LID SPN TRAP

1457295_at

Tnrc6a trinucleotide repeat containing 6a

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
0.000 0.983 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.595
Method: Least Squares F-statistic: 11.77
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000139
Time: 05:00:06 Log-Likelihood: -101.03
No. Observations: 23 AIC: 210.1
Df Residuals: 19 BIC: 214.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 32.4812 121.459 0.267 0.792 -221.735 286.698
C(dose)[T.1] 96.9249 182.013 0.533 0.601 -284.032 477.882
expression 3.1516 17.595 0.179 0.860 -33.676 39.979
expression:C(dose)[T.1] -6.1958 25.790 -0.240 0.813 -60.174 47.783
Omnibus: 0.248 Durbin-Watson: 1.876
Prob(Omnibus): 0.884 Jarque-Bera (JB): 0.438
Skew: 0.110 Prob(JB): 0.803
Kurtosis: 2.361 Cond. No. 372.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.50
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.83e-05
Time: 05:00:06 Log-Likelihood: -101.06
No. Observations: 23 AIC: 208.1
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 52.3630 86.782 0.603 0.553 -128.661 233.387
C(dose)[T.1] 53.2602 9.483 5.616 0.000 33.479 73.041
expression 0.2677 12.557 0.021 0.983 -25.927 26.462
Omnibus: 0.302 Durbin-Watson: 1.891
Prob(Omnibus): 0.860 Jarque-Bera (JB): 0.473
Skew: 0.060 Prob(JB): 0.789
Kurtosis: 2.308 Cond. No. 143.

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: 05:00:06 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: 0.096
Model: OLS Adj. R-squared: 0.053
Method: Least Squares F-statistic: 2.219
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.151
Time: 05:00:06 Log-Likelihood: -111.95
No. Observations: 23 AIC: 227.9
Df Residuals: 21 BIC: 230.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -110.8454 128.110 -0.865 0.397 -377.265 155.574
expression 27.1019 18.194 1.490 0.151 -10.734 64.938
Omnibus: 4.004 Durbin-Watson: 2.364
Prob(Omnibus): 0.135 Jarque-Bera (JB): 1.916
Skew: 0.398 Prob(JB): 0.384
Kurtosis: 1.832 Cond. No. 134.

CP101

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

F-statistic p-value df difference
3.248 0.097 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.573
Model: OLS Adj. R-squared: 0.457
Method: Least Squares F-statistic: 4.924
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0209
Time: 05:00:06 Log-Likelihood: -68.915
No. Observations: 15 AIC: 145.8
Df Residuals: 11 BIC: 148.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 224.1356 129.410 1.732 0.111 -60.693 508.964
C(dose)[T.1] 132.4796 223.940 0.592 0.566 -360.408 625.367
expression -19.2374 15.833 -1.215 0.250 -54.086 15.612
expression:C(dose)[T.1] -12.1358 28.633 -0.424 0.680 -75.157 50.885
Omnibus: 2.987 Durbin-Watson: 0.501
Prob(Omnibus): 0.225 Jarque-Bera (JB): 2.191
Skew: -0.898 Prob(JB): 0.334
Kurtosis: 2.470 Cond. No. 304.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.566
Model: OLS Adj. R-squared: 0.494
Method: Least Squares F-statistic: 7.831
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00666
Time: 05:00:06 Log-Likelihood: -69.036
No. Observations: 15 AIC: 144.1
Df Residuals: 12 BIC: 146.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 254.3643 104.226 2.441 0.031 27.276 481.452
C(dose)[T.1] 37.8044 15.327 2.466 0.030 4.409 71.199
expression -22.9483 12.733 -1.802 0.097 -50.692 4.795
Omnibus: 2.507 Durbin-Watson: 0.512
Prob(Omnibus): 0.285 Jarque-Bera (JB): 1.918
Skew: -0.796 Prob(JB): 0.383
Kurtosis: 2.269 Cond. No. 121.

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: 05:00:06 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: 0.346
Model: OLS Adj. R-squared: 0.296
Method: Least Squares F-statistic: 6.886
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0210
Time: 05:00:06 Log-Likelihood: -72.112
No. Observations: 15 AIC: 148.2
Df Residuals: 13 BIC: 149.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 376.6086 108.138 3.483 0.004 142.990 610.228
expression -35.9009 13.681 -2.624 0.021 -65.458 -6.344
Omnibus: 2.890 Durbin-Watson: 1.377
Prob(Omnibus): 0.236 Jarque-Bera (JB): 1.117
Skew: -0.085 Prob(JB): 0.572
Kurtosis: 1.674 Cond. No. 106.