# A Rank Based Approach to Modeling Gene Expression

## Documentation for package ‘ROSeq’ version 1.0.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

computeDEG | Computes differential expression for the gene in question, by comparing the optimal parameters for sub-populations one and two
findParams | Finds the optimal values of parameters a and b that model the probability distribution of ranks, by Maximising the Log-Likelihood
getd | Finds the double derivative of A
getDataStatistics | Evaluates statistics of the read counts corresponding to the gene
getdu1da | Finds the first derivative of u1 with respect to a. This first derivative is evaluated at the optimal (a_hat, b_hat).
getdu1db | Finds the first derivative of u1 with respect to b. This first derivative is evaluated at the optimal (a_hat, b_hat).
getdu2da | Finds the first derivative of u2 with respect to a. This first derivative is evaluated at the optimal (a_hat, b_hat).
getdu2db | Finds the first derivative of u2 with respect to b. This first derivative is evaluated at the optimal (a_hat, b_hat).
getdvda | Finds the first derivative of v with respect to a. This first derivative is evaluated at the optimal (a_hat, b_hat).
getdvdb | Finds the first derivative of v with respect to b. This first derivative is evaluated at the optimal (a_hat, b_hat).
getI | Computes the Fisher Information Matrix
getu1 | Computes u1
getu2 | Computes u2
getv | Computes v
initiateAnalysis | Computes differential analysis for a given gene
L_Tung_single | Single cell samples for DE genes analysis
minimizeNLL | Minimizes the Negative Log-Likelihood by iterating across values of parameters a and b
ROSeq | A rank based approach to modeling gene expression with filtered and normalized read count matrix
TMMnormalization | TMM Normalization.