Question

DESeq2 Multi factor, multi time point comparison design formula

0

Entering edit mode

arun.thirumaran • 0

@arunthirumaran-16779

Last seen 6.4 years ago

Hi,

I am new to RNA seq and seq data analysis.

I want to use DESeq2 to extract differential expression data for cells (from three donors) seeded on two different scaffolds (scaffold 1 and 2) across three different time points and compare them with an industry standard control (scaffold 3) and positive control (TCP).

Experimental design is as follows,

scaffold 1 (POC1.1) + cells in normal media (NM)
scaffold 2 (POC1.3) + cells in normal media (NM)
scaffold 3 (PLDLA) + cells in normal media (NM)
cells in tissue culture plastic in osteogenic induction media (OM) (control)

-Three time points day 7, day 14 and day 21

-all samples in biological triplicates (3 donors)

Total of 36 samples

I would like to find out deferentially expressed genes in cells cultured on POC 1.1 and POC 1.3 in normal media against PLDLA and osteogenic differentiated cells in tissue culture plastic (TCP).

My sample table looks like the following

	sample name	file name	condition	Group	time
1	POC1.1	7_1_A.bam	NM	POC1.1	7
2	POC1.3	7_1_B.bam	NM	POC1.3	7
3	PLDLA	7_1_C.bam	NM	PLDLA	7
4	TCP	7_1_D.bam	OM	TCP	7
5	POC1.1	7_2_A.bam	NM	POC1.1	7
6	POC1.3	7_2_B.bam	NM	POC1.3	7
7	PLDLA	7_2_C.bam	NM	PLDLA	7
8	TCP	7_2_D.bam	OM	TCP	7
9	POC1.1	7_3_A.bam	NM	POC1.1	7
10	POC1.3	7_3_B.bam	NM	POC1.3	7
11	PLDLA	7_3_C.bam	NM	PLDLA	7
12	TCP	7_3_D.bam	OM	TCP	7
13	POC1.1	14_1_A.bam	NM	POC1.1	14
14	POC1.3	14_1_B.bam	NM	POC1.3	14
15	PLDLA	14_1_C.bam	NM	PLDLA	14
16	TCP	14_1_D.bam	OM	TCP	14
17	POC1.1	14_2_A.bam	NM	POC1.1	14
18	POC1.3	14_2_B.bam	NM	POC1.3	14
19	PLDLA	14_2_C.bam	NM	PLDLA	14
20	TCP	14_2_D.bam	OM	TCP	14
21	POC1.1	14_3_A.bam	NM	POC1.1	14
22	POC1.3	14_3_B.bam	NM	POC1.3	14
23	PLDLA	14_3_C.bam	NM	PLDLA	14
24	TCP	14_3_D.bam	OM	TCP	14
25	POC1.1	21_1_A.bam	NM	POC1.1	21
26	POC1.3	21_1_B.bam	NM	POC1.3	21
27	PLDLA	21_1_C.bam	NM	PLDLA	21
28	TCP	21_1_D.bam	OM	TCP	21
29	POC1.1	21_2_A.bam	NM	POC1.1	21
30	POC1.3	21_2_B.bam	NM	POC1.3	21
31	PLDLA	21_2_C.bam	NM	PLDLA	21
32	TCP	21_2_D.bam	OM	TCP	21
33	POC1.1	21_3_A.bam	NM	POC1.1	21
34	POC1.3	21_3_B.bam	NM	POC1.3	21
35	PLDLA	21_3_C.bam	NM	PLDLA	21
36	TCP	21_3_D.bam	OM	TCP	21

I want to test for

POC1.1 Vs PLDLA for day 7, day 14 and day 21
POC1.3 Vs PLDLA for day 7, day 14 and day 21
PLDLA Vs TCP for day 7, day 14 and day 21
POC1.1 Vs TCP for day 7, day 14 and day 21
POC 1.3 Vs TCP for day 7, day 14 and day 21
POC 1.1 Day 7 Vs Day 14 Vs Day 21
POC 1.3 Day 7 Vs Day 14 Vs Day 21
PLDLA Day 7 Vs Day 14 Vs Day 21
TCP Day 7 Vs Day 14 Vs Day 21

Does this comparison make sense? Any suggestions are appreciated.

Can anyone please help with design formulas for differential expression using DESeq2 for the above-mentioned comparisons.

Thank you very much in advance

Best Regards

Arun

rna-seq deseq2 deseq R • 2.4k views

ADD COMMENT • link updated 6.4 years ago by Michael Love 43k • written 6.4 years ago by arun.thirumaran • 0

0

Entering edit mode

If you want to know whether your comparisons make sense, you need to tell us more about the biology of your experiment. What is a "scafffold" and what do you want to find out about it?

ADD REPLY • link 6.4 years ago Simon Anders ★ 3.8k

0

Entering edit mode

Hi Simon,

Thanks for the reply.

I have edited the question above. Here is the brief experimental details.

The scaffold here is a hydroxyapatite dopped polymer. I am looking at osteoinductive properties of two different polymers (scaffold 1 and 2 (POC1.1 AND POC 1.3)) in comparison with an industry standard polymer (scaffold 3 (PLDLA)) and cells that are induced to differentiate in tissue culture plastic (TCP).

