Genetic alterations in cancer cells trigger oncogenic transformation, a process largely mediated by the dysregulation of kinase and transcription factor (TF) activities. While the mutational profiles of thousands of tumours have been extensively characterised, the measurements of protein activities have been technically limited until recently. We compiled public data of matched genomics and (phospho)proteomics measurements for 1,110 tumours and 77 cell lines that we used to estimate activity changes in 218 kinases and 292 TFs. Co‐regulation of kinase and TF activities reflects previously known regulatory relationships and allows us to dissect genetic drivers of signalling changes in cancer. We find that loss‐of‐function mutations are not often associated with the dysregulation of downstream targets, suggesting frequent compensatory mechanisms. Finally, we identified the activities most differentially regulated in cancer subtypes and showed how these can be linked to differences in patient survival. Our results provide broad insights into the dysregulation of protein activities in cancer and their contribution to disease severity. Estimation of activity changes for over 500 kinases and transcription factors in more than 1,000 cancer samples and cell lines identifies signalling regulators often dysregulated in cancer associated with patient survival. We then asked whether TFs and kinases carrying such mutations would have, on average, differences in their predicted activities as measured by changes in their targets. On average, we did not observe reduced predicted activity for proteins carrying different types of mutations in the tumour samples  with those where it was predicted to be decreased (eight samples). We reasoned that phosphatase levels could be a potential mechanism explaining a downregulation of the pathway activity in the presence of an activating mutation. We therefore measured the fold change in phosphatase mRNA levels in these two groups of samples. Of 237 phosphatases tested, 74% tend to have higher expression levels in the samples with low BRAF activity, 17 of these have a significant difference across the two groups of samples with a false discovery rate set at 15%  for the estimated association strength . While there is much variation, it is not unexpected given the differences in methodology and samples. There are only 2% of samples  that overlap between the two analyses, and as such, this analysis shows that the associations can replicate in independent datasets.We replicated the associations between mutations and kinase activity using the RPPA antibody measurements of phosphosites in kinases that can report on kinase activities. We were able to measure the association strength for 29 gene‐kinase pairs that overlap with those measured here. Across these associations, we find a correlation of 0.57 , with AKT1 and the cell‐cycle kinases CDK1/2 and AURKB being highly regulated in both sets of conditions , adjusted P‐value = 1e‐2). Interestingly, we also found that high activity of FOXA1 was a good prognostic factor and FOXM1 was a poor prognostic factor in liver cancer (FOXA1: HR = 0.69 (95% CI 0.55–0.85), adjusted P‐value = 4.7e‐3; FOXM1: HR = 1.39 (95% CI 1.17–1.66), adjusted P‐value = 1.9e‐3; Appendix Fig et al, et al, Survival analyses from multiomics datasets have been largely based on mutation, gene or protein expression differences between groups of patients. However, kinase and TF activities should capture the signalling state of the cancer samples and could be also linked to overall patient survival (OS). To explore this, we first performed a log‐rank test to compare the Kaplan–Meier (KM) survival curves between patients with TF and kinase activities classified as inactive, neutral and active . We founP‐value = 1.1e‐13; PRKCA: HR = 6.15 (95% CI 4.27–8.84), adjusted P‐value = 2.3e‐21; Fig et al, et al, et al, P‐value = 7.5e‐3) on a separate cohort of cancer patients from TCGA using RPPA phosphorylation measures , adjusted Kinases and TFs are important mediators of cell signalling regulation and sensitivity to anticancer drugs. Here, we have compiled multiomics datasets made available by the TCGA and CPTAC consortia and cell line studies and we were able to estimate the activities of 218 kinases and 292 TFs in over 1,000 primary tumours and cancer cell lines. Based on these, we found that kinase activities appear to be primarily regulated by phosphorylation level with fewer cases of significant correlation with the predicted kinase protein abundance levels. Contrary to this, the predicted TF activity is primarily correlated with the mRNA/protein level of the TF itself with a smaller proportion of TFs with significant correlations with the phosphorylation state. This difference in regulation is not simply due to lack of detection of phosphosites as TFs have a median value of six phosphosites detected compared with nine for kinases. A larger fraction of the TF activities is correlated with their mRNA levels than the protein abundance. This result is nonintuitive since the protein abundance should be a better proxy for activity. It is possible that this is due to the fact that TF activities are derived directly from the same mRNA datasets while there will be some degree of technical variation due to sample preparation and analysis when compared to the protein dataset.et al, et al, et al, Intuitively, the activity of a given protein (i.e. kinases and TFs) might be positively or negatively affected by mutations in the same protein or in other proteins it interacts with throughout the signalling networks. Our genetic analysis identified associations between mutated genes and the activities of kinases and TFs, which were significantly enriched for known protein–protein interactions. Moreover, we found that the activities of the transcription factors TP53 and SREBF2 were correlated with their mutational status, as described previously  heterogeneities between individual cancer cells that are unable to be uncovered in bulk. In future, single‐cell multiomics profiling may help to study signalling networks cell‐by‐cell and help to uncover the signalling consequences of intratumoral genetic and nongenetic factors . For the colon cancer samples . The same data for the cancer cell lines of breast and colorectal tumours were downloaded from the respective publications ‐based protein and phosphosite quantifications (absolute [phospho]peptide intensities and ratios relative to controls) for the cancer samples of brain . The data for the brain cancer were compiled from the paediatric cBioPortal ; for the liver ; for the stomach ; and for the colon cancer .The RNA‐seq data were obtained in the format of read counts and Fragments Per Kilobase of transcript per Million mapped reads (FPKM). The data for the tumour tissues of breast  in Mutation Annotation Format (MAF) files. For the breast . The MAF file for the colon cancer samples . Regarding the kidney . For the liver .The whole‐genome sequencing (WGS)‐derived somatic mutations for the brain cancer samples . The same data for the brain cancer samples ; for the colon samples ; and for the liver samples . The segment‐level log2 ratios for the kidney  data were downloaded as discretised GISTIC2 scores  and the cBioPortal  databases. The survival data for these patients were collected from . The clinical data from the cancer cell lines donors . Altogether, we collected the following information about the cancer patients: age, gender, ethnicity, race, height, weight, cancer histological type and subtype, tumour stage, overall survival and survival time in days.The metadata and clinical information from the patients of breast  for the colorectal tumours (Zhang github.com/mskcc/vcf2maf/blob/main/data/isoform_overrides_uniprot). Duplicated phosphosites, arising from multiple phosphopeptide intensities mapping to the same phosphosite, were reduced to a single phosphosite if the log2 fold‐change values were the same across all samples from the respective experimental study. All duplicated phosphosites were discarded otherwise. For the colorectal cancer cell lines . As the phosphorylation sites were annotated at the gene symbol level (see data collection above), we mapped the phosphosites to the protein sequences using the canonical transcripts from UniProt  and the GENCODE v19 human gene annotation (gencodegenes.org). After selecting the protein‐coding genes, as described in the GENCODE v19 annotation, we removed the genes without expression (FPKM > 0) in at least 50% of the samples of the respective dataset. The FPKMs of each gene were subsequently log2 transformed (adding a pseudocount of 1 to avoid taking the log of 0) and converted to log2 fold‐changes by subtracting the log2 median FPKM across samples. The log2 fold‐changes were calculated for each dataset separately. The final gene expression matrix contained 17,056 genes across 1,187 samples and 9 tissues . 14,966 genes were expressed in at least 80% of the samples.The RNA‐seq data (FPKMs and read counts) downloaded from the GDC and GEO websites (see data collection above) were converted to tabular formats using in‐house R scripts. For the liver  with the following parameters: ‐f '%CHROM\t%POS\t%REF\t%ALT\t%QUAL\t%FILTER\t%CSQ\n' ‐d ‐A tab. Only the mutations passing all quality filters (FILTER == “PASS”) were selected for downstream analyses. The mutations from these samples were then collected in a single text file using in‐house bash scripts. In all datasets, we selected the mutations that were annotated using the canonical UniProt transcripts (github.com/mskcc/vcf2maf/blob/main/data/isoform_overrides_uniprot) and classified as frameshift and in‐frame insertions/deletions (Indels), missense, nonsense, stop codon loss (readthrough mutations) and splice site. All mutations (except splice site) were standardised against the Ensembl human proteins (GRCh37—release 98) by filtering out those mutations whose reference (wild type) residues did not match the protein sequences in the mutation positions. The reference/mutated residues and protein position of the mutations were extracted from the HGVSp codes. In total, we collected 284,882 mutations in 17,305 protein‐coding genes, across 1,168 samples  from nine different tissues.We processed the VEP‐annotated VCF files from the kidney . After obtaining the discretised GISTIC2 CNV scores for each dataset, only protein‐coding genes were selected as described in the GENCODE v19 human gene annotation. The final CNV matrix contained 