Omics analyses provide possibilities for molecular classification of cancers to enable personalized medicine. To allow for multi-layered molecular analysis, we developed an automated protocol for the generation of proteomics data of breast cancer tumor tissue that is subjected to parallel transcriptome analysis. We compare different data acquisition strategies for proteomics and settle on data-independent acquisition, achieving high correlation with RNA between samples. The proteomics data were further used for functional analyses and tumor classification, showing the potential of the methodology.In recent years, several advances have been achieved in breast cancer (BC) classification and treatment. However, overdiagnosis, overtreatment, and recurrent disease are still significant causes of complication and death. Here, we present the development of a protocol aimed at parallel transcriptome and proteome analysis of BC tissue samples using mass spectrometry, via Data Dependent and Independent Acquisitions (DDA and DIA). Protein digestion was semi-automated and performed on flowthroughs after RNA extraction. Data for 116 samples were acquired in DDA and DIA modes and processed using MaxQuant, EncyclopeDIA, or DIA-NN. DIA-NN showed an increased number of identified proteins, reproducibility, and correlation with matching RNA-seq data, therefore representing the best alternative for this setup. Gene Set Enrichment Analysis pointed towards complementary information being found between transcriptomic and proteomic data. A decision tree model, designed to predict the intrinsic subtypes based on differentially abundant proteins across different conditions, selected protein groups that recapitulate important clinical features, such as estrogen receptor status, HER2 status, proliferation, and aggressiveness. Taken together, our results indicate that the proposed protocol performed well for the application. Additionally, the relevance of the selected proteins points to the possibility of using such data as a biomarker discovery tool for personalized medicine. Breast cancer (BC) was the leading cause of cancer incidence in 2020, representing an estimated 11.7% of all cases diagnosed in 2020. Despite this number, it ranks fifth in terms of mortality ,4,5,6,7.Several advances have been achieved in the classification of BC, which is mostly done at the genomic or transcriptomic level. This includes transcriptomic analysis of large cohorts, including the TCGA  and the Aiming to evaluate the performance of different proteomic methods and assess their potential use in biomarker discovery, we developed a protocol for parallel RNA and proteome analysis of BC tissue samples, and analyzed proteome samples belonging to a BC cohort using three label-free methods, namely DDA, library-based DIA via EncyclopeDIA , and libFirst, 116 breast cancer samples from SCAN-B ,20 were RNA-seq data was acquired through the Gene Expression Omnibus (series GSE96058), corresponding to gene expression data from 30,800 genes across 3273 samples.In the different analytical steps which follow, i.e., proteomics method comparison, protein/transcript correlation analysis, Gene Set Enrichment Analysis, and decision tree classifier, subsets of these original samples were used to match samples across experiments. All samples were accompanied by clinical data registered prospectively at time of diagnosis from the Swedish National Quality Register for Breast Cancer (NKBC), which included, for instance, information on hormone receptor and HER2 statuses, lymph node status, subtyping, and tumor grade. More detailed information can be found in RNA concentrations were available for all samples. Therefore, a subset of 31 samples were used for quantification using a Total Protein Kit, Micro Lowry . The concentrations were then correlated with RNA concentrations. The protein concentration in all samples was then estimated based on the regression analysis of this subset.The samples were allowed to thaw at +4 °C for 20 min prior to aliquoting. Then, 20 μg of each sample was transferred to KingFisher 96 deep-well microtiter plates . The proteins were reduced and alkylated. First, 1M solutions of DTT and IAA were prepared. DTT was added to the samples to a final concentration of 10 mM. The proteins were reduced at room temperature for 45 min. IAA was added to the samples to a final concentration of 40 mM. The samples were then alkylated for 45 min at room temperature in the dark. Finally, DTT was added to a final concentration of 20 mM to quench the reaction.v/v), and the resulting mixture was dried on a SpeedVac Vacuum Concentrator (ThermoFisher Scientific). The samples were then resuspended in 50 µL 5% formic acid and desalted on BioPureSPN Mini C18 columns . Peptides were eluted in 50 μL 50% acetonitrile, 5% formic acid, and dried. The samples were then resuspended in 14 μL 0.1% formic acid. The final concentration was estimated by performing A280 measures on a NanoDrop 2000 (ThermoFisher Scientific).Protein digestion was automated on a KingFisher Flex robot (ThermoFisher Scientific) in a 96-well format. The tip comb was stored in plate #1. HILIC beads , were placed in plate #2 together with equilibration buffer  in a bead/protein ratio of 10:1, giving a total volume of 300 µL. Plate #3 contained equilibration buffer (500 µL), without the addition of new beads. The samples were placed in plate #4 in binding buffer  to a final volume of 300 µL. Next, 300 µL of washing solution (95% acetonitrile) was placed on plates #5–6. Plate #7 contained 290 µL of digestion buffer (50 mM ammonium bicarbonate) with sequencing-grade modified trypsin  in a trypsin/protein ratio of 1:20. The protocol was carried out in one step equilibration of 1 min, followed by a 30-min protein binding step. The washes were then performed for 1 min each, and digestion took place for 4 h at 37 °C with intermittent mixing every 2 min. Protease activity was then quenched with formic acid to a final concentration of 5%  coupled online to an electrospray ionization Q-Exactive HF-X Hybrid Quadrupole-Orbitrap mass spectrometer (ThermoFisher Scientific) for all samples. Approximately 500 ng peptide solution was injected per sample and separated using a 15 cm long fused silica capillary  packed in house with ReproSil-Pur 1.9 μm C18 material . The peptides were separated in 0.1% FA using a 3-min gradient from 5–10% solvent B , followed by a 60-min linear gradient from 10% to 25% B from 3 to 63 min, followed by a 5 min gradient to 40% B, and finally a 5-min gradient to 90% B followed by 7 min isocratic washing at 90% B at a constant flow rate of 250 nL/min.m/z and normalized collision energy 27 was employed with target 1E5 ions, maximum IT 20 ms, and resolution 15,000.For DDA, a top 20 method was used with MS1 target 3E6 ions, maximum injection time 50 ms, and resolution 120,000. For MS2, an isolation window of 1.2 m/z isolation windows and normalized collision energy 27 with 20 ms maximum injection time at 15000 resolution. For acquisition of a spectral library, a sample pool derived from 30 samples belonging to different subtypes was used .For correlation purposes, a new MaxQuant run was initialized with the DDA RAW files. The parameters were kept the same, except for the database, which corresponded to the Uniprot human FASTA file as of 25 April 2019 and the corresponding predicted Prosit spectral library. The files were downloaded at https://www.proteomicsdb.org/prosit (accessed on 7 July 2020) was used as input for the library generation, as described by Searle et al [The DIA raw data files were converted to mzML with vendor peak-picking and demultiplexing using msconvert in Proteowizard version 3.0.21098 . For genle et al . For quantitative data processing in EncyclopeDIA (version 0.9.5) or DIA-NN (version 1.8), the false discovery rate (FDR) was set to 1% for both peptide and protein levels. For EncyclopeDIA, the chromatography library from above was used and settings were: expectedPeakWidth 25.0, numberOfQuantitativePeaks 5, minNumOfQuantitativePeaks 3.m/z 300–1800 were considered with maximum one missed cleavage. A maximum of one variable modification per peptide was considered. For quantification, ‘robust LC (high accuracy)’ was selected, and mass accuracy was automatically set.For DIA-NN, library free mode was used with the same Uniprot FASTA database as input. Precursors of charge state 1–4, peptide lengths 7–30 and peptide https://github.com/ComputationalProteomics/ProteinRollup, accessed on 4 June 2020), where peptides passing a 1% FDR identification threshold were grouped in protein groups with a minimum of two peptides being required for such group. Samples were matched against available clinical data to add data belonging to different gene expression phenotype and risk predictor signatures. Group-wise differential abundance comparisons were performed using LIMMA statistics in NormalyzerDE. Unsupervised hierarchical clustering was performed on normalized proteome and transcriptome data from 66 samples. Clustering was performed using the ComplexHeatmap package, version 2.10.0. The data were filtered to remove ‘NA’ values prior to being filtered to remove features with low variance and converted to z-scores. Annotation corresponding to PAM50 subtyping, ER status, HER2 status, PGR status, and lymph node status were then added to the generated figures. Quantitative peptide data were cyclic Loess normalized using NormalyzerDE . All proAll statistical analyses were performed in RStudio, using R version 4.1.3 and the R notebook is available as p value adjustment method (pAdjustMethod = “fdr”) and adjusted p value cutoff . The process was independently applied to each subtype for both datasets (proteomics and transcriptomics) and the results were then compiled into a single table.Gene Set Enrichment Analysis (GSEA) was performed using the GSEA function in the R package clusterProfiler using a ranked list of differentially abundant proteins or transcripts in each of the intrinsic subtypes. The list was ranked according to log2 fold change values. The aim was to find pathways, as defined by the MsigDB Hallmark set, enriched separately across the subtypes . Parametp-value < 0.1; absolute log2FoldChange ≥ 1.5) prior to being processed according to the procedure mentioned in The same list of differentially abundant proteins or transcripts was also used as input for unsupervised hierarchical clustering via the ComplexHeatmap package. The data were first filtered . The samples were split into a training and a test set. The training set corresponded to 70% of the data in each subtype (60 samples), i.e., data from 70% of samples in each subtype, used to train and generate the model. The test set, on the other hand, contained the remaining 30% of samples (24 samples) and was used to assess the performance of the model on samples which had never been used before. Normalized intensity values from these 223 protein groups and their corresponding transcripts were further used to generate a dataset used for unsupervised hierarchical clustering via the ComplexHeatmap R package.The analysis was based on a set of 223 unique protein groups differentially abundant in different subtypes as well as in different clinicopathological