Ken Furudate

library(reshape2)
library(ggplot2)

in_f1 = "Cibersortx.txt"      

set.seed(42)
data <- read.table(in_f1, header=TRUE, row.names=1, sep="\t", quote="", stringsAsFactors = FALSE)
data

	OSCC cell	CAF	MSC	MAF	Mst	DC	EC	T.cell	Mac	B.cell	Myo	LNM
N0_1	0.949462	0.037150	0.000109	0.007990	0.000031	0.000000	0.001129	0.003654	0.000000	0.000027	0.000449	0
N0_2	0.960907	0.029300	0.000000	0.003827	0.000033	0.000000	0.004337	0.001596	0.000000	0.000000	0.000000	0
N0_3	0.863627	0.065710	0.000000	0.000000	0.000000	0.000000	0.011142	0.005287	0.050508	0.000000	0.003726	0
N0_4	0.946941	0.037037	0.000000	0.000491	0.000101	0.000000	0.000206	0.004879	0.010343	0.000000	0.000000	0
N0_5	0.978963	0.015044	0.000000	0.000000	0.000000	0.000000	0.000000	0.005931	0.000000	0.000059	0.000004	0
...	...	...	...	...	...	...	...	...	...	...	...	...
LNM_111	0.916204	0.050123	0.000000	0.004877	0.000000	0.000000	0.004764	0.003464	0.001263	0.000000	0.019305	1
LNM_112	0.941422	0.021222	0.000000	0.001101	0.000039	0.001058	0.002139	0.004718	0.028281	0.000020	0.000000	1
LNM_113	0.915116	0.048471	0.000066	0.001132	0.000000	0.000000	0.001310	0.000856	0.032967	0.000000	0.000083	1
LNM_114	0.855557	0.033025	0.000000	0.013933	0.000093	0.000000	0.021723	0.002849	0.059730	0.000000	0.013091	1
LNM_115	0.919173	0.033074	0.000027	0.002856	0.000000	0.000000	0.013717	0.002542	0.028551	0.000036	0.000024	1

201 rows × 12 columns

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

  LNM  variable Relative_fraction
1   0 OSCC cell         0.9494617
2   0 OSCC cell         0.9609070
3   0 OSCC cell         0.8636268
4   0 OSCC cell         0.9469413
5   0 OSCC cell         0.9789630
6   0 OSCC cell         0.8948533

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

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=Relative_fraction), 
				    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(x=col_, y="Relative fraction") +
		    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_$Relative_fraction~mat_$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_$Relative_fraction by mat_$LNM
W = 5098, p-value = 0.7086
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -0.01478162  0.02495532
sample estimates:
difference in location 
           0.003217375 

[1] "CAF"

	Wilcoxon rank sum test with continuity correction

data:  mat_$Relative_fraction by mat_$LNM
W = 4785, p-value = 0.6959
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -0.012900689  0.007322069
sample estimates:
difference in location 
          -0.002079433 

[1] "T.cell"

	Wilcoxon rank sum test with continuity correction

data:  mat_$Relative_fraction by mat_$LNM
W = 5802.5, p-value = 0.03565
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 4.248527e-05 1.656907e-03
sample estimates:
difference in location 
          0.0008028222