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I think we need to either do a larger experiment prior to sending out NovaSeq5. Or, we pool NovaSeq5 as normal, qPCR the first 72 samples singly from 10? plates and then use the sequencing data from it with the qPCR data to decide the pooling standard moving forward. I would advocate for option B.
We are going to add added absorbance into the mix as a cheaper and quicker tool for normalized pooling. We will get got absorbance readings for these same products and do the same comparison to readsdid the same comparison to reads.
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#Read in the absorbance data
AbsLVD <- read_xlsx("/Volumes/Macintosh HD/Users/gregg/Downloads/5LVD5_16S_ITS.xlsx", sheet = "Summary", skip = 2)
AbsLVD <- AbsLVD[, c(3,11)]
AbsDG <- read_xlsx("/Volumes/Macintosh HD/Users/gregg/Downloads/5DG5_16S_ITS.xlsx", sheet = "Summary", skip = 2)
AbsDG <- AbsDG[, c(3,11)]
AbsMC <- read_xlsx("/Volumes/Macintosh HD/Users/gregg/Downloads/LRII_LRIII_MC.xlsx", sheet = "Summary", skip = 2)
AbsMC <- AbsMC[, c(3,11)]
AbsLRIII <- rbind(AbsLVD, AbsDG, AbsMC)
colnames(AbsLRIII) <- c("samplename", "NgPerUl")
LRIIIReads <- left_join(LRIIIReads, AbsLRIII, by = "samplename")
#First lets check the relationship to reads
ggplot(LRIIIReads, aes(NgPerUl, reads, color=plate, shape=plate))+
geom_point(show.legend = TRUE)+
geom_smooth(method='lm', formula= y~x)+
stat_regline_equation(label.y = 200000, aes(label = ..eq.label..)) +
stat_regline_equation(label.y = 150000, aes(label = ..rr.label..))+
facet_wrap(~plate) |
Image AddedThere is still a relationship here. We will proceed with 1 column of qPCR as a check on sequence-able state, and use absorbance to adjust for less read disparity.
Files:
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| name | LRIIIfiltermergestats.csv |
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