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1.502048248 0 0 0 0 2.885821832 0 3.531300161 0 0 0 0 1.461038961 0 0 3.385826772 0 0 0 0.329163924 0 0 0 0 0 0 0 0 0 0 1.369550331 0 +Tessaracoccus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +Tetragenococcus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +Thauera 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +Thermoanaerobacterium 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.502168455 0 +Thermomonas 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.551181102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.136955033 0 +Thermorudis 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +Thermus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5.748031496 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +Thioclava 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +Tropicimonas 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.399024607 +Xylophilus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 \ No newline at end of file diff --git a/R_decontaminer_visualizationScript/decontaminer.R b/R_decontaminer_visualizationScript/decontaminer.R new file mode 100644 index 0000000..c4d0805 --- /dev/null +++ b/R_decontaminer_visualizationScript/decontaminer.R @@ -0,0 +1,99 @@ +### set working directory and load input file + +setwd("test_html/") +originaldata <- read.table("barPlotInfo_ge_CT_5_breast.txt",sep='\t', row.names=1, header=TRUE) ## should be data frame + +### select sub set based on coulmn number or name + + newdata_1=subset(originaldata, select=c(1:10,22,23)) ## col number based OR + #newdata_1=subset(originaldata, select=c("Breast.03","Breast.08","Breast.100")) ##name based + +############# Mean greater than threshold #################### +newdata_1=newdata_1[rowSums(newdata_1) >0.1 ,drop=FALSE, ] #take only those rows which are greater than zero + +## apply filter all sample threshold +allsample_threshhold <- 1 +data_wt=newdata_1[(rowSums(newdata_1)/nrow(newdata_1))>= allsample_threshhold,drop=FALSE, ] +nor <- data_wt +data_wt <- as.data.frame(t(data_wt)) + + +library(tidyverse) +library('dplyr') ##HAS COUNT FUNCTION + +## preparation of data format for plots +data_wt$Sample <- row.names(data_wt) +data_wt <- data_wt %>% + pivot_longer(!Sample, names_to = "Species", values_to = "count") # Gathering the columns to have normalized counts to a single column + + +#### Lets Plot ################# +library(ggplot2) +library(RColorBrewer) + +## setting 2 different color variables + +my_palette <- colorRampPalette(c("#179493","#76c286", "#ebdc96", "#ec9173","#d0587e"), alpha=TRUE)(n=20) +heat_colors <- brewer.pal(6, "Reds") ##"Greens" ##"Blues" + +##### First : Stack plot with count #### + +ggplot(data_wt, aes(x = Sample, y = count, fill = Species)) + + geom_bar(stat = "identity") + theme(axis.text.x = element_text(angle = 60, vjust = 1, hjust=1)) + +##### Second : Stack plot with percent #### + +ggplot(data_wt, aes(x = Sample, y = count, fill = Species)) + + geom_col(position = "fill") + + scale_y_continuous(labels = scales::percent) + theme(axis.text.x = element_text(angle = 60, vjust = 1, hjust=1)) + +#### Third :Dot Plot (Sample vs Species with count size) ########## + +ggplot(data_wt,aes(x=Species,y=Sample )) + + geom_point(aes(size=count, color = Species))+ + scale_color_manual(values = heat_colors )+ ### in case heat_color doesnt work replace with manual or other like c("red","blue") + #scale_color_gradientn('Log2 mean (Molecule 1, Molecule 2)', colors=my_palette) + + ggtitle("Sample vs Species with count size ") + + theme_bw() + + theme(panel.grid.minor = element_blank(), + panel.grid.major = element_blank(), + axis.text=element_text(size=14, colour = "black"), + axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5), + axis.text.y = element_text(size=12, colour = "black"), + axis.title=element_blank(), + panel.border = element_rect(size = 0.5, linetype = "solid", colour = "black")) + + + +####### Fourth : Dot Plot (Sample vs Count) + +ggplot(data_wt) + + geom_point(aes(x = Sample , y = count, color = Species )) + + ## scale_y_log10() + ##want to scale it or not?? + xlab("Sample ID") + + ylab("Counts") + + ggtitle("Sample vs Count") + + theme_bw() + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + + theme(plot.title = element_text(hjust = 0.5)) + + + +################ Fifth : Heat Map ##############3 + +library(ComplexHeatmap) +### Run pheatmap +pheatmap::pheatmap( nor, + color = my_palette, + cluster_cols = T, + cluster_rows = F, + show_rownames = T, + border_color = NA, + fontsize = 10, + scale = "row", #row #column #none ## row wise differences clarity (i.e b/w samples) + fontsize_row = 10, + height = 20, + main= "Heat map (Sample level differences)" +) + + diff --git a/R_decontaminer_visualizationScript/decontaminer.Rmd b/R_decontaminer_visualizationScript/decontaminer.Rmd new file mode 100644 index 0000000..0fd83a9 --- /dev/null +++ b/R_decontaminer_visualizationScript/decontaminer.Rmd @@ -0,0 +1,124 @@ +--- +title: "Decontaminer Visualization support" +author: "Amarinder Singh Thind" +date: "2022 Dec 07" +output: + html_document: + toc: yes + code_folding: hide + pdf_document: + toc: yes +--- +### set working directory and load input file + +```{r} +setwd("test_html/") +originaldata <- read.table("barPlotInfo_ge_CT_5_breast.txt",sep='\t', row.names=1, header=TRUE) ## should be data frame +``` + +### select sub set based on coulmn number or name + +```{r} + + + newdata_1=subset(originaldata, select=c(1:10,22,23)) ## col number based OR + #newdata_1=subset(originaldata, select=c("Breast.03","Breast.08","Breast.100")) ##name based +``` + +#### Mean greater than threshold #################### + +```{r} + +newdata_1=newdata_1[rowSums(newdata_1) >0.1 ,drop=FALSE, ] #take only those rows which are greater than zero + +allsample_threshhold <- 1 ## Define threshold for average count > threshold + +data_wt=newdata_1[(rowSums(newdata_1)/nrow(newdata_1))>= allsample_threshhold,drop=FALSE, ] +nor <- data_wt +data_wt <- as.data.frame(t(data_wt)) +``` + +## preparation of data format for plots +```{r} +library(tidyverse) +library('dplyr') ##HAS COUNT FUNCTION + +data_wt$Sample <- row.names(data_wt) +data_wt <- data_wt %>% + pivot_longer(!Sample, names_to = "Species", values_to = "count") # Gathering the columns to have normalized counts to a single column +``` + +#### Lets Plot ################# +```{r} +library(ggplot2) +library(RColorBrewer) + +## setting 2 different color variables + +my_palette <- colorRampPalette(c("#179493","#76c286", "#ebdc96", "#ec9173","#d0587e"), alpha=TRUE)(n=20) +heat_colors <- brewer.pal(6, "Reds") ##"Greens" ##"Blues" +``` + +##### First : Stack plot with count #### + +```{r} +ggplot(data_wt, aes(x = Sample, y = count, fill = Species)) + + geom_bar(stat = "identity") + theme(axis.text.x = element_text(angle = 60, vjust = 1, hjust=1)) +``` + +##### Second : Stack plot with percent #### +```{r} +ggplot(data_wt, aes(x = Sample, y = count, fill = Species)) + + geom_col(position = "fill") + + scale_y_continuous(labels = scales::percent) + theme(axis.text.x = element_text(angle = 60, vjust = 1, hjust=1)) +``` + +#### Third :Dot Plot (Sample vs Species with count size) ########## +```{r} +ggplot(data_wt,aes(x=Species,y=Sample )) + + geom_point(aes(size=count, color = Species))+ + scale_color_manual(values = heat_colors )+ ### in case heat_color doesnt work replace with manual or other like c("red","blue") + #scale_color_gradientn('Log2 mean (Molecule 1, Molecule 2)', colors=my_palette) + + ggtitle("Sample vs Species with count size ") + + theme_bw() + + theme(panel.grid.minor = element_blank(), + panel.grid.major = element_blank(), + axis.text=element_text(size=14, colour = "black"), + axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5), + axis.text.y = element_text(size=12, colour = "black"), + axis.title=element_blank(), + panel.border = element_rect(size = 0.5, linetype = "solid", colour = "black")) +``` + +##### Fourth : Dot Plot (Sample vs Count) +```{r} +ggplot(data_wt) + + geom_point(aes(x = Sample , y = count, color = Species )) + + ## scale_y_log10() + ##want to scale it or not?? + xlab("Sample ID") + + ylab("Counts") + + ggtitle("Sample vs Count") + + theme_bw() + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + + theme(plot.title = element_text(hjust = 0.5)) + +``` + +#### Fifth : Heat Map ##############3 +```{r} +library(ComplexHeatmap) +### Run pheatmap +pheatmap::pheatmap( nor, + color = my_palette, + cluster_cols = T, + cluster_rows = F, + show_rownames = T, + border_color = NA, + fontsize = 10, + scale = "row", #row #column #none ## row wise differences clarity (i.e b/w samples) + fontsize_row = 10, + height = 20, + main= "Heat map (Sample level differences)" +) + +```