library(agricolae)
library(lattice)
library(Rmpfr)

## Loading required package: gmp

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library(HH)

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library(lawstat)
library(readxl)
library(mvnormtest)
library(ggplot2)

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library(ggdendro)
library(ape)

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library(RColorBrewer)
library(gplots)

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library(graphics)
library(DescTools)

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library(pcr)
library(viridis)

## Loading required package: viridisLite

library(hrbrthemes)

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##       Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and

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library(readr)
library(plotly)

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library(readxl)
library(plyr)

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library(writexl)

library(agricolae)
library(lattice)
library(Rmpfr)
library(HH)
library(lawstat)
library(readxl)
library(mvnormtest)
library(ggplot2)
library(ggdendro)
library(ape)
library(RColorBrewer)
library(gplots)
library(graphics)
library(DescTools)
library(readr)
#library(AICcmodavg)
library(plyr)
library(data.table)

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library(writexl)
library(grid)

#uvozimo podatke

kid<- read_excel("F:/misc/qPCR/qPCR_mouse/qPCR_stare_nove.xlsx", 
    sheet = "Kid22")
#View(kid)

#Kidney CCL5

kidCCL5<-kid[,c('CCL5', 'B2M')]

## add grouping variable
group_var <- rep(c('KM', 'KS', 'WM', 'WS', 'H'), each = 9)

group <- rep(c('KM', 'KS', 'WM', 'WS', 'H'), each = 9)

#calculate all values and errors in one step
## mode == 'separate_tube' default
res <- pcr_analyze(kidCCL5,
                   group_var = group_var,
                   reference_gene = 'B2M',
                   reference_group = 'WM')

res

##   group gene normalized calibrated relative_expression     error     lower
## 1     H CCL5   3.411727 -1.4585156            2.748255 0.7964514 1.5823450
## 2    KM CCL5   3.886780 -0.9834635            1.977206 0.9737038 1.0067879
## 3    KS CCL5   4.331727 -0.5385156            1.452477 0.8339274 0.8148397
## 4    WM CCL5   4.870243  0.0000000            1.000000 0.9063205 0.5335441
## 5    WS CCL5   3.831354 -1.0388889            2.054645 0.6899015 1.2736663
##      upper
## 1 4.773234
## 2 3.882988
## 3 2.589086
## 4 1.874259
## 5 3.314498

gg1 <- pcr_analyze(kidCCL5,
  group_var = group_var,
  reference_gene = 'B2M',
  reference_group = 'WM',
  plot = TRUE) +
  labs(x = '', y = 'Relative mRNA expression') +
  ggtitle(label = 'Kidney')


gg1

tst3 <- pcr_test(kidCCL5,
         group_var = group,
         reference_gene = 'B2M',
         reference_group = 'WM',
         test = 'lm')
knitr::kable(tst3, caption = 'Table 16: Linear regression summary')

Table 16: Linear regression summary
gene	term	estimate	p_value	lower	upper
CCL5	group_varH	-1.4585156	0.0007110	-2.262044	-0.6549872
CCL5	group_varKM	-0.9834635	0.0177169	-1.786992	-0.1799351
CCL5	group_varKS	-0.5385156	0.1831785	-1.342044	0.2650128
CCL5	group_varWS	-1.0388889	0.0125824	-1.842417	-0.2353605

Kidney IL-10

kidIL10<-kid[,c('IL10', 'B2M')]

## add grouping variable
group_var <- rep(c('KM', 'KS', 'WM', 'WS', 'H'), each = 9)

group <- rep(c('KM', 'KS', 'WM', 'WS', 'H'), each = 9)

#calculate all values and errors in one step
## mode == 'separate_tube' default
res <- pcr_analyze(kidIL10,
                   group_var = group_var,
                   reference_gene = 'B2M',
                   reference_group = 'WM')

res

##   group gene normalized calibrated relative_expression    error      lower
## 1     H IL10   9.271727  -2.386293           5.2281242 2.407537 0.98538287
## 2    KM IL10  12.710113   1.052092           0.4822683 3.218801 0.05180038
## 3    KS IL10  10.379505  -1.278516           2.4258925 2.319015 0.48615905
## 4    WM IL10  11.658021   0.000000           1.0000000 2.454947 0.18238426
## 5    WS IL10   8.906910  -2.751111           6.7323543 1.056413 3.23709129
##       upper
## 1 27.738743
## 2  4.489981
## 3 12.104998
## 4  5.482929
## 5 14.001643

gg1 <- pcr_analyze(kidIL10,
  group_var = group_var,
  reference_gene = 'B2M',
  reference_group = 'WM',
  plot = TRUE) +
  labs(x = '', y = 'Relative mRNA expression') +
  ggtitle(label = 'Kidney')


gg1

tst3 <- pcr_test(kidIL10,
         group_var = group,
         reference_gene = 'B2M',
         reference_group = 'WM',
         test = 'lm')
knitr::kable(tst3, caption = 'Table 16: Linear regression summary')

Table 16: Linear regression summary
gene	term	estimate	p_value	lower	upper
IL10	group_varH	-2.386293	0.0408455	-4.668251	-0.1043355
IL10	group_varKM	1.052092	0.3570238	-1.229866	3.3340499
IL10	group_varKS	-1.278516	0.2642295	-3.560474	1.0034423
IL10	group_varWS	-2.751111	0.0193713	-5.033069	-0.4691532

Kidney YBX1

kidYBX1<-kid[,c('YBX1', 'B2M')]

## add grouping variable
group_var <- rep(c('KM', 'KS', 'WM', 'WS', 'H'), each = 9)

group <- rep(c('KM', 'KS', 'WM', 'WS', 'H'), each = 9)

