Hi, I used DESeq2 to analyze RNA-Seq data, and obtained some results that are hard to explain. Could anyone help me?

Please see the following figure. The figure is MA plot which shows the result of differentially expressed genes identification using DESeq2. 

https://drive.google.com/open?id=1tudXNmfH7jm9YV9DBuMv8E8y8FDCZnEI

The red point indicates the gene with p-value < 0.1, whereas black point indicates the gene with p-value >= 0.1. I'm not clear about that why there some black points (genes) have p-value >= 0.1 even if they have large fold-changes. Are the counts of these genes recognized as outliers?

The R code to identify differentially expressed genes and plot MA-plot are here.

library(pasilla)
library(DESeq2)

pasCts <- system.file("extdata", "pasilla_gene_counts.tsv",
                      package="pasilla", mustWork=TRUE)
cts <- as.matrix(read.csv(pasCts,sep="\t",row.names="gene_id"))
countData <- cts[, c('untreated1', 'treated1')]
colData <- data.frame(group = colnames(countData))

dds <- DESeqDataSetFromMatrix(countData = countData, colData = colData, design = ~group)
dds <- DESeq(dds)
res <- as.data.frame(results(dds))
plot(log2(res$baseMean), res$log2FoldChange, col = ifelse(res$pvalue < 0.1, 2, 1))

Outputs of sessionInfo are here.

R version 3.4.2 (2017-09-28)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.2

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
 [1] pasilla_1.6.0              BiocInstaller_1.28.0       MASS_7.3-48                DESeq2_1.18.1              SummarizedExperiment_1.8.1 DelayedArray_0.4.1         matrixStats_0.52.2
 [8] Biobase_2.38.0             GenomicRanges_1.30.1       GenomeInfoDb_1.14.0        IRanges_2.12.0             S4Vectors_0.16.0           BiocGenerics_0.24.0

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.14           locfit_1.5-9.1         lattice_0.20-35        digest_0.6.13          plyr_1.8.4             backports_1.1.2        acepack_1.4.1          RSQLite_2.0
 [9] ggplot2_2.2.1          pillar_1.0.1           zlibbioc_1.24.0        rlang_0.1.6            lazyeval_0.2.1         rstudioapi_0.7         data.table_1.10.4-3    annotate_1.56.1
[17] blob_1.1.0             rpart_4.1-11           Matrix_1.2-12          checkmate_1.8.5        splines_3.4.2          BiocParallel_1.12.0    geneplotter_1.56.0     stringr_1.2.0
[25] foreign_0.8-69         htmlwidgets_0.9        RCurl_1.95-4.10        bit_1.1-12             munsell_0.4.3          compiler_3.4.2         base64enc_0.1-3        htmltools_0.3.6
[33] nnet_7.3-12            tibble_1.4.1           gridExtra_2.3          htmlTable_1.11.1       GenomeInfoDbData_1.0.0 Hmisc_4.0-3            XML_3.98-1.9           bitops_1.0-6
[41] grid_3.4.2             xtable_1.8-2           gtable_0.2.0           DBI_0.7                magrittr_1.5           scales_0.5.0           stringi_1.1.6          XVector_0.18.0
[49] genefilter_1.60.0      latticeExtra_0.6-28    Formula_1.2-2          RColorBrewer_1.1-2     tools_3.4.2            bit64_0.9-7            survival_2.41-3        AnnotationDbi_1.40.0
[57] colorspace_1.3-2       cluster_2.0.6          memoise_1.1.0          knitr_1.18

Check the section of ?results about experiments without replicates and why this is not useful for proper DE analysis.