Merge pull request #30 from pinellolab/jon_dev5

fix markers nnz

stream2/tools/_graph_utils.py

@@ -720,6 +720,41 @@ def extend_leaves(
     TrimmingRadius=float("inf"),
     key="epg",
 ):
+    """Extend leaves with additional nodes
+
+    Parameters
+    -----------
+    Mode: str, the mode used to extend the graph. 
+        "QuantDists","QuantCentroid", "WeigthedCentroid" 
+    LeafIDs: int vector, 
+        The id of nodes to extend. If None, all the vertices will be extended.
+    TrimmingRadius: positive numeric
+        The trimming radius used to control distance
+    DoSA: bool
+        Should optimization (via simulated annealing) 
+        be performed when Mode = "QuantDists"?
+    ControlPar: positive numeric
+        The parameter used to control the contribution of 
+        the different data points
+
+        The value of ControlPar has a different interpretation 
+        depending on the valus of Mode. 
+        In each case, for only the extreme points,
+        i.e., the points associated with the leaf node that 
+        do not have a projection on any edge are considered.
+
+        If Mode = "QuantCentroid", for each leaf node, 
+        the extreme points are ordered by their distance from the node
+        and the centroid of the points farther away 
+        than ControlPar is returned.
+
+        If Mode = "WeightedCentroid", for each leaf node, 
+        a weight is computed for each points 
+        by raising the distance to the ControlPar power.
+        Hence, larger values of ControlPar result in a larger influence 
+        of points farther from the node
+    """
+
     X = _get_graph_data(adata, key)
 
     PG = elpigraph.ExtendLeaves(
@@ -737,6 +772,76 @@ def extend_leaves(
     adata.uns[key]["edge"] = PG["Edges"][0]
     _store_graph_attributes(adata, X, key)
 
+def grow_leaves(
+    adata,
+    n_nodes=20,
+    use_weights=False,
+    epg_mu=None,
+    epg_lambda=None,
+    epg_cycle_mu=None,
+    epg_cycle_lambda=None,
+    key="epg",
+):
+    """Grow leaves using elpigraph optimization
+
+    Parameters
+    ----------
+    use_weights: bool
+        Whether to weight points with adata.obs['pointweights']
+    shift_nodes_pos: dict
+        Optional dict to hold some nodes fixed at specified positions
+        e.g., {2:[.5,.2]} will hold node 2 at coordinates [.5,.2]
+    epg_mu: float
+        ElPiGraph Mu parameter
+    epg_lambda: float
+        ElPiGraph Lambda parameter
+    cycle_epg_mu: float
+        ElPiGraph Mu parameter, specific for nodes that are part of cycles
+    cycle_epg_lambda: float
+        ElPiGraph Lambda parameter, specific for nodes that are part of cycles
+    """
+    # --- Init parameters, variables
+    if epg_mu is None:
+        epg_mu = adata.uns[key]["params"]["epg_mu"]
+    if epg_lambda is None:
+        epg_lambda = adata.uns[key]["params"]["epg_lambda"]
+    if epg_cycle_mu is None:
+        epg_cycle_mu = epg_mu
+    if epg_cycle_lambda is None:
+        epg_cycle_lambda = epg_lambda
+    if use_weights:
+        weights = np.array(adata.obs["pointweights"])[:, None]
+    else:
+        weights = None
+
+    X = _get_graph_data(adata, key)
+    PG = elpigraph.GrowLeaves(
+        X,
+        NumNodes=n_nodes+len(adata.uns["epg"]["node_pos"]),
+        InitNodePositions=adata.uns["epg"]["node_pos"],
+        InitEdges=adata.uns["epg"]["edge"],
+        PointWeights=weights,
+        Mu=epg_mu,
+        Lambda=epg_lambda,
+        verbose=1,
+        Do_PCA=False,
+        CenterData=False
+    )[0]
+
+    adata.uns["epg"]["node_pos"] = PG["NodePositions"]
+    adata.uns["epg"]["edge"] = PG["Edges"][0]
+
+    # update edge_len, conn, data projection
+    _store_graph_attributes(adata, X, key)
+
+def nodes_info(adata,key='epg'):
+    '''Return dict of graph nodes classified into leaf, branching, branch
+    '''
+    g = elpigraph.src.graphs.ConstructGraph(stream2elpi(adata, key=key))
+    leaf = np.where(np.array(g.degree()) == 1)[0]
+    branching = np.where(np.array(g.degree()) > 2)[0]
+    branch = np.where(np.array(g.degree()) == 2)[0]
+    return {'leaf':leaf, 'branching':branching, 'branch':branch}
 
 def use_graph_with_n_nodes(adata, n_nodes):
     """Use the graph at n_nodes.

stream2/tools/_markers.py

@@ -366,7 +366,7 @@ def detect_transition_markers(
         + " cells ..."
     )
     input_markers_expressed = np.array(input_markers)[
-        np.where((df_sc[input_markers] > 0).sum(axis=0) > min_num_cells)[0]
+        np.where((df_sc[input_markers] != 0).sum(axis=0) > min_num_cells)[0]
     ].tolist()
     df_marker_detection = df_sc[input_markers_expressed].copy()