#calculate all values and errors in one step
## mode == 'separate_tube' default
res <- pcr_analyze(kidYBX1,
                   group_var = group_var,
                   reference_gene = 'B2M',
                   reference_group = 'WM')

res

##   group gene normalized   calibrated relative_expression     error     lower
## 1     H YBX1  -4.803828 -1.022960069           2.0320840 0.9265431 1.0691149
## 2    KM YBX1  -2.546554  1.234314236           0.4250445 0.6251726 0.2755743
## 3    KS YBX1  -3.772717  0.008151042           0.9943661 0.7245642 0.6017703
## 4    WM YBX1  -3.780868  0.000000000           1.0000000 0.4390523 0.7376190
## 5    WS YBX1  -3.387535  0.393333333           0.7613684 0.2129755 0.6568752
##       upper
## 1 3.8624150
## 2 0.6555866
## 3 1.6430919
## 4 1.3557134
## 5 0.8824841

gg1 <- pcr_analyze(kidYBX1,
  group_var = group_var,
  reference_gene = 'B2M',
  reference_group = 'WM',
  plot = TRUE) +
  labs(x = '', y = 'Relative mRNA expression') +
  ggtitle(label = 'Kidney')


gg1

tst3 <- pcr_test(kidYBX1,
         group_var = group,
         reference_gene = 'B2M',
         reference_group = 'WM',
         test = 'lm')
knitr::kable(tst3, caption = 'Table 16: Linear regression summary')

Table 16: Linear regression summary
gene	term	estimate	p_value	lower	upper
YBX1	group_varH	-1.0229601	0.0014174	-1.6258921	-0.4200281
YBX1	group_varKM	1.2343142	0.0001753	0.6313822	1.8372462
YBX1	group_varKS	0.0081510	0.9783380	-0.5947810	0.6110831
YBX1	group_varWS	0.3933333	0.1948416	-0.2095987	0.9962653

ggplot(res, aes(x=group, y=relative_expression, fill=group))+
  geom_point()+
  geom_errorbar(aes(ymin=relative_expression-error, ymax=relative_expression+error), width=.2,
                 position=position_dodge(.9))

Spleen

#uvozimo podatke

spl<- read_excel("F:/misc/qPCR/qPCR_mouse/qPCR_stare_nove.xlsx", 
    sheet = "Spl22")
#View(kid)

Spleen CCL5

splCCL5<-kid[,c('CCL5', 'B2M')]

## add grouping variable
group_var <- rep(c('KM', 'KS', 'WM', 'WS', 'H'), each = 9)

group <- rep(c('KM', 'KS', 'WM', 'WS', 'H'), each = 9)

#calculate all values and errors in one step
## mode == 'separate_tube' default
res <- pcr_analyze(splCCL5,
                   group_var = group_var,
                   reference_gene = 'B2M',
                   reference_group = 'WM')

res

##   group gene normalized calibrated relative_expression     error     lower
## 1     H CCL5   3.411727 -1.4585156            2.748255 0.7964514 1.5823450
## 2    KM CCL5   3.886780 -0.9834635            1.977206 0.9737038 1.0067879
## 3    KS CCL5   4.331727 -0.5385156            1.452477 0.8339274 0.8148397
## 4    WM CCL5   4.870243  0.0000000            1.000000 0.9063205 0.5335441
## 5    WS CCL5   3.831354 -1.0388889            2.054645 0.6899015 1.2736663
##      upper
## 1 4.773234
## 2 3.882988
## 3 2.589086
## 4 1.874259
## 5 3.314498

gg1 <- pcr_analyze(splCCL5,
  group_var = group_var,
  reference_gene = 'B2M',
  reference_group = 'WM',
  plot = TRUE) +
  labs(x = '', y = 'Relative mRNA expression') +
  ggtitle(label = 'Spleen')


gg1

tst3 <- pcr_test(splCCL5,
         group_var = group,
         reference_gene = 'B2M',
         reference_group = 'WM',
         test = 'lm')
knitr::kable(tst3, caption = 'Table 16: Linear regression summary')

Table 16: Linear regression summary
gene	term	estimate	p_value	lower	upper
CCL5	group_varH	-1.4585156	0.0007110	-2.262044	-0.6549872
CCL5	group_varKM	-0.9834635	0.0177169	-1.786992	-0.1799351
CCL5	group_varKS	-0.5385156	0.1831785	-1.342044	0.2650128
CCL5	group_varWS	-1.0388889	0.0125824	-1.842417	-0.2353605

ggplot(res, aes(x=group, y=relative_expression, fill=group))+
  geom_point()+
  geom_errorbar(aes(ymin=relative_expression-error, ymax=relative_expression+error), width=.2,
                 position=position_dodge(.9))

Spleen YBX1

splYBX1<-kid[,c('YBX1', 'B2M')]

## add grouping variable
group_var <- rep(c('KM', 'KS', 'WM', 'WS', 'H'), each = 9)

group <- rep(c('KM', 'KS', 'WM', 'WS', 'H'), each = 9)

#calculate all values and errors in one step
## mode == 'separate_tube' default
res <- pcr_analyze(splYBX1,
                   group_var = group_var,
                   reference_gene = 'B2M',
                   reference_group = 'WM')

res

##   group gene normalized   calibrated relative_expression     error     lower
## 1     H YBX1  -4.803828 -1.022960069           2.0320840 0.9265431 1.0691149
## 2    KM YBX1  -2.546554  1.234314236           0.4250445 0.6251726 0.2755743
## 3    KS YBX1  -3.772717  0.008151042           0.9943661 0.7245642 0.6017703
## 4    WM YBX1  -3.780868  0.000000000           1.0000000 0.4390523 0.7376190
## 5    WS YBX1  -3.387535  0.393333333           0.7613684 0.2129755 0.6568752
##       upper
## 1 3.8624150
## 2 0.6555866
## 3 1.6430919
## 4 1.3557134
## 5 0.8824841

gg1 <- pcr_analyze(splYBX1,
  group_var = group_var,
  reference_gene = 'B2M',
  reference_group = 'WM',
  plot = TRUE) +
  labs(x = '', y = 'Relative mRNA expression') +
  ggtitle(label = 'Spleen')


gg1

tst3 <- pcr_test(splYBX1,
         group_var = group,
         reference_gene = 'B2M',
         reference_group = 'WM',
         test = 'lm')
knitr::kable(tst3, caption = 'Table 16: Linear regression summary')

