CellTag-Classifier

Description

This repository contains source functions required for CellTag classification and brief tutorial using a species mixing dataset that involves two distinct CellTags. For detailed explanation, please refer to the paper: Guo et. al., CellTag Indexing: a genetic barcode-based multiplexing tool for single-cell technologies, bioRxiv, 2018 (new version under review).

In brief, we introduce a new multiplexing approach using CellTag - a lentiviral-based barcode system. This system allows CellTags to be transribed and captured using single-cell RNA-sequencing, further enable us to use CellTag as the identifiers for each cell. A step in this process is to identify the true signal among different CellTags; thus, further recover the identities of cells in the single-cell transcriptome. Here, we share the source code and demonstrate the usage and flow of this classification approach on a human-mouse species-mixing dataset with two CellTags tags (Mouse Tag - GTTGGCTA, Human Tag - TGCTATAT).

Method and General Tutorial

Before we start

Clone the Github repository

git clone https://github.com/morris-lab/CellTag-Classifier.git
cd CellTag-Classifier

Open a new R terminal. The source code can be loaded.

setwd("path/to/CellTag-Classifier")
source(src/Classifier_Source.R)

Initial overview of the data

Single-cell transcriptome are collected and processed using 10x and CellRanger. CellTag count matrix can be extracted via previous demonstrated workflow - https://github.com/morris-lab/CellTagWorkflow or R package - CloneHunter:https://github.com/morris-lab/CloneHunter (Biddy et. al., Naure, 2018). Here the example count data file is located at data/2-tag species mixing before collapsing.csv.

# load celltag counts (before Starcode collapse)
count.celltag <- read.csv("data/2-tag species mixing before collapsing.csv", header = TRUE, row.names=1, stringsAsFactors = F)
# check all CellTags before filtering
all.celltags <- rowSums(count.celltag)
hist(all.celltags, breaks = 50)

Starcode CellTag sequence analysis and collapse

CellTags are then extracted and compared. CellTags with similar sequences are collapsed to the centroid CellTag. For more information, please refer to starcode software - https://github.com/gui11aume/starcode. Briefly, starcode clusters DNA sequences based on the Levenshtein distances between each pair of sequences, from which we collapse similar CellTag sequences to correct for potential errors occurred during single-cell RNA-sequencing process. Default maximum distance from starcode was used to cluster the CellTags. Here the example starcode output result is located at data/2-tag species mixing starcode result.txt.

# load starcode collapsed result
starcode.out <- read.table("data/2-tag species mixing starcode result.txt", sep = "\t", stringsAsFactors=FALSE)

# create a list to store consensus celltags for each centroid celltag
consensus.lookup <- list()
for (i in 1:nrow(starcode.out)) { # repeat for each row
  centroid <- 0
  # split consensus celltags separated by comma and populate list
  centroid <- strsplit(starcode.out[i,3], ",")
  names(centroid) <- starcode.out[i,1]
  consensus.lookup[[i]] <- split(unname(centroid), names(centroid))
}

# create new matrix where each count.celltag column is renamed to its centroid CellTag
count.celltag.collapsed <- as.data.frame(count.celltag)
for (j in 1:ncol(count.celltag.collapsed)) {
  n <- grep(colnames(count.celltag.collapsed[j]), consensus.lookup)
  colnames(count.celltag.collapsed)[j] <- names(consensus.lookup[[n]])
}

# collapse count.celltag by consensus CellTags
count.celltag.collapsed <- t(count.celltag.collapsed)
count.celltag.collapsed <- by(count.celltag.collapsed, INDICES=row.names(count.celltag.collapsed), FUN=colSums)
count.celltag.collapsed <- as.data.frame(do.call(cbind,count.celltag.collapsed))

Normalization

The CellTag count matrices are log-normalized (log2-based) as the following. We first normalize the total reads number and UMI counts across cells.

# Log the count matrix
count.norm.expr <- log2(count.celltag.collapsed+1)
ct.dge.norm <- t(count.norm.expr)
rownames(ct.dge.norm) <- colnames(count.celltag.collapsed)
colnames(ct.dge.norm) <- rownames(count.celltag.collapsed)
# Normalize
norm.ct.dge <- normalize.function(ct.dge.norm)

We identify the top two most abundant CellTags, which match with what we knew in the experimental design. We extract those two for further analysis.

count.norm.expr.t <- t(norm.ct.dge)
sort(colSums(count.norm.expr.t))

# pull out dge by 2 most abundant celltags
ct.dge <- count.norm.expr.t[,c("TGCTATAT", "GTTGGCTA")]
collapsed.orig.count <- count.celltag.collapsed[,c("TGCTATAT", "GTTGGCTA")]

we perform another normalization between different CellTags to make them more comparable to each other.

norm.ct.dge.2 <- normalize.function(ct.dge)

Perform permutation sampling and dynamic CellTag detection

Assuming balanced loading of cell number from two groups without overloading on the 10x machine, we expect a relatively low multiplet rate, suggesting most cells should have one or the other CellTag. In such case, each CellTag expression across all cells should have ~50% zero and the remaining as significantly expressed. This allows us to assume that CellTags' expression across cells would share similar density functions. Under this assumption, we will examine the significance of each CellTag expression, i.e. what is the likelihood of occurrence of each expression value.

