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ascl1_atoh1_jon-advanced.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import sys
get_ipython().system('{sys.executable} -m pip -q install palantir fa2')
# In[2]:
import warnings
warnings.filterwarnings("ignore")
from anndata import AnnData
import numpy as np
import pandas as pd
import scanpy as sc
import scFates as scf
import palantir
import matplotlib.pyplot as plt
sc.settings.verbosity = 3
sc.settings.logfile = sys.stdout
## fix palantir breaking down some plots
import seaborn
seaborn.reset_orig()
get_ipython().run_line_magic('matplotlib', 'inline')
sc.set_figure_params()
scf.set_figure_pubready()
# In[345]:
mg = sc.read_h5ad(r"/mnt/c/Users/Emil/10X/Levi_Jon/combined.h5ad")
# In[346]:
mgtotal = sc.read_h5ad(r"/mnt/c/Users/Emil/10X/Levi_Jon/combined.h5ad")
# In[347]:
import pandas as pd
mg.obs['seurat_clusters'] = mg.obs['seurat_clusters'].astype(str)
old_to_new = {
'0':'Transitory',
'1':'Transitory',
'2':'Transitory',
'3':'Transitory',
'4':'Partially muller glia',
'5':'Microglia',
'6':'RGC',
'7':'Cones',
'8':'Mature muller glia',
'9':'Transitory',
'10':'Transitory'}
mg.obs['EK_anno'] = (
mg.obs['seurat_clusters']
.map(old_to_new).astype('category')
)
# In[348]:
mgtotal.obs['seurat_clusters'] = mgtotal.obs['seurat_clusters'].astype(str)
old_to_new = {
'0':'Transitory',
'1':'Transitory',
'2':'Transitory',
'3':'Transitory',
'4':'Partially muller glia',
'5':'Microglia',
'6':'RGC',
'7':'Cones',
'8':'Mature muller glia',
'9':'Transitory',
'10':'Early neurons'}
mgtotal.obs['EK_anno'] = (
mgtotal.obs['seurat_clusters']
.map(old_to_new).astype('category')
)
# In[349]:
mg.obs
# In[350]:
mg = mg[mg.obs['EK_anno'].isin(['Transitory','Partially muller glia','RGC','Cones','Mature muller glia','Early neurons'])]
# In[351]:
mgtotal = mgtotal[mgtotal.obs['EK_anno'].isin(['Transitory','Partially muller glia','RGC','Cones','Mature muller glia','Early neurons'])]
# In[334]:
sc.pp.filter_genes(mg,min_counts=3)
# In[352]:
sc.pp.pca(mg)
pca_projections = pd.DataFrame(mg.obsm["X_pca"],index=mg.obs_names)
# In[353]:
dm_res = palantir.utils.run_diffusion_maps(pca_projections)
ms_data = palantir.utils.determine_multiscale_space(dm_res,n_eigs=4)
# In[354]:
# generate neighbor draph in multiscale diffusion space
mg.obsm["X_palantir"]=ms_data.values
sc.pp.neighbors(mg,n_neighbors=30,use_rep="X_palantir")
# In[355]:
# draw ForceAtlas2 embedding using 3 first PCs as initial positions
mg.obsm["X_pca2d"]=mg.obsm["X_pca"][:,:3]
sc.tl.draw_graph(mg, layout = 'fa',init_pos='X_pca2d')
# In[356]:
sc.pl.draw_graph(mg,color=['orig.ident'], layout = 'fa') #WE USE THAT
# In[357]:
sc.pl.draw_graph(mg,color=['seurat_clusters'], layout = 'fa') #WE USE THAT
# In[358]:
sc.pl.draw_graph(mg,color=['EK_anno'], layout = 'fa') #WE USE THAT
# In[359]:
def cluster_small_multiples(
adata, clust_key, size=60, frameon=False, legend_loc=None, **kwargs
