LID SPN TRAP

1455182_at

Kif1b kinesin family member 1B

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
0.028 0.870 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.671
Model: OLS Adj. R-squared: 0.619
Method: Least Squares F-statistic: 12.91
Date: Thu, 21 Nov 2024 Prob (F-statistic): 7.86e-05
Time: 05:23:50 Log-Likelihood: -100.32
No. Observations: 23 AIC: 208.6
Df Residuals: 19 BIC: 213.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 28.7643 85.582 0.336 0.740 -150.360 207.889
C(dose)[T.1] 302.3187 223.602 1.352 0.192 -165.685 770.323
expression 3.7977 12.742 0.298 0.769 -22.872 30.467
expression:C(dose)[T.1] -34.8532 31.388 -1.110 0.281 -100.550 30.843
Omnibus: 0.035 Durbin-Watson: 1.698
Prob(Omnibus): 0.983 Jarque-Bera (JB): 0.085
Skew: 0.031 Prob(JB): 0.958
Kurtosis: 2.708 Cond. No. 424.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.53
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.80e-05
Time: 05:23:50 Log-Likelihood: -101.05
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 67.2454 78.705 0.854 0.403 -96.931 231.422
C(dose)[T.1] 54.3066 10.529 5.158 0.000 32.344 76.269
expression -1.9459 11.713 -0.166 0.870 -26.378 22.486
Omnibus: 0.396 Durbin-Watson: 1.876
Prob(Omnibus): 0.820 Jarque-Bera (JB): 0.525
Skew: 0.039 Prob(JB): 0.769
Kurtosis: 2.264 Cond. No. 128.

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: 05:23:50 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: 0.183
Model: OLS Adj. R-squared: 0.144
Method: Least Squares F-statistic: 4.715
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0415
Time: 05:23:50 Log-Likelihood: -110.78
No. Observations: 23 AIC: 225.6
Df Residuals: 21 BIC: 227.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -139.0826 100.975 -1.377 0.183 -349.073 70.907
expression 31.5361 14.523 2.171 0.042 1.333 61.739
Omnibus: 1.983 Durbin-Watson: 2.246
Prob(Omnibus): 0.371 Jarque-Bera (JB): 1.376
Skew: 0.368 Prob(JB): 0.503
Kurtosis: 2.054 Cond. No. 110.

CP101

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

F-statistic p-value df difference
0.599 0.454 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.503
Model: OLS Adj. R-squared: 0.368
Method: Least Squares F-statistic: 3.714
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0458
Time: 05:23:50 Log-Likelihood: -70.054
No. Observations: 15 AIC: 148.1
Df Residuals: 11 BIC: 150.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 131.2968 253.733 0.517 0.615 -427.165 689.759
C(dose)[T.1] -195.4481 305.178 -0.640 0.535 -867.140 476.244
expression -8.8723 35.212 -0.252 0.806 -86.373 68.629
expression:C(dose)[T.1] 33.0710 41.870 0.790 0.446 -59.083 125.225
Omnibus: 0.242 Durbin-Watson: 1.086
Prob(Omnibus): 0.886 Jarque-Bera (JB): 0.403
Skew: -0.208 Prob(JB): 0.817
Kurtosis: 2.312 Cond. No. 429.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.475
Model: OLS Adj. R-squared: 0.388
Method: Least Squares F-statistic: 5.429
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0209
Time: 05:23:50 Log-Likelihood: -70.467
No. Observations: 15 AIC: 146.9
Df Residuals: 12 BIC: 149.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -37.0788 135.442 -0.274 0.789 -332.181 258.024
C(dose)[T.1] 45.2491 16.185 2.796 0.016 9.986 80.512
expression 14.5177 18.750 0.774 0.454 -26.336 55.371
Omnibus: 1.173 Durbin-Watson: 0.997
Prob(Omnibus): 0.556 Jarque-Bera (JB): 0.996
Skew: -0.527 Prob(JB): 0.608
Kurtosis: 2.305 Cond. No. 133.

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: 05:23:50 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: 0.133
Model: OLS Adj. R-squared: 0.066
Method: Least Squares F-statistic: 1.995
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.181
Time: 05:23:50 Log-Likelihood: -74.229
No. Observations: 15 AIC: 152.5
Df Residuals: 13 BIC: 153.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -134.2127 161.628 -0.830 0.421 -483.388 214.963
expression 31.0310 21.972 1.412 0.181 -16.436 78.498
Omnibus: 0.577 Durbin-Watson: 1.489
Prob(Omnibus): 0.749 Jarque-Bera (JB): 0.052
Skew: 0.144 Prob(JB): 0.974
Kurtosis: 3.007 Cond. No. 128.