So I was doing a differential expression analysis with RNA-Seq data using limma-voom pipeline. However, the fit for mean-variance is not like a flat line at 1 but a curve (top: plot after voom; bottom: plot after fitting to linear model). Is it normal? Can I still use the results?

Here are my codes:

library(edgeR)
library(limma)

#Import data
txi <- tximport(files = read.hd5s,
                     type = "kallisto",
                     tx2gene = tx2gene,
                     countsFromAbundance = "lengthScaledTPM",
                     ignoreTxVersion = TRUE)

# create DGEList object
raw_data = DGEList(txi$counts)

#Filtering low count reads (TPM < 2)
library(genefilter)
abundance = txi$abundance
abs_crit = pOverA(0.2, 2)
abs_filter <- filterfun(abs_crit)
keep <- genefilter(abundance, abs_filter)
raw_data <- raw_data[keep,,keep.lib.sizes=FALSE]
nrow(raw_data)

# calculate normalization factors to scale the raw library sizes
raw_data = calcNormFactors(raw_data)

# experiment design (cluster from previous clustering analysis)
design <- model.matrix(~condition)

# Batch effect correction
dat0 = as.matrix(raw_data$counts)
mod1 = design
mod0 = cbind(mod1[,1])
set.seed(12345)
svseq = svaseq(dat0,mod1,mod0)

# using limma to find differentially expressed genes
#model fit with batch
new_design <- model.matrix(~0+condition)
new_design = cbind(new_design,svseq$sv)

#voom transformation
voom_diff = voom(raw_data, new_design, normalize="none", plot = TRUE)

#contrast matrix
contr.matrix <- makeContrasts(conditionvscontrol = conditionTRUE - conditionFALSE,
                              levels = colnames(new_design))

#model fitting
vfit <- lmFit(voom_diff_ph, new_design)
vfit <- contrasts.fit(vfit, contrasts=contr.matrix)
efit <- eBayes(vfit,trend=TRUE)
plotSA(efit, main="Final model: Mean-variance trend")

First two columns of design matrix are like this:

> head(new_design[,1:2])
  conditionFALSE conditionTRUE
1            1           0
2            1           0
3            1           0
4            1           0
5            1           0
6            1           0

There are some 1 in TRUE and some 0 in FALSE but it's too long so I didn't include them.