Glyco summary
TS
2023-04-12
library(readxl)## Warning: package 'readxl' was built under R version 4.2.3library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.2.3library(ggrepel)## Warning: package 'ggrepel' was built under R version 4.2.3library(sqldf)## Loading required package: gsubfn## Loading required package: proto## Loading required package: RSQLite## Warning: package 'RSQLite' was built under R version 4.2.3library(Rcpp)
library(biomaRt)
library(UniprotR)
library(ggplot2)
library(ggseqlogo)
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## chisq.test, fisher.testlibrary(stringr)
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## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errorslibrary(EnhancedVolcano)
library(basicPlotteR)# nardimo barvanje po gostoti
plot_colorByDensity = function(x1,x2,
ylim=c(min(x2),max(x2)),
xlim=c(min(x1),max(x1)),
xlab="",ylab="",main="") {
df <- data.frame(x1,x2)
x <- densCols(x1,x2, colramp=colorRampPalette(c("black", "white")))
df$dens <- col2rgb(x)[1,] + 1L
cols <- colorRampPalette(c("#000099", "#45FE4F","#FCFF00", "#FF9400", "#FF3100"))(256)
df$col <- cols[df$dens]
plot(x2~x1, data=df[order(df$dens),],
ylim=ylim,xlim=xlim,pch=20,col=col,
cex=2,xlab=xlab,ylab=ylab,
main=main)
}Kidney ConA 11. 11. 2022
kcprot <- read_excel("Z:/TSever/Misc/glycoproteomics/kidney_cona_proteins.xlsx") # uvozimo podatke za proteine, izvozeno iz perseus tabele
kcpep <- read_excel("Z:/TSever/Misc/glycoproteomics/kidney_cona_peptides.xlsx") # uvozimo podatke za peptide, izvozeno iz perseus tabele
kcprot <- clean_names(kcprot)
kcpep <- clean_names(kcpep)#poiscemo kateri peptidi imajo konsenzusno zaporedje N[^P][STC]
kcpepg <- kcpep[grep("N[^P][STC]", kcpep$t_sequence),]
#pridobimo sekvence teh proteinov
#tega pol ne ponucamo
accessions_kcpepg <-kcpepg$t_leading_razor_protein
Sequences_Information_kc<-GetSequences(accessions_kcpepg, directorypath = 'L:/')## Please wait we are processing your accessions ...#nardimo objekte samo z sekvencami peptidov in samo z sekvencami v istem vrstnem redu
vectorofseq<-Sequences_Information_kc$Sequence
vectorofpep<-kcpepg$t_sequence# for loop ki napise lokacije vseh peptidov v sekvenci
#zacetek, konec, dolzino peptida in sekvenco peptida
#notr v data.frame df jih da
i=1
df = data.frame()
for (i in 1: length(vectorofpep)) {
toFind<-vectorofpep[i]
here<-vectorofseq[i]
output <- matchPattern(toFind, here)
output<-as.data.frame(output)
df=rbind(df,output)
i<-i+1
}#poiscemo, kje se prvic pojavi N X S/T/C v sekvenci peptida
my_string <- df$seq #define string
#find position of first occurrence of 'N X S/T/C '
