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                   Modeling expression ranks for noise-tolerant
                        differential expression analysis of scRNA-Seq
                        data
TMMnormalization        TMM Normalization.
