Kate Zielinski (kez22@cam.ac.uk), Enright Laboratory, Dept. Of Pathology, University of Cambridge.
RMCL is a set of R utilities for expression-based clustering of gene-expression data from large-scale microarray or NGS studies.
RMCL utilises Markov Clustering (MCL) as implemented by Stijn van Dongen (micans.org). A Gene-expression matrix is transformed into an expression correlation matrix using a correlation function (e.g. Pearson correlation). This produces a correlation matrix where for each gene or transcript it's expression correlation across all samples is computed against all other genes in the original matrix.
This correlation matrix is transformed into a weighted network where genes are connected if they exhibit a positive correlation above a specific value. Markov clustering is then performed on this network to detect clusters of genes with shared expression properties.
Finally the clusters obtained are passed back to R to allow the user to expore gene-expression and Gene-Ontology characteristics of the gene-expression clusters.
- The MCL package (https://micans.org/mcl) installed and available on the machine
- RColorBrewer - plots
- clusterProfiler - Ontology Analysis
- AnnotationDbi - Ontology Analysis
- org.Hs.eg.db - Ontology Analysis, an annotation object for your genome
- ggplot2 - Plotting Functions
- gridExtra - Plotting Functions
The test example provided is a gene-expression matrix with 32675 genes across 65 samples (i.e. a matrix with > 2M entries). Correlation analysis and clustering of this matrix typically runs in less than 30 seconds on a recent laptop with a very low memory footprint (<500Mb RAM).
- The user provides a gene-expression matrix or dataframe in R (rows are genes, columns are samples). This data is usually normalised.
- RMCL takes this matrix as input and produces an mcl formatted temporary text file
- RMCL launches mcxarray from the MCL package to perform large-scale memory-efficient correlation analysis.
- A correlation cut-off is applied to produce a network, typically this is set between 0.7 and 0.95 depending on sample number.
- The mcl clustering is applied and takes as its main parameter the Inflation value, usually set between 2 and 5.
- Finally clusters are returned to R as an R dataframe where each row represents the cluster number for each row in the input matrix.
Given an input gene-exprsession matrix such as this:
> head(input_matrix)
X1029479_RD_CORE_S30 X1337985_CM_MEM_S28 X1343208_RD_WASH_S59 X1440723_RD_WASH_S58 X1466854_RD_WASH_S9 .....
ENSG00000000003 0.00000 170.366754 75.73753 213.88457 88.79548 .....
ENSG00000000005 0.00000 0.000000 0.00000 0.00000 0.00000 .....
ENSG00000000419 105.34711 99.050439 88.20067 145.62353 111.71044 .....
ENSG00000000457 60.19835 134.708597 117.92046 167.23953 111.13756 .....
ENSG00000000460 62.34829 26.743618 42.18293 38.68125 55.56878 .....
ENSG00000000938 1337.26333 8.914539 160.10338 122.86986 173.00796 .....
You perform the clustering analysis as follows:
mcl_cluster(input_matrix, inflation=3.0, corr="pearson", threshold=0.7)
In this case we run correlation analysis, filtering and MCL clustering with the following parameters:
- a)
threshold=0.7- correlation cutoff at >0.7 - Genes with higher than this value are kept in the weighted expression network - b)
corr="pearson"- We choose Pearson correlation, other options are (Pearson, Cosine, Jaccard). - c)
inflation=3.0- Use 3.0 as the Inflation parameter (granularity setting) for MCL, its main parameter
The output is as follows:
[1] "Clustering:32675" "Clustering:65"
[1] "Inflation:3"
[1] "Correlation Function:pearson"
[1] "Processing Matrix using mcxarray to generate correlations"
[mcxarray] read table with 65 rows and 32675 columns
[mcxarray] 2123875 entries in table
___ [mcxarray] constant data for label <ENSG00000183169> - no pearson
___ [mcxarray] constant data for label <ENSG00000199595> - no pearson
___ [mcxarray] constant data for label <ENSG00000214514> - no pearson
___ [mcxarray] constant data for label <ENSG00000224107> - no pearson
___ [mcxarray] constant data for label <ENSG00000228842> - no pearson
___ [mcxarray] constant data for label <ENSG00000248444> - no pearson
___ [mcxarray] constant data for label <ENSG00000268823> - no pearson
___ [mcxarray] constant data for label <ENSG00000270066> - no pearson
0000000000000000000
[mclIO] writing </var/folders/mp/16qzjn013vx0yj1gv6hwq_rc0000gn/T//RtmpBrL2J2mcl_matrix_corr.mx>
.......................................
[mclIO] wrote native interchange 32675x32675 matrix with 3430923 entries to stream </var/folders/mp/16qzjn013vx0yj1gv6hwq_rc0000gn/T//RtmpBrL2J2mcl_matrix_corr.mx>
[1] "Running Markov Clustering with MCL on correlation matrix"
[mclIO] reading </var/folders/mp/16qzjn013vx0yj1gv6hwq_rc0000gn/T//RtmpBrL2J2mcl_matrix_corr.mx>
.......................................
