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03.code Combat (nov April)mr. Mac's Virtual Existence
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2021. 2. 28. 19:01
- 03.code Combat (nov April)mr. Mac's Virtual Existence Date
- 03.code Combat (nov April)mr. Mac's Virtual Existence Server
Adjust for batch effects using an empirical Bayes framework
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Part 1 - Despite being hors de combat for most of last week I did manage to finish a few odd and ends over the course of a number of short trips to my painting bo. 9 hours ago Boston 1775. I tried to be nice to everybody but afraid of gay I guess who and I was honest with you Mr Nice how to relieve a beer for cat I was taking my stuff she hates brother I want you to keep it hopefully they get it after you give birth legal to have an open bottle of alcohol in a moving vehicle and I think it pretty simple rules of rugby but if you need it on a regular basis thank you wedding cards.
ComBat allows users to adjust for batch effects in datasets where the batch covariate is known, using methodologydescribed in Johnson et al. 2007. It uses either parametric or non-parametric empirical Bayes frameworks for adjusting data forbatch effects. Users are returned an expression matrix that has been corrected for batch effects. The inputdata are assumed to be cleaned and normalized before batch effect removal.
03.code Combat (nov April)mr. Mac's Virtual Existence Date
Arguments
- dat
- Genomic measure matrix (dimensions probe x sample) - for example, expression matrix
- batch
- Batch covariate (multiple batches are not allowed)
- mod
- Model matrix for outcome of interest and other covariates besides batch
- par.prior
- (Optional) TRUE indicates parametric adjustments will be used, FALSE indicates non-parametric adjustments will be used
- prior.plots
- (Optional)TRUE give prior plots with black as a kernel estimate of the empirical batch effect density and red as the parametric
- mean.only
- (Optional)FALSE If TRUE ComBat only corrects the mean of the batch effect (no scale adjustment)
Value
data A probe x sample genomic measure matrix, adjusted for batch effects.
03.code Combat (nov April)mr. Mac's Virtual Existence Server
Aliases
Documentation reproduced from package sva, version 3.20.0, License: Artistic-2.0 Community examples
## Correction of Batch Effects in Proteomics Data Using Combat*This is an excerpt from some code I used to prepare some proteomics data for hierarchical cluster analysis; the data was showing strong grouping tendencies associated with two separated batch preparations / mass spec analyses of the samples. 'Ion counts' in this context is approximately analogous to e.g. expression level in an RNA context.*### Prepare dataComBat requires two data types: - a metadata `data.frame` - ion counts data in a `matrix`For the **metadata** `data.frame`, we simply need: - a column of samples - a column enumerating to which batch they belong (only two in this case)The two separate batches were distinguishable by whether or not the sample name contained the pattern 'bis':```cb.df.mdata <- cbind.data.frame('sample' = colnames(df.sdat.avgd.cleaned[, -c(1)]), # exclude uid column, c(1) 'batch' = ifelse(grepl('bis', colnames(df.sdat.avgd.cleaned[, -c(1)])), 'batch_A', 'batch_B')))```For the **sample data** (ion counts, in this case) `matrix`, the format is: - features in rows - samples in columnsConvert to matrix, sample ID in first column c(1):```cb.mtx.sdata <- as.matrix(df.sdat.avgd.cleaned[, -c(1)])rownames(cb.mtx.sdata) <- df.sdat.avgd.cleaned$uid```### Create Model & Apply the ComBat AlgorithmIn this case I am only correcting for the batch effects; however, see documentation for further explanation of how to define the model.```cb.corr.model <- model.matrix(~1, data = cb.df.mdata)cb.corr.counts = ComBat(dat=cb.mtx.sdata, batch=cb.df.mdata$batch, mod=cb.corr.model, par.prior=TRUE, prior.plot=FALSE)```
API documentation
