Bulk Tissue QPCR
Lindsay Hayes
Plots
Linear Mixed Effect Model & Type 2 ANOVA
# TNFa: sufficient litter overlap to model Litter
data %>% filter(!is.na(Tnfa)) %>% group_by(Group, Region, Sex) %>% summarise(n = n(), mouse = length(unique(Mouse)), litter = length(unique(Litter)))## `summarise()` has grouped output by 'Group', 'Region'. You can override using the `.groups` argument.## # A tibble: 8 x 6
## # Groups: Group, Region [4]
## Group Region Sex n mouse litter
## <fct> <fct> <fct> <int> <int> <int>
## 1 CON Ctx F 6 6 4
## 2 CON Ctx M 9 9 7
## 3 CON Str F 6 6 4
## 4 CON Str M 8 8 6
## 5 MIA Ctx F 5 5 4
## 6 MIA Ctx M 15 15 9
## 7 MIA Str F 5 5 4
## 8 MIA Str M 15 15 9data %>% group_by(Group, Region, Sex) %>% shapiro_test(log2.TNFa)## # A tibble: 8 x 6
## Region Sex Group variable statistic p
## <fct> <fct> <fct> <chr> <dbl> <dbl>
## 1 Ctx F CON log2.TNFa 0.935 0.623
## 2 Ctx M CON log2.TNFa 0.981 0.969
## 3 Str F CON log2.TNFa 0.921 0.511
## 4 Str M CON log2.TNFa 0.914 0.386
## 5 Ctx F MIA log2.TNFa 0.810 0.0967
## 6 Ctx M MIA log2.TNFa 0.967 0.805
## 7 Str F MIA log2.TNFa 0.859 0.225
## 8 Str M MIA log2.TNFa 0.941 0.401data %>% levene_test((log2.TNFa) ~ Group * Region * Sex)## # A tibble: 1 x 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 7 61 2.54 0.0230Anova(aov(log2.TNFa ~ Group + Region + Sex, data=data))## Anova Table (Type II tests)
##
## Response: log2.TNFa
## Sum Sq Df F value Pr(>F)
## Group 1.6100 1 3.7565 0.05695 .
## Region 0.0181 1 0.0422 0.83784
## Sex 0.1842 1 0.4298 0.51442
## Residuals 27.8585 65
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(lmer(log2.TNFa ~ Group + Region + Sex + (1|Litter), data=data))## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log2.TNFa ~ Group + Region + Sex + (1 | Litter)
## Data: data
##
## REML criterion at convergence: 142
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.72660 -0.63437 -0.01022 0.58688 2.39125
##
## Random effects:
## Groups Name Variance Std.Dev.
## Litter (Intercept) 0.0000 0.0000
## Residual 0.4286 0.6547
## Number of obs: 69, groups: Litter, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.1240 0.1758 65.0000 -0.705 0.4831
## GroupMIA 0.3143 0.1622 65.0000 1.938 0.0569 .
## RegionStr -0.0324 0.1577 65.0000 -0.205 0.8378
## SexM 0.1126 0.1717 65.0000 0.656 0.5144
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpMIA RgnStr
## GroupMIA -0.411
## RegionStr -0.440 -0.019
## SexM -0.578 -0.174 0.013
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular# Iba1: not enough litter overlap to properly model
data %>% filter(!is.na(Iba1)) %>% group_by(Group, Region, Sex) %>% summarise(n = n(), litters = length(unique(Litter)))## `summarise()` has grouped output by 'Group', 'Region'. You can override using the `.groups` argument.## # A tibble: 8 x 5
## # Groups: Group, Region [4]
## Group Region Sex n litters
## <fct> <fct> <fct> <int> <int>
## 1 CON Ctx F 6 4
## 2 CON Ctx M 2 2
## 3 CON Str F 6 4
## 4 CON Str M 2 2
## 5 MIA Ctx F 5 4
## 6 MIA Ctx M 3 2
## 7 MIA Str F 5 4
## 8 MIA Str M 3 2data %>% group_by(Group, Sex) %>% shapiro_test(log2.Iba1)## # A tibble: 4 x 5
## Sex Group variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 F CON log2.Iba1 0.977 0.968
## 2 M CON log2.Iba1 0.993 0.970
## 3 F MIA log2.Iba1 0.934 0.485
## 4 M MIA log2.Iba1 0.890 0.318data %>% levene_test(log2.Iba1 ~ Group * Region * Sex)## # A tibble: 1 x 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 7 24 1.21 0.333Anova(aov(log2.Iba1 ~ Group + Region + Sex, data=data))## Anova Table (Type II tests)
##
## Response: log2.Iba1
## Sum Sq Df F value Pr(>F)
## Group 0.36386 1 4.1953 0.05002 .
## Region 0.39093 1 4.5073 0.04273 *
## Sex 0.13264 1 1.5293 0.22649
## Residuals 2.42850 28
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Session Info
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] rstatix_0.7.0 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
## [5] purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.2
## [9] tidyverse_1.3.1 car_3.0-11 carData_3.0-4 multcomp_1.4-17
## [13] TH.data_1.0-10 MASS_7.3-54 survival_3.2-11 mvtnorm_1.1-2
## [17] lmerTest_3.1-3 lme4_1.1-27.1 Matrix_1.3-4 cowplot_1.1.1
## [21] ggplot2_3.3.5
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.2 sass_0.4.0 jsonlite_1.7.2
## [4] splines_4.0.3 modelr_0.1.8 bslib_0.2.5.1
## [7] assertthat_0.2.1 highr_0.9 cellranger_1.1.0
## [10] yaml_2.2.1 numDeriv_2016.8-1.1 pillar_1.6.1
## [13] backports_1.2.1 lattice_0.20-44 glue_1.4.2
## [16] digest_0.6.27 rvest_1.0.0 minqa_1.2.4
## [19] colorspace_2.0-2 sandwich_3.0-1 htmltools_0.5.1.1
## [22] pkgconfig_2.0.3 broom_0.7.8 haven_2.4.1
## [25] scales_1.1.1 openxlsx_4.2.4 rio_0.5.27
## [28] farver_2.1.0 generics_0.1.0 ellipsis_0.3.2
## [31] withr_2.4.2 cli_3.0.0 magrittr_2.0.1
## [34] crayon_1.4.1 readxl_1.3.1 evaluate_0.14
## [37] fs_1.5.0 fansi_0.5.0 nlme_3.1-152
## [40] xml2_1.3.2 foreign_0.8-81 tools_4.0.3
## [43] data.table_1.14.0 hms_1.1.0 lifecycle_1.0.0
## [46] munsell_0.5.0 reprex_2.0.0 zip_2.2.0
## [49] compiler_4.0.3 jquerylib_0.1.4 rlang_0.4.11
## [52] grid_4.0.3 nloptr_1.2.2.2 rstudioapi_0.13
## [55] labeling_0.4.2 rmarkdown_2.9 boot_1.3-28
## [58] gtable_0.3.0 codetools_0.2-18 abind_1.4-5
## [61] DBI_1.1.1 curl_4.3.2 R6_2.5.0
## [64] zoo_1.8-9 lubridate_1.7.10 knitr_1.33
## [67] utf8_1.2.1 stringi_1.6.2 Rcpp_1.0.7
## [70] vctrs_0.3.8 dbplyr_2.1.1 tidyselect_1.1.1
## [73] xfun_0.24