library(tidyverse)
-library(ggplot2)
-library(reshape2)
-library(extrafont)
-library(ComplexHeatmap)
-library(circlize)
-#font_import()
+
+library(tidyverse)
+
+
+Registered S3 methods overwritten by 'dbplyr':
+ method from
+ print.tbl_lazy
+ print.tbl_sql
+-- Attaching packages ----------------------------------------------------------------------------------------------- tidyverse 1.3.1 --
+v ggplot2 3.3.3 v purrr 0.3.4
+v tibble 3.1.1 v dplyr 1.0.5
+v tidyr 1.1.3 v stringr 1.4.0
+v readr 1.4.0 v forcats 0.5.1
+-- Conflicts -------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
+x dplyr::filter() masks stats::filter()
+x dplyr::lag() masks stats::lag()
+
+
+library(ggplot2)
+library(reshape2)
+
+
+
+Attaching package: 㤼㸱reshape2㤼㸲
+
+The following object is masked from 㤼㸱package:tidyr㤼㸲:
+
+ smiths
+
+
+library(extrafont)
+
+
+Registering fonts with R
+
+
+library(ComplexHeatmap)
+
+
+Loading required package: grid
+========================================
+ComplexHeatmap version 2.6.2
+Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
+Github page: https://github.com/jokergoo/ComplexHeatmap
+Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
+
+If you use it in published research, please cite:
+Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
+ genomic data. Bioinformatics 2016.
+
+This message can be suppressed by:
+ suppressPackageStartupMessages(library(ComplexHeatmap))
+========================================
+
+
+library(circlize)
+
+
+========================================
+circlize version 0.4.12
+CRAN page: https://cran.r-project.org/package=circlize
+Github page: https://github.com/jokergoo/circlize
+Documentation: https://jokergoo.github.io/circlize_book/book/
+
+If you use it in published research, please cite:
+Gu, Z. circlize implements and enhances circular visualization
+ in R. Bioinformatics 2014.
+
+This message can be suppressed by:
+ suppressPackageStartupMessages(library(circlize))
+========================================
+
+
+#font_import()
#loadfonts(device = "win")
@@ -1996,11 +2062,11 @@ vi. Save the figure
vii. Create the accessory function
-This accessory function pathway_heatmap will create a heatmap for specific metabolic pathways using the data variable constructed in the previous code blocks. You can also subset a data frame manually and visualize it that way.
+This accessory function flux_heatmap will create a heatmap for specific metabolic pathways using the data variable constructed in the previous code blocks. You can also subset a data frame manually and visualize it that way.
-
-pathway_heatmap = function(df, pathway){
+
+flux_heatmap = function(df, pathway){
# If pathway variable is missing, just visualize everything
if(missing(pathway)){
@@ -2022,6 +2088,7 @@ vii. Create the accessory function
mid2_data = abs(data[, c("GSE17708_24hrs", "Garcia_24hrs", "GSE147405_24hrs")])
late_data = abs(data[, c("GSE17708_72hrs", "GSE17518_72hrs", "Garcia_48hrs",
"Keshamouni_72hrs", "GSE147405_72hrs")])
+ tmp = cbind(early_data, mid1_data, mid2_data, late_data)
# Create column annotation bars
h1_col = HeatmapAnnotation(h1=anno_block(gp=gpar(fill="#CAC4CE"),
@@ -2058,7 +2125,10 @@ vii. Create the accessory function
)
# Construct the heatmaps
- col_fun = colorRamp2(c(0, 5, 20),
+ col_fun = colorRamp2(c(0,
+ median(as.matrix(tmp),
+ na.rm=TRUE),
+ max(as.matrix(tmp), na.rm=TRUE)),
c("Gray", "#FD7470", "#DC1C13"))
h1 = Heatmap(row_labels=data$name,
row_names_side="left",
@@ -2102,7 +2172,7 @@ vii. Create the accessory function
bottom_annotation=h4_col,
width = ncol(late_data)*unit(5, "mm"),
height = nrow(late_data)*unit(5, "mm"),
- heatmap_legend_param=list(title="Absolute Flux"))
+ heatmap_legend_param=list(title="Absolute flux (gDW/mmol*hr)"))
# Return the final figure
final_figure = h1+h2+h3+h4+hm+bp
@@ -2119,8 +2189,8 @@ C. Visualize Glycolytic Reactions
Now let’s specifically get reactions related to Glycolysis.