Table 16: Linear regression summary
gene	term	estimate	p_value	lower	upper
YBX1	group_varH	-1.0229601	0.0014174	-1.6258921	-0.4200281
YBX1	group_varKM	1.2343142	0.0001753	0.6313822	1.8372462
YBX1	group_varKS	0.0081510	0.9783380	-0.5947810	0.6110831
YBX1	group_varWS	0.3933333	0.1948416	-0.2095987	0.9962653

ggplot(res, aes(x=group, y=relative_expression, fill=group))+
  geom_point()+
  geom_errorbar(aes(ymin=relative_expression-error, ymax=relative_expression+error), width=.2,
                 position=position_dodge(.9))

Spleen IL-10

splIL10<-kid[,c('IL10', 'B2M')]

## add grouping variable
group_var <- rep(c('KM', 'KS', 'WM', 'WS', 'H'), each = 9)

group <- rep(c('KM', 'KS', 'WM', 'WS', 'H'), each = 9)

#calculate all values and errors in one step
## mode == 'separate_tube' default
res <- pcr_analyze(splIL10,
                   group_var = group_var,
                   reference_gene = 'B2M',
                   reference_group = 'WM')

res

##   group gene normalized calibrated relative_expression    error      lower
## 1     H IL10   9.271727  -2.386293           5.2281242 2.407537 0.98538287
## 2    KM IL10  12.710113   1.052092           0.4822683 3.218801 0.05180038
## 3    KS IL10  10.379505  -1.278516           2.4258925 2.319015 0.48615905
## 4    WM IL10  11.658021   0.000000           1.0000000 2.454947 0.18238426
## 5    WS IL10   8.906910  -2.751111           6.7323543 1.056413 3.23709129
##       upper
## 1 27.738743
## 2  4.489981
## 3 12.104998
## 4  5.482929
## 5 14.001643

gg1 <- pcr_analyze(splIL10,
  group_var = group_var,
  reference_gene = 'B2M',
  reference_group = 'WM',
  plot = TRUE) +
  labs(x = '', y = 'Relative mRNA expression') +
  ggtitle(label = 'Spleen')


gg1

tst3 <- pcr_test(splIL10,
         group_var = group,
         reference_gene = 'B2M',
         reference_group = 'WM',
         test = 'lm')
knitr::kable(tst3, caption = 'Table 16: Linear regression summary')

Table 16: Linear regression summary
gene	term	estimate	p_value	lower	upper
IL10	group_varH	-2.386293	0.0408455	-4.668251	-0.1043355
IL10	group_varKM	1.052092	0.3570238	-1.229866	3.3340499
IL10	group_varKS	-1.278516	0.2642295	-3.560474	1.0034423
IL10	group_varWS	-2.751111	0.0193713	-5.033069	-0.4691532

ggplot(res, aes(x=group, y=relative_expression, fill=group))+
  geom_point()+
  geom_errorbar(aes(ymin=relative_expression-error, ymax=relative_expression+error), width=.2,
                 position=position_dodge(.9))

Kidney

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

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

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

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

ANOVA

CCL5

preverjamo kombiniran vpliv starosti in genotipa

razlika med posameznimi vzorci, vse je glede na WM

##             Df Sum Sq Mean Sq F value Pr(>F)
## AG           4  10.14   2.535   1.056  0.427
## Residuals   10  24.00   2.400

post-hoc

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = CCL5 ~ AG, data = lg)
## 
## $AG
##                   diff       lwr      upr     p adj
## KO_ML-H     -0.6175033 -4.780721 3.545714 0.9867384
## KO_ST-H     -1.4408041 -5.604022 2.722414 0.7834614
## WT_ML-H     -1.8867536 -6.049971 2.276464 0.5893660
## WT_ST-H      0.2658939 -3.897324 4.429112 0.9994843
## KO_ST-KO_ML -0.8233008 -4.986518 3.339917 0.9625772
## WT_ML-KO_ML -1.2692502 -5.432468 2.893967 0.8481586
## WT_ST-KO_ML  0.8833972 -3.279820 5.046615 0.9522533
## WT_ML-KO_ST -0.4459494 -4.609167 3.717268 0.9961244
## WT_ST-KO_ST  1.7066980 -2.456520 5.869916 0.6698095
## WT_ST-WT_ML  2.1526475 -2.010570 6.315865 0.4741446

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for CCL5 
## 
## Mean Square Error:  2.400336 
## 
## AG,  means
## 
##           CCL5       std r       Min      Max
## H     3.080926 1.5668249 3 1.3291976 4.348614
## KO_ML 2.463423 2.0350172 3 1.1023295 4.802844
## KO_ST 1.640122 0.8502364 3 0.6832832 2.308926
## WT_ML 1.194173 0.8826256 3 0.5354742 2.197032
## WT_ST 3.346820 1.9757314 3 1.9892467 5.613455
## 
## Alpha: 0.01 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 4.009129 4.177984 4.284076 4.357207 
## 
## Means with the same letter are not significantly different.
## 
##           CCL5 groups
## WT_ST 3.346820      a
## H     3.080926      a
## KO_ML 2.463423      a
## KO_ST 1.640122      a
## WT_ML 1.194173      a

ggplot(lg, aes(x=AG, y=CCL5, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Kidney CCL5")

IL-10

preverjamo kombiniran vpliv starosti in genotipa

razlika med posameznimi vzorci, vse je glede na WM

##             Df Sum Sq Mean Sq F value Pr(>F)
## AG           4  204.5   51.13   1.587  0.252
## Residuals   10  322.2   32.22

post-hoc

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = IL10 ~ AG, data = lg)
## 
## $AG
##                  diff        lwr       upr     p adj
## KO_ML-H     -7.391525 -22.645390  7.862340 0.5317609
## KO_ST-H     -2.151794 -17.405659 13.102071 0.9889948
## WT_ML-H     -6.287149 -21.541014  8.966716 0.6656979
## WT_ST-H      2.369762 -12.884103 17.623627 0.9842666
## KO_ST-KO_ML  5.239731 -10.014134 20.493596 0.7877615
## WT_ML-KO_ML  1.104376 -14.149489 16.358241 0.9991554
## WT_ST-KO_ML  9.761287  -5.492578 25.015152 0.2888020
## WT_ML-KO_ST -4.135355 -19.389220 11.118510 0.8934464
## WT_ST-KO_ST  4.521556 -10.732309 19.775420 0.8602152
## WT_ST-WT_ML  8.656911  -6.596954 23.910775 0.3910090