characteristics. The list was generated by filtering the protein groups across the different comparisons . We then applied Gene Set Enrichment Analysis (GSEA) to identify pathways that would be enriched (both positively and negatively) in each subtype and dataset .For the most part, the pathways showed similar trends in both datasets. For instance, in luminal tumors, characterized by being ER-positive, early and late estrogen response were significantly enriched in the positive side, which is expected. Additionally, pathways associated with proliferation –a well-established hallmark of cancer–are among the highest absolute enrichments . The resTo assess the potential use of proteomics data in a biomarker discovery platform, we constructed a decision tree to stratify patients in the five intrinsic subtypes based on normalized protein intensities. In the context of machine learning strategies, decision trees are part of the toolbox of supervised methods, i.e., methods that are trained based on combined input and output data in order to predict the output from the input data belonging to new samples . The maip-value < 0.01. A list containing 223 proteins was generated after filtering. The original DIA-NN dataset was then filtered to only contain these proteins, and samples were split into a training and a test set. The data were split by intrinsic subtype to have representation of all the subtypes.To construct the tree, a set of differentially abundant proteins was identified. This list was created based on different group comparisons: ER+ versus ER-; grade 3 versus grade 1; lymph node positive versus lymph node negative; HER2+ versus HER2-; Basal-like versus Luminal A; Luminal A versus Luminal B; Luminal B versus HER2-enriched; Basal versus HER2-enriched; and Luminal A versus HER2-enriched. A combination of features originating from the intrinsic subtypes as well as clinical subtypes and other clinical parameters was selected in an attempt to capture variability in BC. Additionally, using only intrinsic subtypes to predict intrinsic subtypes could potentially lead to overfitting of the model, thereby reducing its generalizability and possibly the relevance of the discriminating proteins as biomarkers. From such comparisons, the resulting proteins were further filtered based on log2 Fold Change > 2 and adjusted After these two sets were generated, the training set was used to train a recursive partitioning algorithm for continuous data in a conditional inference framework . BootstrAs a way of comparing the performance of different models, the proteins selected by the model generated in Bouchal, P. et al. (2019) were used to create a new model to predict the intrinsic subtypes with the same dataset used to generate the model previously described. This model with the respective group distribution during training can be seen in Finally, the 223 differentially abundant protein groups were used to filter the transcriptomics data, and new hierarchical clustering was performed for each omics data type across the same samples . A totalSeveral advances have been achieved in the molecular classification of BC, with the intrinsic subtypes providing relevant information about prognosis and treatment response, and even contributing to a reduction in the mortality rate. However, in the context of personalized medicine, challenges such as overtreatment, overdiagnosis, and late recurrence still point towards the need for improved patient stratification. Given the direct relationship between proteins and functions, proteomics is expected to provide useful complementary information.In the present study, we used both DDA and DIA LC-MS/MS to acquire data from 116 BC tissue samples and compared the results with the objective to assess the impact of the choice of acquisition method on the potential use of such data in biomarker discovery. In the context of personalized medicine, the field of proteomics is moving in the direction of peptide-centric data querying strategies. Targeted approaches and DIA mitigate the issues surrounding the reproducibility and stochasticity found in DDA, but the much more complex and computationally intense DIA methods are subjected to greater variation from data processing strategies. On the other hand, biomarker discovery efforts have mostly been performed via DDA, and the often-used spectrum-centric data querying strategy makes FDR determination more robust and straightforward ,30. For When evaluating the different acquisition methods, our results indicate that DIA-NN outperformed EncyclopeDIA and DDA in terms of the total number of identified proteins, number of proteins found across all samples, and correlation to RNA-seq data, suggesting that DIA-NN as used here in the library-free mode offers increased proteome coverage and reproducibility. This is in agreement with what was reported by Gotti, C. et al. (2021). In their setup, different DIA methods and software tools were benchmarked. They report that DIA-NN achieves the highest number of identifications, reproducibility, and sensitivity, especially when in the FASTA mode with narrow acquisition windows . It shouOur suggested method, on the other hand, performed well in this setup. It is backed up by the higher correlation values between transcripts and their respective proteins, as well as the overall increased number of proteins identified, both overall and in terms of proteins found across all samples ,15,16.The good correlation between transcriptome and proteome data is further highlighted in the unsupervised hierarchical clustering of proteome and transcriptome features . The samDespite the different number of features, the unsupervised hierarchical clustering shows good agreement, particularly in relation to PAM50 subtypes, with most Basal and HER2-enriched samples clustering separately from the Luminal and Normal-like samples. Interestingly, Normal-like samples clustered almost entirely separated from other subtypes in the proteome heatmap but not in the transcriptome one. Additionally, Luminal B samples clustered more closely to Basal and HER2-enriched in the proteome data as opposed to the transcriptome data, where they clustered more closely to Luminal A. These differences may serve as evidence for functional differences between transcriptome and proteome data.A GSEA was performed to assess the possibility of getting additional information from different data sources. Our results  indicatePatterns associated with proliferation such as G2M checkpoint, E2F targets, and MYC targets, were among the most highly enriched in basal tumors, both based on transcriptomics and proteomics data. For basal-like tumors especially, pathways associated with MYC targets, the cell cycle checkpoint, and DNA repair seem to be enriched, which is in agreement with previous reports . In lumiA similar study was conducted by Oshi, M. et al. (2020) for the G2M checkpoint pathway . The regAccording to Mertins, P. et al. (2016), agreement of the intrinsic subtypes in transcriptomics and proteomics seems to be high, even when separate tissue sections are used for each analysis. However, consensus clustering of proteome and phosphoproteome data identified three main clusters. The basal-enriched and luminal-enriched clusters showed strong overlap with basal-like and luminal samples, respectively, while a third stromal-enriched cluster had representation from all subtypes. Interestingly, HER2-enriched samples were distributed across all three clusters .Enrichment in pathways associated with immune response and inflammation, e.g., T-cell, B-cell, and neutrophil signatures, was also identified at the proteome level for basal-like tumors . Murthy,The presence of TILs in breast tumors has been associated with improved prognosis . These cIn summary, the intrinsic subtypes seem to be represented both at the transcriptome and proteome level. Our GSEA further supports this, as similar enrichments are for the most part observed in both data layers. However, as it has been observed that unsupervised clustering of proteome and phosphoproteome data results in clusters that do not fully overlap with the intrinsic subtypes, differences in enrichment can also be expected, which is observed particularly for HER2-enriched samples . Our resp-value <0.05. The reason behind some of the nodes containing multiple proteins is technically from the overlap of constituent peptides analyzed using LC-MS/MS and suggests that these sum up as more discriminant than peptides unique to these specific proteins.A decision tree was created to classify samples based on their LC-MS/MS data, ultimately assessing the use of such data in biomarker discovery . OverallAnterior gradient proteins  are members of the protein disulfide isomerase family, present in the endoplasmic reticulum. Their function is associated with protein folding and protein degradation in this cell compartment, through a process known as endoplasmic reticulum-based degradation. AGR2 appears to be overexpressed in ER-positive BC, where it is often associated with poor prognosis ,37. It iThe epidermal growth factor receptor (EGFR) family of receptor tyrosine kinases is composed of four different members, EGFR (ErbB1/HER1), ErbB2 (HER2), ErbB3 (HER3), and ErbB4 (HER4). Members of this family have been extensively associated with the progression of different conditions, often related to a poor outcome ,40. HER2The next protein identified in the decision tree is CHI3L1. It has also been implicated in the promotion of inflammation (via Th2-like immune reaction), macrophage activation, tumor growth, and pro-angiogenesis . EvaluatFinally, Ribonucleotide reductase is an enzyme essential to DNA replication. It is composed of four different subunits, RRM2 being one of them. It was previously shown that high expressions of this protein could serve as a prognostic indicator of poor survival in different types of cancer, including lung and colorectal cancers . It has Besides analyzing the tree in terms of the functionality of the selected protein groups, the predictability of the model can be assessed by means of the confusion matrix . This maAnother method of measuring the model performance is by means of the kappa statistic. The concept was first introduced by Jacob Cohen in the context of psychological behavior as a way of measuring the degree of agreement of at least two people observing the same phenomenon ,49. It iFinally, unsupervised hierarchical clustering using the decision tree data  suggest In the present study, we successfully explored and compared the use of different label-free LC-MS/MS proteomics methods in a BC cohort, establishing a new workflow for the analysis of BC tissue flowthroughs via DIA. A larger number of samples is still required to further evaluate and validate the performance of the decision tree model. However, our results suggest that proteomic technologies can provide additional information to already existing clinical subtyping. Moreover, proteins highlighted by the model show a clear link to BC, elucidating biological processes related to the disease. These findings can potentially enable a better understanding of BC biology and assist in the improvement of personalized medicine, with the development of novel biomarker signatures.


