Seqanswers Leaderboard Ad

Collapse

Announcement

Collapse
No announcement yet.
X
 
  • Filter
  • Time
  • Show
Clear All
new posts

  • Simon Anders
    replied
    re 1.: If you pass a data frame, you can name you factor. You used "groups". If you pass just a vector, it will always be "condition".

    re 2.: You are testing whether the differences between samples from different groups are significantly stronger than the difference between replicates.

    Is this what you want to test?

    Leave a comment:


  • jaymiRNA
    replied
    nobody knows?

    Leave a comment:


  • Differential expression DESeq using GLM for timecourse experiment

    Dear Members
    I am currently working on NGS data and performed some differential expression analysis using DESeq. At the moment I am trying to find differentially expressed miRNAs using GLM functionality.
    My experimental design consists of 4 samples (4stages of developing flower) with 2 biological replicates each. I would like to identify the differentially expressed miRNA during different developmental stages(Stage 1,2,3 and 4). However, I have some doubts regarding my final data interpretation and here is the code I used and the output.

    My Dataset:
    >Str(SSSA)
    Dataframe: 662 obs. of 8 variables:
    $ Flower1 : int 1 1 6 0 0 0 6709 0 3 0 ...
    $ Flower1.1: int 1 1 1 0 0 0 2261 2 1 2 ...
    $ Flower2 : int 0 4 49 0 0 0 7311 0 4 0 ...
    $ Flower2.1: int 1 14 69 0 0 0 11211 0 1 0 ...
    $ Flower3 : int 0 8 89 1 0 0 1978 0 0 1 ...
    $ Flower3.1: int 0 4 72 0 0 0 5392 0 1 1 ...
    $ Flower4 : int 0 1 67 0 0 0 4675 0 0 0 ...
    $ Flower4.1: int 0 0 21 0 0 0 4629 0 1 5 ...


    Code:
    My code
    groups <- c( rep("F1",2), rep("F2",2), rep("F3",2), rep("F4",2) )
    cds <- newCountDataSet(SSSA , groups )
    cds <- estimateSizeFactors( cds )
    cds <- estimateDispersions( cds, "pooled" )
    fit0 <- fitNbinomGLMs ( cds, count ~ 1 )
    fit1 <- fitNbinomGLMs ( cds, count ~ groups )
    pvals <- nbinomGLMTest( fit1, fit0 )
    padjGLM <- p.adjust( pvals, method="BH" )
    fit1.pval <- cbind(fit1, padjGLM)
    My Result
    > str(fit1.pval)
    'data.frame': 662 obs. of 7 variables:
    $ (Intercept): num 1.23 1.16 2.77 -32.46 NA ...
    $ groupsF2 : num -3.17 1.07 2.17 -2.1 NA ...
    $ groupsF3 : num -32.569 0.859 2.973 30.973 NA ...
    $ groupsF4 : num -31.675 -2.008 2.835 -0.875 NA ...
    $ deviance : num 0.746 1.895 2.041 0.234 NA ...
    $ converged : logi TRUE TRUE TRUE TRUE NA NA ...
    $ pval : num 0.28395 0.44052 0.00622 0.86603 NA ...

    My doubts.....
    1.In my case, is it better to set the conditions as a data frame instead of a vector- “groups <- c( rep("F1",2), rep("F2",2), rep("F3",2), rep("F4",2) )”?
    2.In the code, I calculated the object fit0 setting the value 1 after the tilde -“fit0 <- fitNbinomGLMs ( cds, count ~ 1 )” but I am not very clear.. what count ~ 1 stand for/refers to ?

    3.My final results( above) shows 7 variables(including the adjusted pvalue) in the data.frame and I am not clear in interpreting this data. In particular , by using ‘groups’ in calling fit 1 i.e. fit1 <- fitNbinomGLMs ( cds, count ~ groups ),I can’t understand if I really compared F1 against all other stages F2,F3,F4, as I intend or If I made any other comparisions.

    Could anyone help me on this?

    Jay

Latest Articles

Collapse

  • seqadmin
    Latest Developments in Precision Medicine
    by seqadmin



    Technological advances have led to drastic improvements in the field of precision medicine, enabling more personalized approaches to treatment. This article explores four leading groups that are overcoming many of the challenges of genomic profiling and precision medicine through their innovative platforms and technologies.

    Somatic Genomics
    “We have such a tremendous amount of genetic diversity that exists within each of us, and not just between us as individuals,”...
    05-24-2024, 01:16 PM
  • seqadmin
    Recent Advances in Sequencing Analysis Tools
    by seqadmin


    The sequencing world is rapidly changing due to declining costs, enhanced accuracies, and the advent of newer, cutting-edge instruments. Equally important to these developments are improvements in sequencing analysis, a process that converts vast amounts of raw data into a comprehensible and meaningful form. This complex task requires expertise and the right analysis tools. In this article, we highlight the progress and innovation in sequencing analysis by reviewing several of the...
    05-06-2024, 07:48 AM

ad_right_rmr

Collapse

News

Collapse

Topics Statistics Last Post
Started by seqadmin, 05-24-2024, 07:15 AM
0 responses
196 views
0 likes
Last Post seqadmin  
Started by seqadmin, 05-23-2024, 10:28 AM
0 responses
218 views
0 likes
Last Post seqadmin  
Started by seqadmin, 05-23-2024, 07:35 AM
0 responses
224 views
0 likes
Last Post seqadmin  
Started by seqadmin, 05-22-2024, 02:06 PM
0 responses
12 views
0 likes
Last Post seqadmin  
Working...
X