Cell-cell communication of cytokine signalling

The tutorial quick_start_cytokine_enrichment shows you how the enrichment analysis is run in more detail. This tutorial shows how to compute and visualize cytokine-mediated cell-cell communication using huCIRA.

# Import core packages and the hucira package.

import os
import time
import warnings

import numpy as np
from tqdm import TqdmWarning

import hucira as hc

warnings.simplefilter("ignore", TqdmWarning)

start = time.time()

current_wd = os.getcwd()
print(current_wd)

/ictstr01/home/icb/jenni.liu/all_projects/cytokine_dict_project/huCIRA/docs/notebooks

Quick start cytokine signaling

Given a list of cytokines, get_all_senders_and_receivers identifies which cell types act as senders (produce the cytokine) and which act as receivers (express the corresponding receptor). This helps to map potential cytokine-mediated communication between immune cell types in your dataset.

Note: these signals reflect expression potential only and are not filtered for significance or robustness in this quick start tutorial. If you know which cytokines you’re interested in, you can define them and run cell-cell communication analysis directly. If you want to explore enriched signals in your query data, run the full enrichment analysis first and enter the output of all enriched signals after filtering of robust signals as a parameter to plot_communication(tutorial in second part).

For a quick start, follow these steps:

1. Load cytokine information file from huCIRA.

2. Define the cytokine(s) for which you want to compute signaling. They must be present in the cytokine information file to define sender and receiver relationships. The function will then identify which genes are in your query data as well.

3. Load your query adata. It needs to have feature names saved under adata.var.index in HGNC format to match nomenclature of cytokine info file.

4. Adjust threshold as needed. More about that in the advanced tutorial.

The files are saved to or loaded from the current working directory current_wd. The path is explicitly specified here, although it’s also the default saving location if left unspecified.

# Load cytokine info file

cytokine_info = hc.load_cytokine_info(save_dir=current_wd)
cytokine_info.head()

	Unnamed: 0	name	gene	used cytokines	DEG_plot_family	single_family	family_level_1	family_level_2	receptor gene	alternative names	comments	parse_cytokine	single_gene	go_annotations	go_labels	functional_classification
0	0	IL-1-alpha	IL1A	IL-1α	IL-1	IL-1	interleukin	IL-1	IL1R1, IL1RAP	Interleukin 1 alpha	NaN	True	IL1A	['GO:0005125', 'GO:0046688', 'GO:0034605', 'GO...	cytokine activity, extracellular space, positi...	Pro-inflammatory
1	1	IL-1-beta	IL1B	IL-1β	IL-1	IL-1	interleukin	IL-1	IL1R1, IL1RAP	Interleukin 1 beta	NaN	True	IL1B	['GO:0005125', 'GO:0070372', 'GO:0001934', 'GO...	cytokine activity, positive regulation of prot...	Pro-inflammatory
2	2	IL-1Ra	IL1RN	IL-1Ra	IL-1	IL-1	interleukin	IL-1	IL1R1	Interleukin 1 receptor antagonist	NaN	True	IL1RN	['GO:0006953', 'GO:0005813', 'GO:0005149', 'GO...	extracellular space, protein binding, cytosol,...	Anti-inflammatory
3	3	IL-18	IL18	IL-18	IL-1	IL-1	interleukin	IL-1	IL18R1, IL18RAP	Interleukin 18	NaN	True	IL18	['GO:0005125', 'GO:0005615', 'GO:0032819', 'GO...	cytokine activity, extracellular space, positi...	Pro-inflammatory, Th1-associated
4	4	IL-33	IL33	IL-33	IL-1	IL-1	interleukin	IL-1	IL1RL1, IL1RAP	Interleukin 33	NaN	True	IL33	['GO:0005125', 'GO:0005737', 'GO:0005615', 'GO...	cytokine activity, cytoplasm, extracellular sp...	Pro-inflammatory

print("All cytokines present in the file:")
print(cytokine_info.name.unique())

