Hello
Please correct me if I am wrong as I am only getting a few significant genes after differential expression analysis
I have MSC cell lines (non-treated, IT treated and LPS treated) in 24 and 48 hours
I want to compare untreated MSC with IT and LPS so, my main table is this
> design
condition time
First I look at 24 and 48 hours independently
For instance for 24 hours I done so
> design
X condition time
1 CTRL_24_hrs_replicate1 control 24
2 CTRL_24_hrs_replicate2 control 24
3 CTRL_24_hrs_replicate3 control 24
4 treatment_24_hrs_replicate1 t 24
5 treatment_24_hrs_replicate2 t 24
6 treatment_24_hrs_replicate3 t 24
7 CTRL_48_hrs_replicate1 control 48
8 CTRL_48_hrs_replicate2 control 48
9 CTRL_48_hrs_replicate3 control 48
10 treatment_48_hrs_replicate1 t 48
11 treatment_48_hrs_replicate2 t 48
12 treatment_48_hrs_replicate3 t 48
>
dds <- DESeqDataSetFromMatrix(countData=count, colData= design24, design=~condition, tidy = FALSE)
dds <- DESeq(dds)
resIT<- results(dds, contrast=c("condition", "IT", "control"))
resLPS <- results(dds, contrast=c("condition", "LPS", "control"))
resIT_LPS <- results(dds, contrast=c("condition", "IT", "LPS"))
I done the same for 48 hours
Looking at the results, I only see very few significant genes
head(resIT[order(resIT $padj),])
log2 fold change (MLE): condition IT vs control
Wald test p-value: condition IT vs control
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
hsa-miR-122-5p 2021.7353 -3.148194 0.519671 -6.05805 1.37781e-09 5.75699e-05
hsa-mir-122 2021.8461 -3.148297 0.519682 -6.05812 1.37722e-09 5.75699e-05
RNU11 38.5986 -2.882145 0.621620 -4.63651 3.54342e-06 9.87044e-02
hsa-mir-133a-1 13.6656 -2.571007 0.574599 -4.47444 7.66131e-06 1.28047e-01
hsa-miR-133a-3p 13.6656 -2.571007 0.574599 -4.47444 7.66131e-06 1.28047e-01
GNAI21 130.3099 0.993967 0.232581 4.27364 1.92304e-05 2.67838e-01
Please correct me where possibly I am doing something wrong, or how I can get more significant genes?
And, please how I could take time points into the design comparing my conditions without separation my data to 24 and 48 hours?
Anyway thanks a lot in advance
Sorry what you mean by joining? Like IT+4hrs and IT+8hrs ???
Do just like it says in the vignette. The "much simpler approach"
http://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#interactions
Thank you
Like this?
But I think I should have more combinations
Don't you think?
You can use contrasts to specify what subgroup to compare to what, even if it doesn't appear in the resultsNames.
In your case, the warning about changing characters to factors is fine; it's what you want to happen.
Thank you
Basically I want to compare IT versus LPS from 24 hours to 48 hours but I don't know how to add what I want in the contrast
Please can you help me?
What have you tried? Where have you looked?
Thank you
Actually I want to find the difference at time point 24 vs 48 in in each condition
I searched and I found a similar thing here
DESeq2 time series contrasts between timepoints
but I don't know how to adopt that to my data as I am observing such things (time_24_vs_48) in my design
For adopting this code, I tried this but I got error