Differential gene expression

Scanpy Toolkit

Identify genes that are significantly over or under-expressed between conditions in specific cell populations.
Authors

Åsa Björklund

Paulo Czarnewski

Susanne Reinsbach

Roy Francis

Published

05-Feb-2024

Note

Code chunks run Python commands unless it starts with %%bash, in which case, those chunks run shell commands.

In this tutorial we will cover differential gene expression, which comprises an extensive range of topics and methods. In single cell, differential expresison can have multiple functionalities such as identifying marker genes for cell populations, as well as identifying differentially regulated genes across conditions (healthy vs control). We will also cover controlling batch effect in your test.

Differential expression is performed with the function rank_genes_group. The default method to compute differential expression is the t-test_overestim_var. Other implemented methods are: logreg, t-test and wilcoxon.

By default, the .raw attribute of AnnData is used in case it has been initialized, it can be changed by setting use_raw=False.

The clustering with resolution 0.6 seems to give a reasonable number of clusters, so we will use that clustering for all DE tests.

First, let’s import libraries and fetch the clustered data from the previous lab.

import numpy as np
import pandas as pd
import scanpy as sc
import gseapy
import matplotlib.pyplot as plt
import warnings
import os
import urllib.request

warnings.simplefilter(action="ignore", category=Warning)

# verbosity: errors (0), warnings (1), info (2), hints (3)
sc.settings.verbosity = 2

sc.settings.set_figure_params(dpi=80)

Read in the clustered data object.

# download pre-computed data if missing or long compute
fetch_data = True

# url for source and intermediate data
path_data = "https://export.uppmax.uu.se/naiss2023-23-3/workshops/workshop-scrnaseq"

path_results = "data/covid/results"
if not os.path.exists(path_results):
    os.makedirs(path_results, exist_ok=True)

# path_file = "data/covid/results/scanpy_covid_qc_dr_scanorama_cl.h5ad"
path_file = "data/covid/results/scanpy_covid_qc_dr_scanorama_cl.h5ad"
if fetch_data and not os.path.exists(path_file):
    urllib.request.urlretrieve(os.path.join(
        path_data, 'covid/results/scanpy_covid_qc_dr_scanorama_cl.h5ad'), path_file)

adata = sc.read_h5ad(path_file)
adata
AnnData object with n_obs × n_vars = 7222 × 2626
    obs: 'type', 'sample', 'batch', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'percent_mt2', 'n_counts', 'n_genes', 'percent_chrY', 'XIST-counts', 'S_score', 'G2M_score', 'phase', 'doublet_scores', 'predicted_doublets', 'doublet_info', 'leiden_1.0', 'leiden_0.6', 'leiden_0.4', 'leiden_1.4', 'louvain_1.0', 'louvain_0.6', 'louvain_0.4', 'louvain_1.4', 'kmeans5', 'kmeans10', 'kmeans15', 'hclust_5', 'hclust_10', 'hclust_15'
    var: 'gene_ids', 'feature_types', 'genome', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'dendrogram_leiden_0.6', 'dendrogram_louvain_0.6', 'doublet_info_colors', 'hclust_10_colors', 'hclust_15_colors', 'hclust_5_colors', 'hvg', 'kmeans10_colors', 'kmeans15_colors', 'kmeans5_colors', 'leiden', 'leiden_0.4_colors', 'leiden_0.6_colors', 'leiden_1.0_colors', 'leiden_1.4_colors', 'log1p', 'louvain', 'louvain_0.4_colors', 'louvain_0.6_colors', 'louvain_1.0_colors', 'louvain_1.4_colors', 'neighbors', 'pca', 'phase_colors', 'sample_colors', 'tsne', 'umap'
    obsm: 'Scanorama', 'X_pca', 'X_tsne', 'X_umap'
    varm: 'PCs'
    obsp: 'connectivities', 'distances'
print(adata.X.shape)
print(adata.raw.X.shape)
print(adata.raw.X[:10,:10])
(7222, 2626)
(7222, 19468)
  (1, 4)    0.7825693876867097
  (8, 7)    1.1311041336746985

As you can see, the X matrix only contains the variable genes, while the raw matrix contains all genes.

Printing a few of the values in adata.raw.X shows that the raw matrix is normalized.

For DGE analysis we would like to run with all genes, on normalized values, so we will have to revert back to the raw matrix. In case you have raw counts in the matrix you also have to renormalize and logtransform.

adata = adata.raw.to_adata()

Now lets look at the clustering of the object we loaded in the umap. We will use louvain_0.6 clustering in this exercise.

sc.pl.umap(adata, color='louvain_0.6')

1 T-test

sc.tl.rank_genes_groups(adata, 'louvain_0.6', method='t-test', key_added = "t-test")
sc.pl.rank_genes_groups(adata, n_genes=25, sharey=False, key = "t-test")

# results are stored in the adata.uns["t-test"] slot
adata
ranking genes
    finished (0:00:02)

AnnData object with n_obs × n_vars = 7222 × 19468
    obs: 'type', 'sample', 'batch', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'percent_mt2', 'n_counts', 'n_genes', 'percent_chrY', 'XIST-counts', 'S_score', 'G2M_score', 'phase', 'doublet_scores', 'predicted_doublets', 'doublet_info', 'leiden_1.0', 'leiden_0.6', 'leiden_0.4', 'leiden_1.4', 'louvain_1.0', 'louvain_0.6', 'louvain_0.4', 'louvain_1.4', 'kmeans5', 'kmeans10', 'kmeans15', 'hclust_5', 'hclust_10', 'hclust_15'
    var: 'gene_ids', 'feature_types', 'genome', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'n_cells'
    uns: 'dendrogram_leiden_0.6', 'dendrogram_louvain_0.6', 'doublet_info_colors', 'hclust_10_colors', 'hclust_15_colors', 'hclust_5_colors', 'hvg', 'kmeans10_colors', 'kmeans15_colors', 'kmeans5_colors', 'leiden', 'leiden_0.4_colors', 'leiden_0.6_colors', 'leiden_1.0_colors', 'leiden_1.4_colors', 'log1p', 'louvain', 'louvain_0.4_colors', 'louvain_0.6_colors', 'louvain_1.0_colors', 'louvain_1.4_colors', 'neighbors', 'pca', 'phase_colors', 'sample_colors', 'tsne', 'umap', 't-test'
    obsm: 'Scanorama', 'X_pca', 'X_tsne', 'X_umap'
    obsp: 'connectivities', 'distances'

