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Rmd 373fd42 Evgenii O. Tretiakov 2024-07-26 added MTT analysis figure legends
Rmd 160fe90 Evgenii O. Tretiakov 2024-07-26 added MTT analysis with Glutamate for consistency
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Rmd d09b2f1 EugOT 2024-03-20 calculate cell viability MTT assay 
df <- read_tsv(here(data_dir, "ASTRO_MTT_EPA.tsv"))
df <- df %>%
  tidyr::gather(key = Group, value = Measurement)

kable(df)
Group Measurement
Ctr_1 0.647
Ctr_1 0.657
Ctr_1 0.580
Ctr_1 0.622
Ctr_1 0.644
Ctr_1 0.589
Ctr_1 0.604
Ctr_1 0.561
EPA_5μM_1 0.638
EPA_5μM_1 0.643
EPA_5μM_1 0.667
EPA_5μM_1 0.680
EPA_5μM_1 0.630
EPA_5μM_1 0.658
EPA_5μM_1 0.627
EPA_5μM_1 0.649
EPA_10μM_1 0.630
EPA_10μM_1 0.651
EPA_10μM_1 0.669
EPA_10μM_1 0.670
EPA_10μM_1 0.677
EPA_10μM_1 0.670
EPA_10μM_1 0.737
EPA_10μM_1 0.683
EPA_30μM_1 0.731
EPA_30μM_1 0.733
EPA_30μM_1 0.797
EPA_30μM_1 0.758
EPA_30μM_1 0.737
EPA_30μM_1 0.735
EPA_30μM_1 0.510
EPA_30μM_1 0.673
Ctr_2 0.830
Ctr_2 0.847
Ctr_2 0.824
Ctr_2 0.832
Ctr_2 0.900
Ctr_2 0.877
Ctr_2 0.857
Ctr_2 0.767
EPA_5μM_2 0.859
EPA_5μM_2 0.914
EPA_5μM_2 0.928
EPA_5μM_2 0.941
EPA_5μM_2 0.888
EPA_5μM_2 0.907
EPA_5μM_2 0.988
EPA_5μM_2 0.857
EPA_10μM_2 0.850
EPA_10μM_2 0.893
EPA_10μM_2 0.880
EPA_10μM_2 0.934
EPA_10μM_2 0.943
EPA_10μM_2 0.909
EPA_10μM_2 0.898
EPA_10μM_2 0.931
EPA_30μM_2 0.896
EPA_30μM_2 0.975
EPA_30μM_2 0.919
EPA_30μM_2 0.966
EPA_30μM_2 0.952
EPA_30μM_2 0.977
EPA_30μM_2 0.974
EPA_30μM_2 0.872
Ctr_3 0.731
Ctr_3 0.745
Ctr_3 0.705
Ctr_3 0.692
Ctr_3 0.748
Ctr_3 0.744
Ctr_3 0.771
Ctr_3 0.661
EPA_5μM_3 0.726
EPA_5μM_3 0.770
EPA_5μM_3 0.754
EPA_5μM_3 0.725
EPA_5μM_3 0.688
EPA_5μM_3 0.768
EPA_5μM_3 0.753
EPA_5μM_3 0.708
EPA_10μM_3 0.753
EPA_10μM_3 0.787
EPA_10μM_3 0.782
EPA_10μM_3 0.744
EPA_10μM_3 0.747
EPA_10μM_3 0.788
