Ken Furudate

library(reshape2)
library(ggplot2)

in_f1 = "Single_cell_proportion_data.txt"

set.seed(42)
data <- read.table(in_f1, header=TRUE, sep="\t", quote="", stringsAsFactors = FALSE)
data

data.melt <- reshape2::melt(data, 
                            id.vars="LNM", 
                            value.name="Proportion_of_cells_per_sample", 
                            na.rm=TRUE, 
                            stringsAsFactors = FALSE
                            )
data.melt$LNM <- as.character(data.melt$LNM)
head(data.melt)

  LNM variable Proportion_of_cells_per_sample
1   1      Mst                     0.26760563
2   1      Mst                     0.02120141
3   1      Mst                     0.01883239
4   1      Mst                     0.05747126
5   1      Mst                     0.01666667
6   1      Mst                     0.01123596

anot_cols <- c("#0977a9", "#ab162c")
select.col_names <- c("OSCC cell", "CAF", "MAF")

for (i in 1:length(select.col_names)){
	col_ <- select.col_names[i]
	print(col_)
	mat_ <- data.melt[data.melt$variable==col_,]
  ggplot()+theme_set(theme_classic(base_size = 30, base_family = "Arial"))
	p <- ggplot(mat_,
				aes(x=LNM, 
            y=Proportion_of_cells_per_sample), 
				    fill=LNM
            ) +
        stat_boxplot(geom = "errorbar", 
                     width = 0.2
                    ) +
        geom_boxplot(aes(fill=LNM), 
                     outlier.colour=NA, alpha=1.0
                    ) +
        geom_jitter(aes(col=LNM), 
                    position=position_jitter(0.2), 
                    shape=16, 
                    alpha=0.9, 
                    color="Black"
                    ) +
		    labs(title=select.col_names[i], x="", y="Proportion of cells per sample") +
				theme(plot.title = element_text(hjust = 0.5)) +
		    scale_fill_manual(values=anot_cols) +
		    scale_colour_manual(values=anot_cols) +
		    scale_x_discrete(limits=c("0", "1"),
				                labels=c("LNM(-)", "LNM(+)")
                        )
	print(p)

	res_ = wilcox.test(mat_$Proportion_of_cells_per_sample~mat_$LNM,
					  alternative = "two.sided",
					  paired=F,
					  conf.int=T,
					  conf.level=0.95,
					  exact=F
					  )
	print(res_)
}

[1] "OSCC cell"

	Wilcoxon rank sum test with continuity correction

data:  mat_$Proportion_of_cells_per_sample by mat_$LNM
W = 26, p-value = 0.7122
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -0.3721139  0.3237603
sample estimates:
difference in location 
           -0.05673079 

[1] "CAF"

	Wilcoxon rank sum test with continuity correction

data:  mat_$Proportion_of_cells_per_sample by mat_$LNM
W = 30, p-value = 1
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -0.1721137  0.1405347
sample estimates:
difference in location 
          -0.006208771 

[1] "MAF"

	Wilcoxon rank sum test with continuity correction

data:  mat_$Proportion_of_cells_per_sample by mat_$LNM
W = 10, p-value = 0.02604
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -0.2749857  0.0000000
sample estimates:
difference in location 
          -0.008325316

	Mst	DC	EC	Mac	Myo	T cell	MSC	MAF	OSCC cell	B cell	CAF	LNM
Sample_IDs
oscc24	0.267606	0.000000	0.014085	0.028169	0.000000	0.014085	0.000000	0.267606	0.183099	0.000000	0.225352	1
oscc18	0.021201	0.000000	0.031802	0.012367	0.001767	0.365724	0.012367	0.000000	0.464664	0.015901	0.074205	1
oscc25	0.018832	0.013183	0.001883	0.001883	0.015066	0.175141	0.030132	0.274953	0.288136	0.000000	0.180791	1
oscc13	0.057471	0.344828	0.034483	0.034483	0.000000	0.172414	0.000000	0.011494	0.172414	0.068966	0.103448	1
oscc5	0.016667	0.000000	0.045833	0.241667	0.000000	0.116667	0.020833	0.008333	0.291667	0.000000	0.258333	1
oscc10	0.011236	0.000000	0.089888	0.000000	0.000000	0.078652	0.000000	0.000000	0.033708	0.494382	0.292135	1
oscc26	0.005076	0.005076	0.000000	0.000000	0.000000	0.015228	0.000000	0.451777	0.309645	0.000000	0.213198	1
oscc6	0.003247	0.000000	0.168831	0.019481	0.000000	0.000000	0.042208	0.006494	0.509740	0.012987	0.237013	1
oscc28	0.000000	0.000000	0.029350	0.002096	0.018868	0.000000	0.098532	0.446541	0.102725	0.000000	0.301887	1
oscc20	0.000000	0.000000	0.002890	0.000000	0.000000	0.000000	0.000000	0.002890	0.956647	0.000000	0.037572	1
oscc23	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1
oscc7	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.857143	0.000000	0.142857	1
oscc16	0.040268	0.011745	0.072148	0.013423	0.001678	0.503356	0.005034	0.000000	0.137584	0.000000	0.214765	0
oscc22	0.109453	0.004975	0.004975	0.000000	0.000000	0.000000	0.009950	0.000000	0.611940	0.000000	0.258706	0
oscc17	0.010558	0.000000	0.025641	0.003017	0.000000	0.352941	0.000000	0.000000	0.532428	0.010558	0.064857	0
oscc12	0.000000	0.000000	0.000000	0.000000	0.000000	0.898089	0.000000	0.000000	0.095541	0.000000	0.006369	0
oscc8	0.020408	0.000000	0.275510	0.010204	0.000000	0.377551	0.000000	0.000000	0.030612	0.020408	0.265306	0