Last updated: 2025-06-17

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Knit directory: Cinquina_2024/

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Rmd f653eaf EugOT 2025-06-17 mark correctly neurons and astrocytes
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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)%>%
  drop_na()

kable(df)
Group Measurement
Ctr_1 0.6470000
Ctr_1 0.6570000
Ctr_1 0.5800000
Ctr_1 0.6220000
Ctr_1 0.6440000
Ctr_1 0.5890000
Ctr_1 0.6040000
Ctr_1 0.5610000
EPA_5μM_1 0.6380000
EPA_5μM_1 0.6430000
EPA_5μM_1 0.6670000
EPA_5μM_1 0.6800000
EPA_5μM_1 0.6300000
EPA_5μM_1 0.6580000
EPA_5μM_1 0.6270000
EPA_5μM_1 0.6490000
EPA_10μM_1 0.6300000
EPA_10μM_1 0.6510000
EPA_10μM_1 0.6690000
EPA_10μM_1 0.6700000
EPA_10μM_1 0.6770000
EPA_10μM_1 0.6700000
EPA_10μM_1 0.7370000
EPA_10μM_1 0.6830000
EPA_30μM_1 0.7310000
EPA_30μM_1 0.7330000
EPA_30μM_1 0.7970000
EPA_30μM_1 0.7580000
EPA_30μM_1 0.7370000
EPA_30μM_1 0.7350000
EPA_30μM_1 0.5100000
EPA_30μM_1 0.6730000
Ctr_2 0.8300000
Ctr_2 0.8470000
Ctr_2 0.8240000
Ctr_2 0.8320000
Ctr_2 0.9000000
Ctr_2 0.8770000
Ctr_2 0.8570000
Ctr_2 0.7670000
EPA_5μM_2 0.8590000
EPA_5μM_2 0.9140000
EPA_5μM_2 0.9280000
EPA_5μM_2 0.9410000
EPA_5μM_2 0.8880000
EPA_5μM_2 0.9070000
EPA_5μM_2 0.9880000
EPA_5μM_2 0.8570000
EPA_10μM_2 0.8500000
EPA_10μM_2 0.8930000
EPA_10μM_2 0.8800000
EPA_10μM_2 0.9340000
EPA_10μM_2 0.9430000
EPA_10μM_2 0.9090000
EPA_10μM_2 0.8980000
EPA_10μM_2 0.9310000
EPA_30μM_2 0.8960000
EPA_30μM_2 0.9750000
EPA_30μM_2 0.9190000
EPA_30μM_2 0.9660000
EPA_30μM_2 0.9520000
EPA_30μM_2 0.9770000
EPA_30μM_2 0.9740000
EPA_30μM_2 0.8720000
Ctr_3 0.7310000
Ctr_3 0.7450000
Ctr_3 0.7050000
Ctr_3 0.6920000
Ctr_3 0.7480000
Ctr_3 0.7440000
Ctr_3 0.7710000
Ctr_3 0.6610000
EPA_5μM_3 0.7260000
EPA_5μM_3 0.7700000
EPA_5μM_3 0.7540000
EPA_5μM_3 0.7250000
EPA_5μM_3 0.6880000
EPA_5μM_3 0.7680000
EPA_5μM_3 0.7530000
EPA_5μM_3 0.7080000
EPA_10μM_3 0.7530000
EPA_10μM_3 0.7870000
EPA_10μM_3 0.7820000
EPA_10μM_3 0.7440000
EPA_10μM_3 0.7470000
EPA_10μM_3 0.7880000
EPA_10μM_3 0.7480000
EPA_10μM_3 0.7290000
EPA_30μM_3 0.7590000
EPA_30μM_3 0.8090000
EPA_30μM_3 0.7900000
EPA_30μM_3 0.8550000
EPA_30μM_3 0.7970000
EPA_30μM_3 0.8450000
EPA_30μM_3 0.8420000
EPA_30μM_3 0.8240000
Ctr_4 0.8400000
Ctr_4 0.7540000
Ctr_4 0.7440000
Ctr_4 0.7980000
Ctr_4 0.7880000
Ctr_4 0.8100000
