PCA or cluster table of experimental fitness scores I need to find patterns experimental data.

The columns are "experiments" which are chemical treatments for growth experiments.  The rows are individual gene names, the values are a fitness-defect score, which reflect the genes contribution to growth. 

I would like to find patterns that are reflected across all experiments using some type of PCA or clustering.  I have been trying to use sklearn but have not been successful in applying a model. 

The data looks like:

gene     SGTC_1                   SGTC_2                 SGTC_3 
YAL002W  3.56420220283773        1.80774301690328       0.431491057210906
YAL004W -0.885645399324204      -1.76020417788351       0.883034190306176


....

There are 4000 rows for genes and 30 columns for experiments.

Any suggestions would be greatly appreciated.
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.