LID SPN TRAP

1437841_x_at

Csdc2 cold shock domain containing C2, RNA binding

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
1.487 0.237 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.673
Model: OLS Adj. R-squared: 0.622
Method: Least Squares F-statistic: 13.06
Date: Thu, 21 Nov 2024 Prob (F-statistic): 7.34e-05
Time: 04:26:50 Log-Likelihood: -100.24
No. Observations: 23 AIC: 208.5
Df Residuals: 19 BIC: 213.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 268.4300 322.704 0.832 0.416 -406.997 943.857
C(dose)[T.1] 46.2190 385.072 0.120 0.906 -759.746 852.184
expression -20.4099 30.740 -0.664 0.515 -84.750 43.930
expression:C(dose)[T.1] 1.1586 36.415 0.032 0.975 -75.058 77.376
Omnibus: 0.259 Durbin-Watson: 1.773
Prob(Omnibus): 0.879 Jarque-Bera (JB): 0.386
Skew: 0.209 Prob(JB): 0.824
Kurtosis: 2.522 Cond. No. 1.36e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.673
Model: OLS Adj. R-squared: 0.641
Method: Least Squares F-statistic: 20.61
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.38e-05
Time: 04:26:50 Log-Likelihood: -100.24
No. Observations: 23 AIC: 206.5
Df Residuals: 20 BIC: 209.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 259.7640 168.692 1.540 0.139 -92.121 611.649
C(dose)[T.1] 58.4670 9.449 6.187 0.000 38.756 78.178
expression -19.5842 16.062 -1.219 0.237 -53.090 13.921
Omnibus: 0.258 Durbin-Watson: 1.777
Prob(Omnibus): 0.879 Jarque-Bera (JB): 0.386
Skew: 0.209 Prob(JB): 0.825
Kurtosis: 2.522 Cond. No. 428.

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: 04:26:50 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: 0.048
Model: OLS Adj. R-squared: 0.003
Method: Least Squares F-statistic: 1.060
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.315
Time: 04:26:50 Log-Likelihood: -112.54
No. Observations: 23 AIC: 229.1
Df Residuals: 21 BIC: 231.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -182.2768 254.586 -0.716 0.482 -711.717 347.163
expression 24.6670 23.960 1.029 0.315 -25.161 74.495
Omnibus: 1.660 Durbin-Watson: 2.366
Prob(Omnibus): 0.436 Jarque-Bera (JB): 1.254
Skew: 0.358 Prob(JB): 0.534
Kurtosis: 2.107 Cond. No. 388.

CP101

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

F-statistic p-value df difference
4.157 0.064 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.640
Model: OLS Adj. R-squared: 0.541
Method: Least Squares F-statistic: 6.505
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00859
Time: 04:26:50 Log-Likelihood: -67.647
No. Observations: 15 AIC: 143.3
Df Residuals: 11 BIC: 146.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 144.5063 318.866 0.453 0.659 -557.314 846.327
C(dose)[T.1] -366.3361 348.763 -1.050 0.316 -1133.958 401.286
expression -7.8590 32.497 -0.242 0.813 -79.385 63.667
expression:C(dose)[T.1] 43.7097 35.764 1.222 0.247 -35.007 122.427
Omnibus: 2.711 Durbin-Watson: 1.223
Prob(Omnibus): 0.258 Jarque-Bera (JB): 1.881
Skew: -0.844 Prob(JB): 0.390
Kurtosis: 2.604 Cond. No. 781.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.591
Model: OLS Adj. R-squared: 0.522
Method: Least Squares F-statistic: 8.655
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00471
Time: 04:26:50 Log-Likelihood: -68.602
No. Observations: 15 AIC: 143.2
Df Residuals: 12 BIC: 145.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -209.4331 136.156 -1.538 0.150 -506.092 87.226
C(dose)[T.1] 59.5529 14.485 4.111 0.001 27.994 91.112
expression 28.2295 13.846 2.039 0.064 -1.938 58.397
Omnibus: 3.370 Durbin-Watson: 1.523
Prob(Omnibus): 0.185 Jarque-Bera (JB): 2.169
Skew: -0.926 Prob(JB): 0.338
Kurtosis: 2.802 Cond. No. 196.

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: 04:26:50 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: 0.014
Model: OLS Adj. R-squared: -0.062
Method: Least Squares F-statistic: 0.1827
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.676
Time: 04:26:50 Log-Likelihood: -75.195
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 14.2216 186.116 0.076 0.940 -387.859 416.302
expression 8.2653 19.335 0.427 0.676 -33.505 50.035
Omnibus: 0.312 Durbin-Watson: 1.803
Prob(Omnibus): 0.856 Jarque-Bera (JB): 0.459
Skew: -0.067 Prob(JB): 0.795
Kurtosis: 2.154 Cond. No. 179.