):
tmp = adata.copy()
for i, clust in enumerate(adata.obs[clust_key].cat.categories):
tmp.obs[clust] = adata.obs[clust_key].isin([clust]).astype("category")
tmp.uns[clust + "_colors"] = ["#d3d3d3", adata.uns[clust_key + "_colors"][i]]
sc.pl.draw_graph(
tmp,
groups=tmp.obs[clust].cat.categories[1:].values,
color=adata.obs[clust_key].cat.categories.tolist(),
size=size,
frameon=frameon,
legend_loc=legend_loc,
**kwargs,
)
# In[360]:
cluster_small_multiples(mg, clust_key = 'EK_anno', layout = 'fa')
# In[678]:
sc.pp.filter_genes(mgtotal,min_counts=3)
sc.pp.highly_variable_genes(mgtotal, n_top_genes=1500, flavor='cell_ranger')
# In[679]:
mgtotal.var
# In[680]:
sc.pp.pca(mgtotal)
pca_projections = pd.DataFrame(mgtotal.obsm["X_pca"],index=mgtotal.obs_names)
dm_res = palantir.utils.run_diffusion_maps(pca_projections)
ms_data = palantir.utils.determine_multiscale_space(dm_res,n_eigs=4)
mgtotal.obsm["X_palantir"]=ms_data.values
sc.pp.neighbors(mgtotal,n_neighbors=30,use_rep="X_palantir")
mgtotal.obsm["X_pca2d"]=mgtotal.obsm["X_pca"][:,:3]
sc.tl.draw_graph(mgtotal, layout = 'fa',init_pos='X_pca2d')
sc.pl.draw_graph(mgtotal,color=['seurat_clusters'], layout = 'fa') #WE USE THAT
# In[681]:
sc.pl.draw_graph(mgtotal,color=['seurat_clusters'], layout = 'fa') #WE USE THAT
# In[682]:
sc.pl.draw_graph(mgtotal,color=['EK_anno'], layout = 'fa') #WE USE THAT
# In[345]:
sc.pl.draw_graph(mgtotal,color=['EK_anno'], layout = 'fa', save = 'ascl1_total_fa.pdf') #WE USE THAT
# In[613]:
sc.pl.draw_graph(mg,color=['EK_anno'], layout = 'fa', save = 'umap_total.pdf')
# In[614]:
sc.pl.draw_graph(early,color=['EK_anno'], layout = 'fa', save = 'early_umap_total.pdf')
# In[615]:
sc.pl.draw_graph(late,color=['EK_anno'], layout = 'fa', save = 'late_umap_total.pdf')
# In[347]:
mg.obsm['X_draw_graph_fa'] = mgtotal.obsm['X_draw_graph_fa']
# In[348]:
cluster_small_multiples(mgtotal, clust_key = 'EK_anno', layout = 'fa')
# In[362]:
mg.layers['scaled'] = sc.pp.scale(mg, copy=True).X
# In[363]:
sc.pl.matrixplot(mg, ['Trpm1','Nrxn3','Pde6h','Rcvrn','Rlbp1','Id1','Id3','Caln1'], 'EK_anno',
colorbar_title='mean z-score', vmin=-2, vmax=2, cmap='RdBu_r', layer = 'scaled', categories_order = ['RGC','Cones','Mature muller glia','Partially muller glia',
'Transitory'], figsize = [4,1], save = 'heatmap_levi1.pdf')
# In[124]:
import scvelo as scv
# In[125]:
I = scv.read("/mnt/c/Users/Emil/10X/Levi_Jon/early.loom", cache = True)
UI = scv.read("/mnt/c/Users/Emil/10X/Levi_Jon/late.loom", cache = True)
# In[126]:
early = mg[mg.obs['orig.ident'].isin(['early'])]
# In[127]:
late = mg[mg.obs['orig.ident'].isin(['late'])]
# In[128]:
early_velo = scv.utils.merge(early, I)
# In[129]:
late_velo = scv.utils.merge(late, UI)
# In[130]:
scv.pl.proportions(early_velo)
# In[131]:
scv.pl.proportions(late_velo)
# In[132]:
scv.pp.neighbors(late_velo)
# In[133]:
scv.pp.neighbors(early_velo)
