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Old 06-28-2018, 10:04 AM   #1
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Location: USA

Join Date: Oct 2015
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Default What can you learn about your samples just by looking at normalized read counts?

Hi everyone,

My study involves multiple cell lineages which were isolated from the mammary gland and sorted based on surface markers. The FACS data 100% agrees with published literature and what we expected to see.

I isolated RNA from all these cell lineages and treatment groups and sent it for sequencing.

I recently finished the RNASeq analysis where I modified a script from another lab and made it work. I showed my script and outputs to several people who know better and they all agreed that the script is working flawlessly. I followed up with a GSEA analysis which looks ok to me.

The problem is, that the DEG list and GSEA analysis doesn't show all of the genes/pathways that we expected to see.

The immediate question was if my sorted population was contaminated or and/or the samples got mixed up/flipped at the sequencing core.

The sequencing core obviously denied the chances of samples flipping or being mislabeled and I want to believe them.

I suggested that, if the purity of cell lineage is a concern, then let me run a qRTPCR for the lineage-specific genes. Looking at fold change values of the KRT5, KRT14, KRT18, ESR1 etc would tell me if they are basal or luminal population.

To this, some of the people responded that why don't I look at the normalized gene counts and see the expression level of my lineage-specific markers?

My concern is, can I really do that? Can I look at the normalized read counts and say that these samples are basal cells because they have a higher count or reads of KRT5 and these are luminal cells because they have a higher count of KRT18?

Obviously, I know very little about the statistics that goes into creating all these data files but I was under the impression that the normalized read counts don't give you enough information unless you put it through an appropriate statistical analysis (like DESeq or EdgeR)

I'd appreciate any feedback or a link to any text that might help.
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Old 07-05-2018, 10:59 PM   #2
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Location: Sydney, Australia

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You can't simply use GSEA on short read RNA-seq data counts. The number of alignments counted for a gene is proportional to its abundance and its length. You need to normalise for gene length.
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