gene_x 0 like s 706 view s
Tags: plot, packages
#TODOs: next week
#- try install ggplot2_3d
#- try install kaleido
TODO: using python to generate the 3D plot! https://pypi.org/project/plotly/
# -- before pca --
#png("pca.png", 1200, 800)
svg("pca.svg")
plotPCA(rld, intgroup=c("replicates"))
#plotPCA(rld, intgroup = c("replicates", "batch"))
#plotPCA(rld, intgroup = c("replicates", "ids"))
#plotPCA(rld, "batch")
dev.off()
#TODO:adding label in the figure, change Donor I in blue and donor II in orange
#https://loading.io/color/feature/Paired-12/
svg("pca2.svg")
#plotPCA(rld, intgroup=c("replicates"))
#plotPCA(rld, intgroup = c("replicates", "batch"))
plotPCA(rld, intgroup = c("donor"))
#plotPCA(rld, "batch")
dev.off()
#TODO: adding label in the figure
svg("pca3.svg")
plotPCA(rld, intgroup=c("replicates2"))
dev.off()
#https://loading.io/color/feature/Paired-12/
#https://support.bioconductor.org/p/66404/
# -- calculate PC3 from rld --
library(genefilter)
ntop <- 500
rv <- rowVars(assay(rld))
select <- order(rv, decreasing = TRUE)[seq_len(min(ntop, length(rv)))]
mat <- t( assay(rld)[select, ] )
pc <- prcomp(mat)
pc$x[,1:3]
#To my case:
mat <- t( assay(rld) )
pc <- prcomp(mat)
#pca <- yourFavoritePCA( mat )
pc$x[,1:3]