2 T-test overestimated_variance

sc.tl.rank_genes_groups(adata, 'louvain_0.6', method='t-test_overestim_var', key_added = "t-test_ov")
sc.pl.rank_genes_groups(adata, n_genes=25, sharey=False, key = "t-test_ov")
ranking genes
    finished (0:00:01)

3 Wilcoxon rank-sum

The result of a Wilcoxon rank-sum (Mann-Whitney-U) test is very similar. We recommend using the latter in publications, see e.g., Sonison & Robinson (2018). You might also consider much more powerful differential testing packages like MAST, limma, DESeq2 and, for python, the recent diffxpy.

sc.tl.rank_genes_groups(adata, 'louvain_0.6', method='wilcoxon', key_added = "wilcoxon")
sc.pl.rank_genes_groups(adata, n_genes=25, sharey=False, key="wilcoxon")
ranking genes
    finished (0:00:08)

4 Logistic regression test

As an alternative, let us rank genes using logistic regression. For instance, this has been suggested by Natranos et al. (2018). The essential difference is that here, we use a multi-variate appraoch whereas conventional differential tests are uni-variate. Clark et al. (2014) has more details.

sc.tl.rank_genes_groups(adata, 'louvain_0.6', method='logreg',key_added = "logreg")
sc.pl.rank_genes_groups(adata, n_genes=25, sharey=False, key = "logreg")
ranking genes
    finished (0:00:30)

5 Compare genes

Take all significant DE genes for cluster0 with each test and compare the overlap.

#compare cluster1 genes, only stores top 100 by default

wc = sc.get.rank_genes_groups_df(adata, group='0', key='wilcoxon', pval_cutoff=0.01, log2fc_min=0)['names']
tt = sc.get.rank_genes_groups_df(adata, group='0', key='t-test', pval_cutoff=0.01, log2fc_min=0)['names']
tt_ov = sc.get.rank_genes_groups_df(adata, group='0', key='t-test_ov', pval_cutoff=0.01, log2fc_min=0)['names']

from matplotlib_venn import venn3

venn3([set(wc),set(tt),set(tt_ov)], ('Wilcox','T-test','T-test_ov') )
plt.show()

As you can see, the Wilcoxon test and the T-test with overestimated variance gives very similar result. Also the regular T-test has good overlap.

6 Visualization

There are several ways to visualize the expression of top DE genes. Here we will plot top 5 genes per cluster from Wilcoxon test as heatmap, dotplot, violin plot or matrix.

sc.pl.rank_genes_groups_heatmap(adata, n_genes=5, key="wilcoxon", groupby="louvain_0.6", show_gene_labels=True)
sc.pl.rank_genes_groups_dotplot(adata, n_genes=5, key="wilcoxon", groupby="louvain_0.6")
sc.pl.rank_genes_groups_stacked_violin(adata, n_genes=5, key="wilcoxon", groupby="louvain_0.6")
sc.pl.rank_genes_groups_matrixplot(adata, n_genes=5, key="wilcoxon", groupby="louvain_0.6")

7 Compare specific clusters

We can also do pairwise comparisons of individual clusters on one vs many clusters. For instance, clusters 1 & 2 have very similar expression profiles.

sc.tl.rank_genes_groups(adata, 'louvain_0.6', groups=['1'], reference='2', method='wilcoxon')
sc.pl.rank_genes_groups(adata, groups=['1'], n_genes=20)
ranking genes
    finished (0:00:01)

Plot as violins for those two groups.

sc.pl.rank_genes_groups_violin(adata, groups='1', n_genes=10)

# plot the same genes as violins across all the datasets.

# convert numpy.recarray to list
mynames = [x[0] for x in adata.uns['rank_genes_groups']['names'][:10]]
sc.pl.stacked_violin(adata, mynames, groupby = 'louvain_0.6')

8 DGE across conditions

The second way of computing differential expression is to answer which genes are differentially expressed within a cluster. For example, in our case we have libraries comming from patients and controls and we would like to know which genes are influenced the most in a particular cell type. For this end, we will first subset our data for the desired cell cluster, then change the cell identities to the variable of comparison (which now in our case is the type, e.g. Covid/Ctrl).

cl1 = adata[adata.obs['louvain_0.6'] == '4',:]
cl1.obs['type'].value_counts()

sc.tl.rank_genes_groups(cl1, 'type', method='wilcoxon', key_added = "wilcoxon")
sc.pl.rank_genes_groups(cl1, n_genes=25, sharey=False, key="wilcoxon")
ranking genes
    finished (0:00:01)

sc.pl.rank_genes_groups_violin(cl1, n_genes=10, key="wilcoxon")

We can also plot these genes across all clusters, but split by “type”, to check if the genes are also up/downregulated in other celltypes.

import seaborn as sns

genes1 = sc.get.rank_genes_groups_df(cl1, group='Covid', key='wilcoxon')['names'][:5]
genes2 = sc.get.rank_genes_groups_df(cl1, group='Ctrl', key='wilcoxon')['names'][:5]
genes = genes1.tolist() +  genes2.tolist() 
df = sc.get.obs_df(adata, genes + ['louvain_0.6','type'], use_raw=False)
df2 = df.melt(id_vars=["louvain_0.6",'type'], value_vars=genes)

sns.catplot(x = "louvain_0.6", y = "value", hue = "type", kind = 'violin', col = "variable", data = df2, col_wrap=4, inner=None)

As you can see, we have many sex chromosome related genes among the top DE genes. And if you remember from the QC lab, we have inbalanced sex distribution among our subjects, so this may not be related to covid at all.

