library(agricolae)
library(lattice)
library(Rmpfr)
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## Loading required package: gmp
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## 
## Attaching package: 'gmp'
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## 
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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)
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library(hrbrthemes)
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library(readr)
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library(plotly)
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#MPO

table1 <- read_delim("C:/Users/tsever/Documents/HL60qPCR/20210223_lgmn_mpo_epx_pd_kappa.txt", 
    "\t", escape_double = FALSE, trim_ws = TRUE)
## Rows: 9 Columns: 9
## -- Column specification --------------------------------------------------------
## Delimiter: "\t"
## chr (3): Well, Group, WellName
## dbl (5): LGMN, MPO, EPX, GUSB, HPRT1
## lgl (1): Ct
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
#View(table1)
table2<- read_excel("C:/Users/tsever/Documents/HL60qPCR/20210223_lgmn_mpo_epx_pd_kappa.xlsx", 
     sheet = "Sheet2")
# default mode delta_delta_ct
## locate and read raw ct data
fl <- system.file('extdata', "C:/Users/tsever/Documents/HL60qPCR/20210223_lgmn_mpo_epx_pd_kappa.txt", package = 'pcr')
ct1 <- read.delim("C:/Users/tsever/Documents/HL60qPCR/20210223_lgmn_mpo_epx_pd_kappa.txt")


table2$LGMN_Ct <- as.numeric(table2$LGMN_Ct)
table2$MPO_Ct <- as.numeric(table2$MPO_Ct)
table2$EPX_Ct <- as.numeric(table2$EPX_Ct)
table2$HPRT1_Ct <- as.numeric(table2$HPRT1_Ct)
table2$GUSB_Ct <- as.numeric(table2$GUSB_Ct)

table2<-as.data.frame(table2)


## add grouping variable
group_var <- rep(c('wt','sc', 'lgmn'), each = 3)

# calculate all values and errors in one step
## mode == 'separate_tube' default
res <- pcr_analyze(table2,
                   group_var = group_var,
                   reference_gene = 'GUSB_Ct',
                   reference_group = 'sc')
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

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## returning NA

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## returning NA
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introduced by coercion

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## na.rm): NAs introduced by coercion

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## na.rm): NAs introduced by coercion
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res
##    group     gene normalized calibrated relative_expression      error lower
## 1   lgmn    Group         NA         NA                  NA         NA    NA
## 2     sc    Group         NA         NA                  NA         NA    NA
## 3     wt    Group         NA         NA                  NA         NA    NA
## 4   lgmn  LGMN_Ct         NA         NA                  NA 0.13316656    NA
## 5     sc  LGMN_Ct         NA         NA                  NA 0.05567764    NA
## 6     wt  LGMN_Ct         NA         NA                  NA 0.07637626    NA
## 7   lgmn   MPO_Ct         NA         NA                  NA 0.10519823    NA
## 8     sc   MPO_Ct         NA         NA                  NA 0.07831560    NA
## 9     wt   MPO_Ct         NA         NA                  NA 0.23395156    NA
## 10  lgmn   EPX_Ct         NA         NA                  NA 0.22509257    NA
## 11    sc   EPX_Ct         NA         NA                  NA 0.23480488    NA
## 12    wt   EPX_Ct         NA         NA                  NA 0.26166136    NA
## 13  lgmn HPRT1_Ct         NA         NA                  NA 0.13699148    NA
## 14    sc HPRT1_Ct         NA         NA                  NA 0.07302967    NA
## 15    wt HPRT1_Ct         NA         NA                  NA 0.10708252    NA
##    upper
## 1     NA
## 2     NA
## 3     NA
## 4     NA
## 5     NA
## 6     NA
## 7     NA
## 8     NA
## 9     NA
## 10    NA
## 11    NA
## 12    NA
## 13    NA
## 14    NA
## 15    NA
HL60 <- read_excel("F:/misc/qPCR/HL60qPCR.xlsx", 
    sheet = "Sheet8")
View(HL60)
HL60<- as.data.frame(HL60)
ggplot(HL60, aes(x=gene, y=Ct, fill=treatment))+
  geom_boxplot()+
  facet_wrap(~gene, scale="free")

ggplot(HL60, aes(x=gene, y=Ct, fill=treatment))+
  geom_boxplot()+
  facet_wrap(~treatment)