# In[134]:
scv.pp.filter_and_normalize(late_velo, min_shared_counts=20, n_top_genes=2000)
scv.pp.moments(late_velo, n_pcs=30, n_neighbors=30)
# In[ ]:
# In[135]:
scv.pp.filter_and_normalize(early_velo, min_shared_counts=20, n_top_genes=2000)
scv.pp.moments(early_velo, n_pcs=30, n_neighbors=30)
# In[136]:
scv.tl.velocity(late_velo)
# In[137]:
scv.tl.velocity(early_velo)
# In[138]:
scv.tl.velocity_graph(early_velo)
# In[139]:
scv.tl.velocity_graph(late_velo)
# In[140]:
sc.set_figure_params(figsize = [8,8])
sc.pl.draw_graph(early_velo,color=['EK_anno'], layout = 'fa', legend_loc = 'on data',use_raw=False)
# In[141]:
sc.set_figure_params(figsize = [8,8])
sc.pl.draw_graph(late_velo,color=['EK_anno'], layout = 'fa', legend_loc = 'on data',use_raw=False)
# In[142]:
scv.pl.velocity_embedding_grid(early_velo, basis='draw_graph_fa', color = 'EK_anno', dpi = 300, add_margin = 0.2, figsize = (4,4), arrow_length = 8, arrow_size = 2, density = 1)
# In[143]:
scv.pl.velocity_embedding_grid(late_velo, basis='draw_graph_fa', color = 'EK_anno', dpi = 300, add_margin = 0.2, figsize = (4,4), arrow_length = 8, arrow_size = 2, density = 1)
# In[157]:
scv.pl.velocity_embedding_stream(early_velo, basis='draw_graph_fa', color = 'EK_anno', dpi = 300, density = 2, add_margin=0.2, arrow_size = 2, legend_loc = 'right margin', linewidth = 2, max_length = 8, save = 'velo_early_new.svg')
# In[158]:
scv.pl.velocity_embedding_stream(late_velo, basis='draw_graph_fa', color = 'EK_anno', dpi = 300, density = 2, add_margin=0.2, arrow_size = 2, legend_loc = 'right margin', linewidth = 2, max_length = 8, save = 'velo_late_new.svg')
# In[146]:
scv.tl.velocity_confidence(early_velo)
keys = 'velocity_confidence','velocity_length'
scv.pl.scatter(early_velo, c=keys, cmap='coolwarm', perc=[5, 95], basis='draw_graph_fa' )
# In[147]:
scv.tl.velocity_confidence(late_velo)
keys = 'velocity_confidence','velocity_length'
scv.pl.scatter(late_velo, c=keys, cmap='coolwarm', perc=[5, 95], basis='draw_graph_fa' )
# In[148]:
scv.tl.velocity_pseudotime(early_velo)
scv.pl.scatter(early_velo, color='velocity_pseudotime', cmap='gnuplot', basis='draw_graph_fa')
# In[149]:
scv.tl.velocity_pseudotime(late_velo)
scv.pl.scatter(late_velo, color='velocity_pseudotime', cmap='gnuplot', basis='draw_graph_fa')
# In[150]:
scv.tl.recover_dynamics(early_velo)
# In[151]:
scv.tl.recover_dynamics(late_velo)
# In[152]:
scv.tl.velocity(early_velo, mode='dynamical')
scv.tl.velocity_graph(early_velo)
scv.tl.velocity(late_velo, mode='dynamical')
scv.tl.velocity_graph(late_velo)
# In[153]:
scv.pl.velocity_embedding_grid(early_velo, basis='draw_graph_fa', color = 'EK_anno', dpi = 300, add_margin = 0.2, figsize = (4,4), arrow_length = 8, arrow_size = 2, density = 1)
# In[154]:
scv.pl.velocity_embedding_grid(late_velo, basis='draw_graph_fa', color = 'EK_anno', dpi = 300, add_margin = 0.2, figsize = (4,4), arrow_length = 8, arrow_size = 2, density = 1)
