Analysing lipidomic data in Mst1 overexpressing and Gal-3 KO mice
Mark Ziemann
2022-01-27
Intro
Doing sone analysis of lipidomics data.
suppressPackageStartupMessages({
library("dplyr")
library("gplots")
library("ggplot2")
library("limma")
library("kableExtra")
})Import lipidomics data
x<-read.table("2019_05_07-DU_final_database.tsv",header=T,row.names=1,sep="\t")Overall class analysis
#pdf("lipidomics_charts_classes.pdf")
# subset
y<-t(x[,776:816])
#MDS plot
plotMDS(y)
mtext("MDS plot for lipid classes")
# remove PC
y<-y[which(row.names(y)!="PC"),]
# sample correlation
heatmap(cor(y,method="s"),scale="none")
mtext("Spearman correlation b/w samples for lipid classes, dark colours are higher correlation" )
# lipid class correlation
heatmap(cor(t(y),method="s"),scale="none")
mtext("Spearman correlation between lipid classes, dark colours are higher correlation" )
# Make a heatmap of class differences
colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(y,scale="row",trace="none",margins = c(5,5),,col=colfunc(25))
mtext("heatmap of lipid classes")
# set up sample list
group<-sapply(strsplit(colnames(y),"_"),"[[",1)WT vs Mst-TG
# ttest
tres<-apply(y , 1 , function(x) { t.test( x[which(group=="Mst-TG")] , x[which(group=="WT")]) } )
pvals<-sapply(tres,"[[",3)
fdr<-p.adjust(pvals,method="BH")
means<-t(sapply(tres,"[[",5))
lfc<-log2(means[,2]/means[,1])
res<-cbind(pvals,fdr,lfc,y[,c( which(group=="WT") , which(group=="Mst-TG") )])
res<-as.data.frame(res)
res<-res[order(res$pvals),]
head(res,10) %>% kbl(caption = "Top 10 lipid classes for WT vs TG") %>% kable_paper("hover", full_width = F)| pvals | fdr | lfc | WT_1 | WT_10 | WT_2 | WT_3 | WT_4 | WT_5 | WT_6 | WT_7 | WT_8 | WT_9 | Mst-TG_21 | Mst-TG_22 | Mst-TG_23 | Mst-TG_24 | Mst-TG_25 | Mst-TG_26 | Mst-TG_27 | Mst-TG_28 | Mst-TG_29 | Mst-TG_30 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Ubiquinone.1 | 0.0e+00 | 0.00e+00 | 1.9096090 | 69440.50 | 94234.70 | 84368.76 | 91888.87 | 78190.70 | 102821.72 | 89319.69 | 84853.82 | 84420.87 | 81675.58 | 22108.73 | 30522.51 | 19628.26 | 28518.31 | 23993.07 | 20805.09 | 20628.13 | 22532.67 | 21849.45 | 18638.85 |
| DHC | 1.0e-07 | 1.70e-06 | -0.7982886 | 1400.63 | 1660.82 | 1124.54 | 1341.88 | 1080.33 | 1494.04 | 1132.05 | 1117.33 | 964.18 | 1575.07 | 2659.95 | 1697.54 | 2390.99 | 2301.05 | 2263.70 | 2032.43 | 2245.66 | 2047.97 | 2296.20 | 2482.21 |
| SM | 1.0e-07 | 1.70e-06 | -0.8247198 | 129593.56 | 115927.18 | 123323.82 | 124765.14 | 118901.15 | 122924.13 | 119945.68 | 126784.98 | 104776.26 | 120019.92 | 186172.45 | 195166.56 | 240743.14 | 196976.01 | 218784.23 | 209062.21 | 218878.92 | 201137.02 | 208811.66 | 262027.76 |
| MHC | 2.0e-07 | 2.10e-06 | -1.1985088 | 749.30 | 615.98 | 582.60 | 795.49 | 1244.07 | 596.03 | 642.10 | 933.57 | 989.87 | 807.48 | 1766.55 | 1417.66 | 1981.18 | 1982.94 | 2326.77 | 1607.89 | 1751.03 | 2284.39 | 1514.46 | 1627.46 |
| PS | 2.8e-06 | 2.22e-05 | -0.4437706 | 206058.33 | 180052.07 | 206707.82 | 246156.89 | 212697.12 | 188468.88 | 235447.96 | 186304.19 | 185490.48 | 236096.49 | 256900.12 | 324559.01 | 298023.46 | 278198.83 | 286018.18 | 297218.39 | 311293.61 | 267598.99 | 277180.48 | 236864.06 |
| COH.1 | 3.4e-06 | 2.25e-05 | -0.3786773 | 415973.17 | 410295.99 | 410678.35 | 430875.54 | 485252.15 | 475017.09 | 395077.94 | 432397.27 | 403230.79 | 478520.00 | 544449.00 | 514400.53 | 611671.79 | 650897.73 | 552959.80 | 525837.82 | 601992.79 | 580842.08 | 566323.19 | 489786.64 |
| PE | 4.9e-06 | 2.51e-05 | 0.5218124 | 5886110.35 | 5036781.03 | 5477237.33 | 6964716.93 | 4887244.17 | 5667202.15 | 6204941.35 | 5033420.16 | 5237042.41 | 5860565.75 | 3938260.90 | 5043402.09 | 3732113.12 | 4269089.98 | 4340068.38 | 3445959.96 | 3624499.16 | 3792878.03 | 3881193.37 | 3114116.65 |
| PC.P. | 5.0e-06 | 2.51e-05 | 0.4728615 | 40088.22 | 51624.90 | 47535.20 | 47643.24 | 36768.93 | 49757.30 | 47627.95 | 46257.08 | 39526.64 | 43308.40 | 30327.82 | 35370.79 | 31825.80 | 32237.90 | 34620.07 | 31746.62 | 30401.80 | 27747.37 | 34240.79 | 35820.69 |
| TG.O. | 6.2e-06 | 2.77e-05 | 0.6397977 | 5248.90 | 6065.19 | 5251.28 | 5677.64 | 6435.93 | 5318.91 | 5337.77 | 4632.58 | 5384.85 | 6863.77 | 4872.30 | 4633.82 | 2940.97 | 3550.94 | 2602.45 | 3399.24 | 3814.41 | 4188.60 | 2961.04 | 3116.35 |
| LPC | 9.2e-06 | 3.67e-05 | -0.5157493 | 34760.29 | 42331.93 | 36095.46 | 39123.34 | 41049.69 | 39126.90 | 33598.81 | 34991.14 | 29371.54 | 37345.15 | 44929.98 | 56513.64 | 49323.57 | 54633.72 | 63702.44 | 45531.13 | 51893.36 | 46079.23 | 52067.56 | 61174.24 |
write.table(res,file="WTvsMst-TG_lipid_classes.tsv",sep="\t",quote=F,row.names=T)