Genome-wide profiling of chromatin accessibility by DNase-seq or ATAC-seq has been widely used to identify regulatory DNA elements and transcription factor binding sites. However, enzymatic DNA cleavage exhibits intrinsic sequence biases that confound chromatin accessibility profiling data analysis. Existing computational tools are limited in their ability to account for such intrinsic biases and not designed for analyzing single-cell data. Here, we present Simplex Encoded Linear Model for Accessible Chromatin (SELMA), a computational method for systematic estimation of intrinsic cleavage biases from genomic chromatin accessibility profiling data. We demonstrate that SELMA yields accurate and robust bias estimation from both bulk and single-cell DNase-seq and ATAC-seq data. SELMA can utilize internal mitochondrial DNA data to improve bias estimation. We show that transcription factor binding inference from DNase footprints can be improved by incorporating estimated biases using SELMA. Furthermore, we show strong effects of intrinsic biases in single-cell ATAC-seq data, and develop the first single-cell ATAC-seq intrinsic bias correction model to improve cell clustering. SELMA can enhance the performance of existing bioinformatics tools and improve the analysis of both bulk and single-cell chromatin accessibility sequencing data. Genome-wide profiling of chromatin accessibility by DNase-seq or ATAC-seq has been widely used to identify regulatory DNA elements and transcription factor binding sites. Here the authors develop a computational model, SELMA, to estimate and correct enzymatic cleavage biases in chromatin accessibility profiling data. Genome-wide profiles of chromatin accessibility are a means to measure the global landscapes of active regulatory elements in different cell types. DNaseI hypersensitivity sequencing (DNase-seq) and the assay for transposase-accessible chromatin using sequencing (ATAC-seq) have become widely used for the genomic profiling of chromatin structure and accessibility4. Signal enrichments, or “peaks”, from DNase-seq or ATAC-seq data are considered to represent accessible chromatin regions and can be used for inferring regulatory elements or TF binding sites. In addition, DNase-seq and ATAC-seq data also exhibit footprint patterns, which are fine structures in the accessible chromatin where high-affinity protein-DNA interactions protect the DNA from DNaseI or Tn5-transposase cleavages5. DNase/ATAC-seq footprint detection has been implicated as an effective approach for identifying accurate TF binding sites at base-pair resolution7. A few computational tools have been developed for detecting footprints from DNase-seq  or ATAC-seq data . A recent study integrated 243 DNase-seq samples from different human cell and tissue types and reported approximately 4.5 million DNaseI consensus footprints associated with TF occupancy across the human genome as one of the largest maps of human regulatory DNA7.Cis-regulatory elements in the genome play a critical role in transcription regulation by interaction with protein molecules such as transcription factors (TFs). These DNA elements are usually unwrapped from packed nucleosomes and are accessible in the chromatin structure16. Such intrinsic enzymatic biases in sequencing data can potentially confound observed cleavage patterns and footprint detection. The characterization and correction of intrinsic cleavage biases are essential to DNase/ATAC-seq data analysis. To characterize intrinsic cleavage biases, most studies use a k-mer model, in which the k-mer DNA sequence centered at a cleavage site of DNaseI/Tn5 is used as the signature of this cleavage17. The sequence bias can be estimated by counting the occurrences of cleavages with each k-mer in one dataset relative to the genome-wide occurrences of this k-mer. A naive k-mer model assumes that k-mers are independent of each other, resulting in an exponential increase in the degree of freedom when k increases. Therefore, a naive k-mer model becomes less feasible in practice with a large k, especially with samples having insufficient sequencing depth. Although most studies use 6-mers with 46 = 4096 parameters18, it is unclear whether a different model with a larger k-mer size and more feasible parameter estimation can achieve better performance.The premise of using DNase-seq and ATAC-seq data to profile chromatin accessibility is that enzymatic DNA cleavages represented by sequence reads reflect local chromatin openness only. However, it has been shown that both DNaseI and Tn5 transposase exhibit sequence selection biases in high-throughput sequencing data19 and footprint detection with bias correction20 for bulk DNase/ATAC-seq data. Recently, single-cell ATAC-seq (scATAC-seq) has enabled chromatin accessibility profiling in thousands of individual cells at one time24. Due to the high sparsity of single-cell data and because most chromatin accessibility regions contain only one read in one cell, any potential bias can be substantial in scATAC-seq data compared to bulk data, creating additional challenges in computational analysis. Incorporating intrinsic cleavage bias effects for improved scATAC-seq analysis also requires comprehensive assessment and development of innovative methods.Several studies have used various computational approaches for intrinsic cleavage bias estimationSimplex Encoded Linear Model for Accessible Chromatin (SELMA), a computational framework for the accurate estimation of intrinsic cleavage biases and improved analysis of DNase/ATAC-seq data for both bulk and single-cell experiments. We demonstrate that SELMA generates more accurate and robust bias estimation from bulk DNase/ATAC-seq data than the naive k-mer model and that SELMA can utilize mitochondrial DNA (mtDNA) for bias estimation instead of requiring a separate naked DNA sample. We show an improved TF occupancy inference on ENCODE consensus footprints by including SELMA-estimated biases for each footprint. Finally, we show that SELMA-estimated biases can be incorporated with existing scATAC-seq computational tools to generate more accurate cell clustering analysis.Here, we present 16, derived from a simplex encoding model, in which the k-mer sequences are encoded as the vertices of a regular 0-centered simplex26. Intuitively, a k-mer can be represented as k mononucleotides and k − 1 adjacent dinucleotides. Each mononucleotide is encoded as the 3-dimensional vector of one of the four tetrahedral vertices of the cube of side 2 centered at the origin. Each dinucleotide is encoded as the outer product of the 2 vectors representing the associated nucleotides  = 12k − 8 parameters, much fewer than the naive k-mer model (4k). We use a linear model to fit these 12k − 8 parameters from the naive k-mer biases, and use the fitted values as the SELMA-modeled cleavage biases , the cleavage biases are expected to be invariant across cell types and independent of chromatin states  patterns across these footprint regions Fig. . Interes35 and collected two sets of footprint regions: “TF binding hotspots”, the footprint regions overlapping with TF binding sites detected from more than 3000 ChIP-seq datasets, and “TF binding deserts”, the footprint regions that do not overlap with any TF binding sites from any ChIP-seq dataset or any human TF motif sequence from the HOCOMOCO database36 , indicating that FBS might be an informative feature of the consensus footprint regions and might help separate true TF binding footprints from false-positive DNaseI footprint patterns. Next, we used a model-based approach to assess the potential power of SELMA-derived FBS in boosting the performance of TF binding inference from DNase-seq signal patterns on consensus footprint regions containing the TF motif sequence. For every TF with a known motif in a cell type with both ChIP-seq and DNase-seq data available, we employed a multinomial logistic regression model using different sets of features, each of which may include DNase-seq read count, DNaseI footprint score produced from an existing method, and an optional FBS, to infer the TF binding occupancy (determined by peak occurrence in ChIP-seq data) in the motif-containing footprint regions. For each footprint method, we compared the TF binding inference performance of the original method (read count + footprint score as features), the original method plus a randomized naive k-mer bias score feature as a control, and the original method plus the SELMA bias score (FBS), using an integrated rank score strategy. We included our previous footprint method13 and several representative methods that outperform others, Wellington9 and HINT19 (with and without bias correction mode) in this comparison. We surveyed all human cell types that have both DNase-seq and more than 20 TF ChIP-seq samples available from ENCODE, including in 7 cell lines and a total of 375 ChIP-seq samples for 156 different TFs  received a higher inference rank score when considering SELMA FBS, covering 117 (75%) of the 156 TFs  and HLH family  TFs rarely showed improved inference performance after considering footprint bias  as the relative SELMA-estimated bias score at the two spikes compared to the average SELMA bias score across the rest of the region for each footprint. Consistent with what we observed in the aggregate view, the FBSs for TF binding deserts were significantly higher, while the FBSs for TF binding hotspots were significantly lower, than the background of all consensus footprints  in these subsets of footprints. We found that different TFs had various preferences for binding at low-FBS footprints or high-FBS footprints. Among the 156 TFs included, a significantly larger amount of TFs can be improved with SELMA FBS than those that cannot be improved by SELMA Fig. . These r23. Due to the scarcity of cleavage events in an individual cell, most chromatin accessibility regions in a single cell have only one aligned fragment, making the potential influence of intrinsic cleavage biases more substantial in scATAC-seq data than in bulk ATAC-seq data. We collected scATAC-seq datasets generated using different platforms for different biological samples, human hematopoietic cells37, mixed human cell lines21, and mouse primitive gut tube38, and found that the scATAC-seq data contained similar intrinsic cleavage biases to bulk data with highly correlated bias scores estimated by SELMA  technologies enable the detection of accessible chromatin regions at single-cell resolution in thousands of cells at a time39 to quantify the accuracy of an unsupervised clustering result. As scATAC-seq-based cell clustering can be performed on all chromatin accessibility regions (ATAC-seq peaks), we sought to address whether removing peaks with high intrinsic biases can increase the clustering accuracy. We first tested a simple clustering approach that involved principal component analysis (PCA) dimensionality reduction followed by K-means clustering. For most cases in the 6 scATAC-seq datasets, cell clustering after removing 1–50% of peaks with the highest PBS can increase ARI from using all peaks 40, Seurat41, scran42, and snapATAC43, in addition to K-means, for scATAC-seq cell clustering, and compared the cell clustering accuracy of the uncorrected raw data with the bias-corrected data. While different tools showed various performances, across the 6 biological samples we tested, the bias-corrected data yielded a significantly higher ARI than the uncorrected data did for most cases , under the clustered one-sided paired t-test taking correlation