All cytokines present in the file:
['IL-1-alpha' 'IL-1-beta' 'IL-1Ra' 'IL-18' 'IL-33' 'IL-36-alpha'
 'IL-36-beta' 'IL-36-gamma' 'IL-36Ra' 'IL-37' 'IL-38' 'IL-2' 'IL-7' 'IL-9'
 'IL-15' 'IL-21' 'IL-4' 'IL-13' 'IL-3' 'IL-5' 'GM-CSF' 'IL-6' 'IL-11'
 'IL-27' 'IL-30' 'IL-31' 'LIF' 'OSM' 'CT-1' 'IL-12' 'IL-23' 'IL-35'
 'IL-10' 'IL-19' 'IL-20' 'IL-22' 'IL-24' 'IL-26' 'IL-17A' 'IL-17B'
 'IL-17C' 'IL-17D' 'IL-17E' 'IL-17F' 'IL-16' 'IL-32-beta' 'TSLP'
 'IFN-alpha1' 'IFN-alpha2' 'IFN-alpha4' 'IFN-alpha5' 'IFN-alpha6'
 'IFN-alpha7' 'IFN-alpha8' 'IFN-alpha10' 'IFN-alpha13' 'IFN-alpha14'
 'IFN-alpha16' 'IFN-alpha17' 'IFN-alpha21' 'IFN-beta' 'IFN-epsilon'
 'IFN-kappa' 'IFN-omega' 'IFN-gamma' 'IFN-lambda1' 'IFN-lambda2'
 'IFN-lambda3' 'IFN-lambda4' 'TNF-alpha' 'LT-alpha1-beta2'
 'LT-alpha2-beta1' 'FasL' 'TRAIL' 'CD27L' 'CD30L' 'CD40L' 'OX40L' '4-1BBL'
 'GITRL' 'APRIL' 'BAFF' 'LIGHT' 'TL1A' 'RANKL' 'TWEAK' 'EDA' 'C1q' 'C3a'
 'C4a' 'C4b' 'C5a' 'Properdin' 'M-CSF' 'G-CSF' 'TGF-beta1' 'TGF-beta2'
 'TGF-beta3' 'BMP2' 'BMP4' 'BMP6' 'BMP7' 'GDF5' 'GDF15' 'EGF' 'TGF-alpha'
 'FGF-alpha' 'FGF-beta' 'FGF7' 'FGF9' 'FGF21' 'PDGF-AA' 'PDGF-BB'
 'PDGF-CC' 'PDGF-DD' 'VEGF' 'VEGF-B' 'VEGF-C' 'VEGF-D' 'IGF-1' 'IGF-2'
 'GDNF' 'PSPN' 'HGF' 'SCF' 'FLT3L' 'IL-34' 'EPO' 'MYDGF' 'PRL' 'Leptin'
 'ADSF' 'Noggin' 'Decorin' 'TPO' 'CXCL1' 'CXCL2' 'CXCL3' 'CXCL5' 'CXCL6'
 'CXCL7' 'IL-8' 'CXCL14' 'CXCL4' 'CXCL4V1' 'CXCL9' 'CXCL10' 'CXCL11'
 'CXCL12' 'CXCL13' 'CXCL16' 'CXCL17' 'CCL1' 'CCL2' 'CCL3' 'CCL3L1'
 'CCL3L3' 'CCL4' 'CCL4L1' 'CCL4L2' 'CCL5' 'CCL7' 'CCL8' 'CCL11' 'CCL13'
 'CCL14a' 'CCL15' 'CCL16' 'CCL17' 'CCL18' 'CCL19' 'CCL20' 'CCL21' 'CCL22'
 'CCL23' 'CCL24' 'CCL25' 'CCL26' 'CCL27' 'CCL28' 'CX3CL1' 'XCL1' 'XCL2']

# Load your query data

adata = hc.load_multiple_sclerosis_data(save_dir=current_wd)
adata

AnnData object with n_obs × n_vars = 65326 × 10266
    obs: 'labels', 'MS', 'CSF', 'valid_clusters', 'CD4_labels'
    obsm: 'X_umap'

# Compute senders and receivers for (a) chosen cytokine(s) in your query data

df_senders, df_receivers = hc.get_all_senders_and_receivers(
    adata=adata,
    cytokine_info=cytokine_info,
    cytokine_list=["IL-15", "IL-7"],
    celltype_colname="labels",
    sender_pvalue_threshold=0.1,
    receiver_mean_X_threshold=0.1,
)

df_senders

	min_logfoldchanges	min_pvals	min_pvals_adj	gene	mean_X	frac_X	mean_X>0	cytokine	celltype
Gran	1.655668	0.0	0.0	IL15	0.083014	0.062886	0.083014	IL-15	Gran
Mono	2.027854	0.000022	0.000022	IL15	0.122385	0.113548	0.122385	IL-15	Mono
Mono Doublet	1.321865	0.000265	0.000265	IL15	0.077380	0.086847	0.077380	IL-15	Mono Doublet
ncMono	2.071975	0.0	0.0	IL15	0.122466	0.115276	0.122466	IL-15	ncMono
B1	2.646197	0.087981	0.087981	IL7	0.038835	0.024137	0.038835	IL-7	B1
B2	3.863949	0.000447	0.000447	IL7	0.086458	0.063812	0.086458	IL-7	B2

