PROGENy pathway signatures: Application to Bulk transcriptomics • progeny
Description: progeny

# PROGENy pathway signatures

This R package provides the model we inferred in the publication "Perturbation-response genes reveal signaling footprints in cancer gene expression" and a function to obtain pathway scores from a gene expression matrix. It is available on bioRxiv.

## Scoring the `airway` package data for pathway scores

This is to outline how to prepare expression data, in this case from the `airway` package for pathway activity analysis using PROGENy.

### Preparing the gene expression matrix

    library(airway)
    library(DESeq2)
    data(airway)

    # import data to DESeq2 and variance stabilize
    dset = DESeqDataSetFromMatrix(assay(airway),
        colData=as.data.frame(colData(airway)), design=~dex)
    dset = estimateSizeFactors(dset)
    dset = estimateDispersions(dset)
    gene_expr = getVarianceStabilizedData(dset)

    # annotate matrix with HGNC symbols
    library(biomaRt)
    mart = useDataset("hsapiens_gene_ensembl", useMart("ensembl"))
    genes = getBM(attributes = c("ensembl_gene_id","hgnc_symbol"),
                  values=rownames(gene_expr), mart=mart)
    matched = match(rownames(gene_expr), genes$ensembl_gene_id)
    rownames(gene_expr) = genes$hgnc_symbol[matched]

### Obtaining pathway scores

We can then use the `progeny` function to score the expression matrix. Note that we are scaling the pathway scores with respect to the controls only.

    library(progeny)
    pathways = progeny(gene_expr, scale=FALSE)
    controls = airway$dex == "untrt"
    ctl_mean = apply(pathways[controls,], 2, mean)
    ctl_sd = apply(pathways[controls,], 2, sd)
    pathways = t(apply(pathways, 1, function(x) x - ctl_mean))
    pathways = apply(pathways, 1, function(x) x / ctl_sd)

### Checking for differences between the groups

So now we might be interested how the treatment with dexamethasone affects signaling pathways. To do this, we check if the control is different to the perturbed condition using a linear model:

    library(dplyr)
    result = apply(pathways, 1, function(x) {
        broom::tidy(lm(x ~ !controls)) %>%
            filter(term == "!controlsTRUE") %>%
            dplyr::select(-term)
    })
    mutate(bind_rows(result), pathway=names(result))

    ## # A tibble: 14 x 5
    ##    estimate std.error statistic   p.value pathway
    ##       <dbl>     <dbl>     <dbl>     <dbl> <chr>
    ##  1    8.89      0.830    10.7   0.0000392 Androgen
    ##  2    1.25      3.22      0.389 0.711     EGFR
    ##  3   -6.90      0.830    -8.31  0.000165  Estrogen
    ##  4    0.783     1.92      0.408 0.698     Hypoxia
    ##  5   -0.972     0.657    -1.48  0.190     JAK-STAT
    ##  6    1.04      1.34      0.774 0.469     MAPK
    ##  7    0.456     0.697     0.655 0.537     NFkB
    ##  8   -4.16      0.774    -5.37  0.00171   p53
    ##  9   -0.635     1.89     -0.337 0.748     PI3K
    ## 10    2.34      1.09      2.15  0.0756    TGFb
    ## 11    0.715     0.681     1.05  0.335     TNFa
    ## 12    0.257     0.792     0.324 0.757     Trail
    ## 13    0.703     0.705     0.996 0.358     VEGF
    ## 14   -1.75      0.566    -3.10  0.0211    WNT

What we see is that indeed the p53/DNA damage response pathway is less active after treatment than before.

## Reproducing drug associations on the GDSC panel

Below is an example on how to calculate pathway scores for cell lines in the Genomics of Drug Sensitivity in Cancer (GDSC) panel, and to check for associations with drug response.

The code used for the analyses is available on Github.

### Getting the data

This example shows how to use the GDSC gene expression data of multiple cell lines together with PROGENy to calculate pathway activity and then to check for associations with drug sensitivity.

First, we need the GDSC data for both gene expression and drug response. They are available on the GDSC1000 web site:

    # set up a file cache so we download only once
    library(BiocFileCache)
    bfc = BiocFileCache(".")
    # gene expression and drug response
    base = "http://www.cancerrxgene.org/gdsc1000/GDSC1000_WebResources/Data/"
    paths = bfcrpath(bfc, paste0(base, c("suppData/TableS4A.xlsx",
                "preprocessed/Cell_line_RMA_proc_basalExp.txt.zip")))

You can also download the files manually (adjust the file names when loading):

- Processed gene expression matrix
- Drug sensitivities

### Creating the right objects to work with

First, we need to load the files we just downloaded into R to be able to perform the analysis:

    # load the downloaded files
    drug_table <- readxl::read_excel(paths[1], skip=5, na="NA")
    drug_table <- replace(drug_table, is.na(drug_table), 0)
    gene_table <- readr::read_tsv(paths[2])

    # we need drug response with COSMIC IDs
    drug_response <- data.matrix(drug_table[,3:ncol(drug_table)])
    rownames(drug_response) <- drug_table[[1]]

    # we need genes in rows and samples in columns
    gene_expr <- data.matrix(gene_table[,3:ncol(gene_table)])
    colnames(gene_expr) <- sub("DATA.", "", colnames(gene_expr), fixed=TRUE)
    rownames(gene_expr) <- gene_table$GENE_SYMBOLS