# PC1 PC2 PC3
#untreated DI -27.5379705 1.4478299 -6.389731
#untreated DII -28.3320463 0.6794066 2.073768
#mCh d3 DII 2.8988953 -6.4372647 10.252829
#sT d3 DII 5.1869876 2.6116282 13.816117
#mCh d8 DII -20.8047275 1.0708861 3.394721
#sT d8 DII -4.5144119 19.6230473 8.357902
#mCh d3 DI -4.5690693 -8.8938297 -7.391567
#sT d3 DI -7.6326832 5.3781061 2.214181
#mCh d8 DI 0.8536828 -5.0593045 -13.325567
#sT d8 DI 1.9232111 24.8795741 -4.162946
#GFP d3 DII -12.5042914 -3.3424106 15.207755
#LTtr d3 DII 5.2309178 -9.6124712 8.328132
#GFP d8 DII -13.0652347 -8.2058086 15.078469
#LTtr d8 DII 13.0678654 -2.0677676 9.188943
#GFP d3 DI -13.9999251 -1.4988226 -3.335085
#LTtr d3 DI 2.6090782 -9.5753559 -10.022324
#GFP d8 DI -12.4430571 -6.0670545 -14.725450
#LTtr d8 DI 8.9794396 3.4918629 -14.410118
#LT d8 DII 18.8388058 0.2459081 2.334700
#LT d8 DI 15.2986278 -0.6055500 -11.034778
#GFP+mCh d9/12 DI -17.3162152 3.2939931 -6.917358
#sT+LTtr d9/12 DI 6.8517730 17.9282911 -6.209778
#GFP+mCh d9/12 DII 2.0874834 -6.7379107 8.810602
#sT+LTtr d9/12 DII 19.3883422 19.6033774 4.314808
#LT d3 DI 6.5376031 -8.5766236 -6.500155
#LT d3 DII 17.8400725 -11.7362896 1.117396
#sT+LT d3 DI 16.6029944 -7.7951798 -5.593658
#sT+LT d3 DII 18.5238521 -4.0422674 5.528193
# vs.
#data
# PC1 PC2 group condition donor name
#untreated DI -27.537970 1.4478299 untreated:DI untreated DI untreated DI
#untreated DII -28.332046 0.6794066 untreated:DII untreated DII untreated DII
#mCh d3 DII 2.898895 -6.4372647 mCh d3:DII mCh d3 DII mCh d3 DII
# -- construct a data structure (merged_df) as above with data and pc --
library(ggplot2)
data <- plotPCA(rld, intgroup=c("condition", "donor"), returnData=TRUE)
#calculate all PCs including PC3 with the following codes
library(genefilter)
ntop <- 500
rv <- rowVars(assay(rld))
select <- order(rv, decreasing = TRUE)[seq_len(min(ntop, length(rv)))]
mat <- t( assay(rld)[select, ] )
pc <- prcomp(mat)
pc$x[,1:3]
df_pc <- data.frame(pc$x[,1:3])
identical(rownames(data), rownames(df_pc)) #-->TRUE
## define the desired order of row names
#desired_order <- rownames(data)
## sort the data frame by the desired order of row names
#df <- df[match(desired_order, rownames(df_pc)), ]
data$PC1 <- NULL
data$PC2 <- NULL
merged_df <- merge(data, df_pc, by = "row.names")
#merged_df <- merged_df[, -1]
row.names(merged_df) <- merged_df$Row.names
merged_df$Row.names <- NULL # remove the "name" column
merged_df$name <- NULL
merged_df <- merged_df[, c("PC1","PC2","PC3","group","condition","donor")]
# -- draw 3D with merged_df using plot3D --
#https://stackoverflow.com/questions/45052188/how-to-plot-3d-scatter-diagram-using-ggplot
devtools::install_github("AckerDWM/gg3D")
library("gg3D")
png("pca10.png",800,800)
#svg("pca10.svg",10,10)
#methods(class = "prcomp")
#summary(pc) #--> Proportion of Variance 0.3647 0.1731 0.1515
#percentVar <- round(100 * attr(data, "percentVar"))
percentVar <- c(36,17,15)
#scatterplot3d
#Unfortunately, ggplot does not support 3D plotting. It is designed for creating 2D plots and visualizations in R. However, there are other packages available in R for creating 3D plots, such as plot3D, scatterplot3d, and rgl. These packages can be used to create 3D scatter plots, surface plots, and more complex 3D visualizations. You can install and load these packages in R using the following commands:
install.packages("plot3D")
library(plot3D)
install.packages("scatterplot3d")
library(scatterplot3d)
install.packages("rgl")
library(rgl)
#Once you have loaded these packages, you can create 3D plots using their respective functions. For example, you can create a 3D scatter plot using the plot3D package with the following code:
#https://plotly.com/r/3d-scatter-plots/
library(plotly)
data(mtcars)
png("xxx.png", 1200, 800)
plot_ly(mtcars, x = ~mpg, y = ~wt, z = ~qsec, type = "scatter3d", mode = "markers")
dev.off()
# zlab(paste0("PC3: ",percentVar[3],"% variance")) +
#scatterplot3d(merged_df[,c("PC1","PC2","PC3")], pch=16, color="blue", main="3D Scatter Plot")
# labs(x = "PC1", y = "PC2", z = "PC3") +
#axes_3D() + stat_3D() +
colors = "Set1",
#marker = list(symbol = ~shapes[group])
#using the corresponding keywords ("square", "triangle-up", "diamond", etc.).
