Bulk functional analysis

Bulk RNA-seq yields many molecular readouts that are hard to interpret by themselves. One way of summarizing this information is by inferring pathway and transcription factor activities from prior knowledge.

In this notebook we showcase how to use decoupler for transcription factor (TF) and pathway activity inference from a human data-set. The data consists of 6 samples of hepatic stellate cells (HSC) where three of them were activated by the cytokine Transforming growth factor (TGF-β), it is available at GEO here.


This tutorial assumes that you already know the basics of decoupler. Else, check out the Usage tutorial first.

Loading packages

First, we need to load the relevant packages, scanpy to handle RNA-seq data and decoupler to use statistical methods.

import scanpy as sc
import decoupler as dc

# Only needed for processing
import numpy as np
import pandas as pd
from anndata import AnnData

Loading the data

We can download the data easily from GEO:

!wget 'https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE151251&format=file&file=GSE151251%5FHSCs%5FCtrl%2Evs%2EHSCs%5FTGFb%2Ecounts%2Etsv%2Egz' -O counts.txt.gz
!gzip -d -f counts.txt.gz
--2024-02-19 18:39:54--  https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE151251&format=file&file=GSE151251%5FHSCs%5FCtrl%2Evs%2EHSCs%5FTGFb%2Ecounts%2Etsv%2Egz
Resolving www.ncbi.nlm.nih.gov (www.ncbi.nlm.nih.gov)..., 2607:f220:41e:4290::110
Connecting to www.ncbi.nlm.nih.gov (www.ncbi.nlm.nih.gov)||:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 1578642 (1,5M) [application/octet-stream]
Saving to: ‘counts.txt.gz’

counts.txt.gz       100%[===================>]   1,50M  2,15MB/s    in 0,7s

2024-02-19 18:39:55 (2,15 MB/s) - ‘counts.txt.gz’ saved [1578642/1578642]

We can then read it using pandas:

# Read raw data and process it
adata = pd.read_csv('counts.txt', index_col=2, sep='\t').iloc[:, 5:].T
GeneName DDX11L1 WASH7P MIR6859-1 MIR1302-11 MIR1302-9 FAM138A OR4G4P OR4G11P OR4F5 RP11-34P13.7 ... MT-ND4 MT-TH MT-TS2 MT-TL2 MT-ND5 MT-ND6 MT-TE MT-CYB MT-TT MT-TP
25_HSCs-Ctrl1 0 9 10 1 0 0 0 0 0 33 ... 93192 342 476 493 54466 17184 1302 54099 258 475
26_HSCs-Ctrl2 0 12 14 0 0 0 0 0 0 66 ... 114914 355 388 436 64698 21106 1492 62679 253 396
27_HSCs-Ctrl3 0 14 10 0 0 0 0 0 0 52 ... 155365 377 438 480 85650 31860 2033 89559 282 448
31_HSCs-TGFb1 0 11 16 0 0 0 0 0 0 54 ... 110866 373 441 481 60325 19496 1447 66283 172 341
32_HSCs-TGFb2 0 5 8 0 0 0 0 0 0 44 ... 45488 239 331 343 27442 9054 624 27535 96 216
33_HSCs-TGFb3 0 12 5 0 0 0 0 0 0 32 ... 70704 344 453 497 45443 13796 1077 43415 192 243

6 rows × 64253 columns


In case your data does not contain gene symbols but rather gene ids like ENSMBL, you can use the function sc.queries.biomart_annotations to retrieve them. In this other vignette, ENSMBL ids are transformed into gene symbols.


In case your data is not from human but rather a model organism, you can find their human orthologs using pypath. Check this GitHub issue where mouse genes are transformed into human.

The obtained data consist of raw read counts for six different samples (three controls, three treatments) for ~60k genes. Before continuing, we will transform our expression data into an AnnData object. It handles annotated data matrices efficiently in memory and on disk and is used in the scverse framework. You can read more about it here and here.

