diff --git a/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/complexity_vs_deseq2.md b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/complexity_vs_deseq2.md
new file mode 100644
index 00000000000000..ab6c21333852cb
--- /dev/null
+++ b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/complexity_vs_deseq2.md
@@ -0,0 +1,14 @@
+---
+title: Why is transcriptome complexity important if DESeq2 already detects changes?
+area: conceptual
+layout: faq
+box_type: tip
+contributors: [gallardoalba]
+---
+
+DESeq2 and edgeR measure **total gene abundance** changes. They miss situations where:
+- A gene maintains constant total expression but **reorganizes its isoforms**
+- Rare isoforms become more common (or vice versa) without affecting overall counts
+- The **diversity** of the isoform landscape changes
+
+These are valid biological signals (isoform switching via splicing regulation) that complement abundance-based methods.
diff --git a/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/effect_size_reporting.md b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/effect_size_reporting.md
new file mode 100644
index 00000000000000..8b2142e6bc74b6
--- /dev/null
+++ b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/effect_size_reporting.md
@@ -0,0 +1,16 @@
+---
+title: A gene shows significant q×condition interaction but small effect size—should I report it?
+area: interpretation
+layout: faq
+box_type: tip
+contributors: [gallardoalba]
+---
+
+Guidelines for meaningful results:
+- **Effect size (Tsallis divergence) D > 0.1**: ~10% information divergence (notable)
+- **D > 0.2**: Substantial divergence (recommended threshold)
+- **Small D with p < 0.05**: May reflect statistical noise; prioritize high-effect genes
+
+Report both for completeness:
+- High-effect genes (D > 0.2): Strong biological signal
+- Borderline genes (D ~ 0.1, p < 0.05): Mention as candidates for validation
diff --git a/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/erratic_q_curves.md b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/erratic_q_curves.md
new file mode 100644
index 00000000000000..06bd7731351bc8
--- /dev/null
+++ b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/erratic_q_curves.md
@@ -0,0 +1,13 @@
+---
+title: Q-curves show erratic patterns. Is this normal?
+area: troubleshooting
+layout: faq
+box_type: tip
+contributors: [gallardoalba]
+---
+
+**Expected**: Small fluctuations in entropy across q-values
+**Concerning**: Large spikes or reversals
+- May indicate normalization issues
+- Check: Are housekeeping genes (GAPDH, ACTB) showing balanced patterns?
+- If not: Revisit pseudocount and filtering parameters
diff --git a/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/index.md b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/index.md
new file mode 100644
index 00000000000000..fde41fcf19a8d4
--- /dev/null
+++ b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/index.md
@@ -0,0 +1,5 @@
+---
+layout: faq-page
+area_order: [conceptual, technical, methodological, workflow, interpretation, troubleshooting]
+title: TSENAT FAQs
+---
diff --git a/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/long_reads.md b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/long_reads.md
new file mode 100644
index 00000000000000..27c40e6663ea2f
--- /dev/null
+++ b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/long_reads.md
@@ -0,0 +1,13 @@
+---
+title: Can I use TSENAT with very long reads (PacBio, Nanopore)?
+area: troubleshooting
+layout: faq
+box_type: tip
+contributors: [gallardoalba]
+---
+
+Conceptually yes, but:
+- TSENAT expects pseudoalignment-based quantification (SALMON, Kallisto)
+- Long reads typically use alignment-based tools (Minimap2, etc.)
+- Workflow would need adaptation to parse alignment-based counts
+- Overall approach (entropy framework) remains valid
diff --git a/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/na_nan_results.md b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/na_nan_results.md
new file mode 100644
index 00000000000000..07062eba33f2eb
--- /dev/null
+++ b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/na_nan_results.md
@@ -0,0 +1,14 @@
+---
+title: Some q-values give NA or NaN results. Why?
+area: troubleshooting
+layout: faq
+box_type: tip
+contributors: [gallardoalba]
+---
+
+Possible causes:
+1. **Zero counts**: Pseudocount selection should handle; check filtering
+2. **Numerical instability**: Very small divergence values can underflow
+3. **Method-specific**: SRH (rank test) may have issues with many tied values
+
+Solution: Ensure pseudocount regularization is enabled; try alternative statistical method.
diff --git a/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/no_significant_genes.md b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/no_significant_genes.md
new file mode 100644
index 00000000000000..2ae152ed5610c6
--- /dev/null
+++ b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/no_significant_genes.md
@@ -0,0 +1,13 @@
+---
+title: My results show no significant genes. What could be wrong?
