acore.utils module

convertToEdgeList(data, cols)

This function converts a pandas dataframe to an edge list where index becomes the source nodes and columns the target nodes.

Parameters:

• data – pandas dataframe.

• cols (list) – names for dataframe columns.

Returns:

Pandas dataframe with columns cols.

check_is_paired(df, subject, group)

Check if samples are paired.

Parameters:

• df – pandas dataframe with samples as rows and protein identifiers as columns (with additional columns ‘group’, ‘sample’ and ‘subject’).

• subject (str) – column with subject identifiers

• group (str) – column with group identifiers

Returns:

True if paired samples.

Return type:

bool

transform_into_wide_format(data, index, columns, values, extra=[])

This function converts a Pandas DataFrame from long to wide format using pandas pivot_table() function.

Parameters:

• data – long-format Pandas DataFrame

• index (list) – columns that will be converted into the index

• columns (str) – column name whose unique values will become the new column names

• values (str) – column to aggregate

• extra (list) – additional columns to be kept as columns

Returns:

Wide-format pandas DataFrame

Example:

result = transform_into_wide_format(df, index='index', columns='x', values='y', extra='group')

transform_into_long_format(data, drop_columns, group, columns=['name', 'y'])

Converts a Pandas DataDrame from wide to long format using pd.melt() function.

Parameters:

• data – wide-format Pandas DataFrame

• drop_columns (list) – columns to be deleted

• group (str or list) – column(s) to use as identifier variables

• columns (list) – names to use for the 1)variable column, and for the 2)value column

Returns:

Long-format Pandas DataFrame.

Example:

result = transform_into_long_format(df, drop_columns=['sample', 'subject'], group='group', columns=['name','y'])

remove_group(data)

Removes column with label ‘group’.

Parameters:

data – pandas dataframe with one column labelled ‘group’

Returns:

Pandas dataframe

Example:

result = remove_group(data)

calculate_fold_change(df, condition1, condition2)

Calculates fold-changes between two groups for all proteins in a dataframe.

Parameters:

• df – pandas dataframe with samples as rows and protein identifiers as columns.

• condition1 (str) – identifier of first group.

• condition2 (str) – identifier of second group.

Returns:

Numpy array.

Example:

result = calculate_fold_change(data, 'group1', 'group2')

pooled_standard_deviation(sample1, sample2, ddof)

Calculates the pooled standard deviation. For more information visit https://www.hackdeploy.com/learn-what-is-statistical-power-with-python/.

Parameters:

• sample1 (array) – numpy array with values for first group

• sample2 (array) – numpy array with values for second group

• ddof (int) – degrees of freedom

cohens_d(sample1, sample2, ddof)

Calculates Cohen’s d effect size based on the distance between two means, measured in standard deviations. For more information visit https://www.hackdeploy.com/learn-what-is-statistical-power-with-python/.

Parameters:

• sample1 (array) – numpy array with values for first group

• sample2 (array) – numpy array with values for second group

• ddof (int) – degrees of freedom

hedges_g(df, condition1, condition2, ddof=0)

Calculates Hedges’ g effect size (more accurate for sample sizes below 20 than Cohen’s d). For more information visit https://docs.scipy.org/doc/numpy/reference/generated/numpy.nanstd.html.

Parameters:

• df – pandas dataframe with samples as rows and protein identifiers as columns.

• condition1 (str) – identifier of first group.

• condition2 (str) – identifier of second group.

• ddof (int) – means Delta Degrees of Freedom.

Returns:

Numpy array.

Example:

result = hedges_g(data, 'group1', 'group2', ddof=0)

unit_vector(vector)

Returns the unit vector of the vector. :param tuple vector: vector :return tuple unit_vector: unit vector

flatten(t, my_list=[])

Code from: https://gist.github.com/shaxbee/0ada767debf9eefbdb6e Acknowledgements: Zbigniew Mandziejewicz (shaxbee) Generator flattening the structure

>>> list(flatten([2, [2, (4, 5, [7], [2, [6, 2, 6, [6], 4]], 6)]]))
[2, 2, 4, 5, 7, 2, 6, 2, 6, 6, 4, 6]

angle_between(v1, v2)

Returns the angle in radians between vectors ‘v1’ and ‘v2’

Parameters:

• v1 (tuple) – vector 1

• v2 (tuple) – vector 2

Return float angle:

angle between two vectors in radians

Example::

angle = angle_between((1, 0, 0), (0, 1, 0))

append_to_list(mylist, myappend)

generator_to_dict(genvar)