EPA_10μM_3 0.748
EPA_10μM_3 0.729
EPA_30μM_3 0.759
EPA_30μM_3 0.809
EPA_30μM_3 0.790
EPA_30μM_3 0.855
EPA_30μM_3 0.797
EPA_30μM_3 0.845
EPA_30μM_3 0.842
EPA_30μM_3 0.824
Ctr_4 0.840
Ctr_4 0.754
Ctr_4 0.744
Ctr_4 0.798
Ctr_4 0.788
Ctr_4 0.810
Ctr_4 0.820
Ctr_4 0.825
EPA_5μM_4 0.778
EPA_5μM_4 0.803
EPA_5μM_4 0.791
EPA_5μM_4 0.761
EPA_5μM_4 0.760
EPA_5μM_4 0.750
EPA_5μM_4 0.765
EPA_5μM_4 0.765
EPA_10μM_4 0.790
EPA_10μM_4 0.763
EPA_10μM_4 0.833
EPA_10μM_4 0.806
EPA_10μM_4 0.787
EPA_10μM_4 0.785
EPA_10μM_4 0.790
EPA_10μM_4 0.839
EPA_30μM_4 0.859
EPA_30μM_4 0.859
EPA_30μM_4 0.881
EPA_30μM_4 0.887
EPA_30μM_4 0.911
EPA_30μM_4 0.839
EPA_30μM_4 0.916
EPA_30μM_4 0.920
Ctr_5 0.571
Ctr_5 0.541
Ctr_5 0.539
Ctr_5 0.529
Ctr_5 0.509
Ctr_5 0.568
Ctr_5 0.504
Ctr_5 0.516
EPA_5μM_5 0.531
EPA_5μM_5 0.529
EPA_5μM_5 0.510
EPA_5μM_5 0.531
EPA_5μM_5 0.531
EPA_5μM_5 0.528
EPA_5μM_5 0.506
EPA_5μM_5 0.519
EPA_10μM_5 0.529
EPA_10μM_5 0.551
EPA_10μM_5 0.552
EPA_10μM_5 0.535
EPA_10μM_5 0.545
EPA_10μM_5 0.545
EPA_10μM_5 0.515
EPA_10μM_5 0.540
EPA_30μM_5 0.611
EPA_30μM_5 0.621
EPA_30μM_5 0.706
EPA_30μM_5 0.577
EPA_30μM_5 0.579
EPA_30μM_5 0.590
EPA_30μM_5 0.560
EPA_30μM_5 0.588
Ctr_6 0.510
Ctr_6 0.520
Ctr_6 0.560
Ctr_6 0.540
Ctr_6 0.520
Ctr_6 0.580
Ctr_6 0.550
Ctr_6 0.510
EPA_5μM_6 0.580
EPA_5μM_6 0.620
EPA_5μM_6 0.710
EPA_5μM_6 0.630
EPA_5μM_6 0.620
EPA_5μM_6 0.620
EPA_5μM_6 0.640
EPA_5μM_6 0.600
EPA_10μM_6 0.570
EPA_10μM_6 0.590
EPA_10μM_6 0.610
EPA_10μM_6 0.560
EPA_10μM_6 0.610
EPA_10μM_6 0.560
EPA_10μM_6 0.580
EPA_10μM_6 0.570
EPA_30μM_6 0.680
EPA_30μM_6 0.670
EPA_30μM_6 0.650
EPA_30μM_6 0.650
EPA_30μM_6 0.650
EPA_30μM_6 0.680
EPA_30μM_6 0.680
EPA_30μM_6 0.660 
df_glut <- read_tsv(here(data_dir, "ASTRO_MTT_Glut_24h.tsv"), col_types = "d")
df_glut <- df_glut %>%
  tidyr::gather(key = Group, value = Measurement)