+area: troubleshooting
+layout: faq
+box_type: tip
+contributors: [gallardoalba]
+---
+
+1. **Sample size too small**: Need ≥6-8 per group (check design)
+2. **Entropy differences too subtle**: Try visualizing q-curves manually
+3. **Filtering too stringent**: Relaxing filters may reveal additional patterns
+4. **Method choice**: Try alternative statistical method (GAM vs. LMM vs. SRH)
+5. **Data quality**: Run M-estimation QC; check for outlier samples
diff --git a/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/q_condition_interaction.md b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/q_condition_interaction.md
new file mode 100644
index 00000000000000..78a25fce8f4839
--- /dev/null
+++ b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/q_condition_interaction.md
@@ -0,0 +1,12 @@
+---
+title: What does "q × condition interaction" mean?
+area: methodological
+layout: faq
+box_type: tip
+contributors: [gallardoalba]
+---
+
+It means the effect of **condition on entropy depends on which q-value** you examine:
+- At q=0.5, one condition might have higher entropy
+- At q=2, the pattern might reverse
+- This reveals **scale-dependent** biological signals
diff --git a/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/q_spectrum_choice.md b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/q_spectrum_choice.md
new file mode 100644
index 00000000000000..0ce452cdf7dbd9
--- /dev/null
+++ b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/q_spectrum_choice.md
@@ -0,0 +1,12 @@
+---
+title: How do I choose the q-spectrum (range and granularity)?
+area: technical
+layout: faq
+box_type: tip
+contributors: [gallardoalba]
+---
+
+**Standard configuration**: q = 0 to 2.0 with Δq = 0.05 (41 q-values)
+- Range: 0-2 covers extreme rare-emphasis to abundant-emphasis
+- Granularity: Δq=0.05 provides good resolution without computational burden
+- For visualization: Can downsample to q = (0, 0.5, 1, 1.5, 2) for quick exploration
diff --git a/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/read_depth_entropy.md b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/read_depth_entropy.md
new file mode 100644
index 00000000000000..15ff56d1149e21
--- /dev/null
+++ b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/read_depth_entropy.md
@@ -0,0 +1,13 @@
+---
+title: Does read depth affect entropy estimates?
+area: technical
+layout: faq
+box_type: tip
+contributors: [gallardoalba]
+---
+
+Yes. TSENAT uses **automatic pseudocount selection** to handle this:
+- Pseudocounts scale with library size
+- Ensures entropy estimates are comparable across samples with different sequencing depths
+- Breaks ties in rank-based validation methods
+- Standard practice in RNA-seq analysis
diff --git a/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/sample_size.md b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/sample_size.md
new file mode 100644
index 00000000000000..6a1c563de8e446
--- /dev/null
+++ b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/sample_size.md
@@ -0,0 +1,12 @@
+---
+title: What sample size do I need?
+area: technical
+layout: faq
+box_type: tip
+contributors: [gallardoalba]
+---
+
+TSENAT assumes **≥6-8 samples per group** for paired/longitudinal designs:
+- Smaller samples are underpowered to detect entropy shifts
+- Larger samples increase power to detect subtle complexity changes
+- Biological replicates are essential (not technical replicates)
diff --git a/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/transcript_counts.md b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/transcript_counts.md
new file mode 100644
index 00000000000000..1f5e5cea5c6abf
--- /dev/null
+++ b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/transcript_counts.md
@@ -0,0 +1,14 @@
+---
+title: Where do I get transcript-level counts for this analysis?
+area: workflow
+layout: faq
+box_type: tip
+contributors: [gallardoalba]
+---
+
+TSENAT expects **SALMON or Kallisto output** including:
+- NumReads: raw fragment counts per transcript
+- TPM: length- and library-normalized estimates
+- EffectiveLength: read-length corrected transcript length
+
+These are standard outputs from pseudoalignment-based quantification.
diff --git a/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/transcript_switching_types.md b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/transcript_switching_types.md
new file mode 100644
index 00000000000000..128dced9b788d2
--- /dev/null
+++ b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/transcript_switching_types.md
@@ -0,0 +1,15 @@
+---
+title: What's the difference between "consistent" vs. "scale-dependent" transcript switching?
