset.txt", vennFormat = 0, nintersects = 7,saveplot = "upset.pdf")

sp_upsetview("upset.wide.data", vennFormat = 0)
sp_upsetview("upset.wide.data", vennFormat = 0,nintersects = 4)
sp_upsetview("upset.wide.data", vennFormat = 0,order.by = "degree")
sp_upsetview("upset.wide.data", vennFormat = 0,decreasing = FALSE)
sp_upsetview("upset.wide.data", vennFormat = 0,scale.intersections = "log2")
sp_upsetview("upset.wide.data", vennFormat = 0,queries_bar1 = c("Samp1","Samp3"),queries_bar1_color = "#FF0000")
sp_upsetview("upset.wide.data", vennFormat = 0,scale.intersections = "log2",sets = c("Samp1","Samp2"))

sp_upsetview(vennDiagram_data, vennFormat = 2, width=10, height=4, saveplot = "upsetview2.pdf")

str(a)

manhattan2 plot

library(YSX)
library(ggplot2)
library(dplyr)

manhattan_data = "manhattan.data"

sp_manhattan2_plot(data=manhattan_data, ID_var='ID', FDR_var='FDR', title="test1", point_size=2, point_label_var = "Labels")

lines plot

Melted data with xvariable, yvariable, legend_variable specified

library(YSX)
library(ggplot2)
library(reshape2)
library(grid)

set.seed(131)
res_output <- data.frame(Pos=1:10,value =runif(20))
value=0.5
res_output$Group <- ifelse(res_output$value<=value,"groupA", "groupB")
head(res_output)
sp_lines(data=res_output, xvariable="Pos", melted=T, yvariable="value", legend_variable="Group",zoom_xlim = c(3,5),zoom_split =F)


library(ggforce)
sp_lines(data=res_output, xvariable="Pos", melted=T, yvariable="value", legend_variable="Group",zoom_xlim = c(3,5),zoom_split =F)

Melted data with xvariable, yvariable specified

sp_lines(data=res_output, xvariable="Pos", melted=T, yvariable="value")

Read in long format matrix file

lines_data_melted = "line.data"

sp_lines(data=lines_data_melted, xvariable = "Pos", yvariable = "value", legend_variable="Variable", melted = T)

Read in long format matrix file (smooth lines, manually set colors and xtics)

sp_lines(data=lines_data_melted, xvariable = "Pos", yvariable = "value", 
         legend_variable="Variable", melted = T, smooth_method = "auto",
         manual_color_vector = c("cyan","purple"), manual_xtics_pos = c(-5000,0,5000),
         manual_xtics_value = c("-5 kb","TSS","5 kb"), xintercept=c(-1000,1000), 
         custom_vline_anno=c("-1 kb","1 kb"))

Specify line_size using a number

sp_lines(data="exprTable.txt", manual_color_vector = "Set2", alpha=1, line_size = 1)

Transform data when necessary

exprTable <- read.table("exprTable.txt", row.names=1, header=T, sep="\t")
exprTable <- as.data.frame(t(exprTable))
sp_lines(data=exprTable, manual_color_vector = "Set2", alpha=1, line_size = 1)

Log transfer Y-axis

exprTable <- read.table("exprTable.txt", row.names=1, header=T, sep="\t")
exprTable <- as.data.frame(t(exprTable))
sp_lines(data=exprTable, manual_color_vector = "Set2", alpha=1, line_size = 1, yaxis_scale_mode = "log2")

exprTable <- read.table("exprTable.txt", row.names=1, header=T, sep="\t")
exprTable <- as.data.frame(t(exprTable))
sp_lines(data=exprTable, manual_color_vector = "Set2", alpha=1, line_size = 1, yaxis_scale_mode = "scale_y_log10()")

Specify line size using one column

sp_lines(data=exprTable, manual_color_vector = "Set2", alpha=1, line_size = "value")

sp_lines(data=exprTable, manual_color_vector = "Set2", alpha=1, line_size = "value", coordinate_flip = T)

