Getting Started

The idea behind Trex is to combine TCR CDR3 amino acid information with phenotypic RNA/protein data to direct the use of single-cell sequencing towards antigen-specific discoveries. This is a growing field - specifically TESSA uses amino acid characteristics and autoencoder as a means to get a dimensional reduction. Another option is CoNGA, which produces an embedding using TCR and RNA with python. Trex was designed to make a customizable approach to this combined approach using R.

More information is available at the Trex GitHub Repo.

Installation

devtools::install_github("ncborcherding/Trex")

The Data Set

To show the multiple options of Trex, the example data is derived from GSE167118, a cohort of CITE-seq data derived from severe COVID-19 patients. More information is available in the corresponding manuscript.

Formation of the Single-cell Object

Here is the basic workflow that was used to make the single-cell object to use in the vignette. Notice there is a removal of TCR-related CITE-seq data and RNA features (using the Trex function quietTCRgenes()). As we are going to combine multimodal data, both the GEX and CITE probes may cause bias in the weighted output.

##################################
#scRNA/ADT loading and processing
#################################
tmp <-  Read10X("~/Patient17/filtered_feature_bc_matrix")

Pt17 <- CreateSeuratObject(counts = tmp$`Gene Expression`)

#Removing TCR-specific antibody
adt_assay <- CreateAssayObject(counts = tmp$`Antibody Capture`[1:37,])
Pt17[["ADT"]] <- adt_assay
Pt17 <- subset(Pt17, subset = nFeature_RNA > 100) 
Pt17  <- RenameCells(object = Pt17 , new.names = paste0("Pt17_", rownames(Pt17[[]])))
Pt17[["mito.genes"]] <- PercentageFeatureSet(Pt17, pattern = "^MT-")
    
#Filtering step
standev <- sd(log(Pt17$nFeature_RNA))*2.5 #cutting off above standard deviation of 2.5
mean <- mean(log(Pt17$nFeature_RNA))
cut <- round(exp(standev+mean))
Pt17 <- subset(Pt17, subset = mito.genes < 10 & nFeature_RNA < cut)

#Processing RNA
DefaultAssay(Pt17) <- 'RNA'
Pt17 <- NormalizeData(Pt17) %>% 
  FindVariableFeatures() %>% 
  quietTCRgenes() %>% 
  ScaleData() %>% 
  RunPCA(verbose = FALSE)

#Processing ADT
DefaultAssay(Pt17) <- 'ADT'
VariableFeatures(Pt17) <- rownames(Pt17[["ADT"]])
Pt17 <- NormalizeData(Pt17, normalization.method = 'CLR', margin = 2) %>% 
  ScaleData() %>% 
  RunPCA(reduction.name = 'apca')


##################################
#Processing and Adding Contig Info
##################################

contigs <- read.csv("~/Patient17/filtered_contig_annotations.csv")
clones <- combineTCR(contigs, samples = "Pt17", filterMulti = TRUE, removeNA = TRUE)
Pt17 <- combineExpression(clones, Pt17, cloneCall="aa")
saveRDS(Pt17, file = "Trex_FullExample.rds")

###################################
#Making Example Data Set for Trex
#################################
meta <- Pt17[[]]
meta <- meta[sample(nrow(meta), nrow(meta)*0.1),]
trex_example <- subset(Pt17, cells = rownames(meta))
save(trex_example, file = "trex_example.rda", compress = "xz")

Loading the Data Object

For the purpose of the vignette, we will load the full object. The data example built into the package (trex_example) is derived from randomly sampling T cells from patient 17 (see above workflow).

