signifinder offers a rapid way to apply and explore gene expression tumor signatures from literature. It allows to compute the signature scores on the user dataset. Further, it supports the exploration of the scores proving functions to visualize single or multiple signatures. Currently, signifinder contains more than 60 distinct signatures collected from the literature relating to multiple tumors and multiple cancer processes.
signifinder 1.6.0
In cancer studies, many works propose transcriptional signatures as good indicators of cancer processes, for their potential to show cancer ongoing activities and that can be used for patient stratification. For these reasons, they are considered potentially useful to guide therapeutic decisions and monitoring interventions. Moreover, transcriptional signatures of RNA-seq experiments are also used to assess the complex relations between the tumor and its microenvironment. In recent years, the new technologies for transcriptome detection (single-cell RNA-seq and spatial transcriptomics) highlighted the highly heterogeneous behaviour of this disease and, as a result, the need to dissect its complexity.
Each of these signatures has a specific gene set (and eventually a set of coefficients to differently weight the gene contributions) whose expression levels are combined in a single-sample score. And each signature has its own method to define the computation of the score. Despite much evidence that computational implementations are useful to improve data applicability and dissemination, the vast majority of signatures in literature are not published along with a computational code and only few of them have been implemented in a software, virtuous examples are: the R package consensusOV
, dedicated to the TCGA ovarian cancer signature; and the R package genefu
which hosts some of the most popular signatures of breast cancer.
signifinder
provides an easy and fast computation of several published signatures. Firstly, users can see all the signatures collected so far in the package, with all the useful information and a description on how to properly interpret the scores. Then, users can decide which signature they want to compute on their dataset. To be easily integrated in the expression data analysis pipelines, signifinder
works with the Bioconductor data structures (SummarizedExperiment
, SingleCellExperiment
and SpatialExperiment
).
Also, several visualization functions are implemented to visualize the scores. These can help in the result interpretations: users can not only browse single signatures independently but also compare them with each other.
To install this package:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("signifinder")
The criteria for the inclusion of the signatures are: (i) signatures should rely on cancer topics, and be developed and used on cancer samples; (ii) signatures must exclusively use transcriptomic data; (iii) the original paper must state the gene list used for the signature definition, where all genes have an official gene symbol (Hugo consortium) or an unambiguous translation (genes without an official gene symbol are removed); (iv) the method to calculate the score must be unambiguously described. While it may not ever be possible to include all cancer signatures proposed in the literature, our package makes easy the addition of new signatures (by us or by others via “pull requests”, see Adding new signatures).
The input expression dataset must be normalized RNA-Seq counts (or normalized data matrix from microarrays) of bulk transcriptomics data, single-cell transcriptomics data or spatial transcriptomics data. They should be provided in the form of a matrix, a data frame or a SummarizedExperiment
(and respectively SingleCellExperiment
/SpatialExperiment
) where rows correspond to genes and columns correspond to samples. In the last case, the name of the assay containing the normalized values should be “norm_expr” (users can also choose another name, but it should be specified in the whichAssay
argument). Regardless the input type class, the output data is a SummarizedExperiment
(SingleCellExperiment
/SpatialExperiment
) where the scores computed are put in the colData
section.
Gene IDs in the input data can either be gene symbols, NCBI entrez or ensembl gene IDs. Users must say which of the three identifiers they use (SYMBOL, ENTREZID or ENSEMBL) to let the package convert the signature gene lists (nametype
argument inside the signature functions).
When a signature is computed a message is shown that says the percentage of genes found in the input data compared to the original list. There is no minimum threshold of genes for signatures to be computed, but a warning
will be given if there are less than the 30% of signature genes. After a signature has been calculated it is possible to visually inspect signature gene expressions using geneHeatmapSignPlot
(see Signature goodness).
