LID SPN TRAP

1456796_at

Snai3 snail homolog 3 (Drosophila)

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
1.570 0.225 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.676
Model: OLS Adj. R-squared: 0.625
Method: Least Squares F-statistic: 13.23
Date: Thu, 03 Apr 2025 Prob (F-statistic): 6.74e-05
Time: 22:53:52 Log-Likelihood: -100.13
No. Observations: 23 AIC: 208.3
Df Residuals: 19 BIC: 212.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 132.7948 123.467 1.076 0.296 -125.624 391.214
C(dose)[T.1] 110.5276 175.227 0.631 0.536 -256.227 477.283
expression -12.3792 19.426 -0.637 0.532 -53.039 28.280
expression:C(dose)[T.1] -8.7732 27.416 -0.320 0.752 -66.156 48.610
Omnibus: 0.246 Durbin-Watson: 1.549
Prob(Omnibus): 0.884 Jarque-Bera (JB): 0.436
Skew: 0.116 Prob(JB): 0.804
Kurtosis: 2.367 Cond. No. 345.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.675
Model: OLS Adj. R-squared: 0.642
Method: Least Squares F-statistic: 20.73
Date: Thu, 03 Apr 2025 Prob (F-statistic): 1.33e-05
Time: 22:53:53 Log-Likelihood: -100.19
No. Observations: 23 AIC: 206.4
Df Residuals: 20 BIC: 209.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 160.7567 85.247 1.886 0.074 -17.066 338.579
C(dose)[T.1] 54.5242 8.498 6.416 0.000 36.798 72.250
expression -16.7839 13.397 -1.253 0.225 -44.729 11.162
Omnibus: 0.311 Durbin-Watson: 1.536
Prob(Omnibus): 0.856 Jarque-Bera (JB): 0.481
Skew: 0.096 Prob(JB): 0.786
Kurtosis: 2.318 Cond. No. 133.

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, 03 Apr 2025 Prob (F-statistic): 3.51e-06
Time: 22:53:53 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: 0.005
Model: OLS Adj. R-squared: -0.043
Method: Least Squares F-statistic: 0.1004
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.754
Time: 22:53:53 Log-Likelihood: -113.05
No. Observations: 23 AIC: 230.1
Df Residuals: 21 BIC: 232.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 125.6622 145.192 0.865 0.397 -176.280 427.605
expression -7.1990 22.722 -0.317 0.754 -54.452 40.054
Omnibus: 4.295 Durbin-Watson: 2.439
Prob(Omnibus): 0.117 Jarque-Bera (JB): 1.730
Skew: 0.278 Prob(JB): 0.421
Kurtosis: 1.776 Cond. No. 132.

CP101

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

F-statistic p-value df difference
0.012 0.915 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.491
Model: OLS Adj. R-squared: 0.352
Method: Least Squares F-statistic: 3.533
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0520
Time: 22:53:53 Log-Likelihood: -70.240
No. Observations: 15 AIC: 148.5
Df Residuals: 11 BIC: 151.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 20.2307 136.793 0.148 0.885 -280.848 321.310
C(dose)[T.1] 307.1443 274.502 1.119 0.287 -297.031 911.320
expression 7.4128 21.408 0.346 0.736 -39.706 54.531
expression:C(dose)[T.1] -43.3045 45.800 -0.946 0.365 -144.110 57.501
Omnibus: 1.108 Durbin-Watson: 0.978
Prob(Omnibus): 0.575 Jarque-Bera (JB): 0.676
Skew: -0.499 Prob(JB): 0.713
Kurtosis: 2.709 Cond. No. 259.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.358
Method: Least Squares F-statistic: 4.896
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0279
Time: 22:53:53 Log-Likelihood: -70.826
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 80.4706 120.514 0.668 0.517 -182.107 343.049
C(dose)[T.1] 48.1820 18.291 2.634 0.022 8.329 88.035
expression -2.0484 18.842 -0.109 0.915 -43.101 39.004
Omnibus: 2.534 Durbin-Watson: 0.788
Prob(Omnibus): 0.282 Jarque-Bera (JB): 1.772
Skew: -0.815 Prob(JB): 0.412
Kurtosis: 2.581 Cond. No. 97.1

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, 03 Apr 2025 Prob (F-statistic): 0.00629
Time: 22:53:53 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: 0.131
Model: OLS Adj. R-squared: 0.064
Method: Least Squares F-statistic: 1.958
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.185
Time: 22:53:54 Log-Likelihood: -74.248
No. Observations: 15 AIC: 152.5
Df Residuals: 13 BIC: 153.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 260.6914 119.748 2.177 0.049 1.991 519.392
expression -27.3679 19.560 -1.399 0.185 -69.624 14.889
Omnibus: 0.836 Durbin-Watson: 1.405
Prob(Omnibus): 0.658 Jarque-Bera (JB): 0.663
Skew: -0.066 Prob(JB): 0.718
Kurtosis: 1.979 Cond. No. 79.4