+area: technical
+layout: faq
+box_type: tip
+contributors: [gallardoalba]
+---
+
+- **Consistent switching**: Same transcripts show high delta-influence values across all q-values
+  - Robust, scale-independent splicing shift
+  - Suggests coordinated regulatory mechanism
+  
+- **Scale-dependent switching**: Different transcripts matter at different q-values
+  - Regulatory complexity varies by scale
+  - Different mechanisms for rare vs. abundant isoforms
diff --git a/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/tsallis_divergence_vs_foldchange.md b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/tsallis_divergence_vs_foldchange.md
new file mode 100644
index 00000000000000..72cb16a5abbd5c
--- /dev/null
+++ b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/tsallis_divergence_vs_foldchange.md
@@ -0,0 +1,18 @@
+---
+title: What's the advantage of Tsallis divergence over fold-change?
+area: methodological
+layout: faq
+box_type: tip
+contributors: [gallardoalba]
+---
+
+**Fold-change**:
+- Simple 1:1 ratio between conditions
+- Misses complexity patterns
+- Unbounded (can be arbitrarily large)
+
+**Tsallis divergence**:
+- Information-theoretic distance between distributions
+- Captures pattern type (rare-driven, balanced, abundant-driven)
+- Symmetric [0] to asymmetric, bounded interpretability
+- Directly quantifies isoform complexity differences
diff --git a/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/tsallis_vs_shannon.md b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/tsallis_vs_shannon.md
new file mode 100644
index 00000000000000..2ed36924ccedbd
--- /dev/null
+++ b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/faqs/tsallis_vs_shannon.md
@@ -0,0 +1,14 @@
+---
+title: What's the difference between Tsallis entropy and Shannon entropy?
+area: conceptual
+layout: faq
+box_type: tip
+contributors: [gallardoalba]
+---
+
+**Shannon entropy** (q=1 in Tsallis framework) treats all isoforms equally. **Tsallis entropy** adds a parameter `q` that acts as a sensitivity dial:
+- When q < 1: emphasizes **rare isoforms**
+- When q = 1: recovers Shannon entropy (balanced)
+- When q > 1: emphasizes **abundant isoforms**
+
+This allows you to examine complexity at different biological scales in a single analysis.
diff --git a/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/slides.html b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/slides.html
new file mode 100644
index 00000000000000..1b6c05abba91ff
--- /dev/null
+++ b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/slides.html
@@ -0,0 +1,87 @@
+---
+layout: tutorial_slides
+logo: "GTN"
+
+title: "Isoform complexity analysis"
+contributions:
+  authorship:
+    - gallardoalba
+
+priority: 4
+
+video: no
+zenodo_link: ""
+
+
+---
+
+## Index of contents
+
+1. Introduction
+
+2. Galaxy workflow
+
+---
+
+> To do science is to be a social actor engaged, whether one likes it or not, in political activity. The denial of the interpenetration of the scientific and the social is itself a political act [...]
+> 
+> .footnote[Source: The Dialectical Biologist, Introduction](https://www.hup.harvard.edu/books/9780674202832)
+
+---
+
+# Introduction
+
+- The problem of measuring biological complexity
+- Parts, wholes, and dialectical relationships
+- Why reductionism fails for isoform complexity
+- Tsallis entropy: a dialectical measure
+
+---
+
+## The problem of measuring biological complexity
+
+- Reductionist approach: Count isoforms, compute ratios, test differences one by one
+- Problem: This treats each isoform as an independent entity with intrinsic properties
+- Cartesian assumption: "The part is ontologically prior to the whole" (Levins & Lewontin)
+
+>  Cartesian reductionism is sometimes spoken of as the "Cartesian method"... But Cartesianism is more than simply a method of investigation; it is a commitment to how things really are.