# Now do a volcano plot
sig<-subset(res,fdr<0.05)
plot(res$lfc,-log10(res$pvals),pch=19,cex=0.9,xlim=c(-2.5,1.5),xlab="log2 fold change",ylab="-log10(p-value)")
points(sig$lfc,-log10(sig$pvals),pch=19,col="red",cex=0.95)
grid()
abline(v=0,lty=2)
text(sig$lfc,-log10(sig$pvals)+0.2,labels=rownames(sig))
mtext("volcano plot of lipid class differences: WT vs Mst1-TG")
# Now do some boxplots with jitter on top
lapply( rownames(res)[1:10] , function(NAME) {
z<-as.data.frame(y[which(rownames(y) %in% NAME ) ,])
colnames(z)=NAME
z$group<-factor(group)
MAX=max(z[,1])*1.1
p<-z %>% ggplot(aes(group,z[,1]))+ geom_boxplot(outlier.shape=NA ) + geom_jitter(width = 0.05,col="red") + ylim(0, MAX) + ggtitle(NAME)
print(p)
} )
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WT vs Gal-3 KO
# ttest
tres<-apply(y , 1 , function(x) { t.test( x[which(group=="Gal-3 KO")] , x[which(group=="WT")] ) } )
pvals<-sapply(tres,"[[",3)
fdr<-p.adjust(pvals,method="BH")
means<-t(sapply(tres,"[[",5))
lfc<-log2(means[,2]/means[,1])
res<-cbind(pvals,fdr,lfc,y[,c( which(group=="WT") , which(group=="Gal-3 KO") )])
res<-as.data.frame(res)
head(res,10) %>% kbl(caption = "Top 10 lipid classes for WT vs KO") %>% kable_paper("hover", full_width = F)| pvals | fdr | lfc | WT_1 | WT_10 | WT_2 | WT_3 | WT_4 | WT_5 | WT_6 | WT_7 | WT_8 | WT_9 | Gal-3 KO_11 | Gal-3 KO_12 | Gal-3 KO_13 | Gal-3 KO_14 | Gal-3 KO_15 | Gal-3 KO_16 | Gal-3 KO_17 | Gal-3 KO_18 | Gal-3 KO_19 | Gal-3 KO_20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Sph | 0.0039170 | 0.0156678 | -0.2415801 | 583.32 | 638.32 | 479.43 | 632.85 | 530.80 | 505.70 | 620.62 | 598.04 | 433.98 | 653.16 | 595.38 | 615.21 | 795.21 | 701.60 | 703.58 | 687.83 | 655.45 | 633.68 | 722.96 | 600.02 |
| S1P | 0.0114421 | 0.0352064 | 0.3345190 | 64.51 | 56.20 | 69.42 | 71.69 | 104.17 | 71.08 | 72.82 | 59.71 | 95.79 | 81.50 | 61.20 | 69.74 | 46.94 | 59.67 | 61.41 | 58.93 | 62.91 | 58.72 | 55.83 | 56.97 |
| dhCer | 0.0005209 | 0.0052092 | -0.2922900 | 872.95 | 731.46 | 823.99 | 890.99 | 756.78 | 815.93 | 924.98 | 805.51 | 804.78 | 907.65 | 831.88 | 1172.06 | 1059.33 | 1073.98 | 1063.81 | 1135.74 | 832.22 | 1031.04 | 1059.82 | 947.04 |
| Cer | 0.0148005 | 0.0422871 | -0.1883775 | 24898.05 | 25519.12 | 20835.14 | 30692.76 | 23722.15 | 25584.07 | 23055.37 | 23090.36 | 19645.09 | 27617.23 | 24286.18 | 25725.38 | 29425.82 | 28216.24 | 27466.34 | 29948.10 | 30495.98 | 27615.47 | 30802.74 | 24802.55 |
| C1P | 0.3634940 | 0.5013710 | -0.1862685 | 1.01 | 2.29 | 1.36 | 1.67 | 1.52 | 2.72 | 2.10 | 1.02 | 2.32 | 2.13 | 1.38 | 2.70 | 2.22 | 2.46 | 2.83 | 2.67 | 1.95 | 1.01 | 1.79 | 1.63 |
| MHC | 0.0307400 | 0.0723295 | 0.3719164 | 749.30 | 615.98 | 582.60 | 795.49 | 1244.07 | 596.03 | 642.10 | 933.57 | 989.87 | 807.48 | 562.72 | 734.90 | 744.74 | 504.42 | 793.46 | 593.81 | 469.95 | 592.06 | 573.99 | 578.37 |
| DHC | 0.0024653 | 0.0133371 | -0.6226913 | 1400.63 | 1660.82 | 1124.54 | 1341.88 | 1080.33 | 1494.04 | 1132.05 | 1117.33 | 964.18 | 1575.07 | 1520.94 | 2248.55 | 2143.51 | 2433.03 | 1780.66 | 1221.04 | 1831.88 | 2389.01 | 2905.43 | 1374.60 |
| THC | 0.1927813 | 0.3084501 | -0.1645101 | 109.96 | 183.48 | 145.65 | 150.23 | 245.99 | 171.27 | 160.96 | 151.57 | 194.49 | 173.11 | 190.10 | 230.13 | 230.75 | 200.94 | 154.94 | 205.83 | 203.99 | 137.58 | 173.10 | 163.08 |
| GM3 | 0.0000401 | 0.0016059 | 0.5230165 | 11130.63 | 10595.92 | 10890.82 | 10605.90 | 15374.76 | 12213.94 | 9906.28 | 10363.66 | 11083.33 | 10590.30 | 7194.44 | 8498.71 | 9204.49 | 7408.90 | 8986.38 | 9263.75 | 7313.35 | 8377.98 | 5752.27 | 6468.03 |
| GM1 | 0.0035320 | 0.0156678 | 0.9656636 | 0.62 | 0.79 | 1.22 | 1.02 | 1.69 | 0.96 | 0.71 | 1.21 | 2.01 | 0.98 | 0.60 | 0.58 | 0.67 | 0.65 | 0.59 | 0.70 | 0.84 | 0.53 | 0.00 | 0.58 |
res<-res[order(res$pvals),]
write.table(res,file="WTvsGal3KO_lipid_classes.tsv",sep="\t",quote=F,row.names=T)
# Now do a volcano plot
sig<-subset(res,fdr<0.05)
plot(res$lfc,-log10(res$pvals),pch=19,cex=0.9,xlim=c(-1.5,1.5),xlab="log2 fold change",ylab="-log10(p-value)")
points(sig$lfc,-log10(sig$pvals),pch=19,col="red",cex=0.95)
grid()
abline(v=0,lty=2)
text(sig$lfc,-log10(sig$pvals)+0.1,labels=rownames(sig))
mtext("volcano plot of lipid class differences: WT vs Gal3KO")
# Now do some boxplots with jitter on top
lapply( rownames(res)[1:10] , function(NAME) {
z<-as.data.frame(y[which(rownames(y) %in% NAME ) ,])
colnames(z)=NAME
z$group<-factor(group)
MAX=max(z[,1])*1.1
p<-z %>% ggplot(aes(group,z[,1]))+ geom_boxplot(outlier.shape=NA ) + geom_jitter(width = 0.05,col="red") + ylim(0, MAX) + ggtitle(NAME)
print(p)
} )