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for IL10 
## 
## Mean Square Error:  32.22356 
## 
## AG,  means
## 
##             IL10       std r       Min        Max
## H      8.0152567 8.2010482 3 1.5746160 17.2477149
## KO_ML  0.6237318 0.4081605 3 0.1555015  0.9043794
## KO_ST  5.8634629 8.5090626 3 0.9255182 15.6888388
## WT_ML  1.7281079 1.6567332 3 0.1899028  3.4822023
## WT_ST 10.3850186 4.3064053 3 7.6387328 15.3482259
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 10.32722 10.79185 11.06535 11.24030 
## 
## Means with the same letter are not significantly different.
## 
##             IL10 groups
## WT_ST 10.3850186      a
## H      8.0152567      a
## KO_ST  5.8634629      a
## WT_ML  1.7281079      a
## KO_ML  0.6237318      a

ggplot(lg, aes(x=AG, y=IL10, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Kidney IL10")

YBX1

preverjamo kombiniran vpliv starosti in genotipa

razlika med posameznimi vzorci, vse je glede na WM

##             Df Sum Sq Mean Sq F value Pr(>F)  
## AG           4  5.777  1.4444   2.863 0.0808 .
## Residuals   10  5.045  0.5045                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

post-hoc

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = YBX1 ~ AG, data = lg)
## 
## $AG
##                     diff       lwr        upr     p adj
## KO_ML-H     -1.896977909 -3.805693 0.01173697 0.0515953
## KO_ST-H     -1.245246141 -3.153961 0.66346874 0.2733801
## WT_ML-H     -1.320026763 -3.228742 0.58868811 0.2291249
## WT_ST-H     -1.238509045 -3.147224 0.67020583 0.2776767
## KO_ST-KO_ML  0.651731768 -1.256983 2.56044664 0.7911622
## WT_ML-KO_ML  0.576951146 -1.331764 2.48566602 0.8519166
## WT_ST-KO_ML  0.658468864 -1.250246 2.56718374 0.7852988
## WT_ML-KO_ST -0.074780622 -1.983495 1.83393425 0.9999257
## WT_ST-KO_ST  0.006737096 -1.901978 1.91545197 1.0000000
## WT_ST-WT_ML  0.081517717 -1.827197 1.99023259 0.9998952

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for YBX1 
## 
## Mean Square Error:  0.5045404 
## 
## AG,  means
## 
##            YBX1       std r       Min       Max
## H     2.3639123 1.3290349 3 0.8682071 3.4092265
## KO_ML 0.4669344 0.2523510 3 0.3034319 0.7575665
## KO_ST 1.1186661 0.7075751 3 0.6987159 1.9355959
## WT_ML 1.0438855 0.3920457 3 0.7979910 1.4960010
## WT_ST 1.1254032 0.1957665 3 1.0077314 1.3513903
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 1.292244 1.350383 1.384606 1.406497 
## 
## Means with the same letter are not significantly different.
## 
##            YBX1 groups
## H     2.3639123      a
## WT_ST 1.1254032     ab
## KO_ST 1.1186661     ab
## WT_ML 1.0438855     ab
## KO_ML 0.4669344      b

ggplot(lg, aes(x=AG, y=YBX1, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Kidney IL10")

Lung

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

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

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

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

ANOVA

CCL5

preverjamo kombiniran vpliv starosti in genotipa

razlika med posameznimi vzorci, vse je glede na WM

##             Df Sum Sq Mean Sq F value Pr(>F)
## AG           4  0.232  0.0580    0.12  0.972
## Residuals   10  4.815  0.4815

post-hoc

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = CCL5 ~ AG, data = lg)
## 
## $AG
##                    diff       lwr      upr     p adj
## KO_ML-H      0.31518192 -1.549520 2.179884 0.9785968
## KO_ST-H      0.14049294 -1.724209 2.005195 0.9990124
## WT_ML-H      0.34275984 -1.521942 2.207462 0.9710781
## WT_ST-H      0.21037169 -1.654330 2.075074 0.9952720
## KO_ST-KO_ML -0.17468898 -2.039391 1.690013 0.9976898
## WT_ML-KO_ML  0.02757792 -1.837124 1.892280 0.9999985
## WT_ST-KO_ML -0.10481023 -1.969512 1.759892 0.9996887
## WT_ML-KO_ST  0.20226690 -1.662435 2.066969 0.9959328
## WT_ST-KO_ST  0.06987875 -1.794823 1.934581 0.9999377
## WT_ST-WT_ML -0.13238815 -1.997090 1.732314 0.9992179

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for CCL5 
## 
## Mean Square Error:  0.4815404 
## 
## AG,  means
## 
##            CCL5       std r        Min      Max
## H     0.7440314 0.6173710 3 0.05397119 1.244012
## KO_ML 1.0592134 0.3663800 3 0.68380964 1.415848
## KO_ST 0.8845244 0.4566527 3 0.37070239 1.244012
## WT_ML 1.0867913 0.5341971 3 0.60709744 1.662476
## WT_ST 0.9544031 1.1825491 3 0.18492344 2.316050
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 1.262446 1.319245 1.352678 1.374065 
## 
## Means with the same letter are not significantly different.
## 
##            CCL5 groups
## WT_ML 1.0867913      a
## KO_ML 1.0592134      a
## WT_ST 0.9544031      a
## KO_ST 0.8845244      a
## H     0.7440314      a

ggplot(lg, aes(x=AG, y=CCL5, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Kidney CCL5")