kable(df_glut)
Group Measurement
Ctr_1 0.702
Ctr_1 0.683
Ctr_1 0.704
Ctr_1 0.714
Ctr_1 0.702
Ctr_1 0.670
Ctr_1 0.688
Ctr_1 0.664
Ctr_1 0.767
Ctr_1 0.723
Ctr_1 0.718
Ctr_1 0.757
Ctr_1 0.710
Ctr_1 0.689
Ctr_1 0.721
Ctr_1 0.674
Glu_1 0.703
Glu_1 0.740
Glu_1 0.715
Glu_1 0.825
Glu_1 0.715
Glu_1 0.755
Glu_1 0.710
Glu_1 0.685
Glu_1 0.679
Glu_1 0.746
Glu_1 0.729
Glu_1 0.693
Glu_1 0.748
Glu_1 0.723
Glu_1 0.700
Glu_1 0.744
Ctr_2 0.647
Ctr_2 0.657
Ctr_2 0.580
Ctr_2 0.622
Ctr_2 0.644
Ctr_2 0.589
Ctr_2 0.604
Ctr_2 0.561
Ctr_2 0.590
Ctr_2 0.609
Ctr_2 0.627
Ctr_2 0.613
Ctr_2 0.604
Ctr_2 0.601
Ctr_2 0.586
Ctr_2 0.590
Glu_2 0.625
Glu_2 0.629
Glu_2 0.624
Glu_2 0.648
Glu_2 0.666
Glu_2 0.675
Glu_2 0.658
Glu_2 0.571
Glu_2 0.621
Glu_2 0.693
Glu_2 0.638
Glu_2 0.630
Glu_2 0.670
Glu_2 0.669
Glu_2 0.698
Glu_2 0.605
Ctr_3 0.830
Ctr_3 0.847
Ctr_3 0.824
Ctr_3 0.832
Ctr_3 0.900
Ctr_3 0.877
Ctr_3 0.857
Ctr_3 0.767
Ctr_3 0.803
Ctr_3 0.855
Ctr_3 0.823
Ctr_3 0.818
Ctr_3 0.783
Ctr_3 0.799
Ctr_3 0.833
Ctr_3 0.855
Glu_3 0.844
Glu_3 0.961
Glu_3 0.910
Glu_3 0.965
Glu_3 0.939
Glu_3 0.891
Glu_3 0.908
Glu_3 0.855
Glu_3 0.915
Glu_3 0.927
Glu_3 0.943
Glu_3 0.913
Glu_3 0.969
Glu_3 0.947
Glu_3 0.875
Glu_3 0.826
Ctr_4 0.731
Ctr_4 0.745
Ctr_4 0.705
Ctr_4 0.692
Ctr_4 0.748
Ctr_4 0.744
Ctr_4 0.771
Ctr_4 0.661
Ctr_4 0.698
Ctr_4 0.691
Ctr_4 0.682
Ctr_4 0.668
Ctr_4 0.672
Ctr_4 0.715
Ctr_4 0.707
Ctr_4 0.659
Glu_4 0.713
Glu_4 0.734
Glu_4 0.729
Glu_4 0.736
Glu_4 0.678
Glu_4 0.718
Glu_4 0.735
Glu_4 0.662
Glu_4 0.724
Glu_4 0.711
Glu_4 0.703
Glu_4 0.716
Glu_4 0.673
Glu_4 0.711
Glu_4 0.704
Glu_4 0.659
Ctr_5 0.840
Ctr_5 0.754
Ctr_5 0.744
Ctr_5 0.798
Ctr_5 0.788
Ctr_5 0.810
Ctr_5 0.820
Ctr_5 0.825
Ctr_5 0.818
Ctr_5 0.773
Ctr_5 0.759
Ctr_5 0.760
Ctr_5 0.741
Ctr_5 0.755
Ctr_5 0.744
Ctr_5 0.737
Glu_5 0.784
Glu_5 0.757
Glu_5 0.779
Glu_5 0.789
Glu_5 0.757
Glu_5 0.784
Glu_5 0.751
Glu_5 0.795
Glu_5 0.777
Glu_5 0.726
Glu_5 0.718
Glu_5 0.739
Glu_5 0.762
Glu_5 0.737
Glu_5 0.776
Glu_5 0.778
Ctr_6 0.571
Ctr_6 0.541
Ctr_6 0.539
Ctr_6 0.529
Ctr_6 0.509
Ctr_6 0.568
Ctr_6 0.504
Ctr_6 0.516
Ctr_6 0.494
Ctr_6 0.491
Ctr_6 0.491
Ctr_6 0.486
Ctr_6 0.504
Ctr_6 0.513
Ctr_6 0.521
Ctr_6 0.504
Glu_6 0.543
Glu_6 0.536
Glu_6 0.540
Glu_6 0.572
Glu_6 0.518
Glu_6 0.534
Glu_6 0.526
Glu_6 0.523
Glu_6 0.514
Glu_6 0.516
Glu_6 0.514
Glu_6 0.505
Glu_6 0.503
Glu_6 0.504
Glu_6 0.529
Glu_6 0.503

