Protein for IL-1b, IL6, and TNFa of microglia, astrocytes and uncaptured cells from frontal cortex and striatum.
Data analysis of protein expression of for IL-1b, IL-6 and TNFa after treatment 4500 EU/g lipopolysaccharide (LPS) for 18 hours. pg/mL of protein was normalized with total protein.
Samples
dat <- read.csv("210209_MSD_v2.csv", header = TRUE, stringsAsFactors = TRUE)
# remove id 20-38 & 20-34 because all data points are below standardSample Size
dat %>% filter((analyte == "IL-6")) %>% filter(cell == "astrocyte") %>% group_by(cell, group, region) %>% summarise(n = n())## `summarise()` has grouped output by 'cell', 'group'. You can override using the `.groups` argument.## # A tibble: 8 x 4
## # Groups: cell, group [4]
## cell group region n
## <fct> <fct> <fct> <int>
## 1 astrocyte CON:CON FC 6
## 2 astrocyte CON:CON Str 6
## 3 astrocyte CON:PLX FC 4
## 4 astrocyte CON:PLX Str 5
## 5 astrocyte MIA:CON FC 6
## 6 astrocyte MIA:CON Str 6
## 7 astrocyte MIA:PLX FC 5
## 8 astrocyte MIA:PLX Str 5dat %>% filter((analyte == "IL-6")) %>% filter(cell == "microglia") %>% group_by(cell, group, region) %>% summarise(n = n())## `summarise()` has grouped output by 'cell', 'group'. You can override using the `.groups` argument.## # A tibble: 8 x 4
## # Groups: cell, group [4]
## cell group region n
## <fct> <fct> <fct> <int>
## 1 microglia CON:CON FC 6
## 2 microglia CON:CON Str 6
## 3 microglia CON:PLX FC 5
## 4 microglia CON:PLX Str 5
## 5 microglia MIA:CON FC 6
## 6 microglia MIA:CON Str 6
## 7 microglia MIA:PLX FC 5
## 8 microglia MIA:PLX Str 5dat %>% filter((analyte == "IL-6")) %>% filter(cell == "negative") %>% group_by(cell, group, region) %>% summarise(n = n())## `summarise()` has grouped output by 'cell', 'group'. You can override using the `.groups` argument.## # A tibble: 8 x 4
## # Groups: cell, group [4]
## cell group region n
## <fct> <fct> <fct> <int>
## 1 negative CON:CON FC 6
## 2 negative CON:CON Str 6
## 3 negative CON:PLX FC 5
## 4 negative CON:PLX Str 5
## 5 negative MIA:CON FC 6
## 6 negative MIA:CON Str 6
## 7 negative MIA:PLX FC 5
## 8 negative MIA:PLX Str 5dat %>% filter((analyte == "IL-1b")) %>% filter(cell == "microglia") %>% group_by(cell, group, region) %>% summarise(n = n())## `summarise()` has grouped output by 'cell', 'group'. You can override using the `.groups` argument.## # A tibble: 8 x 4
## # Groups: cell, group [4]
## cell group region n
## <fct> <fct> <fct> <int>
## 1 microglia CON:CON FC 6
## 2 microglia CON:CON Str 6
## 3 microglia CON:PLX FC 5
## 4 microglia CON:PLX Str 5
## 5 microglia MIA:CON FC 6
## 6 microglia MIA:CON Str 6
## 7 microglia MIA:PLX FC 4
## 8 microglia MIA:PLX Str 5dat %>% filter((analyte == "TNFa")) %>% filter(cell == "microglia") %>% group_by(cell, group, region) %>% summarise(n = n())## `summarise()` has grouped output by 'cell', 'group'. You can override using the `.groups` argument.## # A tibble: 8 x 4
## # Groups: cell, group [4]
## cell group region n
## <fct> <fct> <fct> <int>
## 1 microglia CON:CON FC 5
## 2 microglia CON:CON Str 5
## 3 microglia CON:PLX FC 4
## 4 microglia CON:PLX Str 5
## 5 microglia MIA:CON FC 6
## 6 microglia MIA:CON Str 6
## 7 microglia MIA:PLX FC 4
## 8 microglia MIA:PLX Str 4dat %>% filter(analyte == "IL-6") %>%
ggplot(.,aes(x=region, y=norm.tot, fill = group)) + geom_boxplot() + facet_wrap(~ cell) + scale_fill_manual(values = c("grey", "turquoise3", "red", "mediumpurple3")) + geom_point(position = position_dodge(width = 0.75)) + ylab("IL-6 pg/mL")
