LID SPN TRAP

1451141_at

Mettl8 methyltransferase like 8

AIM Score vs. Gene Expression
Full X range:
Auto X range:
Group Comparisons: Boxplots

CP73

Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)

F-statistic p-value df difference
0.001 0.976 1.0

Model:
AIM ~ expression + C(dose) + expression:C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.651
Model: OLS Adj. R-squared: 0.596
Method: Least Squares F-statistic: 11.82
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000135
Time: 05:26:44 Log-Likelihood: -101.00
No. Observations: 23 AIC: 210.0
Df Residuals: 19 BIC: 214.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 81.7904 137.908 0.593 0.560 -206.854 370.435
C(dose)[T.1] -28.4567 250.111 -0.114 0.911 -551.944 495.031
expression -3.8826 19.393 -0.200 0.843 -44.473 36.708
expression:C(dose)[T.1] 11.8350 36.222 0.327 0.747 -63.978 87.648
Omnibus: 0.048 Durbin-Watson: 1.801
Prob(Omnibus): 0.976 Jarque-Bera (JB): 0.265
Skew: 0.033 Prob(JB): 0.876
Kurtosis: 2.479 Cond. No. 471.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.50
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.83e-05
Time: 05:26:44 Log-Likelihood: -101.06
No. Observations: 23 AIC: 208.1
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 57.6899 113.892 0.507 0.618 -179.885 295.265
C(dose)[T.1] 53.1965 9.900 5.374 0.000 32.546 73.847
expression -0.4901 16.010 -0.031 0.976 -33.885 32.905
Omnibus: 0.319 Durbin-Watson: 1.887
Prob(Omnibus): 0.852 Jarque-Bera (JB): 0.484
Skew: 0.065 Prob(JB): 0.785
Kurtosis: 2.301 Cond. No. 186.

Model:
AIM ~ C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.632
Method: Least Squares F-statistic: 38.84
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.51e-06
Time: 05:26:44 Log-Likelihood: -101.06
No. Observations: 23 AIC: 206.1
Df Residuals: 21 BIC: 208.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 54.2083 5.919 9.159 0.000 41.900 66.517
C(dose)[T.1] 53.3371 8.558 6.232 0.000 35.539 71.135
Omnibus: 0.322 Durbin-Watson: 1.888
Prob(Omnibus): 0.851 Jarque-Bera (JB): 0.485
Skew: 0.060 Prob(JB): 0.785
Kurtosis: 2.299 Cond. No. 2.57

Model:
AIM ~ expression

OLS Regression Results
Dep. Variable: AIM R-squared: 0.142
Model: OLS Adj. R-squared: 0.102
Method: Least Squares F-statistic: 3.487
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0759
Time: 05:26:44 Log-Likelihood: -111.34
No. Observations: 23 AIC: 226.7
Df Residuals: 21 BIC: 228.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 361.2000 150.881 2.394 0.026 47.426 674.974
expression -40.4039 21.636 -1.867 0.076 -85.399 4.591
Omnibus: 1.952 Durbin-Watson: 2.211
Prob(Omnibus): 0.377 Jarque-Bera (JB): 1.306
Skew: 0.328 Prob(JB): 0.520
Kurtosis: 2.035 Cond. No. 161.

CP101

Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)

F-statistic p-value df difference
0.149 0.707 1.0

Model:
AIM ~ expression + C(dose) + expression:C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.461
Model: OLS Adj. R-squared: 0.314
Method: Least Squares F-statistic: 3.137
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0693
Time: 05:26:44 Log-Likelihood: -70.664
No. Observations: 15 AIC: 149.3
Df Residuals: 11 BIC: 152.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 75.3955 188.551 0.400 0.697 -339.603 490.395
C(dose)[T.1] 135.0106 266.494 0.507 0.622 -451.538 721.559
expression -1.0294 24.315 -0.042 0.967 -54.547 52.488
expression:C(dose)[T.1] -11.9838 35.662 -0.336 0.743 -90.475 66.508
Omnibus: 2.136 Durbin-Watson: 0.821
Prob(Omnibus): 0.344 Jarque-Bera (JB): 1.655
Skew: -0.714 Prob(JB): 0.437
Kurtosis: 2.220 Cond. No. 327.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.456
Model: OLS Adj. R-squared: 0.365
Method: Least Squares F-statistic: 5.020
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0261
Time: 05:26:44 Log-Likelihood: -70.741
No. Observations: 15 AIC: 147.5
Df Residuals: 12 BIC: 149.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 118.5108 132.961 0.891 0.390 -171.186 408.208
C(dose)[T.1] 45.6814 18.105 2.523 0.027 6.234 85.129
expression -6.6005 17.117 -0.386 0.707 -43.895 30.694
Omnibus: 2.551 Durbin-Watson: 0.812
Prob(Omnibus): 0.279 Jarque-Bera (JB): 1.893
Skew: -0.823 Prob(JB): 0.388
Kurtosis: 2.433 Cond. No. 130.

Model:
AIM ~ C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.406
Method: Least Squares F-statistic: 10.58
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00629
Time: 05:26:45 Log-Likelihood: -70.833
No. Observations: 15 AIC: 145.7
Df Residuals: 13 BIC: 147.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 67.4286 11.044 6.106 0.000 43.570 91.287
C(dose)[T.1] 49.1964 15.122 3.253 0.006 16.527 81.866
Omnibus: 2.713 Durbin-Watson: 0.810
Prob(Omnibus): 0.258 Jarque-Bera (JB): 1.868
Skew: -0.843 Prob(JB): 0.393
Kurtosis: 2.619 Cond. No. 2.70

Model:
AIM ~ expression

OLS Regression Results
Dep. Variable: AIM R-squared: 0.167
Model: OLS Adj. R-squared: 0.103
Method: Least Squares F-statistic: 2.600
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.131
Time: 05:26:45 Log-Likelihood: -73.933
No. Observations: 15 AIC: 151.9
Df Residuals: 13 BIC: 153.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 304.9762 131.378 2.321 0.037 21.152 588.800
expression -28.3444 17.579 -1.612 0.131 -66.321 9.632
Omnibus: 0.073 Durbin-Watson: 1.387
Prob(Omnibus): 0.964 Jarque-Bera (JB): 0.242
Skew: -0.130 Prob(JB): 0.886
Kurtosis: 2.435 Cond. No. 108.