8.1 Remove sex chromosome genes

To remove some of the bias due to inbalanced sex in the subjects we can remove the sex chromosome related genes.

annot = sc.queries.biomart_annotations(
        "hsapiens",
        ["ensembl_gene_id", "external_gene_name", "start_position", "end_position", "chromosome_name"],
    ).set_index("external_gene_name")

chrY_genes = adata.var_names.intersection(annot.index[annot.chromosome_name == "Y"])
chrX_genes = adata.var_names.intersection(annot.index[annot.chromosome_name == "X"])

sex_genes = chrY_genes.union(chrX_genes)
print(len(sex_genes))
all_genes = cl1.var.index.tolist()
print(len(all_genes))

keep_genes = [x for x in all_genes if x not in sex_genes]
print(len(keep_genes))

cl1 = cl1[:,keep_genes]
551
19468
18917

Rerun differential expression.

sc.tl.rank_genes_groups(cl1, 'type', method='wilcoxon', key_added = "wilcoxon")
sc.pl.rank_genes_groups(cl1, n_genes=25, sharey=False, key="wilcoxon")
ranking genes
    finished (0:00:01)

8.2 Patient batch effects

When we are testing for Covid vs Control we are running a DGE test for 3 vs 3 individuals. That will be very sensitive to sample differences unless we find a way to control for it. So first, lets check how the top DGEs are expressed across the individuals:

genes1 = sc.get.rank_genes_groups_df(cl1, group='Covid', key='wilcoxon')['names'][:5]
genes2 = sc.get.rank_genes_groups_df(cl1, group='Ctrl', key='wilcoxon')['names'][:5]
genes = genes1.tolist() +  genes2.tolist() 

sc.pl.violin(cl1, genes1, groupby='sample')
sc.pl.violin(cl1, genes2, groupby='sample')

As you can see, many of the genes detected as DGE in Covid are unique to one or 2 patients.

We can also plot the top Covid and top Ctrl genes as a dotplot:

genes1 = sc.get.rank_genes_groups_df(cl1, group='Covid', key='wilcoxon')['names'][:20]
genes2 = sc.get.rank_genes_groups_df(cl1, group='Ctrl', key='wilcoxon')['names'][:20]
genes = genes1.tolist() +  genes2.tolist() 

sc.pl.dotplot(cl1,genes, groupby='sample')

Clearly many of the top Covid genes are only high in the covid_17 sample, and not a general feature of covid patients.

This is also the patient with the highest number of cells in this cluster:

cl1.obs['sample'].value_counts()
sample
covid_17    130
ctrl_5      114
covid_1     109
ctrl_13      65
ctrl_14      62
ctrl_19      57
covid_16     38
covid_15     37
Name: count, dtype: int64

8.3 Subsample

So one obvious thing to consider is an equal amount of cells per individual so that the DGE results are not dominated by a single sample.

So we will downsample to an equal number of cells per sample, in this case 34 cells per sample as it is the lowest number among all samples

target_cells = 37

tmp = [cl1[cl1.obs['sample'] == s] for s in cl1.obs['sample'].cat.categories]

for dat in tmp:
    if dat.n_obs > target_cells:
            sc.pp.subsample(dat, n_obs=target_cells)

cl1_sub = tmp[0].concatenate(*tmp[1:])

cl1_sub.obs['sample'].value_counts()
sample
covid_1     37
covid_15    37
covid_16    37
covid_17    37
ctrl_5      37
ctrl_13     37
ctrl_14     37
ctrl_19     37
Name: count, dtype: int64
sc.tl.rank_genes_groups(cl1_sub, 'type', method='wilcoxon', key_added = "wilcoxon")
sc.pl.rank_genes_groups(cl1_sub, n_genes=25, sharey=False, key="wilcoxon")
ranking genes
    finished (0:00:00)

genes1 = sc.get.rank_genes_groups_df(cl1_sub, group='Covid', key='wilcoxon')['names'][:20]
genes2 = sc.get.rank_genes_groups_df(cl1_sub, group='Ctrl', key='wilcoxon')['names'][:20]
genes = genes1.tolist() +  genes2.tolist() 

sc.pl.dotplot(cl1,genes, groupby='sample')

It looks much better now. But if we look per patient you can see that we still have some genes that are dominated by a single patient. Still, it is often a good idea to control the number of cells from each sample when doing differential expression.

There are many different ways to try and resolve the issue of patient batch effects, however most of them require R packages. These can be run via rpy2 as is demonstraded in this compendium: https://www.sc-best-practices.org/conditions/differential_gene_expression.html

However, we have not included it here as of now. So please have a look at the patient batch effect section in the seurat DGE tutorial where we run EdgeR on pseudobulk and MAST with random effect.

9 Gene Set Analysis (GSA)

9.1 Hypergeometric enrichment test

Having a defined list of differentially expressed genes, you can now look for their combined function using hypergeometric test.

