Compute pairwise Pearson correlations between samples from an expression matrix and display them as a clustered heatmap — a standard QC view of replicate consistency.
Upload your data and generate the correlation heatmap
TSV/CSV matrix: first column = feature ID, remaining = samples
| Gene | Active_1 | Active_2 | Active_3 | Active_4 | … |
|---|---|---|---|---|---|
| UCP1 | 8.2 | 8.14 | 7.84 | 8.72 | … |
| CPT1A | 5.75 | 7.01 | 9.12 | 8.04 | … |
| PPARGC1A | 7.79 | 6.3 | 7.86 | 9.18 | … |
| HIF1A | 7.66 | 9.89 | 9.66 | 7.48 | … |
Upload data and click Generate, or run with example data
Input format: a tab- or comma-separated numeric matrix whose first column holds feature IDs and whose remaining columns are samples.
Method: pairwise Pearson correlation between samples; rows/columns are reordered by hierarchical clustering (complete linkage on 1 - r) so that similar samples group together. Each cell is annotated with the correlation coefficient.
Typical use: quality control of biological replicates in RNA-seq studies (e.g., Active vs Torpor sample clustering).