5 μM of EPA - Figure S5b

unpaired5 <- load(df,
  x = Group, y = Measurement,
  idx = list(
    c("Ctr_1", "EPA_5μM_1"),
    c("Ctr_2", "EPA_5μM_2"),
    c("Ctr_3", "EPA_5μM_3"),
    c("Ctr_4", "EPA_5μM_4"),
    c("Ctr_5", "EPA_5μM_5"),
    c("Ctr_6", "EPA_5μM_6")
  ),
  minimeta = TRUE
)
print(unpaired5)
DABESTR v2023.9.12
==================

Good afternoon!
The current time is 12:20 PM on Friday July 26, 2024.

Effect size(s) with 95% confidence intervals will be computed for:
1. EPA_5μM_1 minus Ctr_1
2. EPA_5μM_2 minus Ctr_2
3. EPA_5μM_3 minus Ctr_3
4. EPA_5μM_4 minus Ctr_4
5. EPA_5μM_5 minus Ctr_5
6. EPA_5μM_6 minus Ctr_6
7. weighted delta (only for mean difference)

5000 resamples will be used to generate the effect size bootstraps.
unpaired5.mean_diff <- mean_diff(unpaired5)

print(unpaired5.mean_diff)
DABESTR v2023.9.12
==================

Good afternoon!
The current time is 12:20 PM on Friday July 26, 2024.

The unpaired mean difference between EPA_5μM_1 and Ctr_1 is 0.036 [95%CI 0.011, 0.063].
The p-value of the two-sided permutation t-test is 0.0266, calculated for legacy purposes only.

The unpaired mean difference between EPA_5μM_2 and Ctr_2 is 0.068 [95%CI 0.032, 0.109].
The p-value of the two-sided permutation t-test is 0.0055, calculated for legacy purposes only.

The unpaired mean difference between EPA_5μM_3 and Ctr_3 is 0.012 [95%CI -0.018, 0.043].
The p-value of the two-sided permutation t-test is 0.4823, calculated for legacy purposes only.

The unpaired mean difference between EPA_5μM_4 and Ctr_4 is -0.026 [95%CI -0.048, 0.002].
The p-value of the two-sided permutation t-test is 0.0849, calculated for legacy purposes only.

The unpaired mean difference between EPA_5μM_5 and Ctr_5 is -0.011 [95%CI -0.031, 0.005].
The p-value of the two-sided permutation t-test is 0.2624, calculated for legacy purposes only.

The unpaired mean difference between EPA_5μM_6 and Ctr_6 is 0.091 [95%CI 0.065, 0.125].
The p-value of the two-sided permutation t-test is 0.0001, calculated for legacy purposes only.

5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
Any p-value reported is the probability of observing the effect size (or greater),
assuming the null hypothesis of zero difference is true.
For each p-value, 5000 reshuffles of the control and test labels were performed.
kable(unpaired5.mean_diff$boot_result |> select(-bootstraps))
control_group test_group nboots bca_ci_low bca_ci_high pct_ci_low pct_ci_high ci difference weight
Ctr_1 EPA_5μM_1 5000 0.0105000 0.0626250 0.0105000 0.0625000 95 0.036000 1278.3053
Ctr_2 EPA_5μM_2 5000 0.0320000 0.1085000 0.0315031 0.1076250 95 0.068500 575.6816
Ctr_3 EPA_5μM_3 5000 -0.0180310 0.0429689 -0.0182469 0.0428750 95 0.011875 925.5663
Ctr_4 EPA_5μM_4 5000 -0.0483460 0.0023750 -0.0494969 0.0003719 95 -0.025750 1362.6962
Ctr_5 EPA_5μM_5 5000 -0.0310000 0.0051207 -0.0300000 0.0057500 95 -0.011500 2693.9914
Ctr_6 EPA_5μM_6 5000 0.0650000 0.1250000 0.0625000 0.1225000 95 0.091250 949.9576
Minimeta Overall Test Minimeta Overall Test 5000 0.0024303 0.0274285 0.0148743 0.0152279 95 0.015034 1.0000 
dabest_plot(unpaired5.mean_diff)

Version Author Date
7a9f863 Evgenii O. Tretiakov 2024-07-26
25c1972 Evgenii O. Tretiakov 2024-07-25
5947ab6 EugOT 2024-03-20
d69bcf7 EugOT 2024-03-20

10 μM of EPA - Figure S5b1

unpaired10 <- load(df,
  x = Group, y = Measurement,
  idx = list(
    c("Ctr_1", "EPA_10μM_1"),
    c("Ctr_2", "EPA_10μM_2"),
    c("Ctr_3", "EPA_10μM_3"),
    c("Ctr_4", "EPA_10μM_4"),
    c("Ctr_5", "EPA_10μM_5"),
    c("Ctr_6", "EPA_10μM_6")
  ),
  minimeta = TRUE
)
print(unpaired10)
DABESTR v2023.9.12
==================

Good afternoon!
The current time is 12:20 PM on Friday July 26, 2024.