+>
+> .footnote[Source: The Dialectical Biologist, Introduction](https://www.hup.harvard.edu/books/9780674202832)
+
+--- 
+
+## Parts, wholes, and dialectical relationships
+
+> "Part" and "whole" have a special relationship to each other, in that one cannot exist without the other, any more than "up" can exist without "down." What constitutes the parts is defined by the whole that is being considered. Moreover, parts acquire properties by virtue of being parts of a particular whole, properties they do not have in isolation.
+>
+> .footnote[Source: The Dialectical Biologist, Introduction](https://www.hup.harvard.edu/books/9780674202832)
+
+Key insight: Isoform complexity is not the sum of individual isoform abundances—it is an emergent property of the transcript distribution.
+
+---
+
+## Why reductionism fails for isoform complexity
+
+- A gene with 5 isoforms where one dominates (99% abundance) has low complexity
+- A gene with 5 isoforms at equal abundance (20% each) has high complexity
+- Same number of parts, completely different wholes
+
+> It is not that the whole is more than the sum of its parts, but that the parts acquire new properties. But as the parts acquire properties by being together, they impart to the whole new properties, which are reflected in changes in the parts, and so on.
+> 
+> .footnote[Source: The Dialectical Biologist, Introduction](https://www.hup.harvard.edu/books/9780674202832)
+
+---
+
+## Tsallis entropy: a dialectical measure
+
+Tsallis entropy captures diversity as a function of a single parameter q:
+
+$$S_q = \frac{1 - \sum_{i=1}^N p_i^q}{q - 1}$$
+
+- q → 0: Emphasizes rare isoforms (richness-dominated)
+- q = 1: Shannon entropy (standard measure)
+- q → 2+: Emphasizes abundant isoforms (evenness-dominated)
+
+Dialectical aspect: No single q-value reveals "the truth"—each scale reveals different properties of the whole.
+
+---
\ No newline at end of file
diff --git a/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/tutorial.bib b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/tutorial.bib
new file mode 100644
index 00000000000000..9bd4d3091fbbb7
--- /dev/null
+++ b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/tutorial.bib
@@ -0,0 +1,386 @@
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+}
+
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+
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+}
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+}
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+}
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+}
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+  journal={Entropy},
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+}
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+}
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+}
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diff --git a/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/tutorial.md b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/tutorial.md
new file mode 100644
index 00000000000000..cea134a0a4b83f
--- /dev/null
+++ b/topics/transcriptomics/tutorials/transcriptome_complexity_tsenat/tutorial.md
@@ -0,0 +1,358 @@
+---
+layout: tutorial_hands_on
+
+title: "Transcriptome Complexity Analysis with TSENAT"
+
+tags:
+    - rna-seq
+    - isoform
+    - entropy
+    - transcriptomics
+    - complexity
+    - information-theory
+
+level: Advanced
+time_estimation: 6h
+
+questions:
+    - What is Tsallis entropy and how does it relate to isoform complexity?
+    - How can I quantify transcriptome complexity changes between conditions?
+    - What are scale-dependent diversity measures and why do they matter?
+    - How do I detect isoform switching at different scales?
+    - What's the difference between Shannon entropy and Tsallis entropy?
+
+objectives:
+    - Understand the mathematical foundations of Tsallis entropy
+    - Explain the role of the entropic index (q) as a sensitivity parameter
+    - Describe the concept of scale-dependent isoform complexity
+    - Interpret q-curves and identify complexity patterns
+    - Understand the advantages of entropy-based analysis over traditional abundance measures
+    - Design and interpret scale-adaptive interaction models
+    - Identify scale-dependent isoform switching in RNA-seq data
+
+key_points:
+    - Tsallis entropy captures isoform complexity across multiple scales via the q parameter
+    - Traditional RNA-seq tools miss isoform reorganization without abundance changes
+    - Scale-adaptive interaction models detect condition-specific diversity patterns
+    - The q-spectrum reveals which biological scales are affected (rare vs abundant isoforms)
+    - Entropy-based approaches complement abundance-focused tools like DESeq2
+
+follow_up_training:
+    -
+        type: "internal"
+        topic_name: transcriptomics
+        tutorials:
+            - ref-based
+            - rna-seq-reads-to-counts
+
+contributions:
+  authorship:
+    - gallardoalba
+
+lang: en
+
+---
+
+This tutorial covers the theory and practical application of **TSENAT (Tsallis Entropy Analysis Toolbox)**, a  R package for quantifying and analyzing isoform-usage complexity in RNA-seq data using information-theoretic principles.