16,520 genes across 1,025 samples and seven tissues (947 tumours and 78 cell lines).GISTIC2 (version 2.0.23) was used to process the segment‐level log2 ratios for the kidney  was removed using a linear regression model. This model was implemented with the mRNA expression or protein abundance of a given gene as a dependent variable and the experimental batch as independent variable. The residuals from the linear model were the protein or mRNA variation not driven by the technical differences between cancer datasets.et al, uniprot.org/downloads). Only the phosphosites mapping to serine, threonine and tyrosine residues were selected. The log10 transformed phosphorylation and protein absolute abundances (iBAQ) were set to the original values using powers of 10 (10^abundance). Phospho(peptide) abundances mapping to the same phosphosite or protein were averaged per sample. The absolute abundances were converted to relative values (fold‐changes) by calculating the log2 ratio of the abundances over the median abundance across cell lines. This process was performed for both cancer cell line sets. To detect net phosphorylation changes, we regressed out the protein levels from the respective phosphosites using the residuals of a linear regression model (y ∼ x) where the phosphosites were set as dependent variables (y) and the proteins as independent variables (x). In total, we assembled 11,940 proteins and 45,557 phosphosites across 125 cell lines (60 from NCI60 and 65 from CRC65).The proteomics and phosphoproteomics data for the NCI60 and CRC65 cancer cell lines (trypsin‐digested version) were downloaded from (Frejno discover.nci.nih.gov/cellminer/) and Transcripts Per Million , for the NCI60 and CRC65 cell lines, respectively. Both gene expression measures were log2 transformed and converted to fold‐changes by subtracting the log2 median FPKM/TPM across cell lines. In total, we calculated the log2 fold‐changes in 18,291 genes across 95 cell lines . The whole‐genome mutation data were downloaded from the CellMiner (discover.nci.nih.gov/cellminer/) and the DepMap databases  for the NCI60 and CRC65 cancer cell lines, respectively. Across the NCI60 cell lines, we selected those mutations where more than 50% of the respective reads contained the alternative allele. In both datasets, we selected the mutations annotated as silent, missense, nonsense, stop codon loss, frameshift and in‐frame Indels. The protein position of the mutations and respective reference/mutated residues was obtained from the HGVSp codes. Altogether, we collected 585,904 mutations in 17,259 genes along 96 cell lines (60 from NCI60 and 36 from CRC65).The gene expression data were downloaded in the format of FPKMs , √N the square root of the number of kinase substrates (N) and σ the standard deviation of the background. Then, the z‐score was used to calculate a two‐tailed P‐value using the pnorm R function [2 × pnorm(−abs(z))], which was further log10 transformed and signed based on the position of the z‐score in the standard normal distribution. If the z‐score was in the right part of the distribution, that is positive, the kinase substrates showed an increase in phosphorylation in comparison with the sample background. Thus, the activity of that kinase was also expected to be increased (positive) in that sample, and vice versa. This process was repeated for all kinases across all samples. For the downstream analyses, we selected the kinase–substrate interactions from the databases and text‐mining resources and the kinase activities quantified with three or more substrates, resulting in 218 kinases with activity estimates in an average of 437 cancer samples .Kinase and TF activities were estimated using known kinase and TF regulatory targets. The kinase–substrate relationships were obtained from (i) ProtMapper (preprint: Bachman FunscoR R package (evocellnet.github.io/funscoR/). The scores range from 0 to 1 and reflect the functional consequence of the phosphosites . The functional score was calculated using the in vivo and in vitro) using a MS‐based phosphoproteomic dataset reporting the relative phosphorylation changes in 52,814 phosphosites in 103 human perturbation‐dependent conditions  and negative (kinase–condition pairs with unknown regulation) classes, we generated 100 random sets of negative cases with the size of the positive set. The predictive skill of each classifier was evaluated by the mean area under the receiver operating characteristic curves (AUROCs). As a control, we replicated the 100 random sets of negative and positive pairs (53 pairs each) along the different lists of kinase–substrates. The ROC curves and corresponding AUCs were calculated using the prediction and performance functions from the ROCR R package.We validated the kinase activities calculated from the different sources of kinase targets  using the Benjamini–Hochberg method. The linear models and respective statistics were calculated using the lm and p.adjust R functions.The www.gsea-msigdb.org) using the gene ontology molecular function term GOMF_PHOSPHATASE_ACTIVITY. Out of 19 cancer samples with the BRAFV600E mutation and profiled with BRAF kinase activities, we selected the 14 samples with activities above the noise level  and with gene expression data. Then, we clustered the samples as high (six samples) and low (eight samples) BRAF activity according to the respective kinase activity sign. The differential gene expression analysis was then performed for 237 phosphatases with gene expression values across all cancer samples. The log2 fold‐changes between high and low BRAF activity samples were calculated from the FPKM expression values as log2(mean(low BRAF samples) / mean(high BRAF samples)). The statistical significance of the gene expression differences was assessed using a Wilcoxon rank‐sum test. The respective P‐values were adjusted for false discovery rate (FDR) using the Benjamini–Hochberg method. The Wilcoxon rank‐sum test and respective statistics were calculated using the wilcox.test and p.adjust R functions.We obtained 281 genes with phosphatase activity from the Molecular Signatures Database  and the mutational status of X (Xmut) was assessed across samples by fitting a linear model that took into account possible confounding effects:act represents the activity of protein Y, β0 the intercept, β1 the regression coefficient for the covariate experimental study, β2 the regression coefficient for the mutational status of X and ɛ the noise term. This model was applied to assess the effect of Xmut on the activity of the same protein (Xact ∼ Xmut) and on the activity of other proteins (Yact ∼ Xmut). The P‐values from the coefficients of Xmut (β2) were calculated using the t‐statistic over a Student's t‐distribution and adjusted for false discovery rate (FDR) using the Benjamini–Hochberg method. The linear models and respective statistics were calculated using the lm and p.adjust R functions.The effects of mutations on the kinase and TF activities were assessed by associating the activity of a given protein with the mutational status of the same protein or other proteins it might interact throughout the cellular regulatory networks. First, we built a binary mutation matrix M where the index Mact ∼ Xmut or Xact ∼ Xmut was performed if Xmut was mutated in at least five of all the samples in the pair. Regarding the Yact ∼ Xmut associations, we tested 520,938 pairs between 208 kinases and 3,590 genes and 1,048,216 pairs between 292 TFs and 3,590 genes. In relation to the Xact ∼ Xmut associations, we tested 40 pairs and 64 pairs with the kinases and TFs, respectively.The associations were performed with the genes mutated in more than 20 samples and with the protein activities estimated in at least 10 samples. An association between a pair YmissForest function from the missForest R package. Prior to that, we selected the kinases (columns) with activity measures in at least 60% of the samples and the samples (rows) with measures in at least 80% of the kinases. The imputed kinase activity matrix contained 90 kinases across 727 samples. The PCA analysis was performed using the prcomp R function  and the UMAP analysis using the umap function from the umap R package (with default parameters).We reduced the dimensionality of the kinase and TF activity matrices using the PCA and UMAP methods. Given the sparseness of the kinase activity matrix, we imputed the missing values using the hclust R function, agglomeration method = “complete”) using the Jaccard Index (JI) of shared substrates between kinases as distance measure (1‐JI). Then, the kinase dendrogram was cut at a specific level (height = 0.85) to identify clusters of non‐redundant kinases. We only kept one kinase per cluster (with the largest amount of substrates), reducing the number of kinases from 304 to 208.When correlating the kinase activities with the UMAP projections (Pearson correlation coefficient), we excluded redundant kinases based on the degree of shared substrates. We first performed a hierarchical clustering analysis (omnipathdb.org/interactions). We selected the interactions reported as directed, activating (stimulating relationships) and consensual along the resources (databases). Then, we correlated the activity of the kinase–kinase pairs along the samples using the Spearman's rank correlation coefficient. The kinase pairs were stratified by the number of databases in which the interaction was found as a way of ascertaining the relevance of the interactions.We obtained kinase–kinase regulation pairs from the OmniPath database  using the degree of shared substrates between kinases as distance measure (1—Jaccard Index of shared substrates). The dendrogram tree was cut at a height cut‐off of 0.8. Just one kinase was kept per cluster (with the highest number of substrates). Using this approach, we reduced the number of kinases from 304 to 231.Strong correlations might be due to the amount of shared substrates between kinase pairs, and not because of co‐regulation events. To control for this technical limitation, we repeated the correlation analysis using a set of nonredundant kinases. This set was obtained by performing a hierarchical clustering analysis (act) using the activity of kinase K (Kact), while adjusting for possible confounding effects:act represents the activity of the transcription factor T, β0 the intercept, β1 the regression coefficient for the covariate experimental study, β2 the regression coefficient for the activity of kinase K and ɛ the noise