LID SPN TRAP

1423991_at

Nop14 NOP14 nucleolar protein homolog (yeast)

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
0.067 0.798 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.662
Model: OLS Adj. R-squared: 0.608
Method: Least Squares F-statistic: 12.39
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000101
Time: 05:22:23 Log-Likelihood: -100.64
No. Observations: 23 AIC: 209.3
Df Residuals: 19 BIC: 213.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -28.3061 130.482 -0.217 0.831 -301.408 244.796
C(dose)[T.1] 253.7739 247.305 1.026 0.318 -263.841 771.389
expression 10.1913 16.098 0.633 0.534 -23.503 43.885
expression:C(dose)[T.1] -25.3979 31.502 -0.806 0.430 -91.333 40.537
Omnibus: 0.181 Durbin-Watson: 1.911
Prob(Omnibus): 0.913 Jarque-Bera (JB): 0.338
Skew: -0.170 Prob(JB): 0.845
Kurtosis: 2.513 Cond. No. 531.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.59
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.74e-05
Time: 05:22:23 Log-Likelihood: -101.02
No. Observations: 23 AIC: 208.0
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 25.3934 111.216 0.228 0.822 -206.599 257.386
C(dose)[T.1] 54.5535 9.931 5.493 0.000 33.837 75.270
expression 3.5589 13.716 0.259 0.798 -25.052 32.170
Omnibus: 0.676 Durbin-Watson: 1.914
Prob(Omnibus): 0.713 Jarque-Bera (JB): 0.664
Skew: 0.083 Prob(JB): 0.717
Kurtosis: 2.184 Cond. No. 206.

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: 05:22:23 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: 0.123
Model: OLS Adj. R-squared: 0.081
Method: Least Squares F-statistic: 2.932
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.102
Time: 05:22:23 Log-Likelihood: -111.60
No. Observations: 23 AIC: 227.2
Df Residuals: 21 BIC: 229.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 333.6239 148.425 2.248 0.035 24.957 642.291
expression -32.0062 18.690 -1.712 0.102 -70.875 6.862
Omnibus: 2.130 Durbin-Watson: 2.072
Prob(Omnibus): 0.345 Jarque-Bera (JB): 1.107
Skew: -0.069 Prob(JB): 0.575
Kurtosis: 1.934 Cond. No. 177.

CP101

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

F-statistic p-value df difference
3.711 0.078 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.585
Model: OLS Adj. R-squared: 0.472
Method: Least Squares F-statistic: 5.168
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0180
Time: 05:22:23 Log-Likelihood: -68.705
No. Observations: 15 AIC: 145.4
Df Residuals: 11 BIC: 148.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 297.3396 325.588 0.913 0.381 -419.275 1013.955
C(dose)[T.1] 213.2240 394.959 0.540 0.600 -656.075 1082.523
expression -30.8568 43.676 -0.707 0.495 -126.986 65.272
expression:C(dose)[T.1] -20.9891 52.647 -0.399 0.698 -136.864 94.886
Omnibus: 0.539 Durbin-Watson: 0.740
Prob(Omnibus): 0.764 Jarque-Bera (JB): 0.534
Skew: -0.363 Prob(JB): 0.766
Kurtosis: 2.430 Cond. No. 616.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.579
Model: OLS Adj. R-squared: 0.509
Method: Least Squares F-statistic: 8.251
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00557
Time: 05:22:23 Log-Likelihood: -68.812
No. Observations: 15 AIC: 143.6
Df Residuals: 12 BIC: 145.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 404.9696 175.509 2.307 0.040 22.568 787.371
C(dose)[T.1] 55.8722 14.186 3.939 0.002 24.964 86.780
expression -45.3021 23.517 -1.926 0.078 -96.541 5.937
Omnibus: 0.499 Durbin-Watson: 0.826
Prob(Omnibus): 0.779 Jarque-Bera (JB): 0.579
Skew: -0.277 Prob(JB): 0.749
Kurtosis: 2.213 Cond. No. 197.

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: 05:22:23 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: 0.035
Model: OLS Adj. R-squared: -0.040
Method: Least Squares F-statistic: 0.4671
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.506
Time: 05:22:23 Log-Likelihood: -75.035
No. Observations: 15 AIC: 154.1
Df Residuals: 13 BIC: 155.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 264.3920 249.992 1.058 0.309 -275.683 804.467
expression -22.6742 33.175 -0.683 0.506 -94.345 48.997
Omnibus: 1.023 Durbin-Watson: 1.653
Prob(Omnibus): 0.600 Jarque-Bera (JB): 0.731
Skew: 0.117 Prob(JB): 0.694
Kurtosis: 1.944 Cond. No. 192.