T cell state identifier (TCellSI) is a tool to access eight distinct T cell states including Quiescence, Regulating, Proliferation, Helper, Cytotoxicity, Progenitor exhaustion, Terminal exhaustion, and Senescence. TCellSI provides T cell state scores (TCSS) for samples using specific marker gene sets and a compiled reference spectrum of T cell states from transcriptomic data. The major algorithm of TCellSI is shown as follows:
You can install the development version of TCellSI by:
# install.packages("devtools")
devtools::install_github("GuoBioinfoLab/TCellSI")
library(TCellSI)#sample_expression: Complete gene expression data.frame in TPM format by log2-transformed RNA-seq data.
sample_expression <- TCellSI::exampleSample
# sample_expression
# SRR5088825 SRR5088828 SRR5088830 ...
# 5_8S_rRNA 0.000000 0.000000 0.0000000
# 5S_rRNA 0.000000 0.000000 0.4346853
# 7SK 0.0c0000 0.000000 0.0000000
# A1BG 1.907599 3.284418 4.0821248
# A1BG-AS1 3.083914 2.021501 4.5169002
# ...
ResultScores <- TCSS_Calculate(sample_expression)
#output: The output of the function is a data.frame with TCSS metrics, where each row corresponds to a T cell state and each column represents a sample name.
# ResultScores
# SRR5088825 SRR5088828 SRR5088830
# Quiescence 0.6422158 0.6153140 0.5105856
# Regulating 0.4404173 0.3974391 0.1670356
# Proliferation 0.5780474 0.5761891 0.3994404
# Helper 0.5647604 0.4063729 0.2121070
# Cytotoxicity 0.6482720 0.5826073 0.1829111
# Progenitor_exhaustion 0.5905679 0.5308304 0.2482848
# Terminal_exhaustion 0.6624943 0.6222188 0.3311532
# Senescence 0.5921543 0.5725185 0.2896508
#If you want to apply this method to other cell states that interest you, you should compile a reference spectrum and prepare specific marker gene sets of your cell states. You can then calculate the scores for your cell states using the following function. If you choose not to provide a reference expression spectrum but to do the calculation directly, you can use the parameter ref=FALSE and do not need to provide the reference parameter.
OtherScores <- CSS_Calculate(sample_expression, ref=TRUE, reference = XXX, markers = XXX)
#Forms of reference and markers look like:
#reference
#The self-constructed reference should contain log2-transformed, TPM-normalized gene expression data from RNA-seq or scRNA-seq.
# cell_state1 cell_state2 cell_state3 ...
# DDX11L1 0.32323232 0.54567463 0.32456323
# WASH7P 0.82670591 1.89565638 1.40492732
# MIR6859-1 0.02172025 0.03816506 0.52313432
# MIR1302-2HG 0.00000000 0.00000000 0.00032302
# MIR1302-2 0.00000000 0.00000000 0.00002132
# ...
#markers: A list of multiple cell states containing specific gene sets
#The number of marker genes per cell state can vary.
#$cell_state1
#[1] "XXX" "XXX" "XXX" ...
#$cell_state2
#[1] "XXX" "XXX" "XXX" ...
#$cell_state3
#[1] "XXX" "XXX" "XXX" ...
#You can extract the count expression of single-cell data by reading the count file of single-cell data directly or seurat_obj@assays$RNA@counts in the seurat object, and further convert it to the log(TPM +1) format. Then you can use TCellSI to perform calculations of the states scores for each cell of the single-cell data.
scRNA_scores <- TCSS_Calculate(sample_scRNA)
#Then you can add the score value of the result of the calculation into the metadata data box of the seurat object.
Idents(seurat_obj) <- "TCSS" #the name of the column in which the categorical value is added to the metadata object
DimPlot(seurat_obj, reduction ="umap",label=TRUE,label.size = 5,repel = TRUE) #viewing the distribution of scores in a umap
#In addition, if you have an single-cell population annotation, you can create pseudobulk samples and then calculate the state scores for each samples, which can reduce the problem of drop-out in the single-cell data that leads to less accurate results. The creation of the pseudobulk is as follows:
#How to create pseudobulk samples from single cell data ? If you want to do this, you should prepare an expression data, which should be either log2(TPM+1) or normalized single-cell data. In this data, each row represents a gene and each column represents a cell ID (see example as follows). Also, you should prepare a single-cell annotation file, which includes columns of cell annotation and cell ID in expression file (see example as follows).
# expression data
# NP710.20180123 NP711.20180123 NP71.20180123 ...
#A1BG 0.070079488 0.216835131 6.269805313
#NAT2 0.002001509 0.003654851 0.003190016
#ADA 0.085464008 0.088970085 0.057264107
#...
# single-cell annotation file
# UniqueCell_ID annotation
# NTH5.20180123 CD4_C01_CCR7
# NTH64.20180123 CD4_C01_CCR7
# NTR57.20180123 CD4_C01_CCR7
# ...
#Then, you can use the following function to get pseudobulk samples.
pseudo_bulk <- create_pseudo_bulk(
annotation_data = XXX,
expression_data = XXX,
cluster_col = "annotation", # the column names of annotation in single-cell annotation file
cell_id_col = "UniqueCell_ID", # the column names of Cell_ID in single-cell annotation file
n_clusters = 18, # number of cell types annotated
factor = 5, # number of samples for downsampling, default is 5
sampling_rate = 0.6 # percentage of cells downsampled, default is 0.6
)
# see examples of the result
# pseudo_bulk, each column represents a newly pseudobulk samples, each row represents a gene.
# CD4_C01_CCR7_bulk CD4_C01_CCR7_bulk.1 CD4_C01_CCR7_bulk.2
#A1BG 0.495165739 0.67542360 0.737122107
#NAT2 0.006033183 0.00337272 0.007104438
#ADA 0.855647562 1.06058830 0.898952625
