MSN electrophysiology data analysis
Analysis of the ephys data from CON and MIA mice treated with PIC at E9. Recordings were performed on D1TdTom+ (D1R) and D1TdTom- (D2R) MSNs in the ventral striatum.
Paired Pulse Ratio
Repeated measures two-way ANOVA
PPR %>% filter(ms == "400") %>% group_by(Type, Group) %>% summarise(cells = n(), mouse = length(unique(ID)), litter = length(unique(Litter)))## `summarise()` has grouped output by 'Type'. You can override using the `.groups` argument.## # A tibble: 4 x 5
## # Groups: Type [2]
## Type Group cells mouse litter
## <chr> <chr> <int> <int> <int>
## 1 D1R CON 14 8 5
## 2 D1R MIA 11 5 6
## 3 D2R CON 13 5 5
## 4 D2R MIA 11 5 5## Tom+ (D1R)## Analysis of Variance Table
##
## Response: ppr
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 1 0.13 0.126 0.99 0.323
## ms 1 0.52 0.516 4.03 0.047 *
## ID 11 9.79 0.890 6.96 8e-09 ***
## Group:ms 1 0.00 0.002 0.01 0.913
## Residuals 110 14.07 0.128
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Tom- (D2R)## Analysis of Variance Table
##
## Response: ppr
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 1 2.73 2.730 14.50 0.00023 ***
## ms 1 1.37 1.373 7.30 0.00802 **
## ID 8 2.19 0.274 1.46 0.18210
## Group:ms 1 0.05 0.045 0.24 0.62479
## Residuals 108 20.33 0.188
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sEPSC Frequency and Amplitude
T-test
data %>% group_by(Type, Group) %>% summarise(cells = n(), mouse = length(unique(ID)), litter = length(unique(Litter)))## `summarise()` has grouped output by 'Type'. You can override using the `.groups` argument.## # A tibble: 4 x 5
## # Groups: Type [2]
## Type Group cells mouse litter
## <chr> <chr> <int> <int> <int>
## 1 D1R CON 11 8 5
## 2 D1R MIA 10 4 3
## 3 D2R CON 10 6 4
## 4 D2R MIA 13 6 4## Analysis of sEPSC Frequency## Number of D1R Cells
## CON MIA
## 11 10## Number of D2R Cells
## CON MIA
## 10 13## # A tibble: 4 x 5
## Group Type variable statistic p
## <chr> <chr> <chr> <dbl> <dbl>
## 1 CON D1R sFreq 0.916 0.284
## 2 MIA D1R sFreq 0.952 0.697
## 3 CON D2R sFreq 0.940 0.556
## 4 MIA D2R sFreq 0.959 0.742##
## Welch Two Sample t-test
##
## data: D1R$sFreq by D1R$Group
## t = 0.4, df = 19, p-value = 0.7
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.71 1.09
## sample estimates:
## mean in group CON mean in group MIA
## 3.68 3.50##
## Welch Two Sample t-test
##
## data: D2R$sFreq by D2R$Group
## t = 2, df = 16, p-value = 0.03
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.087 1.939
## sample estimates:
## mean in group CON mean in group MIA
## 3.72 2.71## Analysis of sEPSC Amplitude## # A tibble: 4 x 5
## Group Type variable statistic p
## <chr> <chr> <chr> <dbl> <dbl>
## 1 CON D1R sAmp 0.922 0.333
## 2 MIA D1R sAmp 0.850 0.0580
## 3 CON D2R sAmp 0.864 0.0847
## 4 MIA D2R sAmp 0.919 0.242##
## Welch Two Sample t-test
##
## data: D1R$sAmp by D1R$Group
## t = -1, df = 15, p-value = 0.2
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.56 0.84
## sample estimates:
## mean in group CON mean in group MIA
## 15.5 16.9##
## Welch Two Sample t-test
##
## data: D2R$sAmp by D2R$Group
## t = 1, df = 17, p-value = 0.2
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.25 4.90