structures within datasets into account44. Furthermore, regardless of which clustering method was used, the approach that yielded the highest ARI in each of the 6 samples is always using the bias-corrected data. These results demonstrated that SELMA can reduce the effect of intrinsic cleavage biases in scATAC-seq data and can be applied to existing single-cell data analytical tools to improve the accuracy of cell clustering analysis.To correct the intrinsic cleavage bias effect in scATAC-seq data in a systematic manner, we developed a general model to weight peaks by the percentile of SELMA PBS Fig. . The weises Fig. , includises Fig.  and mixeses Fig. , the twoses Fig. , the oveThe existence of enzymatic cleavage biases in DNase-seq and ATAC-seq experiments has been widely acknowledged in the field, but to what extent such intrinsic biases affect data analysis, especially on the single-cell level, has not been systematically assessed. SELMA provides a quantitative approach for the accurate and robust estimation of intrinsic cleavage biases in both bulk and single-cell chromatin accessibility sequencing data and requires fewer sequence reads than the naive k-mer model. Taking Tn5 dimerization into consideration, SELMA yields more accurate bias estimation specifically for ATAC-seq data by including k-mer sequences at two Tn5 cleavages 9 bp apart. SELMA can improve functional analysis and interpretation of chromatin accessibility profiles. On the bulk level, we showed that considering SELMA-estimated biases can improve TF binding inference from ENCODE DNaseI consensus footprints for most TFs, with better performances compared to existing tools, including some that already considered “biases”. On single-cell level, we showed widespread existence of intrinsic cleavage biases in single-cell ATAC-seq data, and demonstrated that SELMA single-cell bias reduction model can enhance the performance of existing tools and can increase the accuracy of cell type clustering. Therefore, SELMA can help researchers obtain more biological insights from chromatin accessibility data.45. Following similar encoding strategies, SELMA can potentially be applied to any k-mer-based model or to any high-throughput sequencing data for robust sequence bias estimation and pattern recognition.SELMA is built on top of the widely used k-mer model by combining simplex encoding and a linear model. Simplex encoding has the unique ability to capture the pairwise symmetry and orthogonality between mononucleotides and interactions within each dinucleotide. It significantly reduces the degrees of freedom without losing any variance information compared to the naive k-mer model. These properties enable SELMA to make robust estimations from fewer sequence reads or smaller datasets than are required by other approaches, hence enabling de novo bias estimation from a smaller DNA molecule, such as mtDNA in a DNase/ATAC-seq sample, without having to generate a separate genomic naked DNA sample. However, SELMA still relies on sufficient read counts for each k-mer for an accurate estimation. Although the performance of SELMA may increase with k, this effect is not unlimited. The performance using 12-mer is not as good as using 10-mer possibly because there are not enough reads in the dataset for many 12-mers. Nevertheless, SELMA works well for most existing DNase/ATAC-seq datasets tested in our study. In addition, the feasibility and superior performance of simplex encoding have also been demonstrated for TF motif characterizationThe k-mer biases estimated by SELMA are consistent across species and cell types, reflecting the assumption that the cleavage biases are intrinsic features of the enzymatic assays. Therefore, one can directly use the SELMA-estimated k-mer biases from DNaseI or Tn5-digested naked DNA data as universal intrinsic biases for DNase-seq and ATAC-seq, respectively, and incorporate these precalculated bias scores into the data analysis. However, this bias dataset is not guaranteed to remain accurate in many species that have not been profiled, as there might be unknown biases that have not been characterized. Although we are confident that the SELMA-estimated results should still be largely valid, one can always use the SELMA package for de novo estimation of cleavage biases from one’s own datasets.20, SELMA or SELMA-generated bias information can be incorporated into any of those models to improve the performance for more accurate regulatory DNA identification from footprints. On single-cell ATAC-seq analysis, while we show that SELMA single-cell bias correction model can achieve an overall significantly more accurate cell clustering on several publicly available datasets using a few existing tools, performance still varies across these tools and across different biological samples. One potential issue that limits a larger-scale benchmarking is the lack of ground truth for most existing scATAC-seq datasets. Except for the human hematopoietic cell sample and the mixed human cell line sample that have the known cell type labels as ground truth, we had to use transcriptome profiling scRNA-seq data as a “pseudo” ground truth or solver standard to determine the cell type identities, which not only depends on the quality of as sparse scRNA-seq data, but also implies the strong but debatable assumption that chromatin accessibility profiles should carry the same cell identity characterization as transcriptomic profiles. Such imperfect “pseudo” ground truths might have caused an insignificant improvement in many of the cases we tested. Meanwhile, the absolute level of increase in ARI also varies across clustering methods and datasets, and some are relatively small. One possible explanation is that ARI is a global metric considering all pairs of cells equally and exhaustively. In the real data, however, the intrinsic bias might only affect a small subset of cells, and the clustering accuracy would only be improved on that subset of cells, which results in a small ARI increase. Nevertheless, the overall increase in the cell clustering consistency for different biological systems tested indicates that SELMA bias correction reduces confounding noises in the data while biologically meaningful variances are retained46. In summary, SELMA is a universal and systematic bias reduction model and can be used to enhance the performance of existing methods and to improve single-cell chromatin accessibility profiling analysis.When applying SELMA-estimated FBS to correct biases in DNase footprints, our data were limited to ENCODE DNaseI consensus footprints as a proof-of-principle study. While this ENCODE dataset is thus far the largest DNase footprint repertoire, users might be interested in de novo detection of footprints from their customized DNase/ATAC-seq data. As many bioinformatics tools are already available for such tasks using various computational modelsPublicly available ATAC-seq, single-cell ATAC-seq, DNase-esq and ChIP-seq data used in this study were collected from Gene Expression Omnibus (GEO) and the ENCODE project. The metadata and accession numbers are listed in Supplementary Dataset 47. Low-quality reads (MAPQ < 30) were discarded. For paired-end sequencing data, reads with two ends aligned to different chromosomes (chimeric reads) were also discarded. For paired-end data, reads with identical 5’ end positions for both ends were regarded as redundant reads and discarded. The nonredundant reads were separated into chromosomal DNA (chromatin reads) and mitochondrial DNA (mtDNA reads) based on their genomic location. Peak detection was performed on the nonredundant chromatin reads using MACS248 (v2.1.2)  and ±200 bp centered on the peak summits was collected as the genome-wide chromatin accessible regions. The accessible regions in each dataset were separated into 5 groups from highest accessibility to lowest accessibility based on the read count on each peak  reference genome with bowtie2 (v2.2.9) (-X 2000 for paired-end data)CS248 v2..2 . The fragment files were then used as input for the subsequent analysis. Because the Cell Ranger ATAC/ARC pipeline shifted from the Tn5 cleavage sites to +4/-5 bp in generating the fragment file, the coordinates were shifted back to represent the actual cleavage loci. For scATAC-seq datasets, cells with more than 10,000 reads were retained for analysis. For 10x Single-Cell Multiome datasets, cells pre-selected by Cell Ranger ARC and with more than 10,000 reads in both scRNA-seq and scATAC-seq parts were retained for analysis.Single-cell ATAC-seq data were processed as follows: For the human hematopoietic cells and the mixed human cell line samples, raw sequencing reads were aligned to the GRCh38 (hg38) reference genome with bowtie2 (-X 2000). Low-quality reads (MAPQ < 30), chimeric reads and duplicate reads for each individual cell were discarded. For the mouse gut tube sample, scATAC-seq data from the 10x Genomics platform were preprocessed with Cell Ranger ATAC (v6.1.1) with the default parameters to generate fragments for each individual cell. For the 10x Single-Cell Multiome datasets, the processed fragment files were downloaded from the 10x genomics website (36 (v11), and the genome-wide motif sites of TFs were detected by FIMO (v4.12.0) in the MEME package49. Motif sites located outside of the genome-wide 36 bp unique mappable regions were excluded from the analysis. In total, 61,531,309 motif sites for 156 TF motifs from the human genome were included in the analysis.For transcription factor motif analysis, the sequence motifs of human TFs were collected from the HOCOMOCO database13. In brief, a naive k-mer bias was estimated as the observed frequency of the cleavages with the k-mer relative to the frequency of that k-mer in the background. For each mapped sequence read in a DNase-seq or ATAC-seq dataset, the enzymatic cleavage site was between genomic positions i and i-1 for the plus (+) strand reads and between i and i+1 for the minus (–) strand reads, where i represents the genomic position of the 5′ nucleotide of the reads. The associated k-mer sequence was thus assigned as j is defined as the number of all observed cleavages with that k-mer divided by the occurrences of that k-mer in the background:j, and j in the background ), Mik is the log-scaled minus strand cleavage count at genomic position i with a k-bp shift, Pi, and Mik for all i. All genomic positions on the genome-wide DNase/ATAC-seq peaks were included in the analysis. k is chosen from 1 to 20 . A total of 4,460,438 footprint regions that do not contain unidentified nucleotides (N) in the GRC38 (hg38) reference genome were used for subsequent analyses. The observed DNaseI cleavage profile from a DNase-seq dataset and DNaseI SELMA bias scores across ±50 bp centered on the footprint region were plotted as heatmaps  with default parameters to detect genome-wide footprints and selected significant footprint regions with p values < 1e−10. The output footprint score was assigned to the TF motif overlapping with the footprint region.Wellington footprint: We used Wellington19 (v0.12.3) with default parameters to detect genome-wide footprints. For bias correction mode, we used an additional parameter: --bias-correction. The footprint score of each footprint region was assigned to the overlapping TF motifs.HINT footprint: We used HINTThe following methods were used to calculate footprint scores for comparison:11 to evaluate different models’ prediction power. For each model, we calculated the area under the ROC curve (AUROC) at 100%, 10%, and 1% false-positive