LID SPN TRAP

1452729_at

Dpm3 dolichyl-phosphate mannosyltransferase polypeptide 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
1.545 0.228 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.693
Model: OLS Adj. R-squared: 0.645
Method: Least Squares F-statistic: 14.33
Date: Thu, 21 Nov 2024 Prob (F-statistic): 4.06e-05
Time: 05:24:42 Log-Likelihood: -99.507
No. Observations: 23 AIC: 207.0
Df Residuals: 19 BIC: 211.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -141.6971 122.799 -1.154 0.263 -398.719 115.325
C(dose)[T.1] 365.1220 285.416 1.279 0.216 -232.260 962.504
expression 20.6618 12.937 1.597 0.127 -6.415 47.739
expression:C(dose)[T.1] -32.8954 30.113 -1.092 0.288 -95.923 30.133
Omnibus: 0.382 Durbin-Watson: 1.924
Prob(Omnibus): 0.826 Jarque-Bera (JB): 0.517
Skew: 0.026 Prob(JB): 0.772
Kurtosis: 2.268 Cond. No. 753.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.674
Model: OLS Adj. R-squared: 0.642
Method: Least Squares F-statistic: 20.70
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.35e-05
Time: 05:24:42 Log-Likelihood: -100.21
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 -84.1325 111.453 -0.755 0.459 -316.618 148.354
C(dose)[T.1] 53.4728 8.450 6.328 0.000 35.846 71.100
expression 14.5906 11.739 1.243 0.228 -9.896 39.077
Omnibus: 0.446 Durbin-Watson: 1.952
Prob(Omnibus): 0.800 Jarque-Bera (JB): 0.556
Skew: -0.261 Prob(JB): 0.757
Kurtosis: 2.446 Cond. No. 254.

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: 05:24:42 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: 0.022
Model: OLS Adj. R-squared: -0.025
Method: Least Squares F-statistic: 0.4717
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.500
Time: 05:24:43 Log-Likelihood: -112.85
No. Observations: 23 AIC: 229.7
Df Residuals: 21 BIC: 232.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -49.4622 188.229 -0.263 0.795 -440.905 341.981
expression 13.6307 19.847 0.687 0.500 -27.644 54.905
Omnibus: 3.900 Durbin-Watson: 2.372
Prob(Omnibus): 0.142 Jarque-Bera (JB): 1.808
Skew: 0.357 Prob(JB): 0.405
Kurtosis: 1.827 Cond. No. 253.

CP101

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

F-statistic p-value df difference
9.308 0.010 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.692
Model: OLS Adj. R-squared: 0.609
Method: Least Squares F-statistic: 8.256
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00370
Time: 05:24:43 Log-Likelihood: -66.456
No. Observations: 15 AIC: 140.9
Df Residuals: 11 BIC: 143.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -307.9434 177.303 -1.737 0.110 -698.184 82.297
C(dose)[T.1] -41.9856 288.180 -0.146 0.887 -676.265 592.294
expression 39.2801 18.530 2.120 0.058 -1.504 80.064
expression:C(dose)[T.1] 9.7352 30.203 0.322 0.753 -56.741 76.212
Omnibus: 0.270 Durbin-Watson: 1.823
Prob(Omnibus): 0.874 Jarque-Bera (JB): 0.019
Skew: 0.040 Prob(JB): 0.991
Kurtosis: 2.846 Cond. No. 572.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.690
Model: OLS Adj. R-squared: 0.638
Method: Least Squares F-statistic: 13.33
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000895
Time: 05:24:43 Log-Likelihood: -66.527
No. Observations: 15 AIC: 139.1
Df Residuals: 12 BIC: 141.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -342.9601 134.789 -2.544 0.026 -636.639 -49.281
C(dose)[T.1] 50.8179 11.824 4.298 0.001 25.056 76.579
expression 42.9444 14.076 3.051 0.010 12.276 73.613
Omnibus: 0.055 Durbin-Watson: 1.842
Prob(Omnibus): 0.973 Jarque-Bera (JB): 0.085
Skew: -0.001 Prob(JB): 0.959
Kurtosis: 2.632 Cond. No. 221.

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: 05:24:43 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: 0.212
Model: OLS Adj. R-squared: 0.151
Method: Least Squares F-statistic: 3.491
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0844
Time: 05:24:43 Log-Likelihood: -73.516
No. Observations: 15 AIC: 151.0
Df Residuals: 13 BIC: 152.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -289.9255 205.499 -1.411 0.182 -733.879 154.028
expression 40.2251 21.529 1.868 0.084 -6.285 86.735
Omnibus: 2.398 Durbin-Watson: 2.125
Prob(Omnibus): 0.301 Jarque-Bera (JB): 1.679
Skew: 0.645 Prob(JB): 0.432
Kurtosis: 1.988 Cond. No. 220.