ggplot(HL60, aes(x=gene, y=Ct, fill=treatment))+
geom_violin() +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)
    ) +
    ggtitle("Violin chart") +
    xlab("")
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aov1=aov(Ct~ gene + treatment  , HL60)
summary(aov1)
##             Df Sum Sq Mean Sq F value  Pr(>F)   
## gene         3 22.111   7.370   5.804 0.00544 **
## treatment    1  5.037   5.037   3.966 0.06099 . 
## Residuals   19 24.129   1.270                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
aov2=aov(Ct~ gene * treatment  , HL60)
summary(aov2)
##                Df Sum Sq Mean Sq F value   Pr(>F)    
## gene            3 22.111   7.370   83.58 5.44e-10 ***
## treatment       1  5.037   5.037   57.12 1.15e-06 ***
## gene:treatment  3 22.718   7.573   85.88 4.44e-10 ***
## Residuals      16  1.411   0.088                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
aov3=aov(Ct ~ gene, HL60)
summary(aov3)
##             Df Sum Sq Mean Sq F value Pr(>F)   
## gene         3  22.11   7.370   5.054 0.0091 **
## Residuals   20  29.17   1.458                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov2)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Ct ~ gene * treatment, data = HL60)
## 
## $gene
##                  diff        lwr        upr     p adj
## CCR5-CCL5 -0.01387997 -0.5043864  0.4766264 0.9998012
## IL10-CCL5  2.24246702  1.7519606  2.7329734 0.0000000
## YBX1-CCL5  0.09801023 -0.3924962  0.5885166 0.9390981
## IL10-CCR5  2.25634699  1.7658406  2.7468534 0.0000000
## YBX1-CCR5  0.11189019 -0.3786162  0.6023966 0.9130879
## YBX1-IL10 -2.14445679 -2.6349632 -1.6539504 0.0000000
## 
## $treatment
##                diff     lwr      upr   p adj
## lgmn-ctrl 0.9162554 0.65926 1.173251 1.1e-06
## 
## $`gene:treatment`
##                              diff        lwr        upr     p adj
## CCR5:ctrl-CCL5:ctrl  0.0754648154 -0.7639658  0.9148954 0.9999785
## IL10:ctrl-CCL5:ctrl -0.0028425885 -0.8422732  0.8365880 1.0000000
## YBX1:ctrl-CCL5:ctrl -0.0027384243 -0.8421690  0.8366921 1.0000000
## CCL5:lgmn-CCL5:ctrl -0.2121013771 -1.0515319  0.6273292 0.9844278
## CCR5:lgmn-CCL5:ctrl -0.3153261277 -1.1547567  0.5241044 0.8859303
## IL10:lgmn-CCL5:ctrl  4.2756752512  3.4362447  5.1151058 0.0000000
## YBX1:lgmn-CCL5:ctrl -0.0133425018 -0.8527731  0.8260881 1.0000000
## IL10:ctrl-CCR5:ctrl -0.0783074039 -0.9177380  0.7611232 0.9999724
## YBX1:ctrl-CCR5:ctrl -0.0782032398 -0.9176338  0.7612273 0.9999726
## CCL5:lgmn-CCR5:ctrl -0.2875661925 -1.1269968  0.5518644 0.9247515
## CCR5:lgmn-CCR5:ctrl -0.3907909431 -1.2302215  0.4486396 0.7376102
## IL10:lgmn-CCR5:ctrl  4.2002104358  3.3607799  5.0396410 0.0000000
## YBX1:lgmn-CCR5:ctrl -0.0888073172 -0.9282379  0.7506232 0.9999354
## YBX1:ctrl-IL10:ctrl  0.0001041642 -0.8393264  0.8395347 1.0000000
## CCL5:lgmn-IL10:ctrl -0.2092587886 -1.0486894  0.6301718 0.9855649
## CCR5:lgmn-IL10:ctrl -0.3124835392 -1.1519141  0.5269470 0.8903407
## IL10:lgmn-IL10:ctrl  4.2785178397  3.4390873  5.1179484 0.0000000
## YBX1:lgmn-IL10:ctrl -0.0104999133 -0.8499305  0.8289307 1.0000000
## CCL5:lgmn-YBX1:ctrl -0.2093629528 -1.0487935  0.6300676 0.9855244
## CCR5:lgmn-YBX1:ctrl -0.3125877034 -1.1520183  0.5268429 0.8901809
## IL10:lgmn-YBX1:ctrl  4.2784136755  3.4389831  5.1178442 0.0000000
## YBX1:lgmn-YBX1:ctrl -0.0106040775 -0.8500346  0.8288265 1.0000000
## CCR5:lgmn-CCL5:lgmn -0.1032247506 -0.9426553  0.7362058 0.9998235
## IL10:lgmn-CCL5:lgmn  4.4877766283  3.6483461  5.3272072 0.0000000
## YBX1:lgmn-CCL5:lgmn  0.1987588753 -0.6406717  1.0381894 0.9892334
## IL10:lgmn-CCR5:lgmn  4.5910013789  3.7515708  5.4304319 0.0000000
## YBX1:lgmn-CCR5:lgmn  0.3019836259 -0.5374469  1.1414142 0.9057776
## YBX1:lgmn-IL10:lgmn -4.2890177530 -5.1284483 -3.4495872 0.0000000
CHI3L1:lgmn-CHI3L1:ctrl 1.454205599 0.4751993 2.43321185 0.0008322
CHID1:lgmn-CHID1:ctrl -0.460561225 -1.4395675 0.51844503 0.8532015
CHIT1:lgmn-CHIT1:ctrl 0.375245124 -0.6037611 1.35425137 0.9562775
EPX:lgmn-EPX:ctrl 0.401270782 -0.5777355 1.38027703 0.9328853
LGMN:lgmn-LGMN:ctrl 3.906294973 2.9272887 4.88530122 0.0000000
MPO:lgmn-MPO:ctrl -0.748406513 -1.7274128 0.23059974 0.2589031 
CHI3L1 <- read_excel("C:/Users/tsever/Desktop/HL60qPCR.xlsx", 
    sheet = "CHI3L1")


