LID SPN TRAP

1434973_at

Car7 carbonic anhydrase 7

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
0.002 0.961 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.655
Model: OLS Adj. R-squared: 0.601
Method: Least Squares F-statistic: 12.04
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000121
Time: 04:31:49 Log-Likelihood: -100.86
No. Observations: 23 AIC: 209.7
Df Residuals: 19 BIC: 214.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 93.2225 89.528 1.041 0.311 -94.162 280.607
C(dose)[T.1] -11.2082 111.077 -0.101 0.921 -243.694 221.278
expression -7.7303 17.697 -0.437 0.667 -44.771 29.310
expression:C(dose)[T.1] 12.9407 22.170 0.584 0.566 -33.461 59.342
Omnibus: 1.027 Durbin-Watson: 1.829
Prob(Omnibus): 0.598 Jarque-Bera (JB): 0.788
Skew: -0.037 Prob(JB): 0.674
Kurtosis: 2.096 Cond. No. 178.

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: 18.50
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.83e-05
Time: 04:31:49 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 51.6059 53.249 0.969 0.344 -59.469 162.681
C(dose)[T.1] 53.4129 8.903 5.999 0.000 34.841 71.985
expression 0.5157 10.482 0.049 0.961 -21.350 22.381
Omnibus: 0.322 Durbin-Watson: 1.878
Prob(Omnibus): 0.851 Jarque-Bera (JB): 0.485
Skew: 0.062 Prob(JB): 0.784
Kurtosis: 2.299 Cond. No. 63.4

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: 04:31:49 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: 0.018
Model: OLS Adj. R-squared: -0.029
Method: Least Squares F-statistic: 0.3776
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.545
Time: 04:31:49 Log-Likelihood: -112.90
No. Observations: 23 AIC: 229.8
Df Residuals: 21 BIC: 232.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 131.2724 84.199 1.559 0.134 -43.830 306.375
expression -10.3593 16.858 -0.615 0.545 -45.417 24.698
Omnibus: 1.565 Durbin-Watson: 2.648
Prob(Omnibus): 0.457 Jarque-Bera (JB): 1.229
Skew: 0.365 Prob(JB): 0.541
Kurtosis: 2.134 Cond. No. 61.1

CP101

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

F-statistic p-value df difference
14.662 0.002 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.769
Model: OLS Adj. R-squared: 0.706
Method: Least Squares F-statistic: 12.21
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000798
Time: 04:31:49 Log-Likelihood: -64.307
No. Observations: 15 AIC: 136.6
Df Residuals: 11 BIC: 139.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -214.5813 148.215 -1.448 0.176 -540.800 111.637
C(dose)[T.1] -126.3461 200.053 -0.632 0.541 -566.661 313.969
expression 41.2708 21.661 1.905 0.083 -6.404 88.946
expression:C(dose)[T.1] 26.6319 29.422 0.905 0.385 -38.125 91.389
Omnibus: 1.296 Durbin-Watson: 1.422
Prob(Omnibus): 0.523 Jarque-Bera (JB): 0.584
Skew: -0.482 Prob(JB): 0.747
Kurtosis: 2.925 Cond. No. 354.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.752
Model: OLS Adj. R-squared: 0.711
Method: Least Squares F-statistic: 18.18
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000233
Time: 04:31:49 Log-Likelihood: -64.846
No. Observations: 15 AIC: 135.7
Df Residuals: 12 BIC: 137.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -313.2169 99.708 -3.141 0.009 -530.461 -95.972
C(dose)[T.1] 54.4774 10.649 5.116 0.000 31.275 77.680
expression 55.7056 14.548 3.829 0.002 24.008 87.403
Omnibus: 0.396 Durbin-Watson: 1.735
Prob(Omnibus): 0.820 Jarque-Bera (JB): 0.484
Skew: -0.297 Prob(JB): 0.785
Kurtosis: 2.350 Cond. No. 132.

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: 04:31:49 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: 0.211
Model: OLS Adj. R-squared: 0.150
Method: Least Squares F-statistic: 3.473
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0851
Time: 04:31:49 Log-Likelihood: -73.524
No. Observations: 15 AIC: 151.0
Df Residuals: 13 BIC: 152.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -218.7880 167.898 -1.303 0.215 -581.510 143.934
expression 46.0671 24.718 1.864 0.085 -7.334 99.468
Omnibus: 1.701 Durbin-Watson: 2.136
Prob(Omnibus): 0.427 Jarque-Bera (JB): 0.920
Skew: 0.155 Prob(JB): 0.631
Kurtosis: 1.827 Cond. No. 129.