## sample estimates:
## mean in group CON mean in group MIA
## 16.7 14.9KS Test of Cumulative Distrubtion
## KS Test comparing the control and MIA D1R sEPSC frequency distribution## Warning in ks.test(D1R[D1R$Group == "CON", ]$probability, D1R[D1R$Group == :
## cannot compute exact p-value with ties##
## Two-sample Kolmogorov-Smirnov test
##
## data: D1R[D1R$Group == "CON", ]$probability and D1R[D1R$Group == "MIA", ]$probability
## D = 0.1, p-value = 1
## alternative hypothesis: two-sided## KS Test comparing the control and MIA D2R sEPSC frequency distribution## Warning in ks.test(D2R[D2R$Group == "CON", ]$probability, D2R[D2R$Group == :
## cannot compute exact p-value with ties##
## Two-sample Kolmogorov-Smirnov test
##
## data: D2R[D2R$Group == "CON", ]$probability and D2R[D2R$Group == "MIA", ]$probability
## D = 0.4, p-value = 0.004
## alternative hypothesis: two-sidedmESPC Frequency and Amplitude
T-test
## `summarise()` has grouped output by 'Type'. You can override using the `.groups` argument.## # A tibble: 2 x 4
## # Groups: Type [1]
## Type Group cells mouse
## <chr> <chr> <int> <int>
## 1 D2R CON 16 3
## 2 D2R MIA 12 3## Analysis of mEPSC Frequency## Number of D2R Cells
## CON MIA
## 16 12## # A tibble: 2 x 4
## Group variable statistic p
## <chr> <chr> <dbl> <dbl>
## 1 CON mFreq 0.944 0.396
## 2 MIA mFreq 0.858 0.0464##
## Welch Two Sample t-test
##
## data: data$mFreq by data$Group
## t = 3, df = 25, p-value = 0.01
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.197 1.357
## sample estimates:
## mean in group CON mean in group MIA
## 3.15 2.37## Analysis of mEPSC Amplitude## # A tibble: 2 x 4
## Group variable statistic p
## <chr> <chr> <dbl> <dbl>
## 1 CON mAmp 0.966 0.764
## 2 MIA mAmp 0.867 0.0594##
## Welch Two Sample t-test
##
## data: data$mAmp by data$Group
## t = 1, df = 24, p-value = 0.2
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.823 4.225
## sample estimates:
## mean in group CON mean in group MIA
## 15.1 13.4KS Test
## KS Test comparing the control and MIA D2R mEPSC frequency distribution## Warning in ks.test(cumlFreq[cumlFreq$Group == "CON", ]$probability,
## cumlFreq[cumlFreq$Group == : cannot compute exact p-value with ties##
## Two-sample Kolmogorov-Smirnov test
##
## data: cumlFreq[cumlFreq$Group == "CON", ]$probability and cumlFreq[cumlFreq$Group == "MIA", ]$probability
## D = 0.4, p-value = 0.008
## alternative hypothesis: two-sided## 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
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## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## 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 cowplot_1.1.1 ggplot2_3.3.5
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## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.7 lattice_0.20-44 lubridate_1.7.10 assertthat_0.2.1
## [5] digest_0.6.27 utf8_1.2.1 R6_2.5.0 cellranger_1.1.0
## [9] backports_1.2.1 reprex_2.0.0 evaluate_0.14 highr_0.9
## [13] httr_1.4.2 pillar_1.6.1 rlang_0.4.11 curl_4.3.2
## [17] readxl_1.3.1 rstudioapi_0.13 data.table_1.14.0 car_3.0-11
## [21] jquerylib_0.1.4 Matrix_1.3-4 rmarkdown_2.9 splines_4.0.3
## [25] labeling_0.4.2 foreign_0.8-81 munsell_0.5.0 broom_0.7.8
## [29] compiler_4.0.3 modelr_0.1.8 xfun_0.24 pkgconfig_2.0.3
## [33] mgcv_1.8-36 htmltools_0.5.1.1 tidyselect_1.1.1 rio_0.5.27
## [37] fansi_0.5.0 crayon_1.4.1 dbplyr_2.1.1 withr_2.4.2
## [41] grid_4.0.3 nlme_3.1-152 jsonlite_1.7.2 gtable_0.3.0
## [45] lifecycle_1.0.0 DBI_1.1.1 magrittr_2.0.1 scales_1.1.1
## [49] zip_2.2.0 carData_3.0-4 cli_3.0.0 stringi_1.6.2
## [53] farver_2.1.0 fs_1.5.0 xml2_1.3.2 bslib_0.2.5.1
## [57] ellipsis_0.3.2 generics_0.1.0 vctrs_0.3.8 openxlsx_4.2.4
## [61] tools_4.0.3 glue_1.4.2 hms_1.1.0 abind_1.4-5
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