Investigating differential abundance methods in microbiome data: a benchmark study
Creators: name: Cappellato, Marco
name: Baruzzo, Giacomo
name: Di Camillo, Barbara
Publisher: name: Zenodo
Year: 2022
Subjects: Microbiome
Metagenomics
Benchmark
Differential Abundance
16S
metaSPARSim

description: This is the repository containing the <strong>results</strong> shown in <em>Cappellato M., Baruzzo G., Di Camillo B. "Investigating differential abundance methods in microbiome data: a benchmark study." (2021)</em>. In the GitLab repository here there is the R package <strong>metaBenchDA </strong>used to perform the benchmarking study previously cited. The R package contains the data and the code to perform the simulation framework, to run all the differential abundance analysis methods and to assess methods' performance. The GitLab repository contains also the Docker image <strong>metabenchda:2.0.0</strong>; the Docker image contains the R package <strong>metaBenchDA</strong> and the code to reproduce the results shown in the paper. As an alternative, to reproduce the results in the main manuscript follow the instructions here. The <strong><code>results</code></strong> folder here in Zenodo contains all the files needed to run the code in every point of the framework. The <strong><code>results</code></strong> folder contains: <strong>lib_unbalanced</strong> results in the scenario with DA features and setting both the intensity threshold \(\theta = 1 \cdot f_{2}\)and unbalanced sequencing depth between conditions. <strong>metagenomeSeq_res</strong> results running two models implemented in metagenomeSeq, namely the zero-inflated Gaussian (ZIG) model and the zero-inflated Log-Gaussian (ZILG) mixture model, in the scenario with DA features and setting the intensity threshold \(\theta = 1 \cdot f_{2}\). <strong>new_datasets</strong> results in the scenario with DA features and setting the intensity threshold \(\theta = 1 \cdot f_{2}\) for AnimalGut and Soil datasets. <strong>NOth</strong> results in the scenario with DA features and without setting the intensity threshold \(\theta = 1 \cdot 0\) . <strong>NULL</strong> results in the scenario without DA features. <strong>WITHth</strong> results in the scenario with DA features and setting the intensity threshold \(\theta = 1 \cdot f_{2}\). <strong>WITHth_GMPR</strong> results in the scenario with DA features and setting the intensity threshold \(\theta = 1 \cdot f_{2}\). We test the methods with GMPR normalisation. <strong>WITHth_HALFvar</strong> results in the scenario with DA features and decreasing the variability parameter \(\varphi = \dfrac{\varphi}{2}\). Inside the folders the results considering structural zeros as TN are available in the <strong><code>reassign</code></strong> folder, while considering the choice of methods in <strong><code>methodoutput</code></strong>. Inside each folder there are: <strong>simulation </strong>all the dataset simulated. All results from the SECTION 1: GENERATE DATASET in the script file. In <em>metagenomeSeq_res</em> and <em>WITHth_GMPR</em> this folder is absent since we run the methods on the WITHth simulation scenario. <em>SSXX_PPYY_FCZ1-Z</em>2 where <em>XX</em> is the sample size, <em>YY</em> is the percentage of DA features, <em>Z1-Z2 </em>is the Fold Change limit. In the NULL configuration <em>PPYY</em> is not present. <em>WW_SSXX_PPYY_FCZ1-Z2</em> where <em>WW</em> is the name of the dataset from which the simulation parameters are estimated. <em>NAMECONF_WW_SSXX_PPYY_FCZ1-Z2_simN</em>.RData where NAMECONF is the name of the configuration (e.g. NULL, NOth ...), <em>N</em> is the number of the simulation. <strong>methods </strong>the output of each method involved in the comparison. All results from the SECTION 2: RUN DA METHODS in the script file <em>SSXX_PPYY_FCZ1-Z</em>2 where <em>XX</em> is the sample size, <em>YY</em> is the percentage of DA features, <em>Z1-Z2 </em>is the Fold Change limit. In the NULL configuration <em>PPYY</em> is not present. <em>WW_SSXX_PPYY_FCZ1-Z2</em> where <em>WW</em> is the name of the dataset from which the simulation parameters are estimated. METHODNAME one folder for each method. In <em>WITHth_GMPR</em> folder each metod is labelled with <em>_gmpr. </em> <em>NAMECONF_WW_SSXX_PPYY_FCZ1-Z2_simN</em>_METHODNAME.RData where NAMECONF is the name of the configuration (e.g. NULL, NOth ...), <em>N</em> is the number of the simulation. <strong>metrics </strong>performance evaluation.<strong> </strong>All results from the SECTION 3: RUN METRICS in the script file <em>methodoutput/reassign</em> where <em>methodoutput</em> means that the output of the methods has been considered, while <em>reassign </em>that the structural zeros are considered as TN <em>SSXX_PPYY_FCZ1-Z</em>2 where <em>XX</em> is the sample size, <em>YY</em> is the percentage of DA features, <em>Z1-Z2 </em>is the Fold Change limit. In the NULL configuration <em>PPYY</em> is not present. <em>WW_SSXX_PPYY_FCZ1-Z2</em> where <em>WW</em> is the name of the dataset from which the simulation parameters are estimated. <em>NAMECONF_methodoutput/reassign_WW_SSXX_PPYY_FCZ1-Z2_NAMEMETRIC</em>.RData where NAMECONF is the name of the configuration (e.g. NULL, NOth ...), <em>NAMEMETRIC</em> is the name of the metric. <strong>figure </strong>all the figures in the manuscript. All the figures are generated from the script file Figures.R. <em>methodoutput/reassign</em> where <em>methodoutput</em> means that the output of the methods has been considered, while <em>reassign </em>that the structural zeros are considered as TN <em>NAMECONF_methodoutput/reassign_</em>NAMEFIGURE.jpeg where NAMECONF is the name of the configuration (e.g. NULL, NOth ...), NAMEFIGURE is the name of the metrics e.g. RECALL_boxplot is the recall values of each method in each configuration of SS, PP, dataset.