Merge pull request #9 from saeyslab/interop-pytometry

Better interoperability with pytometry 0.1.6.

docs/api.md

@@ -50,7 +50,6 @@ For more background information, see the paper for this software package {cite:p
  :toctree: generated
 
  pp.aggregate_flowframes
- pp.normalize_estimate_logicle
 ```
 
 ## Models

pyproject.toml

@@ -31,7 +31,6 @@ dependencies = [
  "pandas",
  "scipy",
  "readfcs",
- "pytometry",
  "scikit-learn",
  "igraph",
  "session-info",
@@ -57,6 +56,8 @@ doc = [
  "ipykernel",
  "ipython",
  "sphinx-copybutton",
+ # For example notebooks
+ "pytometry>=0.1.5",
 ]
 test = ["pytest", "coverage"]
 

src/flowsom/pl/plot_functions.py

@@ -224,7 +224,16 @@ def plot_numbers(fsom, level="clusters", max_node_size=0, **kwargs):
 
 
 def plot_variable(
- fsom, variable, cmap=FlowSOM_colors(), labels=None, text_size=5, text_color="black", lim=None, title=None, **kwargs
+ fsom,
+ variable,
+ cmap=FlowSOM_colors(),
+ labels=None,
+ text_size=5,
+ text_color="black",
+ lim=None,
+ title=None,
+ categorical=True,
+ **kwargs,
 ):
  """Plot FlowSOM grid or tree, colored by node values given in variable.
 
@@ -260,7 +269,14 @@ def plot_variable(
  if labels is not None:
  ax = add_text(ax, layout, labels, text_size=text_size, text_color=text_color, ha=["center"], va=["center"])
  ax, fig = add_legend(
- fig=fig, ax=ax, data=variable, title="Marker", cmap=cmap, location="upper left", bbox_to_anchor=(1.04, 1)
+ fig=fig,
+ ax=ax,
+ data=variable,
+ title="Marker",
+ cmap=cmap,
+ location="upper left",
+ bbox_to_anchor=(1.04, 1),
+ categorical=categorical,
  )
  ax.axis("equal")
  if title is not None:
@@ -294,7 +310,7 @@ def plot_marker(fsom, marker, ref_markers=None, lim=None, cmap=FlowSOM_colors(),
  lim = (mfis[:, indices_markers].min(), mfis[:, indices_markers].max())
  marker = list(get_channels(fsom, marker).keys())[0]
  marker_index = np.where(fsom.get_cell_data().var_names == marker)[0][0]
- fig = plot_variable(fsom, variable=mfis[:, marker_index], cmap=cmap, lim=lim, **kwargs)
+ fig = plot_variable(fsom, variable=mfis[:, marker_index], cmap=cmap, lim=lim, categorical=False, **kwargs)
  return fig
 
 

src/flowsom/pp/__init__.py

@@ -1 +1 @@
-from .fcs_functions import aggregate_flowframes, normalize_estimate_logicle
+from .fcs_functions import aggregate_flowframes

src/flowsom/pp/fcs_functions.py

@@ -4,8 +4,6 @@
 
 import anndata as ad
 import numpy as np
-import pandas as pd
-import pytometry as pm
 
 from flowsom.io import read_FCS
 from flowsom.tl import get_markers
@@ -54,33 +52,3 @@ def aggregate_flowframes(filenames, c_total, channels=None, keep_order=False):
  flow_frame.append(f)
  flow_frame = ad.concat(flow_frame, join="outer", uns_merge="first")
  return flow_frame
-
-
-def normalize_estimate_logicle(adata, channels, m=4.5, q=0.05):
- """Normalize and estimate logicle parameters.
-
- :param adata: An AnnData object
- :type adata: AnnData
- :param channels: Channels/markers to normalize
- :type channels: list
- :param m: Logicle parameter. Default=4.5
- :type m: float
- :param q: Quantile to use for negative values. Default=0.05
- :type q: float
- """
- assert isinstance(adata, ad.AnnData), "Please provide an AnnData object"
- assert isinstance(channels, list), "Please provide a list of channels"
- channels = list(get_markers(adata, channels).keys())
- assert all(i in adata.var_names for i in channels), "Channels should be in the AnnData object"
- neg_marker_quantiles = [
- np.quantile(adata[:, channel].X[adata[:, channel].X < 0], q) if (adata[:, channel].X < 0).any() else 0.5
- for channel in channels
- ]
- neg_marker_quantiles = pd.Series(neg_marker_quantiles, index=channels, dtype=float)
- max_range = adata.var["$PnR"][channels].astype(float)
- w = (m - np.log10(max_range / np.abs(neg_marker_quantiles))) / 2
- for channel in channels:
- adata[:, channel].X = pm.tools.normalize_logicle(
- adata[:, channel], t=max_range[channel], m=m, a=0, w=w[channel], inplace=False
- ).X
- return adata