# Transform to AnnData object
adata = AnnData(adata, dtype=np.float32)
AnnData object with n_obs × n_vars = 6 × 64253

Inside an AnnData object, there is the .obs attribute where we can store the metadata of our samples. Here we will infer the metadata by processing the sample ids, but this could also be added from a separate dataframe:

# Process treatment information
adata.obs['condition'] = ['control' if '-Ctrl' in sample_id else 'treatment' for sample_id in adata.obs.index]

# Process sample information
adata.obs['sample_id'] = [sample_id.split('_')[0] for sample_id in adata.obs.index]

# Visualize metadata
condition sample_id
25_HSCs-Ctrl1 control 25
26_HSCs-Ctrl2 control 26
27_HSCs-Ctrl3 control 27
31_HSCs-TGFb1 treatment 31
32_HSCs-TGFb2 treatment 32
33_HSCs-TGFb3 treatment 33

Quality control

Before doing anything we need to ensure that our data passes some quality control thresholds. In transcriptomics it can happen that some genes were not properly profiled and thus need to be removed.

To filter genes, we will follow the strategy implemented in the function filterByExpr from edgeR. It keeps genes that have a minimum total number of reads across samples (min_total_count), and that have a minimum number of counts in a number of samples (min_count).

We can plot how many genes do we keep, you can play with the min_count and min_total_count to check how many genes would be kept when changed:

dc.plot_filter_by_expr(adata, group='condition', min_count=10, min_total_count=15, large_n=1, min_prop=1)

Here we can observe the frequency of genes with different total sum of counts and number of samples. The dashed lines indicate the current thresholds, meaning that only the genes in the upper-right corner are going to be kept. Filtering parameters is completely arbitrary, but a good rule of thumb is to identify bimodal distributions and split them modifying the available thresholds. In this example, with the default values we would keep a good quantity of genes while filtering potential noisy genes.


Changing the value of min_count will drastically change the distribution of “Number of samples”, not change its threshold. In case you want to lower or increase it, you need to play with the group, large_n and min_prop parameters.


This thresholds can vary a lot between datasets, manual assessment of them needs to be considered. For example, it might be the case that many genes are not expressed in just one sample which they would get removed by the current setting. For this specific dataset it is fine.

Once we are content with the threshold parameters, we can perform the actual filtering:

# Obtain genes that pass the thresholds
genes = dc.filter_by_expr(adata, group='condition', min_count=10, min_total_count=15, large_n=1, min_prop=1)

# Filter by these genes
adata = adata[:, genes].copy()
AnnData object with n_obs × n_vars = 6 × 19713
    obs: 'condition', 'sample_id'

Differential expression analysis

In order to identify which are the genes that are changing the most between treatment and control we can perform differential expression analysis (DEA). For this example, we will perform a simple experimental design where we compare the gene expression of treated cells against controls. We will use the python implementation of the framework DESeq2, but we could have used any other one (limma or edgeR for example). For a better understanding how it works, check DESeq2’s documentation. Note that more complex experimental designs can be used by adding more factors to the design_factors argument.

# Import DESeq2
from pydeseq2.dds import DeseqDataSet, DefaultInference
from pydeseq2.ds import DeseqStats
# Build DESeq2 object
inference = DefaultInference(n_cpus=8)
dds = DeseqDataSet(
# Compute LFCs
Fitting size factors...
... done in 0.00 seconds.

Fitting dispersions...
... done in 40.73 seconds.

Fitting dispersion trend curve...
... done in 0.79 seconds.

Fitting MAP dispersions...
... done in 47.51 seconds.

Fitting LFCs...
... done in 3.20 seconds.

Refitting 0 outliers.

# Extract contrast between COVID-19 vs normal
stat_res = DeseqStats(
    contrast=["condition", 'treatment', 'control'],
# Compute Wald test
Running Wald tests...
Log2 fold change & Wald test p-value: condition treatment vs control
                  baseMean  log2FoldChange     lfcSE      stat    pvalue  \
WASH7P           10.349783       -0.013838  0.588150 -0.023528  0.981229
MIR6859-1        10.114621        0.006602  0.593410  0.011125  0.991123
RP11-34P13.7     45.731312        0.077657  0.297658  0.260893  0.794175
RP11-34P13.8     29.498381       -0.075630  0.250611 -0.301783  0.762818
CICP27          106.032654        0.155742  0.175942  0.885185  0.376057
...                    ...             ...       ...       ...       ...
MT-ND6        17914.984375       -0.435304  0.278967 -1.560414  0.118662
MT-TE          1281.293335       -0.332495  0.287849 -1.155101  0.248049
MT-CYB        54955.449219       -0.313285  0.286940 -1.091813  0.274915
MT-TT           204.692215       -0.483703  0.332233 -1.455916  0.145416
MT-TP           345.049713       -0.462065  0.197978 -2.333926  0.019600

WASH7P        0.986735
MIR6859-1     0.994048
RP11-34P13.7  0.854935
RP11-34P13.8  0.831118
CICP27        0.494115
...                ...
MT-ND6        0.192431
MT-TE         0.354693
MT-CYB        0.385998
MT-TT         0.228249
MT-TP         0.039889

[19713 rows x 6 columns]
... done in 1.68 seconds.