Effect size(s) with 95% confidence intervals will be computed for:
1. EPA_10μM_1 minus Ctr_1
2. EPA_10μM_2 minus Ctr_2
3. EPA_10μM_3 minus Ctr_3
4. EPA_10μM_4 minus Ctr_4
5. EPA_10μM_5 minus Ctr_5
6. EPA_10μM_6 minus Ctr_6
7. weighted delta (only for mean difference)

5000 resamples will be used to generate the effect size bootstraps.
unpaired10.mean_diff <- mean_diff(unpaired10)

print(unpaired10.mean_diff)
DABESTR v2023.9.12
==================

Good afternoon!
The current time is 12:20 PM on Friday July 26, 2024.

The unpaired mean difference between EPA_10μM_1 and Ctr_1 is 0.06 [95%CI 0.033, 0.093].
The p-value of the two-sided permutation t-test is 0.0026, calculated for legacy purposes only.

The unpaired mean difference between EPA_10μM_2 and Ctr_2 is 0.063 [95%CI 0.03, 0.096].
The p-value of the two-sided permutation t-test is 0.0036, calculated for legacy purposes only.

The unpaired mean difference between EPA_10μM_3 and Ctr_3 is 0.035 [95%CI 0.009, 0.064].
The p-value of the two-sided permutation t-test is 0.0376, calculated for legacy purposes only.

The unpaired mean difference between EPA_10μM_4 and Ctr_4 is 0.002 [95%CI -0.024, 0.032].
The p-value of the two-sided permutation t-test is 0.9092, calculated for legacy purposes only.

The unpaired mean difference between EPA_10μM_5 and Ctr_5 is 0.004 [95%CI -0.015, 0.021].
The p-value of the two-sided permutation t-test is 0.6694, calculated for legacy purposes only.

The unpaired mean difference between EPA_10μM_6 and Ctr_6 is 0.045 [95%CI 0.024, 0.065].
The p-value of the two-sided permutation t-test is 0.0018, calculated for legacy purposes only.

5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
Any p-value reported is the probability of observing the effect size (or greater),
assuming the null hypothesis of zero difference is true.
For each p-value, 5000 reshuffles of the control and test labels were performed.
kable(unpaired10.mean_diff$boot_result |> select(-bootstraps))
control_group test_group nboots bca_ci_low bca_ci_high pct_ci_low pct_ci_high ci difference weight
Ctr_1 EPA_10μM_1 5000 0.0325000 0.0925793 0.0316250 0.0911250 95 0.0603750 923.3686
Ctr_2 EPA_10μM_2 5000 0.0305000 0.0963750 0.0300031 0.0960000 95 0.0630000 784.2698
Ctr_3 EPA_10μM_3 5000 0.0092500 0.0643750 0.0083750 0.0631250 95 0.0351250 1110.9899
Ctr_4 EPA_10μM_4 5000 -0.0242500 0.0317707 -0.0257500 0.0306250 95 0.0017500 1104.7326
Ctr_5 EPA_10μM_5 5000 -0.0153750 0.0211250 -0.0143750 0.0220000 95 0.0043750 2523.4887
Ctr_6 EPA_10μM_6 5000 0.0237500 0.0650000 0.0237500 0.0650000 95 0.0450000 1872.9097
Minimeta Overall Test Minimeta Overall Test 5000 0.0172908 0.0412704 0.0289180 0.0292622 95 0.0290195 1.0000 
dabest_plot(unpaired10.mean_diff)

Version Author Date
7a9f863 Evgenii O. Tretiakov 2024-07-26
25c1972 Evgenii O. Tretiakov 2024-07-25
5947ab6 EugOT 2024-03-20
d69bcf7 EugOT 2024-03-20