+
+
+> <agenda-title></agenda-title>
+>
+> In this tutorial, we will deal with:
+>
+> 1. TOC
+> {:toc}
+>
+{: .agenda}
+
+# Background: The Isoform Complexity Problem
+
+## Why Transcriptome Complexity Matters
+
+Standard RNA-seq analysis measures whether **transcript abundance** changes between conditions:
+- DESeq2 and edgeR ask: "Do genes produce more or less RNA?"
+- These tools focus on **total gene abundance**
+
+However, a critical complementary question remains largely unexplored:
+- **How does the diversity of isoforms change?**
+
+A gene may show **little change in total abundance** while dramatically **reshuffling its isoform repertoire**—a phenomenon often missed by traditional methods. This **isoform switching** reflects strategic shifts in protein function driven by splicing regulation, and represents genuine biological signal invisible to abundance-focused analyses.
+
+## Motivation: Evidence from the Literature
+
+### Cancer Heterogeneity and Network Entropy
+
+Recent evidence demonstrates that transcriptome complexity constitutes a measurable biological phenomenon:
+
+- **Tarabichi et al. (2013)**: {% cite tarabichi2013systems %} Cancer cells show "increased entropy of signaling and gene interaction networks" as cancer progresses, contributing to cellular heterogeneity and evolvability
+- **Nijman (2020)**: {% cite nijman2020perturbation %} Cancer-associated perturbations increase network entropy, driving phenotypic heterogeneity through disruption of gene regulatory networks
+- **Cao et al. (2017)**: {% cite cao2017comprehensive %} Single-cell transcriptomics reveals that transcript-level complexity varies systematically across cell types and developmental states
+
+**The biological principle**: Changes in isoform organization—not just abundance—are valid biological signals worth measuring.
+
+## Abundance-Based Tools vs. Entropy-Based Tools
+
+| Aspect | DESeq2/edgeR | DRIMSeq | TSENAT |
+|--------|--------------|---------|--------|
+| **What it measures** | Total gene abundance | Differential transcript usage | Isoform heterogeneity/complexity |
+| **Detection capability** | Gene-level fold changes | Transcript switching | Reorganization of isoform landscape |
+| **A gene that doubles total count but reshuffles isoforms** | ✓ Detected as DE | Maybe detected | ✓ Detected as complexity change |
+| **A gene that maintains count but remixes isoforms** | ✗ Missed | Maybe detected | ✓ Detected as complexity change |
+| **Interpretability** | Fold-change magnitude | Proportional shift | Diversity pattern (rare vs abundant) |
+
+---
+
+# Theoretical Foundations: From Shannon to Tsallis Entropy
+
+## Historical Development
+
+### Claude Shannon (1948): Birth of Information Theory
+
+Claude Shannon's landmark 1948 work established that **information content could be quantified mathematically**. Shannon entropy measures the uncertainty when drawing a single observation from a probability distribution:
+
+$$H(p) = -\sum_{i=1}^{n} p_i \log_2(p_i)$$
+
+Where:
+- $p_i$ is the probability of outcome $i$
+- Higher entropy = more unpredictable outcome
+- In transcriptomics: higher entropy = more evenly distributed isoforms
+
+**Key insight**: If you draw a transcript from a gene's isoform pool, how predictable is the outcome?
