time series, block by patient, between and within comparisons limma-voom
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aec ▴ 90
@aec-9409
Last seen 4.5 years ago

Dear all,

I have a time course RNA-seq analysis with the following design: 5 time points, 15 patients, 2 types of patients.

the questions I would like to answer:

a) expression differences across time points irrespective of patient type (t1 vs t2, t1 vs t3, ...)

b) expression differences between the two types of patients for every time point or in general for any time point

should I follow limma's user guide section 9.6.1 or 9.6.2 ?

should I add duplicatecorrelation function to account for repeated measures (same patients each time point)?

should I combine the factors time_point + type_of_patient in one single factor?

thanks for the clarifications.

This is the information I have: patient (p1 to p15), time (t1 to t5), group (A,B)

sample patient time group
1 p1 t1

A

2 p1 t2 A
3 p1 t3 A
4 p1 t4 A
5 p1 t5 A
6 p2 t1 A
7 p2 t2 A
8 p2 t3 A
9 p2 t4 A
10 p2 t5 A
11 p3 t1 B
12 p3 t2 B
13 p3 t3 B
14 p3 t4 B
15 p3 t5 B
16 p4 t1 B
17 p4 t2 B
18 p4 t3 B
19 p4 t4 B
... .... ... ...

optional design formulas:

1) ~patient+timegroup

2) ~0+timegroup and then duplicatecorrelation to block for patient

3) ~patient+time+group

rna-seq time series patient limma-voom • 2.2k views
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A design matrix would be helpful. Or at least some code describing the available sample annotation.

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sure, I will edit my post.

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How many samples do you have in total? If I understand the question it seems that you have three patients for each time points which would be then divided into the 2 types of patients you have. Or it is something helse

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I have 5 time points per patient, in total 15 patients. These 15 patients can be further divided in two groups for question b)

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For question a) Do you want the answer irrespective of the group or not? (for me the only one that makes sense is the 3rd option. because then you can make the contrasts to answer any question you might have.
 

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Yes, irrespective of the group.

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Aaron Lun ★ 28k
@alun
Last seen 9 hours ago
The city by the bay

It seems like the best strategy would be your approach 2 (i.e., a combined time-group factor with blocking on patient in duplicateCorrelation). This will allow you to answer all of your questions with a single design matrix. For your first question, just compare the averages of each time point across groups, and for your second question, you can compare groups within each time point or the averages of each group across all time points.

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Thanks Aaron, it worked perfectly. Now if I am interested in applying LRT method to detect differential genes between the two time-courses over time, how to set up the model with limma? 5 time-points is enough for using splines?

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