#Available databases : ‘Human’, ‘Mouse’, ‘Yeast’, ‘Fly’, ‘Fish’, ‘Worm’ 
gene_set_names = gseapy.get_library_name(organism='Human')
print(gene_set_names)
['ARCHS4_Cell-lines', 'ARCHS4_IDG_Coexp', 'ARCHS4_Kinases_Coexp', 'ARCHS4_TFs_Coexp', 'ARCHS4_Tissues', 'Achilles_fitness_decrease', 'Achilles_fitness_increase', 'Aging_Perturbations_from_GEO_down', 'Aging_Perturbations_from_GEO_up', 'Allen_Brain_Atlas_10x_scRNA_2021', 'Allen_Brain_Atlas_down', 'Allen_Brain_Atlas_up', 'Azimuth_2023', 'Azimuth_Cell_Types_2021', 'BioCarta_2013', 'BioCarta_2015', 'BioCarta_2016', 'BioPlanet_2019', 'BioPlex_2017', 'CCLE_Proteomics_2020', 'CORUM', 'COVID-19_Related_Gene_Sets', 'COVID-19_Related_Gene_Sets_2021', 'Cancer_Cell_Line_Encyclopedia', 'CellMarker_Augmented_2021', 'ChEA_2013', 'ChEA_2015', 'ChEA_2016', 'ChEA_2022', 'Chromosome_Location', 'Chromosome_Location_hg19', 'ClinVar_2019', 'DSigDB', 'Data_Acquisition_Method_Most_Popular_Genes', 'DepMap_WG_CRISPR_Screens_Broad_CellLines_2019', 'DepMap_WG_CRISPR_Screens_Sanger_CellLines_2019', 'Descartes_Cell_Types_and_Tissue_2021', 'Diabetes_Perturbations_GEO_2022', 'DisGeNET', 'Disease_Perturbations_from_GEO_down', 'Disease_Perturbations_from_GEO_up', 'Disease_Signatures_from_GEO_down_2014', 'Disease_Signatures_from_GEO_up_2014', 'DrugMatrix', 'Drug_Perturbations_from_GEO_2014', 'Drug_Perturbations_from_GEO_down', 'Drug_Perturbations_from_GEO_up', 'ENCODE_Histone_Modifications_2013', 'ENCODE_Histone_Modifications_2015', 'ENCODE_TF_ChIP-seq_2014', 'ENCODE_TF_ChIP-seq_2015', 'ENCODE_and_ChEA_Consensus_TFs_from_ChIP-X', 'ESCAPE', 'Elsevier_Pathway_Collection', 'Enrichr_Libraries_Most_Popular_Genes', 'Enrichr_Submissions_TF-Gene_Coocurrence', 'Enrichr_Users_Contributed_Lists_2020', 'Epigenomics_Roadmap_HM_ChIP-seq', 'FANTOM6_lncRNA_KD_DEGs', 'GO_Biological_Process_2013', 'GO_Biological_Process_2015', 'GO_Biological_Process_2017', 'GO_Biological_Process_2017b', 'GO_Biological_Process_2018', 'GO_Biological_Process_2021', 'GO_Biological_Process_2023', 'GO_Cellular_Component_2013', 'GO_Cellular_Component_2015', 'GO_Cellular_Component_2017', 'GO_Cellular_Component_2017b', 'GO_Cellular_Component_2018', 'GO_Cellular_Component_2021', 'GO_Cellular_Component_2023', 'GO_Molecular_Function_2013', 'GO_Molecular_Function_2015', 'GO_Molecular_Function_2017', 'GO_Molecular_Function_2017b', 'GO_Molecular_Function_2018', 'GO_Molecular_Function_2021', 'GO_Molecular_Function_2023', 'GTEx_Aging_Signatures_2021', 'GTEx_Tissue_Expression_Down', 'GTEx_Tissue_Expression_Up', 'GTEx_Tissues_V8_2023', 'GWAS_Catalog_2019', 'GWAS_Catalog_2023', 'GeDiPNet_2023', 'GeneSigDB', 'Gene_Perturbations_from_GEO_down', 'Gene_Perturbations_from_GEO_up', 'Genes_Associated_with_NIH_Grants', 'Genome_Browser_PWMs', 'GlyGen_Glycosylated_Proteins_2022', 'HDSigDB_Human_2021', 'HDSigDB_Mouse_2021', 'HMDB_Metabolites', 'HMS_LINCS_KinomeScan', 'HomoloGene', 'HuBMAP_ASCT_plus_B_augmented_w_RNAseq_Coexpression', 'HuBMAP_ASCTplusB_augmented_2022', 'HumanCyc_2015', 'HumanCyc_2016', 'Human_Gene_Atlas', 'Human_Phenotype_Ontology', 'IDG_Drug_Targets_2022', 'InterPro_Domains_2019', 'Jensen_COMPARTMENTS', 'Jensen_DISEASES', 'Jensen_TISSUES', 'KEA_2013', 'KEA_2015', 'KEGG_2013', 'KEGG_2015', 'KEGG_2016', 'KEGG_2019_Human', 'KEGG_2019_Mouse', 'KEGG_2021_Human', 'KOMP2_Mouse_Phenotypes_2022', 'Kinase_Perturbations_from_GEO_down', 'Kinase_Perturbations_from_GEO_up', 'L1000_Kinase_and_GPCR_Perturbations_down', 'L1000_Kinase_and_GPCR_Perturbations_up', 'LINCS_L1000_CRISPR_KO_Consensus_Sigs', 'LINCS_L1000_Chem_Pert_Consensus_Sigs', 'LINCS_L1000_Chem_Pert_down', 'LINCS_L1000_Chem_Pert_up', 'LINCS_L1000_Ligand_Perturbations_down', 'LINCS_L1000_Ligand_Perturbations_up', 'Ligand_Perturbations_from_GEO_down', 'Ligand_Perturbations_from_GEO_up', 'MAGMA_Drugs_and_Diseases', 'MAGNET_2023', 'MCF7_Perturbations_from_GEO_down', 'MCF7_Perturbations_from_GEO_up', 'MGI_Mammalian_Phenotype_2013', 'MGI_Mammalian_Phenotype_2017', 'MGI_Mammalian_Phenotype_Level_3', 'MGI_Mammalian_Phenotype_Level_4', 'MGI_Mammalian_Phenotype_Level_4_2019', 'MGI_Mammalian_Phenotype_Level_4_2021', 'MSigDB_Computational', 'MSigDB_Hallmark_2020', 'MSigDB_Oncogenic_Signatures', 'Metabolomics_Workbench_Metabolites_2022', 'Microbe_Perturbations_from_GEO_down', 'Microbe_Perturbations_from_GEO_up', 'MoTrPAC_2023', 'Mouse_Gene_Atlas', 'NCI-60_Cancer_Cell_Lines', 'NCI-Nature_2015', 'NCI-Nature_2016', 'NIH_Funded_PIs_2017_AutoRIF_ARCHS4_Predictions', 'NIH_Funded_PIs_2017_GeneRIF_ARCHS4_Predictions', 'NIH_Funded_PIs_2017_Human_AutoRIF', 'NIH_Funded_PIs_2017_Human_GeneRIF', 'NURSA_Human_Endogenous_Complexome', 'OMIM_Disease', 'OMIM_Expanded', 'Old_CMAP_down', 'Old_CMAP_up', 'Orphanet_Augmented_2021', 'PFOCR_Pathways', 'PFOCR_Pathways_2023', 'PPI_Hub_Proteins', 'PanglaoDB_Augmented_2021', 'Panther_2015', 'Panther_2016', 'Pfam_Domains_2019', 'Pfam_InterPro_Domains', 'PheWeb_2019', 'PhenGenI_Association_2021', 'Phosphatase_Substrates_from_DEPOD', 'ProteomicsDB_2020', 'Proteomics_Drug_Atlas_2023', 'RNA-Seq_Disease_Gene_and_Drug_Signatures_from_GEO', 'RNAseq_Automatic_GEO_Signatures_Human_Down', 'RNAseq_Automatic_GEO_Signatures_Human_Up', 'RNAseq_Automatic_GEO_Signatures_Mouse_Down', 'RNAseq_Automatic_GEO_Signatures_Mouse_Up', 'Rare_Diseases_AutoRIF_ARCHS4_Predictions', 'Rare_Diseases_AutoRIF_Gene_Lists', 'Rare_Diseases_GeneRIF_ARCHS4_Predictions', 'Rare_Diseases_GeneRIF_Gene_Lists', 'Reactome_2013', 'Reactome_2015', 'Reactome_2016', 'Reactome_2022', 'Rummagene_kinases', 'Rummagene_signatures', 'Rummagene_transcription_factors', 'SILAC_Phosphoproteomics', 'SubCell_BarCode', 'SynGO_2022', 'SysMyo_Muscle_Gene_Sets', 'TF-LOF_Expression_from_GEO', 'TF_Perturbations_Followed_by_Expression', 'TG_GATES_2020', 'TRANSFAC_and_JASPAR_PWMs', 'TRRUST_Transcription_Factors_2019', 'Table_Mining_of_CRISPR_Studies', 'Tabula_Muris', 'Tabula_Sapiens', 'TargetScan_microRNA', 'TargetScan_microRNA_2017', 'The_Kinase_Library_2023', 'Tissue_Protein_Expression_from_Human_Proteome_Map', 'Tissue_Protein_Expression_from_ProteomicsDB', 'Transcription_Factor_PPIs', 'UK_Biobank_GWAS_v1', 'Virus-Host_PPI_P-HIPSTer_2020', 'VirusMINT', 'Virus_Perturbations_from_GEO_down', 'Virus_Perturbations_from_GEO_up', 'WikiPathway_2021_Human', 'WikiPathway_2023_Human', 'WikiPathways_2013', 'WikiPathways_2015', 'WikiPathways_2016', 'WikiPathways_2019_Human', 'WikiPathways_2019_Mouse', 'dbGaP', 'huMAP', 'lncHUB_lncRNA_Co-Expression', 'miRTarBase_2017']

