1 Background

Kidney, Liver, Lung and Spleen samples from young and old Legumain knock out, wild type and old heterozygots mice were probed for relative gene expression of CHIA, CHIL3, EPX, MPO and SOD1 with qPCR (SYBR).

Above is a figure presenting relative expression of CHIA, EPX, MPO and SOD1 from Human protein atlas. Organs of interest are marked with blue square. Based on this info, we can expect CHIA to be expreseed in lungs, EPX in spleen and lungs, MPO plausibly in spleen and lungs and for SOD1 to appear in all probed organs

The quality of results is mostly in accordance with above listed expectations. Table below indicates which experiments are valid (✓) and which are not (X). Latter is due to no signal (No Ct), too high Ct values or too wide dispersion among technical replicates, all of which indicates to low aboundance of target gene in the sample. To clarify, only results with ✓ should be considerd further.

Gene/ Tissue	Kidney	Liver	Lung	Spleen
CHIA	X	X	✓	X
CHIL3	✓	X	✓	✓
EPX	X	X	X	✓
MPO	X	X	X	✓
SOD1	✓	✓	✓	✓

How to read: KO -> Knock out
WT -> Wild type
H -> Heterozygotes
ML -> Mladi
ST -> Stari

Exsample: KO_ML -> knock outs, mladi

Plots: different letter (and colour) indicate statistically significant difference between samples (eg.: sample marked with “a” is different from sample marked “b”, but it is not different from sample marked “ab”). \(\alpha\) (level of significance) is set to 0.05. On horizontal axis there are samples (different genotype and age of the mice) while on vertical axis there is fold difference in expression. In this file, fold differences are not logarithmised.

Note, expression is calculated relevant to WT_ML (young wild types).

2 Kidney

#uvozimo podatke

kid<- read_excel("F:/misc/qPCR/qPCR_mouse/qPCR_stare_nove.xlsx", 
    sheet = "Kid")
#View(kid)

##  [1] "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO"
## [16] "KO" "KO" "KO" "WT" "WT" "WT" "WT" "WT" "WT" "WT" "WT" "WT" "WT" "WT" "WT"
## [31] "WT" "WT" "WT" "WT" "WT" "WT" "HT" "HT" "HT" "HT" "HT" "HT" "HT" "HT" "HT"

##  [1] "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ST" "ST" "ST" "ST" "ST" "ST"
## [16] "ST" "ST" "ST" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ST" "ST" "ST"
## [31] "ST" "ST" "ST" "ST" "ST" "ST" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML"

Urejanje podatkov povprecimo posamezne skupine, normalizacija na standard (B2M)

#povprecja za ATP5B, za vsako skupino posebaj

meanATP5B_KM<- mean(kid1[1:2,7]) #povprecje za ATP5B, za KO_ML
meanATP5B_WM<- mean(kid1[3:4,7]) #in tako dalje
meanATP5B_KS<- mean(kid1[5:6,7])
meanATP5B_WS<- mean(kid1[7:8,7])
meanATP5B_H<- mean(kid1[9:10,7])


#povprecja za B2M, za vsako skupino posebaj
meanB2M_KM<- mean(kid1[1:2,8]) #povprecje za B2M, za KO_ML
meanB2M_WM<- mean(kid1[3:4,8]) #in tako dalje
meanB2M_KS<- mean(kid1[5:6,8])
meanB2M_WS<- mean(kid1[7:8,8])
meanB2M_H<- mean(kid1[9:10,8])

meanATP5B_KM

## [1] 19.635

meanB2M_KM

## [1] 23.43

#povprecja za testne vzorce z aggregate
#povprecijo se tehnicne ponovitve

aggregate(kid1[, 2], list(Group=kid1$Group), mean)

##    Group        x
## 1     H1 29.61333
## 2     H2 29.34000
## 3     H3 29.57333
## 4    KM1 29.35000
## 5    KM2 28.25000
## 6    KM3 29.62000
## 7    KS1 28.67000
## 8    KS2 28.42000
## 9    KS3 29.42333
## 10   WM1 29.51000
## 11   WM2 29.29333
## 12   WM3 29.17667
## 13   WS1 29.04667
## 14   WS2 29.80000
## 15   WS3 28.37667

#povprecja za testne vzorce z ddply paketom, lahko bi tut z aggregate (in to bi blo lazje)

kid3<-ddply(kid1, .(Group), summarize,  CHIA=mean(CHIA), CHIL3=mean(CHIL3), EPX=mean(EPX), MPO=mean(MPO), SOD1=mean(SOD1))
row.names(kid3) <- c(kid3$Group) #imena vrstic spremenimo iz stevilk v imena skupin

#kid3 = kid3[seq(1, nrow(kid3), 3), ]

\(\Delta\)Ct = Ct (gene of interest ) - Ct (housekeeping gene)

#zbrisemo stolpec z imeni skupin (Group)


kid3$Group<-NULL

#??Ct zracunamo na B2M

dCtH<- kid3[c('H1',"H2", "H3"), ] - meanB2M_H
dCtKM<- kid3[c('KM1',"KM2", "KM3"), ] - meanB2M_KM
dCtKS<- kid3[c('KS1',"KS2", "KS3"), ] - meanB2M_KS
dCtWM<- kid3[c('WM1',"WM2", "WM3"), ] - meanB2M_WM
dCtWS<- kid3[c('WS1',"WS2", "WS3"), ] - meanB2M_WS

dCtH

##        CHIA    CHIL3      EPX      MPO      SOD1
## H1 4.988333 6.245000 5.971667 13.93833 -0.215000
## H2 4.715000 5.888333 5.625000 15.37500 -2.328333
## H3 4.948333 5.958333 5.745000 15.37500 -2.328333

\(\Delta\Delta\)Ct = \(\Delta\)Ct (sample) - \(\Delta\)Ct (control average) control average je WM -> ekspresijo zracunamo relativno na WM

#povprecje $\Delta$Ct od bioloskih ponovitev

dCtHm <-colMeans(dCtH)
dCtKMm <-colMeans(dCtKM)
dCtKSm <-colMeans(dCtKS)
dCtWMm <-colMeans(dCtWM)
dCtWSm <-colMeans(dCtWS)

dCtHm

##      CHIA     CHIL3       EPX       MPO      SOD1 
##  4.883889  6.030556  5.780556 14.896111 -1.623889

#malo preoblikujemo dCtWMm, da bo ustrezne oblike data.frame za odstevanje v naslednjem koraku

dCtWMm<-as.data.frame(dCtWMm)
dCtWMm<-t(dCtWMm)
dCtWMm<-as.data.frame(dCtWMm)
dCtWMm<-dCtWMm[rep(seq_len(nrow(dCtWMm)), each = 3), ]
row.names(dCtWMm)<-c("WM1","WM2","WM3")

#$\Delta\Delta$Ct

ddCtH<- dCtH-dCtWMm
ddCtKM<- dCtKM-dCtWMm
ddCtKS<- dCtKS-dCtWMm
ddCtWM<- dCtWM-dCtWMm
ddCtWS<- dCtWS-dCtWMm

fold gene gxpression relative to WM 2^-(\(\Delta\Delta\)Ct)

H<- 2^-ddCtH
KM<- 2^-ddCtKM
KS<- 2^-ddCtKS
WM<- 2^-ddCtWM
WS<- 2^-ddCtWS

#zdruzimo data.frame
lg<-rbind( H,KM,KS,WM, WS)


lg$Age<- c("ML","ML","ML","ML","ML","ML","ST","ST","ST","ML","ML","ML","ST","ST","ST")
lg$Genotype <-c("H","H","H","KO","KO","KO","KO","KO","KO","WT","WT","WT","WT","WT","WT")
lg$AG<- c("H","H","H","KO_ML","KO_ML","KO_ML","KO_ST","KO_ST","KO_ST","WT_ML","WT_ML","WT_ML","WT_ST","WT_ST","WT_ST")

lg

##           CHIA      CHIL3        EPX        MPO      SOD1 Age Genotype    AG
## H1   1.3088805  3.1882788  3.4461846  4.3219029 0.7836782  ML        H     H
## H2   1.5819091  4.0824761  4.3822344  1.5965968 3.3908963  ML        H     H
## H3   1.3456781  3.8891219  4.0324780  1.5965968 3.3908963  ML        H     H
## KM1  0.6861837  0.5668784  0.8745827  0.6973718 0.4774208  ML       KO KO_ML
## KM2  1.4708669  1.0360626  1.7131680 16.0000000 0.7439788  ML       KO KO_ML
## KM3  0.5690655  0.5655701  0.8970954  0.6973718 0.2126668  ML       KO KO_ML
## KS1 12.0558935 20.0580731  4.3822344  7.6475625 0.6484198  ST       KO KO_ST
## KS2 14.3369543  9.1436981  1.9207459 18.7436931 1.1741906  ST       KO KO_ST
## KS3  7.1519336 35.6569429 40.5510303 25.3105450 2.7479073  ST       KO KO_ST
## WM1  0.8806659  0.6188998  0.3634931  1.0000000 0.8506672  ML       WT WT_ML
## WM2  1.0233739  0.5642649  0.5677522  1.0000000 0.5396141  ML       WT WT_ML
## WM3  1.1095695  2.8634964  4.8455709  1.0000000 2.1784973  ML       WT WT_ML
## WS1  1.6993700  1.7647263  1.4028233  1.3995859 1.4356128  ST       WT WT_ST
## WS2  1.0081195  1.9001471  2.3538135  5.6896241 1.1796291  ST       WT WT_ST
## WS3  2.7038217  3.0796743  1.5035088  1.3995859 0.9850940  ST       WT WT_ST

#izvozimo v excel
write_xlsx(lg, "F:/misc/qPCR/lg_kid.xlsx")

ANOVA

2.0.1 CHIA

preverjamo kombiniran vpliv starosti in genotipa

interaction <- aov(CHIA ~ Age*Genotype, data = lg)

summary(interaction)

##              Df Sum Sq Mean Sq F value   Pr(>F)    
## Age           1 104.36  104.36   36.03 0.000132 ***
## Genotype      2  65.03   32.52   11.23 0.002778 ** 
## Age:Genotype  1  67.31   67.31   23.24 0.000701 ***
## Residuals    10  28.96    2.90                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

razlika med posameznimi vzorci, vse je glede na WM

test<-aov(CHIA~AG, data=lg)
summary(test)

