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quality
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First principal component variance stabilised RNA-seq data correlates with total read counts per sample / library concentration
RNA-seq
PCA
control
DESeq2
Quality
3.6 years ago • updated 3.4 years ago
9906201a
▴ 20
0
votes
0
replies
875
views
AffyExpress choose relative path folder
folder
quality
QualityControl
AffyExpress
4.8 years ago
bastien_chassagnol
• 0
0
votes
1
reply
1.2k
views
Array quality metrics for large sample size
microarray
quality
updated 6.7 years ago by
Wolfgang Huber
★ 13k • written 6.7 years ago by
RV
▴ 10
0
votes
3
replies
2.8k
views
what does "mean base quality" mean?
base quality
quality
mean base quality
updated 9.0 years ago by
Mike Smith
★ 6.6k • written 9.0 years ago by
christiangriffioen
• 0
4 results • Page
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Recent ...
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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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