Help with multi-factor Deseq analysis
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Entering edit mode
MAPK • 0
@mapk-17136
Last seen 2.5 years ago

Hi All,
I need help to resolve this problem with deseq.
I have two  cultivars (`R` and `S`), two time points (`5d` and `30d`) and four treatments (`Scn`, `Aph`, `ScnAph`, `Ctr`). There are two sets of controls (`Ctr`) for each cultivar and I have a total of 48 samples. 
I need to compare:

 1. Scn Vs Ctr within resistance within 5 day
 2. Scn Vs Aph within susceptible within 5 day
 3. Scn Vs Ctr within resistance within 30 day
 4. Scn Vs Aph within susceptible within 30 day
 5. R Vs S in whole dataset

Can someone please guide me through this. I have tried this model which looks like this, but I am not sure if this is the correct way of doing it. Thanks for your help in advance.

    dds <- DESeqDataSetFromMatrix(countData=count.mat,colData=cond,design= ~cultivar+time+cultivar:time)
    ##you  relevel 5d so you'll do "cultivarR.time30d"
    dds$time <- relevel(dds$time, ref = "5d")   
    ##you relevel S so you'll do "cultivarR.time5d"
    dds$cultivar <- relevel(dds$cultivar, ref = "S") 
    dds = DESeq(dds, test="LRT", reduced= ~cultivar + time)
    #5d vs 30d within resistance
    ##in case where dds$time <- relevel(dds$time, ref = "S")
    resultsNames(dds)
    # > resultsNames(dds)
    # [1] "Intercept"        "cultivar_R_vs_S"  "time_5d_vs_30d"   "cultivarR.time5d"
    #5d vs 30d within resistance
    tt<- results(dds, contrast=list(c("time_5d_vs_30d", "cultivarR.time5d")), test="Wald")

My data:

my `count.mat` looks like this:

    count.mat <- structure(list(sam5_4a_counts = c(26, 28, 1, 137, 3, 0), sam5_4b_counts = c(13, 
    6, 0, 95, 3, 0), sam5_4c_counts = c(1, 7, 0, 41, 2, 0), sam5_5a_counts = c(28, 
    15, 0, 84, 0, 0), sam5_5b_counts = c(12, 29, 1, 97, 2, 1), sam5_5c_counts = c(10, 
    11, 0, 77, 2, 0), sam5_6a_counts = c(42, 24, 0, 139, 4, 0), sam5_6b_counts = c(29, 
    28, 1, 166, 1, 1), sam5_6c_counts = c(29, 46, 0, 112, 5, 3), 
        sam5_7a_counts = c(7, 7, 1, 65, 0, 0), sam5_7b_counts = c(37, 
        10, 0, 108, 4, 0), sam5_7c_counts = c(7, 4, 0, 47, 0, 0), 
        sam5_1a_counts = c(44, 56, 4, 107, 2, 0), sam5_1b_counts = c(13, 
        11, 3, 44, 1, 0), sam5_1c_counts = c(39, 55, 1, 166, 1, 0
        ), sam5_2a_counts = c(4, 8, 1, 75, 1, 0), sam5_2b_counts = c(126, 
        160, 10, 414, 5, 1), sam5_2c_counts = c(28, 37, 1, 209, 3, 
        1), sam5_3a_counts = c(38, 70, 5, 138, 3, 1), sam5_3b_counts = c(132, 
        218, 6, 390, 14, 5), sam5_3c_counts = c(10, 2, 0, 39, 0, 
        1), sam5_8a_counts = c(19, 37, 1, 140, 1, 1), sam5_8b_counts = c(5, 
        12, 0, 63, 1, 0), sam5_8c_counts = c(27, 14, 1, 90, 0, 0), 
        sam30_4a_counts = c(29, 31, 0, 68, 2, 0), sam30_4b_counts = c(32, 
        24, 0, 70, 1, 0), sam30_4c_counts = c(13, 8, 0, 38, 2, 1), 
        sam30_5a_counts = c(22, 14, 0, 104, 2, 0), sam30_5b_counts = c(37, 
        49, 2, 88, 1, 0), sam30_5c_counts = c(37, 84, 1, 106, 0, 
        1), sam30_6a_counts = c(74, 58, 3, 110, 2, 1), sam30_6b_counts = c(68, 
        183, 3, 150, 2, 1), sam30_6c_counts = c(38, 86, 1, 161, 1, 
        0), sam30_7a_counts = c(21, 27, 0, 93, 2, 0), sam30_7b_counts = c(27, 
        20, 0, 89, 0, 1), sam30_7c_counts = c(24L, 23L, 0L, 91L, 
        1L, 0L), sam30_1a_counts = c(5, 7, 0, 16, 0, 0), sam30_1b_counts = c(38, 
        35, 3, 102, 0, 0), sam30_1c_counts = c(55, 26, 0, 136, 2, 
        0), sam30_2a_counts = c(20, 6, 0, 65, 2, 0), sam30_2b_counts = c(10, 
        5, 0, 43, 0, 0), sam30_2c_counts = c(86, 88, 7, 167, 6, 0
        ), sam30_3a_counts = c(39, 19, 1, 132, 3, 0), sam30_3b_counts = c(30, 
        31, 0, 113, 2, 0), sam30_3c_counts = c(59, 35, 0, 104, 3, 
        0), sam30_8a_counts = c(37, 22, 0, 133, 1, 0), sam30_8b_counts = c(28, 
        20, 0, 150, 2, 0), sam30_8c_counts = c(13, 20, 1, 104, 0, 
        0)), row.names = c("Glyma.01G000100", "Glyma.01G000200", 
    "Glyma.01G000300", "Glyma.01G000400", "Glyma.01G000500", "Glyma.01G000600"
    ), class = "data.frame")

