The data report below outlines the statistical tests of significance with viral load data from the heterozygous CC lines (F/N + N/f) and the null lines (N/N).
# load dplyr for data transformations
library(dplyr)## Warning: package 'dplyr' was built under R version 3.2.5##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union# display my entire session info
sessionInfo()## R version 3.2.3 (2015-12-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 14393)
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## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] dplyr_0.5.0
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## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.8 digest_0.6.11 rprojroot_1.1 assertthat_0.1
## [5] R6_2.2.0 DBI_0.5-1 backports_1.0.4 magrittr_1.5
## [9] evaluate_0.10 stringi_1.1.2 rmarkdown_1.3 tools_3.2.3
## [13] stringr_1.1.0 yaml_2.1.14 htmltools_0.3.5 knitr_1.15.1
## [17] tibble_1.2# load qpcr data
qpcr_data <- read.csv(file="C:\\gale_lab\\oas1b_manuscript\\revisions\\qpcr_data_4_ttests.csv", header=T)
#Evaluate which fields we are interested in
names(qpcr_data)## [1] "Mating" "UNC_ID" "Oas1b_status" "Virus"
## [5] "Tissue" "Timepoint" "fc.mean"# We will first filter by spleen tissue
qpcr_spleen <- filter(qpcr_data, Tissue=="Spleen")
# Next filter by Oas1b status
qpcr_spleen_FF <- filter(qpcr_spleen, Oas1b_status %in% c("Functional+Functional"))
qpcr_spleen_NF_FN_NN <- filter(qpcr_spleen, Oas1b_status %in% c("Null+Functional","Functional+Null", "Null+Null"))
# Pool heterozgous data
qpcr_spleen_NF_FN <- filter(qpcr_spleen, Oas1b_status %in% c("Null+Functional","Functional+Null"))
# Pool null spleen data
qpcr_spleen_NN <- filter(qpcr_spleen, Oas1b_status %in% c("Null+Null"))
qpcr_spleen_NF_FN_NN <- filter(qpcr_spleen, Oas1b_status %in% c("Null+Functional","Functional+Null", "Null+Null"))
# perform student t-test on viral spleen data
t.test(qpcr_spleen_FF$fc.mean,qpcr_spleen_NN$fc.mean)##
## Welch Two Sample t-test
##
## data: qpcr_spleen_FF$fc.mean and qpcr_spleen_NN$fc.mean
## t = -3.5333, df = 117.64, p-value = 0.0005874
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -65.83950 -18.54456
## sample estimates:
## mean of x mean of y
## 17.36581 59.55784#They do appear significant but this in fairly under powered lets lookat the heterozygous cc lines
# perform student t-test on viral spleen data
t.test(qpcr_spleen_NF_FN$fc.mean,qpcr_spleen_NN$fc.mean)##
## Welch Two Sample t-test
##
## data: qpcr_spleen_NF_FN$fc.mean and qpcr_spleen_NN$fc.mean
## t = -0.65543, df = 292.5, p-value = 0.5127
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -33.18763 16.60523
## sample estimates:
## mean of x mean of y
## 51.26664 59.55784# They do not appear signifcant in the spleen
# next filter by day 4 post infection
qpcr_spleen_NF_FN_D4 <- filter(qpcr_spleen_NF_FN, Timepoint==4)
qpcr_spleen_NN_D4 <- filter(qpcr_spleen_NN, Timepoint==4)
# perform student t-test on viral spleen data at day 4 post infection
t.test(qpcr_spleen_NF_FN_D4$fc.mean,qpcr_spleen_NN_D4$fc.mean)##
## Welch Two Sample t-test
##
## data: qpcr_spleen_NF_FN_D4$fc.mean and qpcr_spleen_NN_D4$fc.mean
## t = -0.74464, df = 73.637, p-value = 0.4589
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -87.65483 39.96524
## sample estimates:
## mean of x mean of y
## 107.3096 131.1544#They are not distinctly significant day 4 post infection either
### Lets compare the two heterozygous CC lines in the spleen with all timepoints (2,4,7,12) inorder to give us greater statistical power
qpcr_spleen_NF <- filter(qpcr_spleen, Oas1b_status %in% c("Null+Functional"))
qpcr_spleen_FN <- filter(qpcr_spleen, Oas1b_status %in% c("Functional+Null"))
