Introduction: Cancer research has significantly improved in recent years, primarily due to next-generation sequencing (NGS) technology. Consequently, an enormous amount of genomic and transcriptomic data has been generated. In most cases, the data needed for research goals are used, and unwanted reads are discarded. However, these eliminated data contain relevant information. Aiming to test this hypothesis, genomic and transcriptomic data were acquired from public datasets.Materials and Methods: Metagenomic tools were used to explore genomic cancer data; additional annotations were used to explore differentially expressed ncRNAs from miRNA experiments, and variants in adjacent to tumor samples from RNA-seq experiments were also investigated.Results: In all analyses, new data were obtained: from DNA-seq data, microbiome taxonomies were characterized with a similar performance of dedicated metagenomic research; from miRNA-seq data, additional differentially expressed sncRNAs were found; and in tumor and adjacent to tumor tissue data, somatic variants were found.Conclusions: These findings indicate that unexplored data from NGS experiments could help elucidate carcinogenesis and discover putative biomarkers with clinical applications. Further investigations should be considered for experimental design, providing opportunities to optimize data, saving time and resources while granting access to multiple genomic perspectives from the same sample and experimental run. Advances in molecular biology and bioinformatics allow for unprecedented data generation, thereby promoting a greater understanding of many physiological and pathological phenomena in human organisms . NeverthThe last three decades have been extremely prolific regarding the generation of cancer genome data , 6. ThouRecently, a new gold rush was launched: metagenomics. Again, large investments have been made toward efforts to discover the role of the microbiome in many cancer types –11. Morehttp://www.ncbi.nlm.nih.gov/geo) [These two gold rushes have several common features: most experiments collect fresh samples from human tumors and paired noncancer tissues; sequencing procedures result in a large and complex amount of data; bioinformatics teams select the desired information; and nonessential information (“trash”) is discarded, aiming at focusing on the research goals and reducing confounding data –15. Nextgov/geo) , and theThe steps of both cancer genomics and metagenomics experiments are similar. Both investigations are usually based on the acquisition of fresh samples from tumors followed by DNA extraction, library preparation and sequencing. In fact, human and nonhuman DNA are available at this point of the experiment. Nevertheless, according to the investigator, focus is driven to either human DNA for genomics or nonhuman DNA for metagenomics. The unexplored data from each case might harbor some of the objectives of other studies, paving the way for an integrative exploration of both human and nonhuman information.Therefore, NGS genomic data have sufficient information for taxonomic investigation with similar precision of dedicated metagenomic experiments. In an attempt to test this hypothesis, we performed additional analyses using publicly available gastric, prostate and bladder cancer genomic data to explore metagenomic information.Total RNA-seq analyses are NGS experiments that have the most significant opportunity to explore new data. The majority of these experiments focus only on specific types of transcripts, such as mRNA, lncRNA, or miRNA. However, this strategy allows for exploration of several RNA molecules simultaneously. Additionally, from total RNA-seq data, it is possible to detect new genomic variants, providing potentially insights to such experiments. Furthermore, nonhuman microbiome expression data (metatranscriptome) are captured from total RNA-seq. Since both human and nonhuman data are usually sequenced, the identification of microbiome gene expression and several downstream investigations are possible, such as taxonomic profiles and host-microbiome interactions .Most small noncoding RNA (sncRNA) investigations in cancer research address miRNA expression as potential cancer biomarkers and targets for therapy –20. UsuaWe hypothesize that evaluating the discarded sequences from miRNA sequencing data enables the identification of sncRNAs with potential value as cancer biomarkers or treatment approaches. To test this hypothesis, gastric, bladder and prostate cancer miRNA-seq data were explored.Most gene expression investigations in solid tumors rely on comparisons between tumors and adjacent to tumor samples, considering adjacent samples as normal controls. Nevertheless, such specimens harbor molecular alterations that are insufficient to cause cancer but differ from normal tissues collected from noncancer patients , 25, 26.In some cases, however, the analyzed transcripts contain neglected information, such as concomitant expression of oncogenes and expression of mutated genes from both adjacent to tumor and cancer tissues. Once more, these relevant data are likely to be discarded because the goal is generally to identify differential expression and not concurrent variant patterns .Thus, adjacent tumor tissue, which is often used only as a gene expression control, potentially has somatic alterations common to cancer that may hold an essential role in understanding the first steps of carcinogenesis. Aiming to prove this concept, we explored sequencing data from paired gastric tumors and adjacent to tumor tissues.https://www.ncbi.nlm.nih.gov/sra) [All data were downloaded from SRA databank (gov/sra) . For metgov/sra) , gastricgov/sra) , and progov/sra) , 34. Forgov/sra) ; periphegov/sra) ; and gasgov/sra) . For vargov/sra) .All downloaded samples were analyzed using FastQC (version 0.11.2) . TrimminTo analyze metagenomic data from genomic sequencing, we used Centrifuge Aligner software (version 1.0.4-beta) , mappinghttp://www.ensembl.org/info/data/ftp/). Since some sncRNAs may be repeated in several sites in human genome, the aligner parameters were adjusted, allowing at least 100 repetitions in genome. To improve identification of other sncRNAs, such as piRNAs in case multimapping occurred, the best alignment score was selected.STAR aligner (version 2.7.0)  was usedhttp://www.ensembl.org/info/data/ftp/) with Burrow Wheeler Aligner  [Exome samples were also mapped to HG19 human reference genome   using dehttps://www.ensembl.org). Since there are several overlapping sequences in piRbase, we used BEDtools (version 2.17) [For each sample, three annotations were performed for transcript quantification using htseq-count software (version 0.6) . First, on 2.17)  to mergeTaxonomic identification consisted of identifying reads that did not align to the human genome but displayed a minimum alignment score of 60%. The results were reclassified with Recentrifuge (version 1.0.3-beta) . This tohttp://broadinstitute.github.io/picard). The Genome Analysis Toolkit (GATK version 4.1.2) [Duplicated reads were removed using Picard Tools   was usedhttps://www.ensembl.org/Homo_sapiens/Tools/VEP) [All variants were called using BCFtools (version 1.8)  on exoniols/VEP)  was usedTo compare taxonomic profiles, we filtered all data for the top 40 most relatively abundant genera . We also converted the data to presence/absence to avoid sequencing biases and compared taxonomic profiles at genus level. The hypergeometric distribution was used to test significant overlap among microorganisms identified by the proposed whole-genome sequencing captured data (WGScd) and other literature data.P values were adjusted for multiple testing using Benjamini-Hochberg false discovery rate (FDR) adjustments [ustments . Alpha dustments , which wp value < 0.05. All graphics were created in the R statistical platform using the Venn [To identify additional differentially expressed (DE) sncRNAs not addressed by the original authors, we used DESeq2 package , which wthe Venn  and ggplthe Venn  packagesThree different cancer types from bladder, prostate, and gastric tumors were explored to obtain additional findings. This methodology is hereafter referred to as “Whole Genome Sequencing captured data” (WGScd) to differentiate it from other metagenomic analyses. The results were compared to metagenomic literature data.We downloaded data from 44 bladder cancer samples (PRJNA185252) and searched for nonhuman sequences aiming to obtain taxonomic information from the bladder cancer microbiome. After quality filtering and human sequence removal, an average of 180 thousand reads per sample were obtained. The most abundant bacterial genera found in bladder cancer tissues are demonstrated in Despite finding some common genera, the hypergeometric enrichment test did not indicate any significant overlap between the analyzed data . However, two genera were present in all six studies: Finegoldia and Streptococcus. Data from Bučević Popović et al. (2018) were the only data that presented detailed taxonomic information from every studied case, and majority of the genera were also found by the WGScd approach .The metagenomic data obtained from genomic experiments from prostate and gastric cancers are shown in p value > 0.05; Supplementary Table 6; To compare the WGScd metagenomic findings with data from exclusively metagenomics studies –65, we fAfter correcting for multiple testing, the hypergeometric enrichment test did not indicate any significant overlap among cancer metagenomic results . In both cancer types, we found several genera present in WGScd analyses and all metagenomic experiments: four in gastric cancer  and ten in prostate cancer   and 3D.For bladder cancer, we analyzed an average of 16 million (Mi) known transcript reads per sample, including ~81% miRNA reads, ~9% piRNA, and ~10% other transcripts. There was an average of 0.8 Mi reads per sample in gastric cancer, of which ~43% were miRNA reads, ~22% were piRNA reads, 13% were small nucleolar RNA (snoRNA) reads, and ~22% were other transcripts. For prostate cancer peripheral blood, we quantified 1.3 Mi reads per sample, from which ~21% were piRNA reads, ~11% were snoRNA reads, ~3% were snRNAs, ~2% were miRNA reads, and ~63% were other transcripts .Comparing gastric cancer sncRNA expression with that of noncancer gastric samples, we identified 57 DE piRNAs, of which 46 were upregulated and 11 were downregulated . RegardiAfter filtering, 7,443 somatic variants in tumor samples and 7,469 variants in adjacent samples were identified. Comparing tumors with adjacent to tumor samples, we found 1,635 common variants in 1,084 genomic positions, most of which were single nucleotide variants . Small deletions  and small insertions  were also found. Most variants had been previously reported , and 48 (4.4%) were unreported mutations.https://cancer.sanger.ac.uk/cosmic) [This analysis was able to identify 23 high-impact common variants  .Obtaining financial support for cancer research remains a challenge. Additionally, recruiting patients for such investigations also represents a critical step. Therefore, optimizing cancer research by reducing the number of