UMAP
UMAP 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?
UMAP 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 UMAP.
Panel assignments (form fields in the program):
- Ozellik Sutunlari:
[omics_01, omics_02, omics_03, ... +22 adet] - Bilesen sayisi:
2 - Komsu:
15 - Min mesafe:
0.1
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/72_umap_omics_5tip.xlsx
🎬 Scenario
We reduce 25-dimensional omics data to 2 dimensions with UMAP. For both local and global structure, UMAP is appropriate.
⚙️ Variable Selection
- Variables: omics_01..25
Data Preview (First 5 Rows)
| patient_id | lower_type | omics_01 | omics_02 | omics_03 | omics_04 | omics_05 | omics_06 | omics_07 | omics_08 | omics_09 | omics_10 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | S1 | 0.926 | -1.212 | 1.186 | -0.689 | -0.947 | -0.757 | 0.929 | 1.308 | 2.063 | -0.819 |
| 2 | S1 | 0.133 | -0.139 | 0.236 | 0.682 | 1.06 | 0.678 | -0.759 | 0.655 | 1.822 | 1.212 |
| 3 | S1 | 0.35 | 0.184 | -0.256 | 0.488 | -0.116 | -1.293 | 0.266 | 1.47 | 1.493 | 0.012 |
| 4 | S1 | 0.691 | -0.174 | 0.337 | -0.681 | 0.448 | -0.273 | -1.381 | -0.239 | 1.401 | 0.137 |
| 5 | S1 | -0.194 | -1.582 | 0.801 | 0.146 | -0.206 | 1.004 | 0.072 | 0.522 | -0.967 | 0.547 |
n = 200 · Columns (first 12 columns): patient_id, lower_type, omics_01, omics_02, omics_03, omics_04, omics_05, omics_06, omics_07, omics_08, omics_09, omics_10
📈 MerQur Output
💬 Interpretation
We reduced 25-dimensional omics (gene/protein) data to 2 dimensions with UMAP. UMAP does nonlinear dimensionality reduction like t-SNE but preserves both local and global structure better and is faster. Larger n_neighbors emphasizes broader groups, larger min_dist emphasizes the gaps between clusters. In medicine/genomics it is a modern choice for visualizing high-dimensional omics data and exploring natural patient subtypes.
⚠ 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.
▶ UMAP — 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.