CHI3L1<-as.data.frame(CHI3L1)
mod<-aov( Ct ~ treatment, data = CHI3L1)
#comparison<-duncan.test(model,'`Well Name`')
duncan.test(mod,'treatment', alpha = 0.01, console=TRUE)
## 
## Study: mod ~ "treatment"
## 
## Duncan's new multiple range test
## for Ct 
## 
## Mean Square Error:  0.01037931 
## 
## treatment,  means
## 
##            Ct        std r       Min      Max
## ctrl 1.003680 0.10596674 3 0.9075192 1.117287
## lgmn 2.457885 0.09762001 3 2.3784142 2.566852
## 
## Alpha: 0.01 ; DF Error: 4 
## 
## Critical Range
##         2 
## 0.3829974 
## 
## Means with the same letter are not significantly different.
## 
##            Ct groups
## lgmn 2.457885      a
## ctrl 1.003680      b
t.test(CHI3L1[1:3,'Ct'],CHI3L1[4:6,'Ct'], conf.level = 0.99)
## 
##  Welch Two Sample t-test
## 
## data:  CHI3L1[1:3, "Ct"] and CHI3L1[4:6, "Ct"]
## t = 17.482, df = 3.9734, p-value = 6.593e-05
## alternative hypothesis: true difference in means is not equal to 0
## 99 percent confidence interval:
##  1.069462 1.838950
## sample estimates:
## mean of x mean of y 
##  2.457885  1.003680
ggplot(CHI3L1, aes(x=gene, y=Ct, fill=treatment))+
  geom_boxplot()+
  ggtitle('Relative expression of CHI3L1 in HL-60 cells')+
  theme_classic()+
  theme(plot.title = element_text(hjust = 0.5))+
  ylim(0,3)+
  annotate("text", x=1, y=2.9, label= "p=6.593e-05") 

