generic cell cluster notebook for nimbus

[HPiyadasa]

Currently in the generic cell clustering notebook, the heatmap widget shows valued that is scaled/Z scored. This is not informative when working with nimbus output that generates a probability score from 0-1 for each marker.

Describe the solution you'd like
Remove any data transformation from the heatmap widget used for nimbus.

@alex-l-kong

[ngreenwald]

I thought there was already the option to do this? Maybe it's not turned on by default in the notebook? Pretty sure @JLrumberger added this in.

[HPiyadasa]

I am not sure. The only way I got this to work was by altering the ZscoreNormalize and update_gui functions to do nothing

`
class ZScoreNormalize(Normalize):
def init(self, vmin=None, vcenter=None, vmax=None):
super().init(vmin, vmax) # Retain superclass initialization

def inverse(self, value):
    return value  # Return the value as-is

def calibrate(self, values):
    pass  # Do nothing

def __call__(self, value: np.ndarray, clip=None):
    return value  # Return the value as-is

`

and

`
def update_gui(self):
"""Update and redraw any updated GUI elements"""
self.im_cs.set_data(self.selection_mask)
self.im_cs.set_extent((0, self.mcd.cluster_count, 0, 1))

    if not self._heatmaps_stale:
        print("skipping other repaints")
        self.fig.canvas.draw()
        return

    # def _preplot(df):
    #     return df.apply(zscore).clip(upper=self.zscore_clamp_slider.value).T
    
    def _preplot(df):
        return df.T  # Avoiding z-score normalization and clipping

`

[JLrumberger]

We only added the option to make normalization optional for cell_som_clustering in issue #1000. But we didn't touch the GUI widget yet.