# Extract results
results_df = stat_res.results_df
baseMean log2FoldChange lfcSE stat pvalue padj
WASH7P 10.349783 -0.013838 0.588150 -0.023528 0.981229 0.986735
MIR6859-1 10.114621 0.006602 0.593410 0.011125 0.991123 0.994048
RP11-34P13.7 45.731312 0.077657 0.297658 0.260893 0.794175 0.854935
RP11-34P13.8 29.498381 -0.075630 0.250611 -0.301783 0.762818 0.831118
CICP27 106.032654 0.155742 0.175942 0.885185 0.376057 0.494115
... ... ... ... ... ... ...
MT-ND6 17914.984375 -0.435304 0.278967 -1.560414 0.118662 0.192431
MT-TE 1281.293335 -0.332495 0.287849 -1.155101 0.248049 0.354693
MT-CYB 54955.449219 -0.313285 0.286940 -1.091813 0.274915 0.385998
MT-TT 204.692215 -0.483703 0.332233 -1.455916 0.145416 0.228249
MT-TP 345.049713 -0.462065 0.197978 -2.333926 0.019600 0.039889

19713 rows × 6 columns

We can plot the obtained results in a volcano plot:

    figsize=(5, 5)

After performing DEA, we can use the obtained gene level statistics to perform enrichment analysis. Any statistic can be used, but we recommend using the t-values instead of logFCs since t-values incorporate the significance of change in their value. We will transform the obtained t-values stored in stats to a wide matrix so that it can be used by decoupler:

mat = results_df[['stat']].T.rename(index={'stat': 'treatment.vs.control'})
GeneName WASH7P MIR6859-1 RP11-34P13.7 RP11-34P13.8 CICP27 FO538757.2 AP006222.2 RP4-669L17.10 MTND1P23 MTND2P28 ... MT-ND4 MT-TH MT-TS2 MT-TL2 MT-ND5 MT-ND6 MT-TE MT-CYB MT-TT MT-TP
treatment.vs.control -0.023528 0.011125 0.260893 -0.301783 0.885185 -2.193677 2.102634 -0.36349 -1.328259 -0.498827 ... -1.435491 0.747514 0.752503 0.777125 -1.243008 -1.560414 -1.155101 -1.091813 -1.455916 -2.333926

1 rows × 19713 columns

Transcription factor activity inference

The first functional analysis we can perform is to infer transcription factor (TF) activities from our transcriptomics data. We will need a gene regulatory network (GRN) and a statistical method.

CollecTRI network

CollecTRI is a comprehensive resource containing a curated collection of TFs and their transcriptional targets compiled from 12 different resources. This collection provides an increased coverage of transcription factors and a superior performance in identifying perturbed TFs compared to our previous DoRothEA network and other literature based GRNs. Similar to DoRothEA, interactions are weighted by their mode of regulation (activation or inhibition).

For this example we will use the human version (mouse and rat are also available). We can use decoupler to retrieve it from omnipath. The argument split_complexes keeps complexes or splits them into subunits, by default we recommend to keep complexes together.


In this tutorial we use the network CollecTRI, but we could use any other GRN coming from an inference method such as CellOracle, pySCENIC or SCENIC+.

# Retrieve CollecTRI gene regulatory network
collectri = dc.get_collectri(organism='human', split_complexes=False)
source target weight PMID
0 MYC TERT 1 10022128;10491298;10606235;10637317;10723141;1...
1 SPI1 BGLAP 1 10022617
2 SMAD3 JUN 1 10022869;12374795
3 SMAD4 JUN 1 10022869;12374795
4 STAT5A IL2 1 10022878;11435608;17182565;17911616;22854263;2...
... ... ... ... ...
43173 NFKB hsa-miR-143-3p 1 19472311
43174 AP1 hsa-miR-206 1 19721712
43175 NFKB hsa-miR-21-5p 1 20813833;22387281
43176 NFKB hsa-miR-224-5p 1 23474441;23988648
43177 AP1 hsa-miR-144 1 23546882

43178 rows × 4 columns

Activity inference with Univariate Linear Model (ULM)

To infer TF enrichment scores we will run the Univariate Linear Model (ulm) method. For each sample in our dataset (mat) and each TF in our network (net), it fits a linear model that predicts the observed gene expression based solely on the TF’s TF-Gene interaction weights. Once fitted, the obtained t-value of the slope is the score. If it is positive, we interpret that the TF is active and if it is negative we interpret that it is inactive.