##             Df Sum Sq Mean Sq F value  Pr(>F)    
## AG           4 236.71   59.18   20.43 8.4e-05 ***
## Residuals   10  28.96    2.90                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

2.0.1.1 post-hoc

tukey<-TukeyHSD(test)

tukey

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = CHIA ~ AG, data = lg)
## 
## $AG
##                     diff        lwr       upr     p adj
## KO_ML-H      -0.50345052  -5.076708  4.069807 0.9956956
## KO_ST-H       9.76943789   5.196180 14.342696 0.0002683
## WT_ML-H      -0.40761946  -4.980877  4.165638 0.9980961
## WT_ST-H       0.39161452  -4.181643  4.964872 0.9983711
## KO_ST-KO_ML  10.27288841   5.699631 14.846146 0.0001759
## WT_ML-KO_ML   0.09583107  -4.477427  4.669089 0.9999939
## WT_ST-KO_ML   0.89506504  -3.678193  5.468323 0.9639090
## WT_ML-KO_ST -10.17705735 -14.750315 -5.603800 0.0001904
## WT_ST-KO_ST  -9.37782337 -13.951081 -4.804566 0.0003763
## WT_ST-WT_ML   0.79923398  -3.774024  5.372492 0.9758572

plot(duncan.test(test,'AG', alpha = 0.05, console=TRUE))

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for CHIA 
## 
## Mean Square Error:  2.896444 
## 
## AG,  means
## 
##             CHIA       std r       Min       Max
## H      1.4121559 0.1481574 3 1.3088805  1.581909
## KO_ML  0.9087053 0.4903553 3 0.5690655  1.470867
## KO_ST 11.1815938 3.6714344 3 7.1519336 14.336954
## WT_ML  1.0045364 0.1156086 3 0.8806659  1.109569
## WT_ST  1.8037704 0.8526582 3 1.0081195  2.703822
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 3.096201 3.235502 3.317499 3.369951 
## 
## Means with the same letter are not significantly different.
## 
##             CHIA groups
## KO_ST 11.1815938      a
## WT_ST  1.8037704      b
## H      1.4121559      b
## WT_ML  1.0045364      b
## KO_ML  0.9087053      b

ggplot(lg, aes(x=AG, y=CHIA, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Kidney CHIA")

2.0.2 CHIL3

razlika med posameznimi vzorci, vse je glede na WM

test<-aov(CHIL3~AG, data=lg)
summary(test)

##             Df Sum Sq Mean Sq F value  Pr(>F)   
## AG           4  938.1  234.53   6.511 0.00758 **
## Residuals   10  360.2   36.02                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

2.0.2.1 post-hoc

tukey<-TukeyHSD(test)

tukey

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = CHIL3 ~ AG, data = lg)
## 
## $AG
##                    diff        lwr       upr     p adj
## KO_ML-H      -2.9971219 -19.124816 13.130573 0.9699321
## KO_ST-H      17.8996124   1.771918 34.027307 0.0285265
## WT_ML-H      -2.3710719 -18.498766 13.756623 0.9871656
## WT_ST-H      -1.4717764 -17.599471 14.655918 0.9979134
## KO_ST-KO_ML  20.8967344   4.769040 37.024429 0.0111870
## WT_ML-KO_ML   0.6260500 -15.501644 16.753744 0.9999283
## WT_ST-KO_ML   1.5253456 -14.602349 17.653040 0.9976029
## WT_ML-KO_ST -20.2706843 -36.398379 -4.142990 0.0135721
## WT_ST-KO_ST -19.3713888 -35.499083 -3.243694 0.0179567
## WT_ST-WT_ML   0.8992955 -15.228399 17.026990 0.9996983

plot(duncan.test(test,'AG', alpha = 0.05, console=TRUE))

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for CHIL3 
## 
## Mean Square Error:  36.02122 
## 
## AG,  means
## 
##           CHIL3        std r       Min       Max
## H      3.719959  0.4704884 3 3.1882788  4.082476
## KO_ML  0.722837  0.2712621 3 0.5655701  1.036063
## KO_ST 21.619571 13.3254172 3 9.1436981 35.656943
## WT_ML  1.348887  1.3119746 3 0.5642649  2.863496
## WT_ST  2.248183  0.7232694 3 1.7647263  3.079674
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 10.91882 11.41007 11.69923 11.88421 
## 
## Means with the same letter are not significantly different.
## 
##           CHIL3 groups
## KO_ST 21.619571      a
## H      3.719959      b
## WT_ST  2.248183      b
## WT_ML  1.348887      b
## KO_ML  0.722837      b

ggplot(lg, aes(x=AG, y=CHIL3, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Kidney CHIL3")

2.0.3 EPX

razlika med posameznimi vzorci, vse je glede na WM

test<-aov(EPX~AG, data=lg)
summary(test)

##             Df Sum Sq Mean Sq F value Pr(>F)
## AG           4  445.5  111.37   1.173  0.379
## Residuals   10  949.8   94.98

2.0.3.1 post-hoc

tukey<-TukeyHSD(test)

tukey

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = EPX ~ AG, data = lg)
## 
## $AG
##                    diff       lwr      upr     p adj
## KO_ML-H      -2.7920170 -28.98006 23.39603 0.9961938
## KO_ST-H      11.6643712 -14.52367 37.85241 0.6038388
## WT_ML-H      -2.0280269 -28.21607 24.16002 0.9989002
## WT_ST-H      -2.2002505 -28.38829 23.98779 0.9984876
## KO_ST-KO_ML  14.4563882 -11.73166 40.64443 0.4155995
## WT_ML-KO_ML   0.7639901 -25.42405 26.95203 0.9999770
## WT_ST-KO_ML   0.5917665 -25.59628 26.77981 0.9999917
## WT_ML-KO_ST -13.6923981 -39.88044 12.49565 0.4641835
## WT_ST-KO_ST -13.8646217 -40.05267 12.32342 0.4529857
## WT_ST-WT_ML  -0.1722236 -26.36027 26.01582 0.9999999

plot(duncan.test(test,'AG', alpha = 0.05, console=TRUE))

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for EPX 
## 
## Mean Square Error:  94.97732 
## 
## AG,  means
## 
##             EPX        std r       Min       Max
## H      3.953632  0.4729797 3 3.4461846  4.382234
## KO_ML  1.161615  0.4777912 3 0.8745827  1.713168
## KO_ST 15.618004 21.6276813 3 1.9207459 40.551030
## WT_ML  1.925605  2.5308258 3 0.3634931  4.845571
## WT_ST  1.753382  0.5224203 3 1.4028233  2.353813
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 17.72991 18.52759 18.99714 19.29750 
## 
## Means with the same letter are not significantly different.
## 
##             EPX groups
## KO_ST 15.618004      a
## H      3.953632      a
## WT_ML  1.925605      a
## WT_ST  1.753382      a
## KO_ML  1.161615      a

ggplot(lg, aes(x=AG, y=EPX, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Kidney EPX")

2.0.4 MPO

razlika med posameznimi vzorci, vse je glede na WM podatki niso pravi, MPO se ni izrazal

test<-aov(MPO~AG, data=lg)
summary(test)

##             Df Sum Sq Mean Sq F value Pr(>F)  
## AG           4  520.3  130.07   3.909 0.0366 *
## Residuals   10  332.7   33.27                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

2.0.4.1 post-hoc

tukey<-TukeyHSD(test)

tukey

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = MPO ~ AG, data = lg)
## 
## $AG
##                    diff         lwr       upr     p adj
## KO_ML-H       3.2932157 -12.2073808 18.793812 0.9520479
## KO_ST-H      14.7289014  -0.7716952 30.229498 0.0644530
## WT_ML-H      -1.5050322 -17.0056287 13.995564 0.9973463
## WT_ST-H       0.3245664 -15.1760301 15.825163 0.9999939
## KO_ST-KO_ML  11.4356857  -4.0649109 26.936282 0.1847257
## WT_ML-KO_ML  -4.7982479 -20.2988445 10.702349 0.8412892
## WT_ST-KO_ML  -2.9686493 -18.4692459 12.531947 0.9665554
## WT_ML-KO_ST -16.2339335 -31.7345301 -0.733337 0.0392604
## WT_ST-KO_ST -14.4043349 -29.9049315  1.096262 0.0716903
## WT_ST-WT_ML   1.8295986 -13.6709980 17.330195 0.9943817

plot(duncan.test(test,'AG', alpha = 0.05, console=TRUE))

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for MPO 
## 
## Mean Square Error:  33.27443 
## 
## AG,  means
## 
##             MPO      std r       Min       Max
## H      2.505032 1.573456 3 1.5965968  4.321903
## KO_ML  5.798248 8.834976 3 0.6973718 16.000000
## KO_ST 17.233934 8.927753 3 7.6475625 25.310545
## WT_ML  1.000000 0.000000 3 1.0000000  1.000000
## WT_ST  2.829599 2.476855 3 1.3995859  5.689624
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 10.49426 10.96641 11.24433 11.42211 
## 
## Means with the same letter are not significantly different.
## 
##             MPO groups
## KO_ST 17.233934      a
## KO_ML  5.798248      b
## WT_ST  2.829599      b
## H      2.505032      b
## WT_ML  1.000000      b

ggplot(lg, aes(x=AG, y=MPO, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Kidney MPO")

2.0.5 SOD1

razlika med posameznimi vzorci, vse je glede na WM

test<-aov(SOD1~AG, data=lg)
summary(test)

##             Df Sum Sq Mean Sq F value Pr(>F)
## AG           4  6.619  1.6549   1.907  0.186
## Residuals   10  8.677  0.8677

2.0.5.1 post-hoc

tukey<-TukeyHSD(test)

tukey

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = SOD1 ~ AG, data = lg)
## 
## $AG
##                    diff       lwr       upr     p adj
## KO_ML-H     -2.04380150 -4.546931 0.4593277 0.1261792
## KO_ST-H     -0.99831771 -3.501447 1.5048115 0.6903003
## WT_ML-H     -1.33223075 -3.835360 1.1708984 0.4482590
## WT_ST-H     -1.32171164 -3.824841 1.1814175 0.4553614
## KO_ST-KO_ML  1.04548379 -1.457645 3.5486130 0.6554885
## WT_ML-KO_ML  0.71157075 -1.791558 3.2146999 0.8767173
## WT_ST-KO_ML  0.72208985 -1.781039 3.2252190 0.8711221
## WT_ML-KO_ST -0.33391304 -2.837042 2.1692161 0.9910728
## WT_ST-KO_ST -0.32339394 -2.826523 2.1797352 0.9920864
## WT_ST-WT_ML  0.01051911 -2.492610 2.5136483 1.0000000

plot(duncan.test(test,'AG', alpha = 0.05, console=TRUE))

