I am currently learning to perform Differential Analysis via DESEQ2 R Package, and I believe I've made progress, able to format the data correctly [maybe] for DDS(). When I run the results function to see the output, the data seems fine, as can be seen below:

log2 fold change (MLE): Group RA vs C  Wald test p-value: Group RA vs
C  DataFrame with 6 rows and 6 columns
baseMean log2FoldChange     lfcSE      stat    pvalue      padj
<numeric>      <numeric> <numeric> <numeric> <numeric> <numeric> Gnai3   95.1978      0.3458366  0.275366  1.255916  0.209146  0.999383
Cdc45   21.9942     -0.0545177  0.346199 -0.157475  0.874870  0.999383
H19     29.0133     -1.4954220  1.516390 -0.986173  0.324048  0.999383
Narf    16.6967      0.2571059  0.370880  0.693232  0.488164  0.999383
Cav2    63.6356      0.5060019  0.354969  1.425480  0.154018  0.999383
Klf6    65.8469      0.2851175  0.265755  1.072858  0.283335  0.999383


However, when I run summary() or any other function, they do not seem to behave correctly, not counting any stats. Same with MAplot not coloring genes that should be marked red:

out of 13184 with nonzero total read count adjusted p-value < 0.1 LFC
> 0 (up)       : 0, 0% LFC < 0 (down)     : 0, 0% outliers [1]       : 150, 1.1% low counts [2]     : 0, 0% (mean count < 1)


I'm confused as to why this is occurring and was wondering if anyone had ideas.

EDIT: Added More Code for context

sergey <- (read.csv("cleanmatrix25+.csv", sep = "\t", header = T,
check.names = F, row.names = "Gene_names"))

dds <- DESeqDataSetFromMatrix(countData = sergey, colData = meta,
design = ~Group)

keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]

dds$$Group <- relevel(dds$$Group, ref = "C")

dds <- DESeq(dds)

res <- results(dds)

summary(res)

plotMA(res, ylim=c(-2,2))


EDIT2:

I sorted result by p-value, so all of these should be red on the following MAplot due to having a p-value < 0.1

You do not have any significantly differential genes after FDR correction according to padj, simple as that, which means that either there are none or your sample size is too small (underpowered) given the observed variability of your data. As the MA-plot shows some logFCs that could qualify as large enough to be called DEG it probably comes down to a too small sample size and quite some variability between the replicates of the groups. A PCA can help diagnosing whether there is simply lots of per-group variability or other factors such as batch effects that need to be taken care of. Please see the DESeq2 vignette for code examples or Bioc packages such as PCAtools for this.
Edit: plotMA by the way colors based on padj, not p-value alone.