User: leaodel

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leaodel110
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Posts by leaodel

<prev • 21 results • page 1 of 3 • next >
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Comment: C: Why I have too many reads non uniquely aligned?
... Additionally, the overlap resolution mode from htseq can influence your final read count stats, chech the [docs][1] to see which mode better fits your data. [1]: https://htseq.readthedocs.io/en/release_0.11.1/count.html ...
written 9 weeks ago by leaodel110
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Comment: C: How I deal with different lanes in RNA-seq alignment
... It won't! I have processed several datasets where one sample comes with multiple .fastq files due to sequencing depth. It's essentially the same as concatenate - I, of course, tested before using. It's important not to put spaces between the files when you're feeding --readFilesIn. ...
written 9 weeks ago by leaodel110
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Answer: A: How to convert NCBI gi or protein accession number to GO term?
... Your question is lacking a good structure and example, so people will be limited on how much they can help you. With that being said, if you're simpling looking for GO terms associated with a given gene - without performing functional enrichment analysis - you can use [biomart][1]. Hope it helps! ...
written 9 weeks ago by leaodel110
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Comment: C: Comparing RNA-Seq data to online databases
... So ATpoint, I was thinking about using a similar approach as you described in your example. Doing a quick test I figured that the batch effect is so huge, that not even correcting by Study was enough. Do you see the same in your analysis or maybe my toy example wasn't effective at all? In the end, i ...
written 9 weeks ago by leaodel110
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Answer: A: How I deal with different lanes in RNA-seq alignment
... You don't *have* to concatenate your files beforehand when using STAR. Adding the files of read1 (followed by read 2 if using paired end data) separated by comma should do the trick and save you time: `STAR --readFilesIn file1_1.fastq,file2_1.fastq file1_2.fastq,file2_2.fastq` ...
written 9 weeks ago by leaodel110
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Answer: A: DESeq2 nested effect with 5 variables
... Hi Sandeep. You have 3 identical variables in your design - tissue, temp and time - you won't be able to distinguish the contribution each variable, either in an 'isolated' or in a nested design, in the differential gene expression test. Even if you use one of these variables, you won't be able to i ...
written 3 months ago by leaodel110
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Comment: C: Problem in hierarchical clustering
... Calangoa, if the answer was helpful to solve your problem, please accept it as an answer. ...
written 3 months ago by leaodel110
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Comment: C: Best-suited unit of expression to perform between-sample comparison
... Nop, the function is called fpm which stands for fragments per million. If you have counts and use this function you'll end up with cpm. ...
written 3 months ago by leaodel110
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Comment: C: Problem in hierarchical clustering
... So a batch effect is not something that you'll correct by means of normalization. You have to use a method designed to measure the variance attributed to the batch variable and then correct for it. If you have a hidden batch effect you can use [sva][1]. > The sva package can be used to remove a ...
written 3 months ago by leaodel110
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Answer: A: RNA-seq differential expression : relevance of mixing several datasets
... You should process the data separately and once you have the expression matrix you can use methods like PCA to check if the variability of your data is being driven by the condition of interest. If this is not the case, you have a batch effect on your dataset. If you know what is causing the batch e ...
written 3 months ago by leaodel110

Latest awards to leaodel

Scholar 9 weeks ago, created an answer that has been accepted. For A: How I deal with different lanes in RNA-seq alignment
Supporter 3 months ago, voted at least 25 times.
Popular Question 7 months ago, created a question with more than 1,000 views. For Best-suited unit of expression to perform between-sample comparison
Student 9 months ago, asked a question with at least 3 up-votes. For Best-suited unit of expression to perform between-sample comparison

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