position<-((str_locate(pattern = "N.[STC]", my_string)) -1) +df$start #str_locate poizce zacetek in konec
position <- position[-c((length(position)/2)+1 : length(position) )] #ker imamo zacetek in konec, pobrisemo konce
#poiscemo aminokislino na mestu position
aa<-substring(vectorofseq, position, position)
#aa<-aa[-c((length(aa)/2) : length(aa) )] #ker imamo zacetek in konec, pobrisemo konce#dodamo pozicije N -jev v kcpepg data.frame
kcpepg["position"]<-c(position)
#zdruzimo stoplca z gene name in position v nov stolpec, kjer je zdruzeno
kcpepg$gn_position <- paste(kcpepg$t_gene_names, kcpepg$position, sep = "-" )#dejmo se tole mal leps nardit, pa pokazat sam te ko so signifikantni
lbls2<-filter(kcpepg, (n_log_students_t_test_p_value_wt_ko > 2 & (n_students_t_test_difference_wt_ko > 1 | n_students_t_test_difference_wt_ko < -1)))## Use densCols() output to get density at each point
dr<-data.frame(kcpep$n_students_t_test_difference_wt_ko, kcpep$n_log_students_t_test_p_value_wt_ko)
x <- densCols(kcpep$n_students_t_test_difference_wt_ko, kcpep$n_log_students_t_test_p_value_wt_ko, colramp=colorRampPalette(c("black", "white")))
dr$dens <- col2rgb(x)[1,] + 1L
## Map densities to colors
cols <- colorRampPalette(c("#55a0fb","#4CC7B0", "#4cc7b0" ,"#b6e366", "#e5c36e", "#f94040"))(256)
dr$col <- cols[dr$dens]#tiff("Plotcolor_kidney_ConA2.tif",height = 12, width = 10, units = 'in', res = 2000)
plot(x=kcpep$n_students_t_test_difference_wt_ko, y=kcpep$n_log_students_t_test_p_value_wt_ko, cex=1, xlab=expression(italic(log[2])~Fold~change), ylab=expression(italic(-log[10])~P-value), xlim=c(-8,8), col=dr$col, pch=20)
abline(v=c(-1,1), h=-log10(0.01))
addTextLabels(lbls2$n_students_t_test_difference_wt_ko, lbls2$n_log_students_t_test_p_value_wt_ko, lbls2$gn_position, cex.label=0.88, col.label='black', col.background=NULL, avoidPoints=TRUE)
points(kcpepg$n_students_t_test_difference_wt_ko, kcpepg$n_log_students_t_test_p_value_wt_ko, col='#000000', type='p', pch=16, cex=0.9)
#dev.off()names(kcprot)[names(kcprot)== "t_majority_protein_i_ds"] <- "prot_names" #spremenimo ime stolpcev z accession numberji, da jih lahka zdruzimo po njih
names(kcpepg)[names(kcpepg)== "t_leading_razor_protein"] <- "prot_names"
kcpg<-merge(kcprot, kcpepg, by = "prot_names") #zdruzimo keri so gliko peptidi z njihovimi proteini
lbls3<-filter(kcpg, (n_log_welchs_t_test_p_value_wt_ko > 2 & (n_welchs_t_test_difference_wt_ko > 1 | n_welchs_t_test_difference_wt_ko < -1)))## Use densCols() output to get density at each point
dr<-data.frame(kcprot$n_welchs_t_test_difference_wt_ko, kcprot$n_log_welchs_t_test_p_value_wt_ko)