+
+### Limitations of Shannon Entropy
+
+Shannon entropy treats all outcomes equally, regardless of frequency:
+- **Rare isoforms** and **common isoforms** receive identical weight
+- Cannot be tuned to emphasize one scale over another
+- Cannot separately examine complexity driven by rare variants vs. dominant isoforms
+
+### Generalized Entropy Families
+
+To address this limitation, mathematicians developed parametric entropy families:
+- **Rényi entropy** (1961): Introduced a tunable parameter α {% cite van2014renyi %}
+- **Tsallis entropy** (1988): Alternative generalization with parameter q {% cite tsallis2017foundations %}
+- **Hill numbers** (2010): Modern ecological reinterpretation unifying all approaches {% cite chao2010phylogenetic %}
+
+#### Remarkable fact
+
+Despite appearing different mathematically, Rényi and Tsallis entropies can be unified through generalized logarithmic and exponential functions—both answer the fundamental question: **What organizational scales matter?**
+
+## Mathematical Definition of Tsallis Entropy
+
+For a discrete probability vector **p** = (p₁, …, pₙ) representing isoform proportions within a gene, Tsallis entropy is defined as:
+
+$$S_q(p) = \frac{1 - \sum_{i=1}^{n} p_i^q}{q - 1}$$
+
+### Properties and Special Cases
+
+#### The q parameter acts as a sensitivity dial
+
+- **Limit as q → 1**: Recovers Shannon entropy (balanced sensitivity)
+- **q = 0**: Richness (number of distinct isoforms present)
+- **q = 2**: Simpson index (emphasizes dominant isoforms)
+- **q < 1** (e.g., 0.5): Emphasizes **rare isoforms**
+- **q > 1** (e.g., 2.0): Emphasizes **abundant isoforms**
+
+### Biological Interpretation: Richness and Evenness
+
+**True diversity** decomposes into two independent components:
+
+1. **Richness**: How many distinct isoforms exist
+2. **Evenness**: How evenly transcripts are distributed
+
+Two genes can have identical Shannon entropy yet differ dramatically:
+
+| Scenario | Isoforms | Shannon Entropy | Interpretation |
+|----------|----------|-----------------|-----------------|
+| **Gene A** | 1 dominant + 99 rare | High (many isoforms contribute) | Heterogeneous: complexity across scales |
+| **Gene B** | 100 equally abundant | High (same total entropy) | Uniform: isoforms evenly distributed |
+
+By varying q, TSENAT reveals these differences: Gene A shows **high entropy at low q, declining at high q**, while Gene B shows **consistent entropy across all q-values**.
+
+---
+
+
+# Quality Control Approaches
+
+## M-Estimation for Sample Influence
+
+Before group-level comparisons, assess whether individual samples exert **disproportionate influence** on entropy estimates.
+
+### Leave-One-Out Influence Assessment
+
+1. **Robust M-estimation**: Iteratively re-weighted least squares with Huber loss
+2. **Design**: Quantifies how much sample removal affects location estimates
+3. **Independence from group assignment**: Sample-level QC at biological level
+4. **Interpretation**: Samples with high influence scores may indicate:
+   - Technical quality issues
+   - Subject-specific outliers
+   - Genuine biological heterogeneity
+
+### Flagging Criteria
+
+Samples warrant review if:
+- Distance from centroid > 1.5
+- Proportion of affected genes > 0.85
+- Outlier status flagged in QC analysis
+
+---
+
+# The Q-Spectrum: Visualization Framework
+
+## What is a Q-Curve?
+
+A **q-curve** plots Tsallis entropy across the entropic index spectrum from q=0 (rare-emphasizing) to q=∞ (abundant-emphasizing). 
+
+### Interpretation by Shape
+
+Three fundamental patterns emerge:
+
+1. **Flat curve (Balanced)**
+   - Entropy constant across q-values
+   - All isoforms shift proportionally
+   - Suggests systematic rebalancing
+   - Example: developmental transition
+
+2. **Declining curve (Rare-driven)**
+   - D(q=0.5) >> D(q=2.0)
+   - Low-abundance isoforms are condition-specific
+   - Treatment preferentially expresses rare transcripts
+   - Example: exploratory isoform expression
+
+3. **Rising curve (Abundant-driven)**
+   - D(q=0.5) << D(q=2.0)
+   - High-abundance isoforms shift between conditions
+   - Treatment remodels dominant transcript landscape
+   - Example: functional specialization
+
+### Multi-Scale Analysis
+
+Different biological processes prioritize different organizational scales:
+
+- **Isoform switching** (discrete change): Detected at specific q-values
+- **Wholesale reorganization** (widespread change): Detected at all q-values
+- **Cryptic isoform activation**: Revealed at low q (rare scale)
+- **Core architecture change**: Revealed at high q (abundant scale)
+
+---
+
+# Scale-Adaptive Interaction Models (SAIT)
+
+## The Core Question
+
+For each gene, we have a **q-curve**: entropy trajectory across entropic scales. We want to test: **Does this curve differ between groups?**
+
+Statistically, this is a **q × condition interaction**: the effect of condition on entropy depends on which q-value you examine.
