beginning work on TCR_BCR_colonal_resconstruction module

_posts/0008-08-01-TCR_BCR_colonal_resconstruction.md

@@ -6,7 +6,7 @@ title: Introduction to TCR/BCR clonal reconstruction
 categories:
     - Module-08-scRNA
 feature_image: "assets/genvis-dna-bg_optimized_v1a.png"
-date: 0008-01-01
+date: 0008-08-01
 ---
 
 https://stream.pinellolab.partners.org/compute/STREAM_e280e8a0-f0b0-4150-9709-38e4dcb7462d
@@ -18,3 +18,209 @@ https://www.borch.dev/uploads/screpertoire/
 - Clonotype reconstruction
 - Comparison of BCR/TCR cells to B and T cell GEX annotations
 - Cell-cell interaction? - CellChat
+
+
+
+## Exploring BCR
+```R
+library("scRepertoire")
+
+Rep1_ICB_b <- read.csv("data/clonotypes_b_posit/Rep1_ICB-b-filtered_contig_annotations.csv")
+Rep1_ICBdT_b <- read.csv("data/clonotypes_b_posit/Rep1_ICBdT-b-filtered_contig_annotations.csv")
+Rep3_ICB_b <- read.csv("data/clonotypes_b_posit/Rep3_ICB-b-filtered_contig_annotations.csv")
+Rep3_ICBdT_b <- read.csv("data/clonotypes_b_posit/Rep3_ICBdT-b-filtered_contig_annotations.csv")
+Rep5_ICB_b <- read.csv("data/clonotypes_b_posit/Rep5_ICB-b-filtered_contig_annotations.csv")
+Rep5_ICBdT_b <- read.csv("data/clonotypes_b_posit/Rep5_ICBdT-b-filtered_contig_annotations.csv")
+
+```
+
+```R
+BCR.contigs <- list(Rep1_ICB_b, Rep1_ICBdT_b, Rep3_ICB_b, Rep3_ICBdT_b, Rep5_ICB_b, Rep5_ICBdT_b)
+
+combined.BCR <- combineBCR(BCR.contigs, 
+                           samples = c("Rep1_ICB", "Rep1_ICBdT", "Rep3_ICB", 
+                                       "Rep3_ICBdT", "Rep5_ICB", "Rep5_ICBdT"), 
+                           threshold = 0.85)
+
+head(combined.BCR[[1]])
+```
+
+cloneCall
+How to call the clone - VDJC gene (gene), CDR3 nucleotide (nt), CDR3 amino acid (aa), VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data.
+
+```R
+
+# view the total number of unique clones
+clonalQuant(combined.BCR, 
+            cloneCall="strict", 
+            chain = "both", 
+            scale = FALSE)
+
+# view the relative percent of unique clones scaled by the total size of the clonal repertoire
+clonalQuant(combined.BCR, 
+            cloneCall="strict", 
+            chain = "both", 
+            scale = TRUE)
+
+# line graph with a total number of clones by the number of instances within the sample or run
+# The relative distribution of clones by abundance
+clonalAbundance(combined.BCR, 
+                cloneCall = "gene", 
+                scale = FALSE)
+
+```
+
+## Exploring TCR
+
+```R
+Rep1_ICB_t <- read.csv("data/clonotypes_t_posit/Rep1_ICB-t-filtered_contig_annotations.csv")
+Rep1_ICBdT_t <- read.csv("data/clonotypes_t_posit/Rep1_ICBdT-t-filtered_contig_annotations.csv")
+Rep3_ICB_t <- read.csv("data/clonotypes_t_posit/Rep3_ICB-t-filtered_contig_annotations.csv")
+Rep3_ICBdT_t <- read.csv("data/clonotypes_t_posit/Rep3_ICBdT-t-filtered_contig_annotations.csv")
+Rep5_ICB_t <- read.csv("data/clonotypes_t_posit/Rep5_ICB-t-filtered_contig_annotations.csv")
+Rep5_ICBdT_t <- read.csv("data/clonotypes_t_posit/Rep5_ICBdT-t-filtered_contig_annotations.csv")
+
+TCR.contigs <- list(Rep1_ICB_t, Rep1_ICBdT_t, Rep3_ICB_t, Rep3_ICBdT_t, Rep5_ICB_t, Rep5_ICBdT_t)
+
+combined.TCR <- combineTCR(TCR.contigs, 
+                           samples = c("Rep1_ICB", "Rep1_ICBdT", "Rep3_ICB", 
+                                       "Rep3_ICBdT", "Rep5_ICB", "Rep5_ICBdT"),
+                           removeNA = FALSE, 
+                           removeMulti = FALSE, 
+                           filterMulti = FALSE)
+
+head(combined.TCR[[1]])
+
+
+```
+
+```R
+## Visualize 
+# view the total number of unique clones
+clonalQuant(combined.TCR, 
+            cloneCall="strict", 
+            chain = "both", 
+            scale = FALSE)
+
+# view the relative percent of unique clones scaled by the total size of the clonal repertoire
+clonalQuant(combined.TCR, 
+            cloneCall="strict", 
+            chain = "both", 
+            scale = TRUE)
+
+# When we change clone call to "gene", we lower the strictness of what we call a clone
+# requiring only VDJC gene
+# strict requires a VDJC gene + CDR3 nucleotide
+clonalQuant(combined.TCR, 
+            cloneCall="gene", 
+            chain = "both", 
+            scale = TRUE)
+
+
