LID SPN TRAP

1459984_at

Mia3 melanoma inhibitory activity 3

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
6.468 0.019 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.735
Model: OLS Adj. R-squared: 0.693
Method: Least Squares F-statistic: 17.55
Date: Tue, 03 Dec 2024 Prob (F-statistic): 1.05e-05
Time: 11:49:02 Log-Likelihood: -97.838
No. Observations: 23 AIC: 203.7
Df Residuals: 19 BIC: 208.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 303.7379 112.985 2.688 0.015 67.258 540.218
C(dose)[T.1] 52.9811 249.148 0.213 0.834 -468.491 574.453
expression -27.7357 12.544 -2.211 0.039 -53.991 -1.481
expression:C(dose)[T.1] 1.5311 26.502 0.058 0.955 -53.938 57.000
Omnibus: 0.805 Durbin-Watson: 2.174
Prob(Omnibus): 0.669 Jarque-Bera (JB): 0.665
Skew: -0.382 Prob(JB): 0.717
Kurtosis: 2.669 Cond. No. 701.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.735
Model: OLS Adj. R-squared: 0.708
Method: Least Squares F-statistic: 27.71
Date: Tue, 03 Dec 2024 Prob (F-statistic): 1.72e-06
Time: 11:49:02 Log-Likelihood: -97.840
No. Observations: 23 AIC: 201.7
Df Residuals: 20 BIC: 205.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 300.6518 97.047 3.098 0.006 98.215 503.089
C(dose)[T.1] 67.3642 9.409 7.159 0.000 47.736 86.992
expression -27.3926 10.771 -2.543 0.019 -49.861 -4.925
Omnibus: 0.850 Durbin-Watson: 2.169
Prob(Omnibus): 0.654 Jarque-Bera (JB): 0.693
Skew: -0.393 Prob(JB): 0.707
Kurtosis: 2.674 Cond. No. 239.

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, 03 Dec 2024 Prob (F-statistic): 3.51e-06
Time: 11:49:02 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: 0.055
Model: OLS Adj. R-squared: 0.010
Method: Least Squares F-statistic: 1.227
Date: Tue, 03 Dec 2024 Prob (F-statistic): 0.280
Time: 11:49:02 Log-Likelihood: -112.45
No. Observations: 23 AIC: 228.9
Df Residuals: 21 BIC: 231.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -84.8617 148.720 -0.571 0.574 -394.142 224.419
expression 17.8085 16.075 1.108 0.280 -15.620 51.237
Omnibus: 3.909 Durbin-Watson: 2.275
Prob(Omnibus): 0.142 Jarque-Bera (JB): 1.620
Skew: 0.248 Prob(JB): 0.445
Kurtosis: 1.798 Cond. No. 198.

CP101

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

F-statistic p-value df difference
0.094 0.765 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.606
Model: OLS Adj. R-squared: 0.498
Method: Least Squares F-statistic: 5.629
Date: Tue, 03 Dec 2024 Prob (F-statistic): 0.0138
Time: 11:49:02 Log-Likelihood: -68.323
No. Observations: 15 AIC: 144.6
Df Residuals: 11 BIC: 147.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -185.5595 216.461 -0.857 0.410 -661.986 290.867
C(dose)[T.1] 681.3321 307.914 2.213 0.049 3.618 1359.046
expression 29.5133 25.224 1.170 0.267 -26.005 85.031
expression:C(dose)[T.1] -75.5532 36.634 -2.062 0.064 -156.184 5.078
Omnibus: 0.809 Durbin-Watson: 1.477
Prob(Omnibus): 0.667 Jarque-Bera (JB): 0.539
Skew: -0.430 Prob(JB): 0.764
Kurtosis: 2.648 Cond. No. 497.

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: 4.970
Date: Tue, 03 Dec 2024 Prob (F-statistic): 0.0268
Time: 11:49:02 Log-Likelihood: -70.775
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 121.4861 177.156 0.686 0.506 -264.503 507.475
C(dose)[T.1] 47.0724 17.149 2.745 0.018 9.709 84.436
expression -6.3063 20.624 -0.306 0.765 -51.241 38.629
Omnibus: 2.618 Durbin-Watson: 0.850
Prob(Omnibus): 0.270 Jarque-Bera (JB): 1.773
Skew: -0.823 Prob(JB): 0.412
Kurtosis: 2.647 Cond. No. 193.

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, 03 Dec 2024 Prob (F-statistic): 0.00629
Time: 11:49:02 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: 0.110
Model: OLS Adj. R-squared: 0.041
Method: Least Squares F-statistic: 1.600
Date: Tue, 03 Dec 2024 Prob (F-statistic): 0.228
Time: 11:49:02 Log-Likelihood: -74.430
No. Observations: 15 AIC: 152.9
Df Residuals: 13 BIC: 154.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 339.0399 194.220 1.746 0.104 -80.548 758.628
expression -29.2376 23.114 -1.265 0.228 -79.173 20.698
Omnibus: 0.858 Durbin-Watson: 1.701
Prob(Omnibus): 0.651 Jarque-Bera (JB): 0.480
Skew: -0.419 Prob(JB): 0.786
Kurtosis: 2.745 Cond. No. 173.