x <- densCols(kcprot$n_welchs_t_test_difference_wt_ko, kcprot$n_log_welchs_t_test_p_value_wt_ko, colramp=colorRampPalette(c("black", "white")))
dr$dens <- col2rgb(x)[1,] + 1L
## Map densities to colors
cols <- colorRampPalette(c("#55a0fb","#4CC7B0", "#4cc7b0" ,"#b6e366", "#e5c36e", "#f94040"))(256)
dr$col <- cols[dr$dens]#tiff("Plotcolor_kidney_ConA2.tif",height = 12, width = 10, units = 'in', res = 2000)
plot(x=kcprot$n_welchs_t_test_difference_wt_ko, y=kcprot$n_log_welchs_t_test_p_value_wt_ko, cex=1, xlab=expression(italic(log[2])~Fold~change), ylab=expression(italic(-log[10])~P-value), xlim=c(-8,8), col=dr$col, pch=20)
abline(v=c(-1,1), h=-log10(0.01))
addTextLabels(lbls3$n_welchs_t_test_difference_wt_ko, lbls3$n_log_welchs_t_test_p_value_wt_ko, lbls3$gn_position, cex.label=0.88, col.label='black', col.background=NULL, avoidPoints=TRUE)
points(kcpg$n_students_t_test_difference_wt_ko, kcpg$n_log_students_t_test_p_value_wt_ko, col='#000000', type='p', pch=16, cex=0.9)
points(kcpg$n_welchs_t_test_difference_wt_ko, kcpg$n_log_welchs_t_test_p_value_wt_ko, col='#000000', type='p', pch=16, cex=0.9)
#dev.off()Kidney WGA 14. 6. 2021
kwprot <- read_excel("Z:/TSever/Misc/glycoproteomics/kidney_wga_proteins.xlsx") # uvozimo podatke za proteine, izvozeno iz perseus tabele
kwpep <- read_excel("Z:/TSever/Misc/glycoproteomics/kidney_wga_peptides.xlsx") # uvozimo podatke za peptide, izvozeno iz perseus tabele
kwprot <- clean_names(kwprot)
kwpep <- clean_names(kwpep)#poiscemo kateri peptidi imajo konsenzusno zaporedje N[^P][STC]
kwpepg <- kwpep[grep("N[^P][STC]", kwpep$t_sequence),]
#pridobimo sekvence teh proteinov
#tega pol ne ponucamo
accessions_kwpepg <-kwpepg$t_leading_razor_protein
Sequences_Information_kw<-GetSequences(accessions_kwpepg, directorypath = 'L:/')## Please wait we are processing your accessions ...#nardimo objekte samo z sekvencami peptidov in samo z sekvencami v istem vrstnem redu
vectorofseq<-Sequences_Information_kw$Sequence
vectorofpep<-kwpepg$t_sequence# for loop ki napise lokacije vseh peptidov v sekvenci
#zacetek, konec, dolzino peptida in sekvenco peptida
#notr v data.frame df jih da
i=1
df = data.frame()
for (i in 1: length(vectorofpep)) {
toFind<-vectorofpep[i]
here<-vectorofseq[i]
output <- matchPattern(toFind, here)
output<-as.data.frame(output)
df=rbind(df,output)
i<-i+1
}#poiscemo, kje se prvic pojavi N X S/T/C v sekvenci peptida
my_string <- df$seq #define string
#find position of first occurrence of 'N X S/T/C '
position<-((str_locate(pattern = "N.[STC]", my_string)) -1) +df$start #str_locate poizce zacetek in konec
position <- position[-c((length(position)/2)+1 : length(position) )] #ker imamo zacetek in konec, pobrisemo konce
#poiscemo aminokislino na mestu position
