LID SPN TRAP

1417040_a_at

Bok BCL2-related ovarian killer protein

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
0.995 0.330 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.691
Model: OLS Adj. R-squared: 0.642
Method: Least Squares F-statistic: 14.14
Date: Thu, 21 Nov 2024 Prob (F-statistic): 4.43e-05
Time: 03:56:24 Log-Likelihood: -99.612
No. Observations: 23 AIC: 207.2
Df Residuals: 19 BIC: 211.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 87.9092 139.722 0.629 0.537 -204.533 380.352
C(dose)[T.1] -181.4422 189.106 -0.959 0.349 -577.246 214.362
expression -4.2731 17.700 -0.241 0.812 -41.321 32.774
expression:C(dose)[T.1] 29.5180 23.847 1.238 0.231 -20.394 79.430
Omnibus: 0.091 Durbin-Watson: 1.530
Prob(Omnibus): 0.956 Jarque-Bera (JB): 0.053
Skew: 0.045 Prob(JB): 0.974
Kurtosis: 2.783 Cond. No. 479.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.666
Model: OLS Adj. R-squared: 0.632
Method: Least Squares F-statistic: 19.91
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.74e-05
Time: 03:56:24 Log-Likelihood: -100.50
No. Observations: 23 AIC: 207.0
Df Residuals: 20 BIC: 210.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -40.3503 94.970 -0.425 0.675 -238.455 157.754
C(dose)[T.1] 52.3984 8.611 6.085 0.000 34.436 70.361
expression 11.9895 12.018 0.998 0.330 -13.080 37.059
Omnibus: 1.156 Durbin-Watson: 1.907
Prob(Omnibus): 0.561 Jarque-Bera (JB): 0.847
Skew: -0.096 Prob(JB): 0.655
Kurtosis: 2.080 Cond. No. 179.

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: 03:56:24 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: 0.047
Model: OLS Adj. R-squared: 0.001
Method: Least Squares F-statistic: 1.030
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.322
Time: 03:56:24 Log-Likelihood: -112.55
No. Observations: 23 AIC: 229.1
Df Residuals: 21 BIC: 231.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -78.6174 156.159 -0.503 0.620 -403.367 246.132
expression 19.9811 19.686 1.015 0.322 -20.959 60.921
Omnibus: 3.503 Durbin-Watson: 2.426
Prob(Omnibus): 0.174 Jarque-Bera (JB): 1.393
Skew: 0.086 Prob(JB): 0.498
Kurtosis: 1.807 Cond. No. 179.

CP101

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

F-statistic p-value df difference
4.028 0.068 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.588
Model: OLS Adj. R-squared: 0.476
Method: Least Squares F-statistic: 5.231
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0174
Time: 03:56:24 Log-Likelihood: -68.651
No. Observations: 15 AIC: 145.3
Df Residuals: 11 BIC: 148.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -183.6350 173.935 -1.056 0.314 -566.464 199.194
C(dose)[T.1] 59.0220 257.319 0.229 0.823 -507.333 625.377
expression 31.8737 22.043 1.446 0.176 -16.642 80.389
expression:C(dose)[T.1] -3.9433 31.091 -0.127 0.901 -72.374 64.487
Omnibus: 2.211 Durbin-Watson: 0.932
Prob(Omnibus): 0.331 Jarque-Bera (JB): 1.085
Skew: -0.242 Prob(JB): 0.581
Kurtosis: 1.775 Cond. No. 407.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.587
Model: OLS Adj. R-squared: 0.519
Method: Least Squares F-statistic: 8.538
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00494
Time: 03:56:24 Log-Likelihood: -68.662
No. Observations: 15 AIC: 143.3
Df Residuals: 12 BIC: 145.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -168.0226 117.741 -1.427 0.179 -424.557 88.512
C(dose)[T.1] 26.4701 17.712 1.494 0.161 -12.121 65.061
expression 29.8916 14.894 2.007 0.068 -2.560 62.343
Omnibus: 2.284 Durbin-Watson: 0.919
Prob(Omnibus): 0.319 Jarque-Bera (JB): 1.086
Skew: -0.225 Prob(JB): 0.581
Kurtosis: 1.761 Cond. No. 147.

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: 03:56:24 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: 0.510
Model: OLS Adj. R-squared: 0.473
Method: Least Squares F-statistic: 13.56
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00276
Time: 03:56:24 Log-Likelihood: -69.943
No. Observations: 15 AIC: 143.9
Df Residuals: 13 BIC: 145.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -271.7725 99.506 -2.731 0.017 -486.742 -56.803
expression 44.1228 11.984 3.682 0.003 18.234 70.012
Omnibus: 1.374 Durbin-Watson: 1.382
Prob(Omnibus): 0.503 Jarque-Bera (JB): 0.940
Skew: 0.307 Prob(JB): 0.625
Kurtosis: 1.939 Cond. No. 118.