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for SOD1 
## 
## Mean Square Error:  0.8677214 
## 
## AG,  means
## 
##            SOD1       std r       Min       Max
## H     2.5218236 1.5052781 3 0.7836782 3.3908963
## KO_ML 0.4780221 0.2656565 3 0.2126668 0.7439788
## KO_ST 1.5235059 1.0924641 3 0.6484198 2.7479073
## WT_ML 1.1895929 0.8704238 3 0.5396141 2.1784973
## WT_ST 1.2001120 0.2259567 3 0.9850940 1.4356128
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 1.694676 1.770921 1.815801 1.844511 
## 
## Means with the same letter are not significantly different.
## 
##            SOD1 groups
## H     2.5218236      a
## KO_ST 1.5235059     ab
## WT_ST 1.2001120     ab
## WT_ML 1.1895929     ab
## KO_ML 0.4780221      b

ggplot(lg, aes(x=AG, y=SOD1, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Kidney SOD1")

3 Liver

#uvozimo podatke

liv<- read_excel("F:/misc/qPCR/qPCR_mouse/qPCR_stare_nove.xlsx", 
    sheet = "Liv")

##  [1] "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO"
## [16] "KO" "KO" "KO" "WT" "WT" "WT" "WT" "WT" "WT" "WT" "WT" "WT" "WT" "WT" "WT"
## [31] "WT" "WT" "WT" "WT" "WT" "WT" "HT" "HT" "HT" "HT" "HT" "HT" "HT" "HT" "HT"

##  [1] "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ST" "ST" "ST" "ST" "ST" "ST"
## [16] "ST" "ST" "ST" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ST" "ST" "ST"
## [31] "ST" "ST" "ST" "ST" "ST" "ST" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML"

Urejanje podatkov povprecimo posamezne skupine, normalizacija na standard (B2M)

#povprecja za ATP5B, za vsako skupino posebaj

meanATP5B_KM<- mean(liv1[1:2,7]) #povprecje za ATP5B, za KO_ML
meanATP5B_WM<- mean(liv1[3:4,7]) #in tako dalje
meanATP5B_KS<- mean(liv1[5:6,7])
meanATP5B_WS<- mean(liv1[7:8,7])
meanATP5B_H<- mean(liv1[9:10,7])


#povprecja za B2M, za vsako skupino posebaj
meanB2M_KM<- mean(liv1[1:2,8]) #povprecje za B2M, za KO_ML
meanB2M_WM<- mean(liv1[3:4,8]) #in tako dalje
meanB2M_KS<- mean(liv1[5:6,8])
meanB2M_WS<- mean(liv1[7:8,8])
meanB2M_H<- mean(liv1[9:10,8])

#povprecja za testne vzorce z aggregate

aggregate(liv1[, 2], list(Group=liv1$Group), mean)

##    Group        x
## 1     H1 32.02000
## 2     H2 32.13333
## 3     H3 32.99667
## 4    KM1 33.70333
## 5    KM2 33.38333
## 6    KM3 34.76000
## 7    KS1 32.96000
## 8    KS2 33.20000
## 9    KS3 32.40667
## 10   WM1 34.59333
## 11   WM2 34.45667
## 12   WM3 33.61333
## 13   WS1 33.70667
## 14   WS2 33.84333
## 15   WS3 33.58667

#povprecja za testne vzorce z ddply paketom, lahko bi tut z aggregate (in to bi blo lazje)

liv3<-ddply(liv1, .(Group), summarize,  CHIA=mean(CHIA), CHIL3=mean(CHIL3), EPX=mean(EPX), MPO=mean(MPO), SOD1=mean(SOD1))
row.names(liv3) <- c(liv3$Group) #imena vrstic spremenimo iz stevilk v imena skupin

#liv3 = liv3[seq(1, nrow(liv3), 3), ]

\(\Delta\)Ct = Ct (gene of interest ) - Ct (housekeeping gene)

#zbrisemo stolpec z imeni skupin (Group)


liv3$Group<-NULL

#??Ct zracunamo na B2M

dCtH<- kid3[c('H1',"H2", "H3"), ] - meanB2M_H
dCtKM<- kid3[c('KM1',"KM2", "KM3"), ] - meanB2M_KM
dCtKS<- kid3[c('KS1',"KS2", "KS3"), ] - meanB2M_KS
dCtWM<- kid3[c('WM1',"WM2", "WM3"), ] - meanB2M_WM
dCtWS<- kid3[c('WS1',"WS2", "WS3"), ] - meanB2M_WS

\(\Delta\Delta\)Ct = \(\Delta\)Ct (sample) - \(\Delta\)Ct (control average) control average je WM, ekspresijo zracunamo relativno na WM

#povprecje ??Ct od bioloskih ponovitev

dCtHm <-colMeans(dCtH)
dCtKMm <-colMeans(dCtKM)
dCtKSm <-colMeans(dCtKS)
dCtWMm <-colMeans(dCtWM)
dCtWSm <-colMeans(dCtWS)

#malo preoblikujemo dCtWMm, da bo ustrezne oblike data.frame za odstevanje v naslednjem koraku

dCtWMm<-as.data.frame(dCtWMm)
dCtWMm<-t(dCtWMm)
dCtWMm<-as.data.frame(dCtWMm)
dCtWMm<-dCtWMm[rep(seq_len(nrow(dCtWMm)), each = 3), ]
row.names(dCtWMm)<-c("WM1","WM2","WM3")

#$\Delta\Delta$Ct

ddCtH<- dCtH-dCtWMm
ddCtKM<- dCtKM-dCtWMm
ddCtKS<- dCtKS-dCtWMm
ddCtWM<- dCtWM-dCtWMm
ddCtWS<- dCtWS-dCtWMm

fold gene gxpression relative to WM 2^-(\(\Delta\Delta\)Ct)

H<- 2^-ddCtH
KM<- 2^-ddCtKM
KS<- 2^-ddCtKS
WM<- 2^-ddCtWM
WS<- 2^-ddCtWS

#zdruzimo data.frame
lg<-rbind( H,KM,KS,WM, WS)


lg$Age<- c("ML","ML","ML","ML","ML","ML","ST","ST","ST","ML","ML","ML","ST","ST","ST")
lg$Genotype <-c("H","H","H","KO","KO","KO","KO","KO","KO","WT","WT","WT","WT","WT","WT")
lg$AG<- c("H","H","H","KO_ML","KO_ML","KO_ML","KO_ST","KO_ST","KO_ST","WT_ML","WT_ML","WT_ML","WT_ST","WT_ST","WT_ST")

#izvozimo v excel
write_xlsx(lg, "F:/misc/qPCR/lg_liv.xlsx")

##ANOVA

3.0.1 CHIA

preverjamo kombiniran vpliv starosti in genotipa

interaction <- aov(CHIA ~ Age*Genotype, data = lg)

summary(interaction)

##              Df Sum Sq Mean Sq F value   Pr(>F)    
## Age           1  89.31   89.31   33.25 0.000181 ***
## Genotype      2  58.96   29.48   10.98 0.003002 ** 
## Age:Genotype  1  30.51   30.51   11.36 0.007117 ** 
## Residuals    10  26.86    2.69                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

razlika med posameznimi vzorci, vse je glede na WM

test<-aov(CHIA~AG, data=lg)
summary(test)

##             Df Sum Sq Mean Sq F value   Pr(>F)    
## AG           4 178.77   44.69   16.64 0.000203 ***
## Residuals   10  26.86    2.69                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

3.0.1.1 post-hoc

tukey<-TukeyHSD(test)

tukey

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = CHIA ~ AG, data = lg)
## 
## $AG
##                   diff        lwr       upr     p adj
## KO_ML-H      1.6515145  -2.752235  6.055264 0.7333737
## KO_ST-H      9.4634191   5.059670 13.867168 0.0002553
## WT_ML-H      0.4947731  -3.908976  4.898522 0.9953462
## WT_ST-H      1.9287735  -2.474976  6.332523 0.6176639
## KO_ST-KO_ML  7.8119046   3.408156 12.215654 0.0011968
## WT_ML-KO_ML -1.1567413  -5.560490  3.247008 0.9034916
## WT_ST-KO_ML  0.2772590  -4.126490  4.681008 0.9995126
## WT_ML-KO_ST -8.9686460 -13.372395 -4.564897 0.0003980
## WT_ST-KO_ST -7.5346456 -11.938395 -3.130897 0.0015794
## WT_ST-WT_ML  1.4340003  -2.969749  5.837749 0.8167033

plot(duncan.test(test,'AG', alpha = 0.05, console=TRUE))

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for CHIA 
## 
## Mean Square Error:  2.685709 
## 
## AG,  means
## 
##            CHIA        std r       Min        Max
## H     0.5097633 0.05348221 3 0.4724827  0.5710412
## KO_ML 2.1612777 1.16626808 3 1.3534735  3.4983307
## KO_ST 9.9731824 3.27465706 3 6.3790135 12.7875384
## WT_ML 1.0045364 0.11560862 3 0.8806659  1.1095695
## WT_ST 2.4385367 1.15271788 3 1.3628877  3.6553258
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 2.981440 3.115577 3.194535 3.245043 
## 
## Means with the same letter are not significantly different.
## 
##            CHIA groups
## KO_ST 9.9731824      a
## WT_ST 2.4385367      b
## KO_ML 2.1612777      b
## WT_ML 1.0045364      b
## H     0.5097633      b

ggplot(lg, aes(x=AG, y=CHIA, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Liver CHIA")

3.0.2 CHIL3

razlika med posameznimi vzorci, vse je glede na WM

test<-aov(CHIL3~AG, data=lg)
summary(test)

##             Df Sum Sq Mean Sq F value  Pr(>F)   
## AG           4  734.2  183.54   6.356 0.00823 **
## Residuals   10  288.8   28.88                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

