I have a normalized data frame for a timecourse experiment (MS/MS). Samples are named as Genotype_Time_Replicate (e.g. AOX_1h_4). Each sample has 4 replicates for each time point. (>1000 Genes/AGIs)
Sample data
df <- structure(list(AGI = c("ATCG01240", "ATCG01310", "ATMG00070"), aox2_0h__1 = c(15.79105291, 14.82652303, 14.70630068), aox2_0h__2 = c(16.06494674, 14.50610036, 14.52189807), aox2_0h__3 = c(14.64596287, 14.73266459, 13.07143141), aox2_0h__4 = c(15.71713641, 15.15430026, 16.32190068 ), aox2_12h__1 = c(14.99030606, 15.08046949, 15.8317372), aox2_12h__2 = c(15.15569857, 14.98996474, 14.64862254), aox2_12h__3 = c(15.12144791, 14.90111092, 14.59618842), aox2_12h__4 = c(14.25648197, 15.09832061, 14.64442686), aox2_24h__1 = c(15.23997241, 14.80968391, 14.22573239 ), aox2_24h__2 = c(15.57551513, 14.94861669, 15.18808897), aox2_24h__3 = c(15.04928714, 14.83758685, 13.06948037), aox2_24h__4 = c(14.79035385, 14.93873234, 14.70402827), aox5_0h__1 = c(15.8245918, 14.9351844, 14.67678306), aox5_0h__2 = c(15.75108628, 14.85867002, 14.45704948 ), aox5_0h__3 = c(14.36545859, 14.79296855, 14.82177912), aox5_0h__4 = c(14.80626019, 13.43330964, 16.33482718), aox5_12h__1 = c(14.66327372, 15.22571466, 16.17761867), aox5_12h__2 = c(14.58089039, 14.98545497, 14.4331578), aox5_12h__3 = c(14.58091828, 14.86139511, 15.83898617 ), aox5_12h__4 = c(14.48097297, 15.1420725, 13.39369381), aox5_24h__1 = c(15.41855602, 14.9890092, 13.92629626), aox5_24h__2 = c(15.78386057, 15.19372889, 14.63254456), aox5_24h__3 = c(15.55321382, 14.82013321, 15.74324956), aox5_24h__4 = c(14.53085803, 15.12196994, 14.81028556 ), WT_0h__1 = c(14.0535031, 12.45484834, 14.89102226), WT_0h__2 = c(13.64720361, 15.07144643, 14.99836235), WT_0h__3 = c(14.28295759, 13.75283646, 14.98220861), WT_0h__4 = c(14.79637443, 15.1108037, 15.21711524 ), WT_12h__1 = c(15.05711898, 13.33689777, 14.81064042), WT_12h__2 = c(14.83846779, 13.62497318, 14.76356308), WT_12h__3 = c(14.77215863, 14.72814995, 13.0835214), WT_12h__4 = c(14.70685445, 14.98527337, 16.12727292), WT_24h__1 = c(15.43813077, 14.56918572, 14.92146565 ), WT_24h__2 = c(16.05986898, 14.70583866, 15.64566505), WT_24h__3 = c(14.87721853, 13.22461859, 16.34119942), WT_24h__4 = c(14.92822133, 14.74382383, 12.79146694)), class = "data.frame", row.names = c(NA, -3L))
How can I plot volcano plots for different comparisons;
i.e.
***'AOX2_0h__vs_WT_0h_',
'AOX2_12h__vs_WT_12h_',
'AOX2_24h__vs_WT_24h_',
'AOX5_0h__vs_WT_0h_',
'AOX5_12h__vs_WT_12h_',
'AOX5_24h__vs_WT_24h_',
'AOX2_0h__vs_AOX5_0h_',
'AOX2_12h__vs_AOX5_12h_',
'AOX2_24h__vs_AOX5_24h_'***
Also at the end, can I get a summary table of the statistics.
Could anyone help with this, please!
P.S: I did this in DEP package, but after I manually worked on the data frame (in Excel; after extract by df_import <- as.data.frame(assays(df) it's no more in (SummarizedExperiment) format to work with those functions.
1 answer
A Volcano Plot just requires p-values and fold changes.
From where did you get your data? - it looks to be already normalised. Which program did you use?
Here is the data, for everyone else:
df
AGI aox2_0h__1 aox2_0h__2 aox2_0h__3 aox2_0h__4 aox2_12h__1 aox2_12h__2
1 ATCG01240 15.79105 16.06495 14.64596 15.71714 14.99031 15.15570
2 ATCG01310 14.82652 14.50610 14.73266 15.15430 15.08047 14.98996
3 ATMG00070 14.70630 14.52190 13.07143 16.32190 15.83174 14.64862
aox2_12h__3 aox2_12h__4 aox2_24h__1 aox2_24h__2 aox2_24h__3 aox2_24h__4
1 15.12145 14.25648 15.23997 15.57552 15.04929 14.79035
2 14.90111 15.09832 14.80968 14.94862 14.83759 14.93873
3 14.59619 14.64443 14.22573 15.18809 13.06948 14.70403
aox5_0h__1 aox5_0h__2 aox5_0h__3 aox5_0h__4 aox5_12h__1 aox5_12h__2
1 15.82459 15.75109 14.36546 14.80626 14.66327 14.58089
2 14.93518 14.85867 14.79297 13.43331 15.22571 14.98545
3 14.67678 14.45705 14.82178 16.33483 16.17762 14.43316
aox5_12h__3 aox5_12h__4 aox5_24h__1 aox5_24h__2 aox5_24h__3 aox5_24h__4
1 14.58092 14.48097 15.41856 15.78386 15.55321 14.53086
2 14.86140 15.14207 14.98901 15.19373 14.82013 15.12197
3 15.83899 13.39369 13.92630 14.63254 15.74325 14.81029
WT_0h__1 WT_0h__2 WT_0h__3 WT_0h__4 WT_12h__1 WT_12h__2 WT_12h__3 WT_12h__4
1 14.05350 13.64720 14.28296 14.79637 15.05712 14.83847 14.77216 14.70685
2 12.45485 15.07145 13.75284 15.11080 13.33690 13.62497 14.72815 14.98527
3 14.89102 14.99836 14.98221 15.21712 14.81064 14.76356 13.08352 16.12727
WT_24h__1 WT_24h__2 WT_24h__3 WT_24h__4
1 15.43813 16.05987 14.87722 14.92822
2 14.56919 14.70584 13.22462 14.74382
3 14.92147 15.64567 16.34120 12.79147
Cool, did you not look here: https://bioconductor.org/packages/release/bioc/vignettes/DEP/inst/doc/DEP.html#differential-enrichment-analysis
You need to perform the differential enrichment analysis.
To generate a volcano, you could then use my package: EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling
Okay, could just use non-parametric t-test, e.g., Wilcoxon Signed Rank test.
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