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Run complete TSENAT analysis pipeline

Source: R/orchestration.R
TSENAT.Rd

Coordinates the full TSENAT workflow: diversity -> jackknife -> LM interactions -> divergence -> gene interactions -> rank-based tests -> concordance -> visualizations.

Usage

TSENAT(
  analysis,
  output_dir = "tsenat_outputs",
  save_output = TRUE,
  output_format = c("tsv", "csv", "txt", "rds"),
  verbose = TRUE,
  tips = FALSE
)

Arguments

analysis

TSENATAnalysis object created by build_analysis.

output_dir

character. Directory to save results and plots. Default: 'tsenat_outputs'. Set to NULL to disable automatic output saving.

save_output

logical. Whether to save output files (results tables). Default: TRUE. If FALSE, no TSV/CSV output files are written to disk.

output_format

character. Format for output files: 'tsv' (tab-separated), 'csv' (comma-separated), 'txt' (text), or 'rds' (R serialized). Default: 'tsv'.

verbose

logical. Print progress messages. Default: TRUE.

tips

logical. Show tips and usage examples at the end of the pipeline run. Default: FALSE. Set to TRUE to see the `[TIPS]` section displaying common result-extraction commands.

Value

TSENATAnalysis object containing complete analysis results, plots, and metadata.

Details

Pipeline execution order (enforced, follows TSENAT.Rmd vignette):

  1. filter_analysis() - Filter low-abundance transcripts

  2. calculate_diversity() - Tsallis entropy per q-value

  3. plot_diversity_spectrum() - Visualize q-spectrum

  4. calculate_m_estimator() - Sample influence QC analysis

  5. calculate_sait() - LM/SAIT interaction testing

  6. plot_sait() - SAIT results visualization

  7. calculate_jis() - Transcript switching detection

  8. plot_jis_delta() - Multi-q influence heatmap (gene switching tables computed lazily via results())

  9. plot_expression() - Top transcript visualization

  10. calculate_divergence() - Pairwise divergence metrics

  11. calculate_effect_sizes() - Effect size computation

  12. plot_divergence_distribution() - Divergence distribution plot

  13. plot_divergence_spectrum() - Divergence spectrum plot

  14. calculate_assumptions() - Validate rank-based test assumptions

  15. calculate_rank_transform() - ART (Aligned Rank Transform) interaction test

  16. calculate_concordance() - Compare LM and rank test results

Examples

# \donttest{
data(readcounts, package = 'TSENAT')
metadata_df <- read.table(
  system.file('extdata', 'metadata.tsv', package = 'TSENAT'),
  header = TRUE, sep = '\t'
)
gff3_file <- system.file('extdata', 'annotation.gff3.gz', package = 'TSENAT')

config <- TSENAT_config(
  sample_col = 'sample',
  condition_col = 'condition',
  q = seq(0, 2, length.out = 10),
  generate_plots = FALSE
)
analysis <- build_analysis(
  readcounts = as.matrix(readcounts),
  tx2gene = gff3_file,
  metadata = metadata_df,
  config = config,
  tpm = tpm,
  effective_length = effective_length
)

result <- TSENAT(analysis)
#> Created output directory: tsenat_outputs
#> 
#> +============================================================+
#> |          TSENAT: Tsallis Entropy Analysis Toolbox          |
#> +============================================================+
#>                                                               
#>       Science is an essentially anarchic enterprise.          
#>                                                               
#>                        -- Paul Feyerabend, Against Method     
#>                                                               
#> [DATA] Data Summary
#>   Transcripts .......... 3,089
#>   Samples .............. 16
#>   Conditions ........... 2
#>   Q-spectrum range ..... 0 to 2 (10 values)
#> 
#> [CONFIG] Analysis Configuration
#>   Design ............... unpaired
#>   Filter stringency .... medium
#>   Normalization ........ enabled [0-1]
#>   Normalization method . range (default)
#>   Pseudocount .......... disabled
#>   Shrinkage ............ disabled
#>   Significance ......... p < 0.050 | FDR < 0.050
#>   SAIT method .......... GAM
#>   SAIT p-corr method ... BH
#>   Jackknife use_sait_fdr . TRUE
#> 
#> =============================================================
#> [>] [ 1/16] Filtering low-abundance transcripts
#>           [OK] Complete - 341 transcripts remaining
#> [>] [ 2/16] Computing Tsallis entropy
#>           [OK] 10 q-values processed
#> [>] [ 3/16] Plotting diversity q-spectrum
#>           [OK] Plot generated
#> [>] [ 4/16] Computing M-estimator influence
#>           [OK] M-estimate QC complete
#> [>] [ 5/16] Fitting SAIT interaction models
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: nlminb problem, convergence error code = 1
#>   message = iteration limit reached without convergence (10)
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#> Warning: Weights are not used by the primary paired model path (lme_ns_car1): the supplied data_adaptive_heteroscedasticity weights only apply to the GAM/GAMM paths. If weighting is required for the paired analysis, select a GAM-based method explicitly.
#>           [OK] SAIT interaction analysis complete
#> [>] [ 6/16] Plotting SAIT results
#>           [OK] SAIT interaction plot generated
#> [>] [ 7/16] Computing jackknife isoform switching
#>           [OK] Jackknife isoform switching complete
#> [>] [ 8/16] Plotting influence heatmap
#>           [OK] Influence heatmap generated
#> [>] [ 9/16] Plotting top transcripts
#>           [OK] Top transcripts plot generated
#> [>] [10/16] Computing divergence metrics
#>           [OK] Divergence computed
#> [>] [11/16] Computing effect sizes
#>           [OK] Effect sizes computed
#> [>] [12/16] Plotting divergence distributions
#>           [OK] Divergence distribution plot generated
#> [>] [13/16] Plotting divergence spectrum
#>           [OK] Global divergence spectrum plot generated
#>           [OK] Multi-gene divergence spectrum plot generated
#> [>] [14/16] Checking statistical assumptions
#>           [OK] Assumptions validated
#> [>] [15/16] Performing ART (Aligned Rank Transform) interaction test
#>           [OK] Aligned Rank Transform (ART) completed
#> [>] [16/16] Computing SAIT-ART concordance
#>           [OK] Concordance analysis completed
#> =============================================================
#> 
#> +============================================================+
#> |               [OK] ANALYSIS COMPLETE                       |
#> +============================================================+
#> 
#> [RESULTS] Results Summary
#> 
#> [PERF] Performance
#>   Total time ........... 44.6s
#>   Slowest steps:
#>     1. jackknife            18.3s (41.0%)
#>     2. rank_transform_test  8.6s (19.4%)
#>     3. sait_interaction     7.1s (15.8%)
#> 
#> [OUTPUT] Output
#>   Directory ........... tsenat_outputs
#>   Files saved ......... 18
# }