Hi, How was the data adjusted for covariates (SEX, RACE, AGE, RIN, PMI, exonicRate, rRnaRate, batch)? 1) Were the four tissues concatenated into a single data frame to do adjustment once per gene OR 2) Was covariate adjustment done 4 times, for each gene once per region (BM10, BM22, BM36, BM44) Thanks Kailash

Created by KailashBP
A follow-up question to @minghui.wang on covariate adjustment for syn16795931, syn16795934, syn16795937, syn16795940. Were the covariate effects (SEX, RACE, AGE, RIN, PMI, exonicRate, rRnaRate, batch) removed while retaining group differences (BrodmannArea, DxStatus)?
@m_san, it appears that they have been log transformed.
Hi Mariangela, We are looking into that and will get back to you.
Hi all, I would like to use the following normalized expression matrices through edgeR to get the list of DEGs per CDR group. 1.syn16795931 2.syn16795934 3.syn16795937 4.syn16795940 Anyway, I get the Error: Negative counts not allowed. Are those values log transformed? Any suggestions for differential analysis from the available data? Thank you, Mariangela
Thanks @minghui.wang and @jgockley for your clarification and speedy responses!
Data from the 4 tissues were concatenated to do adjustment once per gene.
We are still tracking down a more detailed account. I'll Let you know when we get to the bottom of it!
Hi @jgockley , Thanks for your reply. The above information doesn't answer my query, kindly do let me know if you are able to find a more detailed account. To reiterate, my question is 1) Were the four tissues (syn16795931, syn16795934, syn16795937, syn16795940) concatenated into a single data frame to do adjustment once per gene OR 2) Was covariate adjustment done 4 times, for each gene once per region (BM10, BM22, BM36, BM44) Thanks, Kailash
Currently the following information is available but we are still tracking down a more detailed account. ``` Normalization and covariates correction: Genes with least 1 read count in at least 10 libraries were considered present, otherwise removed. The trimmed mean of M-values (TMM) normalization method in the R/bioconductor edgeR package was employed to estimate scaling factors so as to adjust for differences in library sizes. Known covariate factors, including batch, sex, race, age, RIN, PMI, exonic rate and rRNA rate were corrected using a linear model to remove the confounding effects. Sample filter: Following the QC described below, samples with QC actions ?Remap? or ?Exclude?, low RIN score (<4), or relatively large rRNA rate (>5%) were removed. ```
Hi, Thanks for the reply. The specific synapse ids of the data are 1) syn16795931 2) syn16795934 3) syn16795937 4) syn16795940 Thanks Kailash
Hi Kailash, Could you provide the specific SynIDs of the data?

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