-
-glycolysis_figures = pathway_heatmap(data, pathway="Glycolysis/Gluconeogenesis")
+
+glycolysis_figures = flux_heatmap(data, pathway="Glycolysis/Gluconeogenesis")
The input is a data frame, convert it to the matrix.The input is a data frame, convert it to the matrix.The input is a data frame, convert it to the matrix.The input is a data frame, convert it to the matrix.
@@ -2145,8 +2215,8 @@ C. Visualize Glycolytic Reactions
D. Visualize Pentose Phosphate Pathway Reactions
-
-ppp_figures = pathway_heatmap(data, pathway="Pentose Phosphate Pathway")
+
+ppp_figures = flux_heatmap(data, pathway="Pentose Phosphate Pathway")
The input is a data frame, convert it to the matrix.The input is a data frame, convert it to the matrix.The input is a data frame, convert it to the matrix.The input is a data frame, convert it to the matrix.
@@ -2172,7 +2242,7 @@ E. Visualize Specific Reactions
Now let’s specifically get reactions that are unique to each state.
-
+
# Manually subset reactions
reactions_of_interest = c("ribulose 5-phosphate 3-epimerase",
"Biotin transport via sodium symport",
@@ -2183,7 +2253,7 @@ E. Visualize Specific Reactions
specific_data = data[idx, ]
# Create figures
-specific_figures = pathway_heatmap(specific_data)
+specific_figures = flux_heatmap(specific_data)
The input is a data frame, convert it to the matrix.The input is a data frame, convert it to the matrix.The input is a data frame, convert it to the matrix.The input is a data frame, convert it to the matrix.
@@ -2246,7 +2316,7 @@ A. Load Data
B. Create accessory function
-
+
ko_heatmap = function(df, pathway){
# If pathway variable is missing, just visualize everything
@@ -2269,7 +2339,7 @@ B. Create accessory function
mid2_data = data[, c("GSE17708_24hrs", "Garcia_24hrs", "GSE147405_24hrs")] - 1
late_data = data[, c("GSE17708_72hrs", "GSE17518_72hrs", "Garcia_48hrs",
"Keshamouni_72hrs", "GSE147405_72hrs")] - 1
- tmp = cbind(early_data, mid1_data, mid2_data, late_data) - 1
+ tmp = cbind(early_data, mid1_data, mid2_data, late_data)
# Create column annotation bars
h1_col = HeatmapAnnotation(h1=anno_block(gp=gpar(fill="#CAC4CE"),
@@ -2308,7 +2378,7 @@ B. Create accessory function
)
# Construct the heatmaps
- col_fun = colorRamp2(c(min(tmp), 0, max(tmp)),
+ col_fun = colorRamp2(c(min(as.matrix(tmp)), 0, max(as.matrix(tmp))),
c("#073B4C", "#FFFFFF", "#EF476F"))
h1 = Heatmap(row_labels=data$name,
row_names_side="left",
@@ -2353,7 +2423,7 @@ B. Create accessory function
width = ncol(late_data)*unit(5, "mm"),
height = nrow(late_data)*unit(5, "mm"),
heatmap_legend_param=list(title="Normalized KO Growth Score",
- at = c(min(tmp), 0, max(tmp)),
+ at = c(min(as.matrix(tmp)), 0, max(as.matrix(tmp))),
labels = c("Decr. Fitness", "No change", "Incr. fitness")))
# Return the final figure
@@ -2365,18 +2435,18 @@ B. Create accessory function
-
-B. Plot the top 50 reactions based on priority score.
+
+C. Plot the top 50 reactions based on priority score.
-
+
-
-C. Grab high confidence reactions (2 or more in same sample)
+
+D. Grab high confidence reactions (2 or more in same sample)
There’s not that much for high confidence reactions.
