Real data analysis on a breast cancer cohort
2.Fit_breast_data.RmdPackage setup
For detailed explanation, please visit the package setup.
knitr::opts_chunk$set(warning=FALSE, message=FALSE)
configure_environment(envname="bascule-env", use_default=TRUE)
py = reticulate::import("pybascule")Data
We want to analyze 2,682 breast tumour samples from the GEL, ICGC,
and HMF cohorts. For this purpose We load the breast tumor type data
object breast_data (for more details visit here)
from the bascule package, and retrieve the input data
(counts) from it.
The counts object is a list which contains two SBS and
DBS matrices (data.frame), both with 2682 rows
(representing samples), and 96 columns for SBS context and 78 columns
for DBS context (representing mutational contexts). The values of the
matrices are number of the mutations for corresponding sample and
mutational context. The counts matrix can extracted from bascule fit
object using the get_input function in the package.
# load breast fit data from package
data("breast_data")
# extract the bascule fit object for breast tumor type
x = breast_data$x
x
#> ── [ Bascule ] samples with 0 total mutations. ───────────────────────────────
#>
#> ── SBS catalogue signatures (5 columns shown max)
#> A[C>A]A A[C>A]C A[C>A]G A[C>A]T A[C>G]A
#> SBS1 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> SBS2 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> SBS3 0.020808323 0.016506603 0.001750700 0.012204882 0.019707883
#> SBS5 0.011997600 0.009438112 0.001849630 0.006608678 0.010097980
#> SBS13 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> SBS18 0.051533859 0.015810387 0.002431598 0.021414070 0.001731137
#> SBS31 0.009534985 0.018490274 0.001659127 0.006276698 0.008315626
#>
#> ── De novo signatures (5 columns shown max)
#> A[C>A]A A[C>A]C A[C>A]G A[C>A]T A[C>G]A
#> SBSD1 0.0013021588 0.001086904 0.0010003703 0.001298402 0.0009850989
#> SBSD2 0.0086599355 0.003850128 0.0019153069 0.007406929 0.0097875630
#> SBSD4 0.0071512359 0.005733336 0.0339316145 0.004755269 0.0046120340
#> SBSD5 0.0027345754 0.002193904 0.0007122308 0.002187520 0.0017807318
#> SBSD6 0.0021000142 0.001562314 0.0009072816 0.001607518 0.0012848607
#> SBSD7 0.0225498791 0.017800982 0.0031354088 0.006062250 0.0009157615
#> SBSD8 0.0008454395 0.000797856 0.0004881849 0.001464493 0.0012844335
#> SBSD9 0.0010254879 0.001912371 0.0003545345 0.004855627 0.0018162442
#> SBSD10 0.0035070582 0.002635648 0.0003777245 0.002224826 0.0006770983
#> SBSD11 0.0369320129 0.039624678 0.0067355421 0.053949864 0.0037204620
#> SBSD12 0.0012540791 0.001046035 0.0008772321 0.001602001 0.0031040453
#>
#> ── DBS catalogue signatures (5 columns shown max)
#> AC>CA AC>CG AC>CT AC>GA AC>GG
#> DBS11 0.001369907 0.000193987 0.000755948 0.000441970 0.002249847
#> DBS13 0.002155121 0.000051500 0.002853398 0.002762299 0.001464314
#> DBS20 0.005031140 0.001909180 0.002372220 0.001585940 0.002634990
#>
#> ── De novo signatures (5 columns shown max)
#> AC>CA AC>CG AC>CT AC>GA AC>GG
#> DBSD1 0.02705068 0.002431567 0.009581317 0.004772051 0.003079476
#> DBSD2 0.01119519 0.001638339 0.005947791 0.012590619 0.001679650
# retrieve the input data of the breast tumor type
counts = get_input(x, matrix=TRUE, reconstructed=FALSE)
# dimensions of SBS and DBS count matrices
dim(counts[["SBS"]])
#> [1] 2682 96
dim(counts[["DBS"]])
#> [1] 2682 78
# display first 5 rows and first 5 columns of SBS and DBS matrices
head(counts[["SBS"]][1:5, 1:5])
#> A[C>A]A A[C>A]C A[C>A]G A[C>A]T A[C>G]A
#> GEL-2474917-11 131 81 11 70 71
#> GEL-2516072-11 279 199 32 202 68
#> GEL-2905314-11 54 54 11 38 20
#> GEL-2464811-11 48 36 5 35 17
#> GEL-2142020-11 33 18 7 16 8
head(counts[["DBS"]][1:5, 1:5])
#> AC>CA AC>CG AC>CT AC>GA AC>GG
#> GEL-2474917-11 0 0 0 0 0
#> GEL-2516072-11 1 0 0 1 0
#> GEL-2905314-11 0 0 0 0 0
#> GEL-2464811-11 0 0 0 1 0
#> GEL-2142020-11 0 0 0 0 0As a reference catalogue for breast cancer, we selected signatures SBS1, SBS2, SBS3, SBS5, SBS13, SBS17, SBS18, SBS31, for SBS context and signatures DBS11, DBS13, and DBS20, for the DBS context.