+
+---
+
+# Transcript Switching Analysis
+
+## The Challenge: From Genes to Transcripts
+
+SAIT tells us **which genes** have scale-dependent complexity changes. But:
+- Which **specific transcripts** drive those changes?
+- Do the same transcripts switch across all scales?
+- Or do different regulatory mechanisms dominate at rare vs. abundant scales?
+
+## Jackknife Resampling Approach
+
+TSENAT uses **leave-one-out jackknife resampling** to identify robust transcript switches:
+
+### Delta Influence Metric
+
+For each transcript:
+- **Positive delta influence**: More important in control condition
+- **Negative delta influence**: More important in treatment condition
+- **Magnitude**: Robustness across bootstrap iterations
+- **Visualization**: Color intensity reflects consistency
+
+### Switching Classification
+
+1. **Consistent switching**: Same transcripts show strong delta influence across all q-values
+   - Robust, scale-independent splicing shift
+   - Suggests coordinated regulatory mechanism
+
+2. **Mixed/scale-dependent switching**: Different transcripts matter at different q-values
+   - Regulatory complexity varies by scale
+   - Different mechanisms for rare vs. abundant isoforms
+
+
+---
+
+# Tsallis Divergence: Effect Size Quantification
+
+## Definition
+
+For two probability distributions P (control) and Q (treatment) representing isoform proportions, **Tsallis divergence** measures information-theoretic distance:
+
+$$D_q(P||Q) = \frac{1 - \sum_i p_i^q \cdot q_i^{1-q}}{q - 1}$$
+
+Where:
+- D > 0 always (distance measure)
+- D = 0 only when distributions identical
+- Asymmetry: $D_q(P||Q) \ne D_q(Q||P)$ (information-theoretic property)
+
+## Biological Interpretation
+
+**Effect size threshold**: D > 0.1 indicates meaningful information-theoretic separation between conditions
+
+### Pattern Classification
+
+#### Rare-driven complexity change
+- D(q=0.5) >> D(q=2.0)
+- Divergence concentrated at low q
+- Interpretation: Condition differences driven by rare transcripts
+
+#### Abundant-driven complexity change
+- D(q=0.5) << D(q=2.0)
+- Divergence concentrated at high q
+- Interpretation: Condition differences in dominant isoforms
+
+#### Balanced complexity change
+- D(q=0.5) ≈ D(q=2.0)
+- Divergence consistent across scales
+- Interpretation: All isoforms reorganize proportionally
+
+---
+
+# Method Validation: Non-Parametric Concordance
+
+## Why Two Statistical Methods?
+
+Statistical testing in transcriptomics faces a fundamental trade-off:
+- **Parametric methods (GAM)**: Maximum power when assumptions hold, but sensitive to violations
+- **Non-parametric methods (SRH)**: Robust to any distribution, but reduced power
+
+### Scheirer-Ray-Hare (SRH) Rank-Based Test
+
+#### Advantages for entropy analysis
+
+1. **Robustness to distribution violations**: Operates on ranks (uniformly distributed), valid for ANY continuous distribution
+   - Non-normality: No effect on rank ordering
+   - Skewness: Rank transformation corrects automatically
+   - Bimodality: Inherently handled
+
+2. **Paired design for q-ordered structure**: 
+   - Treats q-measurements as within-subject repeated measures
+   - Captures sequential nature of q-spectrum
+   - AR(1) autocorrelation doesn't affect rank ordering (exchangeability property)
+
+3. **Interaction testing**:
+   - Two-way ANOVA on ranks
+   - Tests q × condition effects directly
+   - F-statistic follows F-distribution under null
+
+### Concordance Interpretation
+
+High agreement between GAM and SRH:
+- ✓ Robust discoveries across statistical frameworks
+- ✓ Results likely generalizable
+
+GAM-only discoveries:
+- Potential artifacts if GAM assumptions violated
+- Alternative: Trust only high-confidence genes significant in both methods
+
+SRH-only discoveries:
+- Rare; indicates distributional violations affecting GAM
+- May represent genuine signals from highly skewed distributions