LID SPN TRAP

1448126_at

Fam60a family with sequence similarity 60, 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
1.064 0.315 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.667
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 12.71
Date: Tue, 28 Jan 2025 Prob (F-statistic): 8.68e-05
Time: 17:27:00 Log-Likelihood: -100.45
No. Observations: 23 AIC: 208.9
Df Residuals: 19 BIC: 213.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 2.3976 74.039 0.032 0.975 -152.568 157.363
C(dose)[T.1] 70.7296 87.513 0.808 0.429 -112.436 253.896
expression 11.6374 16.574 0.702 0.491 -23.053 46.328
expression:C(dose)[T.1] -3.6690 19.729 -0.186 0.854 -44.962 37.624
Omnibus: 0.206 Durbin-Watson: 2.129
Prob(Omnibus): 0.902 Jarque-Bera (JB): 0.357
Skew: 0.181 Prob(JB): 0.836
Kurtosis: 2.509 Cond. No. 132.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.667
Model: OLS Adj. R-squared: 0.633
Method: Least Squares F-statistic: 20.01
Date: Tue, 28 Jan 2025 Prob (F-statistic): 1.69e-05
Time: 17:27:00 Log-Likelihood: -100.47
No. Observations: 23 AIC: 206.9
Df Residuals: 20 BIC: 210.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 13.9264 39.493 0.353 0.728 -68.454 96.307
C(dose)[T.1] 54.5380 8.624 6.324 0.000 36.548 72.528
expression 9.0479 8.771 1.032 0.315 -9.248 27.343
Omnibus: 0.254 Durbin-Watson: 2.119
Prob(Omnibus): 0.881 Jarque-Bera (JB): 0.403
Skew: 0.199 Prob(JB): 0.817
Kurtosis: 2.487 Cond. No. 43.2

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: Tue, 28 Jan 2025 Prob (F-statistic): 3.51e-06
Time: 17:27:00 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: 0.001
Model: OLS Adj. R-squared: -0.047
Method: Least Squares F-statistic: 0.01130
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.916
Time: 17:27:00 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 72.8642 64.864 1.123 0.274 -62.027 207.755
expression 1.5616 14.688 0.106 0.916 -28.984 32.107
Omnibus: 3.228 Durbin-Watson: 2.502
Prob(Omnibus): 0.199 Jarque-Bera (JB): 1.555
Skew: 0.290 Prob(JB): 0.460
Kurtosis: 1.865 Cond. No. 41.7

CP101

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

F-statistic p-value df difference
0.521 0.484 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.485
Model: OLS Adj. R-squared: 0.345
Method: Least Squares F-statistic: 3.456
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0549
Time: 17:27:00 Log-Likelihood: -70.320
No. Observations: 15 AIC: 148.6
Df Residuals: 11 BIC: 151.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 29.1227 124.855 0.233 0.820 -245.682 303.927
C(dose)[T.1] -81.6920 239.878 -0.341 0.740 -609.660 446.276
expression 7.6897 24.956 0.308 0.764 -47.237 62.617
expression:C(dose)[T.1] 25.2670 47.011 0.537 0.602 -78.204 128.738
Omnibus: 2.384 Durbin-Watson: 1.109
Prob(Omnibus): 0.304 Jarque-Bera (JB): 1.396
Skew: -0.744 Prob(JB): 0.498
Kurtosis: 2.851 Cond. No. 195.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.472
Model: OLS Adj. R-squared: 0.384
Method: Least Squares F-statistic: 5.358
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0217
Time: 17:27:00 Log-Likelihood: -70.514
No. Observations: 15 AIC: 147.0
Df Residuals: 12 BIC: 149.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -6.3457 102.802 -0.062 0.952 -230.331 217.640
C(dose)[T.1] 46.9396 15.722 2.986 0.011 12.683 81.196
expression 14.8098 20.513 0.722 0.484 -29.884 59.504
Omnibus: 2.386 Durbin-Watson: 1.075
Prob(Omnibus): 0.303 Jarque-Bera (JB): 1.642
Skew: -0.786 Prob(JB): 0.440
Kurtosis: 2.608 Cond. No. 70.9

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: Tue, 28 Jan 2025 Prob (F-statistic): 0.00629
Time: 17:27:00 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: 0.079
Model: OLS Adj. R-squared: 0.009
Method: Least Squares F-statistic: 1.120
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.309
Time: 17:27:00 Log-Likelihood: -74.680
No. Observations: 15 AIC: 153.4
Df Residuals: 13 BIC: 154.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -42.9545 129.458 -0.332 0.745 -322.631 236.722
expression 26.9857 25.498 1.058 0.309 -28.100 82.071
Omnibus: 0.526 Durbin-Watson: 1.831
Prob(Omnibus): 0.769 Jarque-Bera (JB): 0.550
Skew: -0.019 Prob(JB): 0.760
Kurtosis: 2.063 Cond. No. 70.0