Supplementary Figure 5. Effects of low EPA concentrations on astrocyte viability. b, b1. Cumming estimation plots showing astroglia viability (MTT assay) after 5 μM (b) or 10 μM (b1) EPA treatment. Left panel: Colored circles represent individual data points for control (Ctr) and EPA-treated groups from 6 independent experiments. Black circles and vertical lines indicate group means with 95% confidence intervals. Right panel: Floating plots show mean differences (EPA minus Ctr) for each experiment and the overall weighted mean difference (bottom). Circles represent the point estimate of the mean difference, with vertical lines indicating 95% confidence intervals. The shaded curve represents the resampled distribution of the effect size.

30 μM of EPA - Figure 5c

unpaired30 <- load(df,
  x = Group, y = Measurement,
  idx = list(
    c("Ctr_1", "EPA_30μM_1"),
    c("Ctr_2", "EPA_30μM_2"),
    c("Ctr_3", "EPA_30μM_3"),
    c("Ctr_4", "EPA_30μM_4"),
    c("Ctr_5", "EPA_30μM_5"),
    c("Ctr_6", "EPA_30μM_6")
  ),
  minimeta = TRUE
)
print(unpaired30)
DABESTR v2023.9.12
==================

Good afternoon!
The current time is 12:20 PM on Friday July 26, 2024.

Effect size(s) with 95% confidence intervals will be computed for:
1. EPA_30μM_1 minus Ctr_1
2. EPA_30μM_2 minus Ctr_2
3. EPA_30μM_3 minus Ctr_3
4. EPA_30μM_4 minus Ctr_4
5. EPA_30μM_5 minus Ctr_5
6. EPA_30μM_6 minus Ctr_6
7. weighted delta (only for mean difference)

5000 resamples will be used to generate the effect size bootstraps.
unpaired30.mean_diff <- mean_diff(unpaired30)

print(unpaired30.mean_diff)
DABESTR v2023.9.12
==================

Good afternoon!
The current time is 12:20 PM on Friday July 26, 2024.

The unpaired mean difference between EPA_30μM_1 and Ctr_1 is 0.096 [95%CI 0.007, 0.14].
The p-value of the two-sided permutation t-test is 0.0175, calculated for legacy purposes only.

The unpaired mean difference between EPA_30μM_2 and Ctr_2 is 0.1 [95%CI 0.061, 0.136].
The p-value of the two-sided permutation t-test is 0.0002, calculated for legacy purposes only.

The unpaired mean difference between EPA_30μM_3 and Ctr_3 is 0.091 [95%CI 0.059, 0.123].
The p-value of the two-sided permutation t-test is 0.0001, calculated for legacy purposes only.

The unpaired mean difference between EPA_30μM_4 and Ctr_4 is 0.087 [95%CI 0.057, 0.117].
The p-value of the two-sided permutation t-test is 0.0001, calculated for legacy purposes only.

The unpaired mean difference between EPA_30μM_5 and Ctr_5 is 0.069 [95%CI 0.043, 0.116].
The p-value of the two-sided permutation t-test is 0.0031, calculated for legacy purposes only.

The unpaired mean difference between EPA_30μM_6 and Ctr_6 is 0.129 [95%CI 0.108, 0.145].
The p-value of the two-sided permutation t-test is 0.0000, calculated for legacy purposes only.

5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
Any p-value reported is the probability of observing the effect size (or greater),
assuming the null hypothesis of zero difference is true.
For each p-value, 5000 reshuffles of the control and test labels were performed.
kable(unpaired30.mean_diff$boot_result |> select(-bootstraps))
control_group test_group nboots bca_ci_low bca_ci_high pct_ci_low pct_ci_high ci difference weight
Ctr_1 EPA_30μM_1 5000 0.0070740 0.1397500 0.0288781 0.1503750 95 0.0962500 225.2778
Ctr_2 EPA_30μM_2 5000 0.0610000 0.1357500 0.0618813 0.1367500 95 0.0996250 619.8153
Ctr_3 EPA_30μM_3 5000 0.0587500 0.1227395 0.0585031 0.1224969 95 0.0905000 850.7148
Ctr_4 EPA_30μM_4 5000 0.0573750 0.1167127 0.0570000 0.1165000 95 0.0866250 971.4211
Ctr_5 EPA_30μM_5 5000 0.0426250 0.1157298 0.0385000 0.1073750 95 0.0693750 738.6352
Ctr_6 EPA_30μM_6 5000 0.1075000 0.1450000 0.1087500 0.1462500 95 0.1287500 2338.2046
Minimeta Overall Test Minimeta Overall Test 5000 0.0921689 0.1152220 0.1037444 0.1040722 95 0.1039085 1.0000 
dabest_plot(unpaired30.mean_diff)