Furthermore, the original works also specify the type of expression value (e.g. normalized value, TPM (transcript per million), log(TPM), etc…) that should be used to compute the signature. Therefore, during signature computation, data type should be eventually converted as reported in the original work. When using signifinder
, users must supply the input data in the form of normalised counts (or normalised arrays) and, for the signatures which require this, a data transformation step will be automatically performed. The transformed data matrix will be included in the output as an additional assay and the name of the assay will be the name of the conversion (i.e. “TPM”, “CPM” or “FPKM”). Alternatively, if the input data is a SummarizedExperiment
object that already contains (in addition to the normalized count) also an assay of the transformed data, this will be used directly. Note that in order to be used they must be called “TPM”, “CPM” or “FPKM”. Finally, included signatures have been developed both from array and RNA-seq data, therefore it is crucially important for users to specify the type of input data: “microarray” or “rnaseq” (inputType
argument inside the signature functions). In signifinder
, signatures developed with microarray can be applied to RNA-seq data but not vice versa due to input type conversions.
In the following section, we use an example bulk expression dataset of ovarian cancer to show how to use signifinder
with a standard workflow.
# loading packages
library(SummarizedExperiment)
library(signifinder)
library(dplyr)
data(ovse)
ovse
## class: SummarizedExperiment
## dim: 3180 40
## metadata(0):
## assays(4): norm_expr TPM CPM FPKM
## rownames(3180): ABL2 ACADM ... TMSB4Y USP9Y
## rowData names(0):
## colnames(40): sample1 sample2 ... sample39 sample40
## colData names(42): OV_subtype os ... APM_Wang ADO_Sidders
We can check all the signatures available in the package with the function availableSignatures
.
availSigns <- availableSignatures()
The function returns a data frame with all the signatures included in the package and for each signature the following information:
colData
1 | |
---|---|
signature | EMT_Miow |
scoreLabel | EMT_Miow_Epithelial, EMT_Miow_Mesenchymal |
functionName | EMTSign |
topic | epithelial to mesenchymal |
tumor | ovarian cancer |
tissue | ovary |
cellType | bulk |
requiredInput | microarray, rnaseq |
transformationStep | normArray, normCounts |
author | Miow |
reference | Miow Q. et al. Oncogene (2015) |
description | Double score obtained with ssGSEA to establish the epithelial- and the mesenchymal-like status in ovarian cancer patients. |
We can also interrogate the table asking which signatures are available for a specific tissue (e.g. ovary).
ovary_signatures <- availableSignatures(tissue = "ovary", description = FALSE)
signature | scoreLabel | functionName | topic | tumor | tissue | cellType | requiredInput | transformationStep | author | reference | |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | EMT_Miow | EMT_Miow_Epithelial, EMT_Miow_Mesenchymal | EMTSign | epithelial to mesenchymal | ovarian cancer | ovary | bulk | microarray, rnaseq | normArray, normCounts | Miow | Miow Q. et al. Oncogene (2015) |
5 | Pyroptosis_Ye | Pyroptosis_Ye | pyroptosisSign | pyroptosis | ovarian cancer | ovary | bulk | rnaseq | FPKM | Ye | Ye Y. et al. Cell Death Discov. (2021) |
9 | Ferroptosis_Ye | Ferroptosis_Ye | ferroptosisSign | ferroptosis | ovarian cancer | ovary | bulk | microarray, rnaseq | normArray, FPKM | Ye | Ye Y. et al. Front. Mol. Biosci. (2021) |
13 | LipidMetabolism_Zheng | LipidMetabolism_Zheng | lipidMetabolismSign | metabolism | epithelial ovarian cancer | ovary | bulk | rnaseq | normCounts | Zheng | Zheng M. et al. Int. J. Mol. Sci. (2020) |
15 | ImmunoScore_Hao | ImmunoScore_Hao | immunoScoreSign | immune system | epithelial ovarian cancer | ovary | bulk | microarray, rnaseq | normArray, log2(FPKM+0.01) | Hao | Hao D. et al. Clin Cancer Res (2018) |