I want to find out differential gene expression in scaffold 1 and 2 compared to the control material (scaffold 3) and positive control (TCP) across three time points. This will be followed by IPA analysis. Can I do this in one run in DESeq2 or Do I have to run each comparison separately.

Please note cells are human origin and are in biological triplicates and I have single end sequences not paired end.

Thank you in advance

Arun

ADD REPLY • link 6.4 years ago arun.thirumaran • 0

score 0 · Answer 1 · 2018-08-10

0

Entering edit mode

Michael Love 43k

@mikelove

Last seen 2 hours ago

United States

This statistical analysis question is beyond the amount of software support that I can provide at the moment. Maybe Simon or another of the support site answerers can help, but otherwise, since you have a quite complex experimental design, I'd recommend working with a local statistician to form the design and contrasts (note that anyone familiar with R design formula and linear model contrasts can help you, they don't need to know details about DESeq2 specifically, because we leverage the basic R design formula to compute coefficients).

ADD COMMENT • link 6.4 years ago Michael Love 43k

0

Entering edit mode

Thank You Michael,

Unfortunately, I was not able to get any local help with R designs. Could you please comment on my analysis to compare between two polymers at three-time points? For example, I want to compare POC1.1 (treatment) Vs PLDLA (control).

my sample table looks like,

	Groups	time
1	POC1.1	07d
2	POC1.1	07d
3	POC1.1	07d
4	POC1.1	14d
5	POC1.1	14d
6	POC1.1	14d
7	POC1.1	21d
8	POC1.1	21d
9	POC1.1	21d
10	PLDLA	07d
11	PLDLA	07d
12	PLDLA	07d
13	PLDLA	14d
14	PLDLA	14d
15	PLDLA	14d
16	PLDLA	21d
17	PLDLA	21d
18	PLDLA	21d

I followed your guide on using contrasts to get results table and the script I used is below

counts <- read.table("POC1.1VPLDLA_UCSCtable.csv", header=TRUE, sep=",", row.names=1)

countdata <- as.matrix(counts)
head(countdata, 3)

groups <- factor(c("POC1.1", "POC1.1", "POC1.1", "POC1.1", "POC1.1", "POC1.1", "POC1.1", "POC1.1", "POC1.1", "PLDLA", "PLDLA", "PLDLA", "PLDLA", "PLDLA", "PLDLA", "PLDLA", "PLDLA", "PLDLA"))

time <- factor(c( "07d", "07d", "07d","14d", "14d", "14d", "21d", "21d", "21d", "07d", "07d", "07d","14d", "14d", "14d", "21d", "21d", "21d" ))

coldata <- data.frame(row.names=colnames(countdata), groups, time)

#generation of a DESeqDataSet from count matrix
dds = DESeqDataSetFromMatrix(countData = countdata, colData = coldata, design = ~ groups + time + groups:time)
dds
#making factor levels (is this required?)
dds$groups <- relevel(dds$groups, ref = "PLDLA")
dds$time<- relevel(dds$time, ref = "07d")
#Interactions

dds$combined <- factor(paste0(dds$groups, dds$time))
design(dds) <- ~ combined
dds <- DESeq(dds)
resultsNames(dds)

# I get the following results
= [1] "Intercept"

"combined_PLDLA14d_vs_PLDLA07d"

"combined_PLDLA21d_vs_PLDLA07d"

"combined_POC1.107d_vs_PLDLA07d"

"combined_POC1.114d_vs_PLDLA07d"

"combined_POC1.121d_vs_PLDLA07d"

My questions here are,

Is my design formula ~groups+time+groups:time correct for this analysis?
How can I model the design formula or use contrasts or names to get the results, for POC 1.1 Vs PLDLA on the following time points?
1. d07
2. d14
3. d21
4. d14 v d07
5. d21 v d14
6. d21 v d07

Any suggestion will be very helpful.

Thank you in advance.

Arun

ADD REPLY • link 6.3 years ago arun.thirumaran • 0

0

Entering edit mode

Sorry Arun but I don’t have time to provide this level of statistical support here.

ADD REPLY • link 6.3 years ago Michael Love 43k

0

Entering edit mode

Thank you Michael,

Could you please suggest for one of my time point, ie,

POC1.1 V PLDLA for day7 alone?

When I am reordering the results based on padj values for this singkle time points I am getting 0 genes

> sum(res05$padj < 0.05, na.rm=TRUE)
[1] 0

But if i do p value instead of padj I am getting 231 genes

> sum(res05$pvalue < 0.05, na.rm=TRUE)
[1] 231

ADD REPLY • link 6.3 years ago arun.thirumaran • 0

0

Entering edit mode

You simply would use the contrast argument to compare POC1.1 and PLDLA for day7 alone. You can compare any two levels of the combined variables using contrast.

You need to use the adjusted p-values not the p-values. Otherwise you will not control the false discovery rate. This is explained in the workflow, which you should read over:

https://master.bioconductor.org/packages/release/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html#multiple-testing