Case 3: MNP cell

Datasource: Cross-tissue single-cell landscape of human monocytes and macrophages in health and disease.

This example data has been integrated in the SPARKLE (SPARKLE::MNP.metadata)

Input data

Input formats : Seurat metadata

library(SPARKLE)
metadata <-SPARKLE::MNP.metadata 

 
knitr::kable(head(metadata), caption = "Seurat metadata")

Seurat metadata
	orig.ident	nCount_RNA	nFeature_RNA	Initial.No	Study.No	Ascites.1_Blood.2_Breast.3_Colon.4_Stomach.5_Kidney.6_Liver.7_Lung.8_Pancreas.9_Skin.10_Spleen.11_Tonsil.12	Patient.No	MirgDC.1_DC1.2_DC2.3_MacroMono.4	Study	Tissue	Status	Clusters.NO	Clusters	nCount_integrated	nFeature_integrated
ACTTGTTCACCGCTAG.1_1	SeuratProject	1628.662	719	1	1	1	1	3	Tang-Huau	Ascites	Healthy	6	T cell doublets	337.7403	1387
ATTGGACCATGTCCTC.1_1	SeuratProject	1028.799	342	2	1	1	1	3	Tang-Huau	Ascites	Healthy	6	T cell doublets	262.4473	1390
CCTTACGTCCCTGACT.1_1	SeuratProject	1779.581	959	3	1	1	1	3	Tang-Huau	Ascites	Healthy	6	T cell doublets	332.5596	1415
TGTTCCGCAGAAGCAC.1_1	SeuratProject	1493.055	578	4	1	1	1	1	Tang-Huau	Ascites	Healthy	6	T cell doublets	350.6807	1364
TTGACTTGTACTCAAC.1_1	SeuratProject	1555.572	806	5	1	1	1	1	Tang-Huau	Ascites	Healthy	6	T cell doublets	320.1611	1435
AACTCAGGTAAGTGGC.2_1	SeuratProject	1460.183	767	6	1	1	1	3	Tang-Huau	Ascites	Healthy	6	T cell doublets	334.7708	1478

sparkle.data <- cwas_build_model_data(inputdata = metadata,Sample = "Patient.No",Phenotype = "Status",Celltype ="Clusters",Group ="Tissue",Subgroup = "Study.No",Control_label = "Healthy",Disease_label = "Cancer",selected_celltype = c("cDC1","mregDC","Macrophage-4"))

## [1] "Warning: No Covariate1 infomation"
## [1] "Warning: No Covariate2 infomation"
## [1] "No gene infomation added"
## [1] "No geneset score infomation added"

Data exporlation (Optional)

PCA

PCA plot for data exploration

cwas_pcadraw(sparkle.data)

Wilcoxon

Wilcoxon rank sum test and plot

cwas_classic_comparision(sparkle.data)

Hausman_test

Test for Group

df <- cwas_hausman_test_all(sparkle.data,variable = "Group")


knitr::kable(df, caption = "Hausman test")

Hausman test
Celltype	Hausman_Statistic	Pvalue	Model_Selection	Varible
mregDC	-1.0151489	1.0000000	Random Effects Model	Group : Tissue
cDC1	0.2035418	0.9032365	Random Effects Model	Group : Tissue
Macrophage_4	-0.0073076	1.0000000	Random Effects Model	Group : Tissue

Test for Subgroup

df2 <- cwas_hausman_test_all(sparkle.data,variable = "Subgroup")

knitr::kable(df2, caption = "Hausman test")

Hausman test
Celltype	Hausman_Statistic	Pvalue	Model_Selection	Varible
mregDC	15.003049	0.0005522	Fixed Effects Model	Subgroup : Study.No
cDC1	6.989467	0.0303568	Fixed Effects Model	Subgroup : Study.No
Macrophage_4	6.171417	0.0456976	Fixed Effects Model	Subgroup : Study.No

Cell-phenotype association analysis

All model calculation

all.models <- cwas_allmodel_cal(sparkle.data)### calculation for all model

Heatmap plot for all model

p <- cwas_allmodel_heatmap(all.models)# Plot all model AIC and Pvalue

Forest plot for Best model selection

best.model <- cwas_autoselected_model(all.models)# Select the best model and plot

Cell-phenotype covariate analysis

all.model.cov <- cwas_2celltype_allmodel_cal(sparkle.data)# Select the best model and plot
cwas_allmodel_heatmap(all.model.cov)# Plot all model AIC and Pvalue

best.model.cov <- cwas_autoselected_model(all.model.cov)# Select the best model and plot

Cell-phenotype mediation analysis

Your_interested_celltype="mregDC"

all.model.med <- cwas_mediation_analysis(sparkle.data,X.cell = Your_interested_celltype,Best_model =best.model,method = "cell_mediate_cell" )# Select the best model and plot

Cell-phenotype moderation analysis

all.model.mod <- cwas_moderation_analysis(sparkle.data,X.cell =Your_interested_celltype,Best_model =best.model,method = "cell_moderation_cell" )# Select the best model and plot

Moderation network plot

# sparkle.data <-   cwas.test.data <-  cwas_build_model_data(inputdata = metadata,
#                                            Sample = "Sample", Phenotype = "Phenotype",
#                                            Celltype = "Celltype",Group = "Group",Subgroup = "Subgroup",
#                                            Control_label="Control",
#                                            Disease_label="Disease"
#                                            )



SPARKLE::Cell_phenotype_network_plot(sparkle.data )