Get the significant DEGs for the Covid patients.

#?gseapy.enrichr
glist = sc.get.rank_genes_groups_df(cl1_sub, group='Covid', key='wilcoxon', log2fc_min=0.25, pval_cutoff=0.05)['names'].squeeze().str.strip().tolist()
print(len(glist))
7
enr_res = gseapy.enrichr(gene_list=glist, organism='Human', gene_sets='GO_Biological_Process_2018', cutoff = 0.5)
enr_res.results.head()
Gene_set Term Overlap P-value Adjusted P-value Old P-value Old Adjusted P-value Odds Ratio Combined Score Genes
0 GO_Biological_Process_2018 positive regulation of inflammatory response (... 2/73 0.000273 0.021549 0 0 112.236620 921.142995 NFKBIA;S100A9
1 GO_Biological_Process_2018 positive regulation of defense response (GO:00... 2/74 0.000280 0.021549 0 0 110.672222 905.289889 NFKBIA;S100A9
2 GO_Biological_Process_2018 positive regulation of response to external st... 2/90 0.000414 0.021549 0 0 90.477273 704.697251 NFKBIA;S100A9
3 GO_Biological_Process_2018 positive regulation of NF-kappaB transcription... 2/128 0.000836 0.032592 0 0 63.069841 446.990813 NFKBIA;S100A9
4 GO_Biological_Process_2018 cellular protein complex assembly (GO:0043623) 2/144 0.001056 0.032941 0 0 55.918310 383.234256 HSP90AB1;POMP

Some databases of interest:
GO_Biological_Process_2017bKEGG_2019_HumanKEGG_2019_MouseWikiPathways_2019_HumanWikiPathways_2019_Mouse
You visualize your results using a simple barplot, for example:

gseapy.barplot(enr_res.res2d,title='GO_Biological_Process_2018')
<Axes: title={'center': 'GO_Biological_Process_2018'}, xlabel='$- \\log_{10}$ (Adjusted P-value)'>

10 Gene Set Enrichment Analysis (GSEA)

Besides the enrichment using hypergeometric test, we can also perform gene set enrichment analysis (GSEA), which scores ranked genes list (usually based on fold changes) and computes permutation test to check if a particular gene set is more present in the Up-regulated genes, among the DOWN_regulated genes or not differentially regulated.

