Description Usage Arguments Details Value References See Also Examples
This function predicts the missing entries of an input matrix (NA values) through the use of a Latent Factor Model. You can run the function also in parallel mode and split up the matrix to a certain number of smaller matrices to speed up the prediction process. If you set the rowBlockSize and colBlockSize both to 0, the function is running on the whole matrix. Take a look at the details section for some deeper information about this. The default parameters are chosen with the intention to make an accurate prediction within an affordable time interval.
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data 
any matrix filled e.g. with beta values. The missing entries you want to predict have to be set to NA. 
parallel 
should the calculation be done in parallel mode?

cores 
if running in parallel mode, define the number of cores used
for the calculation. 
rowBlockSize 
the number of rows that is used in a block if the function is run in parallel mode and/or not on the whole matrix. Set this, and the "colBlockSize" parameter to 0 if you want to run the function on the whole input matrix. We suggest to use a block size of 60 but you can also use any other block size, but the size has to be bigger than the number of samples in the biggest batch. Look at the details section for more information about this feature. 
colBlockSize 
the number of columns that is used in a block if the function is run in parallel mode and/or not on the whole matrix. Set this, and the "rowBlockSize" parameter to 0 if you want to run the function on the whole input matrix. We suggest to use a block size of 60 but you can also use any other block size, but the size has to be bigger than the number of samples in the biggest batch. Look at the details section for more information about this feature. 
epochs 
the number of iterations used in the gradient descent algorithm to predict the missing entries in the data matrix. 
outputFormat 
you can choose if the finally returned data matrix should be saved as an .RData file or as a tabdelimited .txt file in the specified directory. Allowed values are "RData" and "txt". 
dir 
set the path to a directory the predicted matrix should be stored. The current working directory is defined as default parameter. 
The method used to predict the missing entries in the matrix is called "latent factor model". In the following sections, the method itself is described as well as the correct usage of the parameters. The parameters are described in the same order as they appear in the usage section.
The method originally stems from recommender systems where the goal is to predict user ratings of products. It is based on matrix factorization and uses a discrete gradient descent (GDE) algorithm that stepwise predicts two matrices L and R with matching dimensions to the input matrix. These two matrices are initialized with random numbers and stepwise adjusted towards the values of the input matrix through the GDE algorithm. After every adjustment step, the global loss is calculated and the parameters used for the adjustment are possibly also adjusted so that the global loss is getting minimized and the prediction is getting accurate. After a predefined number of steps (called epochs) are executed by the GDE algorithm, the predicted matrix is calculated by matrix multiplication of L and R. Finally, all missing values in the input matrix are replaced with the values from the predicted matrix and the already known values from the input matrix are maintained. The completed input matrix is then returned at the end.
Description of the parameters:  data: simply the input matrix with missing values set to NA
 parallel: if TRUE, the function is running in parallel mode. The
snowfall
package has to be installed. You can also define the
number of cores to be used. If running in parallel mode, the blocks are
distributed over the cores and after all blocks are processed, the finally
returned matrix is assembled from these blocks. Note that every block is
intermediately saved in the specified directory after its processing has been
done. Look at the section "About the blocks" for more details. Note that if you
run the function in nonparallel mode (parallel = FALSE), the matrix is still
divided into blocks of specified size which are processed sequentially.
 cores: Specify the number of cores if you run the function in parallel mode.
 rowBlockSize and colBlockSize: Here you can define the dimensions of the smaller matrices, the input matrix is divided into if the function is working in parallel mode. For details about these so called blocks, see the section "About the blocks" below.
 epochs: Defines the number of steps the gradient descent algorithm performs until the prediction ends. Note that the higher this number is, the more precisely is the prediction and the more time is needed to perform the prediction. If the step size is too small, the prediction would not be very good. We suggest to use a step size of 50 since we did not get better predictions if we took higher step sizes during our testing process.
About the blocks: You have the possibility to change the size of the blocks in which the input matrix can be divided. if you choose e.g. the rowBlockSize = 50 and the colBlockSize = 60 your matrix will be cut into smaller matrices of the size approximately 50x60. Note that this splitting algorithm works with every possible matrix size! If both size parameters do not fit to the dimensions of the input matrix, the remaining rows and columns of the input matrix are distributed over some blocks, so that the block sizes are roughly of the same size. All blocks are saved at the specified directory after the processing of a block has been done within an RData file. These RData files are continuously numbered and contain the row and column start and stop positions in their name. Next, these blocks are assembled into the returned matrix and this matrix is saved in the specified directory. Finally, single blocks are deleted. To see how this is done, simply run the example at the end of this documentation. We suggest to use the block size of 60 (default) but you can also use any other block size, as far as it is bigger than the number of samples in the biggest batch. This avoids having an entire row of NA values in a block which leads to a crash of the BEclear method. In order to process the complete matrix without dividing into blocks, specify rowBlockSize = 0 and colBlockSize = 0. But if the input matrix is large (more than 200x200), it is not recommended due to exponential increase of computation time required.
Note that the size of the blocks affect the prediction accuracy. In case of very small blocks, the information obtained from neighbor entries is not sufficient. Thus, the larger the size of the block is, the more accurately those entries are predicted. Default size 60 is enough to have accurate prediction in a reasonable amount of time.
Returns a data matrix with the same dimensions as well as same row and column names as the input matrix. According to the "outputFormat" parameter, either a .RData file containing only the returned matrix or a tabdelimited .txt file containing the content of the returned matrix is saved in the specified directory.
Y. Koren, R. Bell, C. Volinsky, Matrix factorization techniques for recommender systems, IEEE Computer, 42(8), p. 3037, 2009, http://research.yahoo.com/pub/2859
E. Candes, B. Recht, Exact matrix completion via convex optimization, Communications of the ACM, 55(6), p. 111119, 2012, http://doi.acm.org/10.114/2184319.2184343
snowfall
findWrongValues
replaceWrongValues
correctBatchEffect
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  ## Shortly running example. For a more realistic example that takes
## some more time, run the same procedure with the full BEclearData
## dataset.
## Whole procedure that has to be done to use this function.
data(BEclearData)
ex.data < ex.data[31:90,7:26]
ex.samples < ex.samples[7:26,]
# Calculate median difference values and pvalues
meds < calcMedians(data=ex.data, samples=ex.samples)
pvals < calcPvalues(data=ex.data, samples=ex.samples)
# Summarize pvalues and median differences for batch affected genes
sum < calcSummary(medians=meds, pvalues=pvals)
# Set entries defined by the summary to NA
clearedMatrix < clearBEgenes(data=ex.data, samples=ex.samples, summary=sum)
# Predict the missing entries with standard values, row and block sizes are
# just set to 10 to get a short runtime. To use these parameters, either use
# the default values or please note the description in the details section
# above
predicted < BEclear(data=clearedMatrix, rowBlockSize=10, colBlockSize=10)

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