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In FocusPaka/SCBN: A statistical normalization method and differential expression analysis for RNA-seq data between different species

Iteration to find the optimal value A iteration process to compute the normalization factor to identify difference expression(DE) of genes between different species

Description

Iteration to find the optimal value A iteration process to compute the normalization factor to identify difference expression(DE) of genes between different species

Set the initial value Using median method to compute the normalization factor to identify difference expression (DE) of genes between different species

Compute the false discovery rate Compute the p-value for each orthologous genes between different species

Usage

Iter_optimal(scale, orth_gene, hkind, a)

MediancalcNorm(orth_gene, hkind)

sageTestNew(x, y, lengthx, lengthy, n1, n2, scale)

Arguments

scale	A value for normalization factor.
orth_gene	Matrix or data.frame containing read counts and gene length for each orthologous gene between different species. The first and third column containing gene length, the second and the fourth column containing read counts.
hkind	A vector shows conserved genes position in orthologous genes.
a	P-value cutoff in iteration process to find the optimal normalization factor.
x	The read counts for the first species.
y	The read counts for the second species.
lengthx	The gene length for the first species.
lengthy	The gene length for the second species.
n1	The total read counts for the first species.
n2	The total read counts for the second species.

Value

factor Computed normalization factor.

scale Computed Normalization factor.

p_value P-values for each orthologous genes between different species.

Functions

• Iter_optimal: obtain the optimal normalization value.

• MediancalcNorm: get scaling factor for different species.

• sageTestNew: obtain the p-value for each orthologous genes between different species.

References

Brawand D, Soumillon M, Necsulea A, Julien P, Csardi G, Harrigan P, et al. The evolution of gene expression levels in mammalian organs. Nature. 2011;478:343-348.

Examples

data(sim_data)
scale <- MediancalcNorm(orth_gene=sim_data, hkind=1:1000)
Iter_optimal(scale=scale, orth_gene=sim_data, hkind=1:1000, a=0.05)
data(sim_data)
MediancalcNorm(orth_gene=sim_data, hkind=1:1000)
data(sim_data)
orth_gene <- sim_data
hkind <- 1:1000
scale <- MediancalcNorm(orth_gene=orth_gene, hkind=hkind)
x <- orth_gene[, 2]
y <- orth_gene[, 4]
lengthx <- orth_gene[, 1]
lengthy <- orth_gene[, 3]
n1 <- sum(x)
n2 <- sum(y)
p_value <- sageTestNew(x, y, lengthx, lengthy, n1, n2, scale)

FocusPaka/SCBN documentation built on April 20, 2023, 3:54 a.m.