Snow Crab Differential Gene Expression
Courtney Skalley
[1] 3294 14[1] 14 2 condition type
1 control single-read
2 control single-read
3 control single-read
4 control single-read
5 control single-read
6 control single-read
7 control single-read
8 control single-read
9 low pH single-read
10 low pH single-read
11 low pH single-read
12 low pH single-read
13 low pH single-read
14 low pH single-readclass: DESeqDataSet
dim: 3294 14
metadata(1): version
assays(1): counts
rownames(3294): contig_2632 contig_4568 ... contig_3686 contig_4058
rowData names(0):
colnames(14): X5010_1_S1_L003_R1 X5010_1_S1_L003_R2 ...
X5010_41_S41_L003_R1 X5010_41_S41_L003_R2
colData names(2): condition type
Snow Crab (Chionoecetes opilio)
Goal: Understand Snow Crab Response to Ocean Acidification
- Compare snow crab response to ocean acidification
- Conduct a differential gene expression analysis
- Using data from Dr. Laura Spencer
Experiment
63 juvenile snow crabs were exposed to different pH treatments for either a long or short duration:
- control (pH 8.1)
- pH 7.8
- short: 8 hours
- long: 12 weeks- pH 7.5
- short: 8 hours
- long: 12 weeksRNA Seq Data
- mRNA sequencing
- Each of the 63 samples were run in both lanes
- 126 paired-end RNA-Seq data sets
Creating a count matrix of sequences
X5010_1_S1_L003_R1 X5010_1_S1_L003_R2 X5010_2_S2_L003_R1
contig_2632 16.2705 25.966000 6.88181
contig_4568 0.0000 0.000000 0.00000
contig_2580 300.0500 268.228000 212.03100
contig_1896 2.0000 2.000000 0.00000
contig_590 1007.6700 543.944000 506.59700
contig_3193 25.7476 0.397017 7.17347
X5010_2_S2_L003_R2 X5010_3_S3_L003_R1 X5010_3_S3_L003_R2
contig_2632 13.27460 9.37402 9.83167
contig_4568 0.00000 0.00000 0.00000
contig_2580 187.48600 157.40300 149.38300
contig_1896 0.00000 0.00000 0.00000
contig_590 260.13800 282.95900 99.79770
contig_3193 2.80061 31.80440 37.00930
X5010_4_S4_L003_R1 X5010_4_S4_L003_R2 X5010_39_S39_L003_R1
contig_2632 11.33060 17.31500 9.82048
contig_4568 0.00000 0.00000 0.00000
contig_2580 656.56300 621.96000 1113.18000
contig_1896 0.00000 0.00000 0.00000
contig_590 314.95100 283.46500 874.03900
contig_3193 1.48772 4.14912 0.00000
X5010_39_S39_L003_R2 X5010_40_S40_L003_R1 X5010_40_S40_L003_R2
contig_2632 22.48480 6.8945 32.849000
contig_4568 0.00000 0.0000 0.000000
contig_2580 1101.10000 143.6890 188.828000
contig_1896 0.00000 0.0000 0.000000
contig_590 521.94300 904.6130 504.779000
contig_3193 1.99916 0.0000 0.984427
X5010_41_S41_L003_R1 X5010_41_S41_L003_R2
contig_2632 13.37540 27.27050
contig_4568 0.00000 0.00000
contig_2580 184.25200 176.12200
contig_1896 0.00000 0.00000
contig_590 899.47000 560.60400
contig_3193 6.91905 6.06559Code for Differential Gene Expression Plot
tmp <- deseq2.res
# The main plot
plot(tmp$baseMean, tmp$log2FoldChange, pch=20, cex=0.45, ylim=c(-3, 3), log="x", col="darkgray",
main="DEG pH 7.8 (pval <= 0.05)",
xlab="mean of normalized counts",
ylab="Log2 Fold Change")
# Getting the significant points and plotting them again so they're a different color
tmp.sig <- deseq2.res[!is.na(deseq2.res$padj) & deseq2.res$padj <= 0.05, ]
points(tmp.sig$baseMean, tmp.sig$log2FoldChange, pch=20, cex=0.45, col="red")
# 2 FC lines
abline(h=c(-1,1), col="blue")The Result: DGE for Snow Crabs
tmp <- deseq2.res
# The main plot
plot(tmp$baseMean, tmp$log2FoldChange, pch=20, cex=0.45, ylim=c(-3, 3), log="x", col="darkgray",
main="DEG pH 7.8 (pval <= 0.05)",
xlab="mean of normalized counts",
ylab="Log2 Fold Change")
# Getting the significant points and plotting them again so they're a different color
tmp.sig <- deseq2.res[!is.na(deseq2.res$padj) & deseq2.res$padj <= 0.05, ]
points(tmp.sig$baseMean, tmp.sig$log2FoldChange, pch=20, cex=0.45, col="red")
# 2 FC lines
abline(h=c(-1,1), col="blue")The Result: DGE for Snow Crabs
Next steps
- Conduct DGE using all 63 samples
- Update code to combine forward and reverse sequences in DESeq
- Create and annotate a de novo snow crab transcriptome