Issue running phlower.tl.harmonic_stream_tree in tutorial

[SammiLyu]

Hi phlower team!

Thank you so much for creating this really neat package!!

I am trying to replicate the kidney organoid tutorial on my machine and ran into this error when trying to run phlower.tl.harmonic_stream_tree. Additionally, the eigen_n I obtained from phlower.tl.knee_eigen is also 27 instead of 28 (not sure if this would change the down stream results but I also tried running phlower.tl.harmonic_stream_tree(eigen_n=28) and got the same error. Any help on identifying the source of error is much appreciated!!

Best wishes,
Sammi

[SammiLyu]

Hi phlower team,

Just want to give an update!
I think the issue lies in this line

def add_branching(tm, val, htree, htree_roots):
    roots = np.array(htree_roots) #this line is the issue
    in_roots = np.where(np.array([True if len(set(r)&set(val))>0 else False for r in roots]))[0]

because htree_roots can be a list with two tuples and unbalanced (shown below)

I attempted patching using

roots = np.array([list(map(str, r)) for r in htree_roots], dtype=object)

and it seems to be working.

This is differentiation tree I obtained.

Would you mind double checking if my patch makes sense since I am pretty new to python? Thank you so much in advance!!

[SammiLyu]

One more issue and I apologize for just keeping messaging!

I am trying to move onto the next step outlined in branch detection and accessing the chromvar data using
!wget --no-check-certificate --no-verbose https://costalab.ukaachen.de/open_data/PHLOWER/multiome_chromvar.h5 -O save/multiome_chromvar.h5. However, I am running into error "failed: Connection timed out." from both my server and my local laptop. Is there any other way to obtain this chromvar data? Thank you so much!

Best,
Sammi

[SammiLyu]

Hi phlower team,

Sorry that I have another question regarding the tutorial.
I cannot find command in the tutorial directly match to generation of Figure 3E in the manuscript. I think plot_stream_sc is used for generating these plots, with argument color set to gene name I would be able to obtain the gene expression along trajectory plots, but I am not very familiar with chromVar and adata so I do not know where in the adata is chromVar information stored for accessing and plotting TFactivity. Any guidance would be great! Thank you!

Best,
Sammi

[chengmingbo]

Hi phlower team,

Just want to give an update! I think the issue lies in this line

def add_branching(tm, val, htree, htree_roots):
    roots = np.array(htree_roots) #this line is the issue
    in_roots = np.where(np.array([True if len(set(r)&set(val))>0 else False for r in roots]))[0]

because htree_roots can be a list with two tuples and unbalanced (shown below)

I attempted patching using

roots = np.array([list(map(str, r)) for r in htree_roots], dtype=object)

and it seems to be working.

This is differentiation tree I obtained.

Would you mind double checking if my patch makes sense since I am pretty new to python? Thank you so much in advance!!

Hi Sammi,

Glad you are trying our tool. And thank you for providing the patch. I will add the patch to the tool after it passes the test.

Your result is slightly different from the tutorial, this appears to be the environment, starting from the eigendecomposition output. I will further check if it can be improved.

[chengmingbo]

multiome_chromvar

Hi Sammi,

I've just tested the download using wget, and it completed successfully on my end.

Anyway, I uploaded it to Dropbox. Please try this if wget from costalab.ukaachen.de is still not working:
multiome_chromvar.h5: https://www.dropbox.com/scl/fi/0k6m66z63j0u6s8myh9th/multiome_chromvar.h5?rlkey=0u3ny15pyh0lvfmpvnjbsaubs&st=ln0vwueb&dl=0
multiome_phlower.pickle: https://www.dropbox.com/scl/fi/gvptuw0ge84n4h0onmrj0/multiome_phlower.pickle?rlkey=mg75saox3mjpslwneznuf6w7v&st=672wg4n3&dl=0

[chengmingbo]

Hi phlower team,

Sorry that I have another question regarding the tutorial. I cannot find command in the tutorial directly match to generation of Figure 3E in the manuscript. I think plot_stream_sc is used for generating these plots, with argument color set to gene name I would be able to obtain the gene expression along trajectory plots, but I am not very familiar with chromVar and adata so I do not know where in the adata is chromVar information stored for accessing and plotting TFactivity. Any guidance would be great! Thank you!

Best, Sammi

Hi Sammi,
A dedicated notebook for Fig. 3E isn't available yet, but I'm planning to create one. In the meantime, I'd be happy to walk you through the plotting process.

import phlower
import pickle
import scanpy as sc
# https://www.dropbox.com/scl/fi/gvptuw0ge84n4h0onmrj0/multiome_phlower.pickle?rlkey=mg75saox3mjpslwneznuf6w7v&st=672wg4n3&dl=0
f = "multiome_phlower.pickle"
adata = pickle.load(open(f, "rb"))
# https://www.dropbox.com/scl/fi/0k6m66z63j0u6s8myh9th/multiome_chromvar.h5?rlkey=0u3ny15pyh0lvfmpvnjbsaubs&st=ln0vwueb&dl=0
tfbdata = sc.read_10x_h5("multiome_chromvar.h5")
tfbdata.var_names = [i.upper() for i in tfbdata.var_names]

assert(all(tfbdata.obs_names == adata.obs_names))

import copy
tfbdata.obs = copy.deepcopy(adata.obs)
tfbdata.obsm = copy.deepcopy(adata.obsm)
tfbdata.uns = copy.deepcopy(adata.uns)

anno_dic = { 
    9:  "Podocytes",
    1:  "Tubular",
    6:  "Muscle",
    13: "Neuron-1",
    0:  "Neuron-2",
    7:  "Stromal-1",
    8:  "Stromal-2",
    5:  "Stromal-3",
    11: "Stromal-4",
}
preference_order =  {"original": list(anno_dic.values())}
# Plot gene activities
greengrey2red = phlower.custom_cmap(['green', 'grey', 'red', 'red'])
phlower.ext.plot_stream_sc(tfbdata, color=['PAX3', 'GRHL2'],
                                      return_fig=False,
                                      show_text=False,
                                      text_attr='original',
                                      show_graph=True,
                                      show_legend=True,
                                      preference=preference_order,
                                      fig_size=(9,7),
                                      s=(2, 20),
                                      alpha=0.8,
                                      dist_scale=.4,
                                      cmap_continous=greengrey2red)

sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
phlower.tl.magic_adata(adata)

# Plot the gene expressions
grey2red = phlower.custom_cmap(['grey', 'red'])
phlower.ext.plot_stream_sc(adata, color=['PAX3', "GRHL2"], ## genes 
                                      return_fig=False, # True will return figure list for save
                                      show_text=True,
                                      text_attr='original',
                                      show_graph=True,
                                      show_legend=True,
                                      preference=preference_order,
                                      fig_size=(9,7),
                                      s=(2,20),
                                      alpha=0.8,
                                      dist_scale=.4,
                                      cmap_continous=grey2red)