df_receivers

	mean_X	frac_X	cytokine	celltype
labels
B cell doublets	0.140399	0.120690	IL-15	B cell doublets
CD8a	0.207754	0.127662	IL-15	CD8a
Mono Doublet	0.117080	0.118044	IL-15	Mono Doublet
NK1	0.909494	0.465402	IL-15	NK1
NK2	0.914678	0.533137	IL-15	NK2
Tdg	0.198221	0.127036	IL-15	Tdg
Tregs	0.117986	0.084524	IL-15	Tregs
contamination1	0.101549	0.047091	IL-15	contamination1
B cell doublets	0.923470	0.596552	IL-7	B cell doublets
CD4	0.715459	0.447919	IL-7	CD4
CD8a	0.864174	0.417114	IL-7	CD8a
CD8n	0.692757	0.456908	IL-7	CD8n
Mono Doublet	0.435859	0.422428	IL-7	Mono Doublet
NK2	0.914678	0.496318	IL-7	NK2
Tdg	0.951255	0.536645	IL-7	Tdg
Tregs	0.902313	0.529644	IL-7	Tregs
contamination1	0.934714	0.387812	IL-7	contamination1
doublet	0.650651	0.590476	IL-7	doublet
mDC1	0.167398	0.151163	IL-7	mDC1
pDC	0.107447	0.080597	IL-7	pDC

# Plots cell-cell communication

expression_threshold = 0.05

legend_handles, legend_labels = hc.plot_communication(
    df_src=df_senders,
    df_tgt=df_receivers,
    mean_cytokine_gene_expression_sender=expression_threshold,
    mean_cytokine_gene_expression_receiver=expression_threshold,
    show_legend=True,
    figsize=(6, 7),
    lw=0.5,
    fontsize=6,
    bbox_to_anchor=(1.1, 1.1),
    loc="center",
)

Run a full enrichment analysis to visualize enriched and significant cytokine signals

To explore biologically relevant signals in your dataset in a more unbiased way, it is helpful to first run the enrichment analysis.
run_all_enrichment_test() is a wrapper around the core function run_one_enrichment_test() and performs enrichment analyses across multiple cell types and across different filtering parameters used during gene set construction. In contrast, run_one_enrichment_test() focuses on a single specified cell type and does not allow assessment of robustness with respect to gene set construction parameters. More details are provided in the advanced tutorial.

In short, the wrapper returns results in a format that allows for more comprehensive downstream visualization, such as cell–cell communication plots.

For this cell–cell communication tutorial, we will compute enriched signals across a few selected cell types and then filter the results to keep only robust and statistically significant signals for visualization. To see how enrichment analyses are ran, refer to the quick start tutorial and/or the advanced tutorial.

# Load the human cytokine dictionary (mini version).

df_hcd_all = hc.load_human_cytokine_dict(save_dir=current_wd, mini_version=True)

# Enter celltype column name and condition column name of your query adata
your_celltype_colname = "labels"
your_contrast_colname = "MS"

# Define celltype_combos, an input parameter for enrichment testing
adata_celltypes = ["B1", "CD8a", "Mono"]
hcd_celltypes = ["B_cell", "CD8_T_cell", "CD14_Mono"]

celltype_combos = hc.create_celltype_combos(adata_celltypes, hcd_celltypes)
print(celltype_combos)

# Define conditions of interest
contrast = ("True", "False")
print(contrast)

[('B1', 'B_cell'), ('CD8a', 'CD8_T_cell'), ('Mono', 'CD14_Mono')]
('True', 'False')

# Run robust enrichment analysis on a several celltypes and gene set parameters:

all_enrichment_results = hc.run_all_enrichment_test(
    adata=adata,
    df=df_hcd_all,
    contrast_combo=contrast,
    celltype_combos=celltype_combos,
    contrast_column=your_contrast_colname,
    celltype_column=your_celltype_colname,
    direction="upregulated",
    threshold_lfc=[0.8],
    threshold_expression=[0.05],
    threshold_pval=0.01,
)

# Look at all enrichment results (reduced view to columns of interest, not all statistics)
all_enrichment_results[
    [
        "celltype_combo",
        "cytokine",
        "contrast",
        "direction",
        "ES",
        "NES",
        "NOM p-val",
        "FDR q-val",
        "FWER p-val",
        "frac_shared_genes_signature",
        "Lead_genes",
        "threshold_lfc",
        "threshold_expression",
    ]
].sort_values("NES", ascending=False)

