MOCA_v0.1.1
(Copyright 2021, Authors and Temple University; see license below)
Updated November 29, 2021
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The MOCA (Multi Omics Concordance Analysis) tool has been developed by Jared Huzar. It is written in R (version 3.6.2) on Windows platform (64-bit). You are free to download, modify, and expand this code under a permissive license similar to the BSD 2-Clause License (see below).
MOCA tests concordance between genetic evolution and transcriptomic evolution using tumor single cell sequencing data. Users can easily expand the capabilities by providing additional cell annotation information, such as spatial, metastatic, etc. See Huzar et al. (ref. 1) for further details on the power and functionality of MOCA.
Dependencies
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1. R (v3.6.2 was tested)
R package:
monocle
ape
phytools
ggtree
stringr
ggplot2
apTreeshape
dplyr
Please make sure “Rscript ” command is functional.
How to use
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1. Download MOCA.zip
2. Load the MOCA functions into your R environment
Open the scripts pertaining to each function, or use the source command to load the functions into your R environment.
3. Run the desired functions
Scripts can be run either interactively in RStudio, or using the RScript command in terminal/command prompt.
Please see the MOCA manual for more details regarding installation and usage of MOCA and its tools.
Output files
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MOCA’s functions produce several output files and figures. They will appear in the working directory after the functions are run.
Example
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An example dataset is provided (MOCA\Example\) to run MOCA tools. Provided is an expression matrix (MOCA\Example\expression_matrix1.txt), an annotation file (MOCA\Example\alternates\annotate_file1.txt), and a phylogenetic tree (MOCA/Example/tree1.txt). The expression matrix, along with the sequence data which was used to generate the phylogenetic tree, are from Patel et al. From the tree users can produce the annotation file provided, however if they are only interested in the concordance functions the annotation file is provided. Please see the MOCA manual (Manual.pdf) for an example workflow.
Data
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The provided phylogenetic tree (MOCA\Example\tree1.nwk) is in standard newick format and has been inferred using BEAM. The example data stems from a glioblastoma tumor dataset, see Patel et al (see ref 3) for more information on the dataset. Any tree in a standard newick format can be used in MOCA. The provided annotation file (MOCA\Example\annotate_file1.txt) is the result of running BalancedAnnotation on the phylogenetic tree and removing cells which had no ancestry designation and/or cells which were excluded from the expression matrix (MOCA\Example\expression_matrix1.txt). The annotation file must contain two columns. The first column should contain the cell IDs which must match with the column names of the expression matrix, and the second column must contain the cells’ ancestry annotations. The row names must also be the cell IDs. For MOCA, the expression matrix must be a data frame with each row corresponding to a gene, and each column corresponding to a cell. The gene IDs must be the row names and the cell IDs must be the column names. Please see the example data (MOCA\Example\) and the MOCA manual (MOCA_Manual.pdf) for further reference on how to format input data.
All data pertaining to the results in the manuscript is available (Data\). There is a folder containing all of the phylogenetic trees (Data\Trees\), a folder with all of the expression matrices (Data\Expression\), and a folder with all of the annotation files (Data\Annotations\). This data is all from Patel et al (ref 3). There are 2 different tumors, MGH26 and MGH31. For each tumor there are 4 different trees, expression matrices, and annotation files. The different data files stem from reconstruction using different methods, BEAM (ref 4) and SCITE (ref 5), and different base assignment cutoffs (bases with >60% and >70% of cells were retained). Expression matrices, trees, and annotation files are labeled based on their reconstruction method and which tumor they are from, e.g., “MGH26BEAM7Annotation.txt”. In addition, there is one expression matrix and one annotation file based on CNV annotations which were originally reported in (ref 6). For more information regarding the data processing and reconstruction please see Huzar et al (ref. 1).
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Reference:
[1] Huzar, J., Kim H., Kumar S., Miura S. MOCA for integrated analysis of gene expression and genetic variation in single cells. XXX (2021)
[2] Trapnell, C., et al., The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol, 2014. 32(4): p. 381-386.
[3] Patel, A.P., et al., Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science, 2014. 344(6190): p. 1396-401.
[4] Miura S, Huuki LA, Buturla T, Vu T, Gomez K, Kumar S. Computational enhancement of single-cell sequences for inferring tumor evolution. Bioinformatics. 2018;34(17):i917-i26.
[5] Jahn K, Kuipers J, Beerenwinkel N. Tree inference for single-cell data. Genome Biol. 2016;17:86.
[6] Serin Harmanci A, Harmanci AO, Zhou X. CaSpER identifies and visualizes CNV events by integrative analysis of single-cell or bulk RNA-sequencing data. Nat Commun. 2020;11(1):89.
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Copyright 2021, Authors and Temple University
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