Lambda Rector

An R package to correct relatvie abundance of sequeneincg reads into 16S rRNA gene copy-number based on an internal Lambda Phage standard.

This is the supporting package for paper DOIXXXXX

Installation

1. Install and library devtools pakcage on your machine

if(
    !require("devtools")
    ){
        install.packages("devtools")
        }
library(
    devtools
    )

2. Download and install lambda_rector

devtools::install_github(
    "farhadm1990/lambda_rector",
    dependencies=TRUE
    )
library(
    lambda.rector
    )

Alternatively you can clone this repository

git clone https://github.com/farhadm1990/lambda_rector.git


devtools::install_local("./lambda_rector")

3. Creating a phyloseq object based on test dataset

# In the test subdirecotry of lambda_rector you can find test dataset.
count = read.table(
    "./lambda.rector/tests/count_test.tsv"
    )
metadata = read.table(
    "./lambda.rector/tests/metadata_test.tsv"
    )

taxa = read.table(
    "./lambda.rector/tests/taxonomy.tsv", header = F
    ) %>% 
    column_to_rownames(
        "V1"
        ) %>% 
tidyr::separate( 
    col = "V2", sep = ";", 
    into = c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species")
    ) %>% apply( 
        2, function(x){
            gsub("[a-zA-Z]+__", "", x)
        }
        ) # parssing the taxa column and tidying the names

#IMPORTANT: giving an arbiterary name to the lambda standard to be passed on to the function later. 

cbind(
    taxa[,1] %>% data.frame() %>% rename("."="king"), count
    ) %>% 
    group_by(king) %>% 
    summarise_all(sum)

taxa[taxa[,1]=="Unassigned",] <- "Lambda"

ps = phyloseq(
    otu_table(
        count, taxa_are_row = TRUE), 
        tax_table(as(taxa, "matrix")), 
        sample_data(metadata)
        )

4. Rinning lambda_rector function

test_ps = lambda_rector(
                        ps, 
                          lambda_id = "Lambda",
                          singletone_threshold = 1, 
                          out_path = "./", 
                          negative_cont = NULL,
                          negative_filt = FALSE, 
                          rare_depth = 10000, 
                          taxa_level = "Kingdom", 
                          std_threshold = 1.48,
                          rel_abund_threshold = c(0.01,0.99)
                          )


# This will return a list of differnt phyloseq objects and saves the output plots



# Extracting the copy-corrected talbe
cbind(sample_data(test_ps$copy_corrected_ps), test_ps$copy_corrected_matrix) %>% 
data.frame() %>% 
rownames_to_column("barcodes") %>% 
select(-loaded_copy_lambda, -samp_id, -volum_mock, -volum_lambda) %>% 
group_by(barcodes, lambda_ng_ul, mock_ng_ul) %>% 
summarise_all(mean) 

3. Output examples

Fig1. An example of filtering output by the package on suspicious samples.

Fig2. Relative abundance of 16S rRNA gene sequencing reads at Genus level in different Mock and Lambda concentrations.

Fig3. 16S rRNA gene Copy-number corrected reads at Genus level in different Mock and Lambda concentrations.