LID SPN TRAP

1459666_at

Trak1 trafficking protein, kinesin binding 1

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
0.012 0.914 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.670
Model: OLS Adj. R-squared: 0.618
Method: Least Squares F-statistic: 12.86
Date: Mon, 27 Jan 2025 Prob (F-statistic): 8.07e-05
Time: 22:25:13 Log-Likelihood: -100.36
No. Observations: 23 AIC: 208.7
Df Residuals: 19 BIC: 213.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 164.9539 151.340 1.090 0.289 -151.804 481.712
C(dose)[T.1] -230.9831 260.948 -0.885 0.387 -777.153 315.187
expression -13.8037 18.848 -0.732 0.473 -53.254 25.647
expression:C(dose)[T.1] 34.9167 31.989 1.092 0.289 -32.037 101.870
Omnibus: 0.103 Durbin-Watson: 1.768
Prob(Omnibus): 0.950 Jarque-Bera (JB): 0.318
Skew: 0.077 Prob(JB): 0.853
Kurtosis: 2.445 Cond. No. 601.

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: 18.51
Date: Mon, 27 Jan 2025 Prob (F-statistic): 2.82e-05
Time: 22:25:13 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 67.6968 122.914 0.551 0.588 -188.697 324.091
C(dose)[T.1] 53.6705 9.278 5.785 0.000 34.318 73.023
expression -1.6812 15.302 -0.110 0.914 -33.600 30.238
Omnibus: 0.284 Durbin-Watson: 1.903
Prob(Omnibus): 0.868 Jarque-Bera (JB): 0.462
Skew: 0.056 Prob(JB): 0.794
Kurtosis: 2.315 Cond. No. 232.

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: Mon, 27 Jan 2025 Prob (F-statistic): 3.51e-06
Time: 22:25:13 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: 0.062
Model: OLS Adj. R-squared: 0.018
Method: Least Squares F-statistic: 1.397
Date: Mon, 27 Jan 2025 Prob (F-statistic): 0.250
Time: 22:25:13 Log-Likelihood: -112.36
No. Observations: 23 AIC: 228.7
Df Residuals: 21 BIC: 231.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -141.6740 187.430 -0.756 0.458 -531.456 248.108
expression 27.2725 23.073 1.182 0.250 -20.710 75.255
Omnibus: 1.332 Durbin-Watson: 2.401
Prob(Omnibus): 0.514 Jarque-Bera (JB): 0.893
Skew: 0.067 Prob(JB): 0.640
Kurtosis: 2.044 Cond. No. 221.

CP101

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

F-statistic p-value df difference
4.909 0.047 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.609
Model: OLS Adj. R-squared: 0.503
Method: Least Squares F-statistic: 5.718
Date: Mon, 27 Jan 2025 Prob (F-statistic): 0.0131
Time: 22:25:13 Log-Likelihood: -68.252
No. Observations: 15 AIC: 144.5
Df Residuals: 11 BIC: 147.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 480.7744 458.765 1.048 0.317 -528.960 1490.509
C(dose)[T.1] -30.3581 490.522 -0.062 0.952 -1109.989 1049.273
expression -58.4396 64.845 -0.901 0.387 -201.163 84.284
expression:C(dose)[T.1] 8.0709 69.924 0.115 0.910 -145.830 161.972
Omnibus: 2.180 Durbin-Watson: 1.573
Prob(Omnibus): 0.336 Jarque-Bera (JB): 0.552
Skew: 0.351 Prob(JB): 0.759
Kurtosis: 3.624 Cond. No. 766.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.609
Model: OLS Adj. R-squared: 0.544
Method: Least Squares F-statistic: 9.338
Date: Mon, 27 Jan 2025 Prob (F-statistic): 0.00358
Time: 22:25:13 Log-Likelihood: -68.261
No. Observations: 15 AIC: 142.5
Df Residuals: 12 BIC: 144.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 431.6798 164.680 2.621 0.022 72.873 790.486
C(dose)[T.1] 26.2235 16.832 1.558 0.145 -10.450 62.897
expression -51.4985 23.242 -2.216 0.047 -102.140 -0.858
Omnibus: 2.421 Durbin-Watson: 1.590
Prob(Omnibus): 0.298 Jarque-Bera (JB): 0.656
Skew: 0.369 Prob(JB): 0.720
Kurtosis: 3.712 Cond. No. 175.

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: Mon, 27 Jan 2025 Prob (F-statistic): 0.00629
Time: 22:25:13 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: 0.530
Model: OLS Adj. R-squared: 0.494
Method: Least Squares F-statistic: 14.64
Date: Mon, 27 Jan 2025 Prob (F-statistic): 0.00210
Time: 22:25:13 Log-Likelihood: -69.642
No. Observations: 15 AIC: 143.3
Df Residuals: 13 BIC: 144.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 598.1275 132.022 4.531 0.001 312.912 883.343
expression -73.8041 19.288 -3.826 0.002 -115.474 -32.134
Omnibus: 12.212 Durbin-Watson: 1.972
Prob(Omnibus): 0.002 Jarque-Bera (JB): 9.878
Skew: 1.170 Prob(JB): 0.00716
Kurtosis: 6.214 Cond. No. 132.