Sankey diagrams for Bacteria and antibiotics

Wed, 24 Jul 2019 21:13:14 -0500

Visually Classifying Bacteria and Antibiotics

After World War II, antibiotics earned the moniker “wonder drugs” for quickly treating previously-incurable diseases. Data was gathered to determine which drug worked best for each bacterial infection. Comparing drug performance was an enormous aid for practitioners and scientists alike. In the fall of 1951, Will Burtin published a graph showing the effectiveness of three popular antibiotics on 16 different bacteria, measured in terms of minimum inhibitory concentration.

image creidt: Ask a biologist

I am reproducing this wonderful visualization from my professor( Silas Bergen.) in ggplot2, who did this in Tableau

Let’s bring the datasets,

library(tidyverse)
library(knitr)
library(kableExtra)
df <- read.csv("https://cdn.rawgit.com/plotly/datasets/5360f5cd/Antibiotics.csv", stringsAsFactors = F)
#String as Factors is a demon. Better not bring it here ! We rarely need that beast.
#There are 16 bacteria so giving them ID to reference later..
df<-df %>% mutate(ID =seq(1:16) )

kable(head(df,n = 16))

Bacteria	Penicillin	Streptomycin	Neomycin	Gram	ID
Mycobacterium tuberculosis	800.000	5.00	2.000	negative	1
Salmonella schottmuelleri	10.000	0.80	0.090	negative	2
Proteus vulgaris	3.000	0.10	0.100	negative	3
Klebsiella pneumoniae	850.000	1.20	1.000	negative	4
Brucella abortus	1.000	2.00	0.020	negative	5
Pseudomonas aeruginosa	850.000	2.00	0.400	negative	6
Escherichia coli	100.000	0.40	0.100	negative	7
Salmonella (Eberthella) typhosa	1.000	0.40	0.008	negative	8
Aerobacter aerogenes	870.000	1.00	1.600	negative	9
Brucella antracis	0.001	0.01	0.007	positive	10
Streptococcus fecalis	1.000	1.00	0.100	positive	11
Staphylococcus aureus	0.030	0.03	0.001	positive	12
Staphylococcus albus	0.007	0.10	0.001	positive	13
Streptococcus hemolyticus	0.001	14.00	10.000	positive	14
Streptococcus viridans	0.005	10.00	40.000	positive	15
Diplococcus pneumoniae	0.005	11.00	10.000	positive	16

Before proceeding further with the data manipulation we need to think about the format of the visualization. Here we will be making our visualization on the bacteria level, that means we will have information for each bacteria, their gram stain , and the concentration of drug required .

If you look at the table above, we do have all the data we need but not on the format we are thinking. We want one information per row for each bacteria unlike above where each row has all the information of each bacteria on one single row. Let’s change the format of the data,

key_value = df %>% gather("Drug","Concentration",Penicillin:Neomycin,-Bacteria)
kable(head(key_value))

Bacteria	Gram	ID	Drug	Concentration
Mycobacterium tuberculosis	negative	1	Penicillin	800
Salmonella schottmuelleri	negative	2	Penicillin	10
Proteus vulgaris	negative	3	Penicillin	3
Klebsiella pneumoniae	negative	4	Penicillin	850
Brucella abortus	negative	5	Penicillin	1
Pseudomonas aeruginosa	negative	6	Penicillin	850

okay so, now what we need to do is add a minimum concentration information for each bacteria for each stain type. so basically a column on the gathered table above. The only thing to keep note of is that here we should group all these bacteria and select the minimum concentration. We could have done this first[basically for eacg ] and gather like above but this is my thought process.

df_min<- key_value  %>% 
  group_by(Bacteria) %>% summarise(Min = min(Concentration))
kable(head(df_min))

Bacteria	Min
Aerobacter aerogenes	1.000
Brucella abortus	0.020
Brucella antracis	0.001
Diplococcus pneumoniae	0.005
Escherichia coli	0.100
Klebsiella pneumoniae	1.000

so now, let’s join this df_min dataframe from above with df to have that minimum information in the dataframe.

df<- inner_join(df,df_min,by = "Bacteria")
df<- df %>% mutate(Best = case_when(
  Penicillin == Min~ "Penicillin",
  Neomycin == Min~ "Neomycin",
  Streptomycin == Min~ "Streptomycin"
))

Now, since the data is ready and in the format we want,

kable(head(df))

