RADanalysis package has tools for normalizing rank abundance distributions (RAD) to a desired number of ranks using MaxRank Normalization method^{1}. RADs are commonly used in biology/ecology and mathematically equivalent to complementary cumulative distributions (CCDFs) which are used in physics, linguistics and sociology and more generally in data science. For using the package you should first install and load the package.

```
#install.packages("RADanalysis")
library(RADanalysis)
```

Rank Abundance Distributions (RADs) are a way to capture the distribution of biological species in communities, where we use the term “species” for all types of distinct biological entities, e.g. microbial species in a microbiome, viral strains in a quasi-species, the diverse variants B cells in a person, etc. A RAD can be thought of as a plot with the two axes rank (x-axis) and abundance (y-axis). For the most abundant species we draw a point at the (x,y) coordinates \((1,a1)\), with \(a1\) the abundance of this most abundant species. For the second most abundant species we draw a point at \((2,a2)\).

This is a first 10 rows of a typical RAD:

```
rad <- data.frame(rank = 1:100, abundance = round(1./(1:100) * 100))
knitr::kable(head(rad, 10))
```

rank | abundance |
---|---|

1 | 100 |

2 | 50 |

3 | 33 |

4 | 25 |

5 | 20 |

6 | 17 |

7 | 14 |

8 | 12 |

9 | 11 |

10 | 10 |

`plot(rad,xlab = "Rank",ylab = "Abundance",pch = 19,type = "b",lwd = 0.5)`

As is typical for a biological community, the RAD curve has a hollow shape. This means that the community has a small number of very abundant species, and a large number of rare species. The RADs may differ between communities, and that makes it interesting to compare them in order to learn about the communities. Since all RADs have such a hollow shape with a very long tail to the right, it is often more instructive to plot RADs in a log-log plot to see better major differences between RADs. This is the plot type we will be using in the following:

`plot(rad,xlab = "Rank",ylab = "Abundance",pch = 19,log = "xy",type = "b",lwd = 0.5)`

Dethlefsen et al. 2008 (http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0060280) have treated healthy individuals with the antibiotic Ciprofloxacin (Cp) and monitored the states of the gut microbiome before the treatment, during the treatment, and some time after the treatment. We will analyse their data using RADs.

In order to use the data you should first load the package. It is contained in the package as OTU table (OTU = operational taxonomic unit). In the following you can see the first \(10\) columns of the data.

```
library(RADanalysis)
data("gut_otu_table")
colnames(gut_otu_table) <- c(paste("OTU_",seq(1:ncol(gut_otu_table)),sep = ""))
knitr::kable(gut_otu_table[,1:10])
```

OTU_1 | OTU_2 | OTU_3 | OTU_4 | OTU_5 | OTU_6 | OTU_7 | OTU_8 | OTU_9 | OTU_10 | |
---|---|---|---|---|---|---|---|---|---|---|

A1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

A2a | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 |

A2b | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

A2c | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

A3a | 0 | 0 | 0 | 2 | 2 | 2 | 0 | 2 | 0 | 0 |

A3b | 6 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 2 | 2 |

A4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

A5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

B1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

B2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

B3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

B4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

B5 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |

C1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

C2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |

C3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

C4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

C5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

The OTU table contains abundances of all OTUs in all samples. Rows are samples, columns are OTUs. E.g. in sample A1, there was no observation of OTU 1 (abundance entry = 0), 2 observations of OTU 2, etc. “Observations” are here High-Throughput Sequencing (HTSeq) reads of 16S ribosomal genes. The sample names encode from which person the sample came (persons A, B, or C), and at which stage they were taken (1, 2: before treatment; 3: during treatment; 4, 5: after treatment). Thus, A1 is a sample of person A taken before the treatment.

Making RADs from an OTU table means sorting the abundances decreasingly.

```
data("gut_otu_table")
rads <- gut_otu_table
#plot original rads
line_cols <- c("green","red","blue")
#to specify different stages of subjects
sample_classes <- c(1,1,1,1,2,2,3,3,1,1,2,3,3,1,1,2,3,3)
plot(1,xlim = c(1,2000),ylim = c(1,20000),col = "white",log = "xy",axes = F,
xlab = "Rank",ylab = "Abundance",main = "")
sfsmisc::eaxis(side = 1,at = c(1,10,100,1000))
sfsmisc::eaxis(side = 2)
for(i in 1:nrow(rads)){
temp <- sort(rads[i,],decreasing = TRUE)
temp <- temp[temp>0]
lines(x = temp,lwd = 2,col = line_cols[sample_classes[i]])
}
legend("bottomleft",bty = "n",legend = c("pre Cp","under Cp","post Cp"),
col = line_cols,lwd = 3)
```

