#### Enrichment

##### 1. Input gene lists

##### 2. Functional annotation parameters

##### 3. Display parameters

##### 4. Filter BMD Values

##### 5. Download gene lists as excel file

Download#### Compute BMD Value

##### Models Description

The formula for the linear model is
$$ f(dose) = \beta_0 + \beta_1 dose $$
The linear model is a special case of the polynomial model, with $$n=1$$

The formula for the polynomial model is
$$ f(dose) = \beta_0 + \beta_1 dose + \beta_2 dose^2 + \ldots + \beta_n dose^n $$
Here n is the degree of the polynomial. The user can choose between $$n = 2, 3$$

The formula for the power model is
$$ f(dose) = \beta_0 + (dose)^\delta $$
The user can choose between $$\delta = 2, 3, 4$$

The formula for the exponential model is
$$ f(dose) = \beta_0 + exp(dose) $$

The formula for the hill model is
$$ f(dose) = \beta_0 + \dfrac{dose^n}{Kd + dose^n} $$
The user can choose between $$n = 0.5,1,2,3,4,5$$ while Kd is fixed to 10.

The formula for the asymptotic regression model is the following:
$$f(dose) = c + (d-c) \times (1-exp(-dose/e)) $$
The parameter c is the lower limit (at x=0), the parameter d is the upper limit and
the parameter e>0 is determining the steepness of the increase of dose.
The AR.3 model is the one depending from c, d and e parameters. The AR.2 model depends only on d and e parameters, while c is set to zero

The model is defined by the three-parameter model (MM.3) function
$$f(dose, (c, d, e)) = c + \dfrac{d-c}{1+(e/dose)}$$
It is increasing as a function of the dose, attaining the lower limit c at dose 0 (x=0) and the upper limit d for infinitely large doses.
The parameter e corresponds to the dose yielding a response halfway between c and d.
The common two-parameter Michaelis-Menten model (MM.2) is obtained by setting c equal to 0.

#### Filter Genes by Anova

#### Filter Genes by Trend Test

#### Import Gene Expression Table

#### Import Phenotype Data

Loading...

Loading...

Loading...

Loading...

Loading...

Loading...

Loading...

Loading...