rate (FPR). We also calculated the area under the precision-recall curve (AUPRC) at 100%, 10%, and 1% recall. We then combined these six performance measures as a rank score S, defined asInferences of TF binding with different features were implemented as follows: For each TF in Fig. To calculate the random k-mer bias, we randomly permuted the SELMA k-mer bias table and generated the k-mer bias table for a “simulated enzyme”. We used this “simulated” bias table to calculate the FBS for each footprint region and performed TF inference modeling similar to what we did for the DNaseI SELMA FBS. This permutation was performed 100 times to generate 100 performance rank scores for random k-mer bias used as controls.36 motif and ChIP-seq data available in human cell lines. We collected data from all human cell lines with both DNase-seq and more than 20 TF ChIP-seq samples available from ENCODE, resulting in 7 cell lines, 375 TF ChIP-seq samples, and 156 TFs , as the “pseudo” ground truth, or the silver standard. In detail, we integrated scRNA-seq and scATAC-seq data using the ArchR package50 (v1.0.1). Individual cells with a high RNA integration score (unconstrained predicted score < 0.56) were collected as high-quality cells for the analysis. The cutoff of the RNA integration score (0.56) was determined by maximizing the interclass variance in the RNA integration score using Otsu’s method51. Cell types represented by fewer than 10 cells were discarded. For the 10x Single-Cell Multiome datasets, scRNA-seq parts from each sample were separated and clustered using Seurat (v4.0)41 with default parameters. The scRNA-seq clustering results were used as the “pseudo” ground truth for scATAC-seq cell clusters.For single-cell ATAC-seq data in the human hematopoietic cells and human cell line samples, the cell-type information for each individual cell was used as the ground truth, or the gold standard. For single-cell ATAC-seq data in the mouse gut tube sample, the cell-type information was assigned based on label transferThe average bias for each individual cell . For the 10x Genomics single-cell data, the q value cutoff of 0.01 was used for peak calling . Potential accessible regions with fewer than 10 covered reads or more than 4000 covered reads were discarded in the subsequent analysis. To consider the effect of intrinsic cleavage bias in scATAC-seq data, we summarized the bias for each potential accessible region by calculating the peak bias score (PBS), defined as the median SELMA bias score of all reads  located in the peak region.Peak detection and peak bias score calculation were performed as follows: We first combined all the single-cell ATAC-seq reads in the dataset and performed MACS2p value < 0.05 was used to represent the relative level of contribution for this percentile of peaks  for each identified peak, using the scATAC-seq read count/signal across different cells as the variable and the known cell-type labels as the target group labels. The F score from the ANOVA output was used to quantify the contribution of each peak to the clustering. Within each percentile of peaks based on their PBS ranks, the median F score of the peaks with ANOVA 40, Seurat41, scran42, and snapATAC43, we applied each method with default parameters. To evaluate the accuracy of cell clustering, we used the adjusted Rand index (ARI)39 between a clustering result with the predefined cell-type labels . To evaluate the robustness of clustering results for each method, we repeated the clustering for 100 times with different random seeds, and used the average and standard deviation of the ARIs from the 100 runs as the evaluation metrics. APEC result is invariant with random seeds so 100 repeats were not applied to APEC. We selected the top 60 dimensions for those methods at the dimensional reduction step . We used the ArchR package50 to implement the Seurat and scran clustering methods. To explore the effect of intrinsic cleavage bias on scATAC-seq analysis, we selected different percentages  of peaks with the lowest PBS  as input to perform cell clustering  in k-means clustering was determined as the actual number of cell types in the dataset. For published methods tested, including APECing Fig. . We alsoing Fig. . To asseFurther information on research design is available in the Supplementary InformationReporting SummaryDescription of Additional Supplementary FilesPeer Review FileSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Supplementary Dataset 4Supplementary Dataset 5


Additionally, we analyzed two datasets (n = 616 samples) containing metastases and primary tumors of one entity, to point out potential familiarities. Using these methods, no tight link between the site of resection and cluster formation outcome could be demonstrated, or for datasets consisting of solely metastasis or mixed datasets. Instead, dimension reduction methods and data transformation significantly impacted visual clustering results. Our findings strongly suggest data transformation to be considered as another key element in the interpretation of visual clustering approaches along with initialization and different parameters. Furthermore, the results highlight the need for a more thorough examination of parameters used in the analysis of clusters.Personalized oncology is a rapidly evolving area and offers cancer patients therapy options that are more specific than ever. However, there is still a lack of understanding regarding transcriptomic similarities or differences of metastases and corresponding primary sites. Applying two unsupervised dimension reduction methods (t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP)) on three datasets of metastases ( From a clinical perspective, characteristic metastatic patterns frequently occur for specific cancer entities . Thus, thallmarks of cancer, for example, the ability to attract endothelial cells (angiogenesis) or the sufficient evasion from immune control [From a biological point of view, tumor cells develop through clonal evolution, favoring tumor heterogeneity reflected by different driver mutations or genomic alterations. To be specific, these alterations need to take place in genes tightly related to cellular traits known as the  control ,8. Due t control . Additio control  and the  control  models oDue to the advances in personalized oncology, the comprehensive elucidation of primary tumors and metastases is an ongoing process. Still, the determination of possible transcriptomic differences or similarities between metastases—especially from several different resection sites—and primary tumors is an unmet need. Previous approaches frequently analyzed the differences between two specific groups, e.g., bone and brain metastasis , or the Visual clustering, based on data dimension reduction methods, is one potential approach to determine the transcriptomic differences and proximities of different metastasis sites. The mainly used visualization methods for this purpose are t-Distributed Stochastic Neighbor Embedding (t-SNE)  and UnifTo search for transcriptomic dependencies caused by the site of metastasis, t-SNE and UMAP were used to analyze three metastasis datasets, prostate cancer (PCa), neuroendocrine PCa, and skin cutaneous melanoma, totaling 682 samples. For a comprehensive analysis, unprocessed Fragments Per Kilobase Million (FPKM) values, obtained after normalization of the mapped sequencing reads, as well as log10 and log10 + 1 transformed data were analyzed, as logarithmic transformations are commonly used when analyzing gene expressions.n = 266 samples from metastatic prostate carcinoma , the seNEPC WCM ), and thtastatic ). All dan = 538), TCGA-KIRP , and TCGA-KICH .For evaluation purposes, we used an additional dataset known to form distinct clusters based on histopathological subgroups. The TCGA-KIPAN dataset, consisting of three renal cell carcinoma (RCC) subgroups, TCGA-KIRC  consistTo obtain a more comprehensive view, t-SNE plots and UMAPs were applied for the analyses, as t-SNE or UMAP have become standard not only for bulk RNA sequencing but also for single cell analysis ,34,35, cSubsequently, results of t-SNE and UMAP dimension reduction were compared. All t-SNE plots were created equally based on a principal component analysis with 50 components, a learning rate of 300, and a perplexity of 27. For the NEPC WCM dataset, 25 components were used. Further details on the procedure are given elsewhere . UMAP plTo further address the question of possible clusters and the associated distinction between primary tumor and metastasis in the SKCM dataset, we additionally performed k-means  and Leiden  —was additionally considered.To investigate the influence of data transformation and data dimension reduction methods on the formation of visually distinct clusters, the TCGA datasets of the three largest RCC subgroups, clear cell (KIRC), papillary (KIRP), and chromophobe (KICH), were combined to one dataset (KIPAN). Due to the nature of the histopathologic origin of the samples in this dataset, a specific clustering could be expected. t-SNE b,c and UAlthough both data transformations showed a separation based on the histopathological subgroups for both data dimension reduction methods, clusters were not exclusively subgroup-specific and displayed certain outliers.Based on the results of the KIPAN cohort, further analyses were performed for the complete TCGA-SKCM dataset as well—to analyse the transcriptomic relation of the primary and metastatic melanoma samples. Interestingly, no distinct separation between the metastatic and primary melanoma samples was observable .Moreover, only the UMAP log10 + 1 transformed approach displayed two distinct clusters, each containing primary and metastatic samples f. For boThis conclusion can also be drawn from the application of two common clustering methods, namely Leiden  and k-meFurther assessment of the maps in terms of local and global structure between the used transformations revealed that the local distances or neighborhoods between data points were not well preserved .t-SNE plotting and UMAP are crucial methods to identify relevant subsets in transcriptomic data consisting of bulk RNA or single cell approaches. Frequently, these subsets or clusters display distinct cellular functionalities and share commonly altered signaling pathways. For druggable pathways, translational researchers have therefore identified therapeutic implications in various cancers, e.g., RCC  and prosIn this work, we were looking for transcriptomic similarities and differences of metastasis representing different resection sites. It has already been shown in several studies that there is no clustering of samples depending on the underlying resection site of the metastasis . NeverthAs already shown in a previous publication, obtained clusters by using unprocessed FPKM values in a t-SNE approach yielded prognostically relevant clusters with biologically distinct characteristics for RCC . These fAnother remarkable element is the bone cluster identified within the Dream Team dataset. This cluster appears, with minor changes and depending on the area considered, in each of our analyses, regardless of the data transformation and the data dimension reduction method. Considering previous results, we conclude that all present methods have their justification and can be used depending on the research question. For example, the UMAP log10 + 1 approach is suitable for bulk RNA sequencing to identify subgroups within specific entities. However, clusters based on different histopathological