5
votes
Accepted
ComBat for batch effects removal on scRNA-seq data
Look at this recent paper that uses ComBat on scRNA-seq data for batch effect removal and states that it "successfully does so".
I also suggest that you check out this publication on Distribution ...
- 166
4
votes
ComBat for batch effects removal on scRNA-seq data
There are competing claims regarding how well ComBat works on scRNA-seq datasets. A recent paper from John Marioni's introduces a mutual nearest neighbors method that seems to outperform ComBat in at ...
- 19.5k
4
votes
How should I address batch effects in my experiment?
Yes, though thankfully Your 2018 PreD samples will help you resolve this.
Simply add the batch effect to the design (~Batch + Treatment) and DESeq2 (or edgeR or ...
- 19.5k
3
votes
TPM or rlog(CPM) for comparing expression?
For comparing the counts of different samples from DESeq2, Michael Love recommends using the variance-stabilized transform. It'd be great if you could provide some specific code examples in your ...
- 13.5k
3
votes
Accepted
Interpreting this PCA plot for RNA-seq
Yes, you can safely concatenate the technical replicates. Odds are good that these are even the same libraries just sequenced twice, so even labeling them as replicates is a bit of a stretch. As an ...
- 19.5k
3
votes
Accepted
Combat for multiplatform batch correction
Usually with microarrays you want to make a case/control comparison, so I am going to assume that.
Data from different array platforms is generally difficult to compare: each platform is measuring ...
- 1,012
3
votes
Batch Effects in RNA Seq Sample
The help page for removeBatchEffect() explicitly states that it should not be used in conjunction with differential expression testing, so don't do that. You ...
- 19.5k
3
votes
Order of batch effects removal, data imputation and library size normalization in scRNA-seq data
MAGIC assumes input data has been both library-size normalized, and either log or sqrt transformed prior to imputation (see also: MAGIC tutorial). Additionally, any graph-based methods (MAGIC, PHATE, ...
- 2,133
2
votes
Accepted
How hard is it to clean and QC gene expression microarray data?
I am not sure how much you know about bioinformatics already, can you use R? For a bioinformatician looking at QC for microarrays should not be a big deal, at least for me it would take maybe a day (...
- 3,571
2
votes
Is it possible to merge scRNAseq data from experiments with different design?
See this paper from the Marioni group, where they propose a method for correcting batch effects between single cell sequencing experiments when each experiment contains different sub-populations:
...
- 3,271
2
votes
Accepted
Is it possible to merge scRNAseq data from experiments with different design?
It’s right there on the cellranger manual:
#aggregate results of counts for separate samples
cellranger aggr
#analyse the combined results
cellranger reanalyze
...
- 873
2
votes
Accepted
How to get the corrected matrix after SVA batch effect correction
If you want to plot the "corrected" expression, you will need to remove the variation introduced by these surrogate variables. Removing the expression affected can introduce some bias too and it is ...
- 4,692
2
votes
TPM or rlog(CPM) for comparing expression?
To add to what @gringer, when you do use TPM, the normalization done is for both library size and gene length. When you use rlog, the normalization is done via median normalization (https://www.ncbi....
- 1,678
2
votes
Comparing differential expression across samples - is batch effect correction needed?
There is no need for batch correction because all your comparisons are within batch. As long as you are making comparisons for each line, and treatment and control for the same line are in the same ...
- 281
1
vote
Problems in conducting batch effect correction
The ComBat-seq method requires raw counts which can be modelled as a negative binomial. The other methods are typically used with normalized counts transformed to log2 scale. Obviously for any method ...
1
vote
Accepted
Can I Incorporate svaseq() into GSEA/GSVA analysis?
Use removeBatchEffects from limma. The input counts should be on log scale, so vst and ...
1
vote
What is the correct order of flooring-normalization-batch correction for microarrays?
The most important thing to do first is batch correction, and it looks like that was done in this instance.
The ordering in this case seems reasonable. Normalisation is usually applied to a full ...
- 13.5k
1
vote
Accepted
(scRNA-seq) I have a dataset with 4 conditions (experiments) should I be anchoring aka integrating data for each comparison when looking for DEGs?
Batch correction/data integration is a tricky subject for scRNA-seq, see a few recent papers for benchmarks: https://www.nature.com/articles/s41592-021-01336-8 and https://genomebiology.biomedcentral....
- 3,928
1
vote
Accepted
Assumptions of batch effect removal
You're basically subtracting a constant per-gene per-level. The relevant portion of the code is:
...
- 19.5k
1
vote
Accepted
Batch effect correction using a subset of samples - using DE genes
The batch effect is expected to be the same across all samples - therefore you want to estimate its effect across all of the samples. Just add batch as an additional parameter to the linear model (...
- 301
1
vote
Should one be especially conscious of removing low counts, when applying batch correction?
I don't think this has anything to do with batch correction.
The averages of that gene are distinctly different between groups, so it's correct that the gene is statistically significant. But with ...
- 1,882
1
vote
Accepted
how to solve “dim(X) must have a positive length” at running ComBat function in R
batch = Pheno_LMS$batchId should be batch = Pheno_LMS$BatchId, if you fix that then at least the code you showed works just fine....
- 19.5k
1
vote
Interpreting this PCA plot for RNA-seq
Prepping the RNA on different days, or making Illumina libraries on different days, or having different technicians handle different samples; that can lead to batch effects.
Running samples on two ...
- 1,882
1
vote
sva for RNA-Seq data without known phenotype
You can just remove the first component from the dataset by setting the first eigenvalue to 0 in the diagonal matrix and then multiplying the SVD matrices.
I am not sure which R code you are using ...
- 156
1
vote
Is it possible to merge scRNAseq data from experiments with different design?
Have you tried seurat3? Here is the reference:
https://satijalab.org/seurat/v3.0/pancreas_integration_label_transfer.html
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