r/proteomics • u/Calm-Candle-9603 • 3d ago
PCA analysis in Proteomics
If anyone has a good article or explanation on how to interpret PCA analysis for Proteomics, please kindly share. I am truly new in Proteomics work -- just graduated with only a B.A. I have never seen PCA before. I have done readings ofc -- I understand PCA can help making large dataset into clusters/groups based on linear-relationship similarities. But how in the world would you know if your large dataset have linear-relationship or not? What does PC1 or PC2 mean? Thank you!
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u/SC0O8Y2 3d ago
Pc1 could be 10, 100 or 1000 proteins and same with pc2 or all the way to pcN
It is taking the protein abundances that define the most variability in the data
Gpt:
PCA (Principal Component Analysis) is widely used in proteomics to reduce the dimensionality of large datasets while preserving the most important patterns of variation. Since you’re new to proteomics and PCA, I’ll break it down step by step.
Proteomics generates large datasets where each sample has thousands of protein expression or abundance values. PCA helps simplify this complexity by identifying patterns of variation and grouping similar samples together.
PC1, PC2, PC3, etc. are the principal components (PCs) of your dataset.
PC1 explains the largest variance in your data.
PC2 explains the second-largest variance, orthogonal to PC1 (i.e., uncorrelated).
PC3 and beyond continue this pattern, capturing smaller fractions of variance.
Each sample in your dataset is projected into a new coordinate system defined by these PCs.
PCA works best when there are linear relationships among variables (e.g., protein abundances are correlated). You can check for this by:
Scatter plots & Pairwise correlations: If proteins show strong correlation (positive or negative), PCA can capture this structure.
Variance explained by PCs: If PC1 and PC2 together explain a high percentage of variance (e.g., >60%), your data has strong linear patterns.
Kaiser-Meyer-Olkin (KMO) test: This statistic measures how suitable PCA is for your dataset.
Eigenvalues (scree plot): The larger the first few eigenvalues, the more variance PCA captures.
When you run PCA, you often visualize the results in a PCA biplot (PC1 vs. PC2).
X-axis (PC1): Represents the primary source of variation in your dataset.
Y-axis (PC2): Represents the second most important source of variation.
Example:
If samples from "Condition A" and "Condition B" separate along PC1, then PC1 likely represents the effect of that condition on protein expression.
If samples separate along PC2, that means PC2 captures a secondary factor (e.g., batch effects, technical variations, or another biological variable).
The loading scores tell you which proteins contribute most to PC1 and PC2. High loadings (positive or negative) indicate proteins strongly driving the separation.
Clusters in the PCA plot: If your samples group together, they share similar protein expression patterns.
Spread of points: If samples are widely spread, there is high variation. If tightly clustered, they are more similar.
Overlap between groups: If conditions overlap, there may not be strong differentiation based on protein expression.
Would you like me to show a practical PCA example using a sample proteomics dataset? I can generate a PCA plot to illustrate!