t.test(qpcr_spleen_NF$fc.mean,qpcr_spleen_FN$fc.mean)##
## Welch Two Sample t-test
##
## data: qpcr_spleen_NF$fc.mean and qpcr_spleen_FN$fc.mean
## t = 0.3956, df = 118.21, p-value = 0.6931
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -24.67300 36.99195
## sample estimates:
## mean of x mean of y
## 54.62636 48.46688###
# next filter by day 7 post infection
qpcr_spleen_NF_FN_D7 <- filter(qpcr_spleen_NF_FN, Timepoint==7)
qpcr_spleen_NN_D7 <- filter(qpcr_spleen_NN, Timepoint==7)
# perform student t-test on viral spleen data at day 7 post infection
t.test(qpcr_spleen_NF_FN_D7$fc.mean,qpcr_spleen_NN_D7$fc.mean)##
## Welch Two Sample t-test
##
## data: qpcr_spleen_NF_FN_D7$fc.mean and qpcr_spleen_NN_D7$fc.mean
## t = -0.032616, df = 48.701, p-value = 0.9741
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -18.41387 17.82577
## sample estimates:
## mean of x mean of y
## 17.73181 18.02586#They are not distinctly significant day 7 post infection either
#Lets look at the viral brain qPCR
qpcr_brain <- filter(qpcr_data, Tissue=="Brain")
# Next filter by Oas1b status
qpcr_brain_FF <- filter(qpcr_brain, Oas1b_status %in% c("Functional+Functional"))
qpcr_brain_NF_FN <- filter(qpcr_brain, Oas1b_status %in% c("Null+Functional","Functional+Null"))
# Null
qpcr_brain_NN <- filter(qpcr_brain, Oas1b_status %in% c("Null+Null"))
# Hetero
qpcr_brain_NF_FN_NN <- filter(qpcr_brain, Oas1b_status %in% c("Null+Functional","Functional+Null", "Null+Null"))
# perform student t-test on viral brain data
t.test(qpcr_brain_FF$fc.mean,qpcr_brain_NN$fc.mean)##
## Welch Two Sample t-test
##
## data: qpcr_brain_FF$fc.mean and qpcr_brain_NN$fc.mean
## t = -2.1708, df = 128, p-value = 0.03179
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -9752.066 -451.542
## sample estimates:
## mean of x mean of y
## 2.12896 5103.93289#They do appear significant but this in fairly under powered lets lookat the heterozygous cc lines
# perform student t-test on viral brain data
t.test(qpcr_brain_NF_FN$fc.mean,qpcr_brain_NN$fc.mean)##
## Welch Two Sample t-test
##
## data: qpcr_brain_NF_FN$fc.mean and qpcr_brain_NN$fc.mean
## t = -2.109, df = 128.39, p-value = 0.03689
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -9613.9006 -306.6125
## sample estimates:
## mean of x mean of y
## 143.6763 5103.9329# this p value of 0.03689 meetings our criteria (p-value <=.05) and therefore shows significance
# Next lets filter by timepoint. Day 7 and 12 post infection
qpcr_brain_NF_FN_D7 <- filter(qpcr_brain_NF_FN, Timepoint==7)
qpcr_brain_NF_FN_D12 <- filter(qpcr_brain_NF_FN, Timepoint==12)
# Null
qpcr_brain_NN_D7 <- filter(qpcr_brain_NN, Timepoint==7)
qpcr_brain_NN_D12 <- filter(qpcr_brain_NN, Timepoint==12)
#perform student t-test on viral brain data at day 7 post infection
t.test(qpcr_brain_NF_FN_D7$fc.mean,qpcr_brain_NN_D7$fc.mean)##
## Welch Two Sample t-test
##
## data: qpcr_brain_NF_FN_D7$fc.mean and qpcr_brain_NN_D7$fc.mean
## t = -1.4545, df = 45.362, p-value = 0.1527
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2703.3604 435.8828
## sample estimates:
## mean of x mean of y
## 61.45759 1195.19636#perform student t-test on viral brain data at day 12 post infection
t.test(qpcr_brain_NF_FN_D12$fc.mean,qpcr_brain_NN_D12$fc.mean)##
## Welch Two Sample t-test
##
## data: qpcr_brain_NF_FN_D12$fc.mean and qpcr_brain_NN_D12$fc.mean
## t = -2.0146, df = 35.083, p-value = 0.05166
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -32872.3115 124.3175
## sample estimates:
## mean of x mean of y
## 385.8939 16759.8909# this p value of 0.05 meetings our criteria (p-value <=.05) and therefore shows significance