scTCRseq

TCR Downstream analysis clonal sharing, diversity, and clonality.

The argument information is written in the R funtion

Clonal Sharing:

Clone sharing between different clusters, celltypes for different samples using TCR Receptor Similarity Score or Bhattacharya Coefficient

where 𝑝𝑖𝑘 and 𝑝𝑗𝑘 are the proportions of cells in group i and j that belongs to clonotype k, with a total of C clonotypes. This coefficient is between 0 and 1, and larger value indicates higher similarity in clonotype distributions.

Code

    source("./R/VZV_clonal_sharing.R")
    
    TCR_AJ_VZV(object = CD8, 
               savedir = "./output/", 
               clus_col = "seurat_clusters", 
               TCR_col = "CDR3",
               group_col = "Age_Ag", 
               group_val = "O_EBNA1", 
               split_col = "orig.ident",
               column_name = c("id","Ag","Age","Age_number","gender","Run"))

Clonal Diversity

Clonal diversity between individuals within the clusters or celltypes using Shannon or Simpson diversity index

Shannon Diveristy: The Shannon diversity index measures the uncertainty of the probability distribution of species abundance. A higher Shannon diversity index indicates a more diverse community, because it means that there is more uncertainty about which species will be encountered. The Shannon diversity index is calculated as follows:

H = -∑_i p_i log_2 p_i

where: H is the Shannon diversity index

p_i is the proportion of individuals of species i in the community

log_2 is the logarithm to base 2

Code

    source("./R/Shannon_Diversity.R")
    
    Shannon_AJ(object = CD8, 
               savedir = "./output/", 
               clus_col = "seurat_clusters", 
               TCR_col = "CDR3",
               group_col = "orig.ident", 
               group_val = "BB2003", 
               split_col = "orig.ident",
               column_name = c("id","Ag","Age","Age_number","gender","Run"), 
               total_clusters = length(cluster_num))

Inverse Simpson Diversity: The Simpson diversity index measures the probability that two individuals randomly selected from the community will be of the same species. A lower Simpson diversity index indicates a more diverse community, because it means that it is less likely that two individuals will be of the same species. The Simpson diversity index is calculated as follows:

D = Σ p_i^2 ---> Simpson Index

1-D = Gini-Simpson Index or Simpson diversity Index

1/D = Inverse Simpson index

where: D is the Simpson diversity index; p_i is the proportion of individuals of species i in the community

Code

    source("./R/Simpson_Diversity.R")
    
    Simpson_AJ(object = CD8, 
               savedir = savedir,
               clus_col = "seurat_clusters", 
               TCR_col = "CDR3",
               group_col = "orig.ident", 
               group_val = sample_id[i], 
               split_col = "orig.ident",
               column_name = c("id","Ag","Age","Age_number","gender","Run"), 
               total_clusters = length(cluster_num))

Gini Clonality Index:

This method calculates the Gini coefficient (G) of inequality with bootstrap confidence intervals. A Lorenz plot is produced when a single variable is specified for analysis, otherwise the summary statistics alone are displayed for a group of variables.

where x is an observed value, n is the number of values observed and i is the rank of values in ascending order.

    source("./R/Gini_coefficient_index.R")
    
    Gini_coef_AJ(object = CD8, 
                 savedir = savedir, 
                 clus_col = "seurat_clusters", 
                 TCR_col = "TRA1_or_TRA2_TRB1_no_TRB2",
                 group_col = "orig.ident", 
                 group_val = sample_id[i], 
                 split_col = "orig.ident",
                 column_name = c("id","Ag","Age","Age_number","gender","Run"), 
                 total_clusters = cluster_name)

These would calculate the Clonal Sharing, Shannon Diversity, Simpson Diversity and Gini Clonality Index