normalization of ChIP-seq data by using the spike-ins or by using total library sizes
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Bogdan ▴ 660
@bogdan-2367
Last seen 23 hours ago
Palo Alto, CA, USA

Dear all,

This question may have been asked before, I have searched the mailing list and I can not find an answer. The question is about the correct way of setting the SizeFactors() in DESeq2 in 3 situations. I would like to double check with you. Although the R code that I do list below refers to DESeq2, the question applies equally to edgeR or to limma / voom. I would appreciate having your suggestions.

I am working on 4 ChIP-seq datasets (2 DMSO samples and 2 TREAT samples). The input is a list of PEAKS with raw counts in all these 4 conditions (2 DMSO, and 2 TREAT). I am doing the analysis in 3 ways :

<> normalization to # Spike-ins (The Spike-ins numerical values have been estimated). I am setting the SizeFactors as the ratio between each Spike-in # , divided by the minimum # of all Spike-ins. Is it the correct way of doing it ?

spikeins=c(68924, 80282, 52023, 47090) 

spikeins_norm = spikeins / min(spikeins)

sizeFactors(dds) = spikeins_norm
dds <- estimateDispersions(dds)
dds <- nbinomLRT(dds, reduced= ~1)
res <- results(dds, name = "group_TZO_vs_DMSO")

<> normalization to Total # or Aligned Reads (in BAM files). I am setting the Size Factors() in the same way as I do it for Spike-Ins (Certainly, considering the Total # of Aligned Reads is not a recommended way to set the SizeFactors(), I am just doing it for comparison purposes).

     counts_BAM=c(1489249, 1568028, 1616202, 1647090) 

     counts_BAM_norm = counts_BAM / min(counts_BAM)

     sizeFactors(dds) = counts_BAM_norm
     dds <- estimateDispersions(dds)
     dds <- nbinomLRT(dds, reduced= ~1)
     res <- results(dds, name = "group_TZO_vs_DMSO")

<> normalization to # Reads in the Peaks. In this case, I follow the standard R workflow :

dds <- estimateSizeFactors(dds)
dds <- estimateDispersions(dds)
dds <- nbinomWaldTest(dds)

Please let me know what you think. Thanks,

Bogdan

DESeq2 edgeR limma • 563 views
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Referring to limma and edgeR, I could add that the R code that has been posted on the web pages (listed below) works and gives results that make sense.

 https://support.bioconductor.org/p/74870/
 https://support.bioconductor.org/p/p132471/
 https://support.bioconductor.org/p/9135179/
 https://www.biostars.org/p/166556/

Any suggestions about the R code in limma/voom ?

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@mikelove
Last seen 29 minutes ago
United States

For spike-ins, just use this built in paradigm:

dds <- estimateSizeFactors(dds, controlGenes= <names or numeric index of control features> )
dds <- DESeq(dds, ...)
...

For the others, I recommend to set sizeFactors by centering them around 0 first, e.g. the mean of the log of size factors should be ~0. So you can do

sizeFactors <- sizeFactors / exp(mean(log(sizeFactors)))
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Thank you very much Mike . In the light of the article on spiker : https://spiker.readthedocs.io/en/latest/reference.html where the size factors are computed as : 1 mil reads / # spike-in

given the amount of spike-ins :

counts_spike_ins = c(1489249, 1568028, 1616202, 1647090)

is it legit to use the size factors as :

size_factors = 1 / counts_spike_ins

or :

 size_factors = 1000000 / counts_spike_ins

Thanks a lot !

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It used to be a larger question, but I have solved the issue on controlGenes. I do keep the post just in case that somebody else is interested.

given the command :

dds <- estimateSizeFactors(dds, controlGenes= <names or numeric index of control features> )

In order to compute the SizeFactors :

I am starting with a data frame with 4 columns that contains the data of interest that is COUNTS_EXONS_dse , and I add a row with the spikes values :

spikeins = data.frame(DMSO_1 = 154815, DMSO_7  = 253491, TAZ_2 = 517331, TAZ_8  = 638848)

rownames(spikeins) = "spikeins"

head(rbind(spikeins, COUNTS_EXONS_dse))

COUNTS_EXONS_dse = rbind(spikeins, COUNTS_EXONS_dse)

head(COUNTS_EXONS_dse)
                    DMSO_1  DMSO_7  TAZ_2 TAZ_8
spikeins                154815     253491    517331    638848
1 : 858863 - 859082         25         17         5         4
1 : 860261 - 860998         82         51        24        21
1 : 862299 - 862760         37         26         7        11
1 : 867468 - 869833        281        179        79        76
1 : 874902 - 875224         25         15        11         5

dds <- estimateSizeFactors(dds, controlGenes=1)

sizeFactors(dds)
DMSO_1_S.0 DMSO_7_S.6  TAZ_2_S.1  TAZ_8_S.7 
 0.4587516  0.7511508  1.5329680  1.8930502 

or :

isControl <- rownames(dds) %in% "spikeins"

 dds <- estimateSizeFactors(dds, controlGenes=isControl) # the row 1 contains the spike-in data

dds$sizeFactors

sizeFactors(dds)
DMSO_1_S.0 DMSO_7_S.6  TAZ_2_S.1  TAZ_8_S.7 
 0.4587516  0.7511508  1.5329680  1.8930502 

There is smaller question though : how the instruction "controlGenes" work ?

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Mike,

I would like to ask you, how is the normalization done when we compute the differential expression and set up ControlGenes = "spike-ins" (how does the algorithm work ?)

Thanks !

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It uses only those rows to compute size factors. So it’s the same algorithm just focused on the features that should be stable aside from library size differences.

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Thank you. At this moment I have included in the matrix of gene expression only 1 spike-in (let's call it S). Therefore, is it legit to compute the size factors based on only on only a spike-in ?

We do have a list of approx 30 spike-ins, however, only spike-in S has the highest intensity (approx 100x than other spike-ins intensity). Shall I include all of spike-ins in the analysis or only spike-in S suffices ?

Thanks again !

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On another topic, talking about edgeR or limma, any thoughts regarding the computation of the normalization factors as Gordon Smith suggested in : Using edgeR and a spike-in to calculate absolute abundance

I have asked Gordon too, but I have not heard from him yet. The code that he suggested is :

 y <- DGEList(counts = counts_without_spike_in, samples = mapping_file)
norm.factors <- spike_in_factor / y$samples$lib.size
norm.factors <- norm.factors / prod(norm.factors)^(1/length(norm.factors))
y$samples$norm.factors <- norm.factors
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This is edgeR specific, how norm.factors and lib.size are defined there.

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I wouldn't recommend with a single spike in, that has too much variance. Include all spike-ins.

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Thanks Mike.

If you do not mind, I would add another question. It is about limma (I have asked Gordon and Aaron Lu, and I have not heard back from them). Is it legit to set up the normalization factors in limma/voom as equal directly to the spike-in amounts ? You must have done the comparisons with edgeR and voom, when you did write the function controlGenes in DEseq2.

For example :

SPIKEINS = c(154815, 253491, 517331, 638848)
y$samples$norm.factors = SPIKEINS
v <- voom(y, design, plot=TRUE)
vfit <- lmFit(v, design)
vfit <- contrasts.fit(vfit, contrasts=contrast.matrix)
efit <- eBayes(vfit)

I would appreciate having your comments. Thanks !

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I don't know the difference, controlGenes is simply computing median ratio over the control genes.

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