vignettes/Sample_size_effect.R

In rprops/Phenoflow_package: Advanced analysis of microbial flow cytometry data

## ----prepPhenoFlow, fig.width=7,fig.height=5, message=FALSE--------------
library("Phenoflow")
library("gridExtra")
library("grid")
library(ggplot2)
### 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`))


## ----subsamplecollcurves, results='hide',fig.width=7,fig.height=5,fig.cap=c("D0 collector curve","D1 collector curve","D2 collector curve")----
### 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))
D0

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))
D1

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))
D2

## ----arranged plots, fig.width=8,fig.height=5----------------------------
# 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()