## Warning: Removed 1 rows containing missing values (geom_point).
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Mst1-TG vs TG KO
# ttest
tres<-apply(y , 1 , function(x) { t.test( x[which(group=="TG KO")] , x[which(group=="Mst-TG")] ) } )
pvals<-sapply(tres,"[[",3)
fdr<-p.adjust(pvals,method="BH")
means<-t(sapply(tres,"[[",5))
lfc<-log2(means[,2]/means[,1])
res<-cbind(pvals,fdr,lfc,y[,c( which(group=="Mst-TG") , which(group=="TG KO") )])
res<-as.data.frame(res)
res<-res[order(res$pvals),]
head(res,10) %>% kbl(caption = "Top 10 lipid classes for TG vs TGKO") %>% kable_paper("hover", full_width = F)| pvals | fdr | lfc | Mst-TG_21 | Mst-TG_22 | Mst-TG_23 | Mst-TG_24 | Mst-TG_25 | Mst-TG_26 | Mst-TG_27 | Mst-TG_28 | Mst-TG_29 | Mst-TG_30 | TG KO_31 | TG KO_32 | TG KO_33 | TG KO_34 | TG KO_35 | TG KO_36 | TG KO_37 | TG KO_38 | TG KO_39 | TG KO_40 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MHC | 0.0003160 | 0.0126417 | 0.6293783 | 1766.55 | 1417.66 | 1981.18 | 1982.94 | 2326.77 | 1607.89 | 1751.03 | 2284.39 | 1514.46 | 1627.46 | 1515.47 | 1180.99 | 1526.62 | 1058.02 | 749.72 | 1100.58 | 1827.61 | 962.91 | 879.73 | 1002.83 |
| CE | 0.0048351 | 0.0967016 | -0.3548359 | 60317.81 | 57642.35 | 58466.46 | 78116.27 | 63169.91 | 50213.34 | 58817.30 | 57223.12 | 77709.94 | 69237.48 | 69873.11 | 62218.87 | 61346.62 | 76803.19 | 101741.62 | 89360.49 | 91084.00 | 74445.99 | 98187.41 | 81776.83 |
| Sph | 0.0086360 | 0.1011539 | 0.2717791 | 667.83 | 758.51 | 664.57 | 683.06 | 921.78 | 670.03 | 888.11 | 721.26 | 844.33 | 839.23 | 896.64 | 575.04 | 599.19 | 610.84 | 638.32 | 678.68 | 647.39 | 554.22 | 573.93 | 569.44 |
| PC.P. | 0.0101154 | 0.1011539 | -0.3471938 | 30327.82 | 35370.79 | 31825.80 | 32237.90 | 34620.07 | 31746.62 | 30401.80 | 27747.37 | 34240.79 | 35820.69 | 39092.11 | 37203.20 | 38318.15 | 43609.07 | 61508.71 | 42616.81 | 27258.64 | 42169.52 | 37765.28 | 43045.76 |
| THC | 0.0134002 | 0.1072015 | 0.3370801 | 203.07 | 238.60 | 261.59 | 318.74 | 294.87 | 261.42 | 313.27 | 364.35 | 211.46 | 274.64 | 279.36 | 224.26 | 187.05 | 221.63 | 183.43 | 219.16 | 295.95 | 159.56 | 207.16 | 193.13 |
| TG.O…NL. | 0.0164175 | 0.1094501 | -0.6991282 | 263.08 | 607.36 | 219.34 | 207.34 | 236.32 | 255.23 | 320.10 | 447.70 | 313.21 | 582.46 | 584.36 | 711.63 | 279.01 | 377.01 | 510.80 | 339.79 | 461.19 | 865.02 | 612.50 | 863.32 |
| PC.O. | 0.0296513 | 0.1536026 | -0.2447030 | 21266.79 | 26951.31 | 21817.71 | 22514.62 | 22538.87 | 21861.51 | 22607.75 | 20939.36 | 24195.60 | 23670.15 | 26510.11 | 25745.89 | 25645.53 | 27317.24 | 40301.84 | 26108.75 | 20700.95 | 25360.28 | 24725.50 | 28160.34 |
| LPE | 0.0307205 | 0.1536026 | 0.2256225 | 52763.02 | 28970.20 | 40037.37 | 44411.38 | 35022.44 | 33762.28 | 36121.02 | 34637.51 | 38419.85 | 39149.95 | 33594.74 | 33781.02 | 30721.73 | 31922.15 | 37457.31 | 35676.41 | 28695.93 | 28725.68 | 31308.30 | 35920.56 |
| PI | 0.0513911 | 0.2188437 | -0.1386641 | 658019.88 | 800978.51 | 749995.65 | 776203.43 | 773124.87 | 624898.72 | 683314.38 | 654400.39 | 701806.59 | 577477.68 | 865885.27 | 727780.66 | 677775.61 | 671856.84 | 861964.80 | 703628.00 | 752329.47 | 769865.32 | 799269.96 | 876083.23 |
| PE | 0.0584529 | 0.2188437 | -0.1681670 | 3938260.90 | 5043402.09 | 3732113.12 | 4269089.98 | 4340068.38 | 3445959.96 | 3624499.16 | 3792878.03 | 3881193.37 | 3114116.65 | 4489186.42 | 4276699.78 | 4271488.13 | 3985580.44 | 5810090.24 | 4005966.37 | 4367211.20 | 4022500.25 | 4620537.85 | 4176337.81 |
write.table(res,file="Mst-TGvsTGKO_lipid_classes.tsv",sep="\t",quote=F,row.names=T)
# Now do a volcano plot
sig<-subset(res,fdr<0.05)
plot(res$lfc,-log10(res$pvals),pch=19,cex=0.9,xlim=c(-1.5,1.5),xlab="log2 fold change",ylab="-log10(p-value)")
points(sig$lfc,-log10(sig$pvals),pch=19,col="red",cex=0.95)
grid()
abline(v=0,lty=2)
text(sig$lfc,-log10(sig$pvals)+0.1,labels=rownames(sig))
mtext("volcano plot of lipid class differences: Mst-TG vs TG-KO")
# Now do some boxplots with jitter on top
lapply( rownames(res)[1:10] , function(NAME) {
z<-as.data.frame(y[which(rownames(y) %in% NAME ) ,])
colnames(z)=NAME
z$group<-factor(group)
MAX=max(z[,1])*1.1
p<-z %>% ggplot(aes(group,z[,1]))+ geom_boxplot(outlier.shape=NA ) + geom_jitter(width = 0.05,col="red") + ylim(0, MAX) + ggtitle(NAME)
print(p)
} )
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dev.off()## null device
## 1Subset x for individual lipid species
#pdf("lipidomics_charts_species.pdf")