dat %>% filter(analyte == "IL-1b") %>%
ggplot(.,aes(x=region, y=log2(norm.tot), fill = group)) + geom_boxplot() + facet_wrap(~ cell) + scale_fill_manual(values = c("grey", "turquoise3", "red", "mediumpurple3")) + geom_point(position = position_dodge(width = 0.75)) + ylab("IL-1b pg/mL")
dat %>% filter(analyte == "TNFa") %>%
ggplot(.,aes(x=region, y=norm.tot, fill = group)) + geom_boxplot() + facet_wrap(~ cell) + scale_fill_manual(values = c("grey", "turquoise3", "red", "mediumpurple3")) + geom_point(position = position_dodge(width = 0.75)) + ylab("TNFa pg/mL")
2-way ANOVA (type 2)
Microglia
TNFa
TNFa.MG <- dat %>% filter(analyte=="TNFa" & cell=="microglia")
TNFa.MG %>% group_by(group, region) %>% shapiro_test(norm.tot)## # A tibble: 8 x 5
## group region variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 CON:CON FC norm.tot 0.883 0.324
## 2 CON:CON Str norm.tot 0.863 0.237
## 3 CON:PLX FC norm.tot 0.937 0.634
## 4 CON:PLX Str norm.tot 0.961 0.818
## 5 MIA:CON FC norm.tot 0.962 0.838
## 6 MIA:CON Str norm.tot 0.907 0.414
## 7 MIA:PLX FC norm.tot 0.890 0.385
## 8 MIA:PLX Str norm.tot 0.982 0.914## # A tibble: 1 x 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 7 31 2.09 0.0744## Anova Table (Type II tests)
##
## Response: norm.tot
## Sum Sq Df F value Pr(>F)
## group 1809.58 3 9.9808 7.281e-05 ***
## region 142.89 1 2.3643 0.1334
## Residuals 2054.81 34
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lm(formula = norm.tot ~ group + region, data = TNFa.MG)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## CON:PLX - CON:CON == 0 4.399 3.575 1.231 0.61163
## MIA:CON - CON:CON == 0 -9.732 3.329 -2.924 0.02991 *
## MIA:PLX - CON:CON == 0 7.940 3.688 2.153 0.15674
## MIA:CON - CON:PLX == 0 -14.130 3.431 -4.119 0.00125 **
## MIA:PLX - CON:PLX == 0 3.541 3.780 0.937 0.78491
## MIA:PLX - MIA:CON == 0 17.672 3.548 4.980 < 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)IL6
IL6.MG <- dat %>% filter(analyte=="IL-6" & cell=="microglia")
IL6.MG %>% group_by(group, region) %>% shapiro_test(norm.tot)## # A tibble: 8 x 5
## group region variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 CON:CON FC norm.tot 0.927 0.555
## 2 CON:CON Str norm.tot 0.910 0.434
## 3 CON:PLX FC norm.tot 0.980 0.936
## 4 CON:PLX Str norm.tot 0.979 0.927
## 5 MIA:CON FC norm.tot 0.853 0.167
## 6 MIA:CON Str norm.tot 0.841 0.133
## 7 MIA:PLX FC norm.tot 0.819 0.115
## 8 MIA:PLX Str norm.tot 0.948 0.723## # A tibble: 1 x 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 7 36 0.469 0.850## Anova Table (Type II tests)
##
## Response: norm.tot
## Sum Sq Df F value Pr(>F)
## group 152.15 3 3.0273 0.04082 *
## region 0.00 1 0.0000 0.99854
## Residuals 653.38 39
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lm(formula = norm.tot ~ group + region, data = IL6.MG)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## CON:PLX - CON:CON == 0 0.2493 1.7526 0.142 0.9990