SeuratObj <- readRDS(url("https://www.borch.dev/uploads/data/Trex_FullExample.rds"))

Running Trex

maTrex Function

Trex has 2 major functions - the first being maTrex(), which is the backbone of the algorithm and returns the encoded values based on the selection of variables. Unlike runTrex() below, maTrex() does not filter the input for only T cells with attached TCR data. In addition, maTrex() is compatible with the list output from the combineTCR() function from the scRepertoire R package, while runTrex() must be performed on a single-cell object.

chains

  • “TRA” for TCR Alpha Chain
  • “TRB” for TCR Beta Chain

method

  • “encoder” for a convolution neural network (CNN) based encoding.
  • “geometric” for a geometric transformation

encoder.model

  • “VAE” for a variational autoencoder
  • “AE” for a traditional autoencoder

encoder.input

  • “AF” to use Atchley factors
  • “KF” to use Kidera factors
  • “both” to use both Atchley and Kidera factors
  • “OHE” for a One Hot Autoencoder

theta
If choosing the geometric transformation, what value of theta to use (default is pi)

Trex_vectors <- maTrex(SeuratObj, 
                       chains = "TRA",
                       encoder.model = "VAE", 
                       encoder.input = "AF")
## [1] "Calculating the encoding values..."
ggplot(data = as.data.frame(Trex_vectors), aes(Trex_1, Trex_2)) + 
  geom_point() + 
  theme_classic()

Trex_vectors2 <- maTrex(SeuratObj, 
                       chains = "TRB",
                       method = "geometric",
                       theta = pi)
## [1] "Performing geometric transformation..."
ggplot(as.data.frame(Trex_vectors2), aes(x = Trex_1, y = Trex_2)) + 
  geom_point() + 
  theme_classic()

runTrex

Additionally, runTrex() can be used to append the Seurat or Single-cell Experiment object with the Trex vectors and allow for further analysis. Importantly, runTrex() will remove single cells that do not have recovered TCR data in the metadata of the object.

SeuratObj <- runTrex(SeuratObj, 
                    chains = "TRB",
                    encoder.model = "VAE", 
                    encoder.input = "KF",
                    reduction.name = "Trex.KF")
## [1] "Calculating the encoding values..."

Using Trex Vectors

After runTrex() we have the encoded values stored under “Trex…”. Using the Trex reduction stored in Seurat, we can calculate the nearest neighbor and shared nearest neighbor indexes and generate a UMAP.

#Generating UMAP from Trex Neighbors
SeuratObj <- RunUMAP(SeuratObj, 
                     reduction = "Trex.KF",
                     dims = 1:30,
                     reduction.name = 'Trex.umap', 
                     reduction.key = 'trexUMAP_')

#Trex UMAP
plot1 <- DimPlot(SeuratObj, 
                 reduction = "Trex.umap") + 
         NoLegend()
plot2 <- DimPlot(SeuratObj, 
                 group.by = "CTaa", 
                 reduction = "Trex.umap") + 
          scale_color_viridis(discrete = TRUE, option = "B")  + 
          theme(plot.title = element_blank()) +
          NoLegend()

plot1 + plot2

We now can use this in a similar way as other single-cell modalities and calculate weighted nearest neighbor (WNN). To check out more on WNN, please read the Satija’s group paper. We will use the RNA, ADT protein levels, and Trex vectors for the WNN calculations.

SeuratObj <- FindMultiModalNeighbors(
                    SeuratObj, 
                    reduction.list = list("pca", "apca", "Trex.KF"), 
                    dims.list = list(1:30, 1:20, 1:30), 
                    modality.weight.name = "RNA.weight")

SeuratObj <- RunUMAP(SeuratObj, 
                     nn.name = "weighted.nn", 
                     reduction.name = "wnn.umap", 
                     reduction.key = "wnnUMAP_")

SeuratObj <- FindClusters(SeuratObj, 
                          graph.name = "wsnn", 
                          resolution = 0.6,
                          algorithm = 3, verbose = FALSE)

#WNN UMAP
plot3 <- DimPlot(SeuratObj, 
                 reduction = "wnn.umap")
plot4 <- DimPlot(SeuratObj, 
                 reduction = "wnn.umap", 
                 group.by = "CTaa") + 
          scale_color_viridis(discrete = TRUE, option = "B")  + 
          theme(plot.title = element_blank()) +
          NoLegend()

plot3 + plot4

Comparing the outcome to just one modality

We can also look at the differences in the UMAP generated from RNA, ADT, or Trex as individual components. Remember, the clusters that we are displaying in UMAP are based on clusters defined by the weighted nearest neighbors calculated above.