tissues, for example, and thus generally showing a quite different transcriptome, can also be seen in the unprocessed data, where t-SNE plot seems to be more suitable for bulk RNA sequencing than UMAP, which in turn does not seem to be suitable for the unprocessed data of bulk RNA sequencing in general.When looking at the number of clusters previously identified for the analysed datasets within this work, it becomes apparent that the log10 + 1 approaches were mostly in line with the previously shown results. For the TCGA-SKCM dataset, three clusters were identified in the first publication of this dataset . The iniLooking more closely at the differences in the resulting clusters between the different data transformations of the individual data dimension reduction methods, changes are similar to those caused by parameters such as the number of neighbors. Considering very recent research, we believe that the data transformation used is just an equally important factor to consider in the initialization of the data , as respIn order to quantify the visible differences between the different methods, and especially between data transformations, Taskesen et al. proposed a solution. This method considers the differences between the nearest neighbors of the data points in order to make a statement about the preservation of the local and global structure between different maps. In the datasets used by us, it was noticeable that the local structures, i.e., a small number of nearest neighbors, were remarkably different between the individual maps and data transformations. This shows that the data transformation used has an influence on the local characteristics of the clusters. The global structure based on many nearest neighbors, however, seems to remain the same between data transformations.This circumstance is most apparent when looking at the TCGA-KIPAN datasets, as t-SNE and UMAP, with and without data transformation, provide visibly different results, but, nevertheless, a clustering based on the histopathological subgroups. Nevertheless, quantitative analysis of the nearest neighbors showed that the local structures of the clusters differ between the data transformations and the individual methods. This problem becomes even more relevant as t-SNE and UMAP are also used for the analysis of single cell sequencing, in which the smallest transcriptional differences can have major effects on the representation and subsequent interpretation or further analysis. In addition to the method, the data transformation used represents one further important parameter in the representation of clusters and local distances .Consequently, our results suggest that a more in-depth investigation of data transformations and visualization methods are necessary to further assess the nature of the obtained clusters.Our findings that primary and metastatic tumors share common transcriptomic features and are inseparable when analysed with data dimension reduction methods appear to match with previous research. In metastatic pancreatic adenocarcinoma, a distinction between primary tumor and metastatic tumor cells was not possible using single cell RNA sequencing ,52. ThisOne limitation of our work is the fact that our conclusions refer to a recurrent observation based on limited datasets. To show a quantifiable statement regarding the performance of t-SNE and UMAP with respect to data transformations, a comprehensive analysis has to be performed in future studies.To address these challenges, we propose an additional standard legend for visual clustering approaches based on data dimension reduction methods and machine learning, as represented by the UTMC legend in all figures of this work. The information required by this additional information includes the unit (U) , data transformations (T), represented visualization or data dimension reduction method (M), and, if applicable, the applied cluster identification algorithm (C). This enables the reproducibility of figures and analyses and makes visual clustering approaches much more transparent.Taken together, our work further extends the knowledge of tumor heterogeneity in different biological contexts , by provUsing two different data dimension reduction methods, we showed that there was no visual association between the resection site and the transcriptome for three considered metastatic datasets. Instead, there was a significant dependence of clustering according to data transformation and the data dimension reduction method applied. Additionally, the analysis of primary and metastatic samples of specific entities did not show distinct clusters or visible differences. Combining recent works and the results of our study, visual clustering seems highly vulnerable towards data and parameter alterations. To avoid pitfalls in analyzing visual clustering and to enhance reproducibility, we recommend extending the standardized nomenclature, e.g., by adding the UTMC legend introduced in this manuscript.


Plasmodium species and for correct gene expression analyses for biases caused by differences in stage composition among samples.Malaria symptoms are caused by the development of the parasites within the blood of an infected host. Bulk RNA sequencing (RNA-seq) of infected blood can reveal interactions between parasites and the host immune system during an infection, but because multiple developmental stages with distinct transcriptional profiles are concurrently present in infected blood, it is necessary to correct such analyses for differences in cell composition among samples. Gene expression deconvolution is a statistical approach that has been developed for inferring the cell composition of complex tissues characterized by bulk RNA-seq using gene expression profiles from reference cell types. Here, we describe the evaluation of a species-agnostic reference data set that can be used for efficient and accurate gene expression deconvolution of bulk RNA-seq data generated from any IMPORTANCE Differences in cell type proportions among samples can introduce artifacts in gene expression analyses and mask genuine differences in gene regulation. Gene expression deconvolution allows estimation of the proportion of each cell type present in one sample directly from bulk RNA sequencing data, but this approach requires a reference data set with the signature profile of each cell type. Here, we evaluate the suitability of a rodent malaria parasite gene expression data set for estimating the proportions of each parasite developmental stage present in bulk RNA sequencing data generated from blood-stage infections with the human parasites Plasmodium falciparum and Plasmodium vivax. These analyses provide a species-agnostic approach for reliably estimating stage proportions in infected human blood and correcting subsequent gene expression analyses for these variations. Plasmodium parasites, the causative agents of malaria, remain endemic in 85 countries  (Plasmodium species) and increases the parasite load by 10- to 30-fold each cycle   of the ple (IDC) . Blood sle (IDC) . Asexualch cycle . A fractch cycle .in vivo is observed for all Plasmodium species, although to a lesser extent with P. falciparum due to the sequestration of mature stages , sufferprofiles –18. A rerofiles –, 19. Unfrofiles ––22 have ofiles ––, which ile stage , 24. We  samples , 7, but Plasmodium species . We then used this signature matrix to deconvolute bulk RNA-seq data generated from a tightly synchronized P. falciparum culture sampled at 4-h intervals  (We first tested whether scRNA-seq data sets could be used to deconvolute BERSORTx  to generdata set  that encplicate) . Overallplicate) . CIBERSOplicate)  determinPlasmodium species was to test whether reducing the number of genes used to generate the signature matrix lowered the efficiency and resolution of the gene expression deconvolution. We therefore reiterated the gene expression deconvolution of the same in vitro cultures but used only the 3,541 genes with 1:1:1 orthologs in the P. berghei, P. falciparum, and P. vivax genomes  to generate the signature matrix. Reducing the number of genes only minimally impacted the deconvolution results and yielded very comparable results , as well as male and female gametocytes, which we expect to be present in at least some of the infected patient blood samples. We used the P. berghei scRNA-seq data set   . Importasamples) .P. vivax blood-stage parasites. Since we lacked bulk RNA-seq data from synchronized P. vivax parasites, we generated mock RNA-seq data by aggregating scRNA-seq data from individual parasites at the same stages (P. vivax scRNA-seq data set did not include ring-stage parasites (due to limitations of the enrichment method used before library preparation [P. vivax parasites across the IDC from early trophozoites (bin 1) to late schizonts (bin 5). In addition, we included two mock populations made of male or female gametocytes. The proportions inferred by gene expression deconvolution for each mock data point approximately matched the expected proportions .We also evaluated how this species-agnostic signature matrix would perform on e stages  (see Matparation ), and weportions , althougP. berghei parasites from different developmental stages. We randomly sampled 100 cells from selected stages from the P. berghei scRNA-seq data set  in simulated mixtures compared to the true proportions (x axis). Note that the maximum proportion estimated is different from the “true” one and likely reflects mislabeling of some of the cells used for generating the mock mixtures (see main text for details). Download FIG S1, JPG file, 0.05 MB.Accuracy of gene expression deconvolution using a species-agnostic signature matrix. Each plot displays the estimated proportion of a specific Copyright © 2022 Tebben et al.2022Tebben et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Plasmodium blood infections characterized by bulk RNA-seq. This approach has been used previously for analyzing blood samples from malaria patients . We showed that CIBERSORTx performance was not dramatically reduced when we used only the subset of genes conserved across fe cycle , 31, is cultures  and mighin vitro , 32.Plasmodium stages are much more transcriptionally active than others  genuine differences in gene expression between parasites or (ii) differences in the developmental stages present in each sample. Due to sequestration . However, even a minority of late-stage parasites might dramatically affect the overall RNA-seq profiles, since those parasites can disproportionally contribute to the mRNA populations due to their higher transcriptional activity , and use of standard bulk RNA-seq methods, which are much cheaper than scRNA-seq and provide a deeper characterization of the transcriptome. Overall, the method described in this study will provide a solid foundation to implement Plasmodium gene expression analyses for patient infections and to better understand the role of these parasites in disease etiology and severity , or to examine the molecular mechanisms underlying immune evasion.By robustly estimating relative proportions of each sexual and asexual stage present in an infection characterized by bulk RNA-seq, gene expression deconvolution alleviates the main limitation of using infected blood samples, without culture, for stration , 12, mosactivity . In addiven time . In non-nfection . Our anaP. falciparum and one for P. berghei that included gametocytes in addition to asexual parasites. Raw count tables were downloaded for 6,737 cells representing the P. falciparum IDC  and 