CHIT1 <- read_excel("C:/Users/tsever/Desktop/HL60qPCR.xlsx", 
    sheet = "CHIT1")
mod<-aov( Ct ~ treatment, data = CHIT1)
#comparison<-duncan.test(model,'`Well Name`')
duncan.test(mod,'treatment', alpha = 0.01, console=TRUE)
## 
## Study: mod ~ "treatment"
## 
## Duncan's new multiple range test
## for Ct 
## 
## Mean Square Error:  0.4302495 
## 
## treatment,  means
## 
##            Ct       std r       Min      Max
## ctrl 1.048011 0.3569773 3 0.6358096 1.254112
## lgmn 1.423257 0.8561928 3 0.7103819 2.372925
## 
## Alpha: 0.01 ; DF Error: 4 
## 
## Critical Range
##        2 
## 2.465879 
## 
## Means with the same letter are not significantly different.
## 
##            Ct groups
## lgmn 1.423257      a
## ctrl 1.048011      a
t.test(CHIT1[1:3,'Ct'],CHI3L1[4:6,'Ct'], conf.level = 0.99)
## 
##  Welch Two Sample t-test
## 
## data:  CHIT1[1:3, "Ct"] and CHI3L1[4:6, "Ct"]
## t = 0.84236, df = 2.0613, p-value = 0.486
## alternative hypothesis: true difference in means is not equal to 0
## 99 percent confidence interval:
##  -4.283792  5.122946
## sample estimates:
## mean of x mean of y 
##  1.423257  1.003680
ggplot(CHIT1, aes(x=gene, y=Ct, fill=treatment))+
  geom_boxplot()+
  ggtitle('Relative expression of CHIT1 in HL-60 cells')+
  theme_classic()+
  theme(plot.title = element_text(hjust = 0.5))+
  ylim(0,3)+
  annotate("text", x=1, y=2.9, label= "p=0.486") 

CHID1 <- read_excel("C:/Users/tsever/Desktop/HL60qPCR.xlsx", 
    sheet = "CHID1")
mod<-aov( Ct ~ treatment, data = CHID1)
#comparison<-duncan.test(model,'`Well Name`')
duncan.test(mod,'treatment', alpha = 0.01, console=TRUE)
## 
## Study: mod ~ "treatment"
## 
## Duncan's new multiple range test
## for Ct 
## 
## Mean Square Error:  0.0137281 
## 
## treatment,  means
## 
##             Ct       std r       Min       Max
## ctrl 1.0085506 0.1615745 3 0.8565840 1.1782671
## lgmn 0.5479894 0.0367410 3 0.5058097 0.5730237
## 
## Alpha: 0.01 ; DF Error: 4 
## 
## Critical Range
##         2 
## 0.4404706 
## 
## Means with the same letter are not significantly different.
## 
##             Ct groups
## ctrl 1.0085506      a
## lgmn 0.5479894      b
t.test(CHID1[1:3,'Ct'],CHI3L1[4:6,'Ct'], conf.level = 0.99)
## 
##  Welch Two Sample t-test
## 
## data:  CHID1[1:3, "Ct"] and CHI3L1[4:6, "Ct"]
## t = -7.0374, df = 2.474, p-value = 0.01077
## alternative hypothesis: true difference in means is not equal to 0
## 99 percent confidence interval:
##  -0.92574619  0.01436559
## sample estimates:
## mean of x mean of y 
## 0.5479894 1.0036797
ggplot(CHID1, aes(x=gene, y=Ct, fill=treatment))+
  geom_boxplot()+
  ggtitle('Relative expression of CHID1 in HL-60 cells')+
  theme_classic()+
  theme(plot.title = element_text(hjust = 0.5))+
  ylim(0,2)+
  annotate("text", x=1, y=1.9, label= "p=0.01077") 

MPO <- read_excel("C:/Users/tsever/Desktop/HL60qPCR.xlsx", 
    sheet = "MPO")
mod<-aov( Ct ~ treatment, data = MPO)
#comparison<-duncan.test(model,'`Well Name`')
duncan.test(mod,'treatment', alpha = 0.01, console=TRUE)
## 
## Study: mod ~ "treatment"
## 
## Duncan's new multiple range test
## for Ct 
## 
## Mean Square Error:  0.01128495 
## 
## treatment,  means
## 
##             Ct         std r       Min       Max
## ctrl 1.0072903 0.150058907 3 0.8745827 1.1701283
## lgmn 0.2588838 0.007226972 3 0.2511579 0.2654785
## 
## Alpha: 0.01 ; DF Error: 4 
## 
## Critical Range
##         2 
## 0.3993572 
## 
## Means with the same letter are not significantly different.
## 
##             Ct groups
## ctrl 1.0072903      a
## lgmn 0.2588838      b
t.test(MPO[1:3,'Ct'],CHI3L1[4:6,'Ct'], conf.level = 0.99)
## 
##  Welch Two Sample t-test
## 
## data:  MPO[1:3, "Ct"] and CHI3L1[4:6, "Ct"]
## t = -12.146, df = 2.0186, p-value = 0.006484
## alternative hypothesis: true difference in means is not equal to 0
## 99 percent confidence interval:
##  -1.3440521 -0.1455396
## sample estimates:
## mean of x mean of y 
## 0.2588838 1.0036797
ggplot(MPO, aes(x=gene, y=Ct, fill=treatment))+
  geom_boxplot()+
  ggtitle('Relative expression of MPO in HL-60 cells')+
  theme_classic()+
  theme(plot.title = element_text(hjust = 0.5))+
  ylim(0,1.9)+
  annotate("text", x=1, y=1.8, label= "p=0.006484") 

