LID SPN TRAP

1460211_a_at

Kdelr1 KDEL (Lys-Asp-Glu-Leu) endoplasmic reticulum protein retention receptor 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
0.446 0.512 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.658
Model: OLS Adj. R-squared: 0.604
Method: Least Squares F-statistic: 12.17
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000113
Time: 05:12:41 Log-Likelihood: -100.77
No. Observations: 23 AIC: 209.5
Df Residuals: 19 BIC: 214.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 5.9582 140.549 0.042 0.967 -288.214 300.130
C(dose)[T.1] 1.8714 216.738 0.009 0.993 -451.766 455.509
expression 6.4935 18.897 0.344 0.735 -33.058 46.045
expression:C(dose)[T.1] 7.1360 29.409 0.243 0.811 -54.418 68.690
Omnibus: 0.551 Durbin-Watson: 2.093
Prob(Omnibus): 0.759 Jarque-Bera (JB): 0.627
Skew: 0.154 Prob(JB): 0.731
Kurtosis: 2.253 Cond. No. 458.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.657
Model: OLS Adj. R-squared: 0.622
Method: Least Squares F-statistic: 19.13
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.27e-05
Time: 05:12:41 Log-Likelihood: -100.81
No. Observations: 23 AIC: 207.6
Df Residuals: 20 BIC: 211.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -15.9345 105.200 -0.151 0.881 -235.377 203.508
C(dose)[T.1] 54.4164 8.823 6.168 0.000 36.012 72.821
expression 9.4399 14.135 0.668 0.512 -20.045 38.925
Omnibus: 0.427 Durbin-Watson: 2.130
Prob(Omnibus): 0.808 Jarque-Bera (JB): 0.549
Skew: 0.099 Prob(JB): 0.760
Kurtosis: 2.270 Cond. No. 183.

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: 05:12:41 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: 0.004
Model: OLS Adj. R-squared: -0.044
Method: Least Squares F-statistic: 0.07987
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.780
Time: 05:12:41 Log-Likelihood: -113.06
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 127.8728 170.541 0.750 0.462 -226.786 482.532
expression -6.5288 23.101 -0.283 0.780 -54.570 41.512
Omnibus: 3.466 Durbin-Watson: 2.416
Prob(Omnibus): 0.177 Jarque-Bera (JB): 1.595
Skew: 0.285 Prob(JB): 0.450
Kurtosis: 1.843 Cond. No. 178.

CP101

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

F-statistic p-value df difference
1.427 0.255 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.533
Model: OLS Adj. R-squared: 0.405
Method: Least Squares F-statistic: 4.177
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0334
Time: 05:12:41 Log-Likelihood: -69.597
No. Observations: 15 AIC: 147.2
Df Residuals: 11 BIC: 150.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -334.9157 288.387 -1.161 0.270 -969.652 299.820
C(dose)[T.1] 399.8659 452.769 0.883 0.396 -596.671 1396.403
expression 49.4187 35.396 1.396 0.190 -28.487 127.324
expression:C(dose)[T.1] -43.0154 55.874 -0.770 0.458 -165.994 79.963
Omnibus: 2.330 Durbin-Watson: 1.064
Prob(Omnibus): 0.312 Jarque-Bera (JB): 1.333
Skew: -0.727 Prob(JB): 0.514
Kurtosis: 2.874 Cond. No. 628.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.507
Model: OLS Adj. R-squared: 0.425
Method: Least Squares F-statistic: 6.179
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0143
Time: 05:12:41 Log-Likelihood: -69.990
No. Observations: 15 AIC: 146.0
Df Residuals: 12 BIC: 148.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -194.3735 219.429 -0.886 0.393 -672.468 283.721
C(dose)[T.1] 51.4952 15.004 3.432 0.005 18.805 84.185
expression 32.1563 26.919 1.195 0.255 -26.494 90.807
Omnibus: 1.353 Durbin-Watson: 0.905
Prob(Omnibus): 0.508 Jarque-Bera (JB): 0.881
Skew: -0.569 Prob(JB): 0.644
Kurtosis: 2.662 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: 05:12:41 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: 0.024
Model: OLS Adj. R-squared: -0.051
Method: Least Squares F-statistic: 0.3163
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.583
Time: 05:12:41 Log-Likelihood: -75.120
No. Observations: 15 AIC: 154.2
Df Residuals: 13 BIC: 155.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -70.8845 292.757 -0.242 0.812 -703.348 561.579
expression 20.3064 36.106 0.562 0.583 -57.697 98.310
Omnibus: 2.499 Durbin-Watson: 1.646
Prob(Omnibus): 0.287 Jarque-Bera (JB): 1.182
Skew: 0.287 Prob(JB): 0.554
Kurtosis: 1.750 Cond. No. 240.