IL-10

preverjamo kombiniran vpliv starosti in genotipa

razlika med posameznimi vzorci, vse je glede na WM

##             Df Sum Sq Mean Sq F value Pr(>F)
## AG           4  7.543   1.886   0.806  0.549
## Residuals   10 23.396   2.340

post-hoc

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = IL10 ~ AG, data = lg)
## 
## $AG
##                     diff       lwr      upr     p adj
## KO_ML-H     -1.541329738 -5.651545 2.568885 0.7334179
## KO_ST-H     -1.549295389 -5.659510 2.560920 0.7299634
## WT_ML-H     -2.076636371 -6.186851 2.033579 0.4948028
## WT_ST-H     -1.647364611 -5.757580 2.462850 0.6866835
## KO_ST-KO_ML -0.007965651 -4.118181 4.102249 1.0000000
## WT_ML-KO_ML -0.535306632 -4.645522 3.574908 0.9918430
## WT_ST-KO_ML -0.106034873 -4.216250 4.004180 0.9999859
## WT_ML-KO_ST -0.527340982 -4.637556 3.582874 0.9922915
## WT_ST-KO_ST -0.098069222 -4.208284 4.012146 0.9999897
## WT_ST-WT_ML  0.429271760 -3.680943 4.539487 0.9964837

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for IL10 
## 
## Mean Square Error:  2.339607 
## 
## AG,  means
## 
##           IL10       std r       Min      Max
## H     3.089799 2.6394119 3 0.4638296 5.742451
## KO_ML 1.548469 1.3346282 3 0.2682531 2.931556
## KO_ST 1.540503 0.6941047 3 1.0127888 2.326778
## WT_ML 1.013163 0.1925914 3 0.7937005 1.154019
## WT_ST 1.442434 1.5593054 3 0.2537829 3.207983
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 2.782711 2.907907 2.981602 3.028743 
## 
## Means with the same letter are not significantly different.
## 
##           IL10 groups
## H     3.089799      a
## KO_ML 1.548469      a
## KO_ST 1.540503      a
## WT_ST 1.442434      a
## WT_ML 1.013163      a

ggplot(lg, aes(x=AG, y=IL10, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Kidney IL10")

YBX1

preverjamo kombiniran vpliv starosti in genotipa

razlika med posameznimi vzorci, vse je glede na WM

##             Df Sum Sq Mean Sq F value  Pr(>F)   
## AG           4  7.162  1.7904   7.347 0.00499 **
## Residuals   10  2.437  0.2437                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

post-hoc

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = YBX1 ~ AG, data = lg)
## 
## $AG
##                    diff        lwr          upr     p adj
## KO_ML-H      1.05905704 -0.2674636  2.385577633 0.1379270
## KO_ST-H      1.79852560  0.4720050  3.125046188 0.0083183
## WT_ML-H      0.47533783 -0.8511828  1.801858421 0.7626330
## WT_ST-H     -0.01974186 -1.3462624  1.306778736 0.9999984
## KO_ST-KO_ML  0.73946855 -0.5870520  2.065989147 0.4068877
## WT_ML-KO_ML -0.58371921 -1.9102398  0.742801380 0.6138382
## WT_ST-KO_ML -1.07879890 -2.4053195  0.247721695 0.1282118
## WT_ML-KO_ST -1.32318777 -2.6497084  0.003332825 0.0506458
## WT_ST-KO_ST -1.81826745 -3.1447880 -0.491746860 0.0077346
## WT_ST-WT_ML -0.49507968 -1.8216003  0.831440907 0.7365865

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for YBX1 
## 
## Mean Square Error:  0.2436923 
## 
## AG,  means
## 
##            YBX1       std r        Min       Max
## H     0.5684313 0.3363702 3 0.19896072 0.8569139
## KO_ML 1.6274884 0.6240759 3 1.18418041 2.3411579
## KO_ST 2.3669569 0.6088919 3 1.77426588 2.9908500
## WT_ML 1.0437692 0.3831783 3 0.73656711 1.4731342
## WT_ST 0.5486895 0.4452761 3 0.08862693 0.9775363
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##         2         3         4         5 
## 0.8980852 0.9384907 0.9622747 0.9774890 
## 
## Means with the same letter are not significantly different.
## 
##            YBX1 groups
## KO_ST 2.3669569      a
## KO_ML 1.6274884     ab
## WT_ML 1.0437692     bc
## H     0.5684313      c
## WT_ST 0.5486895      c

ggplot(lg, aes(x=AG, y=YBX1, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Kidney IL10")

Liver

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

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

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

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

ANOVA

CCL5

preverjamo kombiniran vpliv starosti in genotipa

razlika med posameznimi vzorci, vse je glede na WM

##             Df Sum Sq Mean Sq F value Pr(>F)  
## AG           4  94.23  23.557   5.009 0.0177 *
## Residuals   10  47.03   4.703                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

post-hoc

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = CCL5 ~ AG, data = lg)
## 
## $AG
##                    diff        lwr        upr     p adj
## KO_ML-H     -6.94697358 -12.774692 -1.1192547 0.0187975
## KO_ST-H     -5.35760207 -11.185321  0.4701168 0.0753988
## WT_ML-H     -6.54521077 -12.372930 -0.7174919 0.0266650
## WT_ST-H     -5.44882058 -11.276539  0.3788983 0.0696428
## KO_ST-KO_ML  1.58937151  -4.238347  7.4170904 0.8914510
## WT_ML-KO_ML  0.40176281  -5.425956  6.2294817 0.9993032
## WT_ST-KO_ML  1.49815300  -4.329566  7.3258719 0.9098686
## WT_ML-KO_ST -1.18760870  -7.015328  4.6401102 0.9584561
## WT_ST-KO_ST -0.09121851  -5.918937  5.7365004 0.9999981
## WT_ST-WT_ML  1.09639019  -4.731329  6.9241091 0.9685951