x<-t(x[,1:775])
#MDS plot
plotMDS(x)
mtext("MDS plot for individual lipids")
# sample correlation
heatmap(cor(x,method="s"),scale="none")
mtext("Spearman correlation b/w samples for lipid species, dark colours are higher correlation" )
#remove lipids with SD=0
x<-x[which(!rownames(x) %in% names(which(apply(x,1,sd)==0))),]
# lipid class correlation
heatmap(cor(t(x),method="s"),scale="none")
mtext("Spearman correlation between lipid species, dark colours are higher correlation" )
# Make a heatmap of class differences
colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(x,scale="row",trace="none",margins = c(5,5),,col=colfunc(25))
mtext("heatmap of lipid species")
# set up sample list
group<-sapply(strsplit(colnames(x),"_"),"[[",1)WT vs Mst-TG
# ttest
tres<-apply(x , 1 , function(x) { t.test( x[which(group=="WT")] , x[which(group=="Mst-TG")] ) } )
pvals<-sapply(tres,"[[",3)
fdr<-p.adjust(pvals,method="BH")
means<-t(sapply(tres,"[[",5))
lfc<-log2(means[,2]/means[,1])
res<-cbind(pvals,fdr,lfc,x[,c( which(group=="WT") , which(group=="Mst-TG") )])
res<-as.data.frame(res)
res<-res[order(res$pvals),]
head(res,20) %>% kbl(caption = "Top 20 lipid species for WT vs TG") %>% kable_paper("hover", full_width = F)| pvals | fdr | lfc | WT_1 | WT_10 | WT_2 | WT_3 | WT_4 | WT_5 | WT_6 | WT_7 | WT_8 | WT_9 | Mst-TG_21 | Mst-TG_22 | Mst-TG_23 | Mst-TG_24 | Mst-TG_25 | Mst-TG_26 | Mst-TG_27 | Mst-TG_28 | Mst-TG_29 | Mst-TG_30 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PC.18.0_18.1. | 0 | 0e+00 | 1.7254945 | 24504.86 | 17277.21 | 25676.66 | 26243.62 | 27129.20 | 19208.69 | 20790.91 | 24548.82 | 18295.67 | 22297.25 | 67443.18 | 63714.17 | 78948.50 | 72329.75 | 75042.83 | 75260.25 | 77187.94 | 76003.69 | 74767.80 | 86579.46 |
| PC.P.18.1.18.1. | 0 | 0e+00 | 2.0622720 | 110.74 | 77.81 | 115.53 | 138.28 | 97.57 | 78.95 | 79.42 | 90.25 | 72.54 | 95.41 | 393.11 | 406.49 | 439.68 | 358.74 | 388.44 | 453.10 | 395.28 | 348.75 | 363.15 | 448.02 |
| PC.O.34.1. | 0 | 0e+00 | 1.1263383 | 1839.31 | 2116.37 | 1844.36 | 1717.39 | 1890.96 | 1755.93 | 1536.68 | 1567.26 | 1275.99 | 1687.32 | 3345.06 | 4100.97 | 3561.33 | 3557.31 | 3505.45 | 3695.87 | 4225.42 | 3735.39 | 3823.26 | 4067.14 |
| Ubiquinone | 0 | 0e+00 | -1.9096090 | 69440.50 | 94234.70 | 84368.76 | 91888.87 | 78190.70 | 102821.72 | 89319.69 | 84853.82 | 84420.87 | 81675.58 | 22108.73 | 30522.51 | 19628.26 | 28518.31 | 23993.07 | 20805.09 | 20628.13 | 22532.67 | 21849.45 | 18638.85 |
| PS.36.1. | 0 | 0e+00 | 1.1349891 | 12781.57 | 13240.14 | 11170.60 | 14107.65 | 19779.85 | 11096.07 | 11770.12 | 13071.26 | 10587.40 | 15479.93 | 27624.62 | 25600.78 | 30333.46 | 31983.52 | 29581.94 | 31237.34 | 32170.85 | 31656.31 | 27403.86 | 24683.58 |
| SM.d18.1.24.1. | 0 | 1e-07 | 1.2063640 | 11367.62 | 13602.89 | 9332.74 | 10718.32 | 13081.21 | 11494.75 | 8766.85 | 12189.55 | 7496.89 | 13721.46 | 23867.21 | 21727.38 | 27968.76 | 22801.84 | 29394.05 | 25006.27 | 25164.66 | 24990.41 | 25922.91 | 31077.01 |
| PE.P.16.0.18.1. | 0 | 1e-07 | 1.2023068 | 7202.58 | 8025.94 | 7715.38 | 9667.64 | 8843.98 | 7464.89 | 6989.02 | 6669.02 | 6167.23 | 9880.73 | 15696.66 | 20876.60 | 16430.85 | 20019.82 | 21522.00 | 16439.49 | 17658.73 | 17510.19 | 17595.90 | 17174.88 |
| PE.18.0_18.1. | 0 | 1e-07 | 0.8189492 | 21360.04 | 16666.59 | 17370.51 | 23705.98 | 21341.14 | 18823.88 | 17755.25 | 15346.34 | 16082.06 | 21407.68 | 31458.96 | 37465.01 | 31544.92 | 35432.36 | 36458.04 | 32454.94 | 32535.32 | 35935.39 | 32976.47 | 28673.59 |
| PC.16.0_18.1. | 0 | 1e-07 | 0.5819789 | 63364.51 | 76173.40 | 64386.55 | 67590.70 | 82863.67 | 74768.30 | 62525.18 | 74024.95 | 60597.34 | 70678.64 | 110013.99 | 97425.68 | 103684.64 | 101679.55 | 94976.74 | 104638.87 | 106513.88 | 107527.63 | 101115.69 | 115723.37 |
| PC.P.16.0.16.1. | 0 | 1e-07 | 1.5822021 | 45.24 | 53.16 | 42.55 | 53.99 | 54.64 | 44.64 | 42.26 | 63.27 | 32.78 | 55.73 | 152.84 | 144.47 | 150.03 | 140.18 | 161.98 | 145.82 | 151.10 | 101.49 | 142.27 | 171.80 |
| LPC.20.1…sn1. | 0 | 2e-07 | 0.8160187 | 74.40 | 80.19 | 96.72 | 78.36 | 100.61 | 82.37 | 63.94 | 86.02 | 78.65 | 76.04 | 126.97 | 121.81 | 138.73 | 145.16 | 160.96 | 138.24 | 151.71 | 150.79 | 138.89 | 165.63 |
| SM.d18.1.24.0. | 0 | 2e-07 | 0.9041109 | 9622.53 | 6425.50 | 9313.23 | 8921.02 | 9724.42 | 8296.79 | 10026.16 | 9019.54 | 9744.21 | 7903.94 | 14379.56 | 13944.52 | 18686.11 | 17379.05 | 17086.56 | 16099.73 | 18521.90 | 14979.90 | 17365.45 | 18106.03 |