aa<-substring(vectorofseq, position, position)
#aa<-aa[-c((length(aa)/2) : length(aa) )] #ker imamo zacetek in konec, pobrisemo konce#dodamo pozicije N -jev v kcpepg data.frame
kwpepg["position"]<-c(position)
#zdruzimo stoplca z gene name in position v nov stolpec, kjer je zdruzeno
kwpepg$gn_position <- paste(kwpepg$t_gene_names, kwpepg$position, sep = "-" )#dejmo se tole mal leps nardit, pa pokazat sam te ko so signifikantni
lbls2<-filter(kwpepg, (n_log_students_t_test_p_value_wt_ko > 2 & (n_students_t_test_difference_wt_ko > 1 | n_students_t_test_difference_wt_ko < -1)))## Use densCols() output to get density at each point
dr<-data.frame(kwpep$n_students_t_test_difference_wt_ko, kwpep$n_log_students_t_test_p_value_wt_ko)
x <- densCols(kwpep$n_students_t_test_difference_wt_ko, kwpep$n_log_students_t_test_p_value_wt_ko, colramp=colorRampPalette(c("black", "white")))
dr$dens <- col2rgb(x)[1,] + 1L
## Map densities to colors
cols <- colorRampPalette(c("#55a0fb","#4CC7B0", "#4cc7b0" ,"#b6e366", "#e5c36e", "#f94040"))(256)
dr$col <- cols[dr$dens]#tiff("Plotcolor_kidney_ConA2.tif",height = 12, width = 10, units = 'in', res = 2000)
plot(x=kwpep$n_students_t_test_difference_wt_ko, y=kwpep$n_log_students_t_test_p_value_wt_ko, cex=1, xlab=expression(italic(log[2])~Fold~change), ylab=expression(italic(-log[10])~P-value), xlim=c(-8,8), col=dr$col, pch=20)
abline(v=c(-1,1), h=-log10(0.01))
addTextLabels(lbls2$n_students_t_test_difference_wt_ko, lbls2$n_log_students_t_test_p_value_wt_ko, lbls2$gn_position, cex.label=0.88, col.label='black', col.background=NULL, avoidPoints=TRUE)
points(kwpepg$n_students_t_test_difference_wt_ko, kwpepg$n_log_students_t_test_p_value_wt_ko, col='#000000', type='p', pch=16, cex=0.9)
#dev.off()names(kwprot)[names(kwprot)== "t_majority_protein_i_ds"] <- "prot_names" #spremenimo ime stolpcev z accession numberji, da jih lahka zdruzimo po njih
names(kwpepg)[names(kwpepg)== "t_leading_razor_protein"] <- "prot_names"
kwpg<-merge(kwprot, kwpepg, by = "prot_names") #zdruzimo keri so gliko peptidi z njihovimi proteini
lbls3<-filter(kwpg, (n_log_students_t_test_p_value_wt_ko.x > 2 & (n_students_t_test_difference_wt_ko.x > 1 | n_students_t_test_difference_wt_ko.x < -1)))## Use densCols() output to get density at each point
dr<-data.frame(kwprot$n_students_t_test_difference_wt_ko, kwprot$n_log_students_t_test_p_value_wt_ko)
x <- densCols(kwprot$n_students_t_test_difference_wt_ko, kwprot$n_log_students_t_test_p_value_wt_ko, colramp=colorRampPalette(c("black", "white")))
dr$dens <- col2rgb(x)[1,] + 1L
## Map densities to colors
cols <- colorRampPalette(c("#55a0fb","#4CC7B0", "#4cc7b0" ,"#b6e366", "#e5c36e", "#f94040"))(256)
