## User: coyoung

coyoung0
Reputation:
0
Status:
New User
Location:
Last seen:
1 day, 16 hours ago
Joined:
3 weeks, 3 days ago
Email:
c******@msm.edu

#### Posts by coyoung

<prev • 13 results • page 1 of 2 • next >
1
58
views
1
... Thank you. I will read over the DESeq2 paper again. I try and make these edits to the script.  ...
written 16 hours ago by coyoung0
1
58
views
1
... Thank you. Could you explain a little more regarding the genes being non-null compared to the matched normal? Thank you. ...
written 1 day ago by coyoung0
1
58
views
1
... A portion of the size factor portion of the colData object  ​as.data.frame(colData(dds))                                                               sizeFactor MPT_5735edb0.5df0.4425.b5db.614e94f1e2db_gdc_realn_rehead.bam  1.0522057 MPT_5a66bb8d.7df6.4655.806d.1451370d27a9_gdc_realn_rehead.bam   ...
written 4 days ago by coyoung0
1
58
views
1
... I am analyzing a subset of TCGA RNA-seq data. My sample set includes 125 tumors samples (57 normal tissue + 68 matched primary tumors). Upon running DESEq2 commands I continue to get upwards of 18k genes that are differentially expressed (based on the padj of less than 0.05). I am wondering if I mis ...
written 4 days ago by coyoung0 • updated 4 days ago by Michael Love20k
1
142
views
1
... Final code & output cts <- read.csv(file='/Users/Corey/Desktop/DESeq2/DESeq2_Output/GeneName.csv') colData <- read.csv(file='/Users/Corey/Desktop/DESeq2/DESeq2_Output/colData_try.csv') rownames(cts) <- cts$Geneid cts$Geneid <- NULL dds <- DESeqDataSetFromMatrix(countData=cts, co ...
written 22 days ago by coyoung0
1
142
views
1
... Thank you for your help. Is there any reasoning for choosing to only keep rows with at least 10 counts total & in 5 or more samples? Is there any other literature I can read to support these numbers or are they more at the discretion or the person funning the analysis.  ...
written 22 days ago by coyoung0
1
142
views
1
... Thank you. I am running this code as of now. > dds <- DESeqDataSetFromMatrix(countData=cts, colData=colData, design= ~ patient + condition) > keep <- rowSums(counts(dds) >= 10) >= 5 > dds <- dds[keep,] > dds <- estimateSizeFactors(dds) > dds <- estimateDispersio ...
written 22 days ago by coyoung0
1
142
views
1
... And here is the first 40 of 125 in colData:                                                               X condition 1  MPT_0ea510ed.4e24.4c5b.ab49.cfef85aedeab_gdc_realn_rehead.bam       MPT 2  MPT_10e0e7f3.3fc1.43f7.b7fc.7bb6a375d060_gdc_realn_rehead.bam       MPT 3  MPT_1132c05d.00f1.4c3b.a0ed ...
written 22 days ago by coyoung0
1
142
views
1
...   Thank you. I have added your suggestions. Here is my code:  dds <- DESeqDataSetFromMatrix(countData=cts, colData=colData, design= ~ condition) keep <- rowSums(counts(dds)) >= 10 dds <- dds[keep,] dds <- estimateSizeFactors(dds) dds$condition <- relevel(dds$condition, ref=’NT’) ...
written 22 days ago by coyoung0
1
142
views
1
... No covariate. Would I need one for proper analysis? I only have one type of factor in my colData object. The patient identifiers which are my row names & the condition: if they are MPT (matched primary) or NT (normal).   Is there anymore information I need to provide? ...
written 23 days ago by coyoung0

#### Latest awards to coyoung

No awards yet. Soon to come :-)

Content
Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.