LID SPN TRAP

1435307_at

Ankrd34b ankyrin repeat domain 34B

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
0.381 0.544 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.676
Model: OLS Adj. R-squared: 0.624
Method: Least Squares F-statistic: 13.19
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.88e-05
Time: 04:55:53 Log-Likelihood: -100.16
No. Observations: 23 AIC: 208.3
Df Residuals: 19 BIC: 212.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 63.6620 118.649 0.537 0.598 -184.673 311.997
C(dose)[T.1] 346.9516 270.945 1.281 0.216 -220.144 914.047
expression -1.1893 14.907 -0.080 0.937 -32.391 30.012
expression:C(dose)[T.1] -36.6109 33.833 -1.082 0.293 -107.423 34.201
Omnibus: 0.550 Durbin-Watson: 1.578
Prob(Omnibus): 0.760 Jarque-Bera (JB): 0.609
Skew: 0.307 Prob(JB): 0.738
Kurtosis: 2.492 Cond. No. 590.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.656
Model: OLS Adj. R-squared: 0.621
Method: Least Squares F-statistic: 19.04
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.35e-05
Time: 04:55:53 Log-Likelihood: -100.85
No. Observations: 23 AIC: 207.7
Df Residuals: 20 BIC: 211.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 120.1623 106.997 1.123 0.275 -103.030 343.355
C(dose)[T.1] 53.9069 8.736 6.170 0.000 35.683 72.131
expression -8.2972 13.439 -0.617 0.544 -36.331 19.737
Omnibus: 0.572 Durbin-Watson: 1.903
Prob(Omnibus): 0.751 Jarque-Bera (JB): 0.630
Skew: 0.132 Prob(JB): 0.730
Kurtosis: 2.233 Cond. No. 200.

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: 04:55:53 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: 0.000
Model: OLS Adj. R-squared: -0.048
Method: Least Squares F-statistic: 0.0004331
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.984
Time: 04:55:53 Log-Likelihood: -113.10
No. Observations: 23 AIC: 230.2
Df Residuals: 21 BIC: 232.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 76.0256 177.534 0.428 0.673 -293.177 445.228
expression 0.4625 22.224 0.021 0.984 -45.755 46.680
Omnibus: 3.269 Durbin-Watson: 2.491
Prob(Omnibus): 0.195 Jarque-Bera (JB): 1.563
Skew: 0.290 Prob(JB): 0.458
Kurtosis: 1.862 Cond. No. 200.

CP101

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

F-statistic p-value df difference
0.384 0.547 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.482
Model: OLS Adj. R-squared: 0.341
Method: Least Squares F-statistic: 3.413
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0566
Time: 04:55:53 Log-Likelihood: -70.366
No. Observations: 15 AIC: 148.7
Df Residuals: 11 BIC: 151.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 142.2033 257.367 0.553 0.592 -424.257 708.663
C(dose)[T.1] 454.5755 660.901 0.688 0.506 -1000.058 1909.209
expression -11.3906 39.165 -0.291 0.777 -97.592 74.811
expression:C(dose)[T.1] -53.3779 90.962 -0.587 0.569 -253.585 146.829
Omnibus: 2.148 Durbin-Watson: 1.020
Prob(Omnibus): 0.342 Jarque-Bera (JB): 1.567
Skew: -0.628 Prob(JB): 0.457
Kurtosis: 2.036 Cond. No. 720.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.466
Model: OLS Adj. R-squared: 0.377
Method: Least Squares F-statistic: 5.233
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0232
Time: 04:55:53 Log-Likelihood: -70.597
No. Observations: 15 AIC: 147.2
Df Residuals: 12 BIC: 149.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 207.1627 225.907 0.917 0.377 -285.045 699.371
C(dose)[T.1] 67.2632 33.031 2.036 0.064 -4.705 139.232
expression -21.2859 34.370 -0.619 0.547 -96.171 53.599
Omnibus: 2.276 Durbin-Watson: 1.083
Prob(Omnibus): 0.320 Jarque-Bera (JB): 1.745
Skew: -0.762 Prob(JB): 0.418
Kurtosis: 2.316 Cond. No. 212.

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: 04:55:53 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: 0.281
Model: OLS Adj. R-squared: 0.226
Method: Least Squares F-statistic: 5.087
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0420
Time: 04:55:53 Log-Likelihood: -72.823
No. Observations: 15 AIC: 149.6
Df Residuals: 13 BIC: 151.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -190.7118 126.376 -1.509 0.155 -463.732 82.308
expression 40.5254 17.967 2.255 0.042 1.709 79.342
Omnibus: 1.383 Durbin-Watson: 0.747
Prob(Omnibus): 0.501 Jarque-Bera (JB): 1.091
Skew: -0.592 Prob(JB): 0.580
Kurtosis: 2.413 Cond. No. 105.