We can run ulm with a one-liner:

# Infer TF activities with ulm
tf_acts, tf_pvals = dc.run_ulm(mat=mat, net=collectri, verbose=True)
Running ulm on mat with 1 samples and 19713 targets for 655 sources.
treatment.vs.control -1.660472 -1.518117 -1.422092 -2.579365 -2.427458 -0.039337 -6.223142 1.033265 2.227805 -0.615537 ... -0.033799 2.490092 1.336223 -1.069932 -1.448839 -1.049743 0.46968 2.809017 0.559583 -2.681535

1 rows × 655 columns

Let us plot the obtained scores for the top active/inactive transcription factors:

    figsize=(3, 6)

MYOCD SRF and JUNB seem to be the most activated in this treatment while IRF1, KLF11 AND ARID1A seem to be inactivated.

We can also plot the network of interesting TFs (top and bottom by activity) and color the nodes by activity and target gene expression:

    n_sources=['MYOCD', 'SRF', 'IRF1', 'KLF11'],
    figsize=(5, 5),

SRF seems to be active in treated cells since their positive targets are up-regulated.

If needed, we can also look at a volcano plot of the target genes:

# Extract logFCs and pvals
logFCs = results_df[['log2FoldChange']].T.rename(index={'log2FoldChange': 'treatment.vs.control'})
pvals = results_df[['padj']].T.rename(index={'padj': 'treatment.vs.control'})

# Plot

Pathway activity inference

Another analysis we can perform is to infer pathway activities from our transcriptomics data.

PROGENy model

PROGENy is a comprehensive resource containing a curated collection of pathways and their target genes, with weights for each interaction. For this example we will use the human weights (other organisms are available) and we will use the top 500 responsive genes ranked by p-value. Here is a brief description of each pathway:

  • Androgen: involved in the growth and development of the male reproductive organs.

  • EGFR: regulates growth, survival, migration, apoptosis, proliferation, and differentiation in mammalian cells

  • Estrogen: promotes the growth and development of the female reproductive organs.

  • Hypoxia: promotes angiogenesis and metabolic reprogramming when O2 levels are low.

  • JAK-STAT: involved in immunity, cell division, cell death, and tumor formation.

  • MAPK: integrates external signals and promotes cell growth and proliferation.

  • NFkB: regulates immune response, cytokine production and cell survival.

  • p53: regulates cell cycle, apoptosis, DNA repair and tumor suppression.

  • PI3K: promotes growth and proliferation.

  • TGFb: involved in development, homeostasis, and repair of most tissues.

  • TNFa: mediates haematopoiesis, immune surveillance, tumour regression and protection from infection.

  • Trail: induces apoptosis.

  • VEGF: mediates angiogenesis, vascular permeability, and cell migration.

  • WNT: regulates organ morphogenesis during development and tissue repair.

To access it we can use decoupler.

# Retrieve PROGENy model weights
progeny = dc.get_progeny(top=500)
source target weight p_value
0 Androgen TMPRSS2 11.490631 0.000000e+00
1 Androgen NKX3-1 10.622551 2.242078e-44
2 Androgen MBOAT2 10.472733 4.624285e-44
3 Androgen KLK2 10.176186 1.944414e-40
4 Androgen SARG 11.386852 2.790209e-40
... ... ... ... ...
6995 p53 ZMYM4 -2.325752 1.522388e-06
6996 p53 CFDP1 -1.628168 1.526045e-06
6997 p53 VPS37D 2.309503 1.537098e-06
6998 p53 TEDC1 -2.274823 1.547037e-06
6999 p53 CCDC138 -3.205113 1.568160e-06

7000 rows × 4 columns

Activity inference with Multivariate Linear Model (MLM)

To infer pathway enrichment scores we will run the Multivariate Linear Model (mlm) method. For each sample in our dataset (adata), it fits a linear model that predicts the observed gene expression based on all pathways’ Pathway-Gene interactions weights. Once fitted, the obtained t-values of the slopes are the scores. If it is positive, we interpret that the pathway is active and if it is negative we interpret that it is inactive.


