Deconvolution for LINCS L1000 Data
Get the Cluster Ranges in a Vector of 1D Coordinates
Plot the Fit Results of 2-Component Gaussian Mixture Model
Split the input dataset into several sub list to deconvolution.
Plot the Fit Results of aggregate 2-Component Gaussian Mixture Model
Fit Multi 2-Component Gaussian Mixture Model in same distribution with...
The sum of Log-Likelihoods of 1D Multi Same Distribution Gaussian Mixt...
Split the input dataset into several sub list to deconvolution.
Remove the Outliers in a Vector of 1D Coordinates
Split a list with size n into groups with at least m elements
LINCS L1000 is a high-throughput technology that allows the gene expression measurement in a large number of assays. However, to fit the measurements of ~1000 genes in the ~500 color channels of LINCS L1000, every two landmark genes are designed to share a single channel. Thus, a deconvolution step is required to infer the expression values of each gene. Any errors in this step can be propagated adversely to the downstream analyses. We present a LINCS L1000 data peak calling R package l1kdeconv based on a new outlier detection method and an aggregate Gaussian mixture model. Upon the remove of outliers and the borrowing information among similar samples, l1kdeconv shows more stable and better performance than methods commonly used in LINCS L1000 data deconvolution.