transforming (calour.transforming)

Warning

Some of the functions require dense matrix and thus will change the sparse matrix to dense matrix.

Functions

normalize(exp[, total, axis, inplace]) Normalize the sum of each sample (axis=0) or feature (axis=1) to sum total
normalize_by_subset_features(exp, features) Normalize each sample by their total sums without a list of features
normalize_compositional(exp[, min_frac, …]) Normalize each sample by ignoring the features with mean>=min_frac in all the experiment
scale(exp[, axis, inplace]) Standardize a dataset along an axis
random_permute_data(exp[, normalize]) Shuffle independently the reads of each feature
binarize(exp[, threshold, inplace]) Binarize the data with a threshold.
log_n(exp[, n, inplace]) Log transform the data
transform(exp[, steps, inplace]) Chain transformations together.
center_log_ratio(exp[, method, centralize, …]) Performs a clr transform to normalize each sample.
subsample_count(exp, total[, replace, …]) Randomly subsample each sample to the same number of counts.