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for CCL5 
## 
## Mean Square Error:  4.70339 
## 
## AG,  means
## 
##            CCL5       std r         Min       Max
## H     7.5614010 2.7485949 3 5.858598389 10.732311
## KO_ML 0.6144274 0.6993891 3 0.014185448  1.382445
## KO_ST 2.2037989 3.8128618 3 0.002178004  6.606512
## WT_ML 1.0161902 0.2127968 3 0.773186787  1.169227
## WT_ST 2.1125804 0.9433097 3 1.564341570  3.201813
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 3.945501 4.123012 4.227501 4.294341 
## 
## Means with the same letter are not significantly different.
## 
##            CCL5 groups
## H     7.5614010      a
## KO_ST 2.2037989      b
## WT_ST 2.1125804      b
## WT_ML 1.0161902      b
## KO_ML 0.6144274      b

ggplot(lg, aes(x=AG, y=CCL5, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("liver CCL5")

IL-10

preverjamo kombiniran vpliv starosti in genotipa

razlika med posameznimi vzorci, vse je glede na WM

##             Df  Sum Sq Mean Sq F value   Pr(>F)    
## AG           4 2861608  715402   75.38 1.99e-07 ***
## Residuals   10   94911    9491                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

post-hoc

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = IL10 ~ AG, data = lg)
## 
## $AG
##                      diff        lwr       upr     p adj
## KO_ML-H     -1095.8377574 -1357.6273 -834.0482 0.0000006
## KO_ST-H     -1094.9144694 -1356.7040 -833.1249 0.0000006
## WT_ML-H     -1096.3211338 -1358.1107 -834.5316 0.0000006
## WT_ST-H     -1080.2836853 -1342.0732 -818.4941 0.0000007
## KO_ST-KO_ML     0.9232880  -260.8663  262.7129 1.0000000
## WT_ML-KO_ML    -0.4833764  -262.2729  261.3062 1.0000000
## WT_ST-KO_ML    15.5540721  -246.2355  277.3436 0.9996123
## WT_ML-KO_ST    -1.4066644  -263.1962  260.3829 1.0000000
## WT_ST-KO_ST    14.6307841  -247.1588  276.4203 0.9996956
## WT_ST-WT_ML    16.0374485  -245.7521  277.8270 0.9995625

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for IL10 
## 
## Mean Square Error:  9491.142 
## 
## AG,  means
## 
##              IL10        std r          Min         Max
## H     1097.351039 216.073877 3 850.20484294 1250.538131
## KO_ML    1.513281   1.433431 3   0.11489065    2.979355
## KO_ST    2.436569   3.536856 3   0.39456457    6.520579
## WT_ML    1.029905   0.306321 3   0.74915354    1.356604
## WT_ST   17.067353  27.443226 3   0.07398278   48.727580
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##        2        3        4        5 
## 177.2376 185.2117 189.9054 192.9080 
## 
## Means with the same letter are not significantly different.
## 
##              IL10 groups
## H     1097.351039      a
## WT_ST   17.067353      b
## KO_ST    2.436569      b
## KO_ML    1.513281      b
## WT_ML    1.029905      b

ggplot(lg, aes(x=AG, y=IL10, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("liver IL10")

YBX1

preverjamo kombiniran vpliv starosti in genotipa

razlika med posameznimi vzorci, vse je glede na WM

##             Df Sum Sq Mean Sq F value Pr(>F)
## AG           4  0.271 0.06774    0.25  0.903
## Residuals   10  2.708 0.27084

post-hoc

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = YBX1 ~ AG, data = lg)
## 
## $AG
##                     diff       lwr      upr     p adj
## KO_ML-H     -0.323502903 -1.721972 1.074967 0.9360986
## KO_ST-H     -0.254166464 -1.652636 1.144303 0.9722205
## WT_ML-H     -0.325395477 -1.723865 1.073074 0.9348482
## WT_ST-H     -0.380157006 -1.778627 1.018313 0.8925448
## KO_ST-KO_ML  0.069336439 -1.329133 1.467806 0.9998106
## WT_ML-KO_ML -0.001892574 -1.400362 1.396577 1.0000000
## WT_ST-KO_ML -0.056654103 -1.455124 1.341815 0.9999151
## WT_ML-KO_ST -0.071229013 -1.469699 1.327241 0.9997892
## WT_ST-KO_ST -0.125990542 -1.524460 1.272479 0.9980151
## WT_ST-WT_ML -0.054761530 -1.453231 1.343708 0.9999258

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for YBX1 
## 
## Mean Square Error:  0.2708444 
## 
## AG,  means
## 
##            YBX1        std r       Min      Max
## H     1.3276052 0.23587216 3 1.1159971 1.581909
## KO_ML 1.0041023 0.80983572 3 0.3698469 1.916313
## KO_ST 1.0734387 0.79271811 3 0.5952909 1.988481
## WT_ML 1.0022097 0.08019560 3 0.9096184 1.049717
## WT_ST 0.9474482 0.08898922 3 0.8506672 1.025741
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##         2         3         4         5 
## 0.9467963 0.9893934 1.0144674 1.0305069 
## 
## Means with the same letter are not significantly different.
## 
##            YBX1 groups
## H     1.3276052      a
## KO_ST 1.0734387      a
## KO_ML 1.0041023      a
## WT_ML 1.0022097      a
## WT_ST 0.9474482      a

ggplot(lg, aes(x=AG, y=YBX1, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("liver IL10")

Spleen

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

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

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

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

ANOVA

CCL5

preverjamo kombiniran vpliv starosti in genotipa

razlika med posameznimi vzorci, vse je glede na WM

##             Df Sum Sq Mean Sq F value Pr(>F)
## AG           4  0.505  0.1263    0.72  0.598
## Residuals   10  1.755  0.1755

post-hoc

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = CCL5 ~ AG, data = lg)
## 
## $AG
##                    diff        lwr       upr     p adj
## KO_ML-H     -0.42736705 -1.5530419 0.6983078 0.7251040
## KO_ST-H     -0.14194641 -1.2676213 0.9837285 0.9927795
## WT_ML-H      0.03756988 -1.0881050 1.1632447 0.9999607
## WT_ST-H      0.07573500 -1.0499399 1.2014099 0.9993670
## KO_ST-KO_ML  0.28542064 -0.8402542 1.4110955 0.9137549
## WT_ML-KO_ML  0.46493693 -0.6607379 1.5906118 0.6641008
## WT_ST-KO_ML  0.50310206 -0.6225728 1.6287769 0.6010008
## WT_ML-KO_ST  0.17951629 -0.9461586 1.3051912 0.9826813
## WT_ST-KO_ST  0.21768141 -0.9079934 1.3433563 0.9653989
## WT_ST-WT_ML  0.03816512 -1.0875097 1.1638400 0.9999582