### Running PROGENy to get pathway activity scores

Activity inference is done using a weighted sum of the model genes. We can run this without worrying about the order of genes in the expression matrix using:

    library(progeny)
    pathways <- progeny(gene_expr,scale = TRUE, organism = "Human", top = 100,
        perm = 1, verbose = FALSE)

To visualize the progeny result

    library(pheatmap)
    myColor = colorRampPalette(c("Darkblue", "white","red"))(100)
    pheatmap(pathways,fontsize=14, show_rownames = FALSE,
        color=myColor, main = "PROGENy", angle_col = 45, treeheight_col = 0,
        border_color = NA)

We now have the pathway activity scores for the pathways defined in PROGENy:

    head(pathways)

    ##           Androgen       EGFR     Estrogen     Hypoxia    JAK-STAT        MAPK
    ## 906826  -0.4241254  0.1385179 -0.269674710 -0.06875973 -0.37598907  0.41134699
    ## 687983  -1.7393225 -0.9213671 -0.763490640 -1.32374232 -0.94662180 -0.90704066
    ## 910927  -1.5393075 -0.0695914  1.613512221 -0.76482836 -1.05428545  0.17412564
    ## 1240138  0.9107488 -0.2200467  0.219584368 -0.73615734 -0.07536995 -0.60238190
    ## 1240139 -1.0602951 -0.1846655 -0.556250681  0.13664228 -0.58644833 -0.04550095
    ## 906792   0.7518034  0.8045299  0.008914293  0.40118664 -0.50038060  0.77199885
    ##               NFkB        p53       PI3K       TGFb       TNFa      Trail
    ## 906826  -0.6374683 -1.0903096 -0.1953806 -0.7793323 -0.4329185 -0.8018626
    ## 687983  -1.3157127 -0.8692247  0.2542425 -0.8242281 -1.0195560 -0.5104074
    ## 910927  -0.3059911  0.8896616 -0.7487845  0.7580845 -0.1756034 -0.8092882
    ## 1240138  0.9755970  2.0632167  0.4839630  1.9108565  0.9865983 -0.2377919
    ## 1240139 -0.4751547  0.8763231  1.3814461  0.9999634 -0.3916422 -0.9224493
    ## 906792  -0.1878324 -0.8895494 -3.0379834 -0.3389734 -0.1436331  0.2455631
    ##                VEGF         WNT
    ## 906826  -0.13639016  0.01831199
    ## 687983  -0.08663407 -0.72986989
    ## 910927   0.13321227  2.28602257
    ## 1240138  0.01391272  1.51533198
    ## 1240139  0.90296847  1.71036886
    ## 906792   0.18149340 -1.22160214

### Testing if MAPK activity is significantly associated with Trametinib

Trametinib is a MEK inhibitor, so we would assume that cell lines that have a higher MAPK activity are more sensitive to MEK inhibition.

We can test this the following way:

    cell_lines = intersect(rownames(pathways), rownames(drug_response))
    trametinib = drug_response[cell_lines, "Trametinib"]

    mapk = pathways[cell_lines, "MAPK"]

    associations = lm(trametinib ~ mapk)
    summary(associations)

    ##
    ## Call:
    ## lm(formula = trametinib ~ mapk)
    ##
    ## Residuals:
    ##    Min     1Q Median     3Q    Max
    ## -6.548 -1.255  0.338  1.338  6.819
    ##
    ## Coefficients:
    ##             Estimate Std. Error t value Pr(>|t|)
    ## (Intercept) -0.94703    0.06442  -14.70   <2e-16 ***
    ## mapk        -1.35978    0.06449  -21.09   <2e-16 ***
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ##
    ## Residual standard error: 1.998 on 960 degrees of freedom
    ## Multiple R-squared:  0.3165, Adjusted R-squared:  0.3158
    ## F-statistic: 444.6 on 1 and 960 DF,  p-value: < 2.2e-16

And indeed we find that MAPK activity is strongly associated with sensitivity to Trametinib: the `Pr(>|t|)` is much smaller than the conventional threshold of `0.05`.

The intercept is significant as well, but we're not really interested if the mean drug response is above or below `0` in this case.

Note, however, that we tested all cell lines at once and did not adjust for the effect different tissues may have.

## R version information

    ## R version 4.0.4 (2021-02-15)
    ## Platform: x86_64-w64-mingw32/x64 (64-bit)
    ## Running under: Windows 10 x64 (build 18363)
    ##
    ## Matrix products: default
    ##
    ## locale:
    ## [1] LC_COLLATE=English_Germany.1252  LC_CTYPE=English_Germany.1252
    ## [3] LC_MONETARY=English_Germany.1252 LC_NUMERIC=C
    ## [5] LC_TIME=English_Germany.1252
    ##
    ## attached base packages:
    ## [1] parallel  stats4    stats     graphics  grDevices utils     datasets
    ## [8] methods   base
    ##
    ## other attached packages:
    ##  [1] pheatmap_1.0.12             BiocFileCache_1.14.0
    ##  [3] dbplyr_2.1.1                dplyr_1.0.6
    ##  [5] progeny_1.15.1              biomaRt_2.46.3
    ##  [7] DESeq2_1.30.1               airway_1.10.0
    ##  [9] SummarizedExperiment_1.20.0 Biobase_2.50.0
    ## [11] GenomicRanges_1.42.0        GenomeInfoDb_1.26.7
    ## [13] IRanges_2.24.1              S4Vectors_0.28.1
    ## [15] BiocGenerics_0.36.1         MatrixGenerics_1.2.1
    ## [17] matrixStats_0.59.0          knitr_1.33
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