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Scaling, Normalization and Standardization [https://www
from the top result below
https://www.bioinforesearch.com/posts/mastering-data-scaling-for-biomedical-heatmaps-a-essential-guide-for-accurate-visualization-and-interpretation
This process is particularly crucial in biomedical contexts where diverse data types—from gene expression counts to protein concentrations—must be compared on a common visual scale
https://www.bioinforesearch.com/posts/heatmaps-vs-volcano-plots-a-strategic-guide-for-biomedical-researchers-and-drug-developers
The fundamental strength of a heatmap lies in its ability to provide an intuitive and immediate visual summary of complex information, allowing patterns, trends, and outliers to be discerned quickly [3]
https://www.bioinforesearch.com/posts/a-complete-stepbystep-guide-to-pca-for-rnaseq-data-analysis-from-basics-to-advanced-applications
They are designed to be uncorrelated (orthogonal), and are ordered such that the first component captures the maximum possible variance in the data, the second component captures the next highest variance while being uncorrelated to the first, and so on [1] [5]
https://www.bioinforesearch.com/posts/a-comprehensive-guide-to-singlecell-rna-sequencing-analysis-from-basics-to-advanced-applications-in-drug-discovery
: Co-expression patterns that exist only in specific cell subpopulations cannot be distinguished from random co-variation across cells [6]
These limitations are particularly problematic in complex tissues like tumors, where cellular heterogeneity is a fundamental driver of therapy resistance and disease progression [2]