EPX <- read_excel("C:/Users/tsever/Desktop/HL60qPCR.xlsx", 
    sheet = "EPX")
mod<-aov( Ct ~ treatment, data = EPX)
#comparison<-duncan.test(model,'`Well Name`')
duncan.test(mod,'treatment', alpha = 0.01, console=TRUE)
## 
## Study: mod ~ "treatment"
## 
## Duncan's new multiple range test
## for Ct 
## 
## Mean Square Error:  0.0247299 
## 
## treatment,  means
## 
##            Ct       std r      Min      Max
## ctrl 1.012081 0.1977442 3 0.882703 1.239708
## lgmn 1.413351 0.1017694 3 1.328686 1.526259
## 
## Alpha: 0.01 ; DF Error: 4 
## 
## Critical Range
##         2 
## 0.5911842 
## 
## Means with the same letter are not significantly different.
## 
##            Ct groups
## lgmn 1.413351      a
## ctrl 1.012081      a
t.test(EPX[1:3,'Ct'],CHI3L1[4:6,'Ct'], conf.level = 0.99)
## 
##  Welch Two Sample t-test
## 
## data:  EPX[1:3, "Ct"] and CHI3L1[4:6, "Ct"]
## t = 4.8296, df = 3.9935, p-value = 0.008498
## alternative hypothesis: true difference in means is not equal to 0
## 99 percent confidence interval:
##  0.01869286 0.80065080
## sample estimates:
## mean of x mean of y 
##  1.413351  1.003680
epx<-ggplot(EPX, aes(x=gene, y=Ct, fill=treatment))+
  geom_boxplot()+
  ggtitle('Relative expression of EPX in HL-60 cells')+
  theme_classic()+
  theme(plot.title = element_text(hjust = 0.5))+
  ylim(0,1.9)+
  annotate("text", x=1, y=1.8, label= "p=0.008498") 
LGMN <- read_excel("C:/Users/tsever/Desktop/HL60qPCR.xlsx", 
    sheet = 'LGMN')
mod<-aov( Ct ~ treatment, data = LGMN)
#comparison<-duncan.test(model,'`Well Name`')
duncan.test(mod,'treatment', alpha = 0.01, console=TRUE)
## 
## Study: mod ~ "treatment"
## 
## Duncan's new multiple range test
## for Ct 
## 
## Mean Square Error:  0.1731441 
## 
## treatment,  means
## 
##            Ct       std r       Min      Max
## ctrl 1.093705 0.5884626 3 0.6689638 1.765406
## lgmn 5.000000 0.0000000 3 5.0000000 5.000000
## 
## Alpha: 0.01 ; DF Error: 4 
## 
## Critical Range
##        2 
## 1.564284 
## 
## Means with the same letter are not significantly different.
## 
##            Ct groups
## lgmn 5.000000      a
## ctrl 1.093705      b
t.test(LGMN[1:3,'Ct'],CHI3L1[4:6,'Ct'], conf.level = 0.99)
## 
##  Welch Two Sample t-test
## 
## data:  LGMN[1:3, "Ct"] and CHI3L1[4:6, "Ct"]
## t = 65.321, df = 2, p-value = 0.0002343
## alternative hypothesis: true difference in means is not equal to 0
## 99 percent confidence interval:
##  3.389119 4.603521
## sample estimates:
## mean of x mean of y 
##   5.00000   1.00368
ggplot(LGMN, aes(x=gene, y=Ct, fill=treatment))+
  geom_boxplot()+
  ggtitle('Relative expression of LGMN in HL-60 cells')+
  theme_classic()+
  theme(plot.title = element_text(hjust = 0.5))+
  ylim(0,1.9)+
  annotate("text", x=1, y=1.8, label= "p=0.00000000") 
## Warning: Removed 3 rows containing non-finite values (stat_boxplot).