Version Author Date
7a9f863 Evgenii O. Tretiakov 2024-07-26
25c1972 Evgenii O. Tretiakov 2024-07-25
5947ab6 EugOT 2024-03-20
d69bcf7 EugOT 2024-03-20

Figure 5. Effects of EPA on astrocyte viability. c. Cumming estimation plot showing astroglia viability (MTT assay) after 30 μM EPA treatment. Left panel: Colored circles represent individual data points for control (Ctr) and EPA-treated groups from 6 independent experiments. Black circles and vertical lines indicate group means with 95% confidence intervals. Right panel: Floating plots show mean differences (EPA minus Ctr) for each experiment and the overall weighted mean difference (bottom). Circles represent the point estimate of the mean difference, with vertical lines indicating 95% confidence intervals. The shaded curve represents the resampled distribution of the effect size.

100 μM of Glutamate - Figure 3c

unpaired_glut <- load(df_glut,
  x = Group, y = Measurement,
  idx = list(
    c("Ctr_1", "Glu_1"),
    c("Ctr_2", "Glu_2"),
    c("Ctr_3", "Glu_3"),
    c("Ctr_4", "Glu_4"),
    c("Ctr_5", "Glu_5"),
    c("Ctr_6", "Glu_6")
  ),
  minimeta = TRUE
)
print(unpaired_glut)
DABESTR v2023.9.12
==================

Good afternoon!
The current time is 12:20 PM on Friday July 26, 2024.

Effect size(s) with 95% confidence intervals will be computed for:
1. Glu_1 minus Ctr_1
2. Glu_2 minus Ctr_2
3. Glu_3 minus Ctr_3
4. Glu_4 minus Ctr_4
5. Glu_5 minus Ctr_5
6. Glu_6 minus Ctr_6
7. weighted delta (only for mean difference)

5000 resamples will be used to generate the effect size bootstraps.
unpaired_glut.mean_diff <- mean_diff(unpaired_glut)

print(unpaired_glut.mean_diff)
DABESTR v2023.9.12
==================

Good afternoon!
The current time is 12:20 PM on Friday July 26, 2024.

The unpaired mean difference between Glu_1 and Ctr_1 is 0.02 [95%CI 0, 0.043].
The p-value of the two-sided permutation t-test is 0.0856, calculated for legacy purposes only.

The unpaired mean difference between Glu_2 and Ctr_2 is 0.037 [95%CI 0.016, 0.057].
The p-value of the two-sided permutation t-test is 0.0016, calculated for legacy purposes only.

The unpaired mean difference between Glu_3 and Ctr_3 is 0.08 [95%CI 0.052, 0.104].
The p-value of the two-sided permutation t-test is 0.0000, calculated for legacy purposes only.

The unpaired mean difference between Glu_4 and Ctr_4 is 0.001 [95%CI -0.022, 0.019].
The p-value of the two-sided permutation t-test is 0.9214, calculated for legacy purposes only.

The unpaired mean difference between Glu_5 and Ctr_5 is -0.016 [95%CI -0.037, 0.003].
The p-value of the two-sided permutation t-test is 0.1375, calculated for legacy purposes only.

The unpaired mean difference between Glu_6 and Ctr_6 is 0.006 [95%CI -0.01, 0.02].
The p-value of the two-sided permutation t-test is 0.4451, calculated for legacy purposes only.