term. The P‐values from the coefficients of Kact (β2) were calculated using the t‐statistic over a Student's t‐distribution and adjusted for false discovery rate (FDR) using the Benjamini–Hochberg method. The linear models and respective statistics were calculated using the lm and p.adjust R functions. Using this model, we tested 26,280 kinase–TF associations between 90 kinases and 292 TFs.For a given protein pair K and T, where K is a kinase and T is a transcription factor, we tested whether the changes in the activity of kinase K are linearly associated with changes in the activity of the transcription factor T. To do that, we fitted a linear model to predict the activity of transcription factor T . The human network (version 11.0) was downloaded from the STRING database (string-db.org) as a list of protein–protein interactions with the corresponding combined scores. The scores range from 150 to 999 and represent the confidence of the respective interactions. We filtered the network using a minimum score of 850 to select the most confident protein–protein interactions. Fisher's exact tests were performed by overlapping the protein association pairs with the STRING network across increasing ‐log10 adjusted P‐values. The backgrounds corresponded to all the protein association pairs linearly modelled.The genetic and the kinase–TF associations were tested for enrichment in the STRING protein–protein interactions network using Fisher's exact tests  procedure (p.adjust R function). The KM curves were plotted using the ggsurvplot function from the survminer R package.In order to construct Kaplan–Meier (KM) survival curves, cancer samples were stratified based on their TF and kinase activity scores (AS). For each kinase and TF, we classified the samples as: inactive if AS < −1.75; active if AS > 1.75; neutral if −1.75 < AS < 1.75. The 1.75 activity cut‐off was chosen based on a previous publication  of 28 recurrently mutated genes in our atlas (at least 100 mutations). Such models were applied to the protein–tissue pairs described previously. We extracted the hazard ratios of the protein activity coefficients and corresponding 95% confidence intervals and To cluster the samples based on TF and kinase activities, a cross‐correlation matrix of samples was created based on the Spearman correlation coefficient of a combination of 292 TFs and 90 kinase activity scores in each sample.Then, Euclidean distance was computed between each sample based on their correlation profiles. A hierarchical clustering tree was created using the complete method over the euclidean distance matrix. The hierarchical tree was then cut at the minimum tree height to obtain eight groups. The number of clusters was decided based on visual inspection of the within‐cluster sum of square plot, silhouette profile and of a heatmap of the cross‐correlation matrix.Each kinase and TF was then summarised into a single cluster‐level activity score. The cluster‐level activity scores were calculated as the median activity score in each cluster divided by the standard deviation of the corresponding activities in each cluster.P‐values were adjusted for FDR using the Benjamini–Hochberg method.Clinical annotations for body mass index (BMI) and age were binarised . Clinical annotation levels that were populated for less than five patients in the cohort were excluded. Each clinical annotation level was then tested for over‐representation in each cluster using a hypergeometric test. For each cluster, deregulated kinases and TFs were defined using an absolute value of 2 for the median/standard deviation score. The Carnival R package was used to generate systematic mechanistic networks of interactions between deregulated kinases and TFs for each cluster. Carnival was used with default parameters, except for the time limit parameter that was set to 10,800 s, and the mipGAP parameter was set to 0.2. The optimisation problem was solved using the CPLEX solver. The prior knowledge network was generated from the omnipathR R package, using the “import_all_interactions” function.Abel Sousa: Conceptualization; formal analysis; visualization; writing – original draft; writing – review and editing. Aurelien Dugourd: Formal analysis; visualization; writing – original draft; writing – review and editing. Danish Memon: Formal analysis; writing – review and editing. Borgthor Petursson: Formal analysis; writing – review and editing. Evangelia Petsalaki: Supervision; writing – review and editing. Julio Saez‐Rodriguez: Supervision; writing – review and editing. Pedro Beltrao: Conceptualization; supervision; writing – original draft; writing – review and editing.JS‐R receives funding from GSK and Sanofi and consultant fees from Travere Therapeutics. JS‐R is an EAB member, and PB is an EAB and EPAB member. This has no bearing on the editorial consideration of this article for publication.AppendixClick here for additional data file.Dataset EV1Click here for additional data file.Dataset EV2Click here for additional data file.Dataset EV3Click here for additional data file.Dataset EV4Click here for additional data file.Dataset EV5Click here for additional data file.


The technologies advances of single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) allowed to generate thousands of single cells in a relatively easy and economic manner and it is rapidly advancing the understanding of the cellular composition of complex organisms and tissues. The data structure and feature in scRNA-seq is similar to that in scATAC-seq, therefore, it’s encouraged to identify and classify the cell types in scATAC-seq through traditional supervised machine learning methods, which are proved reliable in scRNA-seq datasets.In this study, we evaluated the classification performance of 6 well-known machine learning methods on scATAC-seq. A total of 4 public scATAC-seq datasets vary in tissues, sizes and technologies were applied to the evaluation of the performance of the methods. We assessed these methods using a 5-folds cross validation experiment, called intra-dataset experiment, based on recall, precision and the percentage of correctly predicted cells. The results show that these methods performed well in some specific types of the cell in a specific scATAC-seq dataset, while the overall performance is not as well as that in scRNA-seq analysis. In addition, we evaluated the classification performance of these methods by training and predicting in different datasets generated from same sample, called inter-datasets experiments, which may help us to assess the performance of these methods in more realistic scenarios.Both in intra-dataset and in inter-dataset experiment, SVM and NMC are overall outperformed others across all 4 datasets. Thus, we recommend researchers to use SVM and NMC as the underlying classifier when developing an automatic cell-type classification method for scATAC-seq. In recent years, scATAC-seq provides the opportunity to accurately and sensitively identify and characterize the cells in complex tissues. scATAC technologies enable profiling the epigenetic landscapes of thousands of individual cells in a relatively easy and cost-effective way, resulting in the development of computational methods to analyze and interpret data. Due to the similarity of data structure and feature selection between scRNA-seq and scATAC-seq data, the procedures for analyzing the composition of cells in complex tissues from scRNA-seq  can alsoIn order to overcome such challenges in cell-type annotation, a plenty of classification methods including support vector machine (SVM) , 4, neurHere, we evaluated 6 popular machine learning methods ) to automatically assign cell labels in scATAC-seq datasets. These 6 machine learning methods were evaluated using 4 public available scATAC-seq datasets  which make the identification and classification easier than other datasets for all the machine learning methods. For Corces2016 dataset, SVM performed best in all 4 cell types. SVM as the top performing scATAC-seq data analysis method had the F1 score of all cell types surpassed 0.85, followed by RF and LDA with the average of F1 obtained 0.75 and 0.79 respectively. However, both KNN with setting 9 nearest neighbors (KNN9) and KNN with setting 50 nearest neighbors (KNN50), only got F1 about 0.50 in LSC, performed worst across all four cell types  classification is essential in the evaluation. We applied these machine learning methods to 10 × PBMCs v1 and 10 × PBMCs Next Gem datasets from a same donor but profiled to a different molecular depth with a different library preparation method and different chromium system (v1 chemistry and Next Gem v1.1 chemistry) into the inter-dataset experiment. In detail, we first trained the model using 10 × PBMCs v1 dataset and then test the classification ability on 10 × PBMCs Next Gem dataset to get the methods that have the stronger ability to predict the cell types with higher performance. All experiment results are summarized in Figs. In this experiment, most of machine learning methods had competent ability to are distinguish B, Memory CD4 + and Naive CD4 + T cell types from each other correctly. However, only NMC, DT and SVM successfully separated part of FCGR3A + Mono from CD14 + Mono, while others mistakenly mix these two cells together Fig. . SimilarTaken together, we evaluated the performance of 6 current state-of-art machine learning methods for identify and classify the cell types automatically using 4 available scATAC-seq datasets. In this study, we designed 2 testing scenarios, that is, an intra-dataset and an inter-dataset experiment, to test the ability to distinguish and profile the cell types of traditional machine learning methods in scATAC-seq comprehensively. Overall the 4 datasets, several traditional methods including SVM, NMC and RF could distinguish the various cell types with relatively high performance in intra-dataset experiment. In contrary, compared to the intra-dataset experiment, we observed relatively worse performance for the inter-dataset experiment, likely due to the technical and biological differences between the datasets. SVM and NMC outperformed among the other methods both in intra-dataset and inter-dataset experiment, however, KNN9 and KNN50 performed worst for the both experiments. It’s worth noting that, although adjusting the settings for a specific dataset might improve the performances, it increases the risk of overtraining. In order to reduce the risk, we evaluated all methods using the default settings