3.0.2.1 post-hoc

tukey<-TukeyHSD(test)

tukey

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = CHIL3 ~ AG, data = lg)
## 
## $AG
##                      diff        lwr       upr     p adj
## KO_ML-H       0.376366506 -14.063612 14.816345 0.9999854
## KO_ST-H      17.940272958   3.500294 32.380251 0.0145921
## WT_ML-H       0.006047697 -14.433931 14.446026 1.0000000
## WT_ST-H       1.696503040 -12.743475 16.136482 0.9944804
## KO_ST-KO_ML  17.563906452   3.123928 32.003885 0.0166321
## WT_ML-KO_ML  -0.370318809 -14.810297 14.069660 0.9999863
## WT_ST-KO_ML   1.320136534 -13.119842 15.760115 0.9978987
## WT_ML-KO_ST -17.934225261 -32.374204 -3.494247 0.0146227
## WT_ST-KO_ST -16.243769918 -30.683748 -1.803791 0.0264219
## WT_ST-WT_ML   1.690455343 -12.749523 16.130434 0.9945550

plot(duncan.test(test,'AG', alpha = 0.05, console=TRUE))

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for CHIL3 
## 
## Mean Square Error:  28.87666 
## 
## AG,  means
## 
##           CHIL3        std r       Min       Max
## H      1.342839  0.1698380 3 1.1509122  1.473702
## KO_ML  1.719206  0.6451736 3 1.3451600  2.464186
## KO_ST 19.283112 11.8853197 3 8.1555251 31.803444
## WT_ML  1.348887  1.3119746 3 0.5642649  2.863496
## WT_ST  3.039342  0.9777957 3 2.3857526  4.163445
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
##  9.77620 10.21604 10.47494 10.64056 
## 
## Means with the same letter are not significantly different.
## 
##           CHIL3 groups
## KO_ST 19.283112      a
## WT_ST  3.039342      b
## KO_ML  1.719206      b
## WT_ML  1.348887      b
## H      1.342839      b

ggplot(lg, aes(x=AG, y=CHIL3, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Liver CHIL3")

3.0.3 EPX

razlika med posameznimi vzorci, vse je glede na WM

test<-aov(EPX~AG, data=lg)
summary(test)

##             Df Sum Sq Mean Sq F value Pr(>F)
## AG           4  337.6   84.41    1.11  0.404
## Residuals   10  760.7   76.07

3.0.3.1 post-hoc

tukey<-TukeyHSD(test)

tukey

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = EPX ~ AG, data = lg)
## 
## $AG
##                    diff       lwr      upr     p adj
## KO_ML-H       1.3356112 -22.10102 24.77224 0.9996712
## KO_ST-H      12.5029515 -10.93368 35.93958 0.4461525
## WT_ML-H       0.4984141 -22.93822 23.93504 0.9999935
## WT_ST-H       0.9432247 -22.49340 24.37985 0.9999173
## KO_ST-KO_ML  11.1673403 -12.26929 34.60397 0.5464475
## WT_ML-KO_ML  -0.8371971 -24.27383 22.59943 0.9999485
## WT_ST-KO_ML  -0.3923866 -23.82902 23.04424 0.9999975
## WT_ML-KO_ST -12.0045374 -35.44117 11.43209 0.4825715
## WT_ST-KO_ST -11.5597268 -34.99636 11.87690 0.5161470
## WT_ST-WT_ML   0.4448105 -22.99182 23.88144 0.9999959

plot(duncan.test(test,'AG', alpha = 0.05, console=TRUE))

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for EPX 
## 
## Mean Square Error:  76.06837 
## 
## AG,  means
## 
##             EPX        std r       Min       Max
## H      1.427191  0.1707373 3 1.2440117  1.581909
## KO_ML  2.762803  1.1363855 3 2.0801199  4.074623
## KO_ST 13.930143 19.2903458 3 1.7131680 36.168620
## WT_ML  1.925605  2.5308258 3 0.3634931  4.845571
## WT_ST  2.370416  0.7062657 3 1.8964921  3.182146
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 15.86714 16.58102 17.00123 17.27003 
## 
## Means with the same letter are not significantly different.
## 
##             EPX groups
## KO_ST 13.930143      a
## KO_ML  2.762803      a
## WT_ST  2.370416      a
## WT_ML  1.925605      a
## H      1.427191      a

ggplot(lg, aes(x=AG, y=EPX, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Liver EPX")

3.0.4 MPO

razlika med posameznimi vzorci, vse je glede na WM podatki niso pravi, MPO se ni izrazal

test<-aov(MPO~AG, data=lg)
summary(test)

##             Df Sum Sq Mean Sq F value Pr(>F)
## AG           4  598.3   149.6   1.448  0.289
## Residuals   10 1033.0   103.3

3.0.4.1 post-hoc

tukey<-TukeyHSD(test)

tukey

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = MPO ~ AG, data = lg)
## 
## $AG
##                     diff       lwr      upr     p adj
## KO_ML-H      12.88636302 -14.42497 40.19769 0.5549712
## KO_ST-H      14.46716456 -12.84417 41.77849 0.4524997
## WT_ML-H       0.09572773 -27.21560 27.40706 1.0000000
## WT_ST-H       2.92109274 -24.39024 30.23242 0.9961468
## KO_ST-KO_ML   1.58080154 -25.73053 28.89213 0.9996504
## WT_ML-KO_ML -12.79063529 -40.10197 14.52069 0.5614064
## WT_ST-KO_ML  -9.96527029 -37.27660 17.34606 0.7512756
## WT_ML-KO_ST -14.37143683 -41.68277 12.93989 0.4584478
## WT_ST-KO_ST -11.54607183 -38.85740 15.76526 0.6460307
## WT_ST-WT_ML   2.82536500 -24.48596 30.13669 0.9966104

plot(duncan.test(test,'AG', alpha = 0.05, console=TRUE))

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for MPO 
## 
## Mean Square Error:  103.2998 
## 
## AG,  means
## 
##              MPO        std r       Min       Max
## H      0.9042723  0.5679899 3 0.5763432  1.560130
## KO_ML 13.7906353 21.0132338 3 1.6586391 38.054628
## KO_ST 15.3714368  7.9629172 3 6.8210791 22.575197
## WT_ML  1.0000000  0.0000000 3 1.0000000  1.000000
## WT_ST  3.8253650  3.3484867 3 1.8921153  7.691864
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 18.49040 19.32230 19.81198 20.12522 
## 
## Means with the same letter are not significantly different.
## 
##              MPO groups
## KO_ST 15.3714368      a
## KO_ML 13.7906353      a
## WT_ST  3.8253650      a
## WT_ML  1.0000000      a
## H      0.9042723      a

ggplot(lg, aes(x=AG, y=MPO, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Liver MPO")

3.0.5 SOD1

razlika med posameznimi vzorci, vse je glede na WM

test<-aov(SOD1~AG, data=lg)
summary(test)

##             Df Sum Sq Mean Sq F value Pr(>F)
## AG           4  0.847  0.2116   0.424  0.788
## Residuals   10  4.990  0.4990

3.0.5.1 post-hoc

tukey<-TukeyHSD(test)

tukey

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = SOD1 ~ AG, data = lg)
## 
## $AG
##                    diff       lwr      upr     p adj
## KO_ML-H      0.22660092 -1.671564 2.124765 0.9941341
## KO_ST-H      0.44852468 -1.449640 2.346689 0.9314387
## WT_ML-H      0.27925918 -1.618905 2.177424 0.9871326
## WT_ST-H      0.71211069 -1.186054 2.610275 0.7331371
## KO_ST-KO_ML  0.22192376 -1.676241 2.120088 0.9945820
## WT_ML-KO_ML  0.05265826 -1.845506 1.950823 0.9999812
## WT_ST-KO_ML  0.48550977 -1.412655 2.383674 0.9113101
## WT_ML-KO_ST -0.16926550 -2.067430 1.728899 0.9980926
## WT_ST-KO_ST  0.26358601 -1.634578 2.161750 0.9896227
## WT_ST-WT_ML  0.43285151 -1.465313 2.331016 0.9390785

plot(duncan.test(test,'AG', alpha = 0.05, console=TRUE))

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for SOD1 
## 
## Mean Square Error:  0.4989781 
## 
## AG,  means
## 
##            SOD1       std r       Min      Max
## H     0.9103337 0.5433787 3 0.2828940 1.224054
## KO_ML 1.1369346 0.6318412 3 0.5058097 1.769490
## KO_ST 1.3588584 0.9743999 3 0.5783441 2.450937
## WT_ML 1.1895929 0.8704238 3 0.5396141 2.178497
## WT_ST 1.6224444 0.3054734 3 1.3317593 1.940820
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 1.285101 1.342919 1.376952 1.398723 
## 
## Means with the same letter are not significantly different.
## 
##            SOD1 groups
## WT_ST 1.6224444      a
## KO_ST 1.3588584      a
## WT_ML 1.1895929      a
## KO_ML 1.1369346      a
## H     0.9103337      a

ggplot(lg, aes(x=AG, y=SOD1, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Liver SOD1")

4 Lung

#uvozimo podatke

lun<- read_excel("F:/misc/qPCR/qPCR_mouse/qPCR_stare_nove.xlsx", 
    sheet = "Lun")

##  [1] "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO"
## [16] "KO" "KO" "KO" "WT" "WT" "WT" "WT" "WT" "WT" "WT" "WT" "WT" "WT" "WT" "WT"
## [31] "WT" "WT" "WT" "WT" "WT" "WT" "HT" "HT" "HT" "HT" "HT" "HT" "HT" "HT" "HT"

##  [1] "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ST" "ST" "ST" "ST" "ST" "ST"
## [16] "ST" "ST" "ST" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ST" "ST" "ST"
## [31] "ST" "ST" "ST" "ST" "ST" "ST" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML"

Urejanje podatkov povprecimo posamezne skupine, normalizacija na standard (B2M)

#povprecja za ATP5B, za vsako skupino posebaj

meanATP5B_KM<- mean(lun1[1:2,7]) #povprecje za ATP5B, za KO_ML
meanATP5B_WM<- mean(lun1[3:4,7]) #in tako dalje
meanATP5B_KS<- mean(lun1[5:6,7])
meanATP5B_WS<- mean(lun1[7:8,7])
meanATP5B_H<- mean(lun1[9:10,7])


#povprecja za B2M, za vsako skupino posebaj
meanB2M_KM<- mean(lun1[1:2,8]) #povprecje za B2M, za KO_ML
meanB2M_WM<- mean(lun1[3:4,8]) #in tako dalje
meanB2M_KS<- mean(lun1[5:6,8])
meanB2M_WS<- mean(lun1[7:8,8])
meanB2M_H<- mean(lun1[9:10,8])

#povprecja za testne vzorce z aggregate

aggregate(lun1[, 2], list(Group=lun1$Group), mean)