expensive rounds of sequencing experiments and exploring the produced data by additional innovative approaches represents an option to overcome the limitations of collecting human biological samples and the scarce availability of resources to cover the costs of high-throughput experiments. Thus, three different approaches are proposed to better explore NGS data: (i) obtaining metagenomic data from genomic sequencing; (ii) capturing additional sncRNAs from miRNA sequencing; and (iii) analyzing common variants in tumor and adjacent to tumor samples.Metagenomics has become a new player in cancer research that has reached clinical practice . AlthougOur results showed that metagenomic analyses using genomic data are viable. However, several discussions may emerge from this approach regarding data reliability, such as whether contamination may mask results and whether combined acquisition of human and nonhuman sequences produces the same results as those from solely human or solely nonhuman experiments.Regarding contamination, both metagenomic and WGScd analyses share similar vulnerabilities. Tissue collection, laboratory handling, and even sterile reagents are not free of contaminants, since sequencing does not require exclusively viable microorganisms, and fragments of inert DNA may be sequenced and included in downstream investigations. Finding such contaminants remains a challenge, even for metagenomics experiments. Filtering contaminants should not be disregarded, both during classical metagenomics experiments and WGScd, and a critical assessment of results is essential for any potential clinical application. Nevertheless, as demonstrated, a significant part of the bacterial presence in NGS data –71 is obA comparison of WGScd results with metagenomics experimental results strongly suggested similarities between the two methods. According to these preliminary results, it seems viable to capture metagenomics information and save time, lab work, and financial resources by looking at already produced data from genomic sequences.Another question arises from the applicability of WGScd for other tumor sites. To shed light on this question, additional analyses were carried out, and gastric and prostate cancer data were also tested. Compared with other reports, our results seem to be as reliable as those from metagenomic investigations.An additional application of the proposed strategy is the investigation of rare tumors and sites with complex accessibility, such as the brain. These data could be reinvestigated from a metagenomic standpoint to provide insights on potential interactions that could be addressed both for carcinogenic understanding and clinical applications.Experiments should be designed to search for both types of data and perform integrative investigation of genomics and metagenomics from the same samples. In addition to conserving time, saving human and financial resources, and reducing the number of recruited patients, as discussed, this integrative analysis provides an additional advantage of joining genomics and metagenomics from the same clinical situation instead of analyzing each from a different set of patients.Another approach for better exploring NGS data is simultaneous analysis of diverse sncRNAs. A significant number of miRNA NGS experiments are currently reported due to their potential role as biomarkers. They are essential for cellular and tissue homeostasis and are involved in posttranscriptional gene regulation . SequencBy exploring this possibility in three cancer types, a large number of additional ncRNAs were identified and quantified, confirming our hypothesis. This relevant information adds strength and value to such analyses, introducing new players and enabling an integrative interpretation of the role of these sncRNAs in cancers and other biological processes.However, it should be noted that library preparation has an essential role in ncRNA identification . Some snFinally, RNA-seq data were analyzed to find common variants for both tumor and adjacent to tumor tissue in gastric cancer. Using RNA-seq data to identify genomic variants is challenging given the technical computational limitations due to intrinsic complexity of transcriptome, which increases the rate of false-positives compared to DNA-seq data , 75. AliIn this particular analysis, removing RNA editing variants was not considered since we aimed to find common variants from tissue adjacent RNA-seq data and tumor DNA-seq data. Somatic variants in both tissues at an RNA edit position are more likely to be true genomic variants than false-positive findings.Another putative limitation is calling only mutations in expressed transcript, leaving out both unexpressed genes and intronic/intergenic regions. However, using RNA-seq data to call variants allows for the identification of tissue-specific variant expressions, which are relevant for translational approaches.Our results identified several common deleterious variants in both tissues. Although these findings may need further experimental investigation, based on the field cancerization hypothesis, this approach may shed light on early steps of carcinogenesis. The proof of concept is again strengthened, especially when regarding the strategy of obtaining as much data as possible from each experiment, allowing more comprehensive interpretations and optimizing resources.Altogether, our results strengthen the hypothesis that abundant additional and potentially useful information can be extracted from NGS. Moreover, the integrated investigation of every available information should provide a broader and more robust interpretation of the molecular scenario from each experiment.


Single-cell RNA-sequencing (scRNA-seq) is widely used to reveal cellular heterogeneity, complex disease mechanisms and cell differentiation processes. Due to high sparsity and complex gene expression patterns, scRNA-seq data present a large number of dropout events, affecting downstream tasks such as cell clustering and pseudo-time analysis. Restoring the expression levels of genes is essential for reducing technical noise and facilitating downstream analysis. However, existing scRNA-seq data imputation methods ignore the topological structure information of scRNA-seq data and cannot comprehensively utilize the relationships between cells.Here, we propose a single-cell Graph Contrastive Learning method for scRNA-seq data imputation, named scGCL, which integrates graph contrastive learning and Zero-inflated Negative Binomial (ZINB) distribution to estimate dropout values. scGCL summarizes global and local semantic information through contrastive learning and selects positive samples to enhance the representation of target nodes. To capture the global probability distribution, scGCL introduces an autoencoder based on the ZINB distribution, which reconstructs the scRNA-seq data based on the prior distribution. Through extensive experiments, we verify that scGCL outperforms existing state-of-the-art imputation methods in clustering performance and gene imputation on 14 scRNA-seq datasets. Further, we find that scGCL can enhance the expression patterns of specific genes in Alzheimer’s disease datasets.https://github.com/zehaoxiong123/scGCL.The code and data of scGCL are available on Github: Bioinformatics online. Single-cell RNA sequencing technology (scRNA-seq) can explain cellular heterogeneity at the single-cell level and discover biological characteristics of different types of cells  can capture the information of adjacent nodes through the graph structure . The architecture of scGCL is shown in X of the scRNA-seq data is taken as the input raw data, where jth gene ith cell The expression matrix t highly variable genes based on the normalized discrete values computed by the scanpy package    and normFor 14 scRNA-seq datasets, the Clustering results of scGCL slightly outperforms on the average all baseline methods on most datasets . The avek and the distances measures are important hyperparameters for constructing the cell graph and determining the cell–cell relationship in the graph. Since among all baselines, only scTAG uses the K-NN method to construct cell graphs like scGCL, we choose it as the only comparison method for hyperparameter experiments. To investigate the effect of k and distance measures in constructing the cell graph on scGCL, we experiment on all datasets across various k and distance measures. In the experiment, we set k to be 5, 10, 15, 20 and 25, respectively, and set the distances measures to be ‘city-block’, ‘cosine’, ‘euclidean’, ‘L2’ and ‘manhattan’, respectively. The To evaluate the impact of different components of scGCL on the imputation results, we ablate different components on Adam and Romanov datasets. In particular, we specify two different cases: (i) No ZINB-based encoder and retain the AFGRL and graph convolutional models. (ii) Only the ZINB-based encoder is kept, and the graph convolution and AFGRL models are removed. As shown in wilcoxon method in the scanpy package to find differentially expressed genes (DEGs) on the raw scRNA-seq data, the GraphSCI-imputed scRNA-seq data, and the scGCL-imputed scRNA-seq data. Among all the DEGs, we identify 20 DEGs related to their types in different cell types.We apply scGCL to the public Alzheimer’s disease (AD) dataset and keep all hyperparameters constant. From As we can observe from scRNA-seq analysis still faces fundamental challenges, including high sparsity of expression profiles and complex differential gene expression patterns. Imputation of scRNA-seq data can effectively restore gene expression levels and facilitate downstream analysis. In this article, we propose an imputation method for scRNA-seq data scGCL based on graph contrastive learning. First, we construct a cell graph from the relationships between cells. Next, we apply a graph contrastive learning framework to scRNA-seq data, which can capture both global semantic and local information to better enhance the representation of nodes in the graph. Finally, we embed the ZINB-based autoencoder into the scGCL framework, which can effectively capture the global probability distribution of scRNA-seq data and reconstruct the data.To validate the performance of scGCL on scRNA-seq data imputation, we evaluate scGCL and other state-of-the-art baseline methods on multiple downstream analysis tasks such as clustering performance, recovering gene expression levels and pseudo-time analysis. scGCL demonstrates its effectiveness on scRNA-seq data imputation through extensive experiments. Furthermore, we verify the influence of different hyperparameters on the clustering results of scGCL, and the importance of each part on scGCL through ablation experiments. In the end, we perform a case study on the Alzheimer’s disease dataset, validating that scGCL enables clearer gene expression patterns from scRNA-seq data. In the future, we will continue to enhance the balance of scGCL and apply it to the integration of single-cell multi-omics data. In addition, we hope to provide stronger interpretability for the model by integrating the topic model.btad098_Supplementary_DataClick here for additional data file.