These signature collections are a subset of the COSMIC_sbs_filt and COSMIC_dbs catalogues.
Based on prior biological knowledge we expect to see the SBS3 as one
of the signatures included in both matrices, but in practice, the SBS3
signature is not present in the output of the model. Here we give higher
weights to SBS3 using hyperparameters arguments (visit here
for more information), to keep this signature in final output.
Fit the model
We run the model by executing the fit function from
bascule package which performs the non-negative matrix factorization to
deconvolute the counts matrix to lower rank matrices. fit.
The analysis identified 19 SBS (12 de novo) and 5 DBS (2 de novo)
signatures. we aim to eliminate any inferred de novo signatures that can
be represented as a linear combination of other signatures. This is done
using the refine_denovo_signatures function, applied to the
bascule object. After a refinement step, one de novo signature from SBS
context was removed due to its explainability by other signatures. we
identified 18 SBS (11 de novo) and 5 DBS (2 de novo) signatures after
refinement.
We use the fit_clustering function from the bascule
package on the bascule object generated from the refinement step. we
proceeded with the merging step and the tool detected 14 clusters among
the breast cancer samples. As we saw in re-clustering step, the tool
detected 14 clusters among the breast cancer samples. We then applied an
iterative merging function, resulting in 5 clusters, using a cutoff
value of 0.8.
Later, with help of convert_dn_names function we map the
de novo signatures to known signatures from the COSMIC catalogue and the
findings from Degasperi et al. within the SBS context, 6 de novo
signatures corresponded to COSMIC signatures SBS7a, SBS17b, SBS26,
SBS40a, SBS90, and SBS98. Additionally, one de novo signature aligned
with the common signature SBS8, and two signatures matched rare
signatures SBS44 and SBS97, both from the Degasperi et al. study. In the
DBS context, we mapped two de novo signatures to COSMIC signatures DBS14
and DBS20.
Visualization
The bascule object obtained from the previous steps is ready for visualization step. all the visualization functions take the bascule object as the input.
Fitting scores
The plot_scores visualize the model selection steps. The
plot display the BIC and likelihood scores of NMF run for each k value
(number of de novo signatures) for both SBS and DBS context. In the
plot, the x axis is related to k values and the
y axis is related to scores (BIC and Likelihood). The model
selects the k value with lowest BIC score in iterative inference
process.
plot_scores(x)
Based on the BIC score, the model with 11 de novo signatures for SBS and 2 de novo signatures for DBS provides the best fit compared to all other models.
Mutational signatures
We utilize the plot_signatures function to visualize the
mutational signatures inferred by the model. The types and
signames arguments allow us to specify the context type and
the list of signatures to plot. The get_denovo_signames
function is used to obtain the list of de novo signature names. Each
plot consist of multiple signatures plot with their name on the right
hand side.
plot_signatures(x, types="SBS", signames=get_denovo_signames(x))
The plot above shows 11 de novo signatures inferred by the tool in
SBS context. The x axis shows the 96 possible SBS contexts
and the y axis declare their respective density.
plot_signatures(x, types="DBS", signames=get_denovo_signames(x))
The plot above shows 2 de novo signatures inferred by the tool in DBS
context. The x axis shows the 78 possible DBS contexts and
the y axis declare their respective density.
