Morphometric analysis of CD68 content in microglia by 63X confocal microscopy
Dependent variables: iba1.vol, cd68.vol, perc.cd68 Unit of measure: n is a cell Fixed Effects: group (MIA, CON), trt (SAL, LPS), region (dorsal striatum (DS) & ventral striatum (VS))
## `summarise()` has grouped output by 'group', 'trt'. You can override using the `.groups` argument.## # A tibble: 8 x 6
## # Groups: group, trt [4]
## group trt region n mouse litter
## <fct> <fct> <fct> <int> <int> <int>
## 1 CON SAL DS 34 4 4
## 2 CON SAL VS 25 4 4
## 3 CON LPS DS 33 4 4
## 4 CON LPS VS 29 4 4
## 5 MIA SAL DS 19 4 3
## 6 MIA SAL VS 26 4 3
## 7 MIA LPS DS 26 3 2
## 8 MIA LPS VS 19 3 2Plot microglia volume, cd68 volume and % CD68, n= per cell
black = CON, red = MIA
3-way ANOVA (group x region x trt)
Iba1.Vol
# get summary data
data %>%
group_by(group, region, trt) %>%
get_summary_stats(iba1.vol, type = "mean_sd")## # A tibble: 8 x 7
## group trt region variable n mean sd
## <fct> <fct> <fct> <chr> <dbl> <dbl> <dbl>
## 1 CON SAL DS iba1.vol 34 1400. 510.
## 2 CON LPS DS iba1.vol 33 1288. 715.
## 3 CON SAL VS iba1.vol 25 1304. 637.
## 4 CON LPS VS iba1.vol 29 1050. 592.
## 5 MIA SAL DS iba1.vol 19 1412. 793.
## 6 MIA LPS DS iba1.vol 26 864. 621.
## 7 MIA SAL VS iba1.vol 26 1620. 952.
## 8 MIA LPS VS iba1.vol 19 611. 540.# Build the linear model
model <- lm(iba1.vol ~ group+trt+region+group:trt, data = data)
# Compute Shapiro-Wilk test of normality and levene test for equal variance
data %>% group_by(trt, region, group) %>% shapiro_test(iba1.vol)## # A tibble: 8 x 6
## group trt region variable statistic p
## <fct> <fct> <fct> <chr> <dbl> <dbl>
## 1 CON SAL DS iba1.vol 0.985 0.919
## 2 MIA SAL DS iba1.vol 0.952 0.423
## 3 CON SAL VS iba1.vol 0.937 0.129
## 4 MIA SAL VS iba1.vol 0.894 0.0116
## 5 CON LPS DS iba1.vol 0.794 0.0000248
## 6 MIA LPS DS iba1.vol 0.871 0.00379
## 7 CON LPS VS iba1.vol 0.862 0.00135
## 8 MIA LPS VS iba1.vol 0.753 0.000254## # A tibble: 1 x 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 7 203 0.858 0.541## Coefficient covariances computed by hccm()## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 group 1 206 1.675 1.97e-01 0.008
## 2 trt 1 206 21.972 5.03e-06 * 0.096
## 3 region 1 206 1.295 2.56e-01 0.006
## 4 group:trt 1 206 10.459 1.00e-03 * 0.048## Anova Table (Type II tests)
##
## Response: iba1.vol
## Sum Sq Df F value Pr(>F)
## group 771716 1 1.6746 0.197099
## trt 10125827 1 21.9721 5.026e-06 ***
## region 596784 1 1.2950 0.256457
## group:trt 4819951 1 10.4588 0.001422 **
## Residuals 94934962 206
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Coefficient covariances computed by hccm()
## Coefficient covariances computed by hccm()
## Coefficient covariances computed by hccm()
## Coefficient covariances computed by hccm()## # A tibble: 4 x 9
## trt region Effect DFn DFd F p `p<.05` ges
## * <fct> <fct> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 SAL DS group 1 206 0.003 0.953 "" 0.0000168
## 2 SAL VS group 1 206 2.78 0.097 "" 0.013
## 3 LPS DS group 1 206 5.68 0.018 "*" 0.027
## 4 LPS VS group 1 206 4.81 0.029 "*" 0.023pwc <- data %>%
group_by(trt, region) %>%
