LID SPN TRAP

1448992_at

Ina internexin neuronal intermediate filament protein, alpha

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
0.101 0.753 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.651
Model: OLS Adj. R-squared: 0.596
Method: Least Squares F-statistic: 11.82
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000135
Time: 05:23:37 Log-Likelihood: -101.00
No. Observations: 23 AIC: 210.0
Df Residuals: 19 BIC: 214.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 170.0362 358.793 0.474 0.641 -580.927 920.999
C(dose)[T.1] -20.4220 671.938 -0.030 0.976 -1426.805 1385.961
expression -10.1866 31.550 -0.323 0.750 -76.221 55.848
expression:C(dose)[T.1] 6.6262 57.506 0.115 0.909 -113.734 126.987
Omnibus: 0.552 Durbin-Watson: 1.862
Prob(Omnibus): 0.759 Jarque-Bera (JB): 0.601
Skew: -0.025 Prob(JB): 0.741
Kurtosis: 2.210 Cond. No. 2.10e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.651
Model: OLS Adj. R-squared: 0.616
Method: Least Squares F-statistic: 18.64
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.69e-05
Time: 05:23:37 Log-Likelihood: -101.00
No. Observations: 23 AIC: 208.0
Df Residuals: 20 BIC: 211.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 147.3574 292.499 0.504 0.620 -462.784 757.499
C(dose)[T.1] 56.9851 14.411 3.954 0.001 26.924 87.047
expression -8.1921 25.719 -0.319 0.753 -61.840 45.456
Omnibus: 0.455 Durbin-Watson: 1.858
Prob(Omnibus): 0.796 Jarque-Bera (JB): 0.554
Skew: -0.019 Prob(JB): 0.758
Kurtosis: 2.240 Cond. No. 783.

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: 05:23:37 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: 0.378
Model: OLS Adj. R-squared: 0.348
Method: Least Squares F-statistic: 12.75
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00180
Time: 05:23:37 Log-Likelihood: -107.65
No. Observations: 23 AIC: 219.3
Df Residuals: 21 BIC: 221.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -761.5514 235.633 -3.232 0.004 -1251.578 -271.525
expression 72.6257 20.336 3.571 0.002 30.335 114.917
Omnibus: 1.514 Durbin-Watson: 2.368
Prob(Omnibus): 0.469 Jarque-Bera (JB): 1.265
Skew: 0.408 Prob(JB): 0.531
Kurtosis: 2.190 Cond. No. 483.

CP101

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

F-statistic p-value df difference
0.809 0.386 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.559
Model: OLS Adj. R-squared: 0.439
Method: Least Squares F-statistic: 4.657
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0246
Time: 05:23:37 Log-Likelihood: -69.151
No. Observations: 15 AIC: 146.3
Df Residuals: 11 BIC: 149.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 103.3256 413.354 0.250 0.807 -806.461 1013.112
C(dose)[T.1] 1173.9250 812.078 1.446 0.176 -613.446 2961.296
expression -3.5479 40.841 -0.087 0.932 -93.437 86.342
expression:C(dose)[T.1] -108.8706 79.064 -1.377 0.196 -282.889 65.148
Omnibus: 3.301 Durbin-Watson: 1.011
Prob(Omnibus): 0.192 Jarque-Bera (JB): 1.586
Skew: -0.784 Prob(JB): 0.452
Kurtosis: 3.278 Cond. No. 1.39e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.484
Model: OLS Adj. R-squared: 0.397
Method: Least Squares F-statistic: 5.618
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0190
Time: 05:23:37 Log-Likelihood: -70.344
No. Observations: 15 AIC: 146.7
Df Residuals: 12 BIC: 148.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 397.2415 366.959 1.083 0.300 -402.295 1196.778
C(dose)[T.1] 55.9245 16.973 3.295 0.006 18.943 92.906
expression -32.5975 36.252 -0.899 0.386 -111.584 46.389
Omnibus: 1.879 Durbin-Watson: 0.835
Prob(Omnibus): 0.391 Jarque-Bera (JB): 1.190
Skew: -0.675 Prob(JB): 0.551
Kurtosis: 2.710 Cond. No. 499.

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: 05:23:37 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: 0.016
Model: OLS Adj. R-squared: -0.059
Method: Least Squares F-statistic: 0.2162
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.650
Time: 05:23:37 Log-Likelihood: -75.176
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -111.4993 441.407 -0.253 0.805 -1065.101 842.103
expression 20.0596 43.146 0.465 0.650 -73.152 113.271
Omnibus: 0.501 Durbin-Watson: 1.520
Prob(Omnibus): 0.778 Jarque-Bera (JB): 0.541
Skew: 0.032 Prob(JB): 0.763
Kurtosis: 2.072 Cond. No. 452.