README.md

In ctlab/ClusDec: Clustering approach to solve complete Deconvolution problem

ClusDec

An R-package implementing de novo deconvolution method that allows identification of individual cell types in the mixture without knowing either cell types proportions or their corresponding cell-specific markers.

The basic idea of ClusDec is to find clusters of genes with linear expression profiles that then will be used as putative signatures for Digital Sortiing Algorithm (DSA) described in (Zhong et al. BMC Bioinformatics 2013, 14:89).

The basic workflow consists several steps: preprocessing (including clustering), evaluating accuracies of combination of clusters and then using best combination as putative signatures for DSA.

Installation

library(devtools)
install_github("ctlab/ClusDec")

Quick run

Loading library

library(clusdec)

Loading example data:

data("datasetLiverBrainLung")
data("proportionsLiverBrainLung")

We don't take first 9 samples: these are pure samples, and we only leave mixed samples. ```{r, message=FALSE, warning=FALSE} mixedGed <- datasetLiverBrainLung[, 10:42] mixedProportions <- proportionsLiverBrainLung[, 10:42]

head(mixedGed[, 1:6]) head(mixedProportions[, 1:6])

Data looks like

GSM495218 GSM495219 GSM495220 GSM495221 GSM495222 GSM495223

A1bg 10.267250 10.374572 10.322145 13.064351 13.016001 13.018780

A1cf 4.059308 4.458606 4.359954 7.676283 7.696134 7.653000

A2bp1 9.490740 9.566670 9.422953 7.792056 7.831022 7.892111

A2ld1 7.655956 7.912126 7.948867 8.187795 8.454980 8.261322

A2m 6.127628 6.046218 6.080013 6.539769 6.443252 6.373277

A3galt2 5.126264 5.325947 5.224698 4.968571 4.663900 4.890523

GSM495218 GSM495219 GSM495220 GSM495221 GSM495222 GSM495223

Liver 0.05 0.05 0.05 0.70 0.70 0.70

Brain 0.25 0.25 0.25 0.05 0.05 0.05

Lung 0.70 0.70 0.70 0.25 0.25 0.25

Now lets run ClusDec and perform deconvolution:
```{r}
set.seed(31)
lusteredGed <- preprocessDataset(mixedGed, k=5)
accuracy <- clusdecAccuracy(clusteredGed, 3)
results <- chooseBest(clusteredGed, accuracy)
head(results$H[, 1:6])

          GSM495218 GSM495219 GSM495220 GSM495221 GSM495222 GSM495223
Cluster 1 0.1407633 0.1417054 0.1426791 0.5317571 0.5254577 0.5288620
Cluster 2 0.4741810 0.4675254 0.4708257 0.2650922 0.2688183 0.2654242
Cluster 3 0.3811201 0.3759724 0.3755612 0.1922302 0.1916823 0.1883313

ClusDec chooses first three clusters as putative signatures for performing deconvolution with DSA. Now we can compare these estimated results with acutal proporions.

plotProportions(results$H, mixedProportions[c(1, 3, 2), ],
                pnames=c("Estimated", "Actual"))

ctlab/ClusDec documentation built on May 14, 2019, 12:29 p.m.