SeuratObj <- RunUMAP(SeuratObj, 
                     reduction = 'pca', 
                     dims = 1:30, 
                     assay = 'RNA', 
                     reduction.name = 'rna.umap', 
                     reduction.key = 'rnaUMAP_')

SeuratObj <- RunUMAP(SeuratObj, 
                     reduction = 'apca', 
                     dims = 1:20, 
                     assay = 'ADT', 
                     reduction.name = 'adt.umap', 
                     reduction.key = 'adtUMAP_')

plot5 <- DimPlot(SeuratObj, reduction = "rna.umap") + NoLegend()
plot6 <- DimPlot(SeuratObj, reduction = "adt.umap") + NoLegend()
plot7 <- DimPlot(SeuratObj, reduction = "Trex.umap") + NoLegend()

plot5 + plot6 + plot7

CoNGA Reduction

Recent work has proposed using representative cells for the characterization of clone and gene expression relationships. In order to generate these representative cells, either a mean expression across a clone or using the PCA dimensional space to identify a single cell that has the minimum euclidean distance across a clone.

In order to generate a single-cell object based on the CoNGA approach, Trex offers the function CoNGAfy(). For method, select either “mean” or “dist” as described above. After performing CoNGAfy(), the user can use any of the above reduction strategies.

library(dplyr, include.only = c("%>%"))

CoNGA.seurat <- CoNGAfy(SeuratObj, 
                         method = "dist")

CoNGA.seurat <- runTrex(CoNGA.seurat, 
                        chains = "TRB",
                        encoder.model = "VAE", 
                        encoder.input = "KF",
                        reduction.name = "Trex.KF")
## [1] "Calculating the encoding values..."
CoNGA.seurat <- CoNGA.seurat %>%
                  FindNeighbors(reduction = "Trex.KF") %>%
                  FindClusters(algorithm = 3)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 805
## Number of edges: 27729
## 
## Running smart local moving algorithm...
## Maximum modularity in 10 random starts: 0.6491
## Number of communities: 7
## Elapsed time: 0 seconds
CoNGA.seurat <- RunUMAP(CoNGA.seurat, 
                     reduction = "Trex.KF", 
                     dims = 1:20, 
                     reduction.name = 'Trex.umap', 
                     reduction.key = 'trexUMAP_')

DimPlot(CoNGA.seurat, reduction = "Trex.umap") + NoLegend()

Annotating possible epitopes

Towards find epitope relationships, Trex has a built in data base of TCRA and TCRB sequences associated with epitopes. To append the database to the single-cell object (either before or after CoNGAfy()), you can use annotateDB().

The database is collated from multiple sources and represents a great deal of effort from these sources:

  1. VDJdb - citation
  2. McPAS-TCR - citation
  3. iedb - citation
  4. TBA (now called PIRD) - citation
CoNGA.seurat <- annotateDB(CoNGA.seurat, 
                           chains = "TRB")

DimPlot(CoNGA.seurat, 
        reduction = "Trex.umap", 
        group.by = "TRB_Epitope.species") + 
  theme(plot.title = element_blank())

DimPlot(CoNGA.seurat, 
        reduction = "Trex.umap", 
        group.by = "TRB_Epitope.sequence") + 
  theme(plot.title = element_blank())

Or annotateDB() can be used on the full single-cell object and examine the sequence information along the RNA-based UMAP. An added feature to the function allows the annotations to be extended to CDR3 sequences that are within defined edit distances from the reference using edit.distance.

SeuratObj <- annotateDB(SeuratObj,
                        chains = "TRB", 
                        edit.distance = 2)

DimPlot(SeuratObj, 
        reduction = "rna.umap", 
        group.by = "TRB_Epitope.species") + 
  theme(plot.title = element_blank())