17 | ConsensusOV_Chen | ConsensusOV_Chen_IMR, ConsensusOV_Chen_DIF, ConsensusOV_Chen_PRO, ConsensusOV_Chen_MES | consensusOVSign | ovarian subtypes | high-grade serous ovarian carcinoma | ovary | bulk | microarray, rnaseq | normArray, normCounts | Chen | Chen G.M. et al. Clin Cancer Res (2018) |
19 | Matrisome_Yuzhalin | Matrisome_Yuzhalin | matrisomeSign | extracellular matrix | ovarian cystadenocarcinoma, gastric adenocarcinoma, colorectal adenocarcinoma, lung adenocarcinoma | ovary, lung, stomach, colon | bulk | microarray, rnaseq | normArray, normCounts | Yuzhalin | Yuzhalin A. et al. Br J Cancer (2018) |
45 | HRDS_Lu | HRDS_Lu | HRDSSign | chromosomal instability | ovarian cancer, breast cancer | ovary, breast | bulk | microarray, rnaseq | normArray, normCounts | Lu | Lu J. et al. J Mol Med (2014) |
47 | DNArep_Kang | DNArep_Kang | DNArepSign | chromosomal instability | serous ovarian cystadenocarcinoma | ovary | bulk | microarray, rnaseq | normArray, log2(normCount+1) | Kang | Kang J. et al. JNCI (2012) |
48 | IPSOV_Shen | IPSOV_Shen | IPSOVSign | immune system | ovarian cancer | ovary | bulk | microarray, rnaseq | normArray, log2(normCount+1) | Shen | Shen S. et al. EBiomed (2019) |
60 | LRRC15CAF_Dominguez | LRRC15CAF_Dominguez | LRRC15CAFSign | cancer associated fibroblasts | pancreatic adenocarcinoma, breast cancer, lung cancer, ovarian cancer, colon cancer, renal cancer, esophageal cancer, stomach adenocarcinoma, bladder cancer, head and neck squamous cell carcinoma | pancreas, breast, lung, ovary, colon, kidney, esophagus, stomach, bladder, head and neck | bulk | rnaseq | log2(normCounts+1) | Dominguez | Dominguez C.X. et al. Cancer Discovery (2020) |
63 | COXIS_Bonavita | COXIS_Bonavita | COXISSign | immune system | melanoma, bladder cancer, gastric cancer, clear cell renal cancer, ovarian cancer, cervical cancer, breast cancer (TNBC), lung cancer, head and neck squamous cell carcinoma | skin, bladder, stomach, kidney, ovary, cervix, breast, lung, head and neck | bulk | rnaseq | log2(normCounts+1) | Bonavita | Bonavita E. et al. Immunity (2020) |
Once we have found a signature of interest, we can compute it by using the corresponding function (indicated in the functionName
field of availableSignatures
table). All the signature functions require the expression data and to indicate the type of input data (inputType
equal to “rnaseq” or “microarray”). Data are supposed to be the normalized expression values.
ovse <- ferroptosisSign(dataset = ovse, inputType = "rnaseq")
## ferroptosisSignYe is using 100% of signature genes
Some signatures are grouped in the same function by cancer topic even if they deal with different cancer types and computation approaches. We can unequivocally choose the one we are interested in by stating the first author of the signature (indicated in the author
field of availableSignatures
table). E.g., currently, there are four different epithelial-to-mesenchymal transition (EMT) signatures implemented inside the EMTSign
function (“Miow”, “Mak”, “Cheng” or “Thompson”). We can choose which one to compute stating the author
argument:
ovse <- EMTSign(dataset = ovse, inputType = "rnaseq", author = "Miow")
## EMTSignMiow is using 96% of epithelial signature genes
## EMTSignMiow is using 91% of mesenchymal signature genes
## Warning in .filterGenes(dataMatrix, removeConstant = removeConstant,
## removeNzConstant = removeNzConstant): 3 genes with constant values throughout
## the samples.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
In this way, “EMT_Miow” is computed. Regardless the expression input type, the output data of all the signature functions is a SummarizedExperiment
with the computed signature scores in the colData
. Thus, the returned object can be resubmitted as input data to another signature function and will be returned as well with the addition of the new signature in the colData
.