| Hex2Cer.d18.1.22.0. | 0 | 2e-07 | 0.8183770 | 438.24 | 362.60 | 338.06 | 464.62 | 252.26 | 412.80 | 369.03 | 333.90 | 337.54 | 417.91 | 686.24 | 555.47 | 718.97 | 635.96 | 655.95 | 588.46 | 642.25 | 645.27 | 730.58 | 713.05 |
| PC.O.18.1.18.1. | 0 | 2e-07 | 0.8672917 | 147.57 | 158.57 | 173.23 | 152.84 | 136.50 | 134.70 | 124.70 | 117.14 | 102.73 | 137.28 | 220.03 | 291.28 | 248.60 | 223.84 | 257.94 | 225.02 | 247.55 | 271.71 | 249.12 | 291.95 |
| PC.P.18.1.22.6. | 0 | 5e-07 | -0.9876941 | 3625.98 | 4034.51 | 3992.15 | 4331.49 | 2995.99 | 3659.25 | 3469.77 | 3279.70 | 3241.57 | 3940.79 | 1633.11 | 2218.98 | 1559.09 | 1876.18 | 2147.81 | 1670.05 | 1673.70 | 1733.68 | 2003.77 | 1925.87 |
| PE.18.1_22.6…a. | 0 | 6e-07 | -1.0530455 | 683129.91 | 826556.51 | 776076.74 | 877109.53 | 669771.58 | 852434.08 | 864780.69 | 689009.51 | 615909.41 | 776951.41 | 371026.85 | 459195.56 | 301933.39 | 406502.98 | 433603.26 | 322963.53 | 341306.87 | 351653.50 | 356831.71 | 333092.08 |
| PC.20.0_20.4. | 0 | 7e-07 | 1.0342401 | 353.51 | 193.53 | 411.45 | 351.91 | 232.13 | 242.17 | 397.62 | 299.10 | 375.50 | 225.44 | 543.72 | 555.75 | 676.92 | 596.04 | 587.28 | 602.77 | 655.35 | 764.13 | 659.67 | 671.15 |
| SM.d18.0.16.0. | 0 | 7e-07 | 0.8971226 | 3642.66 | 3498.69 | 3227.21 | 3321.26 | 3340.91 | 2873.18 | 3836.88 | 3007.61 | 3186.03 | 3761.68 | 5818.58 | 5822.77 | 6236.79 | 5755.50 | 6352.26 | 5644.97 | 6916.77 | 7409.20 | 5613.61 | 7183.43 |
| PC.P.16.0.18.1. | 0 | 8e-07 | 2.2759353 | 820.37 | 919.07 | 1065.36 | 1056.11 | 710.35 | 924.46 | 717.81 | 819.64 | 509.34 | 960.22 | 3423.16 | 4734.08 | 3975.62 | 3952.43 | 4230.07 | 4280.71 | 3653.39 | 3191.61 | 4312.11 | 5426.52 |
| PS.40.5. | 0 | 8e-07 | 0.5347556 | 20112.38 | 18435.13 | 22070.10 | 23067.34 | 22809.89 | 19086.58 | 25071.80 | 20849.23 | 19517.96 | 23550.63 | 27559.96 | 32881.94 | 32765.56 | 29491.06 | 32365.76 | 33384.26 | 34255.46 | 28284.00 | 30675.73 | 29184.64 |
write.table(res,file="WTvsMst-TG_lipid_species.tsv",sep="\t",quote=F,row.names=T)
# Now do a volcano plot
sig<-subset(res,fdr<0.05)
plot(res$lfc,-log10(res$pvals),pch=19,cex=0.9,xlim=c(-4,4),xlab="log2 fold change",ylab="-log10(p-value)")
points(sig$lfc,-log10(sig$pvals),pch=19,col="red",cex=0.95)
grid()
abline(v=0,lty=2)
text(head(sig$lfc,10),-log10(head(sig$pvals,10))+0.2,labels=head(rownames(sig),10),cex=0.6)
mtext("volcano plot of lipid species differences: WT vs Mst1-TG")
heatmap.2(as.matrix(res[1:50,4:ncol(res)]),trace="none",scale="row",cexRow=0.5,col=colfunc(25))
mtext("Top 50 lipid species, by small p-values: WT vs Mst1-TG")
# Now do some boxplots with jitter on top
lapply( rownames(res)[1:20] , function(NAME) {
z<-as.data.frame(x[which(rownames(x) %in% NAME ) ,])
colnames(z)=NAME
z$group<-factor(group)
MAX=max(z[,1])*1.1
p<-z %>% ggplot(aes(group,z[,1]))+ geom_boxplot(outlier.shape=NA ) + geom_jitter(width = 0.05,col="red") + ylim(0, MAX) + ggtitle(NAME)
print(p)
} )
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WT vs Gal-3 KO
# ttest
tres<-apply(x , 1 , function(x) { t.test( x[which(group=="Gal-3 KO")] , x[which(group=="WT")] ) } )
pvals<-sapply(tres,"[[",3)
fdr<-p.adjust(pvals,method="BH")
means<-t(sapply(tres,"[[",5))
lfc<-log2(means[,2]/means[,1])
res<-cbind(pvals,fdr,lfc,x[,c( which(group=="WT") , which(group=="Gal-3 KO") )])
res<-as.data.frame(res)
res<-res[order(res$pvals),]
head(res,20) %>% kbl(caption = "Top 20 lipid species for WT vs KO") %>% kable_paper("hover", full_width = F)| pvals | fdr | lfc | WT_1 | WT_10 | WT_2 | WT_3 | WT_4 | WT_5 | WT_6 | WT_7 | WT_8 | WT_9 | Gal-3 KO_11 | Gal-3 KO_12 | Gal-3 KO_13 | Gal-3 KO_14 | Gal-3 KO_15 | Gal-3 KO_16 | Gal-3 KO_17 | Gal-3 KO_18 | Gal-3 KO_19 | Gal-3 KO_20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PC.16.1_22.6. | 3.00e-07 | 0.0001916 | -0.5772205 | 2005.90 | 2581.91 | 2255.13 | 2110.27 | 2090.41 | 2949.89 | 2272.74 | 2577.04 | 2029.41 | 2338.19 | 2742.98 | 3883.29 | 3790.62 | 3482.08 | 3390.94 | 3754.98 | 3334.15 | 3544.86 | 3537.70 | 3168.40 |
| Cer.d18.2.23.0. | 1.10e-06 | 0.0004012 | -0.8297307 | 313.88 | 257.09 | 393.35 | 438.12 | 286.97 | 339.26 | 443.92 | 364.56 | 375.41 | 343.93 | 505.89 | 517.06 | 646.55 | 660.44 | 573.86 | 731.74 | 804.29 | 608.13 | 699.42 | 573.76 |
