2. Effect of sample size

For an indication on how many cells you would need to get an accurate phenotypic diversity measurement, see below. This analysis was done on sample 10 of the test data set that was published in doi: 10.1111/2041-210X.12607 and is available as a data(flowData) in the package.

In brief, for each specified sample size, 100 random samples (with replacement) were taken, a fingerprint (128x128) was generated for each subsample and the diversity was calculated on all subsamples with Diversity() (100 bootstraps).

library("Phenoflow")
library("gridExtra")
library("grid")

### Load data
data(flowData)

### Preprocess data according to standard protocol
flowData_transformed <- transform(flowData, `FL1-H` = asinh(`FL1-H`), `SSC-H` = asinh(`SSC-H`), 
    `FL3-H` = asinh(`FL3-H`), `FSC-H` = asinh(`FSC-H`))
param = c("FL1-H", "FL3-H", "SSC-H", "FSC-H")
flowData_transformed = flowData_transformed[, param]
remove(flowData)

### Create a PolygonGate for denoising the dataset Define coordinates for
### gate in sqrcut1 in format: c(x,x,x,x,y,y,y,y)
sqrcut1 <- matrix(c(8.5, 8.5, 15, 15, 3, 8, 14, 3), ncol = 2, nrow = 4)
colnames(sqrcut1) <- c("FL1-H", "FL3-H")
polyGate1 <- polygonGate(.gate = sqrcut1, filterId = "Total Cells")

### Gating quality check
xyplot(`FL3-H` ~ `FL1-H`, data = flowData_transformed[1], filter = polyGate1, 
    scales = list(y = list(limits = c(0, 14)), x = list(limits = c(6, 16))), 
    axis = axis.default, nbin = 125, par.strip.text = list(col = "white", 
        font = 2, cex = 2), smooth = FALSE)

### Isolate only the cellular information based on the polyGate1
flowData_transformed <- Subset(flowData_transformed, polyGate1)

summary <- fsApply(x = flowData_transformed, FUN = function(x) apply(x, 
    2, max), use.exprs = TRUE)
max = max(summary[, 1])
mytrans <- function(x) x/max
flowData_transformed <- transform(flowData_transformed, `FL1-H` = mytrans(`FL1-H`), 
    `FL3-H` = mytrans(`FL3-H`), `SSC-H` = mytrans(`SSC-H`), `FSC-H` = mytrans(`FSC-H`))

### Subsample at various depths and calculate diversity metrics with 100
### bootstraps Notice: this will use some CPU/RAM
for (i in c(10, 100, 200, 300, 400, 500, 750, 1000, 1250, 1500, 2000, 2500, 
    3000, 5000, 10000, 15000, 20000, 30000, 40000, 60000)) {
    for (j in 1:100) {
        fs1 <- FCS_resample(flowData_transformed[10], replace = TRUE, sample = i)
        fp <- flowBasis(fs1, param, nbin = 128, bw = 0.01, normalize = function(x) x)
        div.tmp <- Diversity(fp, d = 3, R = 100)
        div.tmp <- cbind(div.tmp, size = i)
        if (j == 1) 
            results <- div.tmp else results <- rbind(results, div.tmp)
    }
    if (i == 10) 
        results.tot <- results else results.tot <- rbind(results.tot, results)
}

### Create plots
D0 <- ggplot(data = results.tot, aes(x = factor(size), y = D0)) + # geom_jitter(alpha=0.7, size=1)+
geom_boxplot(alpha = 0.2, color = "blue", fill = "blue", size = 1) + labs(x = "Sample size (nr. of cells)", 
    y = "Phenotypic diversity - D0") + theme_bw() + theme(axis.text.x = element_text(angle = 45, 
    hjust = 1))

D1 <- ggplot(data = results.tot, aes(x = factor(size), y = D1)) + # geom_jitter(alpha=0.7, size=1)+
geom_boxplot(alpha = 0.2, color = "blue", fill = "blue", size = 1) + labs(x = "Sample size (nr. of cells)", 
    y = "Phenotypic diversity - D1") + theme_bw() + theme(axis.text.x = element_text(angle = 45, 
    hjust = 1))

D2 <- ggplot(data = results.tot, aes(x = factor(size), y = D2)) + # geom_jitter(alpha=0.7, size=1)+
geom_boxplot(alpha = 0.2, color = "blue", fill = "blue", size = 1) + labs(x = "Sample size (nr. of cells)", 
    y = "Phenotypic diversity - D2") + theme_bw() + theme(axis.text.x = element_text(angle = 45, 
    hjust = 1))

png(file = "sample_size_effect.png", width = 12, height = 6, res = 500, 
    units = "in", pointsize = 10)
grid.arrange(D0, D1, D2, ncol = 3, top = textGrob("Sample size effect on phenotypic alpha diversity (n=100)", 
    gp = gpar(fontsize = 20, font = 3)))
dev.off()