# In[765]:
scv.tl.latent_time(early_velo)
scv.pl.scatter(early_velo, color='latent_time', color_map='gnuplot', size=80, basis = 'draw_graph_fa')
# In[766]:
scv.tl.latent_time(late_velo)
scv.pl.scatter(late_velo, color='latent_time', color_map='gnuplot', size=80, basis = 'draw_graph_fa')
# In[767]:
early_velo.uns['neighbors']['distances'] = early_velo.obsp['distances']
early_velo.uns['neighbors']['connectivities'] = early_velo.obsp['connectivities']
scv.tl.paga(early_velo, groups='EK_anno')
df = scv.get_df(early_velo, 'paga/transitions_confidence', precision=2).T
df.style.background_gradient(cmap='Blues').format('{:.2g}')
# In[768]:
late_velo.uns['neighbors']['distances'] = late_velo.obsp['distances']
late_velo.uns['neighbors']['connectivities'] = late_velo.obsp['connectivities']
scv.tl.paga(late_velo, groups='EK_anno')
df = scv.get_df(late_velo, 'paga/transitions_confidence', precision=2).T
df.style.background_gradient(cmap='Blues').format('{:.2g}')
# In[769]:
scv.pl.paga(early_velo, basis='draw_graph_fa', size=50, alpha=.1,
min_edge_width=2, node_size_scale=1.5)
# In[770]:
scv.pl.paga(late_velo, basis='draw_graph_fa', size=50, alpha=.1,
min_edge_width=2, node_size_scale=1.5)
# In[771]:
sc.tl.leiden(early_velo)
# In[772]:
sc.tl.leiden(late_velo)
# In[773]:
early_velo.uns['neighbors']['distances'] = early_velo.obsp['distances']
early_velo.uns['neighbors']['connectivities'] = early_velo.obsp['connectivities']
scv.tl.paga(early_velo, groups='leiden')
df = scv.get_df(early_velo, 'paga/transitions_confidence', precision=2).T
df.style.background_gradient(cmap='Blues').format('{:.2g}')
# In[774]:
late_velo.uns['neighbors']['distances'] = late_velo.obsp['distances']
late_velo.uns['neighbors']['connectivities'] = late_velo.obsp['connectivities']
scv.tl.paga(late_velo, groups='leiden')
df = scv.get_df(late_velo, 'paga/transitions_confidence', precision=2).T
df.style.background_gradient(cmap='Blues').format('{:.2g}')
# In[775]:
scv.pl.paga(early_velo, basis='draw_graph_fa', size=50, alpha=.1,
min_edge_width=2, node_size_scale=1.5)
# In[776]:
scv.pl.paga(late_velo, basis='draw_graph_fa', size=50, alpha=.1,
min_edge_width=2, node_size_scale=1.5)
# In[777]:
import cellrank as cr
# In[778]:
cr.tl.terminal_states(early_velo, cluster_key="leiden", weight_connectivities=0.2, force_recompute = True, n_states = 1)
# In[786]:
cr.tl.terminal_states(late_velo, cluster_key="leiden", weight_connectivities=0.2, n_states = 3)
# In[780]:
cr.pl.terminal_states(early_velo, basis = 'draw_graph_fa')
# In[787]:
cr.pl.terminal_states(late_velo, basis = 'draw_graph_fa')
# In[790]:
cr.tl.initial_states(early_velo, cluster_key="leiden", n_states = 4)
cr.pl.initial_states(early_velo, discrete=True, basis = 'draw_graph_fa')
# In[792]:
cr.tl.initial_states(late_velo, cluster_key="leiden", n_states = 2)
cr.pl.initial_states(late_velo, discrete=True, basis = 'draw_graph_fa')