+# line graph with a total number of clones by the number of instances within the sample or run
+# The relative distribution of clones by abundance
+clonalAbundance(combined.TCR, 
+                cloneCall = "strict", 
+                scale = FALSE)
+
+# the length distribution of the CDR3 sequences by calling 
+# clone call must be CDR3 nucleotide (nt) OR CDR3 amino acid (aa)
+clonalLength(combined.TCR, 
+             cloneCall="aa", 
+             chain = "both")
+
+# We can also look at clones between samples 
+clonalCompare(combined.TCR, 
+              top.clones = 25, 
+              samples = c("Rep3_ICB", "Rep3_ICBdT"), 
+              cloneCall="aa", 
+              graph = "alluvial")
+
+clonalCompare(combined.TCR, 
+              top.clones = 25, 
+              samples = c("Rep3_ICB", "Rep3_ICBdT"), 
+              cloneCall="gene", 
+              graph = "alluvial")
+
+clonalCompare(combined.TCR, 
+              top.clones = 25, 
+              samples = c("Rep3_ICB", "Rep3_ICBdT"), 
+              cloneCall="nt", 
+              graph = "alluvial")
+
+clonalCompare(combined.TCR, 
+              top.clones = 10, 
+              samples = c("Rep3_ICB", "Rep3_ICBdT"), 
+              cloneCall="strict", 
+              graph = "alluvial")
+
+
+clonotype_table <- clonalCompare(combined.TCR, 
+                                 top.clones = 50, 
+                                 samples = c("Rep1_ICB", "Rep1_ICBdT", "Rep3_ICB", "Rep3_ICBdT", "Rep5_ICB", "Rep5_ICBdT"), 
+                                 cloneCall="aa", 
+                                 graph = "alluvial", exportTable = TRUE)
+
+clonalCompare(combined.TCR, 
+              top.clones = 25, 
+              samples = c("Rep1_ICB", "Rep1_ICBdT", "Rep3_ICB", "Rep3_ICBdT", "Rep5_ICB", "Rep5_ICBdT"), 
+              cloneCall="aa", 
+              graph = "alluvial")
+```
+### Visualizing Clonal Dynamics -- idk if this is useful
+
+```R
+clonalHomeostasis(combined.TCR, 
+                  cloneCall = "gene",
+                  cloneSize = c(Rare = 1e-04, 
+                                 Small = 0.001, 
+                                 Medium = 0.01, 
+                                 Large = 0.1, 
+                                 Hyperexpanded = 1))
+
+# rank the clones by total number and place them into bins.
+clonalProportion(combined.TCR, 
+                 cloneCall = "nt",
+                 clonalSplit = c(1, 5, 10, 100, 1000, 10000)) 
+
+
+clonalDiversity(combined.TCR, cloneCall = "gene", n.boots = 100)
+```
+
+
+### Adding the BCR and TCR Data to your seurat object
+
+```R
+
+rep135 <- readRDS("processed_joined_celltyped_object_0418.rds")
+
+rep135 <- combineExpression(combined.TCR, rep135, 
+                         cloneCall="gene", proportion = FALSE,
+                         group.by = "sample",
+                         cloneSize=c(Single=1, Small=5, Medium=20, Large=100, Hyperexpanded=500))
+
+# added CTgene, CTnt, CTaa, CTstrict, clonalProportion, clonalFrequency, cloneSize
+# Lets rename these columns to keep our TCR and BCR data separate
+# if there are duplicate barcodes (if a cell has both Ig and TCR), the immune receptor information will not be added
+
+columns_to_modify <- c("CTgene", "CTnt", "CTaa", "CTstrict", "clonalProportion", "clonalFrequency", "cloneSize")
+names(rep135@meta.data)[names(rep135@meta.data) %in% columns_to_modify] <- paste0(columns_to_modify, "_TCR")
+
+colnames(rep135@meta.data) # make sure the column names are changed 
+DimPlot(rep135) 
+DimPlot(rep135, group.by = c("cloneSize_TCR", "immgen_singler_main"))
+
+rep135 <- combineExpression(combined.BCR, rep135, 
+                            cloneCall="gene", proportion = FALSE,
+                            group.by = "sample",
+                            cloneSize=c(Single=1, Small=5, Medium=20, Large=100, Hyperexpanded=500))
+
+# added CTgene, CTnt, CTaa, CTstrict, clonalProportion, clonalFrequency, cloneSize
+names(rep135@meta.data)[names(rep135@meta.data) %in% columns_to_modify] <- paste0(columns_to_modify, "_BCR")
+colnames(rep135@meta.data) # make sure the column names are changed 
+
+DimPlot(rep135, group.by = c("cloneSize_TCR", "cloneSize_BCR", "immgen_singler_main"))
+
+```
+
+![Uploading to Cytotrace](/assets/module_8/BCR_TCR_celltype_comparison.png)