Bar plot

library(YSX)
library(ggplot2)
library(RColorBrewer)

bar_test_data <- data.frame(ID = letters[1:4],Gene = letters[c(8,8,9,9,10,10,11,11)], Exper = runif(16))
sp_barplot(data = bar_test_data, xvariable = "ID", yvariable = "Exper", color_variable = "Gene")




bar_data = "bar.data"

sp_barplot(data = bar_data, xvariable = "ID", yvariable = "Exper", color_variable = "Gene",height=12.36,width=20,filename = "bar.pdf")


library(eoffice)
sp_barplot(data = bar_data, xvariable = "ID", yvariable = "Exper", color_variable = "Gene",height=12.36,width=20,filename = "bar.pdf",saveppt = T  )


library(htmlwidgets)
library(plotly)
sp_barplot(data = bar_data, xvariable = "ID", yvariable = "Exper", color_variable = "Gene",height=12.36,width=20,filename = "bar.pdf",savehtml = T  )

library(tidyr)
library(dplyr)
bar_data = "bar.txt"
sp_barplot(data = bar_data, melted =TRUE,xvariable= "ID",color_variable = "Gene", yvariable = "Expression",add_bar_link = T)

sp_barplot(data = bar_data, melted =TRUE,xvariable= "ID",color_variable = "Gene", yvariable = "Expression",add_bar_link = T, bar_mode = "fill")

sp_barplot(data = bar_data, melted =TRUE,xvariable= "ID",color_variable = "Gene", yvariable = "Expression",add_bar_link = T, bar_mode = "stack",xvariable_order = c("2_cell","4_cell"))

sp_barplot(data = bar_data, melted =TRUE,xvariable= "ID",color_variable = "Gene", yvariable = "Expression",add_bar_link = T, bar_mode = "stack",xvariable_order = c("2_cell","4_cell"),color_variable_order = c("Tet1","Tet3"))

sp_barplot(data = bar_data, melted =TRUE,xvariable= "ID",color_variable = "Gene", yvariable = "Expression",add_bar_link = T, bar_mode = "fill",xvariable_order = c("2_cell","4_cell"),color_variable_order = c("Tet1","Tet3","Pou5f1","Sox2"))

box_demo1_data <- "exprTable.txt"
sp_barplot(data = box_demo1_data, melted = F)
sp_barplot(data = box_demo1_data, melted = F, ylim = c(0,20))
sp_barplot(data = box_demo1_data, melted = F, ylim = c(20,40))

library(dplyr)
box_demo2_data <- "exprTable.txt"
sp_barplot(data = box_demo2_data, melted = F, bar_mode="fill", add_text=T)

random_v <- c(rnorm(10, mean=1, sd=0.1), rnorm(10, mean=5), rnorm(20, mean=10),
               rnorm(20, mean=20), rnorm(20, mean=2, sd=0.2),
               rnorm(10, mean=20), rnorm(10, mean=1, sd=0.01), rnorm(20, mean=2),
               rnorm(20, mean=2), rnorm(20, mean=3))
data <- data.frame(Gene=c(paste0('SOX', rep(2,80)), paste0('SOX', rep(3,80))),
                    Expr=random_v, Cluster=rep(c(rep("C1",10), rep("C2",10),rep("C3",20),rep("C4",20), rep("C5",20)),2))

data[data["Expr"]<0,"Expr"] = 0

sp_writeTable(data, file="boxplot_singlecell.txt", keep_rownames = F)

library(dplyr)
library(YSX)
library(ggplot2)
library(RColorBrewer)
library(ggbeeswarm)

box_demo3_data <- "boxplot_singlecell.txt"
sp_barplot(data = box_demo3_data, melted = T, 
           xvariable="Gene", yvariable="Expr", 
           color_variable = "Cluster",
           #bar_mode = "stack",
           add_point = F,
           group_variable = c("Gene","Cluster"),
           add_text = F)

library(dplyr)
library(YSX)
library(ggplot2)
library(RColorBrewer)
library(ggbeeswarm)

box_demo4_data <- "barplot_demo4.txt"
p <- sp_barplot(data = box_demo4_data, melted = T, 
           xvariable="Gene", yvariable="Mean_value", 
           color_variable = "Cluster",
           bar_mode = "stack",
           error_bar_variable = "Standard_deviation",
           add_text = F)

Box plot

Simple box-plot with one input file

library(ggplot2)
library(reshape2)
library(scales)
library(ggbeeswarm)
library(multcompView)
library(dplyr)


box_data = "box.data"
sp_boxplot(data = box_data, melted=T, xvariable = "Gene", yvariable = "Expr", legend_variable="Group", statistics = T, violin_nb = F, violin=T, jitter_bp = T)

sp_boxplot(data = box_data, melted=T, xvariable = "Gene", yvariable = "Expr", legend_variable="Group", manual_color_vector = "Set3", violin = T, coordinate_flip = T)

