t-SNE
t-SNE is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Health Sciences sample dataset — how the analysis is run, what the MerQur output looks like, and how the result is reported in APA 7 format.
🎯 What is it for?
t-SNE automatically performs all assumption checks required for the data type (normality, homogeneity of variance, etc.) in the background and presents the results in a clear table + chart. Automatic APA 7-formatted interpretation, effect sizes (Cohen’s d, η², R²) and 95% confidence intervals are reported.
📌 When is it used?
- Statistical analysis of measurements in the Health Sciences domain
- To produce APA 7-compatible result tables for academic publications
- Hypothesis testing and decision-making processes
- Undergraduate / master’s / PhD theses after the appropriate method has been selected
📐 Assumptions
- Appropriate scale — Variables must be at the measurement level required by the analysis (nominal/ordinal/interval/ratio)
- Independent observations — Observations must come from individuals independent of one another
- Sufficient sample size — The minimum n requirement for the analysis must be met
- Outlier check — Outliers must be detected and evaluated
If assumptions are violated, MerQur automatically suggests a non-parametric or robust alternative.
🛠 How to do it in MerQur
Load the data. Select the sample file from File → Open. MerQur auto-detects column types.
Select the analysis. From the left side select t-SNE.
Panel assignments (form fields in the program):
- Columns:
[gen_01, gen_02, gen_03, ... +9 adet] - Perplexity:
15 - Iterasyon:
500 - Missing strategy:
drop
Optional settings. Effect size ✓ · 95% confidence interval ✓ · Assumption checks (automatic).
▶ Run — click the button. Results are produced automatically as a table + chart.
📄 Export to Word. APA 7-formatted report with italic statistical symbols.
📊 Sample Dataset — Health Sciences
ℹ Note: The scenario, MerQur output and interpretation below were produced by actually running the real example dataset in MerQur. Numeric results on your own data will differ; the goal is to show how the analysis is set up and interpreted end-to-end.
🎬 Example File
This analysis is demonstrated on the following example dataset for Health Sciences:
Tip/70_tsne_cancer_5tip.xlsx
🎬 Scenario
We reduce 12-gene-dimensional data to 2 dimensions with t-SNE for visualization. For nonlinear subtype discovery, t-SNE is appropriate.
⚙️ Variable Selection
- Variables: gene_01..12
Data Preview (First 5 Rows)
| patient_id | lower_type | gene_01 | gene_02 | gene_03 | gene_04 | gene_05 | gene_06 | gene_07 | gene_08 | gene_09 | gene_10 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | A | 1.109 | 0.324 | 1.449 | 1.069 | -1.012 | 0.5 | 0.741 | 1.155 | 0.248 | -0.371 |
| 2 | A | -0.772 | -0.098 | -1.212 | -1.319 | 1.213 | 1.904 | -1.012 | 1.201 | 0.506 | -0.321 |
| 3 | A | -0.539 | 1.733 | 0.544 | 0.323 | -0.577 | 0.755 | 0.09 | 0.005 | 0.203 | -1.462 |
| 4 | A | 1.2 | 1.355 | -0.222 | -0.981 | -0.687 | 1.068 | -0.797 | -1.601 | 0.182 | -0.552 |
| 5 | A | -0.299 | -0.817 | -0.026 | 0.199 | -1.969 | 0.447 | -0.347 | 1.953 | 0.328 | 0.13 |
n = 125 · Columns (first 12 columns): patient_id, lower_type, gene_01, gene_02, gene_03, gene_04, gene_05, gene_06, gene_07, gene_08, gene_09, gene_10
📈 MerQur Output
💬 Interpretation
We reduced 12-gene-dimensional data to 2 dimensions for visualization with t-SNE (KL = 0.56, good quality). t-SNE tries to preserve high-dimensional neighborhoods, placing similar observations near and dissimilar ones far; it reveals nonlinear cluster structures visually that PCA misses. Interpretation is visual (the axes have no absolute meaning). In medicine/genomics it is used for exploratory visualization of hidden patient subtypes in high-dimensional gene/omics data.
⚠ Common Mistakes
- Misidentifying the data type (e.g., loading a categorical variable as numeric)
- Skipping assumption checks and going straight to the p-value
- Failing to report effect size — APA 7 requires both p and effect size
- Failing to apply a Type I error correction (Bonferroni/Tukey) in multiple comparisons
- Not switching to a non-parametric alternative when n is insufficient
📹 Video Walkthrough
Watch the video below for an end-to-end walkthrough of this analysis on a Health Sciences file.
▶ t-SNE — video walkthrough
A complete end-to-end walkthrough of this analysis in MerQur, narrated on screen. Narration is in Turkish.
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · 🏛 Architecture, Planning & Design · ⚙ Engineering · 📊 Social, Humanities & Admin Sciences · 🏃 Sport Sciences · 🌾 Agriculture, Forestry & Aquatic
📚 If You Used This Analysis, Cite MerQur
If you performed this analysis using MerQur in a scientific study, please use the citation below as part of your academic citation obligations (APA 7):
Örücü, Ö. K. (2026). MerQur: Integrated Academic Data Analysis & Reporting Platform [Computer software] (Version 1.0.0). https://doi.org/10.53463/merqur.2026001
For BibTeX, RIS, EndNote and the English citation form: all citation formats →
- American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.).
- Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). Sage.
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.