BioProfiling.jl API

For an experiment e, update in place e.selected_features and e.selected_entries based on an array arr of feature selectors and entry filters. Filters and selectors are applied sequentially.

For an experiment e, update in place e.selected_features and e.selected_entries based on a feature selector s.

For an experiment e, update in place e.selected_features and e.selected_entries based on an entry filter f.

Return (all or if provided the top) features varying the most in e (largest absolute log fold change), when comparing entries matching filters f1 and f2. Columns for which the fold change is negative come last.

Returns column of selected data in Experiment e that are never pairwise-correlated more than 'threshold', prioritizing columns by a given order 'ordercol' (defaults to left to right).

Returns column of 'data' that are never pairwise-correlated more than 'threshold', prioritizing columns by a given order 'ordercol' (defaults to left to right).

Returns column of selected data in Experiment e that are never pairwise-correlated more than 'threshold', prioritizing columns with largest median absolute deviation.

Return information in an experiment e in column(s) feature after filtering with filter f.

Get images in an experiment e whose location is stored in feature s after filtering with filter f. Images will be saved at the path provided if saveimages is set to true, while keeping up to keepsubfolders folders from the original folder structure of the images. If center is true, crosses will indicate the center of selected objects, assuming they are stored in variables :AreaShape_Center_X, :AreaShape_Center_X_1, :AreaShape_Center_Y and :AreaShape_Center_Y_1 (default if you merge nuclear and cytoplasm measurements from CellProfiler outputs). Display up to showlimit images if show is true. Save up to savelimit images if saveimages is true. If rgx provides a list of regex substitutions, it will be applied on all image paths (which is useful if you're in a different file system or environment as the one described in the Experiment's data). If rgb provides a list of 3 lists of regex substitutions, it will be applied to generate the path to 3 images.

Get path to images in an experiment e stored in feature s after filtering with filter f. If center is true, the path will be returned together with the center of the filtered entries, assuming they are stored in variables AreaShape_Center_X, AreaShape_Center_X_1, AreaShape_Center_Y and AreaShape_Center_Y_1. If rgx provides a list of regex substitutions, it will be applied on all file paths in the output (which is useful if you're in a different file system or environment as the one described in the Experiment's data).

Compute the Mahalanobis Distance to center (MDC) in a dataset 'data' for a given perturbation of indices 'indpert' compared to a reference of indices 'indref'.

Compute the median Mahalanobis Distance (MD) in a dataset 'data' for a given perturbation of indices 'indpert' compared to a reference of indices 'indref'.

Compute the Robust Hellinger Distance (RHD) in a dataset data for a given perturbation of indices indpert compared to a reference of indices indref. This function calls R using RCall and a seed is set by default to ensure the results are reproducible. If you don't want that, for instance if you also use RCall and rely on another seed, set 'r_seed' to false.

Compute the median Robust Mahalanobis Distance (RMD) in a dataset 'data' for a given perturbation of indices 'indpert' compared to a reference of indices 'indref'. See https://e-archivo.uc3m.es/bitstream/handle/10016/24613/ws201710.pdf This function calls R using RCall and a seed is set by default to ensure the results are reproducible. If you don't want that, for instance if you also use RCall and rely on another seed, set 'r_seed' to false.

Filter entries in an Experiment e based on filter(s) f, updating e.selected_entries in place accordingly.

Return filtered entries in an Experiment e based on filter f

Return a copy of the data in Experiment e for its selected entries and features.

Squared hellinger distance for covariance estimators S and centers µ

Approximate normal distribution of selected entries for all selected features of an Experiment e. Warning: columns are converted to float when necessary.

Approximate normal distribution

Squared mahalanobis distance for covariance estimator S and center µ

Return (all or if provided the top) features in e associated the most with ref (absolute Pearson correlation).

Return the features of e ranked by decreasing median absolute deviation. Trim to the first top features if a value is provided.

Return a negative Filter or Selector by inverting the entries or features that are kept and excluded.

Center and scale all selected entries for each selectead features of an Experiment e on control values matching a Filter f, based on the median and median absolute deviation of the control. Warning: columns are converted to float when necessary.

Center and scale on control values

Compute the Robust Morphological Perturbation Value (RMPV) for a given Experiment e, for all levels of a column s, compared to rows matching a given filter f or where s is equal to ref. The RMPV quantifies the significance of changes between all conditions (levels in s) and a reference condition (defined by the filter f). In brief, the distance of type dist between points of each perturbation and points of the reference is computed and its statistical significance is defined using a permutation test in which the perturbation and reference labels are shuffled nb_rep times. If process_pool is a pool of worker processes, they will be used for parallel computation in the permutation test. This function might call R using RCall and a seed is set by default to ensure the results are reproducible. If you don't want that, for instance if you also use RCall and rely on another seed, set 'r_seed' to false. This returns a DataFrame with three columns:

• Condition: the levels in s
• Distance: the distance between a condition and the

reference

• RMPV: the RMPV (empirical p-value corrected for multiple

testing)

Return selected features in an Experiment e based on selectors s, updating e.selected_features in place accordingly.

Return selected features in an Experiment e based on selectors s

Permute labels and compute the Mahalanobis Distance to center (MDC) in a dataset 'data' for a given perturbation of indices 'indpert' compared to a reference of indices 'indref', to create an empirical distribution.

Permute labels and compute the median Mahalanobis Distance (RMD) in a dataset 'data' for a given perturbation of indices 'indpert' compared to a reference of indices 'indref', to create an empirical distribution.

Permute labels and compute the Robust Hellinger Distance (RHD) in a dataset data for a given perturbation of indices indpert compared to a reference of indices indref, to create an empirical distribution. This function calls R using RCall and a seed is set by default to ensure the results are reproducible. If you don't want that, for instance if you also use RCall and rely on another seed, set 'r_seed' to false.

Permute labels and compute the median Robust Mahalanobis Distance (RMD) in a dataset 'data' for a given perturbation of indices 'indpert' compared to a reference of indices 'indref', to create an empirical distribution. This function calls R using RCall and a seed is set by default to ensure the results are reproducible. If you don't want that, for instance if you also use RCall and rely on another seed, set 'r_seed' to false.

For an experiment e, update in place e.selected_features and e.selected_entries based on an array arr of feature selectors and entry filters. Filters and selectors are applied sequentially.

For an experiment e, update in place e.selected_features and e.selected_entries based on a feature selector s.

For an experiment e, update in place e.selected_features and e.selected_entries based on an entry filter f.

Expand freqtable to support Experiment objects. Find the frequency of the values taken by feature s in Experiment e.

Expand freqtable to support Experiment objects. Find the frequency of the values taken by feature s in Experiment e.