calour.transforming)¶Warning
Some of the functions require dense matrix and thus will change the sparse matrix to dense matrix.
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. |