We can also compute multiple signatures at once with the function multipleSign
. We can specify which signatures we are interested in through the use of the arguments tissue
, tumor
and/or topic
to define the signature list to compute. E.g. here below we compute all the available signature for ovary and pan-tissue:
ovse <- multipleSign(dataset = ovse, inputType = "rnaseq",
tissue = c("ovary", "pan-tissue"))
## EMTSignMiow is using 96% of epithelial signature genes
## EMTSignMiow is using 91% of mesenchymal signature genes
## Warning in .filterGenes(dataMatrix, removeConstant = removeConstant,
## removeNzConstant = removeNzConstant): 3 genes with constant values throughout
## the samples.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## EMTSignMak is using 96% of epithelial signature genes
## EMTSignMak is using 100% of mesenchymal signature genes
## pyroptosisSignYe is using 86% of signature genes
## ferroptosisSignYe is using 100% of signature genes
## lipidMetabolismSign is using 100% of signature genes
## hypoxiaSign is using 92% of signature genes
## immunoScoreSignHao is using 100% of signature genes
## immunoScoreSignRoh is using 100% of signature genes
## 'select()' returned 1:1 mapping between keys and columns
## Loading training data
## Training Random Forest...
## IPSSign is using 98% of signature genes
## matrisomeSign is using 100% of signature genes
## mitoticIndexSign is using 100% of signature genes
## ImmuneCytSignRooney is using 100% of signature genes
## IFNSign is using 100% of signature genes
## expandedImmuneSign is using 100% of signature genes
## TinflamSign is using 100% of signature genes
## CINSign is using 96% of signature genes
## CINSign is using 94% of signature genes
## cellCycleSignLundberg is using 93% of signature genes
## cellCycleSignDavoli is using 100% of signature genes
## ASCSign is using 92% of signature genes
## ImmuneCytSignDavoli is using 100% of signature genes
## ChemokineSign is using 100% of signature genes
## ECMSign is using 100% of up signature genes
## ECMSign is using 93% of down signature genes
## Warning in .filterGenes(dataMatrix, removeConstant = removeConstant,
## removeNzConstant = removeNzConstant): 3 genes with constant values throughout
## the samples.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## HRDSSign is using 89% of signature genes
## VEGFSign is using 100% of signature genes
## DNArepSign is using 87% of signature genes
## IPSOVSign is using 100% of signature genes
## Warning in .filterAndMapGenesAndGeneSets(param, removeConstant = FALSE, : Some
## gene sets have size one. Consider setting 'minSize > 1'.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## APMSign is using 100% of signature genes
