LID SPN TRAP

1436405_at

Dock4 dedicator of cytokinesis 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
0.919 0.349 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.711
Model: OLS Adj. R-squared: 0.666
Method: Least Squares F-statistic: 15.62
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.31e-05
Time: 04:02:34 Log-Likelihood: -98.810
No. Observations: 23 AIC: 205.6
Df Residuals: 19 BIC: 210.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 67.8269 94.932 0.714 0.484 -130.869 266.523
C(dose)[T.1] 430.5903 215.382 1.999 0.060 -20.208 881.389
expression -1.9615 13.649 -0.144 0.887 -30.529 26.606
expression:C(dose)[T.1] -55.3250 31.439 -1.760 0.095 -121.128 10.478
Omnibus: 0.432 Durbin-Watson: 1.469
Prob(Omnibus): 0.806 Jarque-Bera (JB): 0.191
Skew: 0.215 Prob(JB): 0.909
Kurtosis: 2.882 Cond. No. 429.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.664
Model: OLS Adj. R-squared: 0.631
Method: Least Squares F-statistic: 19.80
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.81e-05
Time: 04:02:34 Log-Likelihood: -100.55
No. Observations: 23 AIC: 207.1
Df Residuals: 20 BIC: 210.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 140.2259 89.927 1.559 0.135 -47.359 327.811
C(dose)[T.1] 51.8525 8.714 5.951 0.000 33.676 70.029
expression -12.3892 12.924 -0.959 0.349 -39.348 14.570
Omnibus: 0.512 Durbin-Watson: 1.978
Prob(Omnibus): 0.774 Jarque-Bera (JB): 0.584
Skew: -0.042 Prob(JB): 0.747
Kurtosis: 2.224 Cond. No. 148.

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: 04:02:34 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: 0.070
Model: OLS Adj. R-squared: 0.026
Method: Least Squares F-statistic: 1.591
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.221
Time: 04:02:34 Log-Likelihood: -112.26
No. Observations: 23 AIC: 228.5
Df Residuals: 21 BIC: 230.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 259.1455 142.422 1.820 0.083 -37.037 555.328
expression -26.0583 20.659 -1.261 0.221 -69.022 16.905
Omnibus: 2.787 Durbin-Watson: 2.617
Prob(Omnibus): 0.248 Jarque-Bera (JB): 1.241
Skew: 0.037 Prob(JB): 0.538
Kurtosis: 1.864 Cond. No. 144.

CP101

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

F-statistic p-value df difference
2.641 0.130 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.569
Model: OLS Adj. R-squared: 0.451
Method: Least Squares F-statistic: 4.835
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0220
Time: 04:02:34 Log-Likelihood: -68.992
No. Observations: 15 AIC: 146.0
Df Residuals: 11 BIC: 148.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 272.0636 226.080 1.203 0.254 -225.536 769.663
C(dose)[T.1] 359.2366 411.755 0.872 0.402 -547.030 1265.503
expression -28.4814 31.431 -0.906 0.384 -97.662 40.699
expression:C(dose)[T.1] -40.2758 55.676 -0.723 0.485 -162.818 82.266
Omnibus: 0.064 Durbin-Watson: 0.971
Prob(Omnibus): 0.969 Jarque-Bera (JB): 0.269
Skew: 0.100 Prob(JB): 0.874
Kurtosis: 2.375 Cond. No. 525.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.548
Model: OLS Adj. R-squared: 0.473
Method: Least Squares F-statistic: 7.281
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00850
Time: 04:02:34 Log-Likelihood: -69.341
No. Observations: 15 AIC: 144.7
Df Residuals: 12 BIC: 146.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 364.2902 182.958 1.991 0.070 -34.341 762.921
C(dose)[T.1] 61.6137 16.169 3.811 0.002 26.385 96.842
expression -41.3176 25.423 -1.625 0.130 -96.710 14.075
Omnibus: 0.388 Durbin-Watson: 1.034
Prob(Omnibus): 0.824 Jarque-Bera (JB): 0.493
Skew: -0.041 Prob(JB): 0.781
Kurtosis: 2.115 Cond. No. 193.

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: 04:02:34 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: 0.001
Model: OLS Adj. R-squared: -0.075
Method: Least Squares F-statistic: 0.01945
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.891
Time: 04:02:34 Log-Likelihood: -75.289
No. Observations: 15 AIC: 154.6
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 60.8832 235.281 0.259 0.800 -447.411 569.178
expression 4.4633 32.002 0.139 0.891 -64.674 73.600
Omnibus: 0.363 Durbin-Watson: 1.580
Prob(Omnibus): 0.834 Jarque-Bera (JB): 0.481
Skew: -0.010 Prob(JB): 0.786
Kurtosis: 2.123 Cond. No. 174.