 

 


my condition dataframe `cond`. 

    cond <- structure(list(cultivar = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("R", "S"), class = "factor"), 
        time = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
        2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
        1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
        1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("30d", "5d"), class = "factor"), 
        treatment = structure(c(3L, 3L, 3L, 1L, 1L, 1L, 4L, 4L, 4L, 
        2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 4L, 4L, 4L, 2L, 2L, 2L, 
        3L, 3L, 3L, 1L, 1L, 1L, 4L, 4L, 4L, 2L, 2L, 2L, 3L, 3L, 3L, 
        1L, 1L, 1L, 4L, 4L, 4L, 2L, 2L, 2L), .Label = c("Aph", "Ctr", 
        "Scn", "ScnAph"), class = "factor")), row.names = c(NA, -48L
    ), class = "data.frame")

deseq2 rnaseq deseq • 556 views
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1
Entering edit mode
@mikelove
Last seen 3 hours ago
United States

Your questions 1-4 can be addressed by creating a combined factor using paste(), see the first part of the Interactions section of the vignette. The 5th question is a bit more difficult because there isn’t such a coefficient in these models. Do you want to average the effect over all time points and treatments? You can use contrast=list(...) and listValues with fractional values to average over many coefficients.

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Thanks, Michael. I got these results below. Now, how do I run contrast for 1 to 4 ? Could you please confirm if it's correct what I have below? Yes for 5, I just want to see the overall effect.

 

cond$CultivarTreatment <- paste(cond$cultivar, cond$treatment, sep="_")

head(cond)
  cultivar time treatment CultivarTreatment
1        R   5d       Scn             R_Scn
2        R   5d       Scn             R_Scn
3        R   5d       Scn             R_Scn
4        R   5d       Aph             R_Aph
5        R   5d       Aph             R_Aph
6        R   5d       Aph             R_Aph
dds <- DESeqDataSetFromMatrix(countData=count.mat, colData=cond, design= ~ CultivarTreatment + time + CultivarTreatment:time)

dds <- DESeq(dds, test="LRT", reduced= ~ CultivarTreatment + time)
# Scn Vs Ctr within resistance within 5 day
results(dds, name="CultivarTreatment_R_Scn_vs_R_Ctr", test="Wald") # due to the fact that 5d is reference level

# Scn Vs Aph within susceptible within 5 day
results(dds, name="CultivarTreatment_S_Scn_vs_S_Aph", test="Wald")

# Scn Vs Ctr within resistance within 30 day
results(dds, contrast=list(c("CultivarTreatmentR_Scn.time30d", "CultivarTreatmentR_Ctr.time30d")), test="Wald")

Scn Vs Aph within susceptible within 30 day
results(dds, contrast=list(c("CultivarTreatmentS_Scn.time30d", "CultivarTreatmentS_Aph.time30d")), test="Wald")

 

 

 

> resultsNames(dds)
 [1] "Intercept"                           "CultivarTreatment_R_Ctr_vs_R_Aph"    "CultivarTreatment_R_Scn_vs_R_Aph"    "CultivarTreatment_R_ScnAph_vs_R_Aph"
 [5] "CultivarTreatment_S_Aph_vs_R_Aph"    "CultivarTreatment_S_Ctr_vs_R_Aph"    "CultivarTreatment_S_Scn_vs_R_Aph"    "CultivarTreatment_S_ScnAph_vs_R_Aph"
 [9] "time_30d_vs_5d"                      "CultivarTreatmentR_Ctr.time30d"       "CultivarTreatmentR_Scn.time30d"       "CultivarTreatmentR_ScnAph.time30d"   
[13] "CultivarTreatmentS_Aph.time30d"       "CultivarTreatmentS_Ctr.time30d"       "CultivarTreatmentS_Scn.time30d"       "CultivarTreatmentS_ScnAph.time30d"
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1
Entering edit mode

If you combine all three factors it will be easier to form your contrasts. 

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Entering edit mode

Thanks Michael. I merged `cond` dataframe as you suggested, but got the following error below. How do I reduce for one column factor? Could you please confirm if I am doing it correctly?

cond$TimeTreatmentCultivar<- as.factor(paste(cond$cultivar,cond$time,cond$treatment, sep = "_"))
    dds <- DESeqDataSetFromMatrix(countData=count.mat,colData=cond,design= ~TimeTreatmentCultivar)
    dds = DESeq(dds, test="LRT", reduced= ~ TimeTreatmentCultivar)

 

Error:

estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
Error in nbinomLRT(object, full = full, reduced = reduced, quiet = quiet,  :
  less than one degree of freedom, perhaps full and reduced models are not in the correc
t order

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1
Entering edit mode

Your LRT doesn’t make sense here. Just use DESeq(dds) and then results(dds, ...)

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