##    Group        x
## 1     H1 23.07333
## 2     H2 22.60000
## 3     H3 28.09667
## 4    KM1 26.92667
## 5    KM2 26.10667
## 6    KM3 26.24667
## 7    KS1 24.98333
## 8    KS2 25.02333
## 9    KS3 25.95000
## 10   WM1 24.30667
## 11   WM2 24.59333
## 12   WM3 23.72667
## 13   WS1 22.42000
## 14   WS2 23.53333
## 15   WS3 25.44000

#povprecja za testne vzorce z ddply paketom, lahko bi tut z aggregate (in to bi blo lazje)

lun3<-ddply(lun1, .(Group), summarize,  CHIA=mean(CHIA), CHIL3=mean(CHIL3), EPX=mean(EPX), MPO=mean(MPO), SOD1=mean(SOD1))
row.names(lun3) <- c(lun3$Group) #imena vrstic spremenimo iz stevilk v imena skupin

#lun3 = lun3[seq(1, nrow(lun3), 3), ]

\(\Delta\)Ct = Ct (gene of interest ) - Ct (housekeeping gene)

#zbrisemo stolpec z imeni skupin (Group)


lun3$Group<-NULL

#??Ct zracunamo na B2M

dCtH<- lun3[c('H1',"H2", "H3"), ] - meanB2M_H
dCtKM<- lun3[c('KM1',"KM2", "KM3"), ] - meanB2M_KM
dCtKS<- lun3[c('KS1',"KS2", "KS3"), ] - meanB2M_KS
dCtWM<- lun3[c('WM1',"WM2", "WM3"), ] - meanB2M_WM
dCtWS<- lun3[c('WS1',"WS2", "WS3"), ] - meanB2M_WS

\(\Delta\Delta\)Ct = \(\Delta\)Ct (sample) - \(\Delta\)Ct (control average) control average je WM, ekspresijo zracunamo relativno na WM

#povprecje ??Ct od bioloskih ponovitev

dCtHm <-colMeans(dCtH)
dCtKMm <-colMeans(dCtKM)
dCtKSm <-colMeans(dCtKS)
dCtWMm <-colMeans(dCtWM)
dCtWSm <-colMeans(dCtWS)

#malo preoblikujemo dCtWMm, da bo ustrezne oblike data.frame za odstevanje v naslednjem koraku

dCtWMm<-as.data.frame(dCtWMm)
dCtWMm<-t(dCtWMm)
dCtWMm<-as.data.frame(dCtWMm)
dCtWMm<-dCtWMm[rep(seq_len(nrow(dCtWMm)), each = 3), ]
row.names(dCtWMm)<-c("WM1","WM2","WM3")

#$\Delta\Delta$Ct

ddCtH<- dCtH-dCtWMm
ddCtKM<- dCtKM-dCtWMm
ddCtKS<- dCtKS-dCtWMm
ddCtWM<- dCtWM-dCtWMm
ddCtWS<- dCtWS-dCtWMm

fold gene gxpression relative to WM 2^-(\(\Delta\Delta\)Ct)

H<- 2^-ddCtH
KM<- 2^-ddCtKM
KS<- 2^-ddCtKS
WM<- 2^-ddCtWM
WS<- 2^-ddCtWS

#zdruzimo data.frame
lg<-rbind( H,KM,KS,WM, WS)


lg$Age<- c("ML","ML","ML","ML","ML","ML","ST","ST","ST","ML","ML","ML","ST","ST","ST")
lg$Genotype <-c("H","H","H","KO","KO","KO","KO","KO","KO","WT","WT","WT","WT","WT","WT")
lg$AG<- c("H","H","H","KO_ML","KO_ML","KO_ML","KO_ST","KO_ST","KO_ST","WT_ML","WT_ML","WT_ML","WT_ST","WT_ST","WT_ST")

#izvozimo v excel
write_xlsx(lg, "F:/misc/qPCR/lg_lun.xlsx")

##ANOVA

4.0.1 CHIA

preverjamo kombiniran vpliv starosti in genotipa

interaction <- aov(CHIA ~ Age*Genotype, data = lg)

summary(interaction)

##              Df Sum Sq Mean Sq F value Pr(>F)  
## Age           1  3.994   3.994   5.648 0.0388 *
## Genotype      2  0.437   0.219   0.309 0.7408  
## Age:Genotype  1  1.261   1.261   1.784 0.2113  
## Residuals    10  7.072   0.707                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

razlika med posameznimi vzorci, vse je glede na WM

test<-aov(CHIA~AG, data=lg)
summary(test)

##             Df Sum Sq Mean Sq F value Pr(>F)
## AG           4  5.693  1.4233   2.012  0.169
## Residuals   10  7.072  0.7072

4.0.1.1 post-hoc

tukey<-TukeyHSD(test)

tukey

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = CHIA ~ AG, data = lg)
## 
## $AG
##                   diff        lwr       upr     p adj
## KO_ML-H     -0.5374947 -2.7973351 1.7223457 0.9299429
## KO_ST-H      1.2786303 -0.9812101 3.5384707 0.3936884
## WT_ML-H     -0.1485465 -2.4083869 2.1112939 0.9994224
## WT_ST-H      0.3706934 -1.8891470 2.6305338 0.9808035
## KO_ST-KO_ML  1.8161249 -0.4437155 4.0759653 0.1344059
## WT_ML-KO_ML  0.3889482 -1.8708922 2.6487886 0.9771496
## WT_ST-KO_ML  0.9081881 -1.3516523 3.1680285 0.6846908
## WT_ML-KO_ST -1.4271768 -3.6870171 0.8326636 0.2995282
## WT_ST-KO_ST -0.9079368 -3.1677772 1.3519036 0.6848953
## WT_ST-WT_ML  0.5192399 -1.7406005 2.7790803 0.9375205

plot(duncan.test(test,'AG', alpha = 0.05, console=TRUE))

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for CHIA 
## 
## Mean Square Error:  0.7072441 
## 
## AG,  means
## 
##            CHIA       std r        Min       Max
## H     1.1810261 1.0240715 3 0.04503599 2.0333927
## KO_ML 0.6435314 0.1781955 3 0.44203185 0.7803657
## KO_ST 2.4596564 0.8149452 3 1.51980780 2.9701907
## WT_ML 1.0324796 0.3266307 3 0.76607394 1.3968937
## WT_ST 1.5517195 1.2980452 3 0.36195667 2.9360751
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 1.529964 1.598799 1.639317 1.665235 
## 
## Means with the same letter are not significantly different.
## 
##            CHIA groups
## KO_ST 2.4596564      a
## WT_ST 1.5517195     ab
## H     1.1810261     ab
## WT_ML 1.0324796     ab
## KO_ML 0.6435314      b

ggplot(lg, aes(x=AG, y=CHIA, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Lung CHIA")

4.0.2 CHIL3

razlika med posameznimi vzorci, vse je glede na WM

test<-aov(CHIL3~AG, data=lg)
summary(test)

##             Df Sum Sq Mean Sq F value Pr(>F)
## AG           4  10.95   2.738   1.645  0.238
## Residuals   10  16.64   1.664

4.0.2.1 post-hoc

tukey<-TukeyHSD(test)

tukey

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = CHIL3 ~ AG, data = lg)
## 
## $AG
##                   diff       lwr      upr     p adj
## KO_ML-H     0.05149904 -3.415266 3.518264 0.9999984
## KO_ST-H     0.08085619 -3.385909 3.547621 0.9999906
## WT_ML-H     0.73920027 -2.727565 4.205965 0.9514530
## WT_ST-H     2.24416270 -1.222602 5.710928 0.2795606
## KO_ST-KO_ML 0.02935716 -3.437408 3.496122 0.9999998
## WT_ML-KO_ML 0.68770124 -2.779064 4.154466 0.9621701
## WT_ST-KO_ML 2.19266367 -1.274101 5.659429 0.2983077
## WT_ML-KO_ST 0.65834408 -2.808421 4.125109 0.9675401
## WT_ST-KO_ST 2.16330651 -1.303459 5.630072 0.3094073
## WT_ST-WT_ML 1.50496243 -1.961803 4.971727 0.6248814

plot(duncan.test(test,'AG', alpha = 0.05, console=TRUE))

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for CHIL3 
## 
## Mean Square Error:  1.664419 
## 
## AG,  means
## 
##           CHIL3        std r        Min       Max
## H     0.2633804 0.22095891 3 0.01098491 0.4219079
## KO_ML 0.3148795 0.06728670 3 0.27043117 0.3922920
## KO_ST 0.3442366 0.11300848 3 0.21638414 0.4307731
## WT_ML 1.0025807 0.08769858 3 0.91172249 1.0867349
## WT_ST 2.5075431 2.87198221 3 0.58709532 5.8091752
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 2.347080 2.452677 2.514835 2.554596 
## 
## Means with the same letter are not significantly different.
## 
##           CHIL3 groups
## WT_ST 2.5075431      a
## WT_ML 1.0025807      a
## KO_ST 0.3442366      a
## KO_ML 0.3148795      a
## H     0.2633804      a

ggplot(lg, aes(x=AG, y=CHIL3, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Lung CHIL3")

4.0.3 EPX

razlika med posameznimi vzorci, vse je glede na WM

test<-aov(EPX~AG, data=lg)
summary(test)

##             Df Sum Sq Mean Sq F value Pr(>F)  
## AG           4   2401   600.3   2.659 0.0956 .
## Residuals   10   2258   225.8                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

4.0.3.1 post-hoc

tukey<-TukeyHSD(test)

tukey

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = EPX ~ AG, data = lg)
## 
## $AG
##                   diff       lwr       upr     p adj
## KO_ML-H       1.492970 -38.88643 41.872371 0.9999410
## KO_ST-H      28.953068 -11.42633 69.332470 0.2036595
## WT_ML-H      -5.678702 -46.05810 34.700700 0.9891200
## WT_ST-H      -3.872552 -44.25195 36.506850 0.9974701
## KO_ST-KO_ML  27.460098 -12.91930 67.839500 0.2414979
## WT_ML-KO_ML  -7.171671 -47.55107 33.207730 0.9744164
## WT_ST-KO_ML  -5.365521 -45.74492 35.013880 0.9912030
## WT_ML-KO_ST -34.631770 -75.01117  5.747632 0.1028571
## WT_ST-KO_ST -32.825620 -73.20502  7.553782 0.1284169
## WT_ST-WT_ML   1.806150 -38.57325 42.185552 0.9998741

plot(duncan.test(test,'AG', alpha = 0.05, console=TRUE))