Ctr_4 0.8200000
Ctr_4 0.8250000
EPA_5μM_4 0.7780000
EPA_5μM_4 0.8030000
EPA_5μM_4 0.7910000
EPA_5μM_4 0.7610000
EPA_5μM_4 0.7600000
EPA_5μM_4 0.7500000
EPA_5μM_4 0.7650000
EPA_5μM_4 0.7650000
EPA_10μM_4 0.7900000
EPA_10μM_4 0.7630000
EPA_10μM_4 0.8330000
EPA_10μM_4 0.8060000
EPA_10μM_4 0.7870000
EPA_10μM_4 0.7850000
EPA_10μM_4 0.7900000
EPA_10μM_4 0.8390000
EPA_30μM_4 0.8590000
EPA_30μM_4 0.8590000
EPA_30μM_4 0.8810000
EPA_30μM_4 0.8870000
EPA_30μM_4 0.9110000
EPA_30μM_4 0.8390000
EPA_30μM_4 0.9160000
EPA_30μM_4 0.9200000
Ctr_5 0.5710000
Ctr_5 0.5410000
Ctr_5 0.5390000
Ctr_5 0.5290000
Ctr_5 0.5090000
Ctr_5 0.5680000
Ctr_5 0.5040000
Ctr_5 0.5160000
EPA_5μM_5 0.5310000
EPA_5μM_5 0.5290000
EPA_5μM_5 0.5100000
EPA_5μM_5 0.5310000
EPA_5μM_5 0.5310000
EPA_5μM_5 0.5280000
EPA_5μM_5 0.5060000
EPA_5μM_5 0.5190000
EPA_10μM_5 0.5290000
EPA_10μM_5 0.5510000
EPA_10μM_5 0.5520000
EPA_10μM_5 0.5350000
EPA_10μM_5 0.5450000
EPA_10μM_5 0.5450000
EPA_10μM_5 0.5150000
EPA_10μM_5 0.5400000
EPA_30μM_5 0.6110000
EPA_30μM_5 0.6210000
EPA_30μM_5 0.7060000
EPA_30μM_5 0.5770000
EPA_30μM_5 0.5790000
EPA_30μM_5 0.5900000
EPA_30μM_5 0.5600000
EPA_30μM_5 0.5880000
Ctr_6 0.5100000
Ctr_6 0.5200000
Ctr_6 0.5600000
Ctr_6 0.5400000
Ctr_6 0.5200000
Ctr_6 0.5800000
Ctr_6 0.5500000
Ctr_6 0.5100000
EPA_5μM_6 0.5800000
EPA_5μM_6 0.6200000
EPA_5μM_6 0.7100000
EPA_5μM_6 0.6300000
EPA_5μM_6 0.6200000
EPA_5μM_6 0.6200000
EPA_5μM_6 0.6400000
EPA_5μM_6 0.6000000
EPA_10μM_6 0.5700000
EPA_10μM_6 0.5900000
EPA_10μM_6 0.6100000
EPA_10μM_6 0.5600000
EPA_10μM_6 0.6100000
EPA_10μM_6 0.5600000
EPA_10μM_6 0.5800000
EPA_10μM_6 0.5700000
EPA_30μM_6 0.6800000
EPA_30μM_6 0.6700000
EPA_30μM_6 0.6500000
EPA_30μM_6 0.6500000
EPA_30μM_6 0.6500000
EPA_30μM_6 0.6800000
EPA_30μM_6 0.6800000
EPA_30μM_6 0.6600000
Ctr_7 0.6315370
Ctr_7 0.6767187
Ctr_7 0.6757631
Ctr_7 0.7197545
Ctr_7 0.7327356
Ctr_7 0.6467031
Ctr_7 0.7103541
Ctr_7 0.7756199
Ctr_7 0.6964314
Ctr_7 0.7991241
Ctr_7 0.7714853
Ctr_7 0.7452136
Ctr_7 0.6995220
EPA_1μM_7 0.6043768
EPA_1μM_7 0.9266503
EPA_1μM_7 0.8606042
EPA_1μM_7 0.8710704
EPA_1μM_7 0.9278893
EPA_1μM_7 0.8072892
EPA_1μM_7 0.7782893
EPA_1μM_7 0.7901075
EPA_5μM_7 0.2920579
EPA_5μM_7 0.4137877
EPA_5μM_7 0.5664762
EPA_5μM_7 0.5037649
EPA_5μM_7 0.6254405
EPA_5μM_7 0.5603325
EPA_5μM_7 0.4643089
EPA_5μM_7 0.4429826 
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