Colonomics is a multi-omics dataset that includes 250 samples: 50 samples from healthy colon mucosa donors and 100 paired samples from colon cancer patients (tumor/adjacent). From these samples, Colonomics project includes data from genotyping, DNA methylation, gene expression, whole exome sequencing and micro-RNAs (miRNAs) expression. It also includes data from copy number variation (CNV) from tumoral samples. In addition, clinical data from all these samples is available. The aims of the project were to explore and integrate these datasets to describe colon cancer at molecular level and to compare normal and tumoral tissues. Also, to improve screening by finding biomarkers for the diagnosis and prognosis of colon cancer. This project has its own website including four browsers allowing users to explore Colonomics datasets. Since generated data could be reuse for the scientific community for exploratory or validation purposes, here we describe omics datasets included in the Colonomics project as well as results from multi-omics layers integration.  Also, 50 samples from healthy colon mucosa obtained at colonoscopy from donors (M) without cancer were included and used as reference , single nucleotide polymorphism (SNP) genotypes and CNV (using Affymetrix microarrays) and somatic mutations (through whole exome sequencing) in a subset. RNA was used to obtain gene and miRNAs expression, using Affymetrix microarrays and SOLID sequencing, respectively. Finally, patient clinical data from all samples were obtained, including follow up.The analysis of these datasets allowed us to make a descriptive and a comparative snapshot of the molecular state of cancer cells (T) and, also, to find differences between healthy normal tissue (M), normal tissue adjacent to tumor (N), and tumoral tissue (T).https://www.colonomics.org/) links to the digital data repository of the University of Barcelona (UB-DD)3 and provides four user friendly web tools that allow an agile and direct visualization of data.The Colonomics project web page  between January 1996 and December 2000, and had fresh frozen tumor samples in the hospital biobank. One hundred patients were included, with the criteria of a confirmed adenocarcinoma of the colon, diagnosed in stage II and the tumor was microsatellite stable (tested with five markers). Patients should have not received adjuvant therapy and a minimum follow up of three years was requested at the time of selection. A pathologist reviewed the tumor blocks, confirming diagnosis and that there were no signs of tumor cells when margins were examined. The paired adjacent normal mucosa (N) was obtained from the proximal margin, at least 10 cm distant from the tumor (T). As a control group, 50 healthy mucosa donors (M) were prospectively invited to participate in this study when they underwent a colonoscopy indicated for screening or symptoms with no evidence of lesions in the colon or rectum. Table From these 250 samples , data on gene expression, DNA methylation, SNPs, CNV and miRNAs were obtained. Whole exome sequencing was analyzed in a subset of 42 tumors and their paired adjacent normal mucosa. Figure DNA was extracted from colon tissue by the phenol-chloroform procedure, quantified using a NanoDrop ND 2000c spectrophotometer  and stored at 4 °C. Bisulphite conversion of DNA (200–500 ng) was performed according to the manufacturer’s recommendations for the Illumina Infinium Assay (EZ DNA methylation kit. Zymo Research. Cat. No. D5004). The incubation profile was 16 cycles at 95 °C for 30 seconds, 50 °C for 60 minutes and a final holding step at 4 °C.Total RNA was isolated from tissue samples using Exiqon’s miRCURY™ RNA Isolation Kit . For quantification, NanoDrop® ND-1000 Spectrophotometer  was used. RNA quality was assessed by gel electrophoresis and RNA 6000 Nano Assay . RNA purity was measured with the ratio of absorbance at 269/280 nm , no differences among tissue types were found. RNA integrity numbers showed good quality .affy package (version 1.28) from R/Bioconductor4 was used to normalize data. After quality control and normalization 49,386 probesets in 20,070 genes and 246 samples remain for the analysis: 50 M, 98 N and 98 T (98 paired N/T). Since the array provides multiple probes per gene, a summary gene expression value for each gene was obtained through a principal components analysis (PCA). We extracted the first principal component, that captures the highest expression variability, and was rescaled to have the average expression of the different proves and maximum observed standard deviation. This rescaling was required to avoid negative expression values of the first PC. Therefore, in addition of the dataset with the measured probes, a final dataset with a single expression value for each one of the 20,070 genes was obtained.Affymetrix Human Genome U219 Array Plate platform  was used to obtain gene expression data from isolated RNA. A block experimental design was performed to the 96-array plates to avoid batch effects and sample ids were blinded to laboratory technicians. Robust Multiarray Average (RMA) algorithm in 6. To minimize batch effects, samples were randomly distributed but matching paired samples  in the same array row (M samples were randomly paired in array rows).The Illumina Infinium HumanMethylation 450k BeadChip assay was used to obtain the DNA methylation profile of the samples. The array interrogates the methylation levels of 485,512 CpG sites covering 99% of RefSeq genes and 96% of CpG islandsSample quality and sex concordance was checked using the SNPs of the 450 K array with those of the Affymetrix Genome-Wide Human SNP 6.0 array . A total of 10 samples had to be excluded due to quality problems and the final dataset contained 240 samples: 96 N, 96 T (92 complete pairs of N/T) and 48 M.7. We also excluded probes that contained SNPs within 10 bp of distance. Finally, a set of 430,086 probes was obtained.Background correction was performed filtering out probes with a detection p-value greater than 0.01 in more than 5% of the samples. We discarded probes that ambiguously mapped to multiple locations in the human genome with up to two mismatches8 using the Bioconductor package minfi (version 3.0.1)9. A random forest approach was used for the imputation of missing values.We used β-values, the Illumina’s standard, to measure methylation level at each locus. β-values are calculated using the average probe intensity and they range from 0 to 1, being β = 0 the absence of methylation and β = 1 a complete methylation. Subset-quantile within array normalization (SWAN) was used to reduce technical variation within and between arrayscrlmm (version 1.8)10. A total of 4 samples were excluded due to quality or sex concordance problems (3 M and 1 N) so 146 samples (47 M and 99 N) remained for the analysis.Extracted DNA was hybridized to obtain the genotypes of the samples using Affymetrix Genome-Wide Human SNP 6.0 array , which includes near 1 million of SNP markers. Genotype calling was performed with Corrected Robust Linear Model with Maximum Likelihood Classification (CRLMM) algorithm as implemented in R/Bioconductor package 11 with default parameters.The Affymetrix Genome-Wide Human SNP 6.0 genotyping array was used to infer the copy number aberrations in the tumors. The average inter-marker distance of this array was 700 bp. Copy number estimate (CNE) was calculated using Affymetrix Power Tools (Version 1.16.1) software12 and split into discrete segments. To avoid masking effects, the stromal content of the tumor samples was taken into account to calculate the thresholds to split the segments. Stromal content was estimated using the ESTIMATE R package (version 1.0.13)13 using gene expression data14. A hierarchical cluster analysis was performed to divide the samples in 4 clusters reflecting their different levels of stromal content. A different threshold to split the segments was used for each cluster: ±0.5 for low stroma, ±0.4 for medium-low stroma, ±0.3 for medium-high stroma and ±0.2 for high stroma15. The third and final step of the segmentation process consisted in a t-test to compare the mean of consecutive segments and to merge them in case there are no statistical differences . For each segment, the mean of CNE was assigned as the representative value of the region. 26,423 segments of CNV were found in the 99 T samples .Adjacent locus with a similar CNE were grouped in regions using a segmentation pipeline that had three steps. First, a smoothing spline was applied to normalize data. Second, change points in CNE pattern were located using the Vega R package (version 1.35)16, removing reads containing a miscall as well as reads containing negative quality scores. All 250 samples showed adequate quality. Quantification of specific miRNAs was performed mapping the reads to the reference of mature miRNA sequences annotated in miRBase release 22.1 (October 2018) that contained 2,654 human miRNA sequences17. The FASTX-toolkit18 was used to preprocess the miRNA data and to provide compatible sequences for mapping with the Bowtie 1.2.2 aligner19. Cutadapt20 software was used to trim read adapter and the final table of counts was generated with SAMtools mpileup v1.821. Finally, 2,641 miRNAs remained for the analysis.Isolated RNA was used to obtain the expression of miRNAs. Small RNA Assay of the Agilent 2100 Bioanalyzer  was used to assess the quality control of the miRNA fraction following manufacturer’s recommendation. Small RNA-seq was performed through the SOLiD platform. The PureLink miRNA isolation kit was used to construct the libraries of compatible fragments with SOLiD from an enriched fraction of small RNA. Sequencing microspheres were obtained by applying an emulsion PCR into an equimolar mixture of 48 libraries followed by an enrichment process before charging in the reaction chamber. Finally, the reaction to obtain the sequences (of 35nt + 10nt barcode) from miRNA fraction was performed by blinded laboratory technicians through the Applied Biosystems SOLiD 4 System. The samples were randomly distributed among the different sequencing slides to minimize batch effects. The data quality was estimated using the SOLiD Experimental Tracking System (SETS) software. Parameters such as total number of reads, proportion of reads with miscalls or proportion of reads with low average quality value (QV) were evaluated for quality control with HTS SOLiD Preprocessing softwareGenomic DNA from a subset of 42 N and T paired samples was sequenced in the National Center of Genomic Analysis  using the Illumina HiSeq- 2000 platform. Samples selection was required due to budgetary constraints. Samples included were those from all patients that had experienced tumor progression (n = 21), and a matched sample from a patient with more than 5 years of follow-up without progression.22 and aligned over the human genome hg19 version, with Bowtie 2.023. Unmapped reads, reads with unmapped mate, nonprimary alignments, and reads that were PCR or optical duplicates were discarded with Picard24. A local realignment around indels found in our samples and defined in dbSNP 135.b37 version25 and 1000 genomes project26 was applied. Variant calling was performed with GATK software27, and low-quality variants  were filtered