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for EPX 
## 
## Mean Square Error:  225.805 
## 
## AG,  means
## 
##             EPX       std r       Min       Max
## H      7.384493  6.399709 3 3.0915564 14.740021
## KO_ML  8.877463  8.182013 3 3.1748021 18.252219
## KO_ST 36.337561 31.699586 3 6.2984638 69.470729
## WT_ML  1.705792  2.100952 3 0.4622248  4.131503
## WT_ST  3.511942  3.441754 3 0.7569833  7.370010
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 27.33779 28.56774 29.29173 29.75485 
## 
## Means with the same letter are not significantly different.
## 
##             EPX groups
## KO_ST 36.337561      a
## KO_ML  8.877463      b
## H      7.384493      b
## WT_ST  3.511942      b
## WT_ML  1.705792      b

ggplot(lg, aes(x=AG, y=EPX, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Lung EPX")

4.0.4 MPO

razlika med posameznimi vzorci, vse je glede na WM podatki niso pravi, MPO se ni izrazal

test<-aov(MPO~AG, data=lg)
summary(test)

##             Df Sum Sq Mean Sq F value Pr(>F)  
## AG           4 155.59   38.90   5.345 0.0145 *
## Residuals   10  72.78    7.28                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

4.0.4.1 post-hoc

tukey<-TukeyHSD(test)

tukey

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = MPO ~ AG, data = lg)
## 
## $AG
##                   diff         lwr        upr     p adj
## KO_ML-H      4.9498060  -2.2992970 12.1989090 0.2384822
## KO_ST-H      8.1221891   0.8730861 15.3712921 0.0270309
## WT_ML-H      0.5524596  -6.6966435  7.8015626 0.9989671
## WT_ST-H      0.3457424  -6.9033607  7.5948454 0.9998376
## KO_ST-KO_ML  3.1723831  -4.0767199 10.4214861 0.6183351
## WT_ML-KO_ML -4.3973464 -11.6464495  2.8517566 0.3331631
## WT_ST-KO_ML -4.6040636 -11.8531667  2.6450394 0.2949079
## WT_ML-KO_ST -7.5697295 -14.8188326 -0.3206265 0.0398835
## WT_ST-KO_ST -7.7764467 -15.0255498 -0.5273437 0.0344754
## WT_ST-WT_ML -0.2067172  -7.4558202  7.0423858 0.9999790

plot(duncan.test(test,'AG', alpha = 0.05, console=TRUE))

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for MPO 
## 
## Mean Square Error:  7.277502 
## 
## AG,  means
## 
##             MPO       std r       Min        Max
## H     0.5581983 0.3598352 3 0.2001904  0.9198334
## KO_ML 5.5080043 4.0233691 3 1.9603498  9.8795439
## KO_ST 8.6803874 4.4099831 3 4.5507714 13.3254386
## WT_ML 1.1106579 0.5305322 3 0.4980783  1.4218584
## WT_ST 0.9039407 0.5840507 3 0.2551548  1.3877789
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 4.907811 5.128617 5.258591 5.341733 
## 
## Means with the same letter are not significantly different.
## 
##             MPO groups
## KO_ST 8.6803874      a
## KO_ML 5.5080043     ab
## WT_ML 1.1106579      b
## WT_ST 0.9039407      b
## H     0.5581983      b

ggplot(lg, aes(x=AG, y=MPO, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Lung MPO")

4.0.5 SOD1

razlika med posameznimi vzorci, vse je glede na WM

test<-aov(SOD1~AG, data=lg)
summary(test)

##             Df Sum Sq Mean Sq F value   Pr(>F)    
## AG           4  6.261  1.5653    13.6 0.000471 ***
## Residuals   10  1.151  0.1151                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

4.0.5.1 post-hoc

tukey<-TukeyHSD(test)

tukey

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = SOD1 ~ AG, data = lg)
## 
## $AG
##                    diff        lwr        upr     p adj
## KO_ML-H      0.24889807 -0.6627233  1.1605194 0.8910781
## KO_ST-H      1.48818658  0.5765652  2.3998079 0.0022484
## WT_ML-H     -0.03297658 -0.9445979  0.8786448 0.9999459
## WT_ST-H     -0.39199403 -1.3036154  0.5196273 0.6325498
## KO_ST-KO_ML  1.23928851  0.3276672  2.1509098 0.0081725
## WT_ML-KO_ML -0.28187464 -1.1934960  0.6297467 0.8418091
## WT_ST-KO_ML -0.64089210 -1.5525134  0.2707292 0.2173601
## WT_ML-KO_ST -1.52116315 -2.4327845 -0.6095418 0.0019088
## WT_ST-KO_ST -1.88018061 -2.7918019 -0.9685593 0.0003588
## WT_ST-WT_ML -0.35901745 -1.2706388  0.5526039 0.6994595

plot(duncan.test(test,'AG', alpha = 0.05, console=TRUE))

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for SOD1 
## 
## Mean Square Error:  0.1150914 
## 
## AG,  means
## 
##            SOD1       std r        Min      Max
## H     1.0394439 0.3105703 3 0.71449707 1.333299
## KO_ML 1.2883420 0.2629440 3 1.04246576 1.565547
## KO_ST 2.5276305 0.3377030 3 2.25271791 2.904588
## WT_ML 1.0064673 0.1403753 3 0.87458267 1.154019
## WT_ST 0.6474499 0.5254663 3 0.09034989 1.134193
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##         2         3         4         5 
## 0.6171887 0.6449566 0.6613016 0.6717573 
## 
## Means with the same letter are not significantly different.
## 
##            SOD1 groups
## KO_ST 2.5276305      a
## KO_ML 1.2883420      b
## H     1.0394439      b
## WT_ML 1.0064673      b
## WT_ST 0.6474499      b

ggplot(lg, aes(x=AG, y=SOD1, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Lung SOD1")

5 Spleen

#uvozimo podatke

spl<- read_excel("F:/misc/qPCR/qPCR_mouse/qPCR_stare_nove.xlsx", 
    sheet = "Spl")

##  [1] "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO" "KO"
## [16] "KO" "KO" "KO" "WT" "WT" "WT" "WT" "WT" "WT" "WT" "WT" "WT" "WT" "WT" "WT"
## [31] "WT" "WT" "WT" "WT" "WT" "WT" "HT" "HT" "HT" "HT" "HT" "HT" "HT" "HT" "HT"

##  [1] "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ST" "ST" "ST" "ST" "ST" "ST"
## [16] "ST" "ST" "ST" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ST" "ST" "ST"
## [31] "ST" "ST" "ST" "ST" "ST" "ST" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML" "ML"

Urejanje podatkov povprecimo posamezne skupine, normalizacija na standard (B2M)

#povprecja za ATP5B, za vsako skupino posebaj

meanATP5B_KM<- mean(spl1[1:2,7]) #povprecje za ATP5B, za KO_ML
meanATP5B_WM<- mean(spl1[3:4,7]) #in tako dalje
meanATP5B_KS<- mean(spl1[5:6,7])
meanATP5B_WS<- mean(spl1[7:8,7])
meanATP5B_H<- mean(spl1[9:10,7])


#povprecja za B2M, za vsako skupino posebaj
meanB2M_KM<- mean(spl1[1:2,8]) #povprecje za B2M, za KO_ML
meanB2M_WM<- mean(spl1[3:4,8]) #in tako dalje
meanB2M_KS<- mean(spl1[5:6,8])
meanB2M_WS<- mean(spl1[7:8,8])
meanB2M_H<- mean(spl1[9:10,8])

#povprecja za testne vzorce z aggregate

aggregate(spl1[, 2], list(Group=spl1$Group), mean)

##    Group        x
## 1     H1 33.69667
## 2     H2 35.68667
## 3     H3 33.82000
## 4    KM1 36.67667
## 5    KM2 34.59333
## 6    KM3 34.74000
## 7    KS1 35.36000
## 8    KS2 31.47000
## 9    KS3 34.95000
## 10   WM1 33.42333
## 11   WM2 34.93000
## 12   WM3 33.47333
## 13   WS1 33.53000
## 14   WS2 34.38667
## 15   WS3 34.78667

#povprecja za testne vzorce z ddply paketom, lahko bi tut z aggregate (in to bi blo lazje)

spl3<-ddply(spl1, .(Group), summarize,  CHIA=mean(CHIA), CHIL3=mean(CHIL3), EPX=mean(EPX), MPO=mean(MPO), SOD1=mean(SOD1))
row.names(spl3) <- c(spl3$Group) #imena vrstic spremenimo iz stevilk v imena skupin

#spl3 = spl3[seq(1, nrow(spl3), 3), ]

\(\Delta\)Ct = Ct (gene of interest ) - Ct (housekeeping gene)

#zbrisemo stolpec z imeni skupin (Group)


spl3$Group<-NULL

#??Ct zracunamo na B2M

dCtH<- spl3[c('H1',"H2", "H3"), ] - meanB2M_H
dCtKM<- spl3[c('KM1',"KM2", "KM3"), ] - meanB2M_KM
dCtKS<- spl3[c('KS1',"KS2", "KS3"), ] - meanB2M_KS
dCtWM<- spl3[c('WM1',"WM2", "WM3"), ] - meanB2M_WM
dCtWS<- spl3[c('WS1',"WS2", "WS3"), ] - meanB2M_WS

\(\Delta\Delta\)Ct = \(\Delta\)Ct (sample) - \(\Delta\)Ct (control average) control average je WM, ekspresijo zracunamo relativno na WM

#povprecje ??Ct od bioloskih ponovitev

dCtHm <-colMeans(dCtH)
dCtKMm <-colMeans(dCtKM)
dCtKSm <-colMeans(dCtKS)
dCtWMm <-colMeans(dCtWM)
dCtWSm <-colMeans(dCtWS)

#malo preoblikujemo dCtWMm, da bo ustrezne oblike data.frame za odstevanje v naslednjem koraku

dCtWMm<-as.data.frame(dCtWMm)
dCtWMm<-t(dCtWMm)
dCtWMm<-as.data.frame(dCtWMm)
dCtWMm<-dCtWMm[rep(seq_len(nrow(dCtWMm)), each = 3), ]
row.names(dCtWMm)<-c("WM1","WM2","WM3")

#$\Delta\Delta$Ct

ddCtH<- dCtH-dCtWMm
ddCtKM<- dCtKM-dCtWMm
ddCtKS<- dCtKS-dCtWMm
ddCtWM<- dCtWM-dCtWMm
ddCtWS<- dCtWS-dCtWMm

fold gene gxpression relative to WM 2^-(\(\Delta\Delta\)Ct)

