LID SPN TRAP

1435138_at

Fam155a family with sequence similarity 155, member A

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
0.188 0.669 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.597
Method: Least Squares F-statistic: 11.88
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000131
Time: 03:41:08 Log-Likelihood: -100.95
No. Observations: 23 AIC: 209.9
Df Residuals: 19 BIC: 214.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 109.8872 149.786 0.734 0.472 -203.618 423.393
C(dose)[T.1] 41.4873 262.362 0.158 0.876 -507.642 590.617
expression -6.9037 18.556 -0.372 0.714 -45.743 31.935
expression:C(dose)[T.1] 1.1028 34.005 0.032 0.974 -70.071 72.277
Omnibus: 0.514 Durbin-Watson: 1.853
Prob(Omnibus): 0.774 Jarque-Bera (JB): 0.590
Skew: 0.080 Prob(JB): 0.745
Kurtosis: 2.232 Cond. No. 555.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.618
Method: Least Squares F-statistic: 18.76
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.58e-05
Time: 03:41:08 Log-Likelihood: -100.95
No. Observations: 23 AIC: 207.9
Df Residuals: 20 BIC: 211.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 107.2386 122.388 0.876 0.391 -148.059 362.536
C(dose)[T.1] 49.9872 11.654 4.289 0.000 25.677 74.297
expression -6.5753 15.157 -0.434 0.669 -38.192 25.041
Omnibus: 0.522 Durbin-Watson: 1.849
Prob(Omnibus): 0.770 Jarque-Bera (JB): 0.594
Skew: 0.080 Prob(JB): 0.743
Kurtosis: 2.229 Cond. No. 224.

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: 03:41:08 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: 0.333
Model: OLS Adj. R-squared: 0.301
Method: Least Squares F-statistic: 10.46
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00397
Time: 03:41:09 Log-Likelihood: -108.46
No. Observations: 23 AIC: 220.9
Df Residuals: 21 BIC: 223.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 468.0475 120.208 3.894 0.001 218.062 718.033
expression -49.6498 15.351 -3.234 0.004 -81.573 -17.726
Omnibus: 0.084 Durbin-Watson: 2.009
Prob(Omnibus): 0.959 Jarque-Bera (JB): 0.248
Skew: -0.118 Prob(JB): 0.883
Kurtosis: 2.549 Cond. No. 162.

CP101

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

F-statistic p-value df difference
0.054 0.820 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.452
Model: OLS Adj. R-squared: 0.302
Method: Least Squares F-statistic: 3.019
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0757
Time: 03:41:09 Log-Likelihood: -70.795
No. Observations: 15 AIC: 149.6
Df Residuals: 11 BIC: 152.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -23.4016 424.763 -0.055 0.957 -958.299 911.496
C(dose)[T.1] 97.7971 590.336 0.166 0.871 -1201.523 1397.117
expression 10.1523 47.458 0.214 0.835 -94.302 114.607
expression:C(dose)[T.1] -5.3749 66.346 -0.081 0.937 -151.401 140.651
Omnibus: 3.295 Durbin-Watson: 0.885
Prob(Omnibus): 0.193 Jarque-Bera (JB): 2.146
Skew: -0.919 Prob(JB): 0.342
Kurtosis: 2.771 Cond. No. 868.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.451
Model: OLS Adj. R-squared: 0.360
Method: Least Squares F-statistic: 4.934
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0273
Time: 03:41:09 Log-Likelihood: -70.799
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 1.2037 284.391 0.004 0.997 -618.432 620.840
C(dose)[T.1] 49.9913 16.070 3.111 0.009 14.977 85.005
expression 7.4021 31.761 0.233 0.820 -61.800 76.604
Omnibus: 3.380 Durbin-Watson: 0.857
Prob(Omnibus): 0.185 Jarque-Bera (JB): 2.165
Skew: -0.926 Prob(JB): 0.339
Kurtosis: 2.812 Cond. No. 327.

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: 03:41:09 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: 0.009
Model: OLS Adj. R-squared: -0.068
Method: Least Squares F-statistic: 0.1146
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.740
Time: 03:41:09 Log-Likelihood: -75.234
No. Observations: 15 AIC: 154.5
Df Residuals: 13 BIC: 155.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 214.2690 356.427 0.601 0.558 -555.744 984.282
expression -13.5669 40.079 -0.339 0.740 -100.153 73.019
Omnibus: 0.800 Durbin-Watson: 1.589
Prob(Omnibus): 0.670 Jarque-Bera (JB): 0.654
Skew: 0.084 Prob(JB): 0.721
Kurtosis: 1.991 Cond. No. 317.