| PC.P.17.0.20.4…b. | 2.30e-06 | 0.0004012 | -0.6793725 | 60.98 | 55.66 | 71.99 | 76.11 | 51.33 | 56.85 | 73.37 | 45.02 | 58.96 | 76.19 | 105.45 | 68.92 | 110.41 | 100.07 | 101.73 | 104.14 | 107.48 | 109.24 | 110.58 | 85.22 |
| PI.36.2. | 2.70e-06 | 0.0004012 | 0.4042892 | 66461.56 | 83395.60 | 70628.62 | 76388.92 | 81113.86 | 80454.68 | 78704.32 | 80084.05 | 66223.20 | 77079.46 | 54551.99 | 57771.11 | 46554.39 | 62868.96 | 52125.67 | 52126.91 | 67431.81 | 59573.43 | 59033.67 | 62628.22 |
| PC.P.17.0.20.4…a. | 2.90e-06 | 0.0004012 | -0.6883036 | 30.25 | 30.16 | 30.39 | 36.81 | 26.59 | 24.50 | 38.63 | 23.79 | 27.55 | 38.68 | 47.14 | 36.86 | 61.93 | 49.04 | 53.09 | 54.16 | 48.34 | 53.67 | 47.59 | 43.44 |
| LPC.19.0…a…sn1…104_sn1. | 3.20e-06 | 0.0004012 | -1.0402278 | 116.46 | 62.06 | 124.63 | 95.58 | 58.73 | 54.18 | 82.21 | 57.18 | 99.27 | 71.24 | 112.48 | 144.31 | 213.26 | 199.08 | 164.28 | 137.47 | 179.44 | 190.91 | 192.83 | 155.48 |
| Cer.d17.1.22.0. | 4.90e-06 | 0.0004480 | -1.0673785 | 198.64 | 95.07 | 176.86 | 269.41 | 97.26 | 126.02 | 210.75 | 127.51 | 173.78 | 143.47 | 276.63 | 209.45 | 392.59 | 405.61 | 340.97 | 310.22 | 401.75 | 322.08 | 417.99 | 315.04 |
| Cer.d17.1.24.0. | 5.70e-06 | 0.0004480 | -0.7402628 | 100.00 | 72.12 | 100.85 | 136.47 | 61.95 | 81.86 | 93.50 | 76.09 | 96.37 | 86.35 | 118.24 | 112.67 | 178.77 | 153.29 | 170.09 | 157.19 | 158.52 | 149.65 | 173.91 | 140.39 |
| Cer.d17.1.20.0. | 5.90e-06 | 0.0004480 | -0.7533047 | 67.65 | 70.98 | 73.58 | 97.80 | 57.90 | 71.39 | 77.99 | 71.59 | 56.77 | 92.03 | 104.08 | 82.17 | 146.84 | 134.29 | 115.61 | 133.87 | 134.73 | 128.64 | 145.61 | 117.63 |
| PC.39.5..b. | 6.00e-06 | 0.0004480 | -0.8417115 | 268.18 | 261.02 | 333.50 | 255.35 | 168.42 | 253.74 | 301.05 | 223.64 | 280.33 | 202.25 | 388.79 | 400.66 | 409.99 | 580.97 | 359.63 | 530.06 | 478.48 | 373.69 | 548.08 | 495.18 |
| Cer.d18.0.22.0. | 9.10e-06 | 0.0006183 | -0.5765950 | 279.42 | 194.50 | 290.22 | 337.77 | 198.18 | 246.72 | 285.89 | 240.10 | 287.83 | 226.78 | 295.33 | 413.52 | 393.90 | 413.44 | 355.00 | 440.13 | 337.50 | 416.82 | 436.70 | 356.33 |
| PI.38.6. | 1.14e-05 | 0.0007089 | -0.5901676 | 3985.77 | 2320.35 | 3894.46 | 3856.82 | 2309.54 | 3499.73 | 3684.30 | 2642.95 | 3491.17 | 2594.10 | 4483.15 | 5531.18 | 4761.39 | 4372.28 | 4822.92 | 5274.06 | 4914.87 | 5433.40 | 4815.89 | 4184.65 |
| PE.18.0_18.2. | 1.29e-05 | 0.0007366 | 0.4253943 | 84087.61 | 92241.40 | 94072.14 | 98088.36 | 96865.75 | 94344.96 | 104900.82 | 104838.46 | 74884.75 | 96293.97 | 70859.87 | 75818.13 | 56679.74 | 77345.46 | 60130.50 | 58320.77 | 80002.33 | 66668.56 | 73880.54 | 80711.53 |
| PC.O.16.0.22.6. | 1.65e-05 | 0.0008751 | -0.4184049 | 15901.12 | 19765.67 | 17360.50 | 17013.95 | 13248.82 | 18913.11 | 15805.49 | 13732.36 | 13792.92 | 15374.44 | 20458.81 | 22941.15 | 22303.39 | 20914.58 | 18376.87 | 20720.27 | 22659.39 | 21122.02 | 25460.41 | 20088.97 |
| Cer.d16.1.20.0. | 2.02e-05 | 0.0009582 | -0.5540988 | 17.96 | 19.43 | 17.47 | 25.37 | 21.29 | 21.88 | 19.83 | 17.55 | 16.80 | 21.01 | 24.83 | 22.05 | 33.24 | 30.55 | 27.46 | 33.25 | 31.80 | 28.11 | 34.21 | 26.08 |
| SM.d18.2.23.0. | 2.08e-05 | 0.0009582 | -0.7563716 | 797.29 | 429.61 | 1022.86 | 904.04 | 654.48 | 658.24 | 999.76 | 688.44 | 940.65 | 577.74 | 1146.94 | 1075.95 | 1310.04 | 1253.31 | 1255.30 | 1258.02 | 1399.53 | 1101.59 | 1838.84 | 1322.18 |
| LPC.20.0…sn2. | 2.19e-05 | 0.0009582 | -0.7941525 | 39.26 | 31.30 | 40.81 | 28.69 | 33.48 | 26.39 | 36.64 | 39.20 | 46.75 | 20.45 | 53.76 | 50.16 | 50.80 | 72.76 | 47.32 | 54.79 | 72.30 | 55.93 | 78.93 | 57.98 |
| Cer.d18.2.21.0. | 2.72e-05 | 0.0010663 | -0.8164422 | 59.77 | 62.34 | 84.97 | 87.40 | 68.42 | 97.45 | 102.13 | 74.96 | 85.01 | 67.95 | 96.45 | 107.26 | 148.05 | 151.62 | 124.06 | 175.13 | 182.48 | 124.99 | 143.83 | 138.07 |
| PE.P.17.0.22.6…a. | 2.99e-05 | 0.0010663 | -0.7212752 | 2647.38 | 2183.71 | 2251.27 | 3451.15 | 1499.42 | 2163.31 | 3199.44 | 1745.78 | 1826.15 | 3488.75 | 3815.83 | 3319.29 | 3823.00 | 4361.89 | 3810.77 | 4355.55 | 4689.81 | 3436.46 | 3981.49 | 4725.61 |
| Cer.d18.2.24.0. | 3.02e-05 | 0.0010663 | -0.4960995 | 372.92 | 439.76 | 488.60 | 432.20 | 501.65 | 494.31 | 479.64 | 515.60 | 457.86 | 424.97 | 546.32 | 562.57 | 671.73 | 621.56 | 617.96 | 713.55 | 786.20 | 630.63 | 779.87 | 568.02 |
write.table(res,file="WTvsGal3KO_lipid_species.tsv",sep="\t",quote=F,row.names=T)
# Now do a volcano plot
sig<-subset(res,fdr<0.05)
plot(res$lfc,-log10(res$pvals),pch=19,cex=0.9,xlim=c(-2,2),xlab="log2 fold change",ylab="-log10(p-value)")