dr$col <- cols[dr$dens]#tiff("Plotcolor_kidney_ConA2.tif",height = 12, width = 10, units = 'in', res = 2000)
plot(x=kwprot$n_students_t_test_difference_wt_ko, y=kwprot$n_log_students_t_test_p_value_wt_ko, cex=1, xlab=expression(italic(log[2])~Fold~change), ylab=expression(italic(-log[10])~P-value), xlim=c(-8,8), col=dr$col, pch=20)
abline(v=c(-1,1), h=-log10(0.01))
addTextLabels(lbls3$n_students_t_test_difference_wt_ko.x, lbls3$n_log_students_t_test_p_value_wt_ko.x, lbls3$gn_position, cex.label=0.88, col.label='black', col.background=NULL, avoidPoints=TRUE)
points(kwpg$n_students_t_test_difference_wt_ko.x, kwpg$n_log_students_t_test_p_value_wt_ko.x, col='#000000', type='p', pch=16, cex=0.9)
#dev.off()Kidney ConA + WGA
((KO/WT)WGA/(KO/WT)ConA)e-1
#poiscemo kateri proteini so isti pri ConA in WGA
kcwt_mean <- rowMeans(kcprot[,1:3]) #povprecje wt od kidney conA
kcko_mean <- rowMeans(kcprot[,4:6])
kwwt_mean <- rowMeans(kwprot[,1:3])
kwko_mean <- rowMeans(kwprot[,4:6])
kw_ratio <-as.data.frame(kwko_mean/kwwt_mean) #zracunamo razmerje ko / wt za ((KO/WT)WGA/(KO/WT)ConA)e-1
kc_ratio <- as.data.frame(kcko_mean/kcwt_mean)
kw_ratio <-cbind(kw_ratio, prot_names = kwprot$prot_names) # dodamo accesion numberje
kw_ratio <-cbind(kw_ratio, gene_names = kwprot$t_gene_names)# dodamo accesion numberje
kc_ratio <-cbind(kc_ratio, prot_names = kcprot$prot_names)
kc_ratio <-cbind(kc_ratio, gene_names = kcprot$t_gene_names)
kwkc_ratio <- merge(kw_ratio, kc_ratio, by = "prot_names") # zdruzimo tabeli z razmerji, da dobimo, kateri so isti proteini v ConA in WGA #jih je 143 skupnih
k_ratio <- kwkc_ratio[,2]/kwkc_ratio[,4]
kwkc_ratio <- cbind(kwkc_ratio, ratio = k_ratio)
kwkc_ratio[order(kwkc_ratio$ratio),]## prot_names kwko_mean/kwwt_mean gene_names.x kcko_mean/kcwt_mean
## 17 P07724 0.9879545 Alb 1.2550516
## 76 Q3TCN2 0.9553904 Plbd2 1.1576327
## 136 Q9QWR8 1.0247777 Naga 1.2395989
## 96 Q80X19 1.0344967 Col14a1 1.2329237
## 82 Q60866 1.0039729 Pter 1.1400904
## 9 O88338 1.0062677 Cdh16 1.1366099
## 4 O08677 0.9727639 Kng1 1.0905051
## 10 O89017 0.7849635 Lgmn 0.8760891
## 7 O35295 1.0538098 Purb 1.1631792
## 1 A2ARV4 0.9965171 Lrp2 1.0977707
## 38 P20152 1.0173560 Vim 1.1138367
## 120 Q99LB7 0.9640948 Sardh 1.0552008
## 108 Q8R146 0.9958301 Apeh 1.0795551
## 115 Q91X72 0.9896533 Hpx 1.0694848
## 25 P13020 0.9928666 Gsn 1.0705698
## 105 Q8K0L3 0.9559687 Acsm2 1.0286056
## 69 Q00897 0.8490337 Serpina1d 0.9128690
## 41 P23953 0.9892532 Ces1c 1.0615658
## 125 Q9DBX3 0.9996846 Susd2 1.0716075
## 57 P51855 0.9838664 Gss 1.0545982
## 18 P07759 0.9692034 Serpina3k 1.0364934
## 92 Q6P5E4 0.9981430 