Question: Low count of differential expression data using Deseq2
gravatar for aristotele_m
3.8 years ago by
aristotele_m30 wrote:

I have compare 2 group of  sample (4 vs 2 control).  I use standar ùDESEq2 pipeline but I have obtain this results:

Pca show not homogeneous group .


out of 35000 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)     : 2, 0.0063%
LFC < 0 (down)   : 1, 0.0031%
outliers [1]     : 13, 0.041%
low counts [2]   : 19532, 62%
(mean count < 36.7)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

Any idea in what could be wrong on this situation?


R version 3.2.0 (2015-04-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 14.04.3 LTS

 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] genefilter_1.50.0         rafalib_1.0.0             ggplot2_1.0.1             limma_3.24.15            
 [5] RColorBrewer_1.1-2        gplots_2.17.0           RSQLite_1.0.0            
 [9] DBI_0.3.1                 annotate_1.46.1           XML_3.98-1.2              AnnotationDbi_1.30.1     
[13] Biobase_2.28.0            biomaRt_2.24.0            DESeq2_1.8.1              RcppArmadillo_0.5.500.2.0
[17] Rcpp_0.12.1               GenomicRanges_1.20.6      GenomeInfoDb_1.4.2        IRanges_2.2.7            
[21] S4Vectors_0.6.5           BiocGenerics_0.14.0       Nozzle.R1_1.1-1          

loaded via a namespace (and not attached):
 [1] gtools_3.5.0         locfit_1.5-9.1       reshape2_1.4.1       splines_3.2.0       
 [5] lattice_0.20-33      colorspace_1.2-6     survival_2.38-3      foreign_0.8-66      
 [9] BiocParallel_1.2.21  lambda.r_1.1.7       plyr_1.8.3           stringr_1.0.0       
[13] munsell_0.4.2        gtable_0.1.2         futile.logger_1.4.1  caTools_1.17.1      
[17] labeling_0.3         latticeExtra_0.6-26  geneplotter_1.46.0   proto_0.3-10        
[21] KernSmooth_2.23-15   acepack_1.3-3.3      xtable_1.7-4         scales_0.3.0        
[25] gdata_2.16.1         Hmisc_3.16-0         XVector_0.8.0        gridExtra_2.0.0     
[29] digest_0.6.8         stringi_0.5-5        grid_3.2.0           tools_3.2.0         
[33] bitops_1.0-6         magrittr_1.5         RCurl_1.95-4.7       Formula_1.2-1       
[37] cluster_2.0.1        futile.options_1.0.0 MASS_7.3-44          rpart_4.1-10        
[41] nnet_7.3-11



deseq2 • 913 views
ADD COMMENTlink modified 3.8 years ago by Michael Love24k • written 3.8 years ago by aristotele_m30
Answer: Low count of differential expression data using Deseq2
gravatar for Michael Love
3.8 years ago by
Michael Love24k
United States
Michael Love24k wrote:

Having no significant genes (even when increasing the independent filtering threshold to the optimal value of 36.7 here) means that the biological and technical variation in the experiment dominates any true log fold changes across condition given your sample size. Another way to say this is that the experiment was underpowered to detect the changes across condition. Ways to increase power in RNA-seq include increasing the sequencing depth and/or the number of biological replicates.

ADD COMMENTlink written 3.8 years ago by Michael Love24k
Please log in to add an answer.


Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 16.09
Traffic: 308 users visited in the last hour