Skip to content

Tutorial_multisample.md

Latest commit

History

History
148 lines (123 loc) · 6.83 KB

Tutorial_multisample.md

File metadata and controls

148 lines (123 loc) · 6.83 KB
Raw

Overview

This document presents the steps to analyze 4 samples from the reduced dataset Xenium Acute Neonatal Lung injury - Slide 2 using the TranspaceR pipeline.

Loading

path='~/Spatial_atlas/Datasets/Xenium/Neonatal_lung/'Output_path='~/Spatial_atlas/Picture_pipeline/Xenium/Neonatal_lung/'Method="Xenium"Tissue="Neonatal_lung"Expression_file= read.csv(paste(path, "Neonatal_lung_ExprMat.csv", sep=""), header=TRUE)
Meta_data= read.csv(paste(path,"Neonatal_lung_Metadata.csv",sep=""), header=TRUE)
# METADATA MUST CONTAIN A SAMPLE COLUMN
dim(Expression_file)
dim(Meta_data)
[1] 328078 541
[1] 328078 11

Step 1: Quality control

The analysis is performed on the whole dataset.

Meta_data= Compute_radius(Meta_data,Method)
QC1_results= QC_RNA_size_threshold(Expression_file,Meta_data,Method,Tissue,Output_path)
QC2_results= QC_Gene_threshold(Expression_file,Meta_data,Method,Tissue,Output_path)
print('Otsu'sthresholdis', QC2_results$Otsu_threshold)
[1] Otsu's threshold is 786.8563

Data curation

Data_curated= Curate_data(Expression_file,Meta_data,QC2_results,min_lib_size=10,max_lib_size=750,min_cell_radius=2,max_cell_radius=15)
Expression_file=Data_curated$Expression_fileMeta_data=Data_curated$Meta_data
dim(Expression_file)
dim(Meta_data)
[1] 322770328
[1] 32277011

Step 2 : Gene selection

Total variance

Variance_computation= Excess_variance_ratio_NB(Expression_file,Output_path,Method,Tissue)
Variance_genes=Variance_computation$Selected_genes

Zero proportion genes

Zero_score= Excess_zero_score_NB(Expression_file,Output_path,Method,Tissue,P_value_threshold=0.01,Delta_threshold=0.01)
Zero_genes=Zero_score_computation$Selected_genes

Spatially variable genes

This step is performed on each sample separately. To compute the Geary's C score and variograms for each sample, versions of functions Geary_C_score and Compute_variogram for multiple samples are implemented.

Geary's C

Geary_computation= Geary_C_score_multiple(Expression_file,Meta_data,Output_path,Method,Tissue,pvalue=0.01)
[1] "Sample1"ComputingGeary's C score for each gene +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ done ![1] "Sample4"Computing Geary'sCscoreforeachgene+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++done![1] "Sample2"ComputingGeary's C score for each gene +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ done ![1] "Sample3"Computing Geary'sCscoreforeachgene+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++done!
>Geary_computation$Sample3
[1] "SCGB3A2""CCL21""SCGB1A1""CEACAM6""COL1A1""KRT5""NKX2.1""EPCAM""COL3A1""RTKN2""VEGFA" [12] "MSLN""ICAM1""AGER""VIM""KRT8""AGR3""ACKR1""APLNR""COL1A2""KRT18""SFRP2" [23] "GLP1R""SFTPD""KRT15""PTGDS""FN1""ACTA2""LUM""CD44""MGST1""SLC2A1""C20orf85"
[34] "POSTN""SFTPC""S100A9""FOXJ1""EPAS1""SPARCL1""HES1""PLVAP""PECAM1""LAMP3""DCN" [45] "ITGA3""XBP1""CCN2""HSPA5""AKR1C1""MEG3""IL7R""CD34""GKN2""EGFR""KRT17" [56] "RNASE1""HEY1""SPRY2""HIST1H1C""CD68""SLC25A37""AKR1C2""FCGR3A""ITGB1""SOX4""FCER1G" [67] "CCL2""GNG11""COL4A3""CFTR""PGC""ATF4""NAPSA""BCL2L1""PKM""HMGA1""KDR" [78] "CCNA1""WFDC2""ATF3""HIF1A""SEC11C""PDIA3""CDK1""PDGFRB""SNCA""KIT""FGF2" [89] "PLIN2""RHOA""HLA.DRA""PDGFRA""CA4""GDF15""RAMP2""CDH26""CTNNB1""CST3""ZEB1" [100] "YAP1""PDIA4""FABP4""AIF1""IDH1""CTHRC1""AXL""DDIT3""TGFB1""WNT5A""CLDN5" [111] "SOX2""LMAN1""NUCB2""ITGB6""PPARG""RSPO3""CXCL14""FGF7""STAT1""KRT14""IRF1" [122] "SOD2""TGFB2""ITM2C""UGDH""BMPR2""HERPUD1""SFTA2""NUTF2""SFRP4""SMAD4""BMP4" [133] "SNAI2""CD4""WNT2""WWTR1""IFIT1""HAS2""ERLEC1""HYOU1""UBE2J1""GSR""GCLM" [144] "NHSL2""PDIA6""SPCS2""ANKRD28""SSR3""CSPG4""SPCS3""AXIN2"# To select the spatially variable genes from all samplesGeary_genes= unique(unlist(Geary_computation))

The Upset plot of the shared geary selected genes across the samples is saved in the output path.

Variogram

For each sample the fitting parameters and distribution are saved, a variogram plots folder is created. The Upset plot of the shared variogram selected genes across the samples is saved.

Variogram_computation= Compute_variogram_multiple(Expression_file,Meta_data,automatic_pad=FALSE,n_pad=100,save_plot=TRUE,ncores=5,Output_path)
Variogram_genes= unique(unlist(Variogram_computation))

Step 3 :Clustering

From here, we resume performing the analysis on the whole dataset.

Clustering_output= Clusters_maker(Expression_file, Shared_genes, K=50, metric_used="L2", nThreads=20, resolution=1)
Clustering=Clustering_output$ClusteringPCA_data=Clustering_output$PCA_dataMean_expression=Clustering_output$Mean_expressionData_correction=Clustering_output$Data_correctionLog2FC_table=Clustering_output$Log2FC_table
Save_heatmap_markers(Expression_file, object=Clustering,Data_correction, Output_path,Method,Tissue,name_object='Clustering',n_top_genes=5)


Save_tissue_visualization(Meta_data,object=Clustering,Output_path,Method,Tissue,name_object='Clustering',scaling_factor=0.4)

For more steps refer to the one sample tutorial here.