pytometry.tools.normalize_biExp#

pytometry.tools.normalize_biExp(adata, negative=0.0, width=-10.0, positive=4.41854, max_value=262144.000029, inplace=True)#

Biexponential transformation.

Biex transform as implemented in FlowJo 10. Adapted from FlowKit Python package. This transform is applied exactly as the FlowJo 10 is implemented, using lookup tables with only a limited set of parameter values.

Information on the input parameters from the FlowJo docs can be found in the details section.

Parameters

  • adata – AnnData object representing the FCS data

  • negative (float, optional) – Value for the FlowJo biex option ‘negative’ (float) or pd.Series. Defaults to 0.0.

  • width (float, optional) – Value for the FlowJo biex option ‘width’ (float) or pd.Series. Defaults to -10.0.

  • positive (float, optional) – Value for the FlowJo biex option ‘positive’ (float) or pd.Series. Defaults to 4.418540.

  • max_value (float, optional) – parameter for the top of the linear scale or pd.Series. Defaults to 262144.000029.

  • inplace (bool, optional) – Return a copy instead of writing to adata. Defaults to True.

Returns

Depending on inplace, returns or updates adata in the following field adata.X is then a normalised adata object

Details:
Adjusting width: The value for w will determine the amount of channels to be

compressed into linear space around zero. The space of linear does not change, but rather the number of channels or bins being compressed into the linear space. Width should be set high enough that all of the data in the histogram is visible on screen, but not so high that extra white space is seen to the left hand side of your dimmest distribution. For most practical uses, once all events have been shifted off the axis and there is no more axis ‘pile-up’, then the optimal width basis value has been reached.

Negative:

Another component in the biexponential transform calculation is the negative decades or negative space. This is the only other value you will probably ever need to adjust. In cases where a high width basis may start compressing dim events into the negative cluster, you may want to lower the width basis (less compression around zero) and instead, increase the negative space by 0.5 - 1.0. Doing this will expand the space around zero so the dim events are still visible, but also expand the negative space to remove the cells from the axis and allow you to see the full distribution.

Positive:

The presence of the positive decade adjustment is due to the algorithm used for logicle transformation, but is not useful in 99.9% of the cases that require adjusting the biexponential transform. It may be appropriate to adjust this value only if you use data that displays data with a data range greater than 5 decades.

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