LID SPN TRAP

1458541_at

Dctn4 dynactin 4

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
0.176 0.679 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.597
Method: Least Squares F-statistic: 11.88
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000131
Time: 03:37:56 Log-Likelihood: -100.96
No. Observations: 23 AIC: 209.9
Df Residuals: 19 BIC: 214.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 88.9766 93.962 0.947 0.356 -107.687 285.640
C(dose)[T.1] 39.7000 142.679 0.278 0.784 -258.932 338.332
expression -5.1170 13.799 -0.371 0.715 -33.998 23.764
expression:C(dose)[T.1] 2.0128 20.935 0.096 0.924 -41.805 45.831
Omnibus: 0.649 Durbin-Watson: 1.900
Prob(Omnibus): 0.723 Jarque-Bera (JB): 0.646
Skew: -0.052 Prob(JB): 0.724
Kurtosis: 2.185 Cond. No. 280.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 18.75
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.60e-05
Time: 03:37:56 Log-Likelihood: -100.96
No. Observations: 23 AIC: 207.9
Df Residuals: 20 BIC: 211.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 83.0352 69.006 1.203 0.243 -60.908 226.979
C(dose)[T.1] 53.3909 8.732 6.114 0.000 35.175 71.607
expression -4.2426 10.117 -0.419 0.679 -25.346 16.861
Omnibus: 0.600 Durbin-Watson: 1.890
Prob(Omnibus): 0.741 Jarque-Bera (JB): 0.623
Skew: -0.035 Prob(JB): 0.732
Kurtosis: 2.197 Cond. No. 110.

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: 03:37:56 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: 0.002
Model: OLS Adj. R-squared: -0.046
Method: Least Squares F-statistic: 0.03977
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.844
Time: 03:37:56 Log-Likelihood: -113.08
No. Observations: 23 AIC: 230.2
Df Residuals: 21 BIC: 232.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 102.3951 113.947 0.899 0.379 -134.572 339.362
expression -3.3346 16.722 -0.199 0.844 -38.109 31.440
Omnibus: 2.975 Durbin-Watson: 2.495
Prob(Omnibus): 0.226 Jarque-Bera (JB): 1.485
Skew: 0.278 Prob(JB): 0.476
Kurtosis: 1.886 Cond. No. 110.

CP101

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

F-statistic p-value df difference
0.304 0.592 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.561
Model: OLS Adj. R-squared: 0.442
Method: Least Squares F-statistic: 4.690
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0241
Time: 03:37:56 Log-Likelihood: -69.122
No. Observations: 15 AIC: 146.2
Df Residuals: 11 BIC: 149.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 234.2651 170.345 1.375 0.196 -140.661 609.191
C(dose)[T.1] -274.8856 206.018 -1.334 0.209 -728.329 178.558
expression -27.6058 28.131 -0.981 0.348 -89.521 34.309
expression:C(dose)[T.1] 53.3323 33.882 1.574 0.144 -21.243 127.907
Omnibus: 2.156 Durbin-Watson: 1.340
Prob(Omnibus): 0.340 Jarque-Bera (JB): 0.736
Skew: -0.514 Prob(JB): 0.692
Kurtosis: 3.349 Cond. No. 255.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.462
Model: OLS Adj. R-squared: 0.373
Method: Least Squares F-statistic: 5.160
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0241
Time: 03:37:56 Log-Likelihood: -70.646
No. Observations: 15 AIC: 147.3
Df Residuals: 12 BIC: 149.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 12.0951 101.068 0.120 0.907 -208.113 232.304
C(dose)[T.1] 48.5674 15.586 3.116 0.009 14.608 82.527
expression 9.1558 16.618 0.551 0.592 -27.051 45.362
Omnibus: 3.379 Durbin-Watson: 0.921
Prob(Omnibus): 0.185 Jarque-Bera (JB): 2.096
Skew: -0.913 Prob(JB): 0.351
Kurtosis: 2.873 Cond. No. 81.9

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: 03:37:56 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: 0.027
Model: OLS Adj. R-squared: -0.047
Method: Least Squares F-statistic: 0.3655
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.556
Time: 03:37:56 Log-Likelihood: -75.092
No. Observations: 15 AIC: 154.2
Df Residuals: 13 BIC: 155.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 14.9375 130.603 0.114 0.911 -267.213 297.088
expression 12.9485 21.417 0.605 0.556 -33.320 59.217
Omnibus: 0.146 Durbin-Watson: 1.706
Prob(Omnibus): 0.929 Jarque-Bera (JB): 0.249
Skew: -0.188 Prob(JB): 0.883
Kurtosis: 2.492 Cond. No. 81.6