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

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Documentation for package ‘ROSeq’ version 1.19.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

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