out. Germline variants from our study that are the variants found in normal adjacent paired tissue but not in tumor were removed. Other germline variants found in normal tissue from 1000 genomes project were also filtered out. Only single-nucleotide variants (SNV) were included in this study. Finally, variants were annotated using the SeattleSeq Variant Annotation web tool28. No correlation was observed between the number of mutations per sample and the control quality parameters . Finally, 13,015 somatic variants  were found.Exome capture was performed with the commercial Agilent kit Sure Select XT Human All Exon 50MB. Exomes from T were sequenced at 60x coverage (2 × 75 bp reads), and exomes from N were sequenced at 40x (2 × 75 bp reads). The sequences were quality controlled using FastQC softwarehttps://www.colonomics.org/), and in the digital data repository of the University of Barcelona (UB-DD)3, to ensure permanent availability.All the Colonomics data is publicly available, though it has been deposited in diverse repositories. Table 29. Raw data was upload as 246 CEL files and the normalized data is included within sample table as text file. The normalized data as log2 RMA signals include 49,386 probesets and can also be downloaded from UB-DD3. Finally, the gene expression summarized at gene level is available at the UB-DD3 and includes 20,070 genes.Gene expression includes 246 samples: 50 M, 98 N and 98 T. Gene expression raw and normalized data are available in the Gene Expression Omnibus database (GEO) through BioProject PRJNA188510 and accession number GSE4407630. Methylation data includes 240 samples: 48 M, 96 N and 96 T. We provide 240 IDAT files that correspond to raw data and a text file with the normalized beta values of the 240 samples and 430,086 CpG probes. The normalized methylation data is also available at the UB-DD3 together with the annotation of the 430,086 CpG methylation probes. Both gene expression and DNA methylation data are part of the GEO super series GSE16642731.DNA methylation data, both, raw and normalized are also available in GEO through BioProject PRJNA542323 and accession number GSE13101332. It includes 146 CEL files that corresponds to 47 M and 99 patients of colon cancer.Raw genotyped SNP data can be obtained, under controlled access, at the European Genome-phenome Archive (EGA) through accession number EGAD000100012533. It includes 54,002 segments: 15,646 losses, 10,777 gains and 27,579 segments that are not gains nor losses.The processed CNV data of 99 T samples is accessible in the UB-DD33. We provide 250 fastq files, one for each sample . Count data of the 2,641 miRNAs and the 250 samples are accessible at UB-DD3.Raw small RNA sequencing data is also available under request in EGA through accession number EGAD0000100482734. The tumors mutations identified are available at UB-DD3.Raw whole exome sequencing data of 84 samples (42 N and 42 T) with 2 fastq files per sample are accessible in EGA under request through accession number EGAD0000100482635.SNPs, miRNAs and whole exome data are in the EGA repository under the Data Access Committee through accession number GAC000010006623. 13,015 somatic variants were included.Finally, somatic mutations of the 42 samples can be downloaded from UB-DD3. This file is txt formatted and contains a row for each sample and a column for each variable. The variables included are: id_clx: the identifier of the Colonomics sample; type: tissue type ; id_clx_individual: the individual identifier; stage: the stage of the tumor (IIA or IIB); sex: ; age; site: the site of the colon (Left/Right); event_free: a binary variable that indicates if the patient has a recurrence of colon cancer (1) or not (0); time_free: a continuous variable that indicates the time from surgery to recurrence of colon cancer; event_global: a binary variable that indicates if the patient is still alive (1) or not (0); time_global: a continuous variable that indicates the time from surgery to death; metastasis_site: the organ where a metastasis has appear; BRAF_mutated: a binary variable that indicates if the tumor is BRAF mutated (Yes) or not (No); KRAS_mutated: a binary variable that indicates if the tumor is KRAS mutated (Yes) or not (No); stromal_score: stromal score to predict the level of infiltrating stromal cells in tumor samples obtained using ESTIMATE package (version 1.0.13)13 from R and CMS: consensus molecular subtype classification of the tumoral samples in 4 groups  obtained using CMScaller package (version 0.1)36 from R and based on the paper of Guinney et al.37.In addition to raw and preprocess multiomics data, clinical information of the samples is also upload at UB-DD38 has also been created. This object includes clinical data, data from gene expression, methylation, miRNAs and copy number variation and it is accessible in UB-DD3.To facilitate working with all these data and to avoid downloading each dataset individually, a MultiAssayExperiment object from R (version 1.22)39. These models were built for normal colon tissue (samples N + M) using elastic net regression, following the PredictDB pipeline41. They include significant prediction for 1,758 genes, 17,281 CpG probes and 39 miRNAs. See the usage notes section for more details.In addition to clinical and multiomics data, gene expression prediction models from SNP data, useful for Transcriptome-Wide Association Studies (TWAS), can be download from Zenodo through accession number 6334768ACTB, TPT1, and UBC were used as control genes in the assay, see Solé et al.42 for details. Also, differentially expressed genes were validated using 45 N/T paired samples from The Cancer Genome Atlas (TCGA) data43, obtaining that the 97.86% of our differentially expressed genes were also differentially expressed in TCGA data44. Differentially expressed genes were found using limma R package (version 3.42)45. Genes with an absolute log fold change >1 and a Bonferroni adjusted P-value < 0.05 were defined as differentially expressed.We performed technical validations and results replication in other datasets for some of the Colonomics assays. For gene expression, a selected group of genes were assessed with multiplexed RT-qPCR using BioMark Dynamic Array 96 × 96 Plates . 44. Also, CNV was validated using TCGA data. In this case, 222 colon cancer samples were used to compare the obtained 13,279 minimal recurrent regions and 66% of these regions were also found in TCGA data15.Regarding DNA methylation data, the list of the differentially methylated CpGs obtained using Colonomics data was compared with the one obtained using the 45 N/T paired samples from TCGA data obtaining that the 99.96% of the common CpGs were also differentially methylated in TCGA data46. Also, to validate recurrent mutations found in AMER1 gene, sanger sequencing was used. All mutations were validated.In the analysis of whole exome sequencing data, 13 SNPs in the 84 samples were genotyped using KASPar genotyping assays  on the Fluidigm genotyping platform . These data were used to assess concordance between samples. All 42 N samples correctly matched with their corresponding T sampleAs a validation of the discovery pipeline of SNVs, 6 mutations in KRAS  and 7 in TP53  were tested using KASPar genotyping assays in the Fluidigm Biomark platform (dynamic arrays). A 65% of concordance were achieved. It is noteworthy that 10 out of 11 no concordant mutations were only found by exome sequencing thus confirming the higher sensitivity of this technique.All the data in the Colonomics project has been analyzed and used in published papers except for miRNAs data. The first paper involving Colonomics data was published eight years ago in 2014. The data were analyzed with the best algorithms and programs at that time, but nowadays there may exist improved analysis pipelines. That’s why we have shared raw data for each omic layer in addition to preprocessed data. Specifically, for the case of whole exome sequencing data that was processed 8 years ago, we are aware that the calling algorithms may have produced some false positive mutations.When re-using Colonomics data we also need to consider that all samples were confirmed adenocarcinoma of the colon, diagnosed in stage II and the tumor was microsatellite stable. Colonomics data is a very homogeneous cohort, but the absence of rectal tumors, colon tumors in other stages rather than stage II and microsatellite instable samples may limit building generalizable hypothesis of colorectal cancer. Patients were selected to have received only radical surgery, without adjuvant chemotherapy, which also reduces potential analysis to prognosis but not prediction.47 web applications that are freely accessible at https://www.colonomics.org/data-browser. There are four different web applications: the expression browser, the methylation browser, the expression quantitative trait loci (eQTL) browser and the regulatory networks browser. In these browsers, expression, methylation, and genotyping data have been exploited developing different types of analysis. A web browser integrating all the types of omics datasets available would be an interesting future application.In order to visualize the different types of data, we developed different intuitive and user friendly shinyThe genetic prediction models represent reference imputation panels for normal colon tissue methylation and gene and miRNA expression, which are of high interest for performing TWAS studies for colon-related diseases.39. Models were trained using the elastic net procedure employed in the PredictDB pipeline41, according to which only models with a predictive performance p-value < 0.05 and R2 >0.1 are considered significant. We adjusted the models by basic covariates, i.e., sex, age, tissue type and colon anatomic location where biopsies were collected (left and right colon). Genome coordinates refer to GRCh37/hg19.We provided significant prediction models for 1,758 genes, 17,281 CpG probes and 39 miRNAs obtained from normal biopsy samples. These features can be predicted from SNPs located within ± 1 Mb, which we assumed they act through cis mechanisms. We included the model’s summary statistics and corresponding SNP weights in SQLite objectsPrediction models for gene expression were trained using 144 normal mucosal biopsy samples (97 N and 47 M), the common samples between gene expression and SNPs. We trained models for 13,939 genes , which had more than 3 sequencing reads in more than 10% of the samples. In cases where multiple probes mapped to a single gene, the first principal component from PCA was used to capture the largest common variability. Trimmed Mean of M-values (TMM) normalization was applied. TMMs were transformed with inverse normal transformation.48 to capture additional technical variability.Prediction models for CpG probes were trained using 132 normal mucosal biopsies (95 N and 37 M). Only CpG annotated in islands, shores and shelves were taken into account as CpGs in open-sea are not functionally interesting. We also filter in CpGs which had more than 3 sequencing reads in more than 5% of the samples. After these filters, 257,809 CpG remained for the analysis. Inverse normal transformation on Beta MIxture Quantile (BMIQ)-normalized values was applied. In addition to the basic covariates for adjustment, we adjusted the models by 10 probabilistic estimation of expression residuals (PEER) factors49.Prediction models for miRNAs were trained using 146 normal mucosal biopsies (99 N and 47 M), the common samples between miRNAs and SNPs. From the 2,641 miRNAs, we included 739 miRNAs, which had more than 3 sequencing counts in more than 10% of the samples. TMM normalization was applied and transformed with inverse normal transformation. As SNP data is in hg19 genome built, annotations of miRNAs refer to the miRBase release 20