	celltype_combo	cytokine	contrast	direction	ES	NES	NOM p-val	FDR q-val	FWER p-val	frac_shared_genes_signature	Lead_genes	threshold_lfc	threshold_expression
0	CD8a (CD8_T_cell)	IL-2	True_vs_False	upregulated	0.612847	2.079503	0.0	0.0	0.0	0.509091	ACTB;HSPA8;PRF1;IL32;UBB;PFN1;ACTG1;MYL12A;GZM...	0.8	0.05
1	CD8a (CD8_T_cell)	IL-15	True_vs_False	upregulated	0.57136	2.022814	0.0	0.0	0.0	0.498920	ACTB;HSPA8;PRF1;IL32;PFN1;ACTG1;MYL12A;HNRNPF;...	0.8	0.05
2	CD8a (CD8_T_cell)	IL-7	True_vs_False	upregulated	0.539797	1.830362	0.0	0.0	0.0	0.407186	ACTB;HSPA8;PRF1;IL32;MYL12A;TAP1;ENO1;GZMB;PSM...	0.8	0.05
0	B1 (B_cell)	IL-21	True_vs_False	upregulated	0.604502	1.560405	0.019305	0.117342	0.1	0.246154	ACTB;CD180;ACTG1;ITGB2;PNP;MYO1G;CD19	0.8	0.05
0	Mono (CD14_Mono)	M-CSF	True_vs_False	upregulated	0.443602	1.542205	0.0	0.126455	0.12	0.286533	AP1S2;DUSP6;BCL2A1;KCNN4;GIMAP6;HSP90AB1;ADAP2...	0.8	0.05
1	B1 (B_cell)	CD40L	True_vs_False	upregulated	0.604784	1.518806	0.021684	0.09108	0.146	0.200000	TLR10;MARCKSL1;ANXA6;VIM	0.8	0.05
3	CD8a (CD8_T_cell)	IL-1-beta	True_vs_False	upregulated	0.507223	1.509405	0.011173	0.031313	0.089	0.517241	TAP1;MT-CO1;PSME2;GAPDH;STOM;IFITM2;ISG15;GBP1...	0.8	0.05
4	CD8a (CD8_T_cell)	IFN-omega	True_vs_False	upregulated	0.452022	1.507522	0.002064	0.02549	0.09	0.406015	HSPA8;PRF1;TAP1;PSME2;IFI16;IFITM2;ALOX5AP;PGA...	0.8	0.05
5	CD8a (CD8_T_cell)	IL-1-alpha	True_vs_False	upregulated	0.614064	1.498569	0.029446	0.023439	0.1	0.375000	ISG15;GBP1;APOL2;EPSTI1;MX1;GBP5;PARP9;RTP4;FB...	0.8	0.05
2	B1 (B_cell)	IL-7	True_vs_False	upregulated	0.637046	1.444307	0.054161	0.129635	0.276	0.133333	HSPA8;TNFSF10;MX1	0.8	0.05
6	CD8a (CD8_T_cell)	IFN-beta	True_vs_False	upregulated	0.416523	1.420999	0.003061	0.046616	0.218	0.423631	HSPA8;PRF1;MYL12A;TAP1;CD164;PSMB8;STOM;IFI16;...	0.8	0.05
2	Mono (CD14_Mono)	IL-13	True_vs_False	upregulated	0.413988	1.397061	0.028322	0.267065	0.383	0.211699	CD52;PTPRE;TRAF3IP3;SCIMP;FBP1;MAT2A;CD1D;SESN...	0.8	0.05
3	Mono (CD14_Mono)	IL-3	True_vs_False	upregulated	0.420398	1.396357	0.035599	0.178673	0.384	0.233677	DUSP6;HSPA8;BCL2A1;KCNN4;PNPLA6;FBP1;SLC27A3;C...	0.8	0.05
3	B1 (B_cell)	IL-1-beta	True_vs_False	upregulated	0.43831	1.365439	0.067836	0.190542	0.445	0.525641	RPS26;ACTB;NOP10;ACTG1;SIPA1;TNFSF10;PCBP1;TAP...	0.8	0.05
4	B1 (B_cell)	IL-2	True_vs_False	upregulated	0.522763	1.36093	0.08872	0.158244	0.451	0.271186	HSPA8;CLIC1;TNFSF10	0.8	0.05
5	B1 (B_cell)	IFN-omega	True_vs_False	upregulated	0.41305	1.306813	0.10095	0.200972	0.602	0.300000	HSPA8;PRELID1;TNFSF10;TYMP;MX1;PCBP1;FCHSD2;CH...	0.8	0.05
4	Mono (CD14_Mono)	IL-1-beta	True_vs_False	upregulated	0.353617	1.285755	0.045128	0.373938	0.707	0.389831	C3AR1;DUSP6;INSIG1;MS4A7;TNFSF10;GCH1;LILRB1;C...	0.8	0.05
7	CD8a (CD8_T_cell)	IFN-gamma	True_vs_False	upregulated	0.419743	1.283557	0.120482	0.141732	0.51	0.462185	TAP1;PSMB8;ISG15;GBP1;OASL;CASP1;GBP2;IFITM1;B...	0.8	0.05
5	Mono (CD14_Mono)	IL-4	True_vs_False	upregulated	0.349172	1.259552	0.058462	0.365209	0.767	0.264476	HLA-DRB5;CD52;TRAF3IP3;SCIMP;CHCHD10;FBP1;CD18...	0.8	0.05
6	Mono (CD14_Mono)	GM-CSF	True_vs_False	upregulated	0.362683	1.230153	0.15229	0.37559	0.833	0.207595	DUSP6;TRAF3IP3;BCL2A1;KCNN4;FBP1;SLC27A3;CISD3...	0.8	0.05
7	Mono (CD14_Mono)	IL-15	True_vs_False	upregulated	0.333228	1.214327	0.09165	0.361704	0.865	0.331570	DUSP6;RNF149;HSPA8;TNFSF10;GCH1;PTPRE;BCL2A1;D...	0.8	0.05
9	Mono (CD14_Mono)	IL-7	True_vs_False	upregulated	0.352927	1.207278	0.150923	0.332062	0.877	0.230769	TNFSF10;GCH1;BCL2A1;KCNN4;FBP1;SERPINB9;BTG1;I...	0.8	0.05