emmeans_test(iba1.vol ~ group, p.adjust.method = "fdr")
pwc## # A tibble: 4 x 11
## trt region term .y. group1 group2 df statistic p p.adj
## * <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 LPS DS group iba1.vol CON MIA 203 2.39 0.0179 0.0179
## 2 LPS VS group iba1.vol CON MIA 203 2.20 0.0293 0.0293
## 3 SAL DS group iba1.vol CON MIA 203 -0.0589 0.953 0.953
## 4 SAL VS group iba1.vol CON MIA 203 -1.67 0.0968 0.0968
## # … with 1 more variable: p.adj.signif <chr>## NOTE: Results may be misleading due to involvement in interactions## # A tibble: 1 x 9
## term .y. group1 group2 df statistic p p.adj p.adj.signif
## * <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 group iba1.vol CON MIA 206 1.24 0.215 0.215 nspwc <- pwc %>% add_xy_position(x = "region")
bxp <- ggboxplot(data, x = "region", y = "iba1.vol", color = "group", palette = "jco", facet.by = "trt", add.params = list(size=0.5))
bxp +
stat_pvalue_manual(pwc) +
labs(
subtitle = get_test_label(res.aov, detailed = TRUE),
caption = get_pwc_label(pwc))
Cd68.Vol
# get summary data
data %>%
group_by(group, region, trt) %>%
get_summary_stats(cd68.vol, type = "mean_sd")## # A tibble: 8 x 7
## group trt region variable n mean sd
## <fct> <fct> <fct> <chr> <dbl> <dbl> <dbl>
## 1 CON SAL DS cd68.vol 34 50.0 28.7
## 2 CON LPS DS cd68.vol 33 56.2 39.9
## 3 CON SAL VS cd68.vol 25 44.5 35.3
## 4 CON LPS VS cd68.vol 29 41.7 33.3
## 5 MIA SAL DS cd68.vol 19 43.9 32.7
## 6 MIA LPS DS cd68.vol 26 29.3 26.2
## 7 MIA SAL VS cd68.vol 26 37.9 31.2
## 8 MIA LPS VS cd68.vol 19 19.5 23.6# Build the linear model
model <- lm(cd68.vol ~ group+trt+region+group:trt, data = data)
# Compute Shapiro-Wilk test of normality and levene test for equal variance
data %>% group_by(trt, region, group) %>% shapiro_test(cd68.vol)## # A tibble: 8 x 6
## group trt region variable statistic p
## <fct> <fct> <fct> <chr> <dbl> <dbl>
## 1 CON SAL DS cd68.vol 0.917 0.0130
## 2 MIA SAL DS cd68.vol 0.896 0.0407
## 3 CON SAL VS cd68.vol 0.883 0.00784
## 4 MIA SAL VS cd68.vol 0.841 0.000945
## 5 CON LPS DS cd68.vol 0.863 0.000669
## 6 MIA LPS DS cd68.vol 0.838 0.000830
## 7 CON LPS VS cd68.vol 0.764 0.0000205
## 8 MIA LPS VS cd68.vol 0.712 0.0000772## # A tibble: 1 x 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 7 203 0.902 0.506## Coefficient covariances computed by hccm()## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 group 1 206 11.934 0.000669 * 0.055
## 2 trt 1 206 1.772 0.185000 0.009
## 3 region 1 206 4.321 0.039000 * 0.021
## 4 group:trt 1 206 4.441 0.036000 * 0.021## Anova Table (Type II tests)
##
## Response: cd68.vol
## Sum Sq Df F value Pr(>F)
## group 12199 1 11.9340 0.0006691 ***
## trt 1811 1 1.7721 0.1845986
## region 4417 1 4.3213 0.0388761 *
## group:trt 4540 1 4.4414 0.0362863 *
## Residuals 210579 206
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Coefficient covariances computed by hccm()
## Coefficient covariances computed by hccm()
## Coefficient covariances computed by hccm()
## Coefficient covariances computed by hccm()## # A tibble: 4 x 9
## trt region Effect DFn DFd F p `p<.05` ges
## * <fct> <fct> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 SAL DS group 1 206 0.453 0.502 "" 0.002
## 2 SAL VS group 1 206 0.535 0.465 "" 0.003
## 3 LPS DS group 1 206 10.3 0.002 "*" 0.048
## 4 LPS VS group 1 206 5.54 0.02 "*" 0.026pwc <- data %>%
group_by(trt, region) %>%