20210824 -> CCR5, CCL5, YBX1, IL10

CCR5

CCR5 <- read_excel("F:/misc/qPCR/HL60qPCR.xlsx", 
    sheet = "CCR5")
mod<-aov( Ct ~ treatment, data = CCR5)
#comparison<-duncan.test(model,'`Well Name`')
duncan.test(mod,'treatment', alpha = 0.01, console=TRUE)
## 
## Study: mod ~ "treatment"
## 
## Duncan's new multiple range test
## for Ct 
## 
## Mean Square Error:  0.2262163 
## 
## treatment,  means
## 
##             Ct       std r       Min      Max
## ctrl 1.0786327 0.4655877 3 0.5703819 1.484524
## lgmn 0.6878418 0.4854489 3 0.4005349 1.248331
## 
## Alpha: 0.01 ; DF Error: 4 
## 
## Critical Range
##        2 
## 1.788025 
## 
## Means with the same letter are not significantly different.
## 
##             Ct groups
## ctrl 1.0786327      a
## lgmn 0.6878418      a
t.test(CCR5[1:3,'Ct'],CCR5[4:6,'Ct'], conf.level = 0.99)
## 
##  Welch Two Sample t-test
## 
## data:  CCR5[1:3, "Ct"] and CCR5[4:6, "Ct"]
## t = 1.0063, df = 3.993, p-value = 0.3713
## alternative hypothesis: true difference in means is not equal to 0
## 99 percent confidence interval:
##  -1.399310  2.180892
## sample estimates:
## mean of x mean of y 
## 1.0786327 0.6878418
ggplot(CCR5, aes(x=gene, y=Ct, fill=treatment))+
  geom_boxplot()+
  ggtitle('Relative expression of CCR5 in HL-60 cells')+
  theme_classic()+
  theme(plot.title = element_text(hjust = 0.5))+
  ylim(0,3)+
  annotate("text", x=1, y=2.9, label= "p=0.0.3713")

CCL5

CCL5 <- read_excel("F:/misc/qPCR/HL60qPCR.xlsx", 
    sheet = "CCL5")
mod<-aov( Ct ~ treatment, data = CCL5)
#comparison<-duncan.test(model,'`Well Name`')
duncan.test(mod,'treatment', alpha = 0.01, console=TRUE)
## 
## Study: mod ~ "treatment"
## 
## Duncan's new multiple range test
## for Ct 
## 
## Mean Square Error:  0.005918376 
## 
## treatment,  means
## 
##             Ct        std r       Min       Max
## ctrl 1.0031679 0.09697302 3 0.9012505 1.0942937
## lgmn 0.7910665 0.04932531 3 0.7422618 0.8408964
## 
## Alpha: 0.01 ; DF Error: 4 
## 
## Critical Range
##         2 
## 0.2892098 
## 
## Means with the same letter are not significantly different.
## 
##             Ct groups
## ctrl 1.0031679      a
## lgmn 0.7910665      a
t.test(CCL5[1:3,'Ct'],CCL5[4:6,'Ct'], conf.level = 0.99)
## 
##  Welch Two Sample t-test
## 
## data:  CCL5[1:3, "Ct"] and CCL5[4:6, "Ct"]
## t = 3.3767, df = 2.97, p-value = 0.04385
## alternative hypothesis: true difference in means is not equal to 0
## 99 percent confidence interval:
##  -0.1584911  0.5826938
## sample estimates:
## mean of x mean of y 
## 1.0031679 0.7910665
ggplot(CCL5, aes(x=gene, y=Ct, fill=treatment))+
  geom_boxplot()+
  ggtitle('Relative expression of CCL5 in HL-60 cells')+
  theme_classic()+
  theme(plot.title = element_text(hjust = 0.5))+
  ylim(0,3)+
  annotate("text", x=1, y=2.9, label= "p=0.04385")