MaxRank normalization generates from a RAD with given richness \(S\) a “normalized RAD” (NRAD) with a user-chosen smaller richness (MaxRank) \(R\) . This is done by randomly re-sampling the original input of the RAD as long as the richness does not exceed \(R\) . Since this is a random procedure, we repeat it \(n\) times and average. MaxRank normalization allows us to turn several samples with different richness into a set of samples with the same richness. Mathematically, we turn vectors of different dimensions into vectors of the same dimension. It is easy to compare such vectors of the same dimension quantitatively. In the following we will normalize one of the RADs form gut_otu_table and will see the effect of averaging.

```
data(gut_otu_table)
rads <- gut_otu_table
original_rad <- sort(rads[1,],decreasing = TRUE)
#removing zeros
original_rad <- original_rad[original_rad > 0]
plot(original_rad,ylim = c(1,max(original_rad)),log = "xy", xlab = "Rank",
ylab = "Abundance", main = "",pch = 19,type = "b",cex = 0.5)
norm_rad <- RADnormalization(input = rads[1,],max_rank = 400,average_over = 50)
points(x = norm_rad$norm_rad * sum(norm_rad$norm_rad_count[1,]) ,pch = 19,cex = 1, type = "l",
col = "blue",lwd = 4)
points(x = norm_rad$norm_rad_count[1,],pch = 19,cex = 1, type = "l",col = "red",lwd = 3)
points(x = norm_rad$norm_rad_count[2,],pch = 19,cex = 1, type = "l",col = "green",lwd = 3)
legend("bottomleft",legend = c("original RAD","possible norm rad","possible norm rad",
paste("nrad averaged over 50 realizations, times",
sum(norm_rad$norm_rad_count[1,]))),
col = c("black","red","green","blue"),lwd = 2,bty = "n")
```

In this example the first sample from gut_otu_table has been used. The original RAD has \(1116\) ranks which is the total number of entries in the corresponding otu_table row which have positive numbers. In red and green you can see two of possible NRADs with \(R = 400\). The blue curve shows the average over 50 possible NRADs. In the following we will work on NRADs that have been created by averaging. With the function RADnormalization() you can access all the NRADs used for averaging. In case you want to normalize a complete OTU table we recommend to use RADnormalization_matrix(). This function returns less data compared to RADnormalization() because sometimes the OTU tables are very big.

Let us generate from the RADs, the corresponding NRADs. We should first decide about \(R\). To do so, we first look at the distribution of richness in our data set.

```
data("gut_otu_table")
rads <- gut_otu_table
richness <- sapply(X = 1:nrow(rads), function(i) length(which(rads[i,] > 0)))
boxplot(richness,horizontal = T,xlab = "Richness")
quantile(richness)
```

```
## 0% 25% 50% 75% 100%
## 492.00 848.00 984.50 1122.75 1465.00
```

So the richness (total number of ranks of each sample) is between \(492\) and \(1465\). In case richness is distributed normally, one can use the minimum richness for normalizing the complete OTU table. In case there are some outliers with very low richness we recommend to remove them from data set so that you could normalize table with higher \(R\) and therefore lose less information.

In this example I will use \(R=400\) for simplicity.

```
data("gut_otu_table")
rads <- gut_otu_table
#plot original rads
line_cols <- c("green","red","blue")
sample_classes <- c(1,1,1,1,2,2,3,3,1,1,2,3,3,1,1,2,3,3)
#Normalization
nrads <- RADnormalization_matrix(input = rads,max_rank = 400,average_over = 10,
sample_in_row = TRUE,verbose = FALSE)
nrads <- nrads$norm_matrix
plot(1,xlim = c(1,400),ylim = c(4e-5,1),col = "white",log = "xy",
xlab = "Rank",ylab = "Abundance",
main = "")
for(i in 1:nrow(nrads)){
lines(x = nrads[i,],lwd = 2,col = line_cols[sample_classes[i]])
}
legend("bottomleft",bty = "n",legend = c("pre Cp","under Cp","post Cp"),
col = line_cols,lwd = 3)
```

Note that NRADs are treated as probability distributions, so they sum up to one. For the next examples I will use gut_nrads data which is included in the package. In gut_nrads I stored the result of RADnormalization_matrix(input = rads,max_rank = 400,average_over = 2000) to avoid computing them again and save a couple of minutes.

Now that we have NRADs we can continue processing them with ordination, clustering or classification methods. Here we only study an ordination example.