Apart from continuous and binary traits that measure the extent or presence of a disease, multi-categorical outcomes including variations/subtypes of a disease or ordinal levels of disease severity are commonly seen in clinical studies. On top of that, studies with clustered design  are popular alternatives to population-based ones as they are able to identify characteristics on both individual and population levels and to investigate the trajectory of traits of interest over time. However, existing methods for microbiome association analysis are inadequate to handle multi-categorical outcomes, neither independent nor clustered data. We propose a microbiome kernel association test with multi-categorical outcomes (MiRKAT-MC). Our method is versatile to deal with both nominal and ordinal outcomes for independent and clustered data. In addition, it incorporates multiple ecological distances to allow for different association patterns between outcomes and microbiome compositions to be incorporated. A computationally efficient pseudo-permutation strategy is used to evaluate the statistical significance. Comprehensive simulations show that MiRKAT-MC preserves the nominal type I error and increases statistical powers under various scenarios and data types. We also apply MiRKAT-MC to real data sets with nominal and ordinal outcomes to gain biological insights. MiRKAT-MC is easy to implement, and freely available via an R package at  The diverse microbial cells including bacteria, archaea, and fungi that colonize the mucosal and skin environment constitute the human microbiome . It is bIdentifying links between microbiome and diseases is often achieved by microbiome-wide association studies (MWAS) , which ip-value calculated in multiple ways, including analytical  or when small p-values are needed for multiple comparison adjustment. These drawbacks also exist for PERMANOVA.Beyond population-based studies in which all samples are independent, nowadays, researchers frequently collect microbiome data that are clustered or longitudinal in nature. For instance, Goodrich et al.  collectep-values. This approach features several advantages over its potential competitors: 1) it avoids the complication in designing a suitable permutation scheme for inference; 2) it is computationally efficient and much faster than direct permutations; 3) it controls the type I error and maintains high statistical power compared to the analytical approach. For the last point, due to the small sample size and the over-dispersion in microbiome data, it is quite difficult to approximate the MiRKAT test statistics, especially for clustered/longitudinal data and for outcomes that are not normally distributed.In this paper, we propose a new distance-based microbiome kernel association test for multi-categorical outcomes (MiRKAT-MC), when samples are independent or clustered. MiRKAT-MC works for both nominal and ordinal outcomes, through the use of the generalized logit model (GLM) and the proportional odds model (POM), respectively. We utilize a fast pseudo-permutation technique  to calcup-value yields inflated type I errors. On the other hand, naively adjusting the results by Bonferroni correction will reduce the statistical power substantially, mainly because the individual tests are highly correlated. We propose an omnibus test that combines the individual p-values from tests with different kernels through the harmonic mean procedure (HMP)  . The omnp-value calculation procedure via a novel fast pseudo-permutation technique  and microbiome composition under different study designs . Our major contributions are two-fold. First, we have cast the association analysis between a multi-categorical outcome and microbiome composition into frameworks of generalized logit models and proportional odds models . Our second contribution is proposing a robust echnique , avoidinTo associate microbiome compositions with a multi-categorical outcome, we build upon generalized logit models (GLM) for nominal outcomes and proportional odds models (POM) for ordinal outcomes, and relate the microbiome profile with the outcome through the flexible semi-parametric kernel machine regression framework . Our proYi denote the multi-categorical outcome with total J categories for the i-th subject. Here, bmYi is a vector with the j-th element being yji, a binary variable denoting whether the i-th sample belongs to the j-th category, i = 1, … , N, j = 1, … , J. That is, yji = 1 means subject i is of category j and otherwise, yji = 0. In practice, yji can represent any mutually-exclusive categorical traits , such as subtypes of cancers and increasing levels of disease severity that Yi can be considered as from a multinomial distribution with J categories. Let πj (xi) = Pr (yji = 1|xi) be the conditional probability that subject i is of category j with ∑jπj (xi) = 1, where xi denotes the set of covariates that we want to associate Yi with . If bmYi is nominal, we can set the last category J as a reference without loss of generalization, and form the following GLM:j = 1, … , J − 1. The left-hand side of βj represents the increase in log-odds of falling into category j vs. the reference category J resulting from a one-unit increase in the corresponding covariate while holding the other covariates constant. This model simultaneously describes the effects of xi on all outcome categories in contrast to the reference. In this model, parameters βj, j = 1, … , J − 1 can be different among categories. If the categories are ordinal, we can utilize the order information and form the following POM:j = 1, … , J − 1, andνj (xi) is the conditional cumulative probability, and the corresponding response, defined by β here keeps constant across J − 1 logits and the intercepts have to satisfy α1 < … < αJ−1 in the proportional odds model.We first describe the GLM and POM model without considering the high dimensional microbiome data. Let p-values is novel and tends to outperform the asymptotic results as in existing methods when sample sizes are small, which is usually the case in microbiome data.Finally, we notice that there are other recent attempts to develop association analysis for multi-categorical outcomes using multinomial logistic regression ), usually in the context of genome wide association studies . Despitei has mi observations. Let k-th observation in cluster i, i = 1, … , n, k = 1, … , mi and πj (xik|bji) = Pr  and setting the J-th category as reference, the GLMM for clustered/longitudinal data can be written as:uik is the design matrix for the random effect term bji. We introduce bji to model correlations among observations within cluster i of category j. The model definition is completed by specifying the distribution of the random effect Gj for the j-th category is unstructured. We also allow bji to be correlated across categories.Similarly, we first describe the GLMM and POMM models without considering the complex microbiome data. Suppose cluster bi to be identical across category comparisions, and thus G. Here, we essentially assume that the random effects across the ordered categories are the same, which guarantees in proportional odds. Specifically, for a fixed cluster i, the random effect bi has identical value across different categories j. But for different clusters i and i′, bi and bi′ may be different and both have normal distribution G is unstructured as well. The same constraints for αj and β as in model . We relate the multivariate outcome to the microbiome community and the covariates with the following modeli = 1, … , N, j = 1, … , J, where η = g (⋅) and g (⋅) is a link function. For GLM, g (πji) = log (πji/πJi), πji = E (yji|hji), and hji = hj (zi); for POM, g (νji) = log{νji/(1 − νji)}, hj (⋅) are unknown real functions corresponding to the effects of microbiome on the j-th category. For POM, hj (⋅) are identical across categories, and αj and βj are subject to the constraints described in model ], hjik = hj (zik), and g (⋅) is the same link function as model , Gjj is specified as “unstructured” in generalized linear mixed effect models, providing much flexibility to capture cluster specific correlations. Again, for POMM, αj, βjm, and bji are subject to the constraints described in model (hjik (⋅) should be identical across categories.For clustered studies, let as model . To illuin model , and hH0: h1 (⋅) = … = hJ−1 (⋅) = 0 in 0 and variance K*. We assume that each K* = IJ−1 ⊗ τK, where IJ−1 denote (J − 1)-th order identity matrix, τ is an unspecified constant, K is an N × N kernel matrix, and ⊗ denotes Kronecker product. Following ’s (which are assumed to be in the space spanned by K). As demonstrated in previous studies, the embedding of such ecological information may boost statistical power for detecting an underlying association under many scenarios  × (J − 1) block matrix with entries In ⊗Gjh, j, h = 1, … , J − 1. For POMM, W−1 = DνVνDν and G* is a (J − 1) block diagonal matrix with entries In ⊗Gjj.For clustered study design, we write model  in matriQ1 and Q2 is relatively straightforward in the pseudo-likelihood framework  and the over-dispersion in microbiome data  and a kernel matrix for microbiome data  for independent data or ϵ = Σ−1 (y* − Xβ) for longitudinal data. The outcome kernel will be N × (J − 1) matrix, where ϵj is the weighted residuals for the j-th category. Originally, N (J − 1), where Vec (⋅) denotes the operator that transforms a matrix into a column vector by vertically stacking the columns of the matrix. We refer the readers to previous publications for further details of p-values using the Pearson type III distribution . And then combines these individual p-values (corresponding to different microbiome kernels) using the harmonic mean p-value (HMP) procedure  is used and gains substantial power compared to when a poor choice of kernel is used.Finally, recall that rocedure  for an oWe conducted comprehensive simulations to evaluate empirical type I error of MiRKAT-MC when there is no true associations, and statistical powers under different association patterns. For both independent and clustered study designs, microbiome compositions were simulated similarly as in previous studies . BrieflyJ = 3) and when there are five categories (J = 5). Data from each sample was simulated independently, according to following modeli = 1, … , N and j = 1, … , J − 1. We set the sample size N = 80 or 200 for when J = 3, and N = 150 or 300 when J = 5. We simulated both nominal and ordinal outcomes, using appropriate link functions of η. For nominal data (GLM), αj = −2, and for ordinal data (POM), αj = j − 4. xi1 is a Bernoulli variable with probability of 0.5, whereas xi2 is a standard normal variable with mean 