LID SPN TRAP

1452333_at

Smarca2 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 2

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
4.416 0.048 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.748
Model: OLS Adj. R-squared: 0.708
Method: Least Squares F-statistic: 18.75
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.64e-06
Time: 04:59:49 Log-Likelihood: -97.275
No. Observations: 23 AIC: 202.5
Df Residuals: 19 BIC: 207.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 96.3403 337.386 0.286 0.778 -609.818 802.498
C(dose)[T.1] 752.7887 433.830 1.735 0.099 -155.228 1660.805
expression -3.9770 31.843 -0.125 0.902 -70.626 62.672
expression:C(dose)[T.1] -66.7429 41.111 -1.623 0.121 -152.790 19.304
Omnibus: 0.098 Durbin-Watson: 2.329
Prob(Omnibus): 0.952 Jarque-Bera (JB): 0.278
Skew: 0.119 Prob(JB): 0.870
Kurtosis: 2.517 Cond. No. 1.64e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.713
Model: OLS Adj. R-squared: 0.684
Method: Least Squares F-statistic: 24.79
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.85e-06
Time: 04:59:49 Log-Likelihood: -98.769
No. Observations: 23 AIC: 203.5
Df Residuals: 20 BIC: 206.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 520.5457 221.990 2.345 0.029 57.483 983.609
C(dose)[T.1] 48.5977 8.252 5.890 0.000 31.385 65.810
expression -44.0196 20.948 -2.101 0.048 -87.717 -0.322
Omnibus: 0.228 Durbin-Watson: 2.395
Prob(Omnibus): 0.892 Jarque-Bera (JB): 0.425
Skew: 0.056 Prob(JB): 0.809
Kurtosis: 2.344 Cond. No. 596.

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: 04:59:49 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: 0.214
Model: OLS Adj. R-squared: 0.177
Method: Least Squares F-statistic: 5.716
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0263
Time: 04:59:49 Log-Likelihood: -110.34
No. Observations: 23 AIC: 224.7
Df Residuals: 21 BIC: 226.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 899.3047 342.870 2.623 0.016 186.267 1612.343
expression -77.7422 32.517 -2.391 0.026 -145.366 -10.119
Omnibus: 2.068 Durbin-Watson: 2.719
Prob(Omnibus): 0.356 Jarque-Bera (JB): 1.181
Skew: -0.202 Prob(JB): 0.554
Kurtosis: 1.966 Cond. No. 570.

CP101

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

F-statistic p-value df difference
4.096 0.066 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.644
Model: OLS Adj. R-squared: 0.547
Method: Least Squares F-statistic: 6.626
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00807
Time: 04:59:49 Log-Likelihood: -67.559
No. Observations: 15 AIC: 143.1
Df Residuals: 11 BIC: 146.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 233.2027 227.821 1.024 0.328 -268.228 734.633
C(dose)[T.1] 489.1785 344.705 1.419 0.184 -269.513 1247.870
expression -17.8694 24.536 -0.728 0.482 -71.872 36.133
expression:C(dose)[T.1] -48.8523 37.588 -1.300 0.220 -131.584 33.879
Omnibus: 3.934 Durbin-Watson: 0.756
Prob(Omnibus): 0.140 Jarque-Bera (JB): 2.631
Skew: -1.020 Prob(JB): 0.268
Kurtosis: 2.778 Cond. No. 624.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.589
Model: OLS Adj. R-squared: 0.521
Method: Least Squares F-statistic: 8.600
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00482
Time: 04:59:49 Log-Likelihood: -68.630
No. Observations: 15 AIC: 143.3
Df Residuals: 12 BIC: 145.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 426.3011 177.597 2.400 0.033 39.351 813.251
C(dose)[T.1] 41.5320 14.108 2.944 0.012 10.793 72.271
expression -38.6842 19.114 -2.024 0.066 -80.330 2.961
Omnibus: 3.323 Durbin-Watson: 0.924
Prob(Omnibus): 0.190 Jarque-Bera (JB): 2.066
Skew: -0.709 Prob(JB): 0.356
Kurtosis: 1.862 Cond. No. 244.

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: 04:59:49 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: 0.292
Model: OLS Adj. R-squared: 0.238
Method: Least Squares F-statistic: 5.368
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0375
Time: 04:59:49 Log-Likelihood: -72.707
No. Observations: 15 AIC: 149.4
Df Residuals: 13 BIC: 150.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 586.9777 213.084 2.755 0.016 126.637 1047.319
expression -53.7885 23.215 -2.317 0.037 -103.942 -3.635
Omnibus: 0.537 Durbin-Watson: 2.015
Prob(Omnibus): 0.765 Jarque-Bera (JB): 0.588
Skew: -0.339 Prob(JB): 0.745
Kurtosis: 2.306 Cond. No. 232.