PCA or cluster table of experimental fitness scores
PCA is a dimensionality reduction algorithm - it projects your high dimensional data onto a lower dimensional plane. This is useful for either visualisation (if you reduce to 2 or 3 dimensions to plot), or for training machine learning models.

You say you want to find patterns in your data - I’m not quite sure what you mean by this. Do you want to visualise your data, train a model on it and make some prediction, or something else?

To visualise high dimensional data, you could use either the tSNE (t-stochastic neighbour embedding) algorithm, or PCA.

Depending on the type of data you have, you can “find patterns” in different ways.

If your data is unlabelled (you don’t know the classes of each sample or there is no dependent variable) you can use unsupervised learning algorithms such as K-means clustering, K nearest neighbours, Gaussian mixture model. If your data has dependent variables, depending on whether your dependent variable is categorical or continuous, you could use classification algorithms for the former and regression algorithms for the latter. Classification algorithms include logistic regression or decision trees. Regression models include linear regression.