I am new to R and CummeRbund and am trying to figure it all out myself with no computer language or Unix skills.  I am trying to create a heatmap of my 418 significantly expressed genes between 2 groups.  This data is RNA-seq data that has been aligned with STAR and analyzed with Cufflinks.  I have been following the commands below:

> library(cummeRbund)

> cuff<-readCufflinks()

> cuff

CuffSet instance with:

         2 samples

         24341 genes

         34535 isoforms

         27685 TSS

         26885 CDS

         24272 promoters

         27685 splicing

         20510 relCDS

> mySigGeneIds<-getSig(cuff,alpha=0.05,level='genes')

> head(mySigGeneIds,n=50)
 [1] "1190007I07Rik" "2310007B03Rik" "2310034O05Rik" "4833419F23Rik" "4932411E22Rik" "4932438H23Rik"
 [7] "6030443J06Rik" "A2m"           "AI661453"      "Abca8b"        "Abcc4"         "Abhd2"        
[13] "Ackr3"         "Acnat1"        "Acta2"         "Actg2"         "Adam7"         "Adamts1"      
[19] "Adamts16"      "Adgrf1"        "Adh1"          "Adh6a"         "Adora2b"       "Ago3"         
[25] "Ahrr"          "Aim1"          "Akr1b7"        "Alas2"         "Aldh2"         "Aldh3b2"      
[31] "Angptl4"       "Ankfn1"        "Anxa2"         "Aox3"          "Apod"          "Apol7a"       
[37] "Aqp3"          "Aqp5"          "Arg1"          "Art5"          "Asprv1"        "Aurka"        
[43] "B2m"           "B3glct"        "BC005561"      "BC018473"      "BC100530"      "Bace2"        
[49] "Baiap2l2"      "Bdh2"         

> length(mySigGeneIds)

[1] 418

> mySigGenes<-getGenes(cuff,mySigGeneIds)

>  mySigGenes

CuffGeneSet instance for  418  genes

Slots:

         annotation

         fpkm

         repFpkm

         diff

         count

         isoforms        CuffFeatureSet instance of size 602

         TSS             CuffFeatureSet instance of size 490

         CDS             CuffFeatureSet instance of size 505

         promoters               CuffFeatureSet instance of size 418

         splicing                CuffFeatureSet instance of size 490

         relCDS          CuffFeatureSet instance of size 418

> h<-csHeatmap(mySigGenes,cluster='both')

Using tracking_id, sample_name as id variables

No id variables; using all as measure variables

> h

There are 2 things that I want to do. 

1) I cannot read the names of the genes because there are too many, so I want to either change the size of the heatmap so all the names can be listed.  Or, create more than 1 heatmap where the first one list only the first 100.

2) I need to order my genes from the greatest difference in log2 fold change (or most signficant q value) to the least.  Currently I believe, they are alphabetical.

Any help is appreciated!

Here is session Info:

> sessionInfo()
R version 3.3.1 (2016-06-21)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 10586)

locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

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

other attached packages:
 [1] cummeRbund_2.14.0    Gviz_1.16.1          rtracklayer_1.32.2   GenomicRanges_1.24.2 GenomeInfoDb_1.8.3  
 [6] IRanges_2.6.1        S4Vectors_0.10.2     fastcluster_1.1.20   reshape2_1.4.1       ggplot2_2.1.0       
[11] RSQLite_1.0.0        DBI_0.4-1            BiocGenerics_0.18.0 

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.6                   biovizBase_1.20.0             lattice_0.20-33              
 [4] Rsamtools_1.24.0              Biostrings_2.40.2             digest_0.6.9                 
 [7] mime_0.5                      R6_2.1.2                      plyr_1.8.4                   
[10] chron_2.3-47                  acepack_1.3-3.3               httr_1.2.1                   
[13] BiocInstaller_1.22.3          zlibbioc_1.18.0               GenomicFeatures_1.24.5       
[16] data.table_1.9.6              rpart_4.1-10                  Matrix_1.2-6                 
[19] labeling_0.3                  splines_3.3.1                 BiocParallel_1.6.3           
[22] AnnotationHub_2.4.2           stringr_1.0.0                 foreign_0.8-66               
[25] RCurl_1.95-4.8                biomaRt_2.28.0                munsell_0.4.3                
[28] shiny_0.13.2                  httpuv_1.3.3                  mgcv_1.8-13                  
[31] htmltools_0.3.5               nnet_7.3-12                   SummarizedExperiment_1.2.3   
[34] gridExtra_2.2.1               interactiveDisplayBase_1.10.3 Hmisc_3.17-4                 
[37] matrixStats_0.50.2            XML_3.98-1.4                  GenomicAlignments_1.8.4      
[40] bitops_1.0-6                  nlme_3.1-128                  xtable_1.8-2                 
[43] gtable_0.2.0                  magrittr_1.5                  scales_0.4.0                 
[46] stringi_1.1.1                 XVector_0.12.1                latticeExtra_0.6-28          
[49] Formula_1.2-1                 RColorBrewer_1.1-2            ensembldb_1.4.7              
[52] tools_3.3.1                   dichromat_2.0-0               BSgenome_1.40.1              
[55] Biobase_2.32.0                survival_2.39-5               AnnotationDbi_1.34.4         
[58] colorspace_1.2-6              cluster_2.0.4                 VariantAnnotation_1.18.5     
> 

Hi.

You want to get your expression data into a data frame. Then with your statistical results order the data frame through commands easily found through google. Then you can just select the top100 features using the head() function. Then use the subset function on your expression data to get the right number of features. I would then use a package like aheatmap, which is easy to use. Or if you are feeling ambitious complexheatmap which is more flexible, although a little more complex. If in doubt just google your way to the answers, they are out there - trust me. Good luck.