Jun 19, 2024
LIANA+_Integrating_MultiOmics_Data
- Daniel Dimitrov1,
- Philipp Sven Lars Schäfer1,
- Elias Farr1,
- Pablo Rodriguez-Mier1,
- Sebastian Lobentanzer1,
- Pau Badia-i-Mompel1,2,
- Aurelien Dugourd1,
- Jovan Tanevski1,
- Ricardo Omar Ramirez Flores1,
- Julio Saez-Rodriguez1
- 1Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany;
- 2GSK, Cellzome, Heidelberg, Germany
- Julio Saez-Rodriguez: [email protected];
External link: https://liana-py.readthedocs.io/en/latest/notebooks/sma.html
Protocol Citation: Daniel Dimitrov, Philipp Sven Lars Schäfer, Elias Farr, Pablo Rodriguez-Mier, Sebastian Lobentanzer, Pau Badia-i-Mompel, Aurelien Dugourd, Jovan Tanevski, Ricardo Omar Ramirez Flores, Julio Saez-Rodriguez 2024. LIANA+_Integrating_MultiOmics_Data. protocols.io https://dx.doi.org/10.17504/protocols.io.eq2lyw6orvx9/v1
Manuscript citation:
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: June 17, 2024
Last Modified: June 19, 2024
Protocol Integer ID: 101923
Keywords: multi-omics, ccc, python, spatial relationships with transcriptomics data, global spatial relationships of metabolite peak, molecular mechanisms of dopamine regulation, transcriptomics data, resolved transcriptomic, unknown metabolite peak, dopamine regulation, transcriptome dataset, metabolite peak, parkinson, recent murine parkinson, msi data, specific metabolite, resolved metabolite, metabolite, molecular mechanism, striatum, global spatial relationship
Funders Acknowledgements:
ITN Strategy-CKD
Grant ID: 860329
ITN DECIDER
Grant ID: 965193
ITN PerMedCoE
Grant ID: 951773
Deutsche Forschungsgemeinschaft
Grant ID: SPP 2395
Deutsche Forschungsgemeinschaft
Grant ID: CRC 1550
Abstract
A Protocol describing the application of LIANA+ on a spatially-resolved metabolite-transcriptome dataset from a recent murine Parkinson’s disease model Vicari et al., 2023.
We demonstrate LIANA+’s utility in harmonizing spatially-resolved transcriptomics and MALDI-MSI data to unravel metabolite-mediated interactions and the molecular mechanisms of dopamine regulation in the
striatum.
Two particular challenges with this data are: - The unaligned spatial locations of the two omics technologies and the untargeted nature of the MALDI-MSI data, which results in a large number of features with unknown
identities. Only few of which were previously identified as specific metabolites.
Here, we show untargeted modelling of known and unknown metabolite peaks and their spatial relationships with transcriptomics data. Specifically, we use a multi-view modelling strategy MISTy to decipher global spatial relationships of metabolite peaks with cell types and brain-specific receptors. Then, we use LIANA+’s local metrics to pinpoint the subregions of interaction. We also show strategies to enable spatial multi-omics analysis from diverse omics technologies with unaligned locations and observations.
This protocol is associated with our Nature Cell Biology paper describing LIANA+.
Materials
Hardware:
A desktop or laptop computer with at least 8GB RAM.
Software:
Python version 3.8 or newer.
liana-py (installable via pip or github) version 1.2.0 or newer.
numpy version < 2.0
Troubleshooting
Safety warnings
Please ensure numpy <= 2.0 is installed in any new environment, as some packages might misbehave due to the recent update.
Before start
Install Python. We recommend using conda, miniconda, or mamba for this.
Install LIANA+
# In command line / or jupyter notebook
pip install liana[common]
pip install adjustText==0.8
Import Required Packages
import numpy as np
import liana as li
import mudata as mu
import scanpy as sc
from matplotlib import pyplot as plt
from adjustText import adjust_text
# save figure parameters
kwargs = {'frameon':False, 'size':1.5, 'img_key':'lowres'}
Obtain and Examine the Data
# let's download the data
rna = sc.read("sma_rna.h5ad", backup_url="https://figshare.com/ndownloader/files/44624974?private_link=4744950f8768d5c8f68c")
msi = sc.read("sma_msi.h5ad", backup_url="https://figshare.com/ndownloader/files/44624971?private_link=4744950f8768d5c8f68c")
ct = sc.read("sma_ct.h5ad", backup_url="https://figshare.com/ndownloader/files/44624968?private_link=4744950f8768d5c8f68c")
# and create a MuData object
mdata = mu.MuData({'rna':rna, 'msi':msi, 'ct':ct})
# examine the object
mdata
# examine data
fig, axes = plt.subplots(1, 3, figsize=(12, 3))
sc.pl.spatial(rna, color='log1p_total_counts', ax=axes[0], **kwargs, show=False)
sc.pl.spatial(msi, color='Dopamine', cmap='magma', ax=axes[1], **kwargs, show=False)
sc.pl.spatial(ct, color='MSN1', cmap='viridis', ax=axes[2], **kwargs, show=False)
fig.subplots_adjust(wspace=0, hspace=0)
fig.tight_layout()
# examine experimental design
sc.pl.spatial(rna, color=['lesion', 'region'], **kwargs, wspace=0.25)
Remove Features with little-to-no variation
sc.pp.highly_variable_genes(rna, flavor='cell_ranger', n_top_genes=5000)
sc.pp.highly_variable_genes(msi, flavor='cell_ranger', n_top_genes=150)
ct.var['cv'] = ct.X.A.var(axis=0) / ct.X.A.mean(axis=0)
ct.var['highly_variable'] = ct.var['cv'] > np.percentile(ct.var['cv'], 20)
msi = msi[:, msi.var['highly_variable']]
rna = rna[:, rna.var['highly_variable']]
ct = ct[:, ct.var['highly_variable']]
# scale msi intensities
sc.pp.scale(msi, max_value=5)
Obtain brain-specific metabolite-receptor interactions
metalinks = li.rs.get_metalinks(tissue_location='Brain',
biospecimen_location='Cerebrospinal Fluid (CSF)',
source=['CellPhoneDB', 'NeuronChat']
)
metalinks.head()
# Obtain Human to Murine gene symbols Map
map_df = li.rs.get_hcop_orthologs(columns=['human_symbol', 'mouse_symbol'],
min_evidence=3
).rename(columns={'human_symbol':'source',
'mouse_symbol':'target'})
metalinks = li.rs.translate_column(resource=metalinks,
map_df=map_df,
column='gene_symbol',
one_to_many=1)
metalinks.head()
# Intersect the RNA modality with the receptors
receptors = np.intersect1d(metalinks['gene_symbol'].unique(), rna.var_names)
rec = rna[:, receptors].copy()
