# PRObabilistic Pathway Scores (PROPS)

#### Lichy Han

#### 2019-05-02

This R package calculates PRObabilistic Pathway Scores (PROPS), which are pathway-based features, from gene-based data. For more information, see:

Lichy Han, Mateusz Maciejewski, Christoph Brockel, William Gordon, Scott B. Snapper, Joshua R. Korzenik, Lovisa Afzelius, Russ B. Altman. A PRObabilistic Pathway Score (PROPS) for Classification with Applications to Inflammatory Bowel Disease.

## Example Data

In the package, example healthy data and patient data are included. Note that each data frame is patient x gene, and the column names are by gene Entrez ID.

    data(example_healthy)
    example_healthy[1:5,1:5]

| 10 | 100 | 1000 | 10000 | 10005
--- | --- | --- | --- | --- | ---
HealthySample1 | 6.993072 | 7.890769 | 1.187037 | 3.186920 | 2.243749
HealthySample2 | 7.170742 | 5.786940 | 6.681429 | 4.765805 | 5.404951
HealthySample3 | 4.458645 | 4.302536 | 3.893047 | 1.921196 | 8.437868
HealthySample4 | 3.215118 | 5.515139 | 4.178101 | 6.063425 | 6.152115
HealthySample5 | 4.456360 | 8.889095 | 6.342727 | 8.625195 | 2.956368

    data(example_data)
    example_data[1:5,1:5]

| 10 | 100 | 1000 | 10000 | 10005
--- | --- | --- | --- | --- | ---
Sample1 | 3.848834 | 3.485361 | 5.650030 | 7.020021 | 3.996484
Sample2 | 4.165301 | 1.808756 | 5.927112 | 10.229086 | 6.963043
Sample3 | 4.874789 | 4.627511 | 6.410964 | 6.036542 | 4.334305
Sample4 | 1.295941 | 4.173276 | 2.408115 | 4.329023 | 3.800146
Sample5 | 8.924654 | 4.229810 | 3.939608 | 7.241261 | 5.378624

## Calculating PROPS using KEGG

KEGG edges have been included as part of this package, and will be used by default. To run PROPS, simply call the props command with the healthy data and the disease data.

    props_features <- props(example_healthy, example_data)
    props_features[1:5,1:5]

pathway_ID | Sample1 | Sample2 | Sample3 | Sample4
--- | --- | --- | --- | ---
00010.xml | -156.48159 | -156.60303 | -146.66342 | -147.22782
00020.xml | -66.02293 | -62.20247 | -68.86954 | -63.09377
00030.xml | -66.36162 | -64.25114 | -62.38084 | -66.41753
00040.xml | -75.33802 | -58.61511 | -64.90238 | -60.49886
00051.xml | -67.76268 | -68.93474 | -71.99290 | -72.98498

## Optional Batch Correction

As part of this package, we have included an optional flag to use ComBat via the sva package. Let us have two batches of data, where the first 50 samples from example_healthy and first 20 samples from example_data are from batch 1, and the remaining are from batch 2. Run the props command with batch_correct as TRUE, followed by the batch numbers.

    healthy_batches = c(rep(1, 25), rep(2, 25))
    dat_batches = c(rep(1, 20), rep(2, 30))

    props_features_batchcorrected <- props(example_healthy, example_data, batch_correct = TRUE, healthy_batches = healthy_batches, dat_batches = dat_batches)

    ## Found2batches

    ## Adjusting for0covariate(s) or covariate level(s)

    ## Fitting L/S model and finding priors

    ## Finding parametric adjustments

    ## Adjusting the Data

    props_features_batchcorrected[1:5,1:5]

pathway_ID | Sample1 | Sample2 | Sample3 | Sample4
--- | --- | --- | --- | ---
00010.xml | -155.40390 | -157.15251 | -145.16141 | -146.00772
00020.xml | -66.49358 | -61.77105 | -68.12498 | -62.50916
00030.xml | -65.45724 | -63.23627 | -61.91169 | -64.95805
00040.xml | -74.70681 | -58.32661 | -64.12064 | -60.15456
00051.xml | -67.53138 | -67.46415 | -71.16755 | -72.69114

## Calculating PROPS using User Input Pathways

A user can also input his or her own pathways, and thus our package is compatible with additional pathway databases and hand-curated pathways from literature or data. To do so, one needs to format the pathways into three columns, where the first column is the source or “from” node of the edge, the second column is the sink or “to” node of the edge, and the third column is the pathway ID (e.g. “glucose_metabolism”).

    data(example_edges)
    example_edges[1:8, ]

from | to | pathway_ID
--- | --- | ---
7476 | 8322 | pathway1
3913 | 3690 | pathway1
26060 | 836 | pathway1
163688 | 5532 | pathway1
84812 | 10423 | pathway1
57104 | 1056 | pathway1
9651 | 8396 | pathway1
3976 | 3563 | pathway1

Run props with the user specified edges as follows:

    props_features_userpathways <- props(example_healthy, example_data, pathway_edges = example_edges)
    props_features_userpathways[,1:5]

pathway_ID | Sample1 | Sample2 | Sample3 | Sample4
--- | --- | --- | --- | ---
pathway1 | -370.3345 | -372.7717 | -397.3746 | -370.7386
pathway2 | -355.6405 | -343.6026 | -354.3261 | -339.1284
pathway3 | -357.4726 | -354.8797 | -352.8343 | -353.7832