LID SPN TRAP

1423732_at

Tram1 translocating chain-associating membrane protein 1

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
0.354 0.559 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.679
Model: OLS Adj. R-squared: 0.628
Method: Least Squares F-statistic: 13.39
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.25e-05
Time: 03:55:41 Log-Likelihood: -100.04
No. Observations: 23 AIC: 208.1
Df Residuals: 19 BIC: 212.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 127.6788 61.270 2.084 0.051 -0.562 255.919
C(dose)[T.1] -65.7338 101.917 -0.645 0.527 -279.049 147.581
expression -13.5790 11.271 -1.205 0.243 -37.169 10.011
expression:C(dose)[T.1] 21.6364 18.246 1.186 0.250 -16.553 59.825
Omnibus: 0.452 Durbin-Watson: 2.179
Prob(Omnibus): 0.798 Jarque-Bera (JB): 0.571
Skew: 0.146 Prob(JB): 0.751
Kurtosis: 2.285 Cond. No. 168.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.655
Model: OLS Adj. R-squared: 0.621
Method: Least Squares F-statistic: 19.00
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.38e-05
Time: 03:55:41 Log-Likelihood: -100.86
No. Observations: 23 AIC: 207.7
Df Residuals: 20 BIC: 211.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 83.0112 48.812 1.701 0.105 -18.809 184.831
C(dose)[T.1] 54.6617 8.974 6.091 0.000 35.942 73.382
expression -5.3234 8.953 -0.595 0.559 -23.999 13.352
Omnibus: 0.028 Durbin-Watson: 2.013
Prob(Omnibus): 0.986 Jarque-Bera (JB): 0.084
Skew: -0.007 Prob(JB): 0.959
Kurtosis: 2.705 Cond. No. 64.7

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: 03:55:41 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: 0.015
Model: OLS Adj. R-squared: -0.031
Method: Least Squares F-statistic: 0.3298
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.572
Time: 03:55:41 Log-Likelihood: -112.93
No. Observations: 23 AIC: 229.9
Df Residuals: 21 BIC: 232.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 34.3021 79.401 0.432 0.670 -130.821 199.425
expression 8.2131 14.301 0.574 0.572 -21.527 37.953
Omnibus: 1.977 Durbin-Watson: 2.401
Prob(Omnibus): 0.372 Jarque-Bera (JB): 1.135
Skew: 0.178 Prob(JB): 0.567
Kurtosis: 1.972 Cond. No. 63.5

CP101

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

F-statistic p-value df difference
3.679 0.079 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.674
Model: OLS Adj. R-squared: 0.585
Method: Least Squares F-statistic: 7.588
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00503
Time: 03:55:41 Log-Likelihood: -66.889
No. Observations: 15 AIC: 141.8
Df Residuals: 11 BIC: 144.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 143.0464 93.406 1.531 0.154 -62.539 348.632
C(dose)[T.1] 392.7247 184.607 2.127 0.057 -13.591 799.041
expression -12.1721 14.962 -0.814 0.433 -45.103 20.759
expression:C(dose)[T.1] -50.7281 28.163 -1.801 0.099 -112.715 11.259
Omnibus: 0.684 Durbin-Watson: 1.669
Prob(Omnibus): 0.710 Jarque-Bera (JB): 0.199
Skew: -0.280 Prob(JB): 0.905
Kurtosis: 2.922 Cond. No. 238.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.578
Model: OLS Adj. R-squared: 0.508
Method: Least Squares F-statistic: 8.222
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00564
Time: 03:55:41 Log-Likelihood: -68.827
No. Observations: 15 AIC: 143.7
Df Residuals: 12 BIC: 145.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 231.9890 86.383 2.686 0.020 43.776 420.203
C(dose)[T.1] 61.1509 15.115 4.046 0.002 28.219 94.083
expression -26.4892 13.811 -1.918 0.079 -56.580 3.601
Omnibus: 0.802 Durbin-Watson: 1.421
Prob(Omnibus): 0.670 Jarque-Bera (JB): 0.768
Skew: -0.394 Prob(JB): 0.681
Kurtosis: 2.220 Cond. No. 83.9

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: 03:55:41 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: 0.003
Model: OLS Adj. R-squared: -0.074
Method: Least Squares F-statistic: 0.03443
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.856
Time: 03:55:41 Log-Likelihood: -75.280
No. Observations: 15 AIC: 154.6
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 115.9224 120.365 0.963 0.353 -144.111 375.955
expression -3.4489 18.586 -0.186 0.856 -43.601 36.704
Omnibus: 0.445 Durbin-Watson: 1.710
Prob(Omnibus): 0.800 Jarque-Bera (JB): 0.520
Skew: 0.062 Prob(JB): 0.771
Kurtosis: 2.096 Cond. No. 78.6