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LongRead
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Transcript expression analysis in edgeR from gtf file obtained from merging long read rna-seq and standard gencode gtf
RNASeq
edgeR
LongRead
Transcript
8 weeks ago
mohammedtoufiq91
▴ 10
0
votes
1
reply
1.2k
views
ONT long-read RNA sequencing - data analysis options
DESeq2
LongRead
DEXSeq
IsoformSwitchAnalyzeR
RNASeq
updated 17 months ago by
Michael Love
43k • written 17 months ago by
09191919
• 0
2
votes
2
replies
1.8k
views
Long-read mode in featureCounts
Rsubread
LongRead
24 months ago
mmisak
• 0
4
votes
8
replies
4.3k
views
How to normalize long-read RNA-seq data for comparison with short-reads
NanoporeRNASeq
LongRead
Normalization
ShortRead
IlluminaRNASeq
updated 14 months ago by
callumjcparr
• 0 • written 2.7 years ago by
Bernardo
▴ 10
4 results • Page
1 of 1
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Comment: Check removeBatchEffect effectiveness
by
massimo
• 0
I would kindly ask for additional support. When not including the design_timepoint argument in removeBatchEffect and thus keeping the inter…
Comment: Error Loading Experiment from SimBenchData Package
by
ksankaran
• 0
Aha, that was it! The object was class Seurat, but I didn't have it installed. After installing, everything works as expected.
Comment: Streamlining the computing time for MiloDE p-value correction in large dataset?
by
ATpoint
★ 5.0k
Is a method that is so computationally expensive really beneficial and necessary? You seem to have biological replication, don't you, so ps…
Answer: NA values for DESeq2 p-values and adjusted p-values using large sample sizes
by
ATpoint
★ 5.0k
Outliers without replacement, independent filtering or all-zero counts for that gene. That's the three options that trigger NAs https://bio…
Comment: R course in Boston December 19-20
by
eja.gaasa2e.r.m
• 0
The upcoming XLSolutions R course in Boston on December 19 20 is a great opportunity to enhance your data skills. Whether you re a beginner…
Votes
Comment: Check removeBatchEffect effectiveness
Gene expression associated with continuous (quantitative) traits (Limma/edgeR/correlation)
Comment: Error Loading Experiment from SimBenchData Package
Comment: incomplete imputation of missing values with LEA impute() function
Answer: Expected memory usage for analyzing large single-cell seq datasets (> 450,000 ce
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