0 and variance 1. p OTUs in the community. β = 0 for type I error simulations, for which the choice of scale is the operation that standardize the data to be mean 0 and variance 1 across all the samples.We considered simulations when there are three categories , the unweighted UniFrac (Ku), the Bray-Curtis (KBC) and a generalized UniFrac kernel with the parameter of 0.5 (K5) for association testing. We also conducted the omnibus test by combining the p-values from all individual tests. To obtain convincing results, we generated 10,000 replicates to estimate the empirical type I errors and 2,000 replicates for statistical powers. Statistical significance was established under the nominal level of α = 0.05 for all the simulation studies.For each scenario, we employed the weighted UniFrac (n = 30 or 60 for three categories (J = 3), and n = 50 or 100 for five categories (J = 5). We simulated data under an unbalanced design: i.e., clusters may have a different number of observations. To achieve this, n/2 of the clusters have three observations and the other n/2 of the clusters have four observations. In this way, the total numbers of observations are N = 105 (n = 30) and N = 210 (n = 60) when J = 3 and N = 175 (n = 50) and N = 350 (n = 100) when J = 5. Within each cluster, the outcome category may vary over observations; e.g., in longitudinal studies, a person may be of one disease category at one time point and of a different disease category at a different time point.We simulated two scenarios to assess MiRKAT-MC when data is clustered. We simulated a family based study and a longitudinal study. For family-based data, we included only a random intercept in the model to capture the correlation between samples, while for longitudinal data, both a random intercept and a random slope of time were involved in the model. We set the number of clusters i = 1, … , n, j = 1, … , J − 1, and k = 1, … , mi. The definition of the parameters η, αj, β xik1, xik2, scale function are identical to the counterparts in model  of MiRKAT-MC are all very close to the expected level. Empirical type I error rates under different mixed models for clustered data are reported in Empirical type I error rates of MiRKAT-MCN  and MiRKAT-MCO  for independent data are reported in Gjj in In the original study , 555 nonThe goal of this application is to test the association between treatment groups  and gut microbiome. Here, we exclusively analyzed the fecal samples, leaving 499 samples from 140 clusters over time. The gut microbiome was profiled from each sample and the raw sequence data is available on the Qiita database (study ID 10508). Specifically, the V4 region of the bacterial 16S rRNA gene was PCR amplified, followed by performing paired-end sequencing of the amplicon library. We reprocessed the pre-joined and trimmed sequencing data through DADA2 pipeline in R . As a reHere we first visually checked the relationship between gut microbiome composition and antibiotic treatment groups under different dissimilarity measures with PCoA plots . All 499p-values p-values less than 0.0001, except for when using the unweighted kernel .To show the performance of MiRKAT-MCN on independent nominal data, we selected samples at week 3 only. All 140 clusters had microbiome data available. By setting treatment groups as the dependent variable and adjusting for gender of mice, we observed very significant association between gut microbiome and the antibiotic treatment groups using weighted, unweighted, and generalized UniFrac kernels, Bray-Curtis kernel, and the omnibus test  from the original dataset for analysis, where there were 15, 6, and 9 clusters in STAT, PAT, and control group, respectively. We applied MiRKAT-MCN for clustered data to evaluate the association between antibiotic treatment and microbiome, adjusting for sex and time (in weeks), and accounting for the cluster-specific correlation through a random intercept and a random slope of time. Again, we employed the same kernels as above and the omnibus test for analysis. Apart from the test using the unweighted UniFrac kernel with p-values by using different kernels, although all significant, suggest that the antibiotic use may have affected the relative abundance of OTUs, because the unweighted UniFrac kernel, which only accounts presence/absence of taxa and gives higher weight to rare taxa, provides the least significant result.These two analyses indicate that antibiotic exposure during early life did alter the microbiome composition in non-obese diabetic mice, no matter we stared at the week 3 or inspected over time. Moreover, the disparities of % similarity using the reference-based OTU-picking approach, and to generate a rooted phylogenetic tree. All samples were rarefied to 10,000 counts per sample before calculating the distance measures.A study was conducted by Goodrich et al.  to invesp-values, in order to ensure comparability.For this analysis, we focused on 311 samples from 145 monozygotic twin pairs. All the twins were female, aged from 27 to 83 with an median age of 63. In order to compare the performance of different methods, we treated the BMI as continuous, binary, three-category ordinal and three-category nominal data, and applied CSKAT , GLMM-Mip-value of each kernel across four methods is bolded. At the first glance, all the individual tests provided significant association at type I error of 0.05 except when the weighted UniFrac kernel was used. The omnibus test also provided significant association. However, MiRKAT-MCO gave the smallest p-values when using the unweighted UniFrac, the generalized UniFrac and the omnibus test. MiRKAT-MCO was always more powerful than MiRKAT-MCN in this analysis, which is reasonable because MiRKAT-MCO utilized the order information in data. Both MiRKAT-MCO and MiRKAT-MCN were more powerful than GLMM-MiRKAT except when the weighted UniFrac kernel was used, for which none of the methods was significant. Our results are also consistent with the conclusion of the previous study  and POM(M) to achieve efficient and accurate p-values and combine them via the harmonic mean approach . HoweveComputational efficiency of MiRKAT-MC is investigated and reported in In summary, we propose MiRKAT-MC, a microbiome regression association test for multi-categorical outcomes with independent and clustered study designs. The proposed methods show well controlled type I errors and high power over multiple scenarios through extensive simulations and better performance than competitors in real data analyses. It is easy to use and fast to compute. We believe that MiRKAT-MC will enrich the toolbox of researchers to conduct microbiome research with multi-categorical outcomes.


Prostate cancer is a widespread form of cancer that affects patients globally and is challenging to diagnose, especially in its early stages. The common means of diagnosing cancer involve mostly invasive methods, such as the use of patient’s blood as well as digital biopsies, which are relatively expensive and require a considerable amount of expertise. Studies have shown that various cancer biomarkers can be present in urine samples from patients who have prostate cancers; this paper aimed to leverage this information and investigate this further by using urine samples from a group of patients alongside FTIR analysis for the prediction of prostate cancer. This investigation was carried out using three sets of data where all spectra were preprocessed with the linear series decomposition learner (LSDL) and post-processed using signal processing methods alongside a contrast across nine machine-learning models, the results of which showcased that the proposed modeling approach carries potential to be used for clinical prediction of prostate cancer. This would allow for a much more affordable and high-throughput means for active prediction and associated care for patients with prostate cancer. Further investigations on the prediction of cancer stage  were carried out, where high prediction accuracy was obtained across the various metrics that were investigated, further showing the promise and capability of urine sample analysis alongside the proposed and presented modeling approaches. In men, prostate cancer is the most frequently diagnosed cancer in 112 countries from North and South America, Australia, Europe and Africa, with a mortality rate of 7.7 per 100,000 worldwide . Liquid The use of computational methods within clinical medicine has seen a rapid rise in its applications over recent years, largely due to the availability of greater computational power and greater awareness of what these approaches can offer clinical settings in terms of improved disease diagnosis capability, while also allowing for an enhancement in care strategies that can be offered to patients ,7. In paRecently, it has been observed that urine carries relevant biomarker antigens which, when analyzed using the appropriate spectroscopic tools alongside the necessary signal processing, can allow for the reliable prediction of the presence of prostate cancer ,18,19,20The linear series decomposition learner (LSDL) is an artificial intelligence (AI) approach formed in the area of metaheuristics, that is capable of performing a sequence of decompositions on a candidate signal with the use of a linear basis function with the goal of performing signal separation in order to obtain the optimal region of the signal which minimizes uncertainty and redundancy within the signal and maximizes the information quality within the candidate signal ,25,26,27Recently, the LSDL has been trialed on spectroscopic waveforms—which can be noted to differ from a typical time series—where FTIR spectra were obtained from blood samples for the diagnosis of endometrial cancer . The pilApplication of the LSDL towards decomposition of the obtained spectra from FTIR urine from a patient population in order to postprocess and build prediction models that can predict the presence of prostate cancer using urine samplefs.Comparison of the results and predictive performance of the LSDL and raw spectroscopy across nine different machine-learning models.Further prediction exercises around the stage of prostate cancer for early- and late-grade variants of the disease.Thus, in this paper, given an acquired urine sample which is subsequently analyzed with FTIR instrumentation to obtain its respective spectrum, the application of the LSDL to the decomposition of urine spectroscopy signals for enhanced and improved prediction of the presence of prostate cancer is observed for the first time. Specifically, the contributions of this paper are as follows:In the clinical screening of prostate cancer, the use of the prostate-specific antigen (PSA) is the predominant means towards the diagnosis of prostate cancer, although this approach is limited due to the potential of the PSA levels to also be elevated in cases where there is benign prostate hyperplasia and prostatic inflammation ,17,18,19It is known that after the manipulation of the prostate, prostatic fluids from prostate cancer cells are released alongside urine, and in certain cases these fluids can also be released even without urinary manipulation . Here, tIn the previous pilot study , urinaryDataset 1: this dataset involved the