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for CCL5 
## 
## Mean Square Error:  0.1754849 
## 
## AG,  means
## 
##            CCL5       std r       Min      Max
## H     1.0385085 0.3150740 3 0.7202984 1.350350
## KO_ML 0.6111415 0.3490796 3 0.2819152 0.977160
## KO_ST 0.8965621 0.4718398 3 0.4223956 1.366040
## WT_ML 1.0760784 0.4593233 3 0.5770094 1.481098
## WT_ST 1.1142435 0.4718957 3 0.7845841 1.654811
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##         2         3         4         5 
## 0.7621079 0.7963958 0.8165787 0.8294894 
## 
## Means with the same letter are not significantly different.
## 
##            CCL5 groups
## WT_ST 1.1142435      a
## WT_ML 1.0760784      a
## H     1.0385085      a
## KO_ST 0.8965621      a
## KO_ML 0.6111415      a

ggplot(lg, aes(x=AG, y=CCL5, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Spleen CCL5")

IL-10

preverjamo kombiniran vpliv starosti in genotipa

razlika med posameznimi vzorci, vse je glede na WM

##             Df Sum Sq Mean Sq F value  Pr(>F)   
## AG           4  6.494  1.6234   6.144 0.00922 **
## Residuals   10  2.642  0.2642                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

post-hoc

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = IL10 ~ AG, data = lg)
## 
## $AG
##                   diff         lwr        upr     p adj
## KO_ML-H     -1.3210312 -2.70233674 0.06027428 0.0624667
## KO_ST-H     -0.8608557 -2.24216124 0.52044978 0.3104635
## WT_ML-H     -0.7227432 -2.10404873 0.65856230 0.4635281
## WT_ST-H      0.5314414 -0.84986412 1.91274690 0.7159558
## KO_ST-KO_ML  0.4601755 -0.92113002 1.84148100 0.8047279
## WT_ML-KO_ML  0.5982880 -0.78301750 1.97959352 0.6267063
## WT_ST-KO_ML  1.8524726  0.47116711 3.23377813 0.0089410
## WT_ML-KO_ST  0.1381125 -1.24319299 1.51941803 0.9970275
## WT_ST-KO_ST  1.3922971  0.01099161 2.77360264 0.0480080
## WT_ST-WT_ML  1.2541846 -0.12712091 2.63549012 0.0798620

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for IL10 
## 
## Mean Square Error:  0.2642368 
## 
## AG,  means
## 
##            IL10       std r       Min       Max
## H     1.7293937 0.6235237 3 1.2814505 2.4415169
## KO_ML 0.4083625 0.3020353 3 0.1515408 0.7411193
## KO_ST 0.8685380 0.1760156 3 0.6962985 1.0481010
## WT_ML 1.0066505 0.1380757 3 0.8493579 1.1078617
## WT_ST 2.2608351 0.8894551 3 1.3266408 3.0975147
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##         2         3         4         5 
## 0.9351758 0.9772501 1.0020164 1.0178590 
## 
## Means with the same letter are not significantly different.
## 
##            IL10 groups
## WT_ST 2.2608351      a
## H     1.7293937     ab
## WT_ML 1.0066505     bc
## KO_ST 0.8685380     bc
## KO_ML 0.4083625      c

ggplot(lg, aes(x=AG, y=IL10, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Spleen IL10")

YBX1

preverjamo kombiniran vpliv starosti in genotipa

razlika med posameznimi vzorci, vse je glede na WM

##             Df Sum Sq Mean Sq F value Pr(>F)
## AG           4 0.0911 0.02278   0.186   0.94
## Residuals   10 1.2244 0.12244

post-hoc

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = YBX1 ~ AG, data = lg)
## 
## $AG
##                     diff        lwr       upr     p adj
## KO_ML-H      0.058358446 -0.8819138 0.9986307 0.9995394
## KO_ST-H     -0.080893727 -1.0211660 0.8593785 0.9983411
## WT_ML-H      0.001344793 -0.9389275 0.9416170 1.0000000
## WT_ST-H     -0.165498250 -1.1057705 0.7747740 0.9752317
## KO_ST-KO_ML -0.139252174 -1.0795244 0.8010201 0.9868130
## WT_ML-KO_ML -0.057013653 -0.9972859 0.8832586 0.9995799
## WT_ST-KO_ML -0.223856696 -1.1641290 0.7164156 0.9297195
## WT_ML-KO_ST  0.082238520 -0.8580337 1.0225108 0.9982311
## WT_ST-KO_ST -0.084604522 -1.0248768 0.8556677 0.9980247
## WT_ST-WT_ML -0.166843043 -1.1071153 0.7734292 0.9745020

## 
## Study: test ~ "AG"
## 
## Duncan's new multiple range test
## for YBX1 
## 
## Mean Square Error:  0.1224394 
## 
## AG,  means
## 
##            YBX1       std r       Min       Max
## H     1.0184707 0.2917317 3 0.6867123 1.2349430
## KO_ML 1.0768291 0.6419441 3 0.4615134 1.7424426
## KO_ST 0.9375770 0.1482087 3 0.8376645 1.1078617
## WT_ML 1.0198155 0.2543739 3 0.8280431 1.3083765
## WT_ST 0.8529724 0.1683010 3 0.6587385 0.9555773
## 
## Alpha: 0.05 ; DF Error: 10 
## 
## Critical Range
##         2         3         4         5 
## 0.6365861 0.6652266 0.6820853 0.6928696 
## 
## Means with the same letter are not significantly different.
## 
##            YBX1 groups
## KO_ML 1.0768291      a
## WT_ML 1.0198155      a
## H     1.0184707      a
## KO_ST 0.9375770      a
## WT_ST 0.8529724      a

ggplot(lg, aes(x=AG, y=YBX1, fill=AG))+
  geom_boxplot()+
    theme_classic()+
    theme(plot.title = element_text(hjust = 0.5))+
    ggtitle("Spleen YBX1")