For each CellTag expression, we compute the density function D of its expression across all cells. Each cell's expression for each tag is then examined via a modified permutation test. In brief, for expression of a CellTag in a cell to be tested, we draw 1,000 samples from the density functions and calculate the proportion of samples that are greater than or equal to expression level at test. If the proportion calculated is small, it indicates that it is unlikely to get an expression at such level suggesting relatively higher expression overall, which, we consider, as an identification signal. For instance, consider expression (C_ij) of CellTag j in Cell i, we draw 1,000 sample S from the density of CellTag j, D_j. The proportion was computed as shown below. This process is iterated for at least 50 times to make sure that the samples are representative of the overall density.

This function takes the normalized matrix, sample size and iteration number as inputs. It outputs the result in a list format. For each item in the list, there are two parts with the first part = Cell barcode and second part = data frame that contains proportion information from each CellTag and each iteration (sample) as below.

**result[[2]] for Cell i **

	CellTag 1	CellTag 2	<More CellTags>	CellTag N
1	P₁₁	P₁₂	...	P_1N
2	P₂₁	P₂₂	...	P_2N
...	...	...	...	...
50	P₅₀₁	P₅₀₂	...	P_50N

perc.ls <- dynamic.celltag.detection(norm.ct.dge.2)

Binarization and Classificaiton

In this step, we classify each cell to their corresponding CellTag based on the proportions calculated above. We first look for an overall minimum column in each proportion matrix. For instance, in the matrix below for Cell i, minimum of each row if calculated. If all minimums are coming from the same CellTag column, we identify such tag as one of the Cell i's identities. On the other hand, if there are some mixing source of different CellTag columns, it is more likely to be a multiplet. Nevertheless, the uniqueness of minimum column does not eliminate the probability for the cell to be a multiplet as other CellTags could share similar level but slightly higher proportions. Hence, for cells with unique minimum column, we examine the pair-wise differences between the minimum tag and other tags. Using a difference cutoff of 0.238, we characterize the CellTag within less than 0.238 difference from the minimum tag to be another identity for the cell. This difference cutoff is obtained via benchmarking and training against the species mixing result based on 10x classification. In brief, we iterate through this function with different thresholds and calculate Cohen's Kappa score for the correspondence between CellTag and 10x classification. The difference that gives the optimal Kappa score is selected to be our base line threshold.

This function takes the normalized matrix, proportion list, and the users' threshold. It outputs a data frame in the same structure as the normalized matrix but with 0 and 1, where 0 = not identified and 1 = identified.

bin.class.ct <- binarization.classification(norm.ct.dge.2, perc.ls)
# Check the binary result
hist(rowSums(bin.class.ct))
table(rowSums(bin.class.ct))

Multiplet Calling based on Binarized Matrix

We will call and label multiplets and singlets in the binary matrix with an additional column.

bin.class.ct <- multiplet.calling(bin.class.ct)
# check results
barplot(table(bin.class.ct$ct.call))

Optional Multiplet Checkpoint

We notice the difference threshold selected is quite large. Hence, we implemented a secondary check on the multiplets by comparing our multiplet rate with the expected rate from 10x genomics. The table is located at data/Expected Multiplet Rate.csv. Briefly, we compare the multiplet number identified from our pipeline to the expected number. If the number is higher, all multiplets are pulled. The proportions and differences are gathered. By sorting the differences, the most likely occuring (smallest differences) multiplets were identified using a cutoff at the quantile of (1.5 * expected num/multiplet). The remaining cells were then characterized to their singlet identities.

This function takes the binary matrix, proportion list and the path to the multiplet rate table.

bin.class.ct <- multiplet.checkpoint(bin.class.ct, perc.ls, multiplet.table.path = "data/Expected Multiplet Rate.csv")

Compare with 10x classification

We first call the species based on the CellTag information known in experimental design.

mouse.human.tag <- data.frame(row.names = c("Mouse", "Human"), Tag = c("GTTGGCTA", "TGCTATAT"))
bin.class.ct$ct.call.2 <- bin.class.ct$ct.call
bin.class.ct[which(bin.class.ct$ct.call == mouse.human.tag["Mouse", "Tag"]),"ct.call.2"] <- "mouse"
bin.class.ct[which(bin.class.ct$ct.call == mouse.human.tag["Human", "Tag"]),"ct.call.2"] <- "human"

Compare with the 10x classification result. It is located at data/2-tag species mixing 10x gem_classification.csv.

# load 10x classification of human, mouse, and doublet cells
class.10x <- read.table("data/2-tag species mixing 10x gem_classification.csv", sep = ",", header = TRUE)
rownames(class.10x) <- gsub('-.*$', "", class.10x$barcode)
colnames(class.10x)[4] <- "10x.call"

# remove nd cells and compare with 10x
class.ct.filt <- subset(bin.class.ct, ct.call.2!="nd")
class.10x.filt <- class.10x[row.names(class.ct.filt),]

# creat composite of human and mouse classification, by 10x and CellTag
classification <- merge(class.10x.filt[,"10x.call"], class.ct.filt$ct.call.2, by=0)
classification <- classification[,2:3]
colnames(classification) <- c("10x", "CellTag")
table(classification)

Calculate Cohen's Kappa Score

library(psych)
rslt <- cohen.kappa(matrix(table(classification), 3, 3))
print(paste0("Cohen's Kappa Score = ", rslt$kappa))

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