Rationale

Testing the new QuantStudio3 machine for any potential variation among wells using a plate with 96 technical replicates, i.e., the same reaction in each well, that includes amplification of both C and D actin loci (VIC and FAM dyes, respectively).

Methods

  • Prepare DNA template: (200 µL total)
    • 100 µL clade C actin @ 64,000 copies/µL
    • 100 µL clade D actin @ 64,000 copies/µL
  • Prepare qPCR MasterMix:
    • Each reaction: 5 µL GTMM; 0.5 µL each of CFor, CRev, CProbe(VIC), DFor, DRev, DProbe(FAM); (no water)
    • MasterMix (n=98): 490 µL GTMM; 49 µL each oligo
  • Add 8 µL MasterMix to each well
  • Add 2 µL mixed C+D DNA template to each well

Results

Raw data files:

.txt

library(reshape2)
df <- read.delim("2018-01-23_Uniformity-Test.txt", skip=7923, sep="\t")
df <- dcast(df, formula=Well.Position~Target.Name, value.var="CT")

Visualize plates:

library(platetools)
raw_map(data=df$C, well=df$Well, plate=96)

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raw_map(data=df$D, well=df$Well, plate=96)

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Run some stats:

library(effects)
library(lsmeans)
## Loading required package: estimability
## Loading required package: methods
df$row <- factor(substr(df$Well, 1,1))
df$col <- factor(as.numeric(substr(df$Well, 2,3)))
df$corner <- factor(ifelse(df$Well %in% c("A1", "A12", "H1", "H12"), "corner", "not_corner"))
df$border <- factor(ifelse(df$row %in% c("A", "H") | df$col %in% c(1, 12), "border", "not_border"))
str(df)
## 'data.frame':	96 obs. of  7 variables:
##  $ Well.Position: Factor w/ 96 levels "A1","A10","A11",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ C            : num  24.6 24.1 24.1 24.3 24 ...
##  $ D            : num  24.4 24.2 24 24.3 24 ...
##  $ row          : Factor w/ 8 levels "A","B","C","D",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ col          : Factor w/ 12 levels "1","2","3","4",..: 1 10 11 12 2 3 4 5 6 7 ...
##  $ corner       : Factor w/ 2 levels "corner","not_corner": 1 2 2 1 2 2 2 2 2 2 ...
##  $ border       : Factor w/ 2 levels "border","not_border": 1 1 1 1 1 1 1 1 1 1 ...
# Are corners different?
mod <- lm(C ~ corner, data=df)
pairs(lsmeans(mod, specs="corner"))
##  contrast             estimate         SE df t.ratio p.value
##  corner - not_corner 0.4512174 0.08782635 94   5.138  <.0001
plot(effect("corner", mod), main="Corner effect - C assay")

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mod <- lm(D ~ corner, data=df)
pairs(lsmeans(mod, specs="corner"))
##  contrast             estimate         SE df t.ratio p.value
##  corner - not_corner 0.1955435 0.07748637 94   2.524  0.0133
plot(effect("corner", mod), main="Corner effect - D assay")

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# Are columns different? (excluding corners)
mod <- lm(C ~ col, data=subset(df, corner=="not_corner"))
anova(mod)
## Analysis of Variance Table
## 
## Response: C
##           Df  Sum Sq  Mean Sq F value Pr(>F)
## col       11 0.36627 0.033297  1.1377 0.3441
## Residuals 80 2.34145 0.029268
plot(effect("col", mod), main="Column effect - C assay (excluding corners)")

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mod <- lm(D ~ col, data=subset(df, corner=="not_corner"))
anova(mod)
## Analysis of Variance Table
## 
## Response: D
##           Df Sum Sq  Mean Sq F value   Pr(>F)   
## col       11 0.5442 0.049473  2.5245 0.008732 **
## Residuals 80 1.5677 0.019597                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(effect("col", mod), main="Column effect - D assay (excluding corners)")

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# Are rows different? (excluding corners)
mod <- lm(C ~ row, data=subset(df, corner=="not_corner"))
anova(mod)
## Analysis of Variance Table
## 
## Response: C
##           Df Sum Sq Mean Sq F value    Pr(>F)    
## row        7 1.0537 0.15053   7.645 4.027e-07 ***
## Residuals 84 1.6540 0.01969                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cld(lsmeans(mod, specs="row"))
##  row   lsmean         SE df lower.CL upper.CL .group
##  F   23.79792 0.04050762 84 23.71736 23.87847  1    
##  E   23.83150 0.04050762 84 23.75095 23.91205  1    
##  D   23.92058 0.04050762 84 23.84003 24.00114  12   
##  G   23.92608 0.04050762 84 23.84553 24.00664  12   
##  C   23.93683 0.04050762 84 23.85628 24.01739  12   
##  H   24.05800 0.04437388 84 23.96976 24.14624   23  
##  B   24.06542 0.04050762 84 23.98486 24.14597   23  
##  A   24.12670 0.04437388 84 24.03846 24.21494    3  
## 
## Confidence level used: 0.95 
## P value adjustment: tukey method for comparing a family of 8 estimates 
## significance level used: alpha = 0.05
plot(effect("row", mod), main="Row effect - C assay (excluding corners)")

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mod <- lm(D ~ row, data=subset(df, corner=="not_corner"))
anova(mod)
## Analysis of Variance Table
## 
## Response: D
##           Df  Sum Sq  Mean Sq F value  Pr(>F)  
## row        7 0.32654 0.046648  2.1947 0.04268 *
## Residuals 84 1.78542 0.021255                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cld(lsmeans(mod, specs="row"))
##  row   lsmean         SE df lower.CL upper.CL .group
##  F   24.02392 0.04208617 84 23.94022 24.10761  1    
##  E   24.05117 0.04208617 84 23.96747 24.13486  1    
##  G   24.09233 0.04208617 84 24.00864 24.17603  1    
##  C   24.09558 0.04208617 84 24.01189 24.17928  1    
##  H   24.11900 0.04610309 84 24.02732 24.21068  1    
##  D   24.14767 0.04208617 84 24.06397 24.23136  1    
##  A   24.19000 0.04610309 84 24.09832 24.28168  1    
##  B   24.20550 0.04208617 84 24.12181 24.28919  1    
## 
## Confidence level used: 0.95 
## P value adjustment: tukey method for comparing a family of 8 estimates 
## significance level used: alpha = 0.05
plot(effect("row", mod), main="Row effect - D assay (excluding corners)")

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Conclusions:

Detection in the corners is 0.45 cycles later for C (VIC) and 0.2 cycles later for D (FAM).

There is a significant column effect for D (FAM) detection.

There is a significant row effect for C (VIC) detection.