Test for digital xvariable and digital legend_variable

set.seed(3)
box_test_data <- data.frame(Gene = 1:4, Expr = runif(16), Group=1:4)
head(box_test_data)
sp_boxplot(data = box_test_data, melted=T, xvariable = "Gene", yvariable = "Expr",  manual_color_vector = c("green","yellow","red"), jitter_bp = T, statistics = T, violin = T)

random_v <- c(rnorm(10, mean=1, sd=0.1), rnorm(10, mean=5), rnorm(20, mean=10),
              rnorm(10, mean=10), rnorm(10, mean=0.2, sd=0.01), rnorm(20, mean=1),  rnorm(20, mean=1),  rnorm(20, mean=1), rnorm(20, mean=2,sd=0.5), rnorm(20, mean=2,sd=0.5))
data <- data.frame(Gene=c(paste0('SOX', rep(2,80)), paste0('SOX', rep(3,80))),
                   Expr=random_v, Cluster=rep(c(rep(1,10), rep(2,10),rep(3,20),rep(4,20),rep(5,20)),2))


sp_boxplot(data, melted = T, xvariable = "Cluster", yvariable = "Expr", facet_variable="Gene", 
           facet_ncol=1, facet_scales="free_y", facet_singlecell_style = T, violin = T)

Different types of color assignment

sp_boxplot(data = box_test_data, melted=T, xvariable = "Gene", yvariable = "Expr", legend_variable="Group", manual_color_vector = c("green","red"), violin_nb = T, statistics=T)

box_test_data2 <- box_test_data[box_test_data$Gene %in% c(1,2),]
sp_boxplot(data = box_test_data2, melted=T, xvariable = "Gene", yvariable = "Expr", legend_variable="Group", manual_color_vector = c("green","red"), violin_nb = T, statistics=T)

Test for unmelted data

exprTableWithReps = "exprTableWithReps.txt"
metadata = "metadata.txt"
sp_boxplot(data = exprTableWithReps, melted=F, metadata = metadata,
           legend_variable = "Group", statistics = T)

#test ylim
sp_boxplot(data = exprTableWithReps, melted=F, metadata = metadata,
           legend_variable = "Class",ylim= c(0,8))

单细胞Marker基因小提琴图展示

data = "boxplot_singlecell.txt"
sp_boxplot(data, melted = T, xvariable = "Cluster", yvariable = "Expr", facet_variable="Gene",
           facet_ncol=1, facet_scales="free_y", facet_singlecell_style = T, violin = T)

Enrichment plot

library(YSX)
library(stringr)
enrichment_data <- "enrichment.data"

sp_enrichment(data = enrichment_data, xvariable = "SampleGroup", 
              yvariable = "Description", color_variable = "Qvalue", 
              log10_transform_variable="Qvalue", size_variable = "Count")

enrichment.data <- sp_readTable(enrichment_data) 
head(enrichment.data)
sp_enrichment(data = enrichment.data, xvariable = "GeneRatio", yvariable = "Description",
              log10_transform_variable = "Qvalue", sqrt_transform_variable = "Count",
              shape_variable = "SampleGroup")

library(YSX)
enrichment_data <- "goeast.enrich.txt"
enrichment.data <- sp_readTable(enrichment_data) 
head(enrichment.data)
flower_plot()
p <- sp_enrichment(data = enrichment.data, xvariable = "log_odds_ratio", 
              yvariable = "Term", color_variable = "p", 
              log10_transform_variable="p", size_variable = "q",
              sqrt_transform_variable = "q",
              shape_variable = "Ontology")

Histogram plot

library(YSX)
library(ggplot2)
library(reshape2)
library(grid)
library(dplyr)



# demo1

sp_histogram(data = "histogram.demo1.txt", xvariable = "weight", melted=T,
             group_variable = "sex", plot_type = "both", 
             yaxis_statistics  = "density", fill_area = T, add_mean_value_vline = T)

Demo2

sp_histogram(data = "histogram.demo1.txt", xvariable = "weight", melted=T,
             group_variable = "sex", plot_type = "both", 
             yaxis_statistics  = "density", fill_area = T, 
             add_mean_value_vline = T,
             facet_variable = "sex")