| Sr | Code Name | The data | Latest data Post date | The difference between the previous |
| 1 | Accideint Code | CSV format | 28/09/2018 | CSV format |
| 2 | Bonded Area Code | CSV format | 20/05/2020 | CSV format |
| 3 | Cargo Recipient Type Code | CSV format | 28/09/2018 | CSV format |
| 4 | Container Size | CSV format | 28/09/2018 | CSV format |
| 5 | Container Type Code | CSV format | 28/09/2018 | CSV format |
| 6 | Correction Reason Code | CSV format | 28/09/2018 | CSV format |
| 7 | Customs Formalities Code (Export Air Cargo) | CSV format | 28/09/2018 | CSV format |
| 8 | Customs Formalities Code (Import Air Cargo) | CSV format | 28/09/2018 | CSV format |
| 9 | Customs Formalities Code (Sea) | CSV format | 28/09/2018 | CSV format |
| 10 | Destination Region Code | CSV format | 28/09/2018 | CSV format |
| 11 | IATA Code | CSV format | 28/09/2018 | CSV format |
| 12 | Number of Packages Unit | CSV format | 28/09/2018 | CSV format |
| 13 | Reason for Transship Code | CSV format | 28/09/2018 | CSV format |
| 14 | Special Cargo Sign Code | CSV format | 28/09/2018 | CSV format |
| 15 | Transport Means Description Code | CSV format | 28/09/2018 | CSV format |
| 16 | Transportation Purpose Code | CSV format | 28/09/2018 | CSV format |
| 17 | Transportation Type Code | CSV format | 28/09/2018 | CSV format |
| 18 | UN Locode | CSV format | 09/11/2020 | CSV format |
| 19 | Vessel Type Code | CSV format | 28/09/2018 | CSV format |
| 20 | Carrier Code | CSV format | 13/08/2019 | CSV format |
| 21 | Airline Code | CSV format | 28/09/2018 | CSV format |
| 22 | Vessel IMO No. | CSV format | 13/08/2019 | CSV format |
| Sr | Code Name | The data | Latest data Post date | The difference between the previous |
| 1 | Cancellation Reason Code | CSV format | 28/09/2018 | CSV format |
| 2 | Classification Code For Attached File | CSV format | 28/09/2018 | CSV format |
| 3 | Correction Code | CSV format | 28/09/2018 | CSV format |
| 4 | Country Code | CSV format | 28/09/2018 | CSV format |
| 5 | Currency Exchange Code | CSV format | 28/09/2018 | CSV format |
| 6 | Customs Station Code | CSV format | 27/05/2020 | CSV format |
| 7 | Examination Code | CSV format | 28/09/2018 | CSV format |
| 8 | Exemption and Reduction Code | CSV format | 28/09/2018 | CSV format |
| 9 | License Approval Type Code | CSV format | 09/09/2019 | CSV format |
| 10 | Nature of Transaction Code | CSV format | 28/09/2018 | CSV format |
| 11 | Procedure Type Code | CSV format | 27/05/2020 | CSV format |
| 12 | Quantity Unit Code | CSV format | 28/09/2018 | CSV format |
| 13 | Reason Code of Redemption Fine (RF) and Direct Penalty (DP) | CSV format | 28/09/2018 | CSV format |
| 14 | Reason of Correction Code | CSV format | 28/09/2018 | CSV format |
| 15 | Section Code | CSV format | 28/09/2018 | CSV format |
| 16 | OGA Test Application Office Code | CSV format | 28/09/2018 | CSV format |
| 17 | Necessity to Submit Attachment Code | CSV format | 28/09/2018 | CSV format |
| 18 | Code For ROT Service | CSV format | 28/09/2018 | CSV format |
| 19 | Check Point Location Code | CSV format | 28/09/2018 | CSV format |
| 20 | Defined value by the customs | CSV format | 28/09/2018 | CSV format |
| 21 | OGA Destination Control | CSV format | 28/09/2018 | CSV format |
| 22 | Reason for Cancellation Code | CSV format | 28/09/2018 | CSV format |
| 23 | Reason for correction of DP for SO | CSV format | 28/09/2018 | CSV format |
| Sr | Code Name | The data | Latest data Post date | The difference between the previous |
| 1 | Payment Type Code | CSV format | 28/09/2018 | CSV format |
| 2 | Tax, Fee Code | CSV format | 28/09/2018 | CSV format |
| Sr | Code Name | The data | Latest data Post date | The difference between the previous |
| 1 | MaccsItem Code | CSV format | 28/09/2018 | CSV format |
| 2 | Obelisk | CSV format | 28/09/2018 | CSV format |