Hi,

I´m using the DESseq package and trying the cook´s distance to filter genes.

boxplot(log10(assays(dds)[["cooks"]]), range=0, las=2,main="Cook's distance")

My sample "control_1" seems to be quite different from the others ??

thanks

You could explore your data further to figure out what is going on:

• heatmap of VST-transformed data matrix
• PCA plot
• heatmap of sample-sample correlation matrix
• pairwise scatterplots

Is the number of sequenced or aligned reads very different for that library? Etc., ...

Hi,

I haven´t seen anything strange with that sample in my PCA, neither my clustering. (see PCA attached and hclust )(see images below).

In the meantime disease_2 has become control_6. Otherwise don´t understand the cook´s values for control 1? How should i interpret it ?

> sessionInfo()
R version 3.2.3 (2015-12-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 14.04.3 LTS

locale:
 [1] LC_CTYPE=en_US.UTF-8          LC_NUMERIC=C                 
 [3] LC_TIME=en_US.UTF-8           LC_COLLATE=en_US.UTF-8       
 [5] LC_MONETARY=ca_AD.UTF-8       LC_MESSAGES=en_US.UTF-8      
 [7] LC_PAPER=ca_AD.UTF-8          LC_NAME=ca_AD.UTF-8          
 [9] LC_ADDRESS=ca_AD.UTF-8        LC_TELEPHONE=ca_AD.UTF-8     
[11] LC_MEASUREMENT=ca_AD.UTF-8    LC_IDENTIFICATION=ca_AD.UTF-8

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

other attached packages:
 [1] psych_1.5.8                randomForest_4.6-12       
 [3] genefilter_1.52.1          ggdendro_0.1-17           
 [5] pheatmap_1.0.8             vsn_3.38.0                
 [7] gridExtra_2.0.0            xlsx_0.5.7                
 [9] xlsxjars_0.6.1             rJava_0.9-8               
[11] RColorBrewer_1.1-2         DESeq2_1.10.1             
[13] RcppArmadillo_0.6.500.4.0  Rcpp_0.12.3               
[15] SummarizedExperiment_1.0.2 Biobase_2.30.0            
[17] GenomicRanges_1.22.4       GenomeInfoDb_1.6.3        
[19] IRanges_2.4.6              S4Vectors_0.8.11          
[21] BiocGenerics_0.16.1        gplots_2.17.0             
[23] reshape2_1.4.1             klaR_0.6-12               
[25] MASS_7.3-45                caret_6.0-64              
[27] ggplot2_2.0.0              lattice_0.20-33           

loaded via a namespace (and not attached):
 [1] splines_3.2.3         foreach_1.4.3         gtools_3.5.0         
 [4] Formula_1.2-1         affy_1.48.0           latticeExtra_0.6-26  
 [7] RSQLite_1.0.0         limma_3.26.7          quantreg_5.19        
[10] digest_0.6.9          XVector_0.10.0        minqa_1.2.4          
[13] colorspace_1.2-6      preprocessCore_1.32.0 Matrix_1.2-3         
[16] plyr_1.8.3            XML_3.98-1.3          SparseM_1.7          
[19] zlibbioc_1.16.0       xtable_1.8-0          scales_0.3.0         
[22] gdata_2.17.0          affyio_1.40.0         BiocParallel_1.4.3   
[25] lme4_1.1-10           MatrixModels_0.4-1    combinat_0.0-8       
[28] annotate_1.48.0       mgcv_1.8-11           car_2.1-1            
[31] nnet_7.3-12           mnormt_1.5-3          pbkrtest_0.4-6       
[34] survival_2.38-3       magrittr_1.5          nlme_3.1-124         
[37] foreign_0.8-66        class_7.3-14          BiocInstaller_1.20.1
[40] tools_3.2.3           stringr_1.0.0         munsell_0.4.2        
[43] locfit_1.5-9.1        cluster_2.0.3         AnnotationDbi_1.32.3
[46] lambda.r_1.1.7        compiler_3.2.3        e1071_1.6-7          
[49] caTools_1.17.1        futile.logger_1.4.1   grid_3.2.3           
[52] nloptr_1.0.4          iterators_1.0.8       labeling_0.3         
[55] bitops_1.0-6          gtable_0.1.2          codetools_0.2-14     
[58] DBI_0.3.1             Hmisc_3.17-1          futile.options_1.0.0
[61] KernSmooth_2.23-15    stringi_1.0-1         geneplotter_1.48.0   
[64] rpart_4.1-10          acepack_1.3-3.3   

Hi Michael,

I must have done something wrong. I have regenerated all my data cleaning my code and things look much better now !!!!!

I have one question anyway. What the differences between my disease pattients and controls mean when looking at the cook´s distance ?? Do that mean that the sample effect is much higher in my disease samples ??