8	CD8a (CD8_T_cell)	IFN-alpha1	True_vs_False	upregulated	0.432258	1.201905	0.199074	0.23207	0.696	0.508197	ISG15;OASL;BST2;EPSTI1;MX1;GBP5;LAP3;PARP9;RTP...	0.8	0.05
9	CD8a (CD8_T_cell)	IL-27	True_vs_False	upregulated	0.527981	1.198174	0.241255	0.215125	0.702	0.392857	GBP1;GBP2;EPSTI1;IRF1;GBP5;PARP9;GBP4;FBXO6;STAT1	0.8	0.05
10	CD8a (CD8_T_cell)	IL-4	True_vs_False	upregulated	0.39526	1.129785	0.299312	0.300944	0.834	0.340000	PTGER2;IMMT;ID2;CD226;PTGDR;CISH;VCL;BATF;XBP1...	0.8	0.05
7	B1 (B_cell)	IL-4	True_vs_False	upregulated	0.309784	1.117536	0.276619	0.492838	0.937	0.368243	ACTB;CD180;IL4R;SYPL1;GRK6;EVI2A;CYSLTR1;CD40;...	0.8	0.05
10	Mono (CD14_Mono)	IL-6	True_vs_False	upregulated	0.426901	1.079522	0.357236	0.672539	0.988	0.168675	CD14;MTSS1;HMOX1;ADGRE1;CD300E	0.8	0.05
11	Mono (CD14_Mono)	IL-2	True_vs_False	upregulated	0.291784	1.048299	0.400621	0.714281	0.998	0.279352	DUSP6;RNF149;INSIG1;TNFSF10;GCH1;BCL2A1;KCNN4;...	0.8	0.05
8	B1 (B_cell)	IFN-beta	True_vs_False	upregulated	0.296887	1.043391	0.421111	0.580006	0.977	0.360656	HSPA8;MYL12A;RAN;TNFSF10;PSMA4;TYMP;UNC93B1;MX...	0.8	0.05
13	Mono (CD14_Mono)	IFN-beta	True_vs_False	upregulated	0.249525	0.955876	0.667	0.956133	1.0	0.370980	GIMAP4;INSIG1;RAB10;TNFSF10;HLA-B;CFD;GCH1;DNA...	0.8	0.05
14	Mono (CD14_Mono)	IL-12	True_vs_False	upregulated	0.324546	0.948705	0.574341	0.896194	1.0	0.185897	TNFSF10;GCH1;MYOF;VAMP5;LRRK2;JAK2;GLS;KCNMA1;...	0.8	0.05
9	B1 (B_cell)	IL-15	True_vs_False	upregulated	0.285618	0.935012	0.59838	0.721562	0.997	0.344371	HSPA8;TNFSF10;TYMP;UNC93B1;PCBP1;TAP1;ENO1;CAR...	0.8	0.05
15	Mono (CD14_Mono)	IFN-gamma	True_vs_False	upregulated	0.258536	0.933028	0.626159	0.867399	1.0	0.296375	INSIG1;GCH1;BCL2A1;IRF1;SNX10;SERPINB9;BTG1;MY...	0.8	0.05
16	Mono (CD14_Mono)	IFN-alpha1	True_vs_False	upregulated	0.297473	0.915052	0.61015	0.848043	1.0	0.258065	TNFSF10;GCH1;IFIT3;SAMD9;ISG15;C6orf47;APOL2;C...	0.8	0.05
11	CD8a (CD8_T_cell)	IL-32-beta	True_vs_False	upregulated	0.390356	0.884804	0.642857	0.695752	0.997	0.196429	GNLY;CD7;MT-CO2;CXCR6;HIF1A;MIAT;ARID5A	0.8	0.05
17	Mono (CD14_Mono)	IL-32-beta	True_vs_False	upregulated	0.233987	0.884189	0.805416	0.858383	1.0	0.282110	RNF149;SLC16A3;HCK;LILRB1;TNFRSF8;LYST;TRAF3IP...	0.8	0.05
18	Mono (CD14_Mono)	Leptin	True_vs_False	upregulated	0.333247	0.856392	0.68401	0.855045	1.0	0.484848	RHOB;EGR1;ARSA;MAPK7;NBEAL2	0.8	0.05
19	Mono (CD14_Mono)	IFN-omega	True_vs_False	upregulated	0.222289	0.848093	0.881	0.816961	1.0	0.333594	INSIG1;RAB10;TNFSF10;GCH1;BCL2A1;SLFN5;DYNLT1;...	0.8	0.05
10	B1 (B_cell)	IL-34	True_vs_False	upregulated	0.338078	0.77143	0.790026	0.897724	1.0	0.833333	TPI1;AIP;PPIA;BRMS1;DYNLL2	0.8	0.05
11	B1 (B_cell)	IL-13	True_vs_False	upregulated	0.2549	0.766178	0.801453	0.821498	1.0	0.372093	IL4R;JCHAIN;BLOC1S6;GCSAM;LAIR1;PHF20L1;SYT17;...	0.8	0.05
20	Mono (CD14_Mono)	IL-1-alpha	True_vs_False	upregulated	0.241813	0.691372	0.889029	0.936877	1.0	0.198473	GCH1;VAMP5;APOL2;LILRA2;SFMBT2;MAP3K7CL;TNF;IS...	0.8	0.05
12	Mono (CD14_Mono)	IL-10	True_vs_False	upregulated	-0.227726	-1.023002	0.373134	0.40135	0.581114	0.229050	CXCR4;MAP3K8;TESC;FOS;NEAT1;MXD1;NINJ2	0.8	0.05
6	B1 (B_cell)	IL-32-beta	True_vs_False	upregulated	-0.407307	-1.172439	0.272321	0.245712	0.448276	0.225806	MT-CO2;SLC2A3;TP53INP1;PTPN1;SPOCK2	0.8	0.05
8	Mono (CD14_Mono)	IL-34	True_vs_False	upregulated	-0.404031	-1.213996	0.230435	0.258567	0.319613	0.520000	FOS;CDKN1A;MT-ND4L;EIF4H	0.8	0.05
1	Mono (CD14_Mono)	C5a	True_vs_False	upregulated	-0.439019	-1.434618	0.084211	0.176012	0.123487	0.180180	DDIT4;DUSP1;MSR1	0.8	0.05