use of samples which were acquired using a repetition format where three iterations were taken from the urine sample and averaged out in order to form the final spectroscopic readings that were eventually used. With the aid of the SMOTE synthetic sample generator, the number of data samples was increased to a total of 62 samples, of which 31 belonged to cancer patients and the remaining 31 were from noncancer patients in a balanced dataset.Dataset 2: for this dataset, the same patients’ urine samples were used, but this time, no repetition or averaging process was conducted as only a single acquisition from each urine sample was used to create the final FTIR readings. Similarly, to Dataset 1, a sum of 62 samples was obtained for this group with a balance in both classes, driven by the use of the SMOTE algorithm.Dataset 3: for this dataset group, concatenation of the data from Datasets 1 and 2 was performed, which meant that this group comprised the FTIR samples acquired using the averaging method and also the ones acquired using the single-acquisition approach, resulting in a total of 124 samples, which meant that this dataset was the largest group.The cumulative number of sample points used as part of this exercise was 248 across all three data groups.For this exercise, only the dataset of patients who had cancer was used. Patients who had prostate cancer scores of 7 were designated to the class of low-grade cancer, while patients whom had prostate cancer scores in the range of 8–10 were designated to the class of a high-grade cancer.Dataset 1: this dataset was comprised of FTIR data which were averaged from three different acquisitions, as previously mentioned, using the SMOTE algorithm for class balancing purposes. The sum of samples in this group was 40, with a balanced group of samples within each data class.Dataset 2: this group of data was formed using single acquisitions from the urine samples to acquire the FTIR readings, and also comprised of a class of 40 samples, which were balanced using the SMOTE algorithm.Dataset 3: this dataset was comprised of a concatenation of the data from the prior groups to form a total of 80 samples of data from both the averaged and single sampled approach.For this case, the cumulative number of 160 samples was utilized for the analysis carried out for this exercise.The LSDL, as mentioned, is an AI-driven method towards the signal deconvolution process built upon metaheuristic reasoning, with the original application of the approach based around source separation of a high-frequency acoustic emission signal in order to infer the particle size distribution of a heterogenous powder mixture ,31,32,33In the case of events which occur simultaneously, the resulting time domain signal contains an overlapping impulse, with magnitude  being a key characteristic towards effective characterization of the source events, as described by Nsugbe et al. ,38,39,40Following its original application involving source separation and estimation of particle size distribution, the LSDL has seen further applications in case studies that involve brain–machine interface control of bionic upper limb prostheses, prediction of preterm pregnancies from uterine contraction signals, prediction of adolescent schizophrenia from EEG brainwave signals and, more recently, prediction of depth of anesthesia during surgery using frontal cortex neural oscillations ,33,37,38The LSDL is based around the separation of a given signal into various amplitude bands by way of selection of tuned linear thresholds, which serve as the basis function for the decomposition problem, alongside a peak detection algorithm which identifies characteristics of a given signal as a “peak” of sorts within a tuned threshold level. The term “LSDL” represents the fact that a linear basis function is used for the signal decomposition process to yield a series of sub-signals, from which an optimal signal and the associated decomposition level are obtained and evaluated via an embedded learning process within the algorithm.p and q, w is the wth feature in feature vector All decomposed regions from the tuned thresholds are subjected to an evaluation by a performance index to evaluate the information quality and discriminatory power of a particular signal threshold region with respect to a performance index of sorts. The performance index utilized as part of the LSDL is the normalized Euclidean distance which, given two sets of samples from two different classes, estimates the separation between them in Euclidean space. The mathematical framework for the normalized version of the Euclidean distance used is covered by Equations (1)–(3) :(1)ED.For the calculation of the optimal threshold region with the LSDL, the Cancer vs. No Cancer datasets were used, obtained using the implementation parameters from Nsugbe et al.  and NsugAccording to the results in Note that decomposition parameters were not computed for Dataset 3 as this dataset group comprised a concatenation of Datasets 1 and 2.The following features were extracted from the spectroscopy signals, as had been applied in previous studies ,40,41: mIt should be noted that due to the nature of the LSDL algorithm, the ZC, DFA and MF features were seen to be redundant.Decision tree (DT): a nonparametric model which works with a Boolean-like approach towards a systematic classification of samples into various data classes in a hierarchical tree-like flow, also viewed as a nonparametric approach .Discriminant analysis: classification model based around statistical reasoning, works with the projection of what would ordinarily be a high-dimensional data vector into a lower-dimensional projection, followed by the imposition of class boundaries . Linear K-nearest neighbors (KNN): model utilizing a voting strategy based on the nearest neighbor to designate samples into respective data classes . Here, tSupport vector machine: an iterative classification method based around the computation of the optimal decision boundary that maximizes the separation between data classes while using a subset of the data referred to as support vectors . The insLogistic regression (LR): A statistically driven method which applies the sigmoid activation function alongside an accompanying threshold towards the classification of data from different classes in a binary-like fashion .All classification models were validated using a K-fold cross-validation method where K was chosen as 10.All model training and development were performed with the MATLAB classification learner application which tunes for the optimal hyperparameter for each selected model given the criteria selected.For the quantification of the results, the following metrics were employed: classification accuracy , sensitivity , specificity , and area under the curve  .The results for Dataset 1 for this exercise can be seen in In terms of the LSDL results for Dataset 1, a considerable increase in the overall predictive performance could be observed for all the models, where the best performing model was LDA alongside LR. This showcases the impact of the LSDL on the overall model performance, with a strongly improved predictive prowess all round, which highlights the effect of the algorithm on enhancing the prediction capability of the detection of prostate cancer using urine samples.In terms of Dataset 2, which represents the data acquisition process where repetitions were utilized, the results showed an overall drop in the model’s predictive performance with exceptions of KNN, CSVM and FGSVM, as seen in As in the case of the raw data, the results of the LSDL reflected the same findings in the sense that there appeared to be an overall drop in model performance across the majority of the classifiers considered, again reflecting the degradation in performance when the repetition-based acquisition method is used, although it can be seen that the DT and LSVM’s performances appeared to be marginally improved. Overall, however, the LSDL continued to show superior model’s predictive performance when compared with the raw data despite the mode of data acquisition, as seen in The case of Dataset 3 showcased a mixture of model-based performance, with the performance of certain models improving while others degraded slightly, as seen in The same trend could be observed for the LSDL in this case as well, where it can be seen in On the basis of a positive cancer prediction, this section looked at a trained model’s ability to predict the associated stage of a cancer and serves as a useful source of auxiliary information in the prioritization of patient care, etc.For the prediction exercises carried out in this section, the Gleason scores were chinked into two classes, i.e., scores of less than 7 and equal to 7 were grouped into one class  while the Gleason scores over 7 were taken to form a separate class comprising 13 samples. As per the previous section, the SMOTE algorithm was utilized towards increasing the number of samples as well as class balancing.As the best results in the prior sections were obtained for the LSDL, the subsequent prediction exercises in this section were performing using the LSDL decomposition as opposed to the raw data.The results for Dataset 1 can be seen in For this case, although the best model performance still belonged to LDA, it can be noted in The results for Dataset 3, which, as mentioned, represented a realistic group of data with a mixture of variabilities, can be seen in A full flow of the proposed process can be seen in In In this work, we conducted a pilot study based around the feasibility of using urinary EVs alongside FTIR analysis data for the prediction of prostate cancer and the subsequent inference of the extent of cancer due to the apparent presence of key cancer biomarkers within urinary EVs. Using three sets of data representing different variants of sample processing and acquisition with FTIR instrumentation, this work investigated the application of the LSDL decomposition method towards the preprocessing of FTIR spectra prior to machine-learning modeling. The results showed an improved predictive performance when benchmarked with the raw spectroscopic data for the three dataset variants, providing quantitative evidence that EVs isolated from urine samples can serve as an affordable means towards early and noninvasive detection of prostate cancer. Additional analysis also showed that using the presented method, it is possible to predict the associated stage of cancer where a positive cancer status has been identified. In terms of the model acceptability criteria, this is envisaged to differ between various clinics and hospitals but an arbitrary threshold cutoff of 70% could be viewed as proof of acceptable model performance.The key limitations of this work revolve around the learning and tuning process required as part of the implementation of the LSDL which, due to the nature of the algorithm, is iterative, in addition to the sample size used as part of this pilot, which is believed to comprise a homogenous ethnic population. Further work in this area would involve the collection of data from a larger pool of patients, hopefully of various ethnicities, which would provide further reflective data for the approach to be validated against .