Combined box plots

combk<- read_excel("F:/misc/qPCR/qPCR_mouse/qPCR_stare_nove.xlsx", 
    sheet = "combk2")

combli<- read_excel("F:/misc/qPCR/qPCR_mouse/qPCR_stare_nove.xlsx", 
    sheet = "combli2")

comblu<- read_excel("F:/misc/qPCR/qPCR_mouse/qPCR_stare_nove.xlsx", 
    sheet = "comblu2")

combs<- read_excel("F:/misc/qPCR/qPCR_mouse/qPCR_stare_nove.xlsx", 
    sheet = "combs2")

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 4.2.0 (2022-04-22 ucrt)
## 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.utf8 
## [2] LC_CTYPE=English_United Kingdom.utf8   
## [3] LC_MONETARY=English_United Kingdom.utf8
## [4] LC_NUMERIC=C                           
## [5] LC_TIME=English_United Kingdom.utf8    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] data.table_1.14.0   writexl_1.4.0       plyr_1.8.7         
##  [4] plotly_4.10.0       readr_2.1.1         hrbrthemes_0.8.0   
##  [7] viridis_0.6.2       viridisLite_0.4.0   pcr_1.2.2          
## [10] DescTools_0.99.44   gplots_3.1.1        RColorBrewer_1.1-2 
## [13] ape_5.5             ggdendro_0.1.22     ggplot2_3.3.5      
## [16] mvnormtest_0.1-9    readxl_1.3.1        lawstat_3.4        
## [19] HH_3.1-43           gridExtra_2.3       multcomp_1.4-17    
## [22] TH.data_1.1-0       MASS_7.3-56         survival_3.3-1     
## [25] mvtnorm_1.1-3       latticeExtra_0.6-29 Rmpfr_0.8-7        
## [28] gmp_0.6-2.1         lattice_0.20-45     agricolae_1.3-5    
## 
## loaded via a namespace (and not attached):
##   [1] backports_1.2.1    Hmisc_4.6-0        systemfonts_1.0.3 
##   [4] lazyeval_0.2.2     splines_4.2.0      AlgDesign_1.2.0   
##   [7] digest_0.6.27      htmltools_0.5.2    fansi_0.5.0       
##  [10] magrittr_2.0.1     checkmate_2.0.0    cluster_2.1.3     
##  [13] tzdb_0.2.0         extrafont_0.17     Kendall_2.2       
##  [16] sandwich_3.0-1     extrafontdb_1.0    jpeg_0.1-9        
##  [19] colorspace_2.0-2   haven_2.4.3        rbibutils_2.2.7   
##  [22] xfun_0.29          dplyr_1.0.7        crayon_1.4.2      
##  [25] jsonlite_1.7.2     Exact_3.1          zoo_1.8-9         
##  [28] glue_1.4.2         gtable_0.3.0       questionr_0.7.5   
##  [31] Rttf2pt1_1.3.9     abind_1.4-5        scales_1.1.1      
##  [34] DBI_1.1.2          miniUI_0.1.1.1     Rcpp_1.0.7        
##  [37] xtable_1.8-4       htmlTable_2.3.0    foreign_0.8-82    
##  [40] proxy_0.4-26       Formula_1.2-4      vcd_1.4-9         
##  [43] htmlwidgets_1.5.4  httr_1.4.2         ellipsis_0.3.2    
##  [46] farver_2.1.0       pkgconfig_2.0.3    nnet_7.3-17       
##  [49] sass_0.4.0         utf8_1.2.2         labeling_0.4.2    
##  [52] tidyselect_1.1.1   rlang_0.4.11       reshape2_1.4.4    
##  [55] later_1.3.0        munsell_0.5.0      cellranger_1.1.0  
##  [58] tools_4.2.0        generics_0.1.1     evaluate_0.14     
##  [61] stringr_1.4.0      fastmap_1.1.0      yaml_2.2.1        
##  [64] knitr_1.37         caTools_1.18.2     purrr_0.3.4       
##  [67] rootSolve_1.8.2.3  nlme_3.1-157       mime_0.12         
##  [70] leaps_3.1          compiler_4.2.0     rstudioapi_0.13   
##  [73] png_0.1-7          e1071_1.7-9        klaR_0.6-15       
##  [76] tibble_3.1.4       bslib_0.3.1        stringi_1.7.4     
##  [79] highr_0.9          forcats_0.5.1      gdtools_0.2.3     
##  [82] Matrix_1.4-1       vctrs_0.3.8        pillar_1.6.4      
##  [85] lifecycle_1.0.1    combinat_0.0-8     Rdpack_2.1.3      
##  [88] lmtest_0.9-39      jquerylib_0.1.4    bitops_1.0-7      
##  [91] lmom_2.8           httpuv_1.6.4       R6_2.5.1          
##  [94] promises_1.2.0.1   KernSmooth_2.23-20 gld_2.6.4         
##  [97] codetools_0.2-18   boot_1.3-28        gtools_3.9.2      
## [100] assertthat_0.2.1   withr_2.4.3        expm_0.999-6      
## [103] parallel_4.2.0     hms_1.1.1          rpart_4.1.16      
## [106] labelled_2.9.0     tidyr_1.1.4        class_7.3-20      
## [109] rmarkdown_2.11     shiny_1.7.1        base64enc_0.1-3

qPCR_mouse_YBX1_IL10_CCl5

TS

07 June 2022

Kidney IL-10

Kidney YBX1

Spleen

Spleen CCL5

Spleen YBX1

Spleen IL-10

Kidney

CCL5

post-hoc

IL-10

post-hoc

YBX1

post-hoc

Lung

CCL5

post-hoc

IL-10

post-hoc

YBX1

post-hoc

Liver

CCL5

post-hoc

IL-10

post-hoc

YBX1

post-hoc

Spleen

CCL5

post-hoc

IL-10

post-hoc

YBX1

post-hoc

Combined box plots