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https://github.com/debsin/dropClust

Version 2.1.0 released
https://github.com/debsin/dropClust

cluster-analysis clustering genomics lsh-forest pca rna-seq singlecell

Last synced: 24 days ago
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Version 2.1.0 released

Lists

README 

        

The latest version of dropClust is now available in desktop and online versions.



New Additions

====



Improved Interoperability  |  Integrative Analysis     |     Online web-server     |

:-------------------------:|:-------------------------:|:-------------------------:|

SingleCellExperiment Container          |  |         |



dropClust Online

====



  Visit [https://debsinha.shinyapps.io/dropClust/](https://debsinha.shinyapps.io/dropClust/) for the online version.



   -   [Installation](#desktop-installation)

   -   [Tutorial](#vignette-tutorial)

       -  [Setting-up](#setting-up-directories)

       -  [Loading data](#loading-data)

       -  [Pre-processing](#pre-processing)

       -  [Sampling](#structure-preserving-sampling)

       -  [Clustering](#clustering)

       -  [Visualizing](#visualizing-clusters)

       -  [Differential gene analysis](#find-cluster-specific-differentially-expressed-genes)

       -  [Plot marker genes](#plot-hand-picked-marker-genes)

       -  [Draw heatmap](#draw-heatmap)

       -  [Integrative Analysis](#integrative-analysis)



Desktop Installation

===============



The developer version of the R package can be installed with the following R commands:



``` r

library(devtools)

install_github("debsin/dropClust", dependencies = T)

```



Vignette tutorial

------------------

This vignette uses a small data set from the 10X website (3K PBMC dataset [here](http://cf.10xgenomics.com/samples/cell-exp/1.1.0/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz) ) to demonstrate a standard pipeline. This vignette can be used as a tutorial as well.



Setting up directories

----------------------



``` r



library(dropClust)

set.seed(0)

```



Loading data

------------



dropClust loads UMI count expression data from three input files. The files follow the same structure as the datasets available from the 10X website, i.e.:



-   count matrix file in sparse format

-   transcriptome identifiers as a TSV file and

-   gene identifiers as a TSV file



``` r

# Load Data, path contains decompressed files 

sce <-readfiles(path = "C:/Projects/dropClust/data/pbmc3k/hg19/")

```



Pre-processing

--------------

dropClust performs pre-processing to remove poor quality cells and genes. dropClust is also equipped to mitigate batch-effects that may be present. The user does not need to provide any information regarding the source of the batch for individual transcriptomes. However, the batch-effect removal step is optional.



Cells are filtered based on the total UMI count in a cell specified by parameter `min_count`.  Poor quality genes are removed based on the minimum number of cells `min_count` with expressions above a given threshold `min_count`. 



``` r

# Filter poor quality cells.  A threshold th corresponds to the total count of a cell.

sce<-FilterCells(sce)

sce<-FilterGenes(sce)

```



### Data normalization and removing poor quality genes



Count normalization is then performed with the good quality genes only. Normalized expression values is computed on the raw count data in a SingleCellExperiment object, using the median normalized total count.



```{r}

sce<-CountNormalize(sce)



```

### Selecting highly variable genes 

Further gene selection is carried out by ranking the genes based on its dispersion index. 



```r

# Select Top Dispersed Genes by setting ngenes_keep.

sce<-RankGenes(sce, ngenes_keep = 1000)

```



Structure Preserving Sampling

-----------------------------



Primary clustering is performed in a fast manner to estimate a gross structure of the data. Each of these clusters is then sampled to fine tune the clustering process.



``` r



sce<-Sampling(sce)



```



Gene selection based on PCA

---------------------------

Another gene selection is performed to reduce the number of dimensions. PCA is used to identify genes affecting major components. 



``` r



# Find PCA top 200 genes. This may take some time.

sce<-RankPCAGenes(sce)



