LID SPN TRAP

1460361_at

5033414D02Rik RIKEN cDNA 5033414D02 gene

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
3.439 0.078 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.718
Model: OLS Adj. R-squared: 0.673
Method: Least Squares F-statistic: 16.10
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.89e-05
Time: 03:32:18 Log-Likelihood: -98.560
No. Observations: 23 AIC: 205.1
Df Residuals: 19 BIC: 209.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -326.3147 185.066 -1.763 0.094 -713.662 61.033
C(dose)[T.1] 317.4966 263.145 1.207 0.242 -233.273 868.266
expression 46.1189 22.420 2.057 0.054 -0.806 93.044
expression:C(dose)[T.1] -33.0225 30.749 -1.074 0.296 -97.381 31.336
Omnibus: 0.597 Durbin-Watson: 1.623
Prob(Omnibus): 0.742 Jarque-Bera (JB): 0.420
Skew: -0.310 Prob(JB): 0.810
Kurtosis: 2.770 Cond. No. 745.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.701
Model: OLS Adj. R-squared: 0.671
Method: Least Squares F-statistic: 23.39
Date: Thu, 21 Nov 2024 Prob (F-statistic): 5.80e-06
Time: 03:32:18 Log-Likelihood: -99.238
No. Observations: 23 AIC: 204.5
Df Residuals: 20 BIC: 207.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -181.4688 127.207 -1.427 0.169 -446.819 83.881
C(dose)[T.1] 35.2209 12.691 2.775 0.012 8.748 61.693
expression 28.5638 15.402 1.854 0.078 -3.565 60.693
Omnibus: 0.879 Durbin-Watson: 1.671
Prob(Omnibus): 0.644 Jarque-Bera (JB): 0.374
Skew: -0.312 Prob(JB): 0.830
Kurtosis: 3.012 Cond. No. 274.

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: 03:32:18 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: 0.585
Model: OLS Adj. R-squared: 0.565
Method: Least Squares F-statistic: 29.63
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.12e-05
Time: 03:32:18 Log-Likelihood: -102.98
No. Observations: 23 AIC: 210.0
Df Residuals: 21 BIC: 212.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -446.0976 96.710 -4.613 0.000 -647.216 -244.979
expression 61.4683 11.292 5.443 0.000 37.984 84.952
Omnibus: 0.025 Durbin-Watson: 1.910
Prob(Omnibus): 0.988 Jarque-Bera (JB): 0.219
Skew: 0.046 Prob(JB): 0.896
Kurtosis: 2.530 Cond. No. 181.

CP101

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

F-statistic p-value df difference
2.369 0.150 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.542
Model: OLS Adj. R-squared: 0.417
Method: Least Squares F-statistic: 4.333
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0302
Time: 03:32:18 Log-Likelihood: -69.449
No. Observations: 15 AIC: 146.9
Df Residuals: 11 BIC: 149.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -156.3264 155.294 -1.007 0.336 -498.126 185.473
C(dose)[T.1] 128.5356 412.177 0.312 0.761 -778.659 1035.730
expression 30.6368 21.210 1.444 0.176 -16.046 77.320
expression:C(dose)[T.1] -11.8446 54.004 -0.219 0.830 -130.707 107.018
Omnibus: 4.443 Durbin-Watson: 1.050
Prob(Omnibus): 0.108 Jarque-Bera (JB): 2.476
Skew: -0.988 Prob(JB): 0.290
Kurtosis: 3.242 Cond. No. 501.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.540
Model: OLS Adj. R-squared: 0.463
Method: Least Squares F-statistic: 7.034
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00952
Time: 03:32:18 Log-Likelihood: -69.482
No. Observations: 15 AIC: 145.0
Df Residuals: 12 BIC: 147.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -142.9824 137.096 -1.043 0.318 -441.689 155.724
C(dose)[T.1] 38.2083 16.057 2.379 0.035 3.222 73.195
expression 28.8098 18.716 1.539 0.150 -11.969 69.589
Omnibus: 5.078 Durbin-Watson: 0.992
Prob(Omnibus): 0.079 Jarque-Bera (JB): 2.813
Skew: -1.043 Prob(JB): 0.245
Kurtosis: 3.384 Cond. No. 147.

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: 03:32:18 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: 0.322
Model: OLS Adj. R-squared: 0.270
Method: Least Squares F-statistic: 6.187
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0272
Time: 03:32:18 Log-Likelihood: -72.380
No. Observations: 15 AIC: 148.8
Df Residuals: 13 BIC: 150.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -271.2254 146.932 -1.846 0.088 -588.653 46.202
expression 48.6077 19.541 2.487 0.027 6.391 90.824
Omnibus: 2.132 Durbin-Watson: 1.859
Prob(Omnibus): 0.344 Jarque-Bera (JB): 0.978
Skew: 0.066 Prob(JB): 0.613
Kurtosis: 1.756 Cond. No. 134.