Compute Spatial Proximies for the Multi-view Model
# We use the metabolite modality as a reference to which we align the other modalities.
# We use the `spatial_neighbors` function to compute the spatial proximity from cell types and brain-specific receptors to the metabolite intensities.
plot, _ = li.ut.query_bandwidth(coordinates=rna.obsm['spatial'], start=0, end=1500)
plot
# choose bandwidth
bandwidth = 500
cutoff = 0.1
# distances of metabolties to RNA
reference = mdata.mod["msi"].obsm["spatial"]
# Compute proximities to the reference for each modality
li.ut.spatial_neighbors(ct, bandwidth=bandwidth, cutoff=cutoff, spatial_key="spatial", reference=reference, set_diag=False, standardize=False)
li.ut.spatial_neighbors(rec, bandwidth=bandwidth, cutoff=cutoff, spatial_key="spatial", reference=reference, set_diag=False, standardize=False)
Construct and Run the Multi-view model
# MISTy
mdata.update_obs()
misty = li.mt.MistyData({"intra": msi, "receptor": rec, "ct": ct}, enforce_obs=False, obs=mdata.obs)
misty
# Learn the relationships between intra and the extra views
misty(model=li.mt.sp.LinearModel, verbose=True, bypass_intra=True, maskby='lesion')
Examine Results
li.pl.target_metrics(misty, stat='multi_R2', return_fig=True, top_n=20, filter_fun=lambda x: x['intra_group']=='intact')
interactions = misty.uns['interactions']
interactions = interactions[(interactions['intra_group'] == 'intact') & (interactions['target'] == 'Dopamine')]
# Create scatter plot
plt.figure(figsize=(5, 4))
# rank rank by abs importances
interactions['rank'] = interactions['importances'].rank(ascending=False)
plt.scatter(interactions['rank'], interactions['importances'], s=11,
c=interactions['view'].map({'ct': '#008B8B', 'receptor': '#a11838'}))
# add for top 10
top_n = interactions[interactions['rank'] <= 10]
texts = []
for i, row in top_n.iterrows():
texts.append(plt.text(row['rank'], row['importances'], row['predictor'], fontsize=10))
adjust_text(texts, arrowprops=dict(arrowstyle="->", color='grey', lw=1.5))
plt.tight_layout()
Identifying Local Interactions
# Focusing on Dopamine, we can next use LIANA+'s local metrics to identify the subregions of interactions with MSN1/2 cells.
# to do so, we need to interpolate one modality to the other, such that they are on the same coordinate system, while this is done internally for the multi-view learning, we need to interpolate it as:
metabs = li.ut.interpolate_adata(target=msi, reference=rna, use_raw=False, spatial_key='spatial')
# let's rebuild this with the update modalities:
mdata = mu.MuData({'msi': metabs, 'rna':rna, 'deconv':ct}, obsm=rna.obsm, obs=rna.obs, uns=rna.uns)
# re-calculate neighbours
li.ut.spatial_neighbors(mdata, bandwidth=bandwidth, cutoff=cutoff, set_diag=True)
# define interactions of interest
interactions = metalinks[['metabolite', 'gene_symbol']].apply(tuple, axis=1).tolist()
# Let's calculate the local metrics for the Dopamine intensities with and Drd1/2 receptors.
li.mt.bivariate(mdata,
local_name='cosine',
x_mod='msi',
y_mod='rna',
key_added='lr',
x_use_raw=False,
y_use_raw=False,
verbose=True,
mask_negatives=True,
n_perms=1000,
interactions=interactions,
x_transform=sc.pp.scale,
y_transform=sc.pp.scale,
)
Plot Bivariate Similarity between Dopamine and Drd1/2 receptors
sc.pl.spatial(mdata.mod['lr'],
color=['Dopamine^Drd1', 'Dopamine^Drd2'],
cmap='cividis_r', vmax=1, layer='pvals',
**kwargs)
Protocol references
Vicari, M. et al. Spatial multimodal analysis of transcriptomes and metabolomes in tissues. Nat. Biotechnol. (2023) doi:10.1038/s41587-023-01937-y.