Uggt1 1.0645184
## 39 P21614 0.9725143 Gc 1.0362754
## 94 Q78PY7 0.9445096 Snd1 1.0060781
## 90 Q62468 0.9781518 Vil1 1.0363452
## 14 P06281;P00796 0.9709177 Ren1;Ren2 1.0275636
## 122 Q9D051 0.9942156 Pdhb 1.0402195
## 130 Q9JIL4 0.9927476 Pdzk1 1.0368438
## 50 P29699 0.9645175 Ahsg 1.0051071
## 84 Q61147 0.9887061 Cp 1.0300435
## 54 P42669 1.0330132 Pura 1.0727478
## 47 P28825 0.9899860 Mep1a 1.0275969
## 128 Q9ET22 1.0167715 Dpp7 1.0512239
## 72 Q02053 1.0008372 Uba1 1.0334624
## 24 P11438 1.0195232 Lamp1 1.0516343
## 22 P10518 0.9984498 Alad 1.0285769
## 35 P19221 0.9578069 F2 0.9866030
## 6 O09159 1.0264222 Man2b1 1.0563053
## 111 Q8VCM7 0.9532918 Fgg 0.9799219
## 23 P10605 1.0040996 Ctsb 1.0313348
## 139 Q9WV92 0.9981262 Epb41l3 1.0222345
## 86 Q61702 0.9755605 Itih1 0.9965040
## 142 Q9Z0L8 1.3455019 Ggh 1.3729419
## 11 P01027 1.0165668 C3 1.0369860
## 106 Q8K2I4 1.0636696 Manba 1.0836513
## 129 Q9JHI5 0.9787855 Ivd 0.9971660
## 93 Q6ZQM8 0.9678428 Ugt1a7c 0.9857858
## 87 Q61703 0.9727369 Itih2 0.9899456
## 40 P23780 1.0561425 Glb1 1.0737701
## 52 P35486 0.9816811 Pdha1 0.9968941
## 112 Q8VDN2 1.0077316 Atp1a1 1.0228374
## 49 P29341 1.0482064 Pabpc1 1.0610897
## 97 Q80X90 1.0004789 Flnb 1.0117489
## 43 P26040 0.9892980 Ezr 0.9983557
## 85 Q61316 0.9972769 Hspa4 1.0029733
## 134 Q9JLB4 0.9988731 Cubn 1.0032368
## 101 Q8BJ64 1.0269744 Chdh 1.0278354
## 80 Q60597 0.9726489 Ogdh 0.9723290
## 29 P16406 1.0135383 Enpep 1.0126479
## 46 P28665 0.9579499 Mug1 0.9568437
## 140 Q9WVE8 1.0111681 Pacsin2 1.0086526
## 127 Q9ESB3 0.9554982 Hrg 0.9525474
## 116 Q91Y97 1.0013079 Aldob 0.9972383
## 28 P14211 1.0041994 Calr 0.9970556
## 37 P20060 1.0528283 Hexb 1.0435131
## 21 P10126;P62631 0.9914697 Eef1a1;Eef1a2 0.9823132
## 79 Q571E4 1.0048377 Galns 0.9953161
## 117 Q921I1 0.9869232 Tf 0.9771355
## 138 Q9WUU7 1.0449564 Ctsz 1.0333157
## 118 Q922R8 1.0014367 Pdia6 0.9896557
## 71 Q01279 0.9151521 Egfr 0.9042253
## 5 O08795 0.9917041 Prkcsh 0.9790994
## 33 P17047 1.0210657 Lamp2 1.0067325
## 143 Q9Z0N1;Q9Z0N2 0.9924551 Eif2s3x;Eif2s3y 0.9773706
## 73 Q03265 1.0080814 Atp5a1 0.9926229
## 67 P70441 1.0009905 Slc9a3r1 0.9817669
## 59 P51885 1.0217320 Lum 0.9997883
## 88 Q61838 0.9667710 A2m 0.9455529
## 141 Q9WVJ3 1.0148117 Cpq 0.9914167
## 12 P01029 0.9888494 C4b 0.9641355
## 104 Q8K0E8 0.9536515 Fgb 0.9293103
## 30 P16546 0.9906025 Sptan1 0.9651990
## 53 P35822 0.9757327 Ptprk 0.9485469
## 99 Q8BG07 1.1778260 Pld4 1.1447474
## 131 Q9JK53 1.0414649 Prelp 1.0084025
## 77 Q3U1J4 0.9995106 Ddb1 0.9669908
## 63 P58771 1.0083087 Tpm1 0.9754008
## 135 Q9QUM9 0.9921640 Psma6 0.9587254
## 126 Q9ES64 0.9908074 Ush1c 0.9568684
