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GuoBioinfoLab / TCellSI

The First Innovative Tool to Comprehensively Assess T Cell States
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TCellSI

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:

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Installation

最后更新于：2024 年 11 月 8 日

You can install the development version of TCellSI by:

# install.packages("devtools")
devtools::install_github("GuoBioinfoLab/TCellSI")
library(TCellSI)

Example tutorial

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 <- TCellSI::TCSS_Calculate(sample_expression, ref = TRUE) (Optionally, ref = FALSE)

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 states that interest you, you should compile a reference spectrum and prepare specific marker gene sets of your states. You can then calculate the scores for your 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 <- TCellSI::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" ...

How to use TCellSI for scRNA-seq data?

TCellSI still shows excellent results in the calculation of single-cell data and can assist in single-cell annotation.

In terms of operation. First, you should 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. We suggested to 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 <- TCellSI::TCSS_scRNAseqCalculate(sample_scRNA, core= XXX, ref = TRUE) # core: default value is 4; Optionally, ref = FALSE

Then you can add the score value of the result of the calculation into the metadata data box of the seurat object.

FeaturePlot(object = seurat_object, features = "TCSS")  #viewing the distribution of scores in a umap; "TCSS" is the name of the column in which the categorical value is added to the metadata object

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:

Pseudobulk creation tutorial for single-cell data analysis

How to create pseudobulk samples from single cell data ? If you want to do this, you should prepare an expression 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 <- TCellSI::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
Result <- TCSS_Calculate(pseudo_bulk)

Credit

Please cite our paper if TCellSI is helpful.

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