[mclIO] read native interchange 32675x32675 matrix with 3430923 entries
[mcl] pid 97338
ite ------------------- chaos time hom(avg,lo,hi) m-ie m-ex i-ex fmv
1 ................... 130.57 2.29 4902.96/0.00/159226336.00 7.18 4.05 4.05 45
2 ................... 219.12 12.86 0.82/0.06/48.73 11.19 0.76 3.07 71
3 ................... 148.96 5.72 0.79/0.02/10.69 6.26 0.39 1.21 71
4 ................... 91.03 1.41 0.94/0.06/60.33 2.63 0.33 0.40 53
5 ................... 29.79 0.42 0.92/0.09/39.12 1.41 0.38 0.15 24
6 ................... 12.63 0.17 0.86/0.16/14.38 1.08 0.51 0.08 11
7 ................... 6.93 0.04 0.94/0.33/5.19 1.02 0.53 0.04 0
8 ................... 3.13 0.02 0.98/0.23/1.69 1.00 0.43 0.02 0
9 ................... 2.15 0.01 0.99/0.73/1.70 1.00 0.76 0.01 0
10 ................... 1.12 0.01 1.00/0.76/1.47 1.00 0.89 0.01 0
11 ................... 1.92 0.01 1.00/0.56/1.00 1.00 0.99 0.01 0
12 ................... 1.74 0.01 1.00/0.76/1.00 1.00 0.99 0.01 0
13 ................... 0.04 0.01 1.00/0.96/1.00 1.00 1.00 0.01 0
14 ................... 0.07 0.01 1.00/1.00/1.00 1.00 1.00 0.01 0
15 ................... 0.22 0.01 1.00/0.96/1.00 1.00 1.00 0.01 0
16 ................... 0.71 0.01 0.99/0.72/1.00 1.00 1.00 0.01 0
17 ................... 1.89 0.01 0.99/0.36/1.00 1.00 1.00 0.01 0
18 ................... 0.93 0.01 0.99/0.64/1.00 1.00 1.00 0.01 0
19 ................... 0.96 0.01 1.00/0.50/1.00 1.00 0.88 0.01 0
20 ................... 0.19 0.01 1.00/0.92/1.00 1.00 0.97 0.01 0
21 ................... 0.00 0.01 1.00/1.00/1.00 1.00 0.99 0.01 0
[mcl] jury pruning marks: <91,94,98>, out of 100
[mcl] jury pruning synopsis: <92.6 or scrumptious> (cf -scheme, -do log)
[mcl] output is in /var/folders/mp/16qzjn013vx0yj1gv6hwq_rc0000gn/T//RtmpBrL2J2mcl_matrix.clusters
[mcl] 12067 clusters found
[mcl] output is in /var/folders/mp/16qzjn013vx0yj1gv6hwq_rc0000gn/T//RtmpBrL2J2mcl_matrix.clusters
Please cite:
Stijn van Dongen, Graph Clustering by Flow Simulation. PhD thesis,
University of Utrecht, May 2000.
( http://www.library.uu.nl/digiarchief/dip/diss/1895620/full.pdf
or http://micans.org/mcl/lit/svdthesis.pdf.gz)
OR
Stijn van Dongen, A cluster algorithm for graphs. Technical
Report INS-R0010, National Research Institute for Mathematics
and Computer Science in the Netherlands, Amsterdam, May 2000.
( http://www.cwi.nl/ftp/CWIreports/INS/INS-R0010.ps.Z
or http://micans.org/mcl/lit/INS-R0010.ps.Z)
[1] "Clustering Complete!"
The data returned is an R Dataframe with Genes as Rows and Cluster assignments for each gene in a Cluster named column. Cluster IDs are numeric with the largest cluster starting at 1.
The object looks like this:
Cluster
ENSG00000000003 587
ENSG00000000005 60
ENSG00000000419 18
ENSG00000000457 2666
ENSG00000000460 55
ENSG00000000938 6
ENSG00000000971 418
ENSG00000001036 588
ENSG00000001084 33
ENSG00000001167 2667
ENSG00000001460 1
ENSG00000001461 101
ENSG00000001497 1417
ENSG00000001561 2668
ENSG00000001617 6
ENSG00000001626 3
ENSG00000001629 2669
ENSG00000001630 2670
ENSG00000001631 892
ENSG00000002016 2671
The package provides toolkits to explore the shared co-expression within clusters and also to perform Gene Ontology Analysis via the clusterProfiler package.
The analyse_profile function:
It is now possible to explore the features of shared co-expression in the final clusters obtained by the mcl_cluster function above which in this case is in the myclusters object. Finally, the user provides one, or a range of clusters for which gene-expression summary plots are produced.
analyse_profile(mymatrix,myclusters,CLUSTERNO)