labs <- list(x = paste0("PC1: ",percentVar[1],"% variance"), y = paste0("PC2: ",percentVar[2],"% variance"), z = paste0("PC3: ",percentVar[3],"% variance"))
#ggplot(merged_df, aes(x=PC1, y=PC2, z=PC3, color=condition, shape=donor)) +
#https://stackoverflow.com/questions/75452609/update-color-in-different-marker-in-plotly-r
dt <- iris
dt$shape_1 <- c("Yes","No")
dt$color_1 <- c("Medium","Large","Small")
library(plotly)
library(kaleido)
fig <- plot_ly(dt,
x=1:nrow(iris),
y=~Sepal.Length,
type="scatter",
mode='markers',
color=~Species,
colors = c("#4477AA","#DDCC77","#CC6677"),
symbol = ~shape_1,
symbols = c("triangle-up", "circle"),
size = 20) %>% add_surface()
# save the chart as an SVG file using Kaleido
kaleido(fig, file = "chart.svg", format = "svg", width = 800, height = 600,
scale = 2, output_options = list(bg = "white"))
# inherit = F,
# size = ~Sepal.Width,
# sizes = c(10, 100) * 10)
plot_ly(dt,
+ x=1:nrow(iris),
+ y=~Sepal.Length,
+ type="scatter",
+ mode='markers',
+ color=~Species,
+ colors = c("#4477AA","#DDCC77","#CC6677"),
+ size = ~Sepal.Width,
+ symbol = ~shape_1,
+ symbols = c("triangle-up", "circle"),
+ inherit = F,
+ sizes = c(10, 100) * 10))
%>%
+ add_trace(type="scatter",
+ mode = "text",
+ text=~Sepal.Width,
+ textposition = "top right",
+ color = ~color_1,
+ colors = c("black","green","blue"),
+ textfont = list(size = 10)
+ )
factors(merged_df$condition)
"GFP d3" "GFP d8" "GFP+mCh d9/12" "LT d3"
[5] "LT d8" "LTtr d3" "LTtr d8" "mCh d3"
[9] "mCh d8" "sT d3" "sT d8" "sT+LT d3"
[13] "sT+LTtr d9/12" "untreated"
merged_df$condition <- factor(merged_df$condition, levels=c("untreated","mCh d3","mCh d8","GFP+mCh d9/12","GFP d3","GFP d8","sT d3","sT d8","LT d3","LT d8","LTtr d3","LTtr d8","sT+LT d3","sT+LTtr d9/12"))
merged_df$donor <- as.character(merged_df$donor)
# Define a list of shapes for each group
shapes <- list("circle", "triangle-up")
plot_ly(merged_df, x=~PC1, y=~PC2, z=~PC3, type = "scatter3d", mode = "markers", color=~condition, colors = c("grey","#a6cee3","#1f78b4","cyan","#b2df8a","#33a02c","#fb9a99","#e31a1c","#fdbf6f","#ff7f00","#cab2d6","#6a3d9a","#ffff99","#a14a1a"), symbol=~donor, symbols = c("triangle-up", "circle"))
geom_point_3d() +
scale_x_continuous(name = labs$x) +
scale_y_continuous(name = labs$y) +
scale_z_continuous(name = labs$z) +
geom_point(size=8) +
scale_color_manual(values = c("untreated" = "grey",
"mCh d3"="#a6cee3",
"mCh d8"="#1f78b4",
"GFP+mCh d9/12"="cyan",
"GFP d3"="#b2df8a",
"GFP d8"="#33a02c",
"sT d3"="#fb9a99",
"sT d8"="#e31a1c",
"LT d3"="#fdbf6f",
"LT d8"="#ff7f00",
"LTtr d3"="#cab2d6",
"LTtr d8"="#6a3d9a",
"sT+LT d3"="#ffff99",
"sT+LTtr d9/12"="#a14a1a")) +
theme(axis.text = element_text(face="bold",size = 21), axis.title = element_text(face="bold",size = 21)) + theme(legend.text = element_text(size = 20)) + theme(legend.title = element_text(size = 22)) + guides(color = guide_legend(override.aes = list(size = 10)), shape = guide_legend(override.aes = list(size = 10)), alpha = guide_legend(override.aes = list(size = 10)))
#axis.title = element_text(face="bold",size = 20)
#p + theme(axis.text.x = element_text(face="bold",size=14), axis.text.y = element_text(face="bold",size=14))
#+ coord_fixed()
#+ theme(
# # Set the width to 6 inches
# fig.width = 6,
# # Set the height to 4 inches
# fig.height = 4
# )
svg("pca4.svg")
plotPCA(rld, intgroup=c("days"))
dev.off()
点赞本文的读者
还没有人对此文章表态
没有评论
MicrobiotaProcess Group2 vs Group6 (v1)
Visualizing Phylogenetic Relationships and Metadata with Circular ggtree and gheatmap in R
© 2023 XGenes.com Impressum