## Warning in .filterGenes(dataMatrix, removeConstant = removeConstant,
## removeNzConstant = removeNzConstant): 3 genes with constant values throughout
## the samples.
## Warning in .filterGenes(dataMatrix, removeConstant = removeConstant,
## removeNzConstant = removeNzConstant): Genes with constant values are discarded.
## ADOSign is using 100% of signature genes
## Warning in .filterGenes(dataMatrix, removeConstant = removeConstant,
## removeNzConstant = removeNzConstant): 3 genes with constant values throughout
## the samples.
## Warning in .filterGenes(dataMatrix, removeConstant = removeConstant,
## removeNzConstant = removeNzConstant): Genes with constant values are discarded.
## LRRC15CAFSign is using 100% of signature genes
## Warning in scoreSingleSamples(gdb, datasetm, methods = "ewm"): 3 row(s) removed
## from expression object (y) due to 0sd
## COXISSign is using 83% of signature genes
Here below, instead, we compute all the available signature for ovary, pan-tissue and that are related to the immune system activity:
ovse <- multipleSign(dataset = ovse, inputType = "rnaseq",
tissue = c("ovary", "pan-tissue"),
topic = "immune system")
## immunoScoreSignHao is using 100% of signature genes
## immunoScoreSignRoh is using 100% of signature genes
## IPSSign is using 98% of signature genes
## ImmuneCytSignRooney is using 100% of signature genes
## IFNSign is using 100% of signature genes
## expandedImmuneSign is using 100% of signature genes
## TinflamSign is using 100% of signature genes
## ImmuneCytSignDavoli is using 100% of signature genes
## ChemokineSign is using 100% of signature genes
## IPSOVSign is using 100% of signature genes
## Warning in .filterAndMapGenesAndGeneSets(param, removeConstant = FALSE, : Some
## gene sets have size one. Consider setting 'minSize > 1'.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## APMSign is using 100% of signature genes
## Warning in .filterGenes(dataMatrix, removeConstant = removeConstant,
## removeNzConstant = removeNzConstant): 3 genes with constant values throughout
## the samples.
## Warning in .filterGenes(dataMatrix, removeConstant = removeConstant,
## removeNzConstant = removeNzConstant): Genes with constant values are discarded.
## COXISSign is using 83% of signature genes
Alternatively, we can state exactly the signature names using the whichSign
argument.
ovse <- multipleSign(dataset = ovse, inputType = "rnaseq",
whichSign = c("EMT_Miow", "IPSOV_Shen"))
## EMTSignMiow is using 96% of epithelial signature genes
## EMTSignMiow is using 91% of mesenchymal signature genes
## Warning in .filterGenes(dataMatrix, removeConstant = removeConstant,
## removeNzConstant = removeNzConstant): 3 genes with constant values throughout
## the samples.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## IPSOVSign is using 100% of signature genes
## Warning in .filterAndMapGenesAndGeneSets(param, removeConstant = FALSE, : Some
## gene sets have size one. Consider setting 'minSize > 1'.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
When computing a signature on a dataset we always have to keep in mind that not all the signature genes may be present in the dataset. Also, these may have many zero values or other issues affecting the goodness of a specific signature for the dataset. We can inspect some signature’s technical parameters to evaluate their reliability for the analysed dataset. First, users can access the complete gene list of a signature with the function getSignGenes
, that returns a dataframe object with “SYMBOL” in the first column. Some signatures have also additional columns: “coeff” for coefficients that weigh the gene contributions; “class” for a classification that divides the signature in two or more groups. Few signatures have other specific columns.
getSignGenes("VEGF_Hu")
## SYMBOL
## 1 RRAGD
## 2 FABP5
## 3 UCHL1
## 4 GAL
## 5 PLOD1
## 6 DDIT4
## 7 VEGFA
## 8 ADM
## 9 ANGPTL4
## 10 NDRG1
## 11 SLC16A3
## 12 FLVCR2
getSignGenes("Pyroptosis_Ye")
## SYMBOL coeff
## 1 AIM2 -0.187
## 2 PLCG1 0.068
## 3 ELANE 0.097
## 4 PJVK -0.143
## 5 CASP3 -0.086
## 6 CASP6 -0.033
## 7 GSDMA 0.130
getSignGenes("EMT_Thompson")
## SYMBOL class
## 1 CDH1 epithelial
## 2 CDH3 epithelial
## 3 CLDN4 epithelial
## 4 EPCAM epithelial
## 5 ST14 epithelial
## 6 MAL2 epithelial
## 7 VIM mesenchymal
## 8 SNAI2 mesenchymal
## 9 ZEB2 mesenchymal
## 10 FN1 mesenchymal
## 11 MMP2 mesenchymal
## 12 AGER mesenchymal
Second, the evaluationSignPlot
function returns a multipanel plot that shows for each signature: (i) a value of the goodness of a signature for the user’s dataset. This goes from 0, worst goodness, to 100, best goodness, and is a combination of the parameters shown in the other pannels; (ii) the percentage of genes from the signature gene list that are actually available in the dataset; (iii) the percentage of zero values in the signature genes, for each sample; (iv) the correlation between signature scores and the sample total read counts; (v) the correlation between signature scores and the percentage of the sample total zero values.
evaluationSignPlot(data = ovse)