H<- 2^-ddCtH
KM<- 2^-ddCtKM
KS<- 2^-ddCtKS
WM<- 2^-ddCtWM
WS<- 2^-ddCtWS

#zdruzimo data.frame
lg<-rbind( H,KM,KS,WM, WS)


lg$Age<- c("ML","ML","ML","ML","ML","ML","ST","ST","ST","ML","ML","ML","ST","ST","ST")
lg$Genotype <-c("H","H","H","KO","KO","KO","KO","KO","KO","WT","WT","WT","WT","WT","WT")
lg$AG<- c("H","H","H","KO_ML","KO_ML","KO_ML","KO_ST","KO_ST","KO_ST","WT_ML","WT_ML","WT_ML","WT_ST","WT_ST","WT_ST")

#izvozimo v excel
write_xlsx(lg, "F:/misc/qPCR/lg_spl.xlsx")

##ANOVA

5.0.1 CHIA

preverjamo kombiniran vpliv starosti in genotipa

interaction <- aov(CHIA ~ Age*Genotype, data = lg)

summary(interaction)

##              Df Sum Sq Mean Sq F value Pr(>F)
## Age           1   6.25   6.246   1.257  0.288
## Genotype      2   3.83   1.915   0.386  0.690
## Age:Genotype  1   8.97   8.974   1.806  0.209
## Residuals    10  49.68   4.968

razlika med posameznimi vzorci, vse je glede na WM

test<-aov(CHIA~AG, data=lg)
summary(test)

##             Df Sum Sq Mean Sq F value Pr(>F)
## AG           4  19.05   4.763   0.959  0.471
## Residuals   10  49.68   4.968

5.0.1.1 post-hoc

tukey<-TukeyHSD(test)

tukey

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = CHIA ~ AG, data = lg)
## 
## $AG
##                   diff       lwr      upr     p adj
## KO_ML-H     -0.3237812 -6.313412 5.665850 0.9997332
## KO_ST-H      2.7309953 -3.258636 8.720626 0.5842517
## WT_ML-H      0.2759131 -5.713718 6.265544 0.9998585
## WT_ST-H     -0.1284295 -6.118060 5.861201 0.9999933
## KO_ST-KO_ML  3.0547765 -2.934854 9.044407 0.4864184
## WT_ML-KO_ML  0.5996943 -5.389936 6.589325 0.9970120
## WT_ST-KO_ML  0.1953517 -5.794279 6.184983 0.9999642
## WT_ML-KO_ST -2.4550822 -8.444713 3.534549 0.6699170
## WT_ST-KO_ST -2.8594248 -8.849056 3.130206 0.5447960
## WT_ST-WT_ML -0.4043426 -6.393973 5.585288 0.9993585

plot(duncan.test(test,'AG', alpha = 0.05, console=TRUE))

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for CHIA 
## 
## Mean Square Error:  4.96837 
## 
## AG,  means
## 
##            CHIA       std r       Min       Max
## H     0.8311363 0.4716140 3 0.2892834 1.1491408
## KO_ML 0.5073551 0.2959930 3 0.1678862 0.7114769
## KO_ST 3.5621316 4.9145259 3 0.6229647 9.2357058
## WT_ML 1.1070494 0.5226064 3 0.5042539 1.4328513
## WT_ST 0.7027068 0.3257830 3 0.4476849 1.0697118
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 4.055119 4.237562 4.344954 4.413651 
## 
## Means with the same letter are not significantly different.
## 
##            CHIA groups
## KO_ST 3.5621316      a
## WT_ML 1.1070494      a
## H     0.8311363      a
## WT_ST 0.7027068      a
## KO_ML 0.5073551      a

ggplot(lg, aes(x=AG, y=CHIA, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Spleen CHIA")

5.0.2 CHIL3

razlika med posameznimi vzorci, vse je glede na WM

test<-aov(CHIL3~AG, data=lg)
summary(test)

##             Df Sum Sq Mean Sq F value Pr(>F)
## AG           4  13.75   3.436   0.485  0.747
## Residuals   10  70.86   7.086

5.0.2.1 post-hoc

tukey<-TukeyHSD(test)

tukey

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = CHIL3 ~ AG, data = lg)
## 
## $AG
##                   diff       lwr      upr     p adj
## KO_ML-H      0.3427559 -6.810550 7.496062 0.9998346
## KO_ST-H      1.0407560 -6.112550 8.194062 0.9876530
## WT_ML-H     -0.8232574 -7.976564 6.330049 0.9948981
## WT_ST-H      2.0048417 -5.148464 9.158148 0.8819349
## KO_ST-KO_ML  0.6980001 -6.455306 7.851306 0.9972950
## WT_ML-KO_ML -1.1660133 -8.319319 5.987293 0.9812398
## WT_ST-KO_ML  1.6620858 -5.491220 8.815392 0.9351522
## WT_ML-KO_ST -1.8640134 -9.017320 5.289293 0.9059015
## WT_ST-KO_ST  0.9640857 -6.189220 8.117392 0.9907218
## WT_ST-WT_ML  2.8280991 -4.325207 9.981405 0.6966598

plot(duncan.test(test,'AG', alpha = 0.05, console=TRUE))

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for CHIL3 
## 
## Mean Square Error:  7.086429 
## 
## AG,  means
## 
##          CHIL3      std r       Min      Max
## H     1.932440 1.019509 3 1.2948435 3.108268
## KO_ML 2.275195 1.740457 3 0.2873958 3.525378
## KO_ST 2.973196 2.113024 3 1.6850341 5.411810
## WT_ML 1.109182 0.528815 3 0.5015427 1.465214
## WT_ST 3.937281 5.159364 3 0.7752738 9.890964
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 4.842954 5.060842 5.189098 5.271142 
## 
## Means with the same letter are not significantly different.
## 
##          CHIL3 groups
## WT_ST 3.937281      a
## KO_ST 2.973196      a
## KO_ML 2.275195      a
## H     1.932440      a
## WT_ML 1.109182      a

ggplot(lg, aes(x=AG, y=CHIL3, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Spleen CHIL3")

5.0.3 EPX

razlika med posameznimi vzorci, vse je glede na WM

test<-aov(EPX~AG, data=lg)
summary(test)

##             Df Sum Sq Mean Sq F value Pr(>F)
## AG           4  3.201  0.8003   0.489  0.744
## Residuals   10 16.382  1.6382

5.0.3.1 post-hoc

tukey<-TukeyHSD(test)

tukey

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = EPX ~ AG, data = lg)
## 
## $AG
##                   diff       lwr      upr     p adj
## KO_ML-H      0.5254257 -2.913931 3.964782 0.9852095
## KO_ST-H      1.3229386 -2.116418 4.762295 0.7161208
## WT_ML-H      0.1869128 -3.252444 3.626269 0.9997275
## WT_ST-H      0.7364552 -2.702901 4.175812 0.9507488
## KO_ST-KO_ML  0.7975130 -2.641844 4.236870 0.9355902
## WT_ML-KO_ML -0.3385129 -3.777870 3.100844 0.9972032
## WT_ST-KO_ML  0.2110295 -3.228327 3.650386 0.9995598
## WT_ML-KO_ST -1.1360259 -4.575383 2.303331 0.8092943
## WT_ST-KO_ST -0.5864834 -4.025840 2.852873 0.9779036
## WT_ST-WT_ML  0.5495425 -2.889814 3.988899 0.9825593

plot(duncan.test(test,'AG', alpha = 0.05, console=TRUE))

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for EPX 
## 
## Mean Square Error:  1.638205 
## 
## AG,  means
## 
##             EPX       std r       Min      Max
## H     0.8985375 0.7167724 3 0.3287357 1.703301
## KO_ML 1.4239632 1.1570281 3 0.2426026 2.555018
## KO_ST 2.2214761 1.1583603 3 1.3456781 3.534893
## WT_ML 1.0854503 0.4968070 3 0.5677522 1.558329
## WT_ST 1.6349927 2.1794340 3 0.2324512 4.145846
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 2.328524 2.433286 2.494953 2.534400 
## 
## Means with the same letter are not significantly different.
## 
##             EPX groups
## KO_ST 2.2214761      a
## WT_ST 1.6349927      a
## KO_ML 1.4239632      a
## WT_ML 1.0854503      a
## H     0.8985375      a

ggplot(lg, aes(x=AG, y=EPX, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Spleen EPX")

5.0.4 MPO

razlika med posameznimi vzorci, vse je glede na WM podatki niso pravi, MPO se ni izrazal

test<-aov(MPO~AG, data=lg)
summary(test)

##             Df Sum Sq Mean Sq F value Pr(>F)
## AG           4  701.3   175.3   0.625  0.655
## Residuals   10 2803.5   280.4

5.0.4.1 post-hoc

tukey<-TukeyHSD(test)

tukey

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = MPO ~ AG, data = lg)
## 
## $AG
##                   diff       lwr      upr     p adj
## KO_ML-H      -2.732215 -47.72483 42.26040 0.9995774
## KO_ST-H       2.626416 -42.36620 47.61903 0.9996384
## WT_ML-H      -9.190419 -54.18303 35.80219 0.9581171
## WT_ST-H      11.664735 -33.32788 56.65735 0.9074076
## KO_ST-KO_ML   5.358630 -39.63398 50.35124 0.9941861
## WT_ML-KO_ML  -6.458204 -51.45082 38.53441 0.9882583
## WT_ST-KO_ML  14.396950 -30.59566 59.38956 0.8255053
## WT_ML-KO_ST -11.816834 -56.80945 33.17578 0.9035294
## WT_ST-KO_ST   9.038319 -35.95429 54.03093 0.9604731
## WT_ST-WT_ML  20.855154 -24.13746 65.84777 0.5702528

plot(duncan.test(test,'AG', alpha = 0.05, console=TRUE))

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for MPO 
## 
## Mean Square Error:  280.3472 
## 
## AG,  means
## 
##             MPO        std r       Min      Max
## H     10.306124  5.4078933 3 5.7734931 16.29222
## KO_ML  7.573909 10.7298239 3 0.7637176 19.94255
## KO_ST 12.932539 14.5994458 3 2.2648954 29.57100
## WT_ML  1.115705  0.5475276 3 0.4934963  1.52391
## WT_ST 21.970859 32.3097194 3 3.2402634 59.27880
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 30.46104 31.83151 32.63821 33.15424 
## 
## Means with the same letter are not significantly different.
## 
##             MPO groups
## WT_ST 21.970859      a
## KO_ST 12.932539      a
## H     10.306124      a
## KO_ML  7.573909      a
## WT_ML  1.115705      a

ggplot(lg, aes(x=AG, y=MPO, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Spleen MPO")

