inst/doc/DistToNearest-Vignette.R

In shazam: Immunoglobulin Somatic Hypermutation Analysis

## ----eval=TRUE, warning=FALSE, message=FALSE----------------------------------
# Import required packages
library(alakazam)
library(dplyr)
library(ggplot2)
library(shazam)

# Load and subset example data (for speed)
data(ExampleDb, package="alakazam")
set.seed(112)
db <- ExampleDb %>% sample_n(size=500)
db %>% count(sample_id)

## ----eval=TRUE, warning=FALSE-------------------------------------------------
# Use nucleotide Hamming distance and normalize by junction length
dist_ham <- distToNearest(db %>% filter(sample_id == "+7d"), 
                          sequenceColumn="junction", 
                          vCallColumn="v_call_genotyped", jCallColumn="j_call",
                          model="ham", normalize="len", nproc=1)

# Use genotyped V assignments, a 5-mer model and no normalization
dist_s5f <- distToNearest(db %>% filter(sample_id == "+7d"), 
                          sequenceColumn="junction", 
                          vCallColumn="v_call_genotyped", jCallColumn="j_call",
                          model="hh_s5f", normalize="none", nproc=1)

## ----eval=FALSE, warning=FALSE------------------------------------------------
#  # Single-cell mode
#  # Group cells in a one-stage process (VJthenLen=FALSE) and using
#  # both heavy and light chain sequences (onlyHeavy=FALSE)
#  
#  data(Example10x, package="alakazam")
#  dist_sc <- distToNearest(Example10x, cellIdColumn="cell_id", locusColumn="locus",
#                           VJthenLen=FALSE, onlyHeavy=FALSE)

## ----eval=TRUE, warning=FALSE, fig.width=7------------------------------------
# Generate Hamming distance histogram
p1 <- ggplot(subset(dist_ham, !is.na(dist_nearest)), 
             aes(x=dist_nearest)) + 
        geom_histogram(color="white", binwidth=0.02) +
        geom_vline(xintercept=0.12, color="firebrick", linetype=2) +
        labs(x = "Hamming distance", y = "Count") +
        scale_x_continuous(breaks=seq(0, 1, 0.1)) +
        theme_bw()
plot(p1)

## ----eval=TRUE, warning=FALSE, fig.width=7------------------------------------
# Generate HH_S5F distance histogram
p2 <- ggplot(subset(dist_s5f, !is.na(dist_nearest)), 
             aes(x=dist_nearest)) + 
        geom_histogram(color="white", binwidth=1) +
        geom_vline(xintercept=7, color="firebrick", linetype=2) +
        labs(x = "HH_S5F distance", y = "Count") +
        scale_x_continuous(breaks=seq(0, 50, 5)) +
        theme_bw()
plot(p2)

## ----eval=TRUE, warning=FALSE, fig.width=7------------------------------------
# Find threshold using density method
output <- findThreshold(dist_ham$dist_nearest, method="density")
threshold <- output@threshold

# Plot distance histogram, density estimate and optimum threshold
plot(output, title="Density Method")

# Print threshold
print(output)

## ----eval=TRUE, warning=FALSE, fig.width=7------------------------------------
# Find threshold using gmm method
output <- findThreshold(dist_ham$dist_nearest, method="gmm", model="gamma-gamma")

# Plot distance histogram, Gaussian fits, and optimum threshold
plot(output, binwidth=0.02, title="GMM Method: gamma-gamma")

# Print threshold
print(output)

## ----fields, eval=TRUE, warning=FALSE-----------------------------------------
dist_fields <- distToNearest(db, model="ham", normalize="len", 
                             fields="sample_id", nproc=1)

## ----eval=TRUE, warning=FALSE, fig.width=7------------------------------------
# Generate grouped histograms
p4 <- ggplot(subset(dist_fields, !is.na(dist_nearest)), 
             aes(x=dist_nearest)) +
        geom_histogram(color="white", binwidth=0.02) +
        geom_vline(xintercept=0.12, color="firebrick", linetype=2) +
        labs(x = "Grouped Hamming distance", y = "Count") + 
      facet_grid(sample_id ~ ., scales="free_y") + 
      theme_bw()
plot(p4)

## ----cross, eval=TRUE, warning=FALSE------------------------------------------
dist_cross <- distToNearest(ExampleDb, sequenceColumn="junction", 
                            vCallColumn="v_call_genotyped", jCallColumn="j_call",
                            model="ham", first=FALSE, 
                            normalize="len", cross="sample_id", nproc=1)

## ----eval=TRUE, warning=FALSE, fig.width=7------------------------------------
# Generate cross sample histograms
p5 <- ggplot(subset(dist_cross, !is.na(cross_dist_nearest)), 
             aes(x=cross_dist_nearest)) + 
        labs(x = "Cross-sample Hamming distance", y = "Count") +
        geom_histogram(color="white", binwidth=0.02) +
        geom_vline(xintercept=0.12, color="firebrick", linetype=2) +
        facet_grid(sample_id ~ ., scales="free_y") +
        theme_bw()
plot(p5)

## ----subsample, eval=TRUE, warning=FALSE--------------------------------------
# Explore V-J-junction length groups sizes to use subsample
# Show the size of the largest groups
top_10_sizes <- ExampleDb %>%
                 group_by(junction_length) %>% # Group by junction length
                 do(alakazam::groupGenes(., first=TRUE)) %>% # Group by V and J call
                 mutate(GROUP_ID=paste(junction_length, vj_group, sep="_")) %>% # Create group ids
                 ungroup() %>%
                 group_by(GROUP_ID) %>% # Group by GROUP_ID
                 distinct(junction) %>% # Count unique junctions per group
                 summarize(SIZE=n()) %>% # Get the size of the group
                 arrange(desc(SIZE)) %>% # Sort by decreasing size
                 select(SIZE) %>% 
                 top_n(10) # Filter to the top 10
top_10_sizes

# Use 30 to subsample
# NOTE: This is a toy example. Subsampling to 30 sequence with real data is unwise
dist <- distToNearest(ExampleDb, sequenceColumn="junction", 
                      vCallColumn="v_call_genotyped", jCallColumn="j_call",
                      model="ham", 
                      first=FALSE, normalize="len",
                      subsample=30)