Step 1: Phyloseq_Generation_IBS.R

Inputs:

1. Reported data from ABIS surveys and Swedish National Registry in .dta form
2. Metadata for ABIS Infants
3. Phyloseq object for ABIS Infant Stool samples
4. CSV file for Age of diagnosis

Outputs:

1. Sample Sheet of Related IBS data
2. Phyloseq object for selected IBS subjects and controls
3. Boxplot of age of diagnosis for selected IBS groups

Step 2: Chi-sq heterogeneity: Heterogeneous.R

Inputs:

1. IBS Sample data

Outputs:

1. CSV of heterogeneous factors
2. Forest plot of odds Ratio

Step 3: Beta diversity: BetaDiversity_Confounders.R

Inputs:

1. IBS Phyloseq object

Outputs:

1. CSV of ANOVA p values

Step 4: Alpha_Diversity.R

Inputs:

1. IBS Phyloseq Object

Outputs:

1. Boxplots of rarefied alpha diversity

Step 5: Phyla Wilcoxon: Phyla_Wilcox.R

Inputs:

1. IBS Phyloseq Object

Outputs:

1. Boxplots of Significant phyla differences

Step 6: PIME OOB: PIME_Prevalence.R

Inputs:

1. IBS Phyloseq object

Outputs:

1. Boxplots of OOB from 50 iterations of Random Forest
2. CSV files of genera with MDA > 0

Step 7: LEfSE and ALDEx2: Differential Abundance

Inputs:

1. IBS Phyloseq Object

Outputs:

1. CSV File of LEFSE markers
2. CSV File of ALDEx2 markers
3. Dotplot of lefse and Aldex2 markers

Step 8: Pheatmap

Inputs:

1. IBS Phyloseq object
2. CSV of PIME markers
3. CSV of LEFSE genera
4. CSV of ALDEX genera

Outputs:

1. Pheatmap of genera found in two or more analyses

Step 9: Picrust File Generation

Inputs:

1. IBS phyloseq object

Outputs:

1. FASTA with identifiers (Genera_RowNumber) and ASV sequence
2. Count of total abundance per subject and identifier
   • picrust2 script:
     • conda activate picrust2
     • picrust2_pipeline.py -s IBS_TotalAbun.fasta -i IBS_count_TotalAbun.tsv -o picrust2_out_pipeline -p 2

Step 10: Picrust Analysis: Picrust_Analysis.R

Inputs:

1. IBS phyloseq object
2. pathways_out/path_abun_unstrat.tsv
3. pathways_out/metacyc_pathways_info.csv

Outputs:

1. Boxplot of relative abundance of predicted pathways
2. Boxplot of predicted pathways vs. heterogeneous factors

Step 11: Environmental Confounders: Genera_Confounders.R

Inputs:

1. IBS Phyloseq
2. Heterogeneous CSV Files from Step 2
3. LEFSE CSV from Step 7
4. ALDEX CSV from Step 7
5. PIME CSV from Step 6 Outputs:
6. Boxplot of significant confounders