5.0.5 SOD1

razlika med posameznimi vzorci, vse je glede na WM

test<-aov(SOD1~AG, data=lg)
summary(test)

##             Df Sum Sq Mean Sq F value Pr(>F)
## AG           4 0.5494  0.1373   0.522  0.722
## Residuals   10 2.6287  0.2629

5.0.5.1 post-hoc

tukey<-TukeyHSD(test)

tukey

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = SOD1 ~ AG, data = lg)
## 
## $AG
##                     diff        lwr       upr     p adj
## KO_ML-H     -0.419652049 -1.7973871 0.9580830 0.8485607
## KO_ST-H      0.008762409 -1.3689726 1.3864975 0.9999999
## WT_ML-H     -0.424642374 -1.8023774 0.9530927 0.8432621
## WT_ST-H     -0.246697272 -1.6244323 1.1310378 0.9736592
## KO_ST-KO_ML  0.428414458 -0.9493206 1.8061495 0.8392036
## WT_ML-KO_ML -0.004990326 -1.3827254 1.3727447 1.0000000
## WT_ST-KO_ML  0.172954777 -1.2047803 1.5506898 0.9929008
## WT_ML-KO_ST -0.433404784 -1.8111398 0.9443303 0.8337655
## WT_ST-KO_ST -0.255459681 -1.6331947 1.1222754 0.9701708
## WT_ST-WT_ML  0.177945103 -1.1997899 1.5556802 0.9920952

plot(duncan.test(test,'AG', alpha = 0.05, console=TRUE))

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for SOD1 
## 
## Mean Square Error:  0.2628725 
## 
## AG,  means
## 
##           SOD1       std r       Min      Max
## H     1.429875 0.4888104 3 0.9177106 1.891387
## KO_ML 1.010223 0.6977118 3 0.4083216 1.774949
## KO_ST 1.438637 0.6130253 3 0.9544740 2.127921
## WT_ML 1.005233 0.1235268 3 0.8692107 1.110424
## WT_ST 1.183178 0.4444843 3 0.6890964 1.550548
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##         2         3         4         5 
## 0.9327585 0.9747241 0.9994263 1.0152280 
## 
## Means with the same letter are not significantly different.
## 
##           SOD1 groups
## KO_ST 1.438637      a
## H     1.429875      a
## WT_ST 1.183178      a
## KO_ML 1.010223      a
## WT_ML 1.005233      a

ggplot(lg, aes(x=AG, y=SOD1, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Spleen SOD1")

6 Combined box plots

combk<- read_excel("F:/misc/qPCR/qPCR_mouse/qPCR_stare_nove.xlsx", 
    sheet = "combk")

combli<- read_excel("F:/misc/qPCR/qPCR_mouse/qPCR_stare_nove.xlsx", 
    sheet = "combli")

comblu<- read_excel("F:/misc/qPCR/qPCR_mouse/qPCR_stare_nove.xlsx", 
    sheet = "comblu")

combs<- read_excel("F:/misc/qPCR/qPCR_mouse/qPCR_stare_nove.xlsx", 
    sheet = "combs")

ggplot(combk, aes(x=gene, y=Ct, fill=AG))+
  geom_boxplot()+
  facet_wrap(~gene, scale="free")+
  ggtitle('Relative expression of targeted genes in kidney')+
  theme_minimal()+
  theme(plot.title = element_text(hjust = 0.5))

ggplot(combli, aes(x=gene, y=Ct, fill=AG))+
  geom_boxplot()+
  facet_wrap(~gene, scale="free")+
  ggtitle('Relative expression of targeted genes in liver')+
  theme_minimal()+
  theme(plot.title = element_text(hjust = 0.5))

ggplot(comblu, aes(x=gene, y=Ct, fill=AG))+
  geom_boxplot()+
  facet_wrap(~gene, scale="free")+
  ggtitle('Relative expression of targeted genes in lungs')+
  theme_minimal()+
  theme(plot.title = element_text(hjust = 0.5))

ggplot(combs, aes(x=gene, y=Ct, fill=AG))+
  geom_boxplot()+
  facet_wrap(~gene, scale="free")+
  ggtitle('Relative expression of targeted genes in spleen')+
  theme_minimal()+
  theme(plot.title = element_text(hjust = 0.5))

sessionInfo()

## R version 4.0.5 (2021-03-31)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_United Kingdom.1252 
## [2] LC_CTYPE=English_United Kingdom.1252   
## [3] LC_MONETARY=English_United Kingdom.1252
## [4] LC_NUMERIC=C                           
## [5] LC_TIME=English_United Kingdom.1252    
## system code page: 1250
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] writexl_1.4.0       data.table_1.12.8   plyr_1.8.6         
##  [4] AICcmodavg_2.3-1    readr_2.0.1         DescTools_0.99.42  
##  [7] gplots_3.1.1        RColorBrewer_1.1-2  ape_5.5            
## [10] ggdendro_0.1.22     ggplot2_3.3.5       mvnormtest_0.1-9   
## [13] readxl_1.3.1        lawstat_3.4         HH_3.1-43          
## [16] gridExtra_2.3       multcomp_1.4-17     TH.data_1.0-10     
## [19] MASS_7.3-53.1       survival_3.2-10     mvtnorm_1.1-2      
## [22] latticeExtra_0.6-29 Rmpfr_0.8-4         gmp_0.6-2          
## [25] lattice_0.20-44     agricolae_1.3-5    
## 
## loaded via a namespace (and not attached):
##   [1] VGAM_1.1-5         colorspace_1.4-1   class_7.3-18      
##   [4] ellipsis_0.3.2     htmlTable_2.2.1    base64enc_0.1-3   
##   [7] gld_2.6.2          proxy_0.4-26       rstudioapi_0.13   
##  [10] farver_2.1.0       fansi_0.4.1        codetools_0.2-18  
##  [13] splines_4.0.5      leaps_3.1          rootSolve_1.8.2.2 
##  [16] knitr_1.33         Formula_1.2-4      jsonlite_1.7.2    
##  [19] cluster_2.1.1      png_0.1-7          Kendall_2.2       
##  [22] shiny_1.6.0        compiler_4.0.5     backports_1.2.1   
##  [25] assertthat_0.2.1   Matrix_1.3-2       fastmap_1.1.0     
##  [28] later_1.3.0        htmltools_0.5.2    tools_4.0.5       
##  [31] lmom_2.8           gtable_0.3.0       glue_1.4.1        
##  [34] reshape2_1.4.4     dplyr_1.0.6        Rcpp_1.0.7        
##  [37] raster_3.4-13      cellranger_1.1.0   jquerylib_0.1.4   
##  [40] vctrs_0.3.8        nlme_3.1-152       lmtest_0.9-38     
##  [43] xfun_0.22          stringr_1.4.0      rbibutils_2.2.3   
##  [46] mime_0.11          miniUI_0.1.1.1     lifecycle_1.0.0   
##  [49] gtools_3.9.2       zoo_1.8-9          scales_1.1.1      
##  [52] hms_1.1.0          promises_1.2.0.1   parallel_4.0.5    
##  [55] sandwich_3.0-1     expm_0.999-6       Exact_2.1         
##  [58] yaml_2.2.1         sass_0.4.0         labelled_2.8.0    
##  [61] rpart_4.1-15       stringi_1.5.3      highr_0.9         
##  [64] klaR_0.6-15        AlgDesign_1.2.0    e1071_1.7-8       
##  [67] checkmate_2.0.0    caTools_1.18.2     boot_1.3-27       
##  [70] bitops_1.0-7       Rdpack_2.1.2       rlang_0.4.10      
##  [73] pkgconfig_2.0.3    evaluate_0.14      purrr_0.3.4       
##  [76] labeling_0.4.2     htmlwidgets_1.5.3  tidyselect_1.1.1  
##  [79] magrittr_2.0.1     R6_2.5.1           generics_0.1.0    
##  [82] Hmisc_4.5-0        combinat_0.0-8     DBI_1.1.1         
##  [85] pillar_1.6.2       haven_2.4.1        foreign_0.8-81    
##  [88] withr_2.4.2        sp_1.4-5           abind_1.4-5       
##  [91] nnet_7.3-15        tibble_3.1.2       crayon_1.4.1      
##  [94] questionr_0.7.4    KernSmooth_2.23-20 utf8_1.2.1        
##  [97] tzdb_0.1.2         rmarkdown_2.10     jpeg_0.1-9        
## [100] forcats_0.5.1      vcd_1.4-8          digest_0.6.27     
## [103] xtable_1.8-4       httpuv_1.6.2       unmarked_1.1.1    
## [106] stats4_4.0.5       munsell_0.5.0      bslib_0.2.5.1

qPCR: young and old Legumain knock out mice compared to their Wild type and Heterozygous counterparts

TS

1 sep 2021

1 Background

2 Kidney

2.0.1 CHIA

2.0.1.1 post-hoc

2.0.2 CHIL3

2.0.2.1 post-hoc

2.0.3 EPX

2.0.3.1 post-hoc

2.0.4 MPO

2.0.4.1 post-hoc

2.0.5 SOD1

2.0.5.1 post-hoc

3 Liver

3.0.1 CHIA

3.0.1.1 post-hoc

3.0.2 CHIL3

3.0.2.1 post-hoc

3.0.3 EPX

3.0.3.1 post-hoc

3.0.4 MPO

3.0.4.1 post-hoc

3.0.5 SOD1

3.0.5.1 post-hoc

4 Lung

4.0.1 CHIA

4.0.1.1 post-hoc

4.0.2 CHIL3

4.0.2.1 post-hoc

4.0.3 EPX

4.0.3.1 post-hoc

4.0.4 MPO

4.0.4.1 post-hoc

4.0.5 SOD1

4.0.5.1 post-hoc

5 Spleen

5.0.1 CHIA

5.0.1.1 post-hoc

5.0.2 CHIL3

5.0.2.1 post-hoc

5.0.3 EPX

5.0.3.1 post-hoc

5.0.4 MPO

5.0.4.1 post-hoc

5.0.5 SOD1

5.0.5.1 post-hoc

6 Combined box plots