We can run mlm with a one-liner:

# Infer pathway activities with mlm
pathway_acts, pathway_pvals = dc.run_mlm(mat=mat, net=progeny, verbose=True)
Running mlm on mat with 1 samples and 19713 targets for 14 sources.
Androgen EGFR Estrogen Hypoxia JAK-STAT MAPK NFkB PI3K TGFb TNFa Trail VEGF WNT p53
treatment.vs.control 2.338243 -1.959179 1.244277 6.153507 -12.091851 8.071877 1.490978 7.937943 39.796871 -6.097804 -2.286829 3.763988 -0.376275 -6.113982

Let us plot the obtained scores:

    figsize=(6, 3)

As expected, after treating cells with the cytokine TGFb we see an increase of activity for this pathway.

On the other hand, it seems that this treatment has decreased the activity of other pathways like JAK-STAT or TNFa.

We can visualize the targets of TFGb in a scatter plot:

dc.plot_targets(results_df, stat='stat', source_name='TGFb', net=progeny, top=15)

The observed activation of TGFb is due to the fact that majority of its target genes with positive weights have positive t-values (1st quadrant), and the majority of the ones with negative weights have negative t-values (3d quadrant).

Functional enrichment of biological terms

Finally, we can also infer activities for general biological terms or processes.

MSigDB gene sets

The Molecular Signatures Database (MSigDB) is a resource containing a collection of gene sets annotated to different biological processes.

msigdb = dc.get_resource('MSigDB')
genesymbol collection geneset
0 MAFF chemical_and_genetic_perturbations BOYAULT_LIVER_CANCER_SUBCLASS_G56_DN
1 MAFF chemical_and_genetic_perturbations ELVIDGE_HYPOXIA_UP
2 MAFF chemical_and_genetic_perturbations NUYTTEN_NIPP1_TARGETS_DN
4 MAFF chemical_and_genetic_perturbations SCHAEFFER_PROSTATE_DEVELOPMENT_12HR_UP
... ... ... ...
3838544 PRAMEF22 go_biological_process GOBP_APOPTOTIC_PROCESS
3838545 PRAMEF22 go_biological_process GOBP_REGULATION_OF_CELL_DEATH

3838548 rows × 3 columns

As an example, we will use the hallmark gene sets, but we could have used any other.


To see what other collections are available in MSigDB, type: msigdb['collection'].unique().

We can filter by for hallmark:

# Filter by hallmark
msigdb = msigdb[msigdb['collection']=='hallmark']

# Remove duplicated entries
msigdb = msigdb[~msigdb.duplicated(['geneset', 'genesymbol'])]

# Rename
msigdb.loc[:, 'geneset'] = [name.split('HALLMARK_')[1] for name in msigdb['geneset']]

genesymbol collection geneset
270 MAFF hallmark HYPOXIA
377 MAFF hallmark COMPLEMENT
... ... ... ...
1450315 ELP4 hallmark PANCREAS_BETA_CELLS
1450526 GCG hallmark PANCREAS_BETA_CELLS
1450731 PCSK2 hallmark PANCREAS_BETA_CELLS
1450916 PAX6 hallmark PANCREAS_BETA_CELLS

7318 rows × 3 columns

Enrichment with Over Representation Analysis (ORA)

To infer functional enrichment scores we will run the Over Representation Analysis (ora) method. As input data it accepts an expression matrix (decoupler.run_ora) or the results of differential expression analysis (decoupler.run_ora_df). For the former, by default the top 5% of expressed genes by sample are selected as the set of interest (S), and for the latter a user-defined significance filtering can be used. Once we have S, it builds a contingency table using set operations for each set stored in the gene set resource being used (net). Using the contingency table, ora performs a one-sided Fisher exact test to test for significance of overlap between sets. The final score is obtained by log-transforming the obtained p-values, meaning that higher values are more significant.