## 48 P28843 1.0100601 Dpp4 0.9727532
## 44 P26043 0.9584794 Rdx 0.9204213
## 26 P14094 1.0126132 Atp1b1 0.9717633
## 114 Q91X17 0.9952419 Umod 0.9543531
## 64 P63017 0.9957007 Hspa8 0.9496610
## 8 O70194 0.9975325 Eif3d 0.9510636
## 55 P50396 0.9948959 Gdi1 0.9462723
## 110 Q8VCI0 1.0725824 Plbd1 1.0156793
## 75 Q07456 0.9919188 Ambp 0.9388655
## 78 Q3ULD5 0.9816379 Mccc2 0.9273254
## 51 P32261 1.0120990 Serpinc1 0.9549416
## 60 P55302 0.9928389 Lrpap1 0.9363159
## 16 P06909 0.9914772 Cfh 0.9346397
## 61 P56480 0.9948366 Atp5b 0.9370248
## 102 Q8BSY0 1.0123216 Asph 0.9525013
## 15 P06683 0.9636498 C9 0.9037837
## 3 E9PV24 0.9634282 Fga 0.9024979
## 56 P50429 1.0149988 Arsb 0.9499063
## 137 Q9QXC1 0.9809546 Fetub 0.9174116
## 100 Q8BHN3 0.9924674 Ganab 0.9253881
## 133 Q9JL35 1.0323918 Hmgn5 0.9619524
## 107 Q8R086 0.9915330 Suox 0.9229719
## 91 Q6IRU2 1.0034869 Tpm4 0.9306871
## 132 Q9JKR6 1.0072260 Hyou1 0.9330855
## 109 Q8VC60 0.9788148 Glb1l 0.9046955
## 65 P63038 0.9959852 Hspd1 0.9184928
## 19 P08003 1.0049371 Pdia4 0.9252604
## 58 P51859 1.0114232 Hdgf 0.9309838
## 13 P01872 0.9654392 Ighm 0.8863319
## 20 P09103 0.9945847 P4hb 0.9105929
## 62 P57780 0.9952418 Actn4 0.9086966
## 42 P24270 0.9221474 Cat 0.8416837
## 81 Q60865 1.0341926 Caprin1 0.9436914
## 45 P27773 1.0020511 Pdia3 0.9119905
## 32 P16858 1.0088592 Gapdh 0.9148319
## 95 Q7TPR4 1.0031255 Actn1 0.9090974
## 113 Q91V76 0.9759533 <NA> 0.8839435
## 2 A6X935 1.1043762 Itih4 0.9997340
## 121 Q99MN9 0.9868384 Pccb 0.8930092
## 89 Q61847 0.9875850 Mep1b 0.8876292
## 36 P20029 1.0000141 Hspa5 0.8986691
## 124 Q9D2G2 1.0001155 Dlst 0.8954093
## 83 Q60928 1.0009879 Ggt1 0.8826287
## 68 P97449 1.0125880 Anpep 0.8897291
## 123 Q9D0G0 0.9959531 Mrps30 0.8742825
## 74 Q03734 0.9221660 Serpina3m 0.8095063
## 119 Q99L47 1.0016682 St13 0.8777669
## 27 P14206 1.0112615 Rpsa 0.8741934
## 66 P70158 1.0206071 Smpdl3a 0.8821818
## 31 P16675 0.9974871 Ctsa 0.8475441
## 103 Q8BTM8 1.0225643 Flna 0.8636848
## 98 Q8BFR4 1.0501233 Gns 0.8703726
## 34 P18242 1.0384339 Ctsd 0.8510612
## 70 Q00898 0.8997457 Serpina1e 0.7180666
## gene_names.y ratio
## 17 Alb 0.7871823
## 76 Plbd2 0.8252967
## 136 Naga 0.8267010
## 96 Col14a1 0.8390598
## 82 Pter 0.8806082
## 9 Cdh16 0.8853237
## 4 Kng1 0.8920306
## 10 Lgmn 0.8959859
## 7 Purb 0.9059737
## 1 Lrp2 0.9077644
## 38 Vim 0.9133798
## 120 Sardh 0.9136600
## 108 Apeh 0.9224449
## 115 Hpx 0.9253552
## 25 Gsn 0.9274189
## 105 Acsm2 0.9293831
## 69 Serpina1d 0.9300718
## 41 Ces1c 0.9318812
## 125 Susd2 0.9328832
## 57 Gss 0.9329301
## 18 Serpina3k 0.9350791
## 92 Uggt1 0.9376475
## 39 Gc 0.9384709
## 94 Snd1 0.9388035
## 