# In[793]:
cr.tl.lineages(early_velo, backward= False)
cr.pl.lineages(early_velo, same_plot=False, basis = 'draw_graph_fa')
# In[794]:
cr.tl.lineages(late_velo, backward= False)
cr.pl.lineages(late_velo, same_plot=False, basis = 'draw_graph_fa')
# In[795]:
scv.tl.recover_latent_time(
early_velo, root_key="initial_states_probs", end_key="terminal_states_probs"
)
# In[796]:
scv.tl.recover_latent_time(
late_velo, root_key="initial_states_probs", end_key="terminal_states_probs"
)
# In[797]:
scv.tl.paga(
early_velo,
groups="leiden",
root_key="initial_states_probs",
end_key="terminal_states_probs",
use_time_prior="latent_time",
)
# In[798]:
scv.tl.paga(
late_velo,
groups="leiden",
root_key="initial_states_probs",
end_key="terminal_states_probs",
use_time_prior="latent_time",
)
# In[799]:
cr.pl.cluster_fates(
early_velo,
mode="paga_pie",
cluster_key="leiden",
basis="draw_graph_fa",
legend_kwargs={"loc": "top right out"},
legend_loc="top left out",
node_size_scale=1,
edge_width_scale=1,
max_edge_width=4,
title="directed PAGA",
)
# In[800]:
cr.pl.cluster_fates(
late_velo,
mode="paga_pie",
cluster_key="leiden",
basis="draw_graph_fa",
legend_kwargs={"loc": "top right out"},
legend_loc="top left out",
node_size_scale=1,
edge_width_scale=1,
max_edge_width=4,
title="directed PAGA",
)
# In[801]:
# compue DPT, starting from CellRank defined root cell
root_idx = np.where(early_velo.obs["initial_states"] == "Transitory")[0][0]
early_velo.uns["iroot"] = root_idx
sc.tl.dpt(early_velo)
scv.pl.scatter(
early_velo, basis = 'draw_graph_fa',
color=["leiden", root_idx, "latent_time", "dpt_pseudotime"],
fontsize=16,
cmap="viridis",
perc=[2, 98],
colorbar=True,
rescale_color=[0, 1],
title=["leiden", "root cell", "latent time", "dpt pseudotime"],
)
# In[471]:
# In[248]:
sc.pl.draw_graph(early_velo,color="leiden", layout = 'fa', legend_loc = 'on data')
# In[606]:
cr.tl.lineage_drivers(early_velo, lineages='5', use_raw=False, backward=False)
# In[257]:
cr.tl.lineage_drivers(early_velo, cluster_key = 'leiden',clusters = ['7','2','8'])
# In[256]:
cr.tl.lineage_drivers(early_velo, cluster_key = 'leiden',clusters = ['5','10','8'])
# In[607]:
cr.pl.lineage_drivers(early_velo, lineage="5", n_genes=5, basis = 'draw_graph_fa')
# In[603]:
early_velo.var_names
# In[802]:
model = cr.ul.models.GAM(early_velo)
cr.pl.gene_trends(
early_velo,
model=model,
data_key="X",
genes=["Trpm3"],
ncols=3,
time_key="latent_time",
same_plot=True,
hide_cells=True,
figsize=(15, 4),
n_test_points=200
)
# In[804]:
cr.pl.heatmap(
late_velo,
model,
genes=late_velo.varm['terminal_lineage_drivers']["5_corr"].sort_values(ascending=False).index[:100],
show_absorption_probabilities=True,
lineages="5",
n_jobs=1,
backend="loky",
)
# In[807]:
late_velo
# In[809]:
cr.pl.circular_projection(late_velo, keys="EK_anno", legend_loc="right")
# In[157]:
cr.pl.circular_projection(early_velo, keys="EK_anno", legend_loc="right", save = 'figures/triangle.pdf') #do reversed to find genes