If the data consists of several groups, it is usually helpful to look at the averaged NRAD of each group. We call this averaged NRAD the \("representative NRAD"\). Here you can see the representative RADs for three groups of pre, under and post CP in the gut_otu_table.

```
data("gut_nrads")
nrads <- gut_nrads
nrads <- nrads$norm_matrix
line_cols <- c("green","red","blue")
sample_classes <- c(1,1,1,1,2,2,3,3,1,1,2,3,3,1,1,2,3,3)
maxrank <- 400
#plot nrads
plot(1e10,xlim = c(1,maxrank),ylim = c(2e-5,1),log="xy",
xlab = "rank",ylab = "abundance",cex.lab = 1.5,axes = FALSE)
sfsmisc::eaxis(side = 1,at = c(1,10,100,1000,10000))
sfsmisc::eaxis(side = 2,at = c(1e-4,1e-3,1e-2,1e-1,1),las = 0)
for(i in 1:nrow(nrads)){
points(nrads[i,],type = 'l',col = line_cols[sample_classes[i]],lwd = 0.8)
}
#plot confidence intervals of representative nrads
a <- representative_RAD(norm_rad = nrads,sample_ids = which(sample_classes == 1),
plot = TRUE,confidence = 0.9,with_conf = TRUE,
col = scales::alpha(line_cols[1],0.5),border = NA)
a <- representative_RAD(norm_rad = nrads,sample_ids = which(sample_classes == 2),
plot = TRUE,confidence = 0.9,with_conf = TRUE,
col = scales::alpha(line_cols[2],0.5),border = NA)
a <- representative_RAD(norm_rad = nrads,sample_ids = which(sample_classes == 3),
plot = TRUE,confidence = 0.9,with_conf = TRUE,
col = scales::alpha(line_cols[3],0.5),border = NA)
#plot representative nrads
a <- representative_RAD(norm_rad = nrads,sample_ids = which(sample_classes == 1),
plot = TRUE,with_conf = FALSE,
col = scales::alpha(line_cols[1],0.8),lwd = 4)
a <- representative_RAD(norm_rad = nrads,sample_ids = which(sample_classes == 2),
plot = TRUE,with_conf = FALSE,
col = scales::alpha(line_cols[2],0.8),lwd = 4)
a <- representative_RAD(norm_rad = nrads,sample_ids = which(sample_classes == 3),
plot = TRUE,with_conf = FALSE,
col = scales::alpha(line_cols[3],0.8),lwd = 4)
legend("bottomleft",bty = "n",legend = c("pre Cp","under Cp","post Cp"),
col = line_cols,lwd = 3)
```

When there are more than \(10-20\) samples, it is hard to recognize a structure in the NRADs. In this case it is usually helpful to use ordination methods such as multi-dimensional scaling. But before doing any ordination, we need to define a distance between NRADs. In the following example, we will use the \(Manhattan\) distance between NRADs. You can also plot the representative points (mean of all the points in the group) to see better if there is a significant difference between groups.

```
data("gut_nrads")
nrads <- gut_nrads
nrads <- nrads$norm_matrix
line_cols <- c("green","red","blue")
sample_classes <- c(1,1,1,1,2,2,3,3,1,1,2,3,3,1,1,2,3,3)
maxrank <- 400
#distance matrix using manhattan distance
d <- dist(x = nrads,method = "manhattan")
#ordination using classical multi-dimensional scaling
mds <- cmdscale(d = d,k = 5,eig = TRUE)
#plot the points
plot(mds$points,xlab = "First coordinate",ylab = "Second coordinate",pch = 19,
cex =1,col = line_cols[sample_classes],
main = "MDS plot with representative points \n of each group and error bars")
#add the representative points wit erorr bar to the previous plot
a <- representative_point(input = mds$points,ids = which(sample_classes == 1),
col = scales::alpha(line_cols[1],0.5),
plot = TRUE,standard_error_mean = TRUE,pch = 19, cex = 4)
a <- representative_point(input = mds$points,ids = which(sample_classes == 2),
col = scales::alpha(line_cols[2],0.5),
plot = TRUE,standard_error_mean = TRUE,pch = 19, cex = 4)
a <- representative_point(input = mds$points,ids = which(sample_classes == 3),
col = scales::alpha(line_cols[3],0.5),
plot = TRUE,standard_error_mean = TRUE,pch = 19, cex = 4)
legend("bottomleft",bty = "n",legend = c("pre Cp","under Cp","post Cp"),
col = line_cols,pch = 19)
```

Saeedghalati et al. 2016 “Quantitative comparison of abundance structures of genetic communities”, submitted↩