robust_results = hc.check_robustness(
    all_results=all_enrichment_results,
    threshold_qval=0.15,  # chosen leniently to visualize more cytokines
    threshold_valid=0.0,
    threshold_below_alpha=0.0,
)
robust_results

	celltype_combo	contrast	cytokine	frac_valid	frac_significant	is_robust	NES_min	NES_max	FDR q-val	qval_threshold	threshold_frac_below_alpha
0	B1 (B_cell)	True_vs_False	IL-21	1.0	1.0	True	1.560405	1.560405	0.117342	0.15	0.0
1	B1 (B_cell)	True_vs_False	CD40L	1.0	1.0	True	1.518806	1.518806	0.091080	0.15	0.0
2	B1 (B_cell)	True_vs_False	IL-7	1.0	1.0	True	1.444307	1.444307	0.129635	0.15	0.0
3	CD8a (CD8_T_cell)	True_vs_False	IL-2	1.0	1.0	True	2.079503	2.079503	0.000000	0.15	0.0
4	CD8a (CD8_T_cell)	True_vs_False	IL-15	1.0	1.0	True	2.022814	2.022814	0.000000	0.15	0.0
5	CD8a (CD8_T_cell)	True_vs_False	IL-7	1.0	1.0	True	1.830362	1.830362	0.000000	0.15	0.0
6	CD8a (CD8_T_cell)	True_vs_False	IL-1-beta	1.0	1.0	True	1.509405	1.509405	0.031313	0.15	0.0
7	CD8a (CD8_T_cell)	True_vs_False	IFN-omega	1.0	1.0	True	1.507522	1.507522	0.025490	0.15	0.0
8	CD8a (CD8_T_cell)	True_vs_False	IL-1-alpha	1.0	1.0	True	1.498569	1.498569	0.023439	0.15	0.0
9	CD8a (CD8_T_cell)	True_vs_False	IFN-beta	1.0	1.0	True	1.420999	1.420999	0.046616	0.15	0.0
10	CD8a (CD8_T_cell)	True_vs_False	IFN-gamma	1.0	1.0	True	1.283557	1.283557	0.141732	0.15	0.0
11	Mono (CD14_Mono)	True_vs_False	M-CSF	1.0	1.0	True	1.542205	1.542205	0.126455	0.15	0.0