except setting 9 and 50 nearest neighbors for KNN9 and KNN50 used to identify smaller and larger population. Moreover, we observed that there is no significant consistent trend between the population size of cell and the accuracy of classification. In another word, the performance of the methods didn’t depend on the population of the cell types.In the inter-dataset experiments, we tested the performance of the methods to classify the populations using the trained model across different scATAC-seq protocols. Due to the limitation of available dataset, two 10 × PBMCs datasets which are sampled from a same healthy donor but sequenced with different prepared libraries and different chromium systems were applied to the inter-dataset experiment. The results showed that these two datasets are compatible, SVM and NMC outperformed than other 4 machine learning methods in 5 cell groups. Unfortunately, KNN9 and KNN50 are still the performed worst with barely correctly identifying and identifying all cells types into one group.These results play an important role for research to choose the most high-performance traditional machine learning method for the analysis of scATAC-seq data as underlying classifier when researcher try to develop an automatic classification method based on scATAC-seq.As far as possible to choose a machine learning method which is most compatible for the specific dataset including the biological problem and biological problem. SVM and NMC may performed better than other traditional method for general datasets. However, the default parameters and settings we used in these methods may not reach the best performance in specific datasets, thus the developer need to adapt and adjust accordingly.A modification to the kernel function or a correction and integration to the read signal in scATAC-seq data is essential and important for the accuracy and sensitivity of following classification. As mentioned before, there is a great loss in read signal because of the sequencing technique, we encourage the research to recover or enhance the read signal before training and predicting, which may enhance the recognition of features.All in Although some methods performed well in the datasets we chose, the existing machine learning methods couldn’t produce convincing results for developing a scATAC-seq automatic classification method, thus they still cannot be used directly as underlying classifier. In order to obtain higher performance, we encourage that researcher to:We evaluated 6 state-of-art machine learning methods on different scATAC-seq datasets, publicly available as Python packages Fig. . These 6https://support.10xgenomics.com/single-cell-gene-expression/datasets/3.0.2/5k_pbmc_v3.Corces2016, Buenrostro2018, 10 × PBMCs v1 and 10 × PBMCs Next Gem, these 4 datasets were used to assess all the 6 machine learning identification methods, from which all the 4 datasets were apply for the intra-dataset experiment using a fivefold cross-validation scheme, and the 10 × PBMCs v1 and 10 × PBMCs Next Gem datasets were used for further inter-dataset experiment. The datasets we used are various both in different tissue , size (from 575 to 4585) and the sequencing protocol (Illumina NextSeq 500 and 10 × Chromium). The human immune cell (Corce2016) is available from the NCBI database (GSE74310). The peak and count file of human hematopoietic system cell could be downloaded at GSE96769.The 10 × PBMCs v1 dataset and 10 × PBMCs Next Gem dataset were obtained from 10 × website  [For 10 × PBMCs v1 and 10 × PBMCs Next Gem data, no label is provided for each cell when the datasets were first downloaded from website. Based on the labeled scRNA-seq data of the same healthy donor, we then predict the labels for scATAC-seq data using a Seurat v3, which is fast and can accurately detect the true biology connection, to select the high confidence labels as golden standard. Using this process, we totally labeled 2927 of 3917 cells and 3670 of 4585 cells for v1 dataset and Next Gem dataset respectively. Finally, only the labeled cells were kept for downstream analysis after filtered the 10 × PBMCs v1 and 10 × PBMCs Next Gem dataset.For intra-dataset experiment, we evaluated the performance by applying a fivefold cross-validation across each dataset for those supervised machine learning methods. The folds were divided in a stratified manner in order to ensure equal proportions of each cell population in each fold. The training and testing folds were exactly the same for all methods. A total of 28 experiments (7 methods and 4 datasets) were applied for all methods.For inter-dataset experiment, we used the 10 × PBMCs v1 and 10 × PBMCs Next Gem datasets to test the ability of machine learning methods in realistic scenarios. We trained a classifier using 10 × PBMCs v1 data and then test the performance of identifying various cell types on 10 × PBMCs Next Gem dataset. A total of 7 experiments were applied for all methods.


Alcohol use disorder (AUD) is highly prevalent and one of the leading causes of disability in the US and around the world. There are some molecular biomarkers of heavy alcohol use and liver damage which can suggest AUD, but these are lacking in sensitivity and specificity. AUD treatment involves psychosocial interventions and medications for managing alcohol withdrawal, assisting in abstinence and reduced drinking , and treating comorbid psychiatric conditions . It has been suggested that various patient groups within the heterogeneous AUD population would respond more favorably to specific treatment approaches. For example, there is some evidence that so-called reward-drinkers respond better to naltrexone than acamprosate. However, there are currently no objective molecular markers to separate patients into optimal treatment groups or any markers of treatment response. Objective molecular biomarkers could aid in AUD diagnosis and patient stratification, which could personalize treatment and improve outcomes through more targeted interventions. Biomarkers of treatment response could also improve AUD management and treatment development. Systems biology considers complex diseases and emergent behaviors as the outcome of interactions and crosstalk between biomolecular networks. A systems approach that uses transcriptomic  can capture genetic and environmental factors associated with AUD and potentially provide sensitive, specific, and objective biomarkers to guide patient stratification, prognosis of treatment response or relapse, and predict optimal treatments. This Review describes and highlights state-of-the-art research on employing transcriptomic data and artificial intelligence (AI) methods to serve as molecular biomarkers with the goal of improving the clinical management of AUD. Considerations about future directions are also discussed. Alcohol use disorder (AUD) is highly prevalent and one of the leading causes of disability in the United States, second only to ischemic heart disease . AUD is Biomarker development and personalized medicine approaches have been identified as top priorities to expedite translational research in AUD . To stanAlcohol use disorder is a complex disorder with both genetic and environmental factors that contribute in roughly equal proportions e.g., . ResearcTechnological advancements in next generation RNA sequencing and other omics technologies have positioned these intermediary molecules to be especially useful biomarkers for complex diseases like AUD. In addition to incorporating both genetic and environmental information, the detection methods are accurate, relatively easy to carry out and interpret, and inexpensive . Furthermore, these technologies involve the measurement of many molecules simultaneously permitting systems-level molecular approaches and facilitating the selection of biomarker panels. While analyzing single targets is appealing for simplicity, expecting a single molecule to capture the state of a complex disorder like AUD is unrealistic. Instead, systems approaches are required for analysis of complex disease states resulting from network perturbations , and bioWhile systems approaches that employ transcriptomics are in clinical use for precision medicine in cancer , successAs -omics-based technologies have emerged, more studies have employed them to explore alcohol’s effects in peripheral tissues. The goal of this Review is to synthesize the literature on transcriptome-based biomarkers for AUD from accessible tissues. Challenges and future directions for RNA biomarkers are also discussed. The discussion of other omics-based intermediary phenotypes in peripheral tissues for use in AUD management is outside the scope of this Review, but interested readers are referred to articles regarding DNA methylation markers , proteomFor this Review, the PubMed database was searched for relevant literature using a Boolean search strategy (inception through 2 July 2022): (microarray OR RNA seq OR transcriptome) AND  AND  NOT cancer NOT liver.The articles were evaluated for the following inclusion characteristics: related to alcohol use disorder (studies on liver disease and carcinoma were excluded), genome-wide study , some metric of biomarker performance , English language, full text availability. The references from the articles were also considered for inclusion in the Review. The included manuscripts were reviewed by the author to provide an overview of the current knowledge regarding transcriptome-based biomarkers in AUD.Before detailing the results of the literature search, it is necessary to provide some definitions and background on the transcriptome and how biomarker performance is evaluated.Some background is provided here to give the reader a basic understanding of classifiers [a type of machine learning (ML)] and the evaluation of classifier performance. A classification problem can be approached using supervised and unsupervised methods. In contrast to unsupervised clustering, supervised classifiers learn a function from training data that consist of pairs of input objects  and desired outputs . The function learned will depend on the specific ML model the user selects. Some examples are Random Forest, Neural Network, Logistic Regression, Naïve Bayes, Support Vector Machines (SVMs), and others. Each has its own set of assumptions and tradeoffs in terms of model complexity, how interpretable the model is, and computational costs for training the model. An overview of the available classification techniques is beyond the scope of this