emmeans_test(cd68.vol ~ group, p.adjust.method = "fdr")
pwc## # A tibble: 4 x 11
## trt region term .y. group1 group2 df statistic p p.adj
## * <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 LPS DS group cd68.vol CON MIA 203 3.19 0.00165 0.00165
## 2 LPS VS group cd68.vol CON MIA 203 2.34 0.0202 0.0202
## 3 SAL DS group cd68.vol CON MIA 203 0.669 0.504 0.504
## 4 SAL VS group cd68.vol CON MIA 203 0.727 0.468 0.468
## # … with 1 more variable: p.adj.signif <chr>## NOTE: Results may be misleading due to involvement in interactions## # A tibble: 1 x 9
## term .y. group1 group2 df statistic p p.adj p.adj.signif
## * <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 group cd68.vol CON MIA 206 3.42 0.000753 0.000753 ***pwc <- pwc %>% add_xy_position(x = "region")
bxp <- ggboxplot(data, x = "region", y = "cd68.vol", color = "group", palette = "jco", facet.by = "trt", add.params = list(size=0.5))
bxp +
stat_pvalue_manual(pwc) +
labs(
subtitle = get_test_label(res.aov, detailed = TRUE),
caption = get_pwc_label(pwc))
Percent CD68
# get summary data
data %>%
group_by(group, region, trt) %>%
get_summary_stats(perc.cd68, type = "mean_sd")## # A tibble: 8 x 7
## group trt region variable n mean sd
## <fct> <fct> <fct> <chr> <dbl> <dbl> <dbl>
## 1 CON SAL DS perc.cd68 34 3.60 1.86
## 2 CON LPS DS perc.cd68 33 4.54 2.38
## 3 CON SAL VS perc.cd68 25 3.49 1.88
## 4 CON LPS VS perc.cd68 29 3.97 1.81
## 5 MIA SAL DS perc.cd68 19 3.35 1.82
## 6 MIA LPS DS perc.cd68 26 3.21 1.48
## 7 MIA SAL VS perc.cd68 26 2.56 2.07
## 8 MIA LPS VS perc.cd68 19 2.74 1.44# Build the linear model
model <- lm(perc.cd68 ~ group+trt+region+group:trt, data = data)
# Compute Shapiro-Wilk test of normality and levene test for equal variance
data %>% group_by(trt, region, group) %>% shapiro_test(perc.cd68)## # A tibble: 8 x 6
## group trt region variable statistic p
## <fct> <fct> <fct> <chr> <dbl> <dbl>
## 1 CON SAL DS perc.cd68 0.953 0.148
## 2 MIA SAL DS perc.cd68 0.954 0.463
## 3 CON SAL VS perc.cd68 0.973 0.729
## 4 MIA SAL VS perc.cd68 0.873 0.00407
## 5 CON LPS DS perc.cd68 0.960 0.257
## 6 MIA LPS DS perc.cd68 0.963 0.456
## 7 CON LPS VS perc.cd68 0.932 0.0608
## 8 MIA LPS VS perc.cd68 0.943 0.300## # A tibble: 1 x 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 7 203 1.31 0.246## Coefficient covariances computed by hccm()## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 group 1 206 12.812 0.00043 * 0.059
## 2 trt 1 206 2.945 0.08800 0.014
## 3 region 1 206 3.189 0.07600 0.015
## 4 group:trt 1 206 1.737 0.18900 0.008model <- lm(perc.cd68 ~ group*trt+region, data = data)
data %>%
group_by(trt) %>%
anova_test(perc.cd68 ~ group+region, error = aov.out)## Coefficient covariances computed by hccm()