4,884 cells representing the P. berghei blood stages . To ensure that enough cells were included to accurately recapitulate the gene expression profile of a specific stage, we pooled cells at slightly different time points within broadly defined developmental stages. For the P. falciparum data set, cells labeled as early and late trophozoites were grouped together as “trophozoites” (while parasites labeled as rings and schizonts were unchanged). For the P. berghei data set, cells labeled as early and late rings were pooled together as “rings”; early, mid-, and late trophozoites were grouped into “trophozoites”; and early, mid-, and late schizonts were grouped into “schizonts.”We used two publicly available 10× Genomics single-cell RNA-seq data sets from Howick et al. : one chaP. vivax parasites . CulturP. falciparum, P. berghei, and P. vivax was obtained from PlasmoDB  a P. falciparum scRNA-seq subset to include only genes with 1:1:1 orthologs; and (iii) a P. berghei scRNA-seq data set subset to include only 1:1:1 orthologs .We generated and evaluated three signature matrices from the scRNA-seq data: (i) P. falciparum or P. berghei scRNA-seq data set using varying proportions of each stage. Randomly sampled cells from the selected stages were collapsed by sum to generate a single mixture data set. We generated these data sets by simply summing the actual scRNA-seq read counts per gene.We generated mock RNA-seq data by “mixing” 500 parasites from the Each reference data set (described above) was uploaded to CIBERSORTx  and usedCIBERSORTx  was thenhttps://github.com/tebbenk/GED.All programs, versions, and parameters used in this study are described in Materials and Methods. Custom codes and gene lists are available at


Through integrating a drug-kinase profiling dataset and several drug response datasets, our method, DepInfeR, correctly identified known protein kinase dependencies, including the EGFR dependence of HER2+ breast cancer cell lines, the FLT3 dependence of acute myeloid leukemia (AML) with FLT3-ITD mutations and the differential dependencies on the B-cell receptor pathway in the two major subtypes of chronic lymphocytic leukemia (CLL). Furthermore, our method uncovered new subgroup-specific dependencies, including a previously unreported dependence of high-risk CLL on Checkpoint kinase 1 (CHEK1). The method also produced a detailed map of the kinase dependencies in a heterogeneous set of 117 CLL samples. The ability to deconvolute polypharmacological phenotypes into underlying causal molecular dependencies should increase the utility of high-throughput drug response assays for functional precision oncology.The development of cancer therapies may be improved by the discovery of tumor-specific molecular dependencies. The requisite tools include genetic and chemical perturbations, each with its strengths and limitations. Chemical perturbations can be readily applied to primary cancer samples at large scale, but mechanistic understanding of hits and further pharmaceutical development is often complicated by the fact that a chemical compound has affinities to multiple proteins. To computationally infer specific molecular dependencies of individual cancers from their  As survival and proliferation of cancer cells depend on molecular aberrations that can be highly specific to cancer types and individual tumors, identifying such dependence is pivotal to designing individualized tumor therapy. Chemical perturbations, through screening of bioactive compounds using primary cancer cells, provide an important tool for identifying tumor-specific dependencies. However, many chemical compounds bind multiple proteins, which complicates interpreting screening results and pinpointing the phenotype-causing target. To overcome this challenge and increase the utility of drug screening approaches for functional precision medicine, we developed a computational framework, DepInfeR, to identify tumor-specific dependencies on druggable proteins through integrating two sources of information: drug sensitivity assays and drug-protein affinity profiling. Our approach correctly identifies known kinase dependencies, which validates our approach. Furthermore, by integrating a newly generated drug screening dataset on primary tumor samples, we discovered a previously unreported survival dependence on Checkpoint kinase 1 (CHEK1) by a molecular subgroup of chronic lymphocytic leukemia samples, highlighting the clinical potential of our method. PLOS Computational Biology Methods paper.This is a The sustained proliferation or apoptosis resistance of cancer cells rely on abnormal pathway activities, often resulting from genetic or epigenetic alterations. The molecular processes underlying these activities, however, can vary greatly across cancer types, and even between tumors from different patients with the same cancer type. Such heterogeneity of driver dependencies can lead to differential treatment responses. A key aim of precision cancer therapy is to exploit core vulnerabilities of each individual tumor.A powerful, scalable, and systematic approach to identify cancer-specific dependencies uses genetic perturbations such as RNAi and CRISPR/Cas9 systems to knock down or knock out a specific gene in cancer cells and observe the subsequent effect on cell growth or survival –4. Whileex vivo cellular effects. By design, all identified protein dependencies are “targetable”. However, a major challenge is the polypharmacological nature of small molecular compounds, as many of them show a broad range of off-target effects, which obstructs the identification of the protein targets related to the desired phenotype [A major goal of cancer dependency mapping is to propose druggable targets for cancer treatment. Chemical perturbation experiments, which rely on high-throughput screening of bioactive compounds on cancer cells, provide an attractive complementary approach to genetic perturbations due to their high translational value for personalized oncology ,13. Chemhenotype .ex vivo (X), and the drug affinities of a set of proteins (Y), to infer information that is not directly observed, the matrix of protein dependencies of each of the cancer samples (https://bioconductor.org/packages/release/bioc/html/DepInfeR.html).To improve the utility of high-throughput drug sensitivity data for functional precision medicine, we developed a computational method that integrates two experimentally accessible input data matrices, the drug sensitivity profiles of cancer cell lines or primary tumors ex vivo assays can be readily performed on cell lines and primary samples, with established platforms and ready to use datasets for both drug sensitivity profiling [As similar rofiling ,15–17 anrofiling , we expeet al. [Kd values, which we converted into binding scores in the range between 0 and 1  a drug-protein profiling matrix, which reports binding affinities of drugs to proteins, and 2) a drug sensitivity matrix , which ret al. , who immnd 1 see . The datnd 1 see . Similarnd 1 see . Altogetclusters .The objective of our approach is to use this network to infer the kinases that a particular sample depends on for viability by overlaying the network with the effects of the drugs on the sample. This task can be posed in terms of multivariate multi-response regression involving three matrices: the known drug-protein binding matrix, a known drug-sample sensitivity matrix—below we consider three different instances of such a matrix—and the unknown protein dependence matrix . While nAs the second data source, we used three separate drug sensitivity datasets: one from cancer cell line collections and two from drug sensitivity studies of primary blood cancer samples (AML and CLL).FLT3 in AML and ALL [As a first test of DepInfeR, we applied it to the cancer cell line drug sensitivity data from the GDSC database . We used and ALL –25.NRAS mutation status was associated with the dependence coefficient of multiple kinases. Its positive association with MAP2K2 , MCL , and T-PLL . This study design enabled us to consider entity specific properties of MCL and T-PLL, and to further resolve the considerable intra-entity heterogeneity of CLL with respect to molecular factors including the mutational status of immunoglobulin heavy chain variable region (IGHV) genes, trisomy 12 status, and utations ,32. We mWe observed that kinases from the cell cycle regulation machinery, such as CDK6, CDK17, and CCNT1, had uniformly high dependence coefficients across samples, reflecting the high toxicity of inhibitors targeting cell cycle . By contThe T-PLL samples were clearly separated from CLL and MCL by their characteristically low dependence on the BTK , which iNext, we zoomed in to study the relationship between the molecular heterogeneity of our CLL samples with the protein dependencies. In a hierarchical clustering of the samples based on their protein dependence profiles, the most striking pattern was the separation between CLLs with mutated IGHV status (M-CLL) and unmutated IGHV status (U-CLL) . The IGHClustering and other forms of pattern detection on the samples can be done on the drug sensitivity data directly, in addition to the protein dependence map. We aimed to compare the two approaches by assessing the results of k-means clustering with the Rand index . We chosWe looked more specifically into the associations identified between genetic features of CLL patients and the protein dependence coefficients. Most associations were found between IGHV status and protein dependence coefficients : as prevTrisomy 12—an important cytogenetic driver presents in around 15% of CLL —was assoHaving validated the CLL protein dependence map with several associations to known molecular stratifiers of CLL, we looked into novel patterns. The most notable finding was the higher dependence of U-CLL on the checkpoint kinase CHEK1 –a major et al. . Amongst the most upregulated pathways in U-CLL   .Moreover, we found that BCR stimulation by anti-IgM antibodies in CLL cells led to a significant upregulation of pathways related to DNA-damage response and cell cycle checkpoint, including UV response, DNA repair, E2F transcription factor targets, and G2/M checkpoints . This fiTaken together, our results suggest a stronger dependence of U-CLL on CHEK1-mediated checkpoint control. This might be either a direct effect of the increased BCR pathway activity in U-CLL or a result of increased replication stress in this more aggressive CLL subtype. Targeted CHEK1 inhibition may therefore be especially beneficial for U-CLL.We developed a computational framework, DepInfeR, that integrates drug response assays and drug-protein affinity data to infer the sample-specific protein dependencies, which reflect how much the survival of the cancer cells (from a specific cell line or patient sample) depends on a certain protein.target addiction score in their study) [Kd values are available, uses these by mapping them to the range [0,We contrast some of the methodological choices of DepInfeR to a recent proposal by Wang et al., who pursued a similar aim by taking a drug-protein binding graph using the mean effect of all drugs connected to a given protein in the graph as a measure of dependence on it (termed r study) . In cont range 0, with a set al. found high sensitivity of U-CLL to two CHEK1 inhibitors, AZD7762 and PF47736[in vitro culture). As a result, U-CLL cells are more vulnerable to CHEK1 inhibition, driving these cells towards apoptosis. Our results suggest targeted CHEK1 inhibition as a potential therapeutic strategy for U-CLL.We