points(sig$lfc,-log10(sig$pvals),pch=19,col="red",cex=0.95)
grid()
abline(v=0,lty=2)
text(head(sig$lfc,10),-log10(head(sig$pvals,10))+0.2,labels=head(rownames(sig),10),cex=0.6)
mtext("volcano plot of lipid species differences: WT vs Gal-3 KO")
# Heatmap top 50 differences
heatmap.2(as.matrix(res[1:50,4:ncol(res)]),trace="none",scale="row",cexRow=0.5,col=colfunc(25))
mtext("Top 50 lipid species, by small p-values: WT vs Gal-3 KO")
# Now do some boxplots with jitter on top
lapply( rownames(res)[1:20] , function(NAME) {
z<-as.data.frame(x[which(rownames(x) %in% NAME ) ,])
colnames(z)=NAME
z$group<-factor(group)
MAX=max(z[,1])*1.1
p<-z %>% ggplot(aes(group,z[,1]))+ geom_boxplot(outlier.shape=NA ) + geom_jitter(width = 0.05,col="red") + ylim(0, MAX) + ggtitle(NAME)
print(p)
} )
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Mst1-TG vs TG KO
# ttest
tres<-apply(x , 1 , function(x) { t.test( x[which(group=="TG KO")] , x[which(group=="Mst-TG")] ) } )
pvals<-sapply(tres,"[[",3)
fdr<-p.adjust(pvals,method="BH")
means<-t(sapply(tres,"[[",5))
lfc<-log2(means[,2]/means[,1])
res<-cbind(pvals,fdr,lfc,x[,c( which(group=="Mst-TG") , which(group=="TG KO") )])
res<-as.data.frame(res)
res<-res[order(res$pvals),]
head(res,20) %>% kbl(caption = "Top 20 lipid species for TG vs TGKO") %>% kable_paper("hover", full_width = F)| pvals | fdr | lfc | Mst-TG_21 | Mst-TG_22 | Mst-TG_23 | Mst-TG_24 | Mst-TG_25 | Mst-TG_26 | Mst-TG_27 | Mst-TG_28 | Mst-TG_29 | Mst-TG_30 | TG KO_31 | TG KO_32 | TG KO_33 | TG KO_34 | TG KO_35 | TG KO_36 | TG KO_37 | TG KO_38 | TG KO_39 | TG KO_40 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Cer.d18.1.24.1. | 0.0000012 | 0.0008849 | 0.6036648 | 3526.01 | 3305.75 | 3921.43 | 4083.66 | 5022.14 | 4075.87 | 4268.58 | 3942.11 | 4032.92 | 4345.82 | 3006.73 | 3381.17 | 2132.27 | 2314.70 | 2260.59 | 2835.40 | 2370.07 | 2804.53 | 2770.54 | 2792.23 |
| PE.18.0_18.1. | 0.0000110 | 0.0023424 | 0.3601459 | 31458.96 | 37465.01 | 31544.92 | 35432.36 | 36458.04 | 32454.94 | 32535.32 | 35935.39 | 32976.47 | 28673.59 | 27016.78 | 31506.59 | 23270.88 | 24044.72 | 21961.07 | 24748.49 | 28401.66 | 26266.53 | 27522.74 | 26203.64 |
| PC.18.0_20.4. | 0.0000116 | 0.0023424 | 0.2891204 | 86691.44 | 103606.27 | 103656.76 | 95137.77 | 103757.42 | 94115.13 | 98681.67 | 101831.12 | 98820.16 | 90786.98 | 86924.72 | 88633.28 | 84800.70 | 86667.40 | 75302.75 | 82004.93 | 69284.80 | 73081.40 | 71262.24 | 81684.79 |
| PC.P.35.2..b. | 0.0000149 | 0.0023424 | -0.5050396 | 15.64 | 22.91 | 16.33 | 18.47 | 19.51 | 19.91 | 16.75 | 13.96 | 15.75 | 16.74 | 26.45 | 19.95 | 24.21 | 22.26 | 27.82 | 29.84 | 25.72 | 22.28 | 24.25 | 26.95 |
| HexCer.d18.1.24.1. | 0.0000157 | 0.0023424 | 0.9034402 | 300.49 | 257.73 | 346.11 | 369.78 | 457.69 | 272.86 | 301.36 | 442.16 | 298.09 | 347.94 | 212.69 | 199.63 | 197.60 | 165.73 | 96.62 | 197.46 | 265.70 | 164.04 | 143.47 | 171.64 |
| PE.P.18.0.22.5…n3. | 0.0000207 | 0.0025648 | -0.6777362 | 12433.00 | 11374.51 | 14402.02 | 16007.38 | 10713.37 | 11445.38 | 11553.77 | 12293.85 | 14164.74 | 10832.54 | 18177.39 | 21534.40 | 19075.93 | 19744.63 | 28710.02 | 19164.45 | 16528.24 | 18329.26 | 20006.42 | 19035.54 |
| Cer.d19.1.22.0. | 0.0000536 | 0.0050541 | -0.5961892 | 5.90 | 9.32 | 7.12 | 6.99 | 6.93 | 5.25 | 8.26 | 8.83 | 6.08 | 7.43 | 13.31 | 9.80 | 11.46 | 9.66 | 14.19 | 10.83 | 10.18 | 8.72 | 11.37 | 9.49 |
| PI.16.0_20.4. | 0.0000543 | 0.0050541 | -0.3017549 | 8692.45 | 10284.39 | 8854.23 | 10836.13 | 10995.99 | 8180.19 | 9368.89 | 10449.17 | 9019.86 | 9754.62 | 12499.46 | 12023.32 | 13009.91 | 10167.07 | 12924.40 | 10543.53 | 12359.58 | 11459.45 | 11753.24 | 12131.09 |
| SM.44.3. | 0.0000629 | 0.0051146 | 0.5058036 | 179.45 | 103.02 | 140.83 | 136.24 | 141.50 | 147.10 | 161.59 | 137.56 | 132.83 | 166.46 | 101.70 | 111.29 | 90.43 | 97.73 | 75.68 | 106.58 | 112.14 | 110.96 | 102.25 | 110.02 |
| PE.P.18.0.20.3…a. | 0.0000722 | 0.0051146 | -0.4143817 | 617.22 | 806.25 | 817.41 | 932.21 | 726.67 | 621.05 | 713.83 | 753.86 | 829.80 | 713.09 | 1118.87 | 975.79 | 854.32 | 924.47 | 1250.78 | 996.87 | 1036.06 | 856.75 | 1006.14 | 1017.24 |
| Cer.d18.1.16.0. | 0.0000756 | 0.0051146 | 0.5608687 | 1971.19 | 1995.44 | 2197.82 | 2337.49 | 3064.00 | 2700.57 | 2789.42 | 2742.38 | 2280.58 | 3019.70 | 2157.74 | 1931.41 | 1638.94 | 1597.68 | 1451.17 | 1993.21 | 1417.09 | 1671.13 | 1507.39 | 1648.42 |