1 and 5 μM of EPA on Neurons - Figure S7a

unpaired1 <- load(df,
  x = Group, y = Measurement,
  idx = list(
    c("Ctr_7", "EPA_1μM_7", "EPA_5μM_7")
  )
)
print(unpaired1)
DABESTR v2025.3.14
==================

Good afternoon!
The current time is 16:40 PM on Tuesday June 17, 2025.

ffect size(s) with 95% confidence intervals will be computed for:
1. EPA_1μM_7 minus Ctr_7
2. EPA_5μM_7 minus Ctr_7

5000 resamples will be used to generate the effect size bootstraps.
unpaired1.mean_diff <- mean_diff(unpaired1)

print(unpaired1.mean_diff)
DABESTR v2025.3.14
==================

Good afternoon!
The current time is 16:40 PM on Tuesday June 17, 2025.

The character(0) mean difference between EPA_1μM_7 and Ctr_7 is 0.107 [95%CI 0.015, 0.168].
The p-value of the two-sided permutation t-test is 0.0242, calculated for legacy purposes only.

The character(0) mean difference between EPA_5μM_7 and Ctr_7 is -0.23 [95%CI -0.309, -0.162].
The p-value of the two-sided permutation t-test is 0.0003, 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(unpaired1.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_7 EPA_1μM_7 5000 0.0153402 0.1678771 0.0277831 0.1742437 95 0.1068644 177.6146
Ctr_7 EPA_5μM_7 5000 -0.3091020 -0.1622632 -0.3050414 -0.1592285 95 -0.2302763 177.0996 
dabest_plot(unpaired1.mean_diff)

Version Author Date
cccdace EugOT 2025-06-17
7a9f863 Evgenii O. Tretiakov 2024-07-26
25c1972 Evgenii O. Tretiakov 2024-07-26
5947ab6 EugOT 2024-03-20
d69bcf7 EugOT 2024-03-20

5 μM of EPA on Astrocytes - Figure S7c1

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 v2025.3.14
==================

Good afternoon!
The current time is 16:40 PM on Tuesday June 17, 2025.

ffect 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 v2025.3.14
==================

Good afternoon!
The current time is 16:40 PM on Tuesday June 17, 2025.

The character(0) 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 character(0) 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 character(0) 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 character(0) 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 character(0) 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 character(0) 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
cccdace EugOT 2025-06-17
7a9f863 Evgenii O. Tretiakov 2024-07-26
25c1972 Evgenii O. Tretiakov 2024-07-26
5947ab6 EugOT 2024-03-20
d69bcf7 EugOT 2024-03-20

10 μM of EPA on Astrocytes - Figure S7c2

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 v2025.3.14
==================

Good afternoon!
The current time is 16:40 PM on Tuesday June 17, 2025.

ffect 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 v2025.3.14
==================

Good afternoon!
The current time is 16:40 PM on Tuesday June 17, 2025.

The character(0) 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 character(0) 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 character(0) 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 character(0) 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 character(0) 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 character(0) 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
cccdace EugOT 2025-06-17
7a9f863 Evgenii O. Tretiakov 2024-07-26
25c1972 Evgenii O. Tretiakov 2024-07-26
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 on Astrocytes - Figure S7c3

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 v2025.3.14
==================

Good afternoon!
The current time is 16:40 PM on Tuesday June 17, 2025.

ffect 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 v2025.3.14
==================

Good afternoon!
The current time is 16:40 PM on Tuesday June 17, 2025.

The character(0) 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 character(0) 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 character(0) 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 character(0) 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 character(0) 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 character(0) 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
cccdace EugOT 2025-06-17
9636b47 Evgenii O. Tretiakov 2024-07-26

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 on Astrocytes - 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 v2025.3.14
==================

Good afternoon!
The current time is 16:41 PM on Tuesday June 17, 2025.

ffect 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 v2025.3.14
==================

Good afternoon!
The current time is 16:41 PM on Tuesday June 17, 2025.

The character(0) 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 character(0) 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 character(0) 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 character(0) 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 character(0) 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 character(0) 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)

Version Author Date
cccdace EugOT 2025-06-17

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 4.3.3 (2024-02-29)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Ubuntu 24.04.2 LTS

Matrix products: default
BLAS/LAPACK: /data/Cinquina_2024/.pixi/envs/default/lib/libopenblasp-r0.3.29.so;  LAPACK version 3.12.0

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: Europe/Vienna
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_1.9.4    forcats_1.0.0     
 [5] stringr_1.5.1      dplyr_1.1.4        purrr_1.0.4        readr_2.1.5       
 [9] tidyr_1.3.1        tibble_3.3.0       ggplot2_3.5.2      tidyverse_2.0.0   
[13] dabestr_2025.3.14  RColorBrewer_1.1-3 knitr_1.50         here_1.0.1        
[17] workflowr_1.7.1   

loaded via a namespace (and not attached):
 [1] beeswarm_0.4.0    gtable_0.3.6      xfun_0.52         bslib_0.9.0      
 [5] processx_3.8.6    callr_3.7.6       tzdb_0.5.0        vctrs_0.6.5      
 [9] tools_4.3.3       ps_1.9.1          generics_0.1.4    parallel_4.3.3   
[13] pkgconfig_2.0.3   lifecycle_1.0.4   compiler_4.3.3    farver_2.1.2     
[17] git2r_0.35.0      ggsci_3.2.0       repr_1.1.7        getPass_0.2-4    
[21] vipor_0.4.7       httpuv_1.6.15     htmltools_0.5.8.1 sass_0.4.10      
[25] yaml_2.3.10       later_1.4.2       pillar_1.10.2     crayon_1.5.3     
[29] jquerylib_0.1.4   whisker_0.4.1     ggmin_0.0.0.9000  cachem_1.1.0     
[33] boot_1.3-31       tidyselect_1.2.1  digest_0.6.37     stringi_1.8.7    
[37] labeling_0.4.3    cowplot_1.1.3     rprojroot_2.0.4   fastmap_1.2.0    
[41] grid_4.3.3        cli_3.6.5         base64enc_0.1-3   effsize_0.8.1    
[45] withr_3.0.2       scales_1.4.0      promises_1.3.3    bit64_4.6.0-1    
[49] ggbeeswarm_0.7.2  timechange_0.3.0  rmarkdown_2.29    httr_1.4.7       
[53] bit_4.6.0         hms_1.1.3         evaluate_1.0.3    rlang_1.1.6      
[57] Rcpp_1.0.14       glue_1.8.0        rstudioapi_0.17.1 vroom_1.6.5      
[61] jsonlite_2.0.0    R6_2.6.1          fs_1.6.6