ScSpTITH

Here, we introduce the ScSpTITH (Single-Cell and Spatial Intra-/Inter-tumoral Heterogeneity), a novel computational framework for quantifying ITH for both scRNA-seq and spatial transcriptomic data.  

Description

ScSpTITH serves as a comprehensive computational framework for dissecting tumor heterogeneity through spatial (across distinct anatomical regions or microenvironments), temporal (during disease progression or therapeutic intervention), and population components (among defined cell types, clusters, or patient cohorts).

 

Details

The function ScSpTITHscore() is used to calculate ITH score. It supports two modalities , containing six parameters:

Parameters

• data : The input dataset, gene expression matrix with rows as genes and columns as cells/samples.
• meta : Metadata containing at minimum.

  • First column : cell/spot IDs (must match column names in data).

  • patient_col :Patient ID column (required).

  • condition_col : Condition/cluster column (optional, only required for cluster mode).

• mode : Calculation mode
  • overall : calculate for entire sample.
  • cluster : calculate separately for each cluster.
• patient_col : Column name in metadata containing patient IDs, default "Patient".
• condition_col : Column name in metadata containing cluster/group information, default "condition".
• top_n_genes : Number of highly variable genes used for correlation calculation, default 5000. 

 

Installation

• Users can install the released version of ScSpTITH with:  

if (!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")

devtools::install_github("WangX-Lab/ScSpTITH")

   

Examples

Install ScSpTITH

library(ScSpTITH)
example_file_path <- system.file("extdata", "example.RData", package = "ScSpTITH")
load(example_file_path)
#ls()
#"data"  "meta"

data

data[1:5,1:5]

row.names	SS12pt.10x.P1_AAACCTGTCACCTTAT_1	SS12pt.10x.P1_AAACCTGTCAGTCAGT_1	SS12pt.10x.P1_AAACCTGTCCAAAGTC_1	SS12pt.10x.P1_AAACCTGTCCGTCATC_1	SS12pt.10x.P1_AAACCTGTCTATGTGG_1
AP006222	0	1	1	0	0
SAMD11	0	0	0	0	0
NOC2L	0	0	2	1	0
PLEKHN1	0	1	0	1	0
HES4	2	0	0	0	3

meta

meta[1:5,]

cell_name	sample	cell_type
SS12pt.10x.P1_AAACCTGTCACCTTAT_1	SyS12pt	Malignant
SS12pt.10x.P1_AAACCTGTCAGTCAGT_1	SyS12pt	Malignant
SS12pt.10x.P1_AAACCTGTCCAAAGTC_1	SyS12pt	Malignant
SS12pt.10x.P1_AAACCTGTCCGTCATC_1	SyS12pt	Malignant
SS12pt.10x.P1_AAACCTGTCTATGTGG_1	SyS12pt	Malignant

 

Apply ScSpTITH to 'overall' mode

ScSpTITH = ScSpTITHscore(data, meta, mode = "overall",patient_col = "sample",top_n_genes = 5000)

ScSpTITH

ScSpTITH

Patient	ScSpTITHscore
SyS12pt	0.6507015
SyS13	0.6242106
SyS14	0.6595786

 

Apply ScSpTITH to 'cluster' mode

ScSpTITH = ScSpTITHscore(data, meta, mode = "cluster",patient_col = "sample",condition_col = 'cell_type',top_n_genes = 5000)

ScSpTITH

ScSpTITH[1:5,]

Patient	ITH_Score	Cluster
SyS12pt	0.5498507	Endothelial
SyS12pt	0.6216496	Fibroblast
SyS12pt	0.5799930	Macrophage
SyS12pt	0.6436497	Malignant
SyS13	0.5706511	Endothelial

Contact

E-mail any questions to Xiaosheng Wang (xiaosheng.wang@hotmail.com)