Demo3

histogram_demo3 = "exprMat.txt"
sp_histogram(data = histogram_demo3,melted=F,
             plot_type = "line", 
             yaxis_statistics  = "density", add_mean_value_vline = F)

pca plot

library(plyr)
library(ggplot2)
library(grid)
library(data.table, quietly=T)
library(ggfortify)
library(ggrepel)

pca_test_data <- matrix(runif(3000,0,100000),ncol=6)
colnames(pca_test_data) <- c(paste("wt",1:3,sep = ""),paste("ko",1:3,sep = ""))
rownames(pca_test_data) <- c(ID = paste0("ENSG",c(1:500)))
pca_data <- as.data.frame(pca_test_data)
sp_pca(data = pca_data, grp_file = NULL) 


data = "pca.data"
group_data = "pca_group.data"
sp_pca(data = data, grp_file = group_data, color="Conditions", size = "Diameters", shape = "Batch", label = TRUE) 


sp_pca(data = data, grp_file = group_data, color="Conditions", size = "Diameters", shape = "Batch", label = FALSE, dimensions = 3)  

pcoa plot

suppressMessages(library("optparse"))
suppressMessages(library("reshape2"))
suppressMessages(library("ggplot2"))
suppressMessages(library("vegan"))
suppressMessages(library("digest"))
suppressMessages(library("ggrepel"))
suppressMessages(library("ggpubr"))

pcoa_data = "pcoa.data"
group_pcoa_data = "group_pcoa.data"
sp_pcoa(data=pcoa_data,grp_file = group_pcoa_data, color = "genotype") 

scatter plot

library(plyr)
library(ggplot2)
library(grid)
library(ggbeeswarm)
library(ggrepel)
library(YSX)

scatter_test_data <- data.frame(Samp = letters[1:6], 
                                Color = sample(c("group1", "group2", "group3"),6,replace = TRUE),
X_val = runif(6), Y_val = runif(6), Size = sample(4:20, size = 6),
Shape = sample(c("cluster1","cluster2"),6,replace = TRUE))

sp_scatterplot(data=scatter_test_data,xvariable = "X_val",yvariable = "Y_val",
color_variable = "Color", shape_variable= "Shape",
size_variable = "Size",label_variable="Samp",Jitter = TRUE)

Demo 1

scatter_data = "scatter_demo1.txt"

sp_scatterplot(data = scatter_data, xvariable = "Gene", yvariable = "Cluster", color_variable = "Expr", size_variable = "Percent", label_variable = "Expr", manual_color_vector = c("#95DF6D","#E78D6C"),height=12.36,width=20,filename="scatter.pdf")

Demo 2

scatter_data = "scatter_demo2.txt"
sp_scatterplot(data=scatter_data, xvariable = "X_variable", yvariable = "Y_variable",
color_variable = "Color", shape_variable= "Shape",
size_variable = "Size",label_variable="Samp",Jitter = F)

sp_scatterplot(data="scatter3.txt", xvariable = "eruptions", yvariable = "waiting",
smooth_method = "lm",line_size=1)

scatter_data = "scatter.txt"

sp_scatterplot(data = scatter_data, xvariable = "X_val", yvariable = "Y_val", 
color_variable = "Color", shape_variable = "Shape", size_variable = "Size",
label = "Samp", xvariable_order = c(1,3,2), yvariable_order = c(2,1,3),
color_variable_order = c("grp2","grp1","grp3"),
shape_variable_order = c("cluster2","cluster1"), label_font_size = 2)

sp_scatterplot(data = scatter_data, xvariable = "X_val", yvariable = "Y_val", color_variable = "Color", shape_variable = "Shape", size_variable = "Size", label_variable = "Samp", Jitter = TRUE)

sp_scatterplot(data = scatter_data, xvariable = "X_val", yvariable = "Y_val", color_variable = "Color", shape_variable = "Shape", size_variable = "Size",label_variable = "Samp", Jitter = TRUE, facet = "Color", scales = "free_y")

scatter_data = "box.data"
sp_scatterplot(data = scatter_data, xvariable = "Gene", yvariable = "Group", color_variable = "Expr", size_variable = "Expr", label_variable = "Expr")