5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
Any p-value reported is the probability of observing the effect size (or greater),
assuming the null hypothesis of zero difference is true.
For each p-value, 5000 reshuffles of the control and test labels were performed.
kable(unpaired_glut.mean_diff$boot_result |> select(-bootstraps))
control_group test_group nboots bca_ci_low bca_ci_high pct_ci_low pct_ci_high ci difference weight
Ctr_1 Glu_1 5000 0.0003849 0.0432387 -0.0006859 0.0420594 95 0.0202500 965.8881
Ctr_2 Glu_2 5000 0.0163698 0.0566875 0.0168125 0.0568125 95 0.0372500 1102.0093
Ctr_3 Glu_3 5000 0.0521250 0.1041250 0.0531891 0.1056234 95 0.0803125 649.9001
Ctr_4 Glu_4 5000 -0.0216248 0.0194375 -0.0196875 0.0210000 95 0.0010625 1098.9137
Ctr_5 Glu_5 5000 -0.0365625 0.0033125 -0.0359359 0.0039984 95 -0.0160625 1136.8508
Ctr_6 Glu_6 5000 -0.0099801 0.0201875 -0.0090625 0.0207500 95 0.0061875 1960.9205
Minimeta Overall Test Minimeta Overall Test 5000 0.0049018 0.0269089 0.0154685 0.0157784 95 0.0155969 1.0000 
dabest_plot(unpaired_glut.mean_diff)

Figure 3. Effects of glutamate on astrocyte viability. c. Cumming estimation plot showing astroglia viability (MTT assay) after 100 μM glutamate treatment. Left panel: Colored circles represent individual data points for control (Ctr) and glutamate-treated (Glu) groups from 6 independent experiments. Black circles and vertical lines indicate group means with 95% confidence intervals. Right panel: Floating plots show mean differences (Glu minus Ctr) for each experiment and the overall weighted mean difference (bottom). Circles represent the point estimate of the mean difference, with vertical lines indicating 95% confidence intervals. The shaded curve represents the resampled distribution of the effect size.


sessionInfo()
R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] skimr_2.1.5          magrittr_2.0.3       lubridate_1.9.3     
 [4] forcats_1.0.0        stringr_1.5.1        dplyr_1.1.4         
 [7] purrr_1.0.2          readr_2.1.5          tidyr_1.3.1         
[10] tibble_3.2.1         ggplot2_3.5.1        tidyverse_2.0.0.9000
[13] dabestr_2023.9.12    RColorBrewer_1.1-3   knitr_1.47          
[16] here_1.0.1           workflowr_1.7.1     

loaded via a namespace (and not attached):
 [1] beeswarm_0.4.0    gtable_0.3.5      xfun_0.45         bslib_0.7.0      
 [5] processx_3.8.4    callr_3.7.6       tzdb_0.4.0        vctrs_0.6.5      
 [9] tools_4.4.0       ps_1.7.6          generics_0.1.3    parallel_4.4.0   
[13] fansi_1.0.6       highr_0.11        pkgconfig_2.0.3   lifecycle_1.0.4  
[17] farver_2.1.2      compiler_4.4.0    git2r_0.33.0      munsell_0.5.1    
[21] ggsci_3.2.0       repr_1.1.7        getPass_0.2-4     vipor_0.4.7      
[25] httpuv_1.6.15     htmltools_0.5.8.1 sass_0.4.9        yaml_2.3.8       
[29] crayon_1.5.2      later_1.3.2       pillar_1.9.0      jquerylib_0.1.4  
[33] whisker_0.4.1     ggmin_0.0.0.9000  cachem_1.1.0      boot_1.3-30      
[37] tidyselect_1.2.1  digest_0.6.35     stringi_1.8.4     labeling_0.4.3   
[41] cowplot_1.1.3     rprojroot_2.0.4   fastmap_1.2.0     grid_4.4.0       
[45] colorspace_2.1-0  cli_3.6.2         base64enc_0.1-3   utf8_1.2.4       
[49] effsize_0.8.1     withr_3.0.0       scales_1.3.0      promises_1.3.0   
[53] bit64_4.0.5       ggbeeswarm_0.7.2  timechange_0.3.0  rmarkdown_2.27   
[57] httr_1.4.7        bit_4.0.5         hms_1.1.3         evaluate_0.24.0  
[61] rlang_1.1.4       Rcpp_1.0.12       glue_1.7.0        rstudioapi_0.16.0
[65] vroom_1.6.5       jsonlite_1.8.8    R6_2.5.1          fs_1.6.4