@@ -2406,11 +2476,22 @@ C. Grab high confidence reactions (2 or more in same sample)
null device
1
+
+# Show
+specific_figures
+
+
+
+
+
+NA
+NA
+
-
-D. Grab low confidence reactions (1 in same sample)
+
+E. Grab low confidence reactions (1 in same sample)
The list of low confidence reactions I’d like to visualize is high.
@@ -2459,20 +2540,9 @@ D. Grab low confidence reactions (1 in same sample)
Subset and create the plot.
-
-idx = data$`BiGG Reaction ID` %in% reactions_of_interest
-specific_data = data[idx, ]
-
-# Create figures
-specific_figures = ko_heatmap(specific_data)
-png(filename="D:/Analysis/EMT/figures/RECON1_rxnko_low_priority_heatmap.png",
- width=20,
- height=12.5,
- units='in',
- res=1200)
-specific_figures
-dev.off()
-
+
+
+
@@ -2491,11 +2561,33 @@ 3. Single-Cell Reaction KOs
A. Load Data
-
+
filename = "D:/Analysis/EMT/data/scRNASeq Reaction KO.csv"
-data = read_csv(filename)
-
-# Create new variable for row names
+data = read_csv(filename)
+
+
+
+-- Column specification ----------------------------------------------------------------------------------------------------------------
+cols(
+ `Reaction Name` = col_character(),
+ `BiGG Reaction ID` = col_character(),
+ `KEGG Subsystem` = col_character(),
+ Average_All = col_double(),
+ Average_Day0 = col_double(),
+ Average_Hour8 = col_double(),
+ Average_Day1 = col_double(),
+ Average_Day3 = col_double(),
+ Average_Day7 = col_double(),
+ Std_All = col_double(),
+ Std_Day0 = col_double(),
+ Std_Hour8 = col_double(),
+ Std_Day1 = col_double(),
+ Std_Day3 = col_double(),
+ Std_Day7 = col_double()
+)
+
+
+# Create new variable for row names
data$name = paste0(data$`BiGG Reaction ID`,
', ',
data$`KEGG Subsystem`)
@@ -2521,12 +2613,14 @@ B. Subset the average data columns
C. Create an annotation heatmap showing the average standard deviation for each reaction across all cells
-
+
hm = Heatmap(as.matrix(data[, "Std_All"]),
rect_gp = gpar(col = "black",
lwd = 1),
col = colorRamp2(c(0, 0.3),
c("#FFFFFF", "orange")),
+ cluster_columns=FALSE,
+ cluster_rows=FALSE,
heatmap_legend_param=list(title="Average Std",
color_bar="continuous"),
width = ncol(data[, "Std_All"])*unit(5, "mm"),
@@ -2539,8 +2633,11 @@ C. Create an annotation heatmap showing the average standard deviation for e
D. Create the Heatmap with additional figures
-
+
final_data = as.matrix(tmp)
+col_fun = colorRamp2(c(min(as.matrix(tmp)), 0, max(as.matrix(tmp))),
+ c("#073B4C", "#FFFFFF", "#EF476F"))
+
ht = Heatmap(row_labels=data$name,
final_data,
row_names_side="left",
@@ -2548,10 +2645,9 @@ D. Create the Heatmap with additional figures
lwd = 1),
cluster_columns=FALSE,
cluster_rows=FALSE,
- col = colorRamp2(c(-0.5, 0, 0.1),
- c("blue", "white", "red")),
+ col = col_fun,
heatmap_legend_param=list(title="Normalized KO Growth Score",
- at = c(-0.6, 0, 0.2),
+ at = c(min(as.matrix(tmp)), 0, max(as.matrix(tmp))),
labels = c("Decr. Fitness", "No change", "Incr. fitness")),
width = ncol(final_data)*unit(5, "mm"),
height = nrow(final_data)*unit(5, "mm")) +
@@ -2560,21 +2656,16 @@ D. Create the Heatmap with additional figures
ht
-
+
+Save figures
-
-png(filename="D:/Analysis/EMT/figures/scRNASeq_avg_rxn_ko.png",
- width=7.5,
- height=10,
- units='in',
- res=1200)
-ht
-dev.off()
-
+
+
+
@@ -2584,7 +2675,7 @@ Conclusion
-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