cytokines_IL_family = [
    "IL-1-alpha",
    "IL-1-beta",
    "IL-10",
    "IL-11",
    "IL-12",
    "IL-13",
    "IL-15",
    "IL-16",
    "IL-17A",
    "IL-17B",
    "IL-17C",
    "IL-17D",
    "IL-17E",
    "IL-17F",
    "IL-18",
    "IL-19",
    "IL-1Ra",
    "IL-2",
    "IL-20",
    "IL-21",
    "IL-22",
    "IL-23",
    "IL-24",
    "IL-26",
    "IL-27",
    "IL-3",
    "IL-31",
    "IL-32-beta",
    "IL-33",
    "IL-34",
    "IL-35",
    "IL-36-alpha",
    "IL-36Ra",
    "IL-4",
    "IL-5",
    "IL-6",
    "IL-7",
    "IL-8",
    "IL-9",
]
print(f"Num of cytokines to be plotted: {len(cytokines_IL_family)}")

Num of cytokines to be plotted: 39

# Compute cytokine senders and receivers per cell type for cytokines
df_senders, df_receivers = hc.get_all_senders_and_receivers(
    adata=adata,
    cytokine_info=cytokine_info,
    cytokine_list=cytokines_IL_family,
    celltype_colname=your_celltype_colname,
    sender_pvalue_threshold=0.1,
    receiver_mean_X_threshold=0,
)

df_senders

	min_logfoldchanges	min_pvals	min_pvals_adj	gene	mean_X	frac_X	mean_X>0	cytokine	celltype
Mono Doublet	3.002076	0.000001	0.000001	IL10	0.101699	0.091906	0.101699	IL-10	Mono Doublet
ncMono	5.035968	0.0	0.0	IL10	0.267259	0.190240	0.267259	IL-10	ncMono
Gran	1.655668	0.0	0.0	IL15	0.083014	0.062886	0.083014	IL-15	Gran
Mono	2.027854	0.000022	0.000022	IL15	0.122385	0.113548	0.122385	IL-15	Mono
Mono Doublet	1.321865	0.000265	0.000265	IL15	0.077380	0.086847	0.077380	IL-15	Mono Doublet
ncMono	2.071975	0.0	0.0	IL15	0.122466	0.115276	0.122466	IL-15	ncMono
B cell doublets	0.849211	0.000001	0.000001	IL16	0.489709	0.406897	0.489709	IL-16	B cell doublets
B2	0.476345	0.000042	0.000042	IL16	0.396229	0.278757	0.396229	IL-16	B2
CD4	0.246106	0.0	0.0	IL16	0.329181	0.223700	0.329181	IL-16	CD4
CD8a	0.276222	0.00017	0.00017	IL16	0.345714	0.212637	0.345714	IL-16	CD8a
CD8n	0.220238	0.004193	0.004193	IL16	0.341423	0.241108	0.341423	IL-16	CD8n
NK1	0.36612	0.009205	0.009205	IL16	0.369055	0.206519	0.369055	IL-16	NK1
NK2	0.572691	0.000256	0.000256	IL16	0.419261	0.284242	0.419261	IL-16	NK2
Tregs	0.614972	0.0	0.0	IL16	0.422423	0.282153	0.422423	IL-16	Tregs
mDC1	0.830383	0.000079	0.000079	IL16	0.485559	0.534884	0.485559	IL-16	mDC1
mDC2	0.087196	0.000206	0.000206	IL16	0.317319	0.318229	0.317319	IL-16	mDC2
pDC	0.316756	0.014826	0.014826	IL16	0.363058	0.316418	0.363058	IL-16	pDC
plasma	0.318229	0.000002	0.000002	IL16	0.363582	0.528169	0.363582	IL-16	plasma
Gran	1.596265	0.0	0.0	IL18	0.119207	0.093933	0.119207	IL-18	Gran
Mono Doublet	3.090332	0.0	0.0	IL18	0.321317	0.310287	0.321317	IL-18	Mono Doublet
NK2	1.827364	0.00061	0.00061	IL18	0.158988	0.113402	0.158988	IL-18	NK2
mDC1	2.412863	0.000042	0.000042	IL18	0.234040	0.302326	0.234040	IL-18	mDC1
mDC2	4.309805	0.0	0.0	IL18	0.596492	0.507724	0.596492	IL-18	mDC2
ncMono	4.584363	0.0	0.0	IL18	0.627767	0.474540	0.627767	IL-18	ncMono
pDC	1.433939	0.012034	0.012034	IL18	0.125188	0.119403	0.125188	IL-18	pDC
CD4	1.484981	0.0	0.0	IL23A	0.077779	0.056323	0.077779	IL-23	CD4
Tregs	0.889414	0.009471	0.009471	IL23A	0.082812	0.060201	0.082812	IL-23	Tregs
B2	4.24254	0.033334	0.033334	EBI3	0.049518	0.038623	0.049518	IL-27	B2
ncMono	5.026896	0.000027	0.000027	EBI3	0.067088	0.067185	0.067088	IL-27	ncMono
CD4	1.400319	0.0	0.0	IL32	2.174868	0.877435	2.174868	IL-32-beta	CD4
CD8a	2.089652	0.0	0.0	IL32	2.747685	0.944876	2.747685	IL-32-beta	CD8a
CD8n	0.582261	0.0	0.0	IL32	2.006130	0.900821	2.006130	IL-32-beta	CD8n
Tdg	1.732243	0.0	0.0	IL32	2.721018	0.956026	2.721018	IL-32-beta	Tdg
Tregs	1.624697	0.0	0.0	IL32	2.617443	0.916084	2.617443	IL-32-beta	Tregs
contamination1	0.820463	0.0	0.0	IL32	2.164150	0.686981	2.164150	IL-32-beta	contamination1
B2	4.24254	0.033334	0.033334	EBI3	0.049518	0.038623	0.049518	IL-35	B2
ncMono	5.026896	0.000027	0.000027	EBI3	0.067088	0.067185	0.067088	IL-35	ncMono
B1	2.646197	0.087981	0.087981	IL7	0.038835	0.024137	0.038835	IL-7	B1
B2	3.863949	0.000447	0.000447	IL7	0.086458	0.063812	0.086458	IL-7	B2