| SM.44.2. | 0.0000969 | 0.0060091 | 0.4112218 | 258.11 | 180.71 | 240.11 | 199.07 | 258.62 | 228.59 | 258.83 | 205.16 | 243.56 | 248.20 | 178.82 | 195.60 | 150.05 | 169.04 | 130.15 | 188.47 | 177.90 | 200.91 | 157.21 | 197.18 |
| Cer.d17.1.22.0. | 0.0001062 | 0.0060795 | -0.6440653 | 133.19 | 272.17 | 137.44 | 139.17 | 193.95 | 127.03 | 145.81 | 184.07 | 159.24 | 133.64 | 254.96 | 264.88 | 255.07 | 280.37 | 329.78 | 245.82 | 267.47 | 223.57 | 189.78 | 228.84 |
| SM.d16.1.23.0..SM.d17.1.22.0. | 0.0001191 | 0.0062500 | -0.5032421 | 1941.30 | 3466.74 | 3056.03 | 1947.53 | 2711.38 | 2330.15 | 2341.65 | 2483.62 | 2352.19 | 2898.93 | 3283.80 | 3920.93 | 3224.24 | 3880.38 | 4217.32 | 3590.20 | 3461.22 | 3486.56 | 2743.97 | 4376.80 |
| PI.38.5…a. | 0.0001260 | 0.0062500 | -0.4758094 | 11996.63 | 13584.02 | 8929.17 | 13043.50 | 12460.26 | 11133.14 | 11256.13 | 12576.95 | 11578.12 | 11287.78 | 15084.24 | 16313.17 | 13941.39 | 15266.50 | 22550.40 | 14719.22 | 16070.92 | 16895.72 | 17821.82 | 15224.43 |
| PE.P.20.0.22.6. | 0.0001344 | 0.0062500 | -0.6131068 | 8676.96 | 15578.79 | 11101.14 | 13163.79 | 12978.24 | 8923.03 | 9711.28 | 9435.58 | 12338.09 | 10596.36 | 19261.87 | 15160.40 | 15504.55 | 13181.92 | 23673.45 | 16546.29 | 17275.01 | 14950.92 | 16207.91 | 20316.98 |
| PC.P.35.2..a. | 0.0001753 | 0.0074497 | -0.4079258 | 30.39 | 36.17 | 27.46 | 26.38 | 31.27 | 25.90 | 27.21 | 29.44 | 27.13 | 29.46 | 35.34 | 27.80 | 40.65 | 33.54 | 41.29 | 44.90 | 42.01 | 42.31 | 36.51 | 41.49 |
| SM.41.0. | 0.0001859 | 0.0074497 | -0.4390849 | 344.00 | 486.10 | 593.89 | 388.80 | 391.06 | 386.30 | 499.65 | 436.41 | 370.80 | 461.91 | 696.59 | 601.59 | 502.42 | 548.05 | 562.46 | 583.04 | 648.53 | 528.18 | 528.91 | 709.81 |
| PI.18.1_18.2. | 0.0001902 | 0.0074497 | -0.9436883 | 4471.72 | 5446.83 | 2558.48 | 3782.57 | 4590.78 | 3400.89 | 3485.65 | 4562.18 | 3602.20 | 4152.42 | 5749.94 | 7611.73 | 5632.66 | 6033.38 | 11871.90 | 5620.12 | 8449.45 | 8224.63 | 9615.17 | 8231.92 |
| PC.18.0_18.1. | 0.0002145 | 0.0078130 | 0.4126401 | 67443.18 | 63714.17 | 78948.50 | 72329.75 | 75042.83 | 75260.25 | 77187.94 | 76003.69 | 74767.80 | 86579.46 | 66237.18 | 63944.90 | 53062.36 | 52850.81 | 30182.28 | 64502.12 | 58705.00 | 58509.77 | 53176.98 | 60218.89 |
write.table(res,file="MstTGvsTGKO_lipid_species.tsv",sep="\t",quote=F,row.names=T)
# Now do a volcano plot
sig<-subset(res,fdr<0.05)
plot(res$lfc,-log10(res$pvals),pch=19,cex=0.9,xlim=c(-2,2),xlab="log2 fold change",ylab="-log10(p-value)")
points(sig$lfc,-log10(sig$pvals),pch=19,col="red",cex=0.95)
grid()
abline(v=0,lty=2)
text(head(sig$lfc,10),-log10(head(sig$pvals,10))+0.2,labels=head(rownames(sig),10),cex=0.6)
mtext("volcano plot of lipid species differences: Mst-TG vs TG KO")
# Heatmap top 50 differences
heatmap.2(as.matrix(res[1:50,4:ncol(res)]),trace="none",scale="row",cexRow=0.5,col=colfunc(25))
mtext("Top 50 lipid species, by small p-values: Mst-TG vs TG KO")
# Now do some boxplots with jitter on top
lapply( rownames(res)[1:20] , function(NAME) {
z<-as.data.frame(x[which(rownames(x) %in% NAME ) ,])
colnames(z)=NAME
z$group<-factor(group)
MAX=max(z[,1])*1.1
p<-z %>% ggplot(aes(group,z[,1]))+ geom_boxplot(outlier.shape=NA ) + geom_jitter(width = 0.05,col="red") + ylim(0, MAX) + ggtitle(NAME)
print(p)
} )
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dev.off()## null device
## 1Session information
For reproducibility.
sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] kableExtra_1.3.4 limma_3.48.1 ggplot2_3.3.5 gplots_3.1.1
## [5] dplyr_1.0.7
##
## loaded via a namespace (and not attached):
## [1] highr_0.9 pillar_1.6.1 bslib_0.2.5.1 compiler_4.1.2
## [5] jquerylib_0.1.4 bitops_1.0-7 tools_4.1.2 digest_0.6.27
## [9] viridisLite_0.4.0 gtable_0.3.0 jsonlite_1.7.2 evaluate_0.14
## [13] lifecycle_1.0.0 tibble_3.1.3 pkgconfig_2.0.3 rlang_0.4.11
## [17] rstudioapi_0.13 DBI_1.1.1 yaml_2.2.1 xfun_0.24
## [21] xml2_1.3.2 httr_1.4.2 withr_2.4.2 stringr_1.4.0
## [25] knitr_1.33 systemfonts_1.0.2 generics_0.1.0 vctrs_0.3.8
## [29] sass_0.4.0 gtools_3.9.2 caTools_1.18.2 webshot_0.5.2
## [33] grid_4.1.2 tidyselect_1.1.1 svglite_2.0.0 glue_1.4.2
## [37] R6_2.5.0 fansi_0.5.0 rmarkdown_2.9 farver_2.1.0
## [41] purrr_0.3.4 magrittr_2.0.1 scales_1.1.1 ellipsis_0.3.2
## [45] htmltools_0.5.1.1 rvest_1.0.0 assertthat_0.2.1 colorspace_2.0-2
## [49] labeling_0.4.2 utf8_1.2.2 KernSmooth_2.23-20 stringi_1.7.3
## [53] munsell_0.5.0 crayon_1.4.1