Tree plot

Simple Tree

library(ggtree)
library(ggplot2)
library(YSX)
library(treeio)

treefile <- "iqtree.treefile"
sp_tree_plot(treefile,debug=T)

Color branches using node attributes.

tree_attrib <- "tree.attribute"
tree_msa = "iqtree.aligned.fa"
sp_tree_plot(treefile,
           tree_type = 'iqtree',
           tree_attrib = tree_attrib,
           tree_msa = NULL,
           color_branches = "Spe",
           layout = "fan",
           ladderize = F,
           branch.length = "none",
           tip_text = "Name",
           tip_text_size = 3,
           bootstrap = TRUE,
           bootstrap_variable = NULL,
           legend.position = "bottom",
           bootstrap_size = 3,
           bootstrap_color = 'red')

tree_msa = "iqtree.aligned.fa"
sp_tree_plot(treefile,
           tree_type = 'iqtree',
           tree_attrib = tree_attrib,
           tree_msa = tree_msa,
           layout = "circular",
           ladderize = F,
           branch.length = "none",
           tip_text = 'label',
           tip_text_size = 3,
           bootstrap = TRUE,
           bootstrap_variable = NULL,
           legend.position = "bottom",
           bootstrap_size = 3,
           bootstrap_color = 'red')

Some codes for generating test data

Gene expression table 3 gene x 20 samples (each with 10 replicates)

expr_matrix <- as.data.frame(round(matrix(c(runif(10, min=1, max=5),
                                      runif(10, min=3, max=7),
                                      runif(10, min=5, max=9),
                                      runif(10, min=1, max=5),
                                      runif(10, min=3, max=7),
                                      runif(10, min=5, max=9)), 
                                    nrow=3, byrow=T),2))
rownames(expr_matrix) <- paste0("Gene_", letters[1:3])
colnames(expr_matrix) <- paste(rep(c("SampleA","SampleB"), each=10),1:20,sep="_")
sp_writeTable(expr_matrix, file="exprTableWithReps.txt")

Metadata table with 20 samples and two attributes

sampleGroup = data.frame(Sample=paste(rep(c("SampleA","SampleB"), each=10),1:20,sep="_"), Group=rep(c("GroupA","GroupB"), each=10), Class=rep(c("ConditionC","ConditionD"), each=5))
sp_writeTable(sampleGroup, file="metadata.txt", keep_rownames = F)

set.seed(1234)
df2 <- data.frame(
  sex=factor(rep(c("F", "M"), each=200)),
  weight=round(c(rnorm(200, mean=55, sd=5), rnorm(200, mean=65, sd=5)))
  )
head(df2)

sp_writeTable(df2,"histogram.demo1.txt", keep_rownames =F)

raincloud

library(dplyr)
library(YSX)
library(grid)
library(tidyverse)
library(readr)
library(tidyr)
library(ggplot2)
library(Hmisc)
library(plyr)
library(RColorBrewer)
library(reshape2)
data <- data.frame(Value = rnorm(300),
                   Repeat = rep(paste("Repeat", 1:3, sep = "_"), 100),
                   Condition = rep(c("Control", "Test"), 150))

sp_raincloud(data = data,xvariable = "Condition", yvariable = "Value")

data = "exprTable.txt"
sp_raincloud(data= data, melted = FALSE,position_nudge_jitter_size = 0.1 )

hclust

library(WGCNA)
x = runif(10)
y = runif(10)
data = cbind(x,y)
rownames(data) = paste("Name",1:10,"")
sp_hclust(data =data)

inflectionpoint

data="inflectionpoint.txt"
sp_inflectionpoint(data=data,which_col = 2,keep_point = "little")

Eulerr plot

library(eulerr)
library(YSX)

data = "Set.data"
sp_EulerDiagrams(data = data, format = "items", # items or counts
                              item_variable = "Gene",
                              set_variable = "Sample",
                 shape="circle",manual_color_vector = c("red","green","blue","yellow","purple"))

library(eulerr)
library(YSX)

data = "Euler.txt"

sp_EulerDiagrams(data = data , format = "counts", # items or counts
                 type = "percent",
                              intersection_variable = "Intersection",
                              count_variable = "Count",
                 manual_color_vector = c("red","green","blue","yellow","purple"))

Tong-Chen/YSX documentation built on Jan. 25, 2021, 2:49 a.m.