Similarity plot
Function plot_similarity_reference plots the similarity
between de novo and COSMIC signatures based on cosine similarity
values.
plot_similarity_reference(x)[[1]]
#> list()
#> attr(,"class")
#> [1] "waiver"The heatmap plot inspects the similarities between the de novo and COSMIC signatures. The rows are de novo signatures, columns are COSMIC signatures and the values are cosine similarity between the corresponding signatures.
Clusters centroids
The plot_centroids() function is used to plot the
clustering centroids.
Here, we visualize the clustering centroids of mutational signatures
clusters. This plot provides a clear representation of how the
mutational signatures are grouped based on their similarities. The
plot_centroids function is used to generate this
visualization, offering insight into the relationships and distances
between different mutational signatures. By examining this plot, we can
better understand the patterns and structure of the signature clustering
results.
You can inspect the centroids for each cluster in two SBS and DBS
context. the x axis defines the clusters, the
y axis defines the density of signatures and the colors
represent the signatures which are present in the clusters. Using the
centroids we can analyze the patients clustering outcome. For detailed
information we can follow the exposure plots of each cluster to find out
the clustering logic and possible etiology behind it.
Exposures matrix
The plot_exposures function is used to visualize the
inferred exposure matrix, where x axis represent samples,
y axis represents the mutational signatures relative
contributions and the colors identify the mutational signatures. The
exposure matrix displays the contribution of each mutational signature
across different samples. This plot provides a clear overview of how
different mutational signatures contribute to the overall mutational
profile across the dataset. For clearer visualization, we plot the
exposure matrix for each cluster separately. The clusters
argument is used to specify which cluster to plot.
plot_exposures(x, clusters=c("G0"))
In the exposure plot for cluster G0 (n=272), the dominant mutational signatures are SBS2 and SBS13, along with DBS11, DBS13, and DBSD2 (mapped to DBS2 from COSMIC). These signatures are associated with APOBEC enzyme activity. APOBEC-driven mutational processes are frequently observed in HER2-positive breast cancers, suggesting a strong link between this cluster and HER2-driven tumorigenesis.
plot_exposures(x, clusters=c("G1"))
The cluster G1 (n=2058) exposure plot, which is the largest among all, shows a strong presence of SBS3, SBSD11 (mapped to SBS8 from Degasperi et al.), DBSD2 (mapped to DBS2 from COSMIC) and DBS13. SBS3 and DBS13 are caused by homologous recombination deficiency (HRD); DBS2, instead, is linked to smoking in some cancers but is often observed also in cancers not directly linked with tobacco exposure. Based on these prevalent signatures, cluster G1 can be associated with triple-negative breast cancers.
Clusters G10 (n=169), G11 (n=69), and G13 (n=114) share similar SBS patterns, including exposure to SBS1, SBS2, SBS3, SBS11, and SBS13, pointing to a mix of APOBEC activity, HRD, and ageing-related mutational processes. BASCULE can differentiate these three clusters based on DBS exposure.
plot_exposures(x, clusters=c("G10"))
Cluster G10 is notably associated with the mutational signatures DBSD2 (mapped to DBS2 from COSMIC) and DBS11. These signatures are linked to the activity of the APOBEC enzyme family. The strong link between G10 and these signatures suggests that APOBEC activity may be a driving factor behind the mutational processes in this cluster.
plot_exposures(x, clusters=c("G11"))
The cluster G11 as the smallest cluster is primarily exposed to DBSD1 (mapped to DBS14 from COSMIC), a signature reported as artifactual in COSMIC. However, the presence of DBS13 in G11, suggests that this cluster is also linked to homologous recombination deficiency (HRD).
plot_exposures(x, clusters=c("G13"))
Cluster G13 shows significant exposure to the mutational signatures DBS13 and DBS20. These particular signatures are associated with homologous recombination deficiency (HRD)-related processes. The presence of DBS13 and DBS20 in cluster G13 suggests that the samples in this group may be influenced by similar underlying genomic instability and repair deficiencies.