Review but interested readers can refer to Although a description of the available ML methods is outside the scope of this Review, we will briefly describe three popular classification methods that are used in two of the RNA biomarker studies discussed below : Random a priori. It is advisable to try as many different methods as possible beginning with the simplest. This is because complex models do not necessarily outperform simpler models , and there is a tradeoff between model complexity and interpretability/computational expense. The simpler the model the less computational load required to train the model and the more interpretable the final model will be.These classification techniques are a good starting place with transcriptomic datasets because they produce relatively interpretable models and include measures of feature importance which could offer biological insights into AUD based on the most differentiating features between the classes being distinguished. However, these are only three of many techniques, and it is difficult to determine what techniques will produce the highest performing models for a given dataset, There are several different metrics used to estimate the performance of a biomarker or panel of biomarkers in classifying samples as one class or another . Some of the most common are accuracy, sensitivity, specificity, and area under receiver operating characteristic curve (AUC). Once a classification model is constructed, its performance can be evaluated on an unseen data set (it is important that the data used to test the model’s performance was not involved in building the model). Given a test set and a classifier, each decision will be one of the following: (1) true positive (a positive example classified as positive), (2) false negative (a positive example misclassified as negative), (3) true negative (a negative example classified as negative), (4) false positive (a negative example misclassified as positive). A contingency table can be constructed from this information as a 2 × 2 matrix where the columns are the known true classes and the rows are the predicted classes. Several common metrics can be calculated from this table: Accuracy (the fraction of predictions the model assigned correctly): resholds . To summresholds .The transcriptome is the complete set of RNA transcripts produced by a genome which includes both protein-coding messenger RNAs (mRNAs) and non-coding RNAs (ncRNAs). The mRNA transcripts reflect which genes are actively expressed. mRNA levels can be compared between two conditions to identify differentially expressed genes (DEGs) pertaining to a biological state of interest . The non-coding RNAs include both housekeeping RNAs  and regulatory RNAs . The transcriptome is affected by both genetic variation and environmental conditions . MicroarTranscriptome studies in the alcohol research field have revealed that alcohol use affects the expression patterns of hundreds of mRNAs and ncRNAs in the brain tissue of humans and animal models, e.g., see reviews and primary research articles . In addiThe search resulted in 17 manuscripts that have employed transcriptome-based methods to identify biomarkers for AUD or alcohol exposure . All 17 Twelve studies did not measure biomarker performance. Although these studies did not meet the inclusion criteria, they provide a basis for RNA biomarkers for AUD and so are tabulated separately . Five weTen studies measured mRNA levels in peripheral tissues . Only onHUS1, TP35, MYC, MUTYH, CDK4, ERCC1, MCM5) were decreased in expression in human AUD blood compared with control. Blood expression levels of two genes (ERCC1 and MCM5) showed a highly significant correlation with AUD-induced decreases in the volume of the left parietal supramarginal gyrus and various neuropsychological measures. This study is not hypothesis-free and does not include an evaluation of the biomarker utility . However, it demonstrated an ethanol-related gene expression signature in human AUD blood and correlated blood gene expression levels with alcohol effects in the CNS.Another study linked peripheral blood mRNA levels with alcohol’s effects in brain . This stImmune response is a prominent theme in peripheral mRNA transcriptomes after alcohol exposure, which is consistent with what is seen in brain . In addiRPL9, RPS3A, and RPS17) form part of the transactivation responsive (TAR)-RNA-binding protein (TRBP)-associated complex and are positively regulated by miR-10a and miR-21. Expression of both miR-10a and miR-21 was measured by qPCR and found to be upregulated following the stress cue in heavy drinking (but not the moderate drinking) subjects. Expression levels of both microRNAs were correlated with amount of beer consumed in heavy drinking (but not moderate drinking) subjects . Together these data that gene expression changes can be detected in response to psychological stress within an hour after cue presentation. Furthermore, expression of miR-10a, miR-21, and several of their target genes is regulated by psychological stress in heavy drinking subjects and correlated with stress-induced drinking in a laboratory setting.To determine the relevance of these changes to stress and alcohol seeking behavior, Immune-related genes were also affected in blood after acute ethanol exposure. in vitro with the Epstein–Barr virus (EBV), a process that makes them immortalized. The group posited that LCLs are a useful model to study the effects of ethanol under controlled conditions and evaluate preexisting differences between those with and without AUD because (1) neuroimmune genes and pathways have been associated with AUD/ethanol in the brain, (2) many of the genes expressed in brain are also expressed in LCLs, and (3) LCLs are accessible. These studies showed that many genes were affected by ethanol exposure in LCLs. This included an immune response seen strongly after 24 h of exposure, that decreased in intensity after 48 h of exposure, potentially due to a reversal of interferon signaling. The genes that were increased in LCLs after ethanol exposure were enriched with genes related to pro-inflammatory pathways . The genes that were decreased in LCLs after ethanol exposure were enriched with genes related to the anti-inflammatory IL10 pathway. There was limited power to detect differences between untreated LCLs from AUD and controls. Combining the data between the two studies on unexposed cells identified 465 genes  with differences ≥1.2-fold. Three of these were identified as either genome-wide or nominally significant by GWAS and were affected by ethanol with a direction opposite those for AUD versus controls: MREG (melanoregulin), CASZ1 (castor zinc finger 1), and ST3GAL1 . Sixty-seven pathways were enriched in the genes that differed at baseline between AUD and control LCLs. One of the most significantly enriched pathways was cholesterol biosynthesis . Ingenuity Pathway Analysis software was used to predict what transcriptional regulators  are likely to produce the observed gene expression differences between AUD and control LCLs. This “upstream analysis” predicted higher activity of TLRs, LPS, TNF, interleukins, interferons, and TP53 [consistent with McClintick and colleagues studied the effects of 24-h  or 48-h Three studies have performed a transcriptome analysis using peripheral blood mononuclear cells (PBMCs) from rhesus macaque monkeys after chronic voluntary ethanol intake , 2019. AConsistent with the PCA results, there is a significant overlap between gene expression changes induced by moderate and heavy chronic alcohol drinking in macaques which is what was observed for human moderate and heavy drinkers as well . TranscrCd14)  .In addition to immune signaling pathways, alcohol affected genes belonging to other major biological categories in peripheral tissues consistently across studies. Other broad categories include cell growth and death, translation, mitochondrial dysfunction/stress responses, and cell-cell signaling (among others). The cell signaling category contained pathways that are most well-studied in the central nervous system (CNS), e.g., GABA and glutamate signaling. Blood cells express ∼80% of genes that are expressed in brain including receptors and enzymes that are required for processing the main excitatory and inhibitory neurotransmitters of the CNS , and other neurotransmitter and neuropeptide systems . In factIncreasing evidence suggests that non-coding RNAs (ncRNAs) play a role in psychiatric disorders including AUD . The effEight studies measured microRNA levels in peripheral tissues , 3. MicrThree studies assessed the diagnostic discrimination of peripheral microRNA profiles for recreational alcohol drinking , AUD Ro, and alcp < 0.05 and fold-change > 2). Three miRNAs  showed similar fold changes and the same direction of expression changes in both AA and EA AUD subjects. miR-10a-5p is especially noteworthy as it has also been independently validated as being upregulated in the saliva of an alcohol abusing population (p < 0.05 and logFC > 1) were included in the prediction analysis, the prediction accuracy was the lowest (63.6% in AAs and 56.2% in EAs), suggesting that feature selection using ML could be more powerful in identifying discriminative biomarkers for predicting AUD status than using differential expression, although the top miRNAs according to the Gini importance measure were also differentially expressed in AUD subjects .pulation  and in tpulation . Enrichmpulation . Inclusip < 0.05) changed microRNAs: 15 downregulated microRNAs , 10 upregulated microRNAs , and 13 de novo microRNAs . The top pathways enriched in the potential targets of the microRNAs differentially expressed in patients chronically abusing alcohol were: Adherens junction [consistent with what was seen in In addition to AUD, there is also evidence that microRNA profiles from saliva might be useful in distinguishing heavy alcohol drinkers from sporadic alcohol users . In thisFive additional studies measured ethanol’s effects on microRNA expression in accessible tissues but did not include any metric of biomarker utility . HoweverThe macaque studies discussed in the Section “mRNA” above also analyzed microRNA profiles in the same PBMC samples , 2019. BPAQR9, NR2F6, and GATA2), two of miR-24 target mRNAs were upregulated , and two of miR-23a target mRNAs were upregulated . This study did not use a small RNA library preparation so there was limited ability to detect microRNAs.Another study profiled microRNAs in PBMCs