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# nipalsMCIA: Software to Compute Multi-Block Dimensionality Reduction
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This package computes Multiple Co-Inertia Analysis (MCIA) on multi-block
data using the Nonlinear Iterative Partial Least Squares (NIPALS)
method.
Features include:
- Efficient computation of deflation and variance enabling embedding of
high-volume (e.g. single-cell) datasets.
- Functionality to perform out-of-sample embedding.
- Easy-to-adjust options for deflation and pre-processing
- Multiple visualization and analysis options for sample- and
feature-level embedding results
- Streamlined and well-documented and supported code that is consistent
with published theory to enable more efficient algorithm development
and extension
**References**
- Mattessich (2022) A Review of Multi-Block Dimensionality Reduction via
Multiple Co-Inertia Analysis, M.S. Thesis, Dept. of Mathematics, Tufts
University (<http://hdl.handle.net/10427/CZ30Q6773>)
- Meng et al. (2014) A multivariate approach to the integration of
multi-omics datasets, BMC Bioinformatics 2014(15)
(<https://doi.org/10.1186/1471-2105-15-162>)
- Hanafi et al. (2011) Connections between multiple co-inertia analysis
and consensus principal component analysis, Chemometrics and
Intelligent Laboratory Systems 106 (1)
(<https://doi.org/10.1016/j.chemolab.2010.05.010>.)
## Installation
This package can be installed [via
Bioconductor](https://bioconductor.org/packages/release/bioc/html/nipalsMCIA.html):
``` r
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("nipalsMCIA")
```
You can install the development version of nipalsMCIA from
[GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("Muunraker/nipalsMCIA", ref = "devel",
force = TRUE, build_vignettes = TRUE)
```
## Basic Example
The package currently includes one test dataset: `data_blocks`. This is
a list of dataframes containing observations of variables from three
omics types (mRNA, proteins, and micro RNA) on 21 cancer cell lines from
the NCI60 cancer cell lines. The data file includes a `metadata` data
frame containing the cancer type associated with each cell line.
``` r
# load the package and set a seed for reproducibility
library(nipalsMCIA)
set.seed(42)
```
``` r
data(NCI60) # import data as "data_blocks" and metadata as "metadata_NCI60"
# examine the data and metadata
summary(data_blocks)
#> Length Class Mode
#> mrna 12895 data.frame list
#> miRNA 537 data.frame list
#> prot 7016 data.frame list
head(metadata_NCI60)
#> cancerType
#> CNS.SF_268 CNS
#> CNS.SF_295 CNS
#> CNS.SF_539 CNS
#> CNS.SNB_19 CNS
#> CNS.SNB_75 CNS
#> CNS.U251 CNS
table(metadata_NCI60)
#> cancerType
#> CNS Leukemia Melanoma
#> 6 6 9
```
*Note: this dataset is reproduced from the [omicade4
package](https://www.bioconductor.org/packages/release/bioc/html/omicade4.html)
(Meng et. al., 2014). This package assumes all input datasets are in
sample by feature format.*
The main MCIA function can be called on `data_blocks` and optionally can
include `metadata_NCI60` for plot coloring by cancer type:
``` r
# to convert data_blocks into an MAE object we provide the simple_mae() function
data_blocks_mae <- simple_mae(data_blocks, row_format = "sample",
colData = metadata_NCI60)
mcia_results <- nipals_multiblock(data_blocks_mae = data_blocks_mae,
col_preproc_method = "colprofile",
num_PCs = 10, tol = 1e-12,
color_col = "cancerType")
```
<img src="man/figures/README-call-mcia-1.png" width="100%" style="display: block; margin: auto;" />
Here `num_PCs` is the dimension of the low-dimensional embedding of the
data chosen by the user.