df_receivers

	mean_X	frac_X	cytokine	celltype
B1	0.099969	0.062755	IL-10	B1
B2	0.152840	0.115029	IL-10	B2
B cell doublets	0.133764	0.131034	IL-10	B cell doublets
CD4	0.114350	0.081498	IL-10	CD4
CD8a	0.121640	0.077910	IL-10	CD8a
...	...	...	...	...
mDC1	0.167398	0.151163	IL-7	mDC1
mDC2	0.072470	0.072091	IL-7	mDC2
ncMono	0.048336	0.043140	IL-7	ncMono
pDC	0.107447	0.080597	IL-7	pDC
plasma	0.084467	0.133803	IL-7	plasma

208 rows × 4 columns

# Plots cell-cell communication: all enrichment results

# Define a minimum expression threshold of genes
expression_threshold = 0.1 / 10_000  # 100 transcripts per million
expression_threshold = np.log2((expression_threshold) + 1)
print(f"expression_threshold is {expression_threshold}")

# All celltypes in query data:
all_query_data_ct = np.array(adata.obs[your_celltype_colname].unique())

legend_handles, legend_labels = hc.plot_communication(
    df_src=df_senders,
    df_tgt=df_receivers,
    frac_expressing_cells_sender=0,
    frac_expressing_cells_receiver=0,
    mean_cytokine_gene_expression_sender=expression_threshold,
    mean_cytokine_gene_expression_receiver=expression_threshold,
    df_enrichment=all_enrichment_results,
    all_celltypes=all_query_data_ct,
    show_legend=True,
    figsize=(6, 7),
    lw=0.5,
    fontsize=6,
    bbox_to_anchor=(1.1, 1.1),
    loc="center",
)

expression_threshold is 1.4426878274712997e-05

# Plots cell-cell communication: only robust and statistically significant results

# Define a minimum expression threshold of genes
expression_threshold = 0.1 / 10_000  # 100 transcripts per million
expression_threshold = np.log2((expression_threshold) + 1)
print(f"expression_threshold is {expression_threshold}")

# All celltypes in query data:
all_query_data_ct = np.array(adata.obs[your_celltype_colname].unique())


legend_handles, legend_labels = hc.plot_communication(
    df_src=df_senders,
    df_tgt=df_receivers,
    frac_expressing_cells_sender=0,
    frac_expressing_cells_receiver=0,
    mean_cytokine_gene_expression_sender=expression_threshold,
    mean_cytokine_gene_expression_receiver=expression_threshold,
    df_enrichment=robust_results,
    all_celltypes=all_query_data_ct,
    show_legend=True,
    figsize=(6, 7),
    lw=0.5,
    fontsize=6,
    bbox_to_anchor=(1.1, 1.1),
    loc="center",
)

expression_threshold is 1.4426878274712997e-05

end = time.time()
print(f"Time: {int((end - start) / 60)} min {int((end - start) % 60)} sec")

Time: 0 min 36 sec

## --END-- ##