If you're trying to do a comparison, the edgeR equivalent would be the "RLE" method in calcNormFactors(). estimateSizeFactors() doesn't take an option in this regard (see help(estimateSizeFactors) for what options it does have).
Seqanswers Leaderboard Ad
Collapse
Announcement
Collapse
No announcement yet.
X
-
Originally posted by Michael Love View PostThe local fit helps you out by dipping down at the high counts, giving you lower estimates of dispersion there than by taking the mean overall. The large circles are genes whose dispersion estimates are not shrunk because they deviate too far from the fitted line. The local fit is useable as well I would say.
I presume -- because you haven't posted the output of sessionInfo() -- that you could be using a version of DESeq2 which performs automatic independent filtering to optimize the number of rejections. Check the Details section of ?results and the argument 'independentFiltering'.
Code:> sessionInfo() R version 3.0.2 (2013-09-25) Platform: x86_64-apple-darwin10.8.0 (64-bit) locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] grid parallel stats graphics grDevices utils datasets methods base other attached packages: [1] gplots_2.11.3 MASS_7.3-29 KernSmooth_2.23-10 caTools_1.14 [5] gdata_2.13.2 gtools_3.1.0 RColorBrewer_1.0-5 DESeq2_1.2.0 [9] RcppArmadillo_0.3.920.1 Rcpp_0.10.5 GenomicRanges_1.14.1 XVector_0.2.0 [13] IRanges_1.20.0 BiocGenerics_0.8.0 loaded via a namespace (and not attached): [1] annotate_1.40.0 AnnotationDbi_1.24.0 Biobase_2.22.0 bitops_1.0-6 [5] DBI_0.2-7 genefilter_1.44.0 lattice_0.20-24 locfit_1.5-9.1 [9] RSQLite_0.11.4 splines_3.0.2 stats4_3.0.2 survival_2.37-4 [13] tools_3.0.2 XML_3.95-0.2 xtable_1.7-1
Code:dds<- estimateSizeFactors(dds) ddsLocal <- estimateDispersions(dds, fitType="local") ddsLocal <- nbinomWaldTest(ddsLocal) res <- results(ddsLocal, name= "condition_MR_vs_BR") res <- res[order(res$padj),]
Code:use <- res$baseMean >= 10 & !is.na(res$pvalue) resFilt <- res [use, ] resFilt$padj <- p.adjust(resFilt$pvalue, method="BH")
Code:#filter for upregulated and downregulated genes resSig <- resFilt[ resFilt$padj < .1, ] write.table(resSig[ order( resSig$log2FoldChange, -resSig$baseMean ), ] ,"~\\DESeq2\\BRvsMR_old_DownRegulated.txt") write.table(resSig[ order( -resSig$log2FoldChange, -resSig$baseMean ), ],"~\\DESeq2\\BRvsMR_old_UpRegulated.txt")
Code:filterThreshold <- 2.0 keep <- rowMeans ( counts(ddsLocal, normalized=TRUE)) > filterThreshold
Thank you again for the help.
Comment
-
You are using DESeq2 v1.2 and reading the documentation off the Bioc website for version v1.0.
This is why I recommended you to look up the man page for ?results.
And it's always safer to use:
browseVignettes("DESeq2")
to ensure you are reading the documentation for the version you are using.
Comment
-
Originally posted by Michael Love View PostYou are using DESeq2 v1.2 and reading the documentation off the Bioc website for version v1.0.
This is why I recommended you to look up the man page for ?results.
And it's always safer to use:
browseVignettes("DESeq2")
to ensure you are reading the documentation for the version you are using.
By using:
Code:browseVignettes("DESeq2")
Code:/library/DESeq2/doc/DESeq2.pdf not found
Comment
-
Hi
I just started using DEseq2, so please correct if i am wrong.
My sample description has five time points (with 2 replicates).
(t0,t0,t1,t1,t2,t2,t3,t3,t4,t4,t5,t5)
if i set the timepoint design, i always get the differential expression through initial time point (control).
In addition to this, i also want to have differential expression with respect to the immediate time points (like t1 to t0, t2 to t1, t3 to t2, t4 to t3 and t5 to t4). Can you please tell me how to set the factor levels to get this type of comparison.
thank youLast edited by i4u412; 10-23-2013, 04:57 AM.