We need a table with all DEGs and their log foldchanges. However, many lowly expressed genes will have high foldchanges and just contribue noise, so also filter for expression in enough cells.

gene_rank = sc.get.rank_genes_groups_df(cl1_sub, group='Covid', key='wilcoxon')[['names','logfoldchanges']]
gene_rank.sort_values(by=['logfoldchanges'], inplace=True, ascending=False)

# calculate_qc_metrics will calculate number of cells per gene
sc.pp.calculate_qc_metrics(cl1, percent_top=None, log1p=False, inplace=True)

# filter for genes expressed in at least 30 cells.
gene_rank = gene_rank[gene_rank['names'].isin(cl1.var_names[cl1.var.n_cells_by_counts>30])]

gene_rank
names logfoldchanges
526 SLFN5 26.829697
368 CXCL8 26.812254
209 EGR1 5.063837
38 PPBP 4.969337
211 PF4 4.870691
... ... ...
18062 NXPH4 -2.804427
18449 MME -3.049736
18380 DHDDS -3.202402
18282 KDM1B -3.256811
18607 ZNF296 -4.392631

7105 rows × 2 columns

Once our list of genes are sorted, we can proceed with the enrichment itself. We can use the package to get gene set from the Molecular Signature Database (MSigDB) and select KEGG pathways as an example.