## MIA:CON - CON:CON == 0 0.7217 1.6710 0.432 0.9726
## MIA:PLX - CON:CON == 0 4.7183 1.7526 2.692 0.0488 *
## MIA:CON - CON:PLX == 0 0.4723 1.7526 0.270 0.9930
## MIA:PLX - CON:PLX == 0 4.4690 1.8305 2.441 0.0860 .
## MIA:PLX - MIA:CON == 0 3.9967 1.7526 2.280 0.1201
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)IL1b
IL1b.MG <- dat %>% filter(analyte=="IL-1b" & cell=="microglia")
IL1b.MG %>% group_by(group, region) %>% shapiro_test(norm.tot)## # A tibble: 8 x 5
## group region variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 CON:CON FC norm.tot 0.923 0.525
## 2 CON:CON Str norm.tot 0.962 0.835
## 3 CON:PLX FC norm.tot 0.894 0.375
## 4 CON:PLX Str norm.tot 0.952 0.749
## 5 MIA:CON FC norm.tot 0.857 0.178
## 6 MIA:CON Str norm.tot 0.862 0.197
## 7 MIA:PLX FC norm.tot 0.882 0.346
## 8 MIA:PLX Str norm.tot 0.869 0.264## # A tibble: 1 x 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 7 35 1.81 0.116## Anova Table (Type II tests)
##
## Response: norm.tot
## Sum Sq Df F value Pr(>F)
## group 24.679 3 1.6471 0.1947
## region 12.668 1 2.5363 0.1195
## Residuals 189.792 38##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lm(formula = norm.tot ~ group + region, data = IL1b.MG)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## CON:PLX - CON:CON == 0 0.79117 0.95690 0.827 0.841
## MIA:CON - CON:CON == 0 -1.28583 0.91237 -1.409 0.501
## MIA:PLX - CON:CON == 0 -0.03656 0.98620 -0.037 1.000
## MIA:CON - CON:PLX == 0 -2.07700 0.95690 -2.171 0.150
## MIA:PLX - CON:PLX == 0 -0.82773 1.02754 -0.806 0.851
## MIA:PLX - MIA:CON == 0 1.24927 0.98620 1.267 0.589
## (Adjusted p values reported -- single-step method)Astrocyte
IL6
IL6.A <- dat %>% filter(analyte=="IL-6" & cell=="astrocyte")
IL6.A %>% group_by(group, region) %>% shapiro_test(norm.tot)## # A tibble: 8 x 5
## group region variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 CON:CON FC norm.tot 0.857 0.179
## 2 CON:CON Str norm.tot 0.870 0.225
## 3 CON:PLX FC norm.tot 0.841 0.197
## 4 CON:PLX Str norm.tot 0.956 0.781
## 5 MIA:CON FC norm.tot 0.880 0.271
## 6 MIA:CON Str norm.tot 0.885 0.291
## 7 MIA:PLX FC norm.tot 0.935 0.631
## 8 MIA:PLX Str norm.tot 0.889 0.355## # A tibble: 1 x 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 7 35 1.24 0.309## Anova Table (Type II tests)
##
## Response: norm.tot
## Sum Sq Df F value Pr(>F)
## group 21.882 3 2.9223 0.04624 *
## region 0.070 1 0.0280 0.86809
## Residuals 94.847 38
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Dunnett Contrasts
##
##
## Fit: lm(formula = norm.tot ~ group + region, data = IL6.A)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## CON:PLX - CON:CON == 0 0.2728 0.6972 0.391 0.9629
## MIA:CON - CON:CON == 0 1.7800 0.6450 2.760 0.0243 *
## MIA:PLX - CON:CON == 0 0.9453 0.6765 1.397 0.3814
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)Negative
IL6
IL6.N <- dat %>% filter(analyte=="IL-6" & cell=="negative")
IL6.N %>% group_by(region, group) %>% shapiro_test(norm.tot)## # A tibble: 8 x 5
## group region variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 CON:CON FC norm.tot 0.803 0.0630
## 2 CON:PLX FC norm.tot 0.747 0.0277
## 3 MIA:CON FC norm.tot 0.787 0.0445
## 4 MIA:PLX FC norm.tot 0.934 0.623
## 5 CON:CON Str norm.tot 0.810 0.0724
## 6 CON:PLX Str norm.tot 0.786 0.0620
## 7 MIA:CON Str norm.tot 0.827 0.102
## 8 MIA:PLX Str norm.tot 0.765 0.0410## # A tibble: 1 x 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 7 36 1.37 0.247## Anova Table (Type II tests)
##
## Response: norm.tot
## Sum Sq Df F value Pr(>F)
## group 474.79 3 2.7079 0.0583 .
## region 4.16 1 0.0712 0.7910
## Residuals 2279.40 39
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Dunnett Contrasts
##
##
## Fit: lm(formula = norm.tot ~ group + region, data = IL6.N)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## CON:PLX - CON:CON == 0 1.082 3.273 0.331 0.977
## MIA:CON - CON:CON == 0 8.198 3.121 2.627 0.033 *
## MIA:PLX - CON:CON == 0 4.181 3.273 1.277 0.451
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)Reproducibility info
## [1] "2021-11-08 08:53:57 EST"## ─ 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 2021-11-08
##
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##
## [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library