## Coefficient covariances computed by hccm()## # A tibble: 4 x 8
## trt Effect DFn DFd F p `p<.05` ges
## * <fct> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 SAL group 1 206 0.009 0.926 "" 0.0000422
## 2 SAL region 1 206 0.004 0.949 "" 0.00002
## 3 LPS group 1 206 0.042 0.838 "" 0.000204
## 4 LPS region 1 206 0.007 0.933 "" 0.0000348## Coefficient covariances computed by hccm()
## Coefficient covariances computed by hccm()## # A tibble: 6 x 8
## region Effect DFn DFd F p `p<.05` ges
## * <fct> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 DS group 1 206 0.018 0.893 "" 0.0000884
## 2 DS trt 1 206 0.007 0.933 "" 0.0000343
## 3 DS group:trt 1 206 0.008 0.931 "" 0.0000365
## 4 VS group 1 206 0.027 0.869 "" 0.000133
## 5 VS trt 1 206 0.003 0.957 "" 0.000014
## 6 VS group:trt 1 206 0.000554 0.981 "" 0.00000269## Coefficient covariances computed by hccm()
## Coefficient covariances computed by hccm()
## Coefficient covariances computed by hccm()
## Coefficient covariances computed by hccm()## # A tibble: 4 x 9
## trt region Effect DFn DFd F p `p<.05` ges
## * <fct> <fct> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 SAL DS group 1 206 0.000739 0.978 "" 0.00000359
## 2 SAL VS group 1 206 0.011 0.917 "" 0.0000524
## 3 LPS DS group 1 206 0.025 0.875 "" 0.000121
## 4 LPS VS group 1 206 0.017 0.896 "" 0.0000833library(emmeans)
pwc <- data %>%
group_by(trt, region) %>%
emmeans_test(perc.cd68 ~ group, p.adjust.method = "bh") %>%
select(-df, -statistic, -p)
pwc <- data %>%
group_by(trt, region) %>%
emmeans_test(perc.cd68 ~ group, p.adjust.method = "fdr")
pwc## # A tibble: 4 x 11
## trt region term .y. group1 group2 df statistic p p.adj
## * <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 LPS DS group perc.cd68 CON MIA 203 2.67 0.00830 0.00830
## 2 LPS VS group perc.cd68 CON MIA 203 2.21 0.0283 0.0283
## 3 SAL DS group perc.cd68 CON MIA 203 0.458 0.647 0.647
## 4 SAL VS group perc.cd68 CON MIA 203 1.75 0.0813 0.0813
## # … with 1 more variable: p.adj.signif <chr>pwc <- pwc %>% add_xy_position(x = "trt")
bxp <- ggboxplot(data, x = "trt", y = "perc.cd68", color = "group", palette = "jco", facet.by = "region", add.params = list(size=0.5))
bxp +
stat_pvalue_manual(pwc) +
labs(
subtitle = get_test_label(res.aov, detailed = TRUE),
caption = get_pwc_label(pwc))
Reproducibility info
## [1] "2021-09-06 14:31:36 EDT"## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.0.3 (2020-10-10)
## os macOS Big Sur 10.16
## system x86_64, darwin17.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/New_York
## date 2021-09-06
##
## ─ Packages ───────────────────────────────────────────────────────────────────
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## tidyr * 1.1.3 2021-03-03 [1] CRAN (R 4.0.2)
## tidyselect 1.1.1 2021-04-30 [1] CRAN (R 4.0.2)
## tidyverse * 1.3.1 2021-04-15 [1] CRAN (R 4.0.2)
## usethis 2.0.1 2021-02-10 [1] CRAN (R 4.0.2)
## utf8 1.2.1 2021-03-12 [1] CRAN (R 4.0.3)
## vctrs 0.3.8 2021-04-29 [1] CRAN (R 4.0.2)
## withr 2.4.2 2021-04-18 [1] CRAN (R 4.0.2)
## xfun 0.24 2021-06-15 [1] CRAN (R 4.0.2)
## xml2 1.3.2 2020-04-23 [1] CRAN (R 4.0.2)
## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.0.2)
## yaml 2.2.1 2020-02-01 [1] CRAN (R 4.0.2)
## zip 2.2.0 2021-05-31 [1] CRAN (R 4.0.2)
## zoo 1.8-9 2021-03-09 [1] CRAN (R 4.0.3)
##
## [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library