90 Vil1 0.9438474
## 14 Ren1;Ren2 0.9448736
## 122 Pdhb 0.9557747
## 130 Pdzk1 0.9574707
## 50 Ahsg 0.9596167
## 84 Cp 0.9598683
## 54 Pura 0.9629600
## 47 Mep1a 0.9633992
## 128 Dpp7 0.9672264
## 72 Uba1 0.9684311
## 24 Lamp1 0.9694655
## 22 Alad 0.9707098
## 35 F2 0.9708129
## 6 Man2b1 0.9717097
## 111 Fgg 0.9728242
## 23 Ctsb 0.9735923
## 139 Epb41l3 0.9764161
## 86 Itih1 0.9789831
## 142 Ggh 0.9800137
## 11 C3 0.9803091
## 106 Manba 0.9815607
## 129 Ivd 0.9815672
## 93 Ugt1a7c 0.9817983
## 87 Itih2 0.9826166
## 40 Glb1 0.9835835
## 52 Pdha1 0.9847396
## 112 Atp1a1 0.9852315
## 49 Pabpc1 0.9878584
## 97 Flnb 0.9888609
## 43 Ezr 0.9909274
## 85 Hspa4 0.9943205
## 134 Cubn 0.9956504
## 101 Chdh 0.9991623
## 80 Ogdh 1.0003290
## 29 Enpep 1.0008793
## 46 Mug1 1.0011561
## 140 Pacsin2 1.0024939
## 127 Hrg 1.0030978
## 116 Aldob 1.0040809
## 28 Calr 1.0071649
## 37 Hexb 1.0089267
## 21 Eef1a1;Eef1a2 1.0093213
## 79 Galns 1.0095664
## 117 Tf 1.0100167
## 138 Ctsz 1.0112654
## 118 Pdia6 1.0119042
## 71 Egfr 1.0120842
## 5 Prkcsh 1.0128738
## 33 Lamp2 1.0142374
## 143 Eif2s3x;Eif2s3y 1.0154337
## 73 Atp5a1 1.0155734
## 67 Slc9a3r1 1.0195806
## 59 Lum 1.0219483
## 88 A2m 1.0224398
## 141 Cpq 1.0235976
## 12 C4b 1.0256332
## 104 Fgb 1.0261927
## 30 Sptan1 1.0263195
## 53 Ptprk 1.0286605
## 99 Pld4 1.0288959
## 131 Prelp 1.0327869
## 77 Ddb1 1.0336299
## 63 Tpm1 1.0337378
## 135 Psma6 1.0348782
## 126 Ush1c 1.0354688
## 48 Dpp4 1.0383519
## 44 Rdx 1.0413486
## 26 Atp1b1 1.0420369
## 114 Umod 1.0428445
## 64 Hspa8 1.0484802
## 8 Eif3d 1.0488599
## 55 Gdi1 1.0513844
## 110 Plbd1 1.0560247
## 75 Ambp 1.0565080
## 78 Mccc2 1.0585690
## 51 Serpinc1 1.0598543
## 60 Lrpap1 1.0603674
## 16 Cfh 1.0608123
## 61 Atp5b 1.0616972
## 102 Asph 1.0628033
## 15 C9 1.0662395
## 3 Fga 1.0675130
## 56 Arsb 1.0685252
## 137 Fetub 1.0692633
## 100 Ganab 1.0724877
## 133 Hmgn5 1.0732254
## 107 Suox 1.0742830
## 91 Tpm4 1.0782215
## 132 Hyou1 1.0794574
## 109 Glb1l 1.0819273
## 65 Hspd1 1.0843691
## 19 Pdia4 1.0861127
## 58 Hdgf 1.0864026
## 13 Ighm 1.0892525
## 20 P4hb 1.0922386
## 62 Actn4 1.0952410
## 42 Cat 1.0955985
## 81 Caprin1 1.0959013
## 45 Pdia3 1.0987517
## 32 Gapdh 1.1027809
## 95 Actn1 1.1034302
## 113 <NA> 1.1040901
## 2 Itih4 1.1046701
## 121 Pccb 1.1050709
## 89 Mep1b 1.1126099
## 36 Hspa5 1.1127723
## 124 Dlst 1.1169367
## 83 Ggt1 1.1340985
## 68 Anpep 1.1380858
## 123 Mrps30 1.1391662
## 74 Serpina3m 1.1391709
## 119 St13 1.1411550
## 27 Rpsa 1.1567937
## 66 Smpdl3a 1.1569125
## 31 Ctsa 1.1769147
## 103 Flna 1.1839554
## 98 Gns 1.2065216
## 34 Ctsd 1.2201636
## 70 Serpina1e 1.2530115chickens[order(chickens$feathers),]