# In[178]:
import sys
get_ipython().system('{sys.executable} -m pip -q install palantir fa2')
# In[179]:
import warnings
warnings.filterwarnings("ignore")
from anndata import AnnData
import numpy as np
import pandas as pd
import scanpy as sc
import scFates as scf
import palantir
import matplotlib.pyplot as plt
sc.settings.verbosity = 3
sc.settings.logfile = sys.stdout
## fix palantir breaking down some plots
import seaborn
seaborn.reset_orig()
get_ipython().run_line_magic('matplotlib', 'inline')
sc.set_figure_params()
scf.set_figure_pubready()
# In[199]:
sc.pp.pca(early_velo)
pca_projections = pd.DataFrame(early_velo.obsm["X_pca"],index=early_velo.obs_names)
dm_res = palantir.utils.run_diffusion_maps(pca_projections)
ms_data = palantir.utils.determine_multiscale_space(dm_res,n_eigs=4)
early_velo.obsm["X_palantir"]=ms_data.values
sc.pp.neighbors(early_velo,n_neighbors=30,use_rep="X_palantir")
early_velo.obsm["X_pca2d"]=early_velo.obsm["X_pca"][:,:2]
sc.tl.draw_graph(early_velo,init_pos='X_pca2d')
# In[211]:
sc.set_figure_params()
sc.pl.draw_graph(early_velo,color="EK_anno", layout = 'fa')
# In[180]:
scf.tl.tree(mg,method="ppt",Nodes=400,use_rep="palantir",
device="cpu",seed=1,ppt_lambda=1,ppt_sigma=0.07,ppt_nsteps=100)
# In[182]:
sc.set_figure_params(figsize = [8,8])
scf.pl.graph(mg)
# In[183]:
sc.set_figure_params()
scf.tl.root(mg,395)
# In[184]:
scf.tl.pseudotime(mg,n_jobs=20,n_map=100,seed=42)
# In[285]:
scf.pl.trajectory(mg)
# In[255]:
sc.pl.draw_graph(early,color=["seg","milestones"])
#scf.tl.rename_milestones(early_velo,["bifA","Ery","DC","Root","BifB","Mono"])
# we change the color of the root milestone for better visualisations
early.uns["milestones_colors"][3]="#17bece"
# In[276]:
sc.pl.draw_graph(mg, layout = 'fa')
# In[275]:
mg
# In[ ]:
scf.tl.test_association(early,n_jobs=20)
# In[ ]:
scf.pl.test_association(early)
# In[ ]:
scf.tl.fit(early,n_jobs=20)
# In[312]:
top_genes = early_velo.var['fit_likelihood'].sort_values(ascending=False).index[:100]
scv.pl.heatmap(early_velo, var_names=top_genes, sortby='latent_time', col_color='EK_anno', n_convolve=300,yticklabels=True, figsize = (8,32))
# In[313]:
early_velo.var
# In[160]:
sc.tl.leiden(early)
# In[161]:
sc.set_figure_params(figsize = [16,16])
sc.pl.draw_graph(early,color="leiden", layout = 'fa', legend_loc = 'on data')
# In[234]:
early_rgc = early[early.obs['leiden'].isin(['20','2','12','19','11','8','5','13','22','21','24','7','10','15','26','27'])]
# In[261]:
early_cones= early[early.obs['leiden'].isin(['18','16','8','5','13','22','21','24','7','10','15','26','27'])]
# In[262]:
early_mg= early[early.obs['leiden'].isin(['17','6','9','25','1','14','0','2','23','3','8','5','13','22','21','24','7','10','15','26','27'])]
# In[263]:
early_mg = early_mg[early_mg.obs['EK_anno'].isin(['Transitory','Partially muller glia','Mature muller glia'])]