Bacteria	Penicillin	Streptomycin	Neomycin	Gram	ID	Min	Best
Mycobacterium tuberculosis	800	5.0	2.00	negative	1	2.00	Neomycin
Salmonella schottmuelleri	10	0.8	0.09	negative	2	0.09	Neomycin
Proteus vulgaris	3	0.1	0.10	negative	3	0.10	Neomycin
Klebsiella pneumoniae	850	1.2	1.00	negative	4	1.00	Neomycin
Brucella abortus	1	2.0	0.02	negative	5	0.02	Neomycin
Pseudomonas aeruginosa	850	2.0	0.40	negative	6	0.40	Neomycin

Okay, this step might be a little unintuitive but if we think with grammer of graphics philosophy this will make sense.

seq1 <- rep(1:16,each=100)
seq2 <-rep(seq(-6,6,length=100),16)
newdat <-data.frame(ID=seq1,T=seq2)
write.csv(newdat,"new_data.csv",row.names=FALSE)

We are making a new dataframe that has data point for the sigmoid curve(you can just draw sigmoid curve in R but this way it is linked with our data with ID)

#Joining the data by ID
final_df<-inner_join(df,newdat,by = "ID")
kable(head(final_df))

Bacteria	Penicillin	Streptomycin	Neomycin	Gram	ID	Min	Best	T
Mycobacterium tuberculosis	800	5	2	negative	1	2	Neomycin	-6.000000
Mycobacterium tuberculosis	800	5	2	negative	1	2	Neomycin	-5.878788
Mycobacterium tuberculosis	800	5	2	negative	1	2	Neomycin	-5.757576
Mycobacterium tuberculosis	800	5	2	negative	1	2	Neomycin	-5.636364
Mycobacterium tuberculosis	800	5	2	negative	1	2	Neomycin	-5.515151
Mycobacterium tuberculosis	800	5	2	negative	1	2	Neomycin	-5.393939

#ggplot
final_df <- final_df %>% mutate(Sigmoid = 1/(1 + exp(-T)))

okay so now we have the final dataset, we can get in the ggplot2 land.

p <- ggplot(data = final_df , aes(x = T , y = Sigmoid ))
p + geom_point()

#Making best slope
#Different slop will separate our curves
final_df<-final_df %>% mutate(bestBacSlope = case_when(
  Best =="Streptomycin" ~ 4 - ID,
  Best =="Neomycin" ~ 9 - ID,
  Best =="Penicillin" ~ 14 - ID
))

final_df<-final_df %>% mutate(curveBest = ID + bestBacSlope * Sigmoid)
#Figuring out ID and labels

label_df<-final_df %>% dplyr::select(c(ID, Bacteria))%>% group_by(Bacteria,ID) %>% summarise(count = n()) %>% dplyr::select(Bacteria,ID) %>% arrange(ID)

Below are the label we will use in y-axis

label_y= c("Mycobacterium tuberculosis" ,  "Salmonella schottmuelleri"  ,    
           "Proteus vulgaris"        ,        "Klebsiella pneumoniae"  ,        
           "Brucella abortus"      ,          "Pseudomonas aeruginosa"    ,     
           "Escherichia coli"    ,            "Salmonella (Eberthella) typhosa",
           "Aerobacter aerogenes"     ,       "Brucella antracis"    ,          
           "Streptococcus fecalis"    ,       "Staphylococcus aureus"      ,    
           "Staphylococcus albus"    ,        "Streptococcus hemolyticus"      ,
           "Streptococcus viridans"    ,      "Diplococcus pneumoniae")

Now it’s a plotting time !

#Plotting the sigmoid plots
library(ggthemes)

## Warning: package 'ggthemes' was built under R version 3.5.2

sankey <- ggplot(data = final_df, aes(x = T , y = curveBest, color =Gram,size = Min,alpha = 0.9,group = Bacteria)) + geom_line() +scale_fill_manual(values=c("green","red")) + 
    scale_y_continuous(breaks = seq(1:16) , labels = label_y)   + theme(axis.title.y = element_blank() , axis.line.x  = element_blank() , axis.ticks.x = element_blank(), axis.title.x =element_blank() , axis.text.x.bottom = element_blank() ) + 
  annotate("text", x = 6, y = 14, label = "Penicillin") +
  annotate("text", x = 6, y = 9, label = "Neomycin") +
  annotate("text", x = 6, y = 4, label = "Streptomycin") +
  annotate("text",x = 5.5,y = 15,label = "Best Antibiotics" ,size = 5, colour = 'blue')+
  theme_minimal()

sankey

Figure 1: Classification of Bacteria

Data Communication | 𝚃𝚛𝚊𝚗𝚜𝚙𝚘𝚗𝚜𝚝𝚎𝚛

Sankey diagrams for Bacteria and antibiotics

Visually Classifying Bacteria and Antibiotics