#Available databases : ‘Human’, ‘Mouse’, ‘Yeast’, ‘Fly’, ‘Fish’, ‘Worm’ 
gene_set_names = gseapy.get_library_name(organism='Human')
print(gene_set_names)
['ARCHS4_Cell-lines', 'ARCHS4_IDG_Coexp', 'ARCHS4_Kinases_Coexp', 'ARCHS4_TFs_Coexp', 'ARCHS4_Tissues', 'Achilles_fitness_decrease', 'Achilles_fitness_increase', 'Aging_Perturbations_from_GEO_down', 'Aging_Perturbations_from_GEO_up', 'Allen_Brain_Atlas_10x_scRNA_2021', 'Allen_Brain_Atlas_down', 'Allen_Brain_Atlas_up', 'Azimuth_2023', 'Azimuth_Cell_Types_2021', 'BioCarta_2013', 'BioCarta_2015', 'BioCarta_2016', 'BioPlanet_2019', 'BioPlex_2017', 'CCLE_Proteomics_2020', 'CORUM', 'COVID-19_Related_Gene_Sets', 'COVID-19_Related_Gene_Sets_2021', 'Cancer_Cell_Line_Encyclopedia', 'CellMarker_Augmented_2021', 'ChEA_2013', 'ChEA_2015', 'ChEA_2016', 'ChEA_2022', 'Chromosome_Location', 'Chromosome_Location_hg19', 'ClinVar_2019', 'DSigDB', 'Data_Acquisition_Method_Most_Popular_Genes', 'DepMap_WG_CRISPR_Screens_Broad_CellLines_2019', 'DepMap_WG_CRISPR_Screens_Sanger_CellLines_2019', 'Descartes_Cell_Types_and_Tissue_2021', 'Diabetes_Perturbations_GEO_2022', 'DisGeNET', 'Disease_Perturbations_from_GEO_down', 'Disease_Perturbations_from_GEO_up', 'Disease_Signatures_from_GEO_down_2014', 'Disease_Signatures_from_GEO_up_2014', 'DrugMatrix', 'Drug_Perturbations_from_GEO_2014', 'Drug_Perturbations_from_GEO_down', 'Drug_Perturbations_from_GEO_up', 'ENCODE_Histone_Modifications_2013', 'ENCODE_Histone_Modifications_2015', 'ENCODE_TF_ChIP-seq_2014', 'ENCODE_TF_ChIP-seq_2015', 'ENCODE_and_ChEA_Consensus_TFs_from_ChIP-X', 'ESCAPE', 'Elsevier_Pathway_Collection', 'Enrichr_Libraries_Most_Popular_Genes', 'Enrichr_Submissions_TF-Gene_Coocurrence', 'Enrichr_Users_Contributed_Lists_2020', 'Epigenomics_Roadmap_HM_ChIP-seq', 'FANTOM6_lncRNA_KD_DEGs', 'GO_Biological_Process_2013', 'GO_Biological_Process_2015', 'GO_Biological_Process_2017', 'GO_Biological_Process_2017b', 'GO_Biological_Process_2018', 'GO_Biological_Process_2021', 'GO_Biological_Process_2023', 'GO_Cellular_Component_2013', 'GO_Cellular_Component_2015', 'GO_Cellular_Component_2017', 'GO_Cellular_Component_2017b', 'GO_Cellular_Component_2018', 'GO_Cellular_Component_2021', 'GO_Cellular_Component_2023', 'GO_Molecular_Function_2013', 'GO_Molecular_Function_2015', 'GO_Molecular_Function_2017', 'GO_Molecular_Function_2017b', 'GO_Molecular_Function_2018', 'GO_Molecular_Function_2021', 'GO_Molecular_Function_2023', 'GTEx_Aging_Signatures_2021', 'GTEx_Tissue_Expression_Down', 'GTEx_Tissue_Expression_Up', 'GTEx_Tissues_V8_2023', 'GWAS_Catalog_2019', 'GWAS_Catalog_2023', 'GeDiPNet_2023', 'GeneSigDB', 'Gene_Perturbations_from_GEO_down', 'Gene_Perturbations_from_GEO_up', 'Genes_Associated_with_NIH_Grants', 'Genome_Browser_PWMs', 'GlyGen_Glycosylated_Proteins_2022', 'HDSigDB_Human_2021', 'HDSigDB_Mouse_2021', 'HMDB_Metabolites', 'HMS_LINCS_KinomeScan', 'HomoloGene', 'HuBMAP_ASCT_plus_B_augmented_w_RNAseq_Coexpression', 'HuBMAP_ASCTplusB_augmented_2022', 'HumanCyc_2015', 'HumanCyc_2016', 'Human_Gene_Atlas', 'Human_Phenotype_Ontology', 'IDG_Drug_Targets_2022', 'InterPro_Domains_2019', 'Jensen_COMPARTMENTS', 'Jensen_DISEASES', 'Jensen_TISSUES', 'KEA_2013', 'KEA_2015', 'KEGG_2013', 'KEGG_2015', 'KEGG_2016', 'KEGG_2019_Human', 'KEGG_2019_Mouse', 'KEGG_2021_Human', 'KOMP2_Mouse_Phenotypes_2022', 'Kinase_Perturbations_from_GEO_down', 'Kinase_Perturbations_from_GEO_up', 'L1000_Kinase_and_GPCR_Perturbations_down', 'L1000_Kinase_and_GPCR_Perturbations_up', 'LINCS_L1000_CRISPR_KO_Consensus_Sigs', 'LINCS_L1000_Chem_Pert_Consensus_Sigs', 'LINCS_L1000_Chem_Pert_down', 'LINCS_L1000_Chem_Pert_up', 'LINCS_L1000_Ligand_Perturbations_down', 'LINCS_L1000_Ligand_Perturbations_up', 'Ligand_Perturbations_from_GEO_down', 'Ligand_Perturbations_from_GEO_up', 'MAGMA_Drugs_and_Diseases', 'MAGNET_2023', 'MCF7_Perturbations_from_GEO_down', 'MCF7_Perturbations_from_GEO_up', 'MGI_Mammalian_Phenotype_2013', 'MGI_Mammalian_Phenotype_2017', 'MGI_Mammalian_Phenotype_Level_3', 'MGI_Mammalian_Phenotype_Level_4', 'MGI_Mammalian_Phenotype_Level_4_2019', 'MGI_Mammalian_Phenotype_Level_4_2021', 'MSigDB_Computational', 'MSigDB_Hallmark_2020', 'MSigDB_Oncogenic_Signatures', 'Metabolomics_Workbench_Metabolites_2022', 'Microbe_Perturbations_from_GEO_down', 'Microbe_Perturbations_from_GEO_up', 'MoTrPAC_2023', 'Mouse_Gene_Atlas', 'NCI-60_Cancer_Cell_Lines', 'NCI-Nature_2015', 'NCI-Nature_2016', 'NIH_Funded_PIs_2017_AutoRIF_ARCHS4_Predictions', 'NIH_Funded_PIs_2017_GeneRIF_ARCHS4_Predictions', 'NIH_Funded_PIs_2017_Human_AutoRIF', 'NIH_Funded_PIs_2017_Human_GeneRIF', 'NURSA_Human_Endogenous_Complexome', 'OMIM_Disease', 'OMIM_Expanded', 'Old_CMAP_down', 'Old_CMAP_up', 'Orphanet_Augmented_2021', 'PFOCR_Pathways', 'PFOCR_Pathways_2023', 'PPI_Hub_Proteins', 'PanglaoDB_Augmented_2021', 'Panther_2015', 'Panther_2016', 'Pfam_Domains_2019', 'Pfam_InterPro_Domains', 'PheWeb_2019', 'PhenGenI_Association_2021', 'Phosphatase_Substrates_from_DEPOD', 'ProteomicsDB_2020', 'Proteomics_Drug_Atlas_2023', 'RNA-Seq_Disease_Gene_and_Drug_Signatures_from_GEO', 'RNAseq_Automatic_GEO_Signatures_Human_Down', 'RNAseq_Automatic_GEO_Signatures_Human_Up', 'RNAseq_Automatic_GEO_Signatures_Mouse_Down', 'RNAseq_Automatic_GEO_Signatures_Mouse_Up', 'Rare_Diseases_AutoRIF_ARCHS4_Predictions', 'Rare_Diseases_AutoRIF_Gene_Lists', 'Rare_Diseases_GeneRIF_ARCHS4_Predictions', 'Rare_Diseases_GeneRIF_Gene_Lists', 'Reactome_2013', 'Reactome_2015', 'Reactome_2016', 'Reactome_2022', 'Rummagene_kinases', 'Rummagene_signatures', 'Rummagene_transcription_factors', 'SILAC_Phosphoproteomics', 'SubCell_BarCode', 'SynGO_2022', 'SysMyo_Muscle_Gene_Sets', 'TF-LOF_Expression_from_GEO', 'TF_Perturbations_Followed_by_Expression', 'TG_GATES_2020', 'TRANSFAC_and_JASPAR_PWMs', 'TRRUST_Transcription_Factors_2019', 'Table_Mining_of_CRISPR_Studies', 'Tabula_Muris', 'Tabula_Sapiens', 'TargetScan_microRNA', 'TargetScan_microRNA_2017', 'The_Kinase_Library_2023', 'Tissue_Protein_Expression_from_Human_Proteome_Map', 'Tissue_Protein_Expression_from_ProteomicsDB', 'Transcription_Factor_PPIs', 'UK_Biobank_GWAS_v1', 'Virus-Host_PPI_P-HIPSTer_2020', 'VirusMINT', 'Virus_Perturbations_from_GEO_down', 'Virus_Perturbations_from_GEO_up', 'WikiPathway_2021_Human', 'WikiPathway_2023_Human', 'WikiPathways_2013', 'WikiPathways_2015', 'WikiPathways_2016', 'WikiPathways_2019_Human', 'WikiPathways_2019_Mouse', 'dbGaP', 'huMAP', 'lncHUB_lncRNA_Co-Expression', 'miRTarBase_2017']

