LID SPN TRAP

1429048_at

Bloc1s2 biogenesis of lysosome-related organelles complex-1, subunit 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
0.121 0.732 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.597
Method: Least Squares F-statistic: 11.86
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000133
Time: 04:31:49 Log-Likelihood: -100.97
No. Observations: 23 AIC: 209.9
Df Residuals: 19 BIC: 214.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 13.3104 226.221 0.059 0.954 -460.176 486.796
C(dose)[T.1] -24.4528 411.545 -0.059 0.953 -885.826 836.920
expression 4.2835 23.684 0.181 0.858 -45.289 53.856
expression:C(dose)[T.1] 8.3852 43.670 0.192 0.850 -83.017 99.788
Omnibus: 0.553 Durbin-Watson: 1.840
Prob(Omnibus): 0.758 Jarque-Bera (JB): 0.602
Skew: 0.036 Prob(JB): 0.740
Kurtosis: 2.211 Cond. No. 1.05e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.651
Model: OLS Adj. R-squared: 0.616
Method: Least Squares F-statistic: 18.67
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.67e-05
Time: 04:31:49 Log-Likelihood: -100.99
No. Observations: 23 AIC: 208.0
Df Residuals: 20 BIC: 211.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -10.2388 185.456 -0.055 0.957 -397.094 376.616
C(dose)[T.1] 54.5468 9.410 5.797 0.000 34.917 74.176
expression 6.7499 19.414 0.348 0.732 -33.746 47.246
Omnibus: 0.569 Durbin-Watson: 1.833
Prob(Omnibus): 0.752 Jarque-Bera (JB): 0.608
Skew: 0.005 Prob(JB): 0.738
Kurtosis: 2.204 Cond. No. 407.

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: 04:31:49 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: 0.065
Model: OLS Adj. R-squared: 0.021
Method: Least Squares F-statistic: 1.463
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.240
Time: 04:31:49 Log-Likelihood: -112.33
No. Observations: 23 AIC: 228.7
Df Residuals: 21 BIC: 230.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 409.5318 272.765 1.501 0.148 -157.713 976.777
expression -34.8562 28.817 -1.210 0.240 -94.785 25.073
Omnibus: 2.992 Durbin-Watson: 2.520
Prob(Omnibus): 0.224 Jarque-Bera (JB): 1.514
Skew: 0.295 Prob(JB): 0.469
Kurtosis: 1.890 Cond. No. 374.

CP101

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

F-statistic p-value df difference
2.304 0.155 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.538
Model: OLS Adj. R-squared: 0.412
Method: Least Squares F-statistic: 4.263
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0316
Time: 04:31:49 Log-Likelihood: -69.515
No. Observations: 15 AIC: 147.0
Df Residuals: 11 BIC: 149.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -678.4337 664.567 -1.021 0.329 -2141.135 784.267
C(dose)[T.1] 83.2404 1017.405 0.082 0.936 -2156.053 2322.534
expression 75.7756 67.507 1.122 0.286 -72.807 224.358
expression:C(dose)[T.1] -4.4885 102.510 -0.044 0.966 -230.112 221.135
Omnibus: 2.579 Durbin-Watson: 1.137
Prob(Omnibus): 0.275 Jarque-Bera (JB): 1.385
Skew: -0.438 Prob(JB): 0.500
Kurtosis: 1.797 Cond. No. 1.75e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.538
Model: OLS Adj. R-squared: 0.460
Method: Least Squares F-statistic: 6.974
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00978
Time: 04:31:49 Log-Likelihood: -69.516
No. Observations: 15 AIC: 145.0
Df Residuals: 12 BIC: 147.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -659.2735 478.917 -1.377 0.194 -1702.744 384.197
C(dose)[T.1] 38.6980 15.990 2.420 0.032 3.858 73.538
expression 73.8291 48.644 1.518 0.155 -32.156 179.814
Omnibus: 2.618 Durbin-Watson: 1.134
Prob(Omnibus): 0.270 Jarque-Bera (JB): 1.376
Skew: -0.427 Prob(JB): 0.503
Kurtosis: 1.786 Cond. No. 668.

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: 04:31:49 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: 0.312
Model: OLS Adj. R-squared: 0.259
Method: Least Squares F-statistic: 5.891
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0305
Time: 04:31:49 Log-Likelihood: -72.497
No. Observations: 15 AIC: 149.0
Df Residuals: 13 BIC: 150.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -1143.7561 509.906 -2.243 0.043 -2245.342 -42.170
expression 124.7543 51.401 2.427 0.030 13.710 235.799
Omnibus: 1.012 Durbin-Watson: 1.937
Prob(Omnibus): 0.603 Jarque-Bera (JB): 0.887
Skew: 0.409 Prob(JB): 0.642
Kurtosis: 2.134 Cond. No. 606.