validated our approach by showing that it could accurately recapitulate known protein dependencies of different cancer entities as well as of their molecular subtypes, both in data from cancer cell line compendia and from primary patient samples. We then made a novel finding, the high dependence of CLL with unmutated IGHV status (U-CLL) on the Checkpoint kinase CHEK1. This protein is an important component of the cellular DNA-damage and cell cycle checkpoint response and has been targeted in cancer therapeutics, especially for MYC-expressing cancers ,45. The d PF47736. Howeverex vivo tumor-drug response matrix. Generally, any increase in the number of drugs, tumor samples or proteins is beneficial. The data preprocessing steps, such as the transformation of drug-protein binding affinity matrix and the summarization of the drug dose-response values, may also impact the final inference results. In addition, collinearities in the drug-protein profiling matrix, which arise if several proteins have similar binding affinity profiles to all tested drugs, can pose challenges to the model fitting, as such proteins are indistinguishable in terms of the data visible to the model. Our method adopts the cluster/proxy set method to deal with collinear features [The inference accuracy of our method relies on the size and quality of both the drug-protein profiling matrix and of the features . Such prWhile our present analyses were limited to kinases and kinase inhibitors, we expect that the approach would be analogously applicable should protein-drug binding matrices for other protein classes become available.In conclusion, we present a method to infer protein dependence maps from drug sensitivity data. Among the possible uses of this method, we envisage both, the discovery of novel disease stratifications by their characteristic dependencies on specific proteins, and improved understanding of existing disease stratifications in terms of differential protein dependencies. Nevertheless, such stratifications gain potential therapeutic value by being directly associated with druggable targets.The use of patient tumor samples in this study was approved by the Ethics Committee Heidelberg . Patients who donated tumor material provided written informed consent prior to study.https://www.cancerrxgene.org) [z-scores, by computing mean and standard deviation for each cell line, across the drugs. These z-scores were used as the drug sensitivity matrix Y, including sensitivity data for 66 drugs. Cell lines with more than 24 (≙ 35%) missing values were removed, resulting in a total of 126 cell lines. The missForest imputation method from the missForest package (version 1.4) [The GDSC1 dataset of drug sensitivities was downloaded from the Genomics of Drug Sensitivity in Cancer Project (ene.org) , separatene.org) . AUC valion 1.4)  was usedThe cancer cell lines were annotated with their cancer types and mutation status obtained from the GDSC project page. Additionally, HER2 status of breast cancer cell lines were annotated based on information from prior publications ,50.ex vivo drug sensitivity data of 528 tumor specimens collected from AML patients. The data were downloaded from the National Cancer Institute’s CTD2 Network (BeatAML_Waves1_2). AUC from a seven-point dose-response curve was used as described by Tyner et al. 2018 and the z-scores of the AUC-values for each cell line across all drugs were used as the response value for generating the drug sensitivity matrix Y, including sensitivity data for 61 drugs. Samples with more than 15 (≙ 24%) missing values were removed, resulting in a total number of 421 samples. Cut-off selection and missing value imputation were done as described for the GDSC1 dataset.The BeatAML dataset  containsex vivo drug response profiling on primary tumor samples from CLL (n = 117), mantle cell lymphoma , or T-cell prolymphocytic leukemia  patients. We termed this drug screen dataset as the EMBL2016 dataset. The samples were annotated with available genomic patient metadata. To obtain a single drug by tumor samples matrix, relative cell viabilities (compared to treatment with DMSO control) were averaged from 9 drug concentrations. Subsequently, the z-score of these values for each patient across all drugs was used as the response value for generating drug sensitivity matrix Y, including sensitivity data for 85 drugs. A list of compounds used in each drug screen dataset included in our study is provided in We performed et al. [X of dissociation constants (Kd) for each drug-protein pair. The Kd values were transformed with a sigmoid function into the range [0, f(x) = (arctan((-log10(x) + 2) * 3) + π/2) / π, with x being the raw Kd values. Values of f(x) close to 1 correspond to evidence of drug-protein binding, values close to 0 to absence of such evidence. The graph of the function f and the distribution of values before and after the transformation are shown in The kinase inhibitor profiling data were obtained from the Supplementary S2 Table of Klaeger et al. . The datrange 0, . We usedKd values within the cluster was used as a cluster name, while the other kinases were also recorded and retained for case-by-case follow-up. The collapsed kinase sets are provided in As some kinases had similar ligand profiles, and since this drug-target matrix was intended to be the design matrix in a multivariate regression model, highly similar kinases were “collapsed” in order to achieve model identifiability and parameter estimate stability. To this end, we computed the cosine similarity between each pair of kinases, applied hierarchical clustering, and identified subtrees in the cluster dendrogram in which all kinases had a cosine similarity > 0.8. For each such cluster, the kinase with the largest sum of transformed For the analysis of the BeatAML and EMBL2016 datasets, for which the gene expression data are also available, the drug-protein profiling matrix was additionally filtered by removing kinases that were not or only weakly expressed in the tumor samples, as follows. In the gene-level count matrix of the RNA sequencing data of the tumor samples, the 80% quantile of its counts across samples was computed for each gene, and a gene was considered not or only weakly expressed if this value was less than 10. Thus, 20 of the 377 kinases were removed from the downstream analysis for the EMBL2016 dataset, and 14 of the 380 kinases in the BeatAML analysis. The final dimensions of the input matrices X were: GDSC: 66 drugs × 118 proteins; BeatAML: 61 drugs × 112 proteins; EMBL2016: 85 drugs × 131 proteins .X is the processed drug-protein binding matrix (drugs × proteins), Y is the processed drug sensitivity matrix (drugs × samples), β is the unknown protein dependence matrix (samples × proteins), and ϵ is a matrix of residuals (drugs x samples). Given the data for X and Y, we computed the model fit for β by minimizing ϵ using a multi-response Gaussian linear model (family = “mgaussian”) with L1-penalty (lasso) on β using the glmnet package (version 3.0–2) [α = 1, and λ shared among samples.Regularized multivariate regression was used to obtain the protein dependence coefficients. We fit the modeln 3.0–2)  with thecv.glmnet from the glmnet package with three-fold cross-validation and fit the model with the optimal choice of λ. As a quality measure, the coefficient of determination R2 from the final ß estimate was used.We repeated the following 100 times and computed the element-wise medians of the 100 β-matrices to obtain the final estimate prcomp from the stats package, which centers and scales the columns (corresponding to samples) of the matrix for equal variance. Visualization graphics were made using the factoextra package (version 1.0.6) [PCA analysis of n 1.0.6) .k-means clustering analysis was performed on eclust function from the factoextra package, which centers and scales the columns of the matrix. Class information  was withheld from the clustering method, but used to set the number of clusters k. 50 different random starting configurations (nstart) were attempted within the eclust function and the best configuration was chosen. As a measure of concordance of the resulting clusters and the ‘ground truth’ classes, the adjusted Rand index [cluster.stats function from the fpc package (version 2.2–9) [k-means clustering of the drug sensitivity matrix was performed analogously.nd index  was calcn 2.2–9) . k-meanshttp://www.ebi.ac.uk/ega/, accession: EGAS00001000374) was analyzed [DESeq2 package (version 1.28.1) [camera method was used to test for differential expression of hallmark gene sets from the MSigDB database v7.2 [limma package (version 3.44.3) [To identify gene expression signatures related to IGHV status, an RNA sequencing dataset from the European Genome-phenome Archive  . Genes wase v7.2 ,57, usin 3.44.3) .limma package was used to perform variance stabilizing normalization and differential expression testing, and the camera method was used to test for differential expression of Hallmark gene sets as above.To identify gene expression signatures related to B-cell stimulation, we analyzed gene expression microarray dataset of anti-IgM triggering in primary CLL samples (E-GEOD-39411) . The limt-test  and ANOVA test. The Benjamini-Hochberg method was applied to the P values to account for multiple testing.Association tests were performed using Student’s S1 FigOnly the high confidence drug-kinase pairs are included. Drugs are shown as blue circles and kinases are shown as red squares. No clear structure can be observed directly from the drug-kinase network.(PDF)Click here for additional data file.S2 FigFor each drug screen dataset, only the kinases that were selected by our protein dependence inference algorithm and share similar drug binding profiles with other kinases are shown here. The full list is provided in (PDF)Click here for additional data file.S3 FigRed indicates high dependence value and blue indicates low dependence value. In the column annotations, cell lines with certain genomic variations are colored by black. Columns are ordered by hierarchical clustering based on Euclidean distance.(PDF)Click here for additional data file.S4 FigP values are from two-sided Student’s t-test with equal variance.(PDF)Click here for additional data file.S5 FigRed indicates high dependence value and blue indicates low dependence values. The column annotations of the heatmap indicate the disease types, genomic variations and epigenetic subtypes of the primary blood cancer samples. Columns are ordered by hierarchical clustering based on Euclidean distance.(PDF)Click here for additional data file.S6 FigP value was calculated by one-way ANOVA test. To avoid potential bias due to the unbalanced sample size or unequal variance, nonparametric Kruskal-Wallis test was also performed and the resulting P value is 0.00019.(PDF)Click here for additional data file.S7 Fig“Up” (colored by red”) indicates the gene sets enriched for the genes up-regulated in U-CLL samples compared to M-CLL samples. The enrichment analysis was performed by using CAMERA against the Cancer Hallmark gene sets from the Molecular Signature Database (MSigDB). Only the gene sets passed 5% FDR are shown. The gene sets that have been shown to be related to CHEK1 functions are colored in red.(PDF)Click here for additional data file.S8 FigP values were calculated by DESeq2.(PDF)Click here for additional data file.S9 Fig(PDF)Click here for additional data file.S10 Fig(PDF)Click here for additional data file.S11 Fig(PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.