Next, we will run GSEA. This will result in a table containing information for several pathways. We can then sort and filter those pathways to visualize only the top ones. You can select/filter them by either p-value or normalized enrichment score (NES).

res = gseapy.prerank(rnk=gene_rank, gene_sets='KEGG_2021_Human')

terms = res.res2d.Term
terms[:10]
0               Cytokine-cytokine receptor interaction
1    AGE-RAGE signaling pathway in diabetic complic...
2    Viral protein interaction with cytokine and cy...
3                                 Rheumatoid arthritis
4                              IL-17 signaling pathway
5                                       Bladder cancer
6                          Chemokine signaling pathway
7                         NF-kappa B signaling pathway
8                                        Legionellosis
9                                       Chagas disease
Name: Term, dtype: object
gseapy.gseaplot(rank_metric=res.ranking, term=terms[0], **res.results[terms[0]])
[<Axes: xlabel='Gene Rank', ylabel='Ranked metric'>,
 <Axes: >,
 <Axes: >,
 <Axes: ylabel='Enrichment Score'>]

Discuss

Which KEGG pathways are upregulated in this cluster? Which KEGG pathways are dowregulated in this cluster? Change the pathway source to another gene set (e.g. CP:WIKIPATHWAYS or CP:REACTOME or CP:BIOCARTA or GO:BP) and check the if you get similar results?

Finally, let’s save the integrated data for further analysis.

adata.write_h5ad('./data/covid/results/scanpy_covid_qc_dr_scanorama_cl_dge.h5ad')

11 Session info

Click here 
sc.logging.print_versions()
-----
anndata     0.10.5.post1
scanpy      1.9.6
-----
PIL                 10.0.0
anyio               NA
asttokens           NA
attr                23.1.0
babel               2.12.1
backcall            0.2.0
certifi             2023.11.17
cffi                1.15.1
charset_normalizer  3.1.0
colorama            0.4.6
comm                0.1.3
cycler              0.12.1
cython_runtime      NA
dateutil            2.8.2
debugpy             1.6.7
decorator           5.1.1
defusedxml          0.7.1
exceptiongroup      1.2.0
executing           1.2.0
fastjsonschema      NA
future              0.18.3
gmpy2               2.1.2
gseapy              1.0.6
h5py                3.9.0
idna                3.4
igraph              0.10.8
ipykernel           6.23.1
ipython_genutils    0.2.0
jedi                0.18.2
jinja2              3.1.2
joblib              1.3.2
json5               NA
jsonpointer         2.0
jsonschema          4.17.3
jupyter_events      0.6.3
jupyter_server      2.6.0
jupyterlab_server   2.22.1
kiwisolver          1.4.5
leidenalg           0.10.1
llvmlite            0.41.1
louvain             0.8.1
markupsafe          2.1.2
matplotlib          3.8.0
matplotlib_inline   0.1.6
matplotlib_venn     0.11.9
mpl_toolkits        NA
mpmath              1.3.0
natsort             8.4.0
nbformat            5.8.0
numba               0.58.1
numpy               1.26.3
opt_einsum          v3.3.0
overrides           NA
packaging           23.1
pandas              2.2.0
parso               0.8.3
patsy               0.5.6
pexpect             4.8.0
pickleshare         0.7.5
pkg_resources       NA
platformdirs        3.5.1
prometheus_client   NA
prompt_toolkit      3.0.38
psutil              5.9.5
ptyprocess          0.7.0
pure_eval           0.2.2
pvectorc            NA
pybiomart           0.2.0
pycparser           2.21
pydev_ipython       NA
pydevconsole        NA
pydevd              2.9.5
pydevd_file_utils   NA
pydevd_plugins      NA
pydevd_tracing      NA
pygments            2.15.1
pyparsing           3.1.1
pyrsistent          NA
pythonjsonlogger    NA
pytz                2023.3
requests            2.31.0
requests_cache      0.4.13
rfc3339_validator   0.1.4
rfc3986_validator   0.1.1
scipy               1.12.0
seaborn             0.13.2
send2trash          NA
session_info        1.0.0
six                 1.16.0
sklearn             1.4.0
sniffio             1.3.0
socks               1.7.1
sparse              0.15.1
stack_data          0.6.2
statsmodels         0.14.1
sympy               1.12
texttable           1.7.0
threadpoolctl       3.2.0
torch               2.0.0
tornado             6.3.2
tqdm                4.65.0
traitlets           5.9.0
typing_extensions   NA
urllib3             2.0.2
wcwidth             0.2.6
websocket           1.5.2
yaml                6.0
zmq                 25.0.2
zoneinfo            NA
zstandard           0.19.0
-----
IPython             8.13.2
jupyter_client      8.2.0
jupyter_core        5.3.0
jupyterlab          4.0.1
notebook            6.5.4
-----
Python 3.10.11 | packaged by conda-forge | (main, May 10 2023, 18:58:44) [GCC 11.3.0]
Linux-5.15.0-92-generic-x86_64-with-glibc2.35
-----
Session information updated at 2024-02-06 00:41