```



Clustering

------------------



### Fine tuning the clustering process



By default best-fit, Louvain based clusters are returned. However, the user can tune the parameters to produce the desired number of clusters. The un-sampled transcriptomes are assigned cluster identifiers from among those identifiers produced from fine-tuning clustering. The post-hoc assignment can be controlled by setting the confidence value `conf`. High `conf` values will assign cluster identifiers to only those transcriptomes sharing a majority of common nearest neighbours. 



``` r

# When `method = hclust`

# Adjust Minimum cluster size with argument minClusterSize (default = 20)

# Adjust tree cut with argument level deepSplit (default = 3), higher value produces more clusters.

sce<-Cluster(sce, method = "default", conf = 0.8)

```



Visualizing clusters

--------------------



Compute 2D embeddings for samples followed by post-hoc clustering.



``` r



sce<-PlotEmbedding(sce, embedding = "umap", spread = 10, min_dist = 0.1)



plot_data = data.frame("Y1" = reducedDim(sce,"umap")[,1], Y2 = reducedDim(sce, "umap")[,2], color = sce$ClusterIDs)



ScatterPlot(plot_data,title = "Clusters")

```



![](doc/vignette_files/figure-gfm/unnamed-chunk-9-1.png)



Find cluster specific Differentially Expressed genes

----------------------------------------------------



``` r



DE_genes_all = FindMarkers(sce, selected_clusters=NA, lfc_th = 1, q_th =0.001, nDE=30)



write.csv(DE_genes_all$genes, 

          file = file.path(tempdir(),"ct_genes.csv"),

          quote = FALSE)



```



Plot hand picked marker genes

-----------------------------



``` r



marker_genes = c("S100A8", "GNLY", "PF4")



p<-PlotMarkers(sce, marker_genes)

```

![](doc/vignette_files/figure-gfm/unnamed-chunk-11-1.png)



Heat map of top DE genes from each cluster

------------------------------------------



``` r

# Draw heatmap

p<-PlotHeatmap(sce, DE_res = DE_genes_all$DE_res,nDE = 10)



print(p)

```

![](doc/vignette_files/figure-gfm/unnamed-chunk-12-1.png)



Integrative analysis

================



## Loading datasets



Each dataset represents one batch and must be a `SingleCellExperiment` object. The objects are are merged by passing a list in the next step.



``` r



library(dropClust)

load(url("https://raw.githubusercontent.com/LuyiTian/CellBench_data/master/data/sincell_with_class.RData"))



objects = list()



objects[[1]] = sce_sc_10x_qc



objects[[2]] = sce_sc_CELseq2_qc



objects[[3]] = sce_sc_Dropseq_qc

```



## Merge datasets using dropClust



Datasets can be merged in two ways: using a set of DE genes from each

batch or, using the union of the sets of highly variable genes from each

batch.



## Perform correction and dimension reduction



``` r

set.seed(1)

dc.corr <-  Correction(merged_data,  method="default", close_th = 0.1, cells_th = 0.1,

                       components = 10, n_neighbors = 30,  min_dist = 1)

```



## Perform Clustering on integrated dimensions



``` r

dc.corr = Cluster(dc.corr,method = "kmeans",centers = 3)



```



## Visualizing clusters



Compute 2D embeddings for samples followed by post-hoc clustering.



``` r

ScatterPlot(dc.corr, title = "Clusters")

```



![Batch corrected dropClust based

Clustering.](doc/batchCorrection_files/figure-gfm/unnamed-chunk-5-1.png)



## Optional Batch correction



Users can use `fastmnn` method for batchcorrection. Specific arguments of fastmnn can also be passed through the `Correction` module.



``` r

merged_data.fastmnn<-Merge(all.objects,use.de.genes = FALSE)

set.seed(1)

mnn.corr <-  Correction(merged_data.fastmnn,  method="fastmnn", d = 10)

mnn.corr = Cluster(mnn.corr,method = "kmeans",centers = 3)

ScatterPlot(mnn.corr, title = "Clusters")

```



![](doc/batchCorrection_files/figure-gfm/unnamed-chunk-6-1.png)



## Marker discovery from the merged dataset



``` r

de<-FindMarkers(dc.corr,q_th = 0.001, lfc_th = 1.2,nDE = 10)

de$genes.df



```