We can run ora with a simple one-liner:

# Infer enrichment with ora using significant deg
top_genes = results_df[results_df['padj'] < 0.05]

# Run ora
enr_pvals = dc.get_ora_df(

Term Set size Overlap ratio p-value FDR p-value Odds ratio Combined score Features
0 ADIPOGENESIS 200 0.625000 0.000209 0.000872 1.257682 10.655318 ABCA1;ACAA2;ACADM;ACADS;ACLY;ACOX1;ADCY6;ADIPO...
1 ALLOGRAFT_REJECTION 200 0.370000 0.999914 1.000000 0.742748 0.000064 ABCE1;ABI1;APBB1;B2M;BCAT1;BCL10;BCL3;BRCA1;C2...
2 ANDROGEN_RESPONSE 100 0.610000 0.016489 0.037476 1.227798 5.040152 ABCC4;ABHD2;ACTN1;ADAMTS1;ALDH1A3;ANKH;ARID5B;...
3 ANGIOGENESIS 36 0.527778 0.428919 0.536149 1.070826 0.906440 COL3A1;CXCL6;FGFR1;FSTL1;ITGAV;JAG1;LRPAP1;NRP...
4 APICAL_JUNCTION 200 0.555000 0.063878 0.106463 1.115798 3.069314 ACTA1;ACTB;ACTC1;ACTG1;ACTG2;ACTN1;ACTN4;ADAM1...

Then we can visualize the most enriched terms:

    enr_pvals.sort_values('Combined score', ascending=False).head(15),
    x='Combined score',
    s='Odds ratio',
    c='FDR p-value',
    figsize=(3, 6)

TNFa and interferons response (JAK-STAT) processes seem to be enriched. We previously observed a similar result with the PROGENy pathways, were they were significantly downregulated. Therefore, one of the limitations of using a prior knowledge resource without weights is that it doesn’t provide direction.

We can also plot the running score for a given gene set:

# Plot

Enrichment of ligand-receptor interactions

Recently, study of interactions between ligands and receptors have gained significant traction, notably pushed by the democratization of single cell sequencing technologies. While most methods (such as the one described in LIANA) are developed for single cell datasets, they rely on a relatively simple assumption of co-expression or co-regulation of two (or more in the context of complexes) genes acting as ligand and receptors to propose hypothetical ligand-receptor interaction events. This concept can seamlessly be applied to a bulk RNA dataset, where the assumption is that sender and receiver cells are convoluted in a single dataset, but the observation of significant co-regulation of ligand and receptors should still correspond to hypothetical ligand-receptor interaction events.

At the core, most current ligand-receptor interaction methods rely on averaging the measurements obtained for ligand and receptor and standardizing them against a background distribution. Thus, an enrichment method based on a weighted mean can emulate this, where the sets are simply the members of a ligand receptor pair (or more in the context of complexes).

Thus, we can extract ligand-receptor interaction ressources from the LIANA package (available both in R and python), and use it as a prior knowledge network with decoupler to find the most significant pairs of ligand-receptors in a given bulk dataset.

While more work is required to fully understand the functional relevance of the highlighted ligand-receptor interactions with such an approach, this represents a very straightforward and intuitive approach to embed a bulk RNA dataset with ligand-receptor interaction prior knowledge.

First, we extract ligand-receptor interactions from liana, and decomplexify them to format them into an appropriate decoupleR input.

import liana as ln

liana_lr = ln.resource.select_resource()
liana_lr = ln.resource.explode_complexes(liana_lr)

# Create two new DataFrames, each containing one of the pairs of columns to be concatenated
df1 = liana_lr[['interaction', 'ligand']]
df2 = liana_lr[['interaction', 'receptor']]

# Rename the columns in each new DataFrame
df1.columns = ['interaction', 'genes']
df2.columns = ['interaction', 'genes']

# Concatenate the two new DataFrames
liana_lr = pd.concat([df1, df2], axis=0)
liana_lr['weight'] = 1

# Find duplicated rows
duplicates = liana_lr.duplicated()

# Remove duplicated rows
liana_lr = liana_lr[~duplicates]

interaction genes weight
... ... ... ...
5775 BMP2&ACTR2 ACTR2 1
5776 BMP15&ACTR2 ACTR2 1
5777 CSF1&CSF3R CSF3R 1
5778 IL36G&IFNAR1 IFNAR1 1
5779 IL36G&IFNAR2 IFNAR2 1

10381 rows × 3 columns

Then we can use ulm to find the significant co-regulated pairs of ligand and receptors.

# Infer lr activities with ulm
lr_score, lr_pvalue = dc.run_ulm(
Running ulm on mat with 1 samples and 19713 targets for 2112 sources.
dc.plot_barplot(lr_score, 'treatment.vs.control', top=25, vertical=True)

Interactions between colagens and the ITG_ITG complexes seems to be quite enrichned. That is especially relevant since the EMT pathway was significantly enriched in the functional pathway analysis.