Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-Seq data

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Documentation for package ‘ROSeq’ version 1.5.0

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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.