YBX1

YBX1 <- read_excel("F:/misc/qPCR/HL60qPCR.xlsx", 
    sheet = "YBX1")
mod<-aov( Ct ~ treatment, data = YBX1)
#comparison<-duncan.test(model,'`Well Name`')
duncan.test(mod,'treatment', alpha = 0.01, console=TRUE)
## 
## Study: mod ~ "treatment"
## 
## Duncan's new multiple range test
## for Ct 
## 
## Mean Square Error:  0.01504641 
## 
## treatment,  means
## 
##             Ct        std r       Min      Max
## ctrl 1.0004295 0.03565023 3 0.9592641 1.021012
## lgmn 0.9898254 0.16977007 3 0.7955365 1.109569
## 
## Alpha: 0.01 ; DF Error: 4 
## 
## Critical Range
##        2 
## 0.461135 
## 
## Means with the same letter are not significantly different.
## 
##             Ct groups
## ctrl 1.0004295      a
## lgmn 0.9898254      a
t.test(YBX1[1:3,'Ct'],YBX1[4:6,'Ct'], conf.level = 0.99)
## 
##  Welch Two Sample t-test
## 
## data:  YBX1[1:3, "Ct"] and YBX1[4:6, "Ct"]
## t = 0.10588, df = 2.176, p-value = 0.9246
## alternative hypothesis: true difference in means is not equal to 0
## 99 percent confidence interval:
##  -0.8583215  0.8795297
## sample estimates:
## mean of x mean of y 
## 1.0004295 0.9898254
ggplot(YBX1, aes(x=gene, y=Ct, fill=treatment))+
  geom_boxplot()+
  ggtitle('Relative expression of YBX1 in HL-60 cells')+
  theme_classic()+
  theme(plot.title = element_text(hjust = 0.5))+
  ylim(0,3)+
  annotate("text", x=1, y=2.9, label= "p=0.9246")

IL-10

IL10 <- read_excel("F:/misc/qPCR/HL60qPCR.xlsx", 
    sheet = "IL10")
mod<-aov( Ct ~ treatment, data = IL10)
#comparison<-duncan.test(model,'`Well Name`')
duncan.test(mod,'treatment', alpha = 0.01, console=TRUE)
## 
## Study: mod ~ "treatment"
## 
## Duncan's new multiple range test
## for Ct 
## 
## Mean Square Error:  0.1055379 
## 
## treatment,  means
## 
##            Ct        std r       Min      Max
## ctrl 1.000325 0.03120818 3 0.9681707 1.030492
## lgmn 5.278843 0.45836877 3 4.9018738 5.789077
## 
## Alpha: 0.01 ; DF Error: 4 
## 
## Critical Range
##        2 
## 1.221282 
## 
## Means with the same letter are not significantly different.
## 
##            Ct groups
## lgmn 5.278843      a
## ctrl 1.000325      b
t.test(IL10[1:3,'Ct'],IL10[4:6,'Ct'], conf.level = 0.99)
## 
##  Welch Two Sample t-test
## 
## data:  IL10[1:3, "Ct"] and IL10[4:6, "Ct"]
## t = -16.13, df = 2.0185, p-value = 0.003673
## alternative hypothesis: true difference in means is not equal to 0
## 99 percent confidence interval:
##  -6.870769 -1.686267
## sample estimates:
## mean of x mean of y 
##  1.000325  5.278843
ggplot(IL10, aes(x=gene, y=Ct, fill=treatment))+
  geom_boxplot()+
  ggtitle('Relative expression of IL10 in HL-60 cells')+
  theme_classic()+
  theme(plot.title = element_text(hjust = 0.5))+
  ylim(0,5.5)+
  annotate("text", x=1, y=5.4, label= "p=0.003673") 
## Warning: Removed 1 rows containing non-finite values (stat_boxplot).

HL60 <- read_excel("F:/misc/qPCR/HL60qPCR.xlsx", 
    sheet = "Sheet8")
View(HL60)
HL60<- as.data.frame(HL60)
ggplot(HL60, aes(x=gene, y=Ct, fill=treatment))+
  geom_boxplot()+
  facet_wrap(~gene, scale="free")

HL60 <- read_excel("F:/misc/qPCR/HL60qPCR.xlsx", 
    sheet = "Sheet9")

HL60<- as.data.frame(HL60)
ggplot(HL60, aes(x=gene, y=Ct, fill=treatment))+
  geom_boxplot()+
  facet_wrap(~gene, scale="free_x")

tiff("boxi.tif",height = 12, width = 10, units = 'in', res = 500)

ggplot(HL60, aes(x=gene, y=Ct, fill=treatment))+
  geom_boxplot()+
  facet_wrap(~gene, scale="free")

dev.off()
## png 
##   2