Comment
-
Hi,
I'm using DESeq2_1.2.0 and I am having this error:
Code:> resSig <- res[ res$padj < .1, ] Error in normalizeSingleBracketSubscript(i, x, byrow = TRUE, exact = FALSE) : subscript contains NAs
Just in case, here is my code:
Code:#Count matrix input Cele_SPvsLR_old = read.csv (file.choose(), header=TRUE, row.names=1) CeleDesign <- data.frame( row.names = colnames(Cele_SPvsLR_old), condition = factor(c("SP", "SP", "LR", "LR"))) dds <- DESeqDataSetFromMatrix(countData = Cele_SPvsLR_old, colData = CeleDesign, design = ~ condition) dds #Est size factor = normalize for library size dds<- estimateSizeFactors(dds) ddsLocal <- estimateDispersions(dds, fitType="local") ddsLocal <- nbinomWaldTest(ddsLocal) plotDispEsts(ddsLocal) #Differential expression analysis resultsNames(ddsLocal) res <- results(ddsLocal, name= "condition_SP_vs_LR") res <- res[order(res$padj),] head(res) plotMA(ddsLocal, ylim=c(-2,2), main="DESeq2") sum(res$padj < .1, na.rm=TRUE) #filter for significant genes resSig <- res[ res$padj < 0.1, ]
Code:> sessionInfo() R version 3.0.2 (2013-09-25) Platform: x86_64-apple-darwin10.8.0 (64-bit) locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] parallel stats graphics grDevices utils datasets methods base other attached packages: [1] DESeq2_1.2.0 RcppArmadillo_0.3.920.1 Rcpp_0.10.5 GenomicRanges_1.14.1 [5] XVector_0.2.0 IRanges_1.20.0 BiocGenerics_0.8.0 loaded via a namespace (and not attached): [1] annotate_1.40.0 AnnotationDbi_1.24.0 Biobase_2.22.0 DBI_0.2-7 [5] genefilter_1.44.0 grid_3.0.2 lattice_0.20-24 locfit_1.5-9.1 [9] RColorBrewer_1.0-5 RSQLite_0.11.4 splines_3.0.2 stats4_3.0.2 [13] survival_2.37-4 tools_3.0.2 XML_3.95-0.2 xtable_1.7-1
Thanks!Last edited by alisrpp; 10-24-2013, 11:50 AM.
Comment
-
contrast argument
Hi,
I have started using DESeq2 on a multi-factor design. Everything goes fine until I try to use the "contrast" argument on the "results" function in order to get somparisons other than the ones concerning the control condition.
This is what I get:
> res.R24.OE1=results(dds.gtype,contrast=c("gtype","R24","OE1"))
Error in results(dds.gtype, contrast = c("gtype", "R24", "OE1")) :
unused argument (contrast = c("gtype", "R24", "OE1"))
"gtype" being my factor and "R24" and "OE1" my two conditions.
I would be very grateful if you could help me out on this one.
Thanks!
Comment
-
Hi,
I am using DESeq2 version 1.2.5 and when I use this design:
> design(dds) <- formula(~ AGE + SEX + CONDITION)
Defining the variables:
> colData(dds)$CONDITION <- relevel(colData(dds)$CONDITION, "R")
> colData(dds)$SEX <- relevel(colData(dds)$SEX, "M")
Whereas AGE is a continuous variable
I get this error:
> dds <- DESeq(dds)
using pre-existing size factors
estimating dispersions
you had estimated dispersions, replacing these
gene-wise dispersion estimates
error: inv(): matrix appears to be singular
On the other hand, there are no problems with these combinations:
> design(dds) <- formula(~ AGE)
> design(dds) <- formula(~ CONDITION)
> design(dds) <- formula(~ SEX + CONDITION)
While I get the same error with:
> design(dds) <- formula(~ AGE + CONDITION)
Any clue what's going on here?
Thank you so much in advance for your help
Comment
-
Originally posted by Michael Love View PostCan you please post colData(dds) (or some scrubbed version of it)? This helps me figure out what the design matrix looks like, and therefore why some designs are causing errors.
> colData(dds)
DataFrame with 122 rows and 4 columns
CONDITION KEEP AGE SEX
<factor> <integer> <numeric> <factor>
ID1 A 1 47.06913 F
ID2 A 1 45.33333 M
ID3 A 1 59.63039 F
ID4 A 1 49.00753 M
ID5 A 1 34.94319 M
... ... ... ... ...
ID118 B 1 30.00684 M
ID119 B 1 41.90828 F
ID120 B 1 39.04449 F
ID121 B 1 39.64682 M
ID122 B 1 16.70363 M
Comment
Latest Articles
Collapse
-
by seqadmin
During the COVID-19 pandemic, scientists observed that while some individuals experienced severe illness when infected with SARS-CoV-2, others were barely affected. These disparities left researchers and clinicians wondering what causes the wide variations in response to viral infections and what role genetics plays.
Jean-Laurent Casanova, M.D., Ph.D., Professor at Rockefeller University, is a leading expert in this crossover between genetics and infectious...-
Channel: Articles
09-09-2024, 10:59 AM -
-
by seqadmin
The first FDA-approved CRISPR-based therapy marked the transition of therapeutic gene editing from a dream to reality1. CRISPR technologies have streamlined gene editing, and CRISPR screens have become an important approach for identifying genes involved in disease processes2. This technique introduces targeted mutations across numerous genes, enabling large-scale identification of gene functions, interactions, and pathways3. Identifying the full range...-
Channel: Articles
08-27-2024, 04:44 AM -
ad_right_rmr
Collapse
News
Collapse
Topics | Statistics | Last Post | ||
---|---|---|---|---|
Started by seqadmin, Today, 06:25 AM
|
0 responses
13 views
0 likes
|
Last Post
by seqadmin
Today, 06:25 AM
|
||
Started by seqadmin, Yesterday, 01:02 PM
|
0 responses
12 views
0 likes
|
Last Post
by seqadmin
Yesterday, 01:02 PM
|
||
Started by seqadmin, 09-18-2024, 06:39 AM
|
0 responses
14 views
0 likes
|
Last Post
by seqadmin
09-18-2024, 06:39 AM
|
||
Started by seqadmin, 09-11-2024, 02:44 PM
|
0 responses
14 views
0 likes
|
Last Post
by seqadmin
09-11-2024, 02:44 PM
|
Comment