from male heavy drinking, moderate drinking, and control monkeys . Chronic heavy alcohol drinking resulted in the downregulation of nine microRNAs  and upregulation of 2 microRNAs  . About 1N = 80; 17 females and 71 males across eight cohorts) and analyzed plasma extracellular vesicles instead of PBMCs  are a class of RNA species that lack 5′ N7-methylguanosine (m7G) caps and 3′ polyadenylated tails, and instead are single-stranded RNAs covalently closed in a circular formation. CircRNAs are usually produced through a process called pre-mRNA “back-splicing” of exons, introns, and intergenic regions . circRNAP < 0.05). CircRNAs can act as microRNA sponges .Circular RNAs were sequenced in AUD and control samples from circulating serum exosomes . Differe sponges . To dete sponges . In factTo explore whether circRNA could be a biomarker for chronic ethanol exposure, The search for objective diagnostic molecular biomarkers is based on identifying the molecular differences between two groups. However, the groups are defined by “biased” diagnostic criteria . This circular process is a major limitation for this research area. Illustrating this point, an epigenome wide association study revealed DNA methylation marks were associated with PETH  but not to AUDIT-C scores , despite there being a positive correlation between PETH and AUDIT-C . FramewoBlood biomarkers may represent a combination of AUD-specific factors  and non-specific factors . First and foremost, the biomarker must be sensitive and specific at its task whether that be diagnosis, prognosis, treatment response, or treatment prioritization. However, an ideal biomarker would not only be a “marker” but would also reflect the underlying pathophysiology .Two studies discussed in this review showed similar changes in brain and blood for some RNA expression levels with alcohol exposure . Other sThe studies in this Review compare an alcohol dependent population to healthy controls with exclusion criteria often including other psychiatric illnesses or diseases. Some of the pathways identified in these studies that are disrupted in peripheral tissue with AUD have been linked with numerous other diseases  . MoreoveAnother confound for AUD biomarker research is the use of medications. The transcriptome can respond to medications, and if individuals with AUD are taking medications to treat their AUD (or other conditions) this could confound the RNA biomarker search. For example, some studies have found very little disorder-related genes independent of treatment for schizophrenia and bipolar disorder . It is iAll the studies discussed in this Review used RNA profiles from peripheral tissues as diagnostic biomarkers to detect ethanol exposure or AUD status. However, in addition to providing data-driven information to guide AUD diagnosis, peripheral RNA profiles could also be utilized as other types of biomarkers listed in Some of these biomarkers are related to one another. For example, stratifying patients into subgroups could facilitate optimal treatment selection. There is some evidence that naltrexone is more effective for reward drinkers  and thatDeveloping these RNA biomarkers will require longitudinal studies, where samples are taken repeatedly before the onset of AUD and throughout AUD progression. Inevitably some participants in these studies would develop AUD (and some not), and some of those who develop an AUD will recover either with intervention or spontaneously (and some not). Data from these studies will be critical in developing other types of RNA biomarkers for AUD, which could arguably represent a greater healthcare need than diagnostic biomarkers because there are existing tools to diagnose AUD , but there are currently no ways to identify “at-risk” individuals, stratify the AUD patient population, select optimal treatments, or monitor treatment response.Gene expression data can be used to identify pharmacological treatments for AUD through a process termed connectivity mapping . The ideThe studies in this Review used bulk sequencing techniques which are low cost, scalable, and can identify differential expression and co-expression between patients and controls. However, a key source of unwanted variability is cellular heterogeneity and unwanted variability can decrease statistical power to detect meaningful differences between groups of interest. Imagine a scenario in which ethanol strongly upregulates an RNA in one cell type, and strongly downregulates the same RNA in another cell type. In bulk sequencing, the cells are lysed and the RNAs in each cell are combined. This effectively averages ethanol’s effects across cell types, and RNAs that are affected strongly by ethanol (but in different directions depending on cell type) would be overlooked. Single-cell techniques use next-generation sequencing to analyze the genetic content of individual cells, providing valuable insights into ethanol’s effects on the functional characteristics of each cell type. Two challenges with single cell data are that it is difficult to generate cell type specific co-expression networks and cost is high which diminishes the clinical utility of single cell approaches. However, single cell data will be critical in understanding the mechanistic relevance of peripheral biomarkers to AUD which is a key part of the process in AUD biomarker development.1  tools, like the ML classifiers described in the Section “Evaluating biomarker performance,” are increasingly being explored in medical and healthcare research, and there are currently 79 AI/ML based, FDA approved medical devices and algorithms1 . The wor1 .Two studies in this Review used ML classifiers to predict AUD or chronic ethanol exposure from transcriptomic data . Other tOne of the biggest challenges when using ML approaches with gene expression data is known as the “curse of dimensionality,” which means that the number of observations is greater than the number of samples. Gene expression data sets (and other -omics data sets) are high dimensional, meaning that there are many observations (tens of thousands of gene expression levels are measured). One way to solve the curse of dimensionality is to increase the sample size. There is scarcity of research in training set size determination methodologies for ML, but it will depend on the complexity of the algorithm and the specific use case. The studies discussed in the above paragraph range in sample size from 20 to 2,034. When large sample sizes are not readily available, another solution is to reduce the number of features  using feature selection techniques. This is advisable as many of the features in high dimensional datasets will be either irrelevant or redundant which will reduce the power of the ML algorithm to identify meaningful patterns . Some MLIn summary, ML models learn a function from training data that consist of pairs of input objects  and assigned labels  and the model’s performance is then evaluated on an independent testing dataset. The training data set is important both in size and nature. The more examples in a training set and the more representative it is of the patient population, the better the model will be in terms of accuracy and generalizability. To increase the ability of ML algorithms to build meaningful (and useful) models, feature selection should be performed in gene expression datasets due to the redundancy and irrelevance of many of the features in these high dimensional datasets. It is highly important that the testing dataset be independent and not involved in the feature selection or model training to get accurate estimates of model performance. Following these guidelines will enable researchers to build on the progress made applying state-of-the-art ML methods to precision medicine in AUD.This Review tabulated and summarized the studies on peripheral RNA biomarkers for AUD. Signatures of chronic and acute alcohol use could be detected in the peripheral transcriptome for both coding and non-coding RNAs, and some of these gene expression responses were detected within relatively short time periods after alcohol or stress cue exposures (60–90 min). Most of the studies did not include a measure of biomarker performance which limits the ability to determine the usefulness of RNAs to serve as biomarkers for AUD. Based on the studies that did include a measure of biomarker performance, the ability of RNA levels to serve as diagnostic biomarkers and predict AUD or other alcohol-related phenotypes was generally good (AUC > 0.70). To facilitate AUD biomarker research, future studies should include a measure of biomarker performance. There is a scarcity of studies investigating the ability of RNAs to serve as other types of biomarkers. While there are existing means for diagnosing AUD, there are no clinical tools for predicting at-risk individuals, stratifying patient groups, prioritizing optimal treatments, or monitoring treatment response. Therefore, this is an unmet healthcare need and future work should include longitudinal studies with repeated sampling to evaluate this potential. It will also be important to see if integrating data across -omics platforms results in an improved biomarker performance using ML approaches. There were no studies assessing alcohol’s effects on some categories of regulatory non-coding RNAs (lncRNAs and piRNAs), so these represent additional unexplored avenues of research for future studies.These studies suggest that there is an AUD-related signature in peripheral tissue that holds diagnostic utility. Based on these findings, we are confident that -omics technologies and systems biology approaches will identify clinically useful biomarkers for AUD, however, establishing models that link mechanisms and biomarkers is likely to be a challenge for the coming decades of basic and clinical research. The identified RNAs in these studies will require further evaluation and validation to find markers with high specificity, low cost, and ease-of-use in routine diagnostic laboratories. Laboratory analysis of RNA has been facilitated by PCR methodologies now available in many diagnostic centers because of the COVID-19 pandemic.LF: conceptualization, data curation, investigation, methodology, project administration, visualization, writing – original draft, review, and editing. RDM: supervision and writing – review and editing. ROM: funding acquisition, resources, supervision, and writing – review and editing. All authors contributed to the article and approved the submitted version.


Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO), cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer, as it is indicative of the biochemical processes in tissue and cells, as well as the genetic characteristics of an organism. Deoxyribonucleic acid (DNA) microarrays and ribonucleic acid (RNA)-sequencing methods for gene expression data allow quantifying the expression levels of genes and produce valuable data for computational analysis. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods. Both conventional and deep learning-based approaches are reviewed, with an emphasis on the application of deep learning models due to their comparative advantages for identifying gene patterns that are distinctive for various types of cancers. Relevant works that employ the most commonly used deep neural network architectures are covered, including multi-layer perceptrons, as well as convolutional, recurrent, graph, and transformer networks. This survey also presents an overview of the data collection methods for gene expression analysis and lists important datasets that are commonly used for supervised machine learning for this task. Furthermore, we review pertinent techniques for feature engineering and data preprocessing that are typically used to handle the high dimensionality of gene expression data, caused by a large number of genes present in data samples. The paper concludes with a discussion of future research directions for machine learning-based gene expression analysis for cancer classification. Cancer describes a class of diseases in which malignant cells form inside the human body due to genetic change. These cells divide indiscriminately upon development, extend throughout the organs, and in many cases, they can result in loss of life. Cancer is the second leading cause of mortality globally after cardiovascular illnesses . RecentlGene expression is the process by which the information contained in DNA is transformed into instructions for making proteins or other molecules. It involves the transcription of DNA into messenger RNA (mRNA), followed by a translation into proteins. Gene expression analysis is employed to assess the order of genetic alterations occurring under certain conditions, in tissue or a single cell . It invoGene expression analysis requires implementing computational methods for understanding how genes are regulated or their role in the functioning of tissues and cells. machine learning (ML)-based approaches have been frequently used to obtain insights related to how variations in genes and regulatory regions result in phenotypic changes, such as traits, wellness, and health ,11. WherAn important component of computational methods for gene expression analysis is feature engineering, employed to handle the challenge of high dimensionality and the relatively small number of samples in gene expression data. This study provides an overview of the feature engineering techniques in gene expression analysis, classified into filter, wrapper, and embedded methods . Filter In the published literature, various architectures of deep neural networks (NN) were applied for cancer classification using gene expression data, including fully connected neural networks , convolutional neural networks (CNN), recurrent neural networks (RNN), graph neural networks (GNN), and transformers neural networks (TNN). MLP networks have connections from each neuron to all neurons in the previous and the next layers. For analysis of gene expression data, the input layer in MLP receives the gene expression profiles with each probe received by one neuron. The output layer of the MLP returns the class probabilities of the gene expression sample . CNN modDespite the recent progress in ML-based cancer classification using gene expression data, various challenges remain to be addressed. Specifically, gene expression datasets typically contain a small sample size, where each sample has a relatively large number of dimensions, i.e., the number of genes. To handle this challenge, ML methods usually rely on feature-engineering techniques for removing redundant information and selecting an optimal set of features for classification. Although conventional ML approaches are more dependent on efficient feature engineering and data preprocessing, prior works have also leveraged feature engineering techniques in the workflows of DL models to improve performance. In addition, transfer learning techniques were also employed to overcome the problem of model training in the small data regime. DL-based methods have generally outperformed conventional ML methods, and it can be expected that most future models for gene expression analysis will be based on DL networks. Currently, several approaches that employed MLP or CNN networks in combination with efficient feature engineering and transfer learning techniques have achieved test accuracies upwards of 90%. However, the performance of current methods is sensitive to various parameters, and further improvements are required for the generalization and robustness of the methods. Other limitations of existing approaches include the lack of interpretability and limited integration with other data types and modalities.Numerous review papers in the published literature have overviewed the advances in computational approaches for gene expression analysis. The most relevant papers that are the closest to this review and were published in the last three years are listed in Gene expression analysis is the process of identifying the number of transcripts present in a particular cell or tissue type to estimate the level of expressed genes. The branch of science that focuses on the quantitative examination of the transcriptome is transcriptomics. Early computational transcriptomics methods employed Sanger sequencing of expressed sequence tag (EST) libraries. EST libraries represent short fragments of mRNA obtained from a single sequencing procedure carried out on randomly chosen clones from cDNA libraries. Whereas, a cDNA library is a collection of DNA sequences that have been cloned and are complementary to mRNA that has been retrieved from an organism or tissue. Over 45 million EST libraries from approximately 1400 distinct cellular species have been produced to date. Although EST libraries provide a base resolution profile of expressed gene sequences, this technology usually does not contain full-length gene sequences, and subsequently, the methods based on EST libraries were superseded by chemical tag-based techniques, such as Serial Analysis of Gene Expression (SAGE). The SAGE method allows for quantitative and simultaneous analysis of a large number of transcripts in any particular cell system, without prior knowledge of the genes. This method is based on a theoretical calculation that assumes a random nucleotide distribution throughout the genome. The methods of Sanger sequencing of EST libraries and SAGE were succeeded by DNA microarrays and NGS methods—most notably RNA-Seq—for estimating gene expression.Microarray data are obtained through a laboratory technique where a DNA sequence is contained in a tool consisting of a two-dimensional array with thousands of microscopic spots. The microarray tools are also known as chips or slides, and each spot on the slide is reserved for a single DNA sequence or gene. The DNA samples bind to the microarray slide through a hybridization process, which is followed by scanning the colors of the spots on the slide to measure the expression of each gene. One row in the microarray data represents the gene expression level, and the columns represent the samples.Microarrays can be used to identify DNA (as in comparative genomic hybridization) or RNA (often as cDNA following reverse transcription), which may or may not be translated into proteins. Microarray data allow for the comprehension of cellular processes for genome-wide expression profiles related to specific conditions or diseases, such as cancer. Similarly, they provide helpful information used in the pursuit of new pharmaceuticals, in pharmacogenomics, and in the development of effective medications for therapeutic methods.One of the main advantages of DNA microarrays is that they allow one to measure the expression level of thousands of genes. Microarrays also have limitations, including relatively poor accuracy, precision, and specificity. Another limitation is the high sensitivity of the experimental setup to changes in the temperature of hybridization, the purity and rate of genetic material degradation, and the amplification process, all of which may affect the quantification of gene expression.RNA-Sequencing (RNA-Seq) belongs to the NGS methods , which aSimilarly, single-cell RNA sequencing (scRNA-Seq)  allows tTo obtain RNA-Seq data, the extracted RNA is first converted into cDNA, and next cDNA libraries are prepared for sequencing, and are sequenced on an NGS platform. The sequencing process involves isolating and purifying mRNA molecules from the data. cDNA is obtained by reverse transcription of mRNA using reverse transcriptase enzyme. cDNA libraries are prepared by amplifying the cDNA fragments, and afterwards, NGS platforms are used to analyze the resultant short-read sequences and estimate the gene expression levels. The procedure depends on a number of experimental factors, including the utilization of biological and technical replicates, level of sequencing, and target transcriptome coverage. In certain instances, these experimental options will not significantly affect the quality of RNA-Seq data. Still, thoughtful experimental design is often required, with a focus on striking a balance between high-quality outcomes, time, and financial expenditure.The research community has dedicated significant efforts to collect, organize, and integrate various types of gene expression data. Feature engineering is the process of turning raw data into features to emphasize relevant information in the data and/or enhance the data analytics capability of machine learning models. Feature engineering can select relevant features or produce novel features for both supervised and unsupervised learning to streamline and accelerate data transformations while simultaneously increasing the predictive potential of computational methods. Such techniques have been used to extract marker genes that influence the discriminative capabilities of ML models ,60 by evExisting algorithms for feature engineering of gene expression data can be categorized into three major groups: filter, wrapper, and embedded methods. The information flow in the three groups of feature engineering methods is depicted in Filter methods include feature engineering techniques that filter out data features that are unlikely to contribute to the performance of a predictive model used in gene expression analysis . Filter Wrapper methods evaluate the significance of data features by employing a classification algorithm. Wrapper methods interact with the classifier to identify a subset of attributes that are optimal for model predictions, i.e., the attributions of various gene subsets are assessed in an initial phase using the classifier to be employed, and afterwards the classifier is re-trained using the genes with high importance. The criterion for selecting a subset of features with wrapper methods is the performance of the learning algorithm. In contrast, the learning algorithm is wrapped in a search algorithm that will discover the best subset of features. In other words, wrapper methods evaluate the subsets of features using the learning algorithm as a black box and the learning algorithm performance as the objective function. Wrapper methods can be categorized as deterministic or randomized search algorithms. Classification models that have been used with wrapper methods include k-Nearest Neighbors , Random Embedded methods comprise algorithms for feature engineering that consider the configuration of a used classifier to explore the space of hypotheses and feature subsets in search of the optimal subset of features. Embedded methods attempt to combine the benefits of filter and wrapper methods by applying the advantages of these methods to the specifics of a single learning algorithm. Embedded methods generally produce an improved performance in comparison to filter and wrapper methods, as they have increased capacity for dealing with the feature interaction problem. This problem occurs when the interactions of a subset of genes with other genes influence the feature