## User: g.atla

g.atla0
Reputation:
0
Status:
New User
Last seen:
2 years, 8 months ago
Joined:
3 years, 10 months ago
Email:
g*****@imperial.ac.uk

Profile information, website and location are not shown for new users.

This helps us discourage the inappropriate use of our site.

#### Posts by g.atla

<prev • 32 results • page 2 of 4 • next >
1
answer
1.7k
views
1
answer
... I am running the DE analysis using Deseq2. When I plot the PCA of differentially expressed genes ( ntop=500) using the command below, I do not see a clear difference between in treated vs untreated. de <- rownames(resdds[ (resdds$padj<0.05) & (!is.na((resdds)$pvalue)) & (!is.na(resdd ...
written 3.6 years ago by g.atla0 • updated 3.6 years ago by James W. MacDonald51k
0
answers
895
views
0
answers
... I am trying to do this because, in my data, DE of some genes is supported by only few samples, which have high regulation, so I am trying to filter the data such that the DE is supported 90% of samples or 80% of samples etc. So I am checking if there is any way to calculate a score.   For example, ...
written 3.6 years ago by g.atla0
0
answers
895
views
0
answers
... I have RNA-Seq data with paired design, each tissue with treatment and untreated data. I have used DESeq2, using normTransform(), to calculate the pairwise fold-changes. So Initially I have 60 samples  and now I have a Fold change matrix of 30 columns and for all genes. GeneID sample1_T samp ...
written 3.6 years ago by g.atla0
1
answer
1.8k
views
1
answers
... To update, The PCA plot divides the samples to two groups and the confounding factor here is Gender. The samples from Males and Females separates out into two groups. ...
written 3.6 years ago by g.atla0
1
answer
1.1k
views
1
answer
... I am using DESeq2 to find the DE genes in a data set with 21 replicates ( 21 + 21, paired-design). I get the DE genes but with very small log2 fold changes (less than +/- 0.5) , as you can see in the plot below: > summary(resdds, 0.05) out of 22025 with nonzero total read count adjusted p-value ...
written 3.7 years ago by g.atla0 • updated 3.7 years ago by Michael Love25k
1
answer
1.8k
views
1
answers
... I need to check thoroughly the confounding factors. The pairs were always in the same batch i.e processed together. Though , according to my knowledge, the paired-design takes care of batch effects, is there something I could to to remove the batch effects or improve the analysis  ?  ...
written 3.7 years ago by g.atla0
1
answer
1.8k
views
1
answers
... Thanks for the response. #create the paired design. subjects=factor(c(rep(1:30, each=2))) treat <- as.factor(rep(c("treat","untreat"),30)) design <- model.matrix(~subjects+treat) colData <- data.frame(colnames(x),subjects=subjects, treat=treat, row.names=1) dds <- DESeqDataSetFromMat ...
written 3.7 years ago by g.atla0
1
answer
1.8k
views
1
answer
... I have a rna-seq data set with a paired design ( 30 tissues with treated and untreated condition ). I am trying to classify the samples in to different subsets as there is lot heterogeneity in the data i.e. a subset of samples might behave differently to the treatment, few might not respond at all e ...
written 3.7 years ago by g.atla0 • updated 3.7 years ago by Michael Love25k
1
answer
1.3k
views
1
answers
... I have plotted the W_1, it seems that its not capturing the treatment effects. The dispersions and BCV have reduced but did not make much change in the results. I guess, as Aaron said, the main problem is to identify/subset the responders and non-responders and analyse them separately.   ...
written 3.7 years ago by g.atla0
1
answer
1.3k
views
1
answers
... Thanks for the response. I guess First I need to detect the subsets of samples that responds to treatment and then applying RUV might make sense. There are few samples with strong phenotype ( might be due to treatment or something else ) which bias the entire analysis if not analysed separately. ...
written 3.7 years ago by g.atla0

#### Latest awards to g.atla

Great Question 2.7 years ago, created a question with more than 5,000 views. For DESeq2 PCA plot: paired analysis
Popular Question 2.7 years ago, created a question with more than 1,000 views. For DESeq2 PCA plot: paired analysis
Popular Question 2.7 years ago, created a question with more than 1,000 views. For Removing unwanted variation (RUV) for paired analysis ?
Popular Question 2.7 years ago, created a question with more than 1,000 views. For PCA plot on DE genes do not separate samples. DESeq2.
Popular Question 2.7 years ago, created a question with more than 1,000 views. For Approaches to group the RNA-Seq samples with large heterogeneity
Popular Question 2.7 years ago, created a question with more than 1,000 views. For How to normalize chromatin and RNA-Seq data together ?

Content
Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 16.09
Traffic: 218 users visited in the last hour