LID SPN TRAP

1442180_at

Dleu7 deleted in lymphocytic leukemia, 7

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
0.042 0.839 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: 11.84
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000134
Time: 03:43:07 Log-Likelihood: -100.98
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 27.7533 74.104 0.375 0.712 -127.349 182.855
C(dose)[T.1] 82.7763 95.858 0.864 0.399 -117.857 283.410
expression 4.0181 11.216 0.358 0.724 -19.457 27.493
expression:C(dose)[T.1] -4.5089 14.989 -0.301 0.767 -35.882 26.864
Omnibus: 0.240 Durbin-Watson: 1.847
Prob(Omnibus): 0.887 Jarque-Bera (JB): 0.433
Skew: 0.009 Prob(JB): 0.805
Kurtosis: 2.328 Cond. No. 186.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.55
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.77e-05
Time: 03:43:07 Log-Likelihood: -101.04
No. Observations: 23 AIC: 208.1
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 44.3738 48.246 0.920 0.369 -56.265 145.012
C(dose)[T.1] 54.0904 9.497 5.696 0.000 34.281 73.900
expression 1.4937 7.270 0.205 0.839 -13.671 16.658
Omnibus: 0.584 Durbin-Watson: 1.900
Prob(Omnibus): 0.747 Jarque-Bera (JB): 0.629
Skew: 0.107 Prob(JB): 0.730
Kurtosis: 2.218 Cond. No. 72.4

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: 03:43:07 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: 0.082
Model: OLS Adj. R-squared: 0.038
Method: Least Squares F-statistic: 1.869
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.186
Time: 03:43:07 Log-Likelihood: -112.12
No. Observations: 23 AIC: 228.2
Df Residuals: 21 BIC: 230.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 171.6241 67.573 2.540 0.019 31.098 312.150
expression -14.4898 10.597 -1.367 0.186 -36.529 7.549
Omnibus: 4.697 Durbin-Watson: 2.390
Prob(Omnibus): 0.096 Jarque-Bera (JB): 1.580
Skew: 0.076 Prob(JB): 0.454
Kurtosis: 1.725 Cond. No. 63.8

CP101

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

F-statistic p-value df difference
0.103 0.754 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.609
Model: OLS Adj. R-squared: 0.502
Method: Least Squares F-statistic: 5.708
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0132
Time: 03:43:07 Log-Likelihood: -68.260
No. Observations: 15 AIC: 144.5
Df Residuals: 11 BIC: 147.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 105.5703 28.072 3.761 0.003 43.785 167.356
C(dose)[T.1] -72.8008 61.889 -1.176 0.264 -209.019 63.417
expression -9.2989 6.384 -1.457 0.173 -23.351 4.753
expression:C(dose)[T.1] 24.1931 11.572 2.091 0.061 -1.277 49.663
Omnibus: 0.395 Durbin-Watson: 1.848
Prob(Omnibus): 0.821 Jarque-Bera (JB): 0.142
Skew: -0.216 Prob(JB): 0.931
Kurtosis: 2.797 Cond. No. 59.9

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.453
Model: OLS Adj. R-squared: 0.362
Method: Least Squares F-statistic: 4.978
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0266
Time: 03:43:07 Log-Likelihood: -70.769
No. Observations: 15 AIC: 147.5
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 75.3664 27.240 2.767 0.017 16.015 134.717
C(dose)[T.1] 52.1542 18.179 2.869 0.014 12.546 91.762
expression -1.9352 6.026 -0.321 0.754 -15.066 11.195
Omnibus: 2.938 Durbin-Watson: 0.880
Prob(Omnibus): 0.230 Jarque-Bera (JB): 1.948
Skew: -0.871 Prob(JB): 0.378
Kurtosis: 2.707 Cond. No. 19.0

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: 03:43:07 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: 0.008
Method: Least Squares F-statistic: 1.109
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.312
Time: 03:43:08 Log-Likelihood: -74.686
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 60.1090 33.328 1.804 0.095 -11.892 132.110
expression 6.8250 6.482 1.053 0.312 -7.177 20.827
Omnibus: 1.399 Durbin-Watson: 1.422
Prob(Omnibus): 0.497 Jarque-Bera (JB): 0.592
Skew: -0.487 Prob(JB): 0.744
Kurtosis: 2.983 Cond. No. 18.2