selection process, making it prone to be stuck with a subset of locally optimal features .Several works in the literature have employed a hybrid approach that combines the advantages of the different feature-engineering methods. For instance, filter methods can be employed in an initial step to reduce the overall number of features that are transmitted to the wrapper stage. Afterward, a classifier model is applied to further refine the features and select the final subset of genes . Such meVarious ML methods have been used in gene expression analysis to identify potential cancers and provide insights into potential treatment options.Conventional machine learning methods, such as Support Vector Machines (SVM), k-Nearest Neighbor (kNN), Naïve Bayes (NB), Random Forest (RF), and related methods were used in a body of works on early cancer detection ,85. For In general, ML algorithms have proven to be a powerful tool for detecting hard-to-discern patterns in complex and high-dimensional data across numerous applications. Therefore, they have been well-suited for analysis and classification of gene expression data . HoweverDeep learning-based methods employ artificial neural networks (NN) with multiple layers of processing units for learning data representations. These methods can learn hierarchical representations in high-dimensional data, which is a key advantage compared to conventional ML algorithms . ConsequMLP is a neural network architecture with fully connected layers where each neuron in a hidden layer is connected to all other neurons in the neighboring layers. MLP classifiers have been designed for cancer classification in a line of prior works on gene expression analysis. For instance, Lai et al.  designedRNN is a subclass of neural networks that introduces recurrent connections between the neuron units, which furnish the network with a memory capability: past observations can be employed to understand the current observation or predict future observations in an input sequence. These characteristics provide RNN with sequential dynamic behavior, making it suitable for processing sequential data and identifying inner relationships and variation tendencies ,102. SahRNN models have multiple advantages in gene expression analysis, as they improve the efficiency by enabling the model to identify and retain sequential feature information . AdditioConvolution Neural Networks (CNN) are deep learning architectures initially designed primarily for image analysis and processing. CNN employ convolutional filters to automatically learn spatial feature hierarchies in input data. The network architectures use a combination of stacked convolutional and pooling layers  . In geneCNN using multi-dimensional 1D, 2D, and 3D convolutional models has been designed for analysis of gene expression data. One-dimensional convolutions were applied to sequences of genomic data and were proven suitable for learning sequential patterns. Two-dimensional convolutions have been designed for processing gene expression data that are first transformed into an image format. Gene expression images are typically created by directly mapping gene expression values to a predetermined palette of colors and utilizing domain-specific data to identify the location of each gene in the images. The main disadvantage of this method is the loss of information that results from utilizing discrete sets of colors instead of the original continuous expression values to calculate the values of the image pixels. Another approach divides the process of creating a gene expression image into two parts that follow one another. First, a biological functional hierarchy represented by a tree-shaped structure is transformed into a functional hierarchy image template. The gene locations are defined by following a certain biological standard. Afterward, gene expression images are created by mapping the expression values to the positions of the genes in the image template. This results in a final set of images where each pixel represents the continuous gene expression value of the corresponding gene, preventing the information loss that results from converting continuous gene expression values into a set of discrete colors . CNN modGraph neural networks (GNN)  are a deGNN models have been designed for the analysis of multi-omics pan-cancer data such as gene expression profile, DNA methylation, gene mutation rates, copy number variation, exon expression, and clinical data, with an emphasis on predicting various types of cancers . PfeiferGNN models have multiple advantages for processing gene expression data due to the intrinsic learnable properties of propagating and aggregating attributes that capture relationships across the entire cell-cell graph. Hence, the learned graph embeddings can be treated as high-order representations of cell–cell relationships in RNA-Seq data in the context of graph topology. GNNs can also effectively aggregate detailed relationships between similar cells using a bottom-up approach, and they can use prior domain knowledge in gene regulation to direct the imputation of missing data . FurtherTNN models employ network architectures that are based on the multi-head self-attention mechanism, which allows capturing long-range dependencies between items in a sequence . It is aTNN models have demonstrated increased robustness in comparison to CNN and RNN and exhibited competitive performance on benchmarks with various data formats. The self-attention mechanism allows one to utilize contextual information for any location in the input sequence and capture long-range dependencies in comparison to CNN, and permits higher parallelization compared to RNN . Among tTransfer learning  aims to Pathway analysis is a widely used technique for extracting biological meaning from high-throughput gene expression data. Existing methods primarily focus on determining which pathway or pathways may have been disrupted because of differential gene expression patterns . For insThis section discusses future directions that may potentially advance the research on ML-based gene expression analysis.One avenue of future research is to consider additional types of input features with existing learning algorithms because the full impact of gene expression cannot be represented by the genetic sequence alone. Specifically, DNA methylations and mutations are possible types of features that can be utilized in cancer classification. DNA methylations can occur at CpG dinucleotides as well as in non-CpG sites. The CpG is used to differentiate between the CG base-pairing of cytosine and guanine from the single-stranded linear sequence. DNA methylation is linked to the normal developmental process and the observable change during the pathological processes. The pathological processes include DNA repair genes and the gene-silencing of tumor suppressors. Therefore, integrating methylations and mutations with RNA-Seq data can produce features that positively impact tumor classification.Along with selecting the feature type that can contribute significantly to enhancing the performance of ML methods, the design of the computational algorithm is also essential. In this regard, researchers can focus on innovative techniques that can perform efficiently on gold-standard datasets, such as the unique molecular identifier (UMI), which has experimentally proven reference genes. Such studies can allow researchers to conduct an experimental comparison of single-cell methods. Also, research studies can validate the performance of algorithms on single-cell sequencing protocols such as SMART-Seq, Cel-Seqs, and droplets.Identifying cancer-related biomarkers can be an important future direction where researchers can investigate methodologies for identifying biomarkers related to each cancer type. For instance, the methods listed for IntPath  and otheGNN can also be designed to support the integration of single-cell multi-omics data by implementing heterogeneous graphs. Such data can include Droplet scRNA-Seq  and the It is also essential to place emphasis on the design of interpretable ML models that help to understand the decision-making process by the employed computational methods, and provide explanations about the cases where the models might fail. Interpretable and explainable models that highlight the local and global properties of ML models based on counterfactuals or feature attribution should receive increased attention in this area.Recently, more studies have considered the analogous genomic probing of pre-malignant lesions in the genomic analysis of various cancers, undertaken as part of The Cancer Genome Atlas (TCGA) project. In addition to genomics, cancer prevention strategies in the future will incorporate a variety of new modalities, including imaging, proteomic, metabolomic, glycemic, and epigenetic, to identify and validate surrogate biomarkers for cancer gene prevention trials. In conjunction with preclinical and clinical studies, these modalities can help establish new biomarkers to improve cancer patients’ treatment. Toxicologists, pathologists, and clinicians involved in early-phase clinical studies may be able to use these novel validated biomarkers for diagnosis, treatment, and cancer patient monitoring.Multidomain genomic data analysis is another important avenue to study feature selection and extraction, and for downstream analysis. Multimodal and multitask ML methods based on early and late fusion may provide improved performance compared to existing methods.Differentiating clinically similar cancers can be challenging, and focusing on genomic and transcriptomic variations may prove beneficial. The omics data describe details on various methods available for ovarian and different types of cancers and renal cell carcinoma for identifying key genes and pathways that might assist in proposing diagnostic and prognostic predictions. Optical genome mapping and structural variant analysis  may be applied to various cancer datasets for improved prognosis and treatment .Further understanding of circRNA localization, transportation, and degradation in live cells, a completed circRNA interactome, and single-cell profiling are important topics in this field that may prove helpful for cancer gene prediction .A summary of future perspectives is provided in Recent advances in deep learning-based approaches for processing complex high-dimensional data offer tremendous potential for pattern recognition and predictive analytics of multi-omics data. This study overviews the progress in the application of both traditional machine learning methods and deep learning methods for gene expression analysis using RNA-sequencing and DNA microarray data for cancer detection. The manuscript presents a brief overview of the data collection methods for gene expression analysis and lists the pertinent commonly used datasets for supervised machine learning. A taxonomy of the techniques for feature engineering and data preprocessing is also provided, as an important component of gene expression analysis. ML-based methods for gene expression analysis are presented next, with a focus on deep learning-based approaches, due to their comparative advantages for gene expression analysis. Prior works that employ neural networks with popular architectures are covered, including multi-layer perceptrons, as well as convolutional, recurrent, graph, and transformer networks. The use of deep learning methods in cancer classification using RNA-Seq data has shown promising results, with several studies reporting high accuracy in classifying different types of cancer. We expect that future research in this area will address the current challenges of generalizability, robustness, and explainability of the results, and will lead to enhanced cancer diagnosis and improved healthcare outcomes. The paper concludes with an outline of promising future directions for cancer classification using gene expression analysis.The main contributions of this study are in the provision of a comprehensive review of recent research works for cancer classification using gene expression analysis, covering feature engineering techniques, datasets for gene expression analysis, and applications of traditional and deep learning ML methods. This study overviews methods based on recent neural network architectures—such as graph and transformer networks—that have not been covered in published reviews, as well as having a focus on RNA-Seq methods as the dominant data format in recent works.