High-throughput single-cell RNA sequencing (scRNA-seq) is a popular method, but it is accompanied by doublet rate problems that disturb the downstream analysis. Several computational approaches have been developed to detect doublets. However, most of these methods may yield satisfactory performance in some datasets but lack stability in others; thus, it is difficult to regard a single method as the gold standard which can be applied to all types of scenarios. It is a difficult and time-consuming task for researchers to choose the most appropriate software. We here propose Chord which implements a machine learning algorithm that integrates multiple doublet detection methods to address these issues. Chord had higher accuracy and stability than the individual approaches on different datasets containing real and synthetic data. Moreover, Chord was designed with a modular architecture port, which has high flexibility and adaptability to the incorporation of any new tools. Chord is a general solution to the doublet detection problem. For the unmet need to choose the suitable doublet detection method, an ensemble machine learning algorithm called Chord was developed, which integrates multiple methods and achieves higher accuracy and stability on different scRNA-seq datasets. In contrast to bulk RNA sequencing, profiling transcriptomes at the single-cell resolution has enabled researchers to recognise the molecular characteristics of all cell types at one time and acquire a better insight into physiology, biological development, and disease2. Among the current state-of-the-art technologies of high-throughput scRNA-seq, droplet-based technologies are currently commonly employed as an unbiased solution of single-cell transcriptomics3. However, these microfluidic methods often encounter the problem of doublets, where one droplet may contain two or more cells with the same barcode during the distribution step of isolating single cells. Then the doublets are counted as a single cell in the data forming technical artefacts4. According to the composition of doublets, doublets can be divided into two major classes: homotypic doublets, which originate from the same cell type, and heterotypic doublets, which arise from distinct transcriptional cells generating an artificial hybrid transcriptome5. Compared to homotypic doublets, heterotypic doublets are considered to have more impact on downstream analyses, including dimensionality reduction, cell clustering, differential expression, and cell developmental trajectories7.Recently, the development of high-throughput single-cell RNA sequencing (scRNA-seq) has provided convenience for dissecting the cellular heterogeneity of tissues8. Several existing experimental techniques can be applied to identify doublets instead of avoiding doublets, such as the cell hashing method using oligo-tagged antibodies as an orthogonal information9, MULTI-seq using lipid-tagged indices10, and demuxlet using natural genetic variations11. However, there are inherent limitations to these experimental techniques. First, since these methods require special experimental operations and additional costs, so they are not helpful for the existing scRNA-seq data. Second, these techniques only experimentally label doublets from different samples but ignore the kind of doublet generated by cells from the same sample or individual. Therefore several computational approaches have been developed to detect doublets in common scRNA-seq data, including data already generated7. A benchmarking study has shown that the performance of these computational methods varies greatly, even the top-performing methods with the noticeable differences7, so there is still a larger challenge in terms of suboptimal accuracy of each method. In addition, because of the unique characteristics brought by these specific mathematical algorithms and their applicability to different scenarios, no method can be considered as the gold standard for each scenario; Thus, it is a challenging and time-consuming task for researchers to choose suitable software for their specific research.To reduce the number of doublets in experiments, decreasing the concentration of loaded cells is an effective control measure. However, this approach also reduces the number of captured cells and dramatically increases the cost per sample12 that can boost the accuracy of somatic mutation detection13 and culprit lesion identification14. Compared to the individual methods, Chord was demonstrated an improved accuracy and stability in doublet detection across different datasets of real and synthetic data. Moreover, Chord was designed with a modular architecture port that is highly flexible and adaptable to incorporate new tools.To address these unmet needs, we propose Chord, which implements an ensemble algorithm that aggregates the results from multiple representative methods to identify doublets accurately. The ensemble algorithm is a widely used technique of machine learning5 adopts this strategy to handle the doublet detection task as a binary classification problem. The other strategy used by cxds in the scds15 package is based on co-expressed ‘marker’ genes that are not simultaneously expressed in the same singlet cell but can appear in doublet cells. However, the performance of existing computational approaches for doublet detection varies greatly in overall detection accuracy, impacts on downstream analyses and computational efficiency7. Here, we describe a strategy based on an ensemble algorithm of machine learning for doublet identification. Our approach, Chord, integrates three representative computational doublet detection methods in R environment  Application of the trained GMB model to the original dataset to predict doublets.The Chord workflow is composed of three main steps Fig. . (i) GenDoublets in the original dataset might cause two types of potential errors which may be introduced into the training set: (i) In the process of generating doublets, the doublets will also be treated as singlets to simulate new doublets, resulting in wrong doublets introduced into the training set. (ii) In the generated training set, the remaining doublets in the original data will be marked as singlets. Therefore, the Chord first roughly estimates the doublets of the input droplet data according to the three built-in methods: DoubletFinder, bcds, and cxds to filter out the likely doublets from the original data before simulating artificial doublets. We called this step “overkill”. Selecting “overkill” could improve the accuracy of training sets, which is beneficial to model fitting . The performance of these methods generally showed an upward trend as the doublet rate increased, except that the DoubletFinder had dropped significantly at some doublet rates. In the doublet rate gradient test of the random sampled DM-A dataset, bcds had the best performance while the effect of Chord was only inferior to that of bcds. In the 15 datasets with doublet rates ranging from 2 to 30% generated from the random sampled HTO8 dataset, Chord ranked first in terms of the AUC and AUPRC most times  was decided based on the mean AUC  and bcds (1.773), both of which had the same high accuracy rate  method for visualising the true positive doublets (TP), true negative doublets (TN), false negative doublets (FN) and false positive doublets (FP)  detection and pseudotime analysis. In the DEG analysis, one of the synthetic scRNA-seq datasets was ‘clean data’ with two cell types and 1126 between-cell-type DEGs, while the other dataset was the ‘contaminated data’ mixed with doublets at a 40% doublet rate 19. Three accuracy measures, namely, the true positive rate (TPR), true negative rate (TNR) and accuracy, were used to evaluate the results of DEG analysis. In Fig. 7. The accuracy of DEG analysis has been improved on these filtered datasets with doublet detection methods. The low TPR for the contaminated data indicated that it was more difficult to identify DEGs in contaminated data. The results of these methods were better than contaminated data, and the results of Chord, DoubletFinder and DoubletDetection are closer to the clean data results. In the pseudotime analysis, the ‘clean data’ is a synthetic scRNA-seq dataset including a bifurcating trajectory, while the ‘contaminated data’ is composed of ‘clean data’ plus 20% doublets  were implemented on the clean data, the contaminated data, and the filtered datasets. In the cell trajectory inferred by monocle, the contaminated data and the filtered datasets from bcds and cxds generated additional bifurcation trajectories due to the influence of doublets , we estimated the proportion of doublets in 5 malignant tumour samples  with the most expected doublet rate , as well as the number and proportion of doublets for different cell types (Supplementary Data https://gbiomed.kuleuven.be/scRNAseq-NSCLC), we can see that T cells contained the largest number of doublets due to their large cell number. The identified doublets of fibroblasts cells accounted for only 1.69%, while the proportion of myeloid was the highest (8.15%)  Fig. . Doublet5%) Fig. , cluster5%) Fig. , in whic5%) Fig. . Cluster5%) Fig. . Cluster24, an entropy-based metric, to assess the purity of cell types in the original and filtered data. The ROGUE index has been scaled in the range of zero to one where the larger value means the higher purity. As a result, the ROGUE value of the filtered data was improved, the increasing trend of ROGUE value after filtering doublets was apparent , endothelial cells (0.935), and epithelial cells (0.91) on the filtered dataset were all greater than those of the B cells (0.705), endothelial cells (0.908), and epithelial cells (0.904) in the unfiltered dataset . After clustering, the cells were divided into 20 clusters.Randomly sample pairs of cells at a ratio of doubletrate/(1-doubletrate) for each cell type, and weight the cells with introduced biological random number from a N distribution which was set to roughly represent experimental randomness.Average the weighted gene expression profiles of the cell pair as simulated doublets.Add the simulated doublets to the pre-filtered data; take this new dataset as the training set.Generating the simulation dataset. To avoid the generation of doublets synthesised from the same cell type, Chord randomly sampled pairs of cells in the pre-filtered data, generated simulated doublets from the raw UMI count by mixing the gene expression profiles of the selected cell pairModel training. For the training set,in which all cells were labelled, Chord used the same parameter settings to evaluate the doublet scores through the bcds, cxds, DoubletFinder methods. GBM (from R package gbm) which performed better than AdaBoost, XGBoost, and LightGBM . The method with the highest SDrank was unstable for different datasets, it represented the generalisability of methods.29, and plotted the ROC curves for individual method by setting the option ‘ROC curve‘ to ‘TRUE’. For the pAUC at 0.9, a 0.95 specificity value was calculated by using the ‘pROC’30 R package.Each method was evaluated using the AUC with ground-truth labels for the original datasets or labels for simulated datasets. In addition, the AUPRC and partial area under the ROC curve (pAUC) were calculated. We calculated the AUC and AUPRC with the PRROC R package7 to test the changes in the number of DEGs correctly detected before and after doublet removal by different methods. The simulated single-cell dataset was generated with scDesign31 and contained 1,667 cells and 18,760 genes. It was divided into two cell types each counted 500 cells, and 667 doublets simulated by those singlets. Among them, high-expression and low-expression DEGs, which were known at the time of data generation, accounted for 6% of all sample (3% upregulated genes and 3% downregulated genes). The dataset without doublets was used as the clean dataset, and the data with doublets were added as the contaminated dataset. After the contaminated dataset was evaluated with a doublet detection method, the dataset of 40% of the cells with the highest score was filtered according to the result. We performed the process described above for each method. Then, we used Seurat’s FindAllmarkers function with the methods ‘wilcox’18 and ‘MAST’19 to perform DEG calculations on the dataset. In order to find DEGs, we removed genes with fold changes below 0.25; then, genes with Bonferroni-corrected p values below 0.05 and genes detected in a minimum fraction of 10% cells in either of the two clusters were defined as DEGