Example 1: M. tuberculosis H37Rv grown on glycerol vs cholesterol (Griffin et al, 2011)

Note: Most of the output files are tab-separated text files that can be opened as spreadsheets in Excel.

Files: (tarball of all the files)

1. Make combined wig file (TTR-normalized by default) 
> transit export combined_wig glycerol_H37Rv_rep1.wig,glycerol_H37Rv_rep2.wig,cholesterol_H37Rv_rep1.wig,cholesterol_H37Rv_rep2.wig,cholesterol_H37Rv_rep3.wig H37Rv.prot_table glyc_chol_combined_wig_TTR.txt
2. Calculate statistics/diagnostics on samples (based on unnormalized data) 
> transit export combined_wig glycerol_H37Rv_rep1.wig,glycerol_H37Rv_rep2.wig,cholesterol_H37Rv_rep1.wig,cholesterol_H37Rv_rep2.wig,cholesterol_H37Rv_rep3.wig H37Rv.prot_table glyc_chol_combined_wig_raw.txt -n nonorm

> transit tnseq_stats -c glyc_chol_combined_wig_raw.txt -o glyc_chol_tnseq_stats.txt
3. Generate spreadsheet with mean insertion counts for each gene, and correlation plot among samples 
> transit export mean_counts glycerol_H37Rv_rep1.wig,glycerol_H37Rv_rep2.wig,cholesterol_H37Rv_rep1.wig,cholesterol_H37Rv_rep2.wig,cholesterol_H37Rv_rep3.wig H37Rv.prot_table glyc_chol_gene_means.txt

> transit corrplot glyc_chol_gene_means.txt glyc_chol_corrplot.png
4. Gumbel analysis of essentiality in glycerol 
> transit gumbel glycerol_H37Rv_rep1.wig,glycerol_H37Rv_rep2.wig H37Rv.prot_table gumbel_H37Rv_glycerol.txt
5. HMM analysis of essentiality in glycerol 
> transit hmm glycerol_H37Rv_rep1.wig,glycerol_H37Rv_rep2.wig H37Rv.prot_table hmm_H37Rv_glycercol.txt

(Note: The HMM also generates 'hmm_H37Rv_glycercol_genes.txt' automatically as output, which contains the gene-level essentiality calls; 'hmm_H37Rv_glycercol.txt' gives analysis of individual TA sites.)

6. Resampling analysis of conditional essentiality in cholesterol versus glycerol 

> transit resampling glycerol_H37Rv_rep1.wig,glycerol_H37Rv_rep2.wig cholesterol_H37Rv_rep1.wig,cholesterol_H37Rv_rep2.wig,cholesterol_H37Rv_rep3.wig H37Rv.prot_table resampling_glyc_chol.txt -a

(Note: Load the output file in Excel and sort by the last column, labeled 'Adj. p-value'; significant genes are those with Padj<0.05.)

7. Pathway enrichment analysis of resampling hits (conditional-essentials) 

> transit pathway_enrichment resampling_glyc_chol.txt $TRANSITDATA/H37Rv_GO_terms.txt $TRANSITDATA/GO_term_names.dat pathways_glyc_chol_GO.txt

> transit pathway_enrichment resampling_glyc_chol.txt $TRANSITDATA/H37Rv_GO_terms.txt $TRANSITDATA/GO_term_names.dat pathways_glyc_chol_GO_GSEA.txt -M GSEA

> transit pathway_enrichment resampling_glyc_chol.txt $TRANSITDATA/H37Rv_sanger_roles.dat $TRANSITDATA/sanger_roles.dat pathways_glyc_chol_Sanger.txt

> transit pathway_enrichment resampling_glyc_chol.txt $TRANSITDATA/H37Rv_COG_roles.dat $TRANSITDATA/COG_roles.dat pathways_glyc_chol_COG.txt
(Note: set environment variable $TRANSITDATA to /PATH/transit/src/pytransit/data/, where 'PATH' is the directory where Transit is installed on your system.)

Example 2: Iron acquisition in M. tuberculosis H37Rv (Zhang et al, 2020)

Files: 1. Run ANOVA to identify significant variable genes: 
> transit anova iron_combined_wig4.txt iron_samples_metadata.txt H37Rv.prot_table anova_iron.txt
2. Run ZINB to identify significantly variable genes: 
> transit zinb iron_combined_wig4.txt iron_samples_metadata.txt H37Rv.prot_table zinb_iron.txt
3. Make correlation plot among conditions (based on significantly variable genes in ANOVA or ZINB output): 
> transit corrplot anova_iron.txt iron_corrplot_anova.png -anova

> transit corrplot zinb_iron.txt iron_corrplot_zinb.png -zinb
4. Make heatmap to show clustering of genes and conditions (based on LFCs for significantly variable genes in ANOVA or ZINB output): 
> transit heatmap -anova anova_iron.txt iron_heatmap_anova.png

> transit heatmap -zinb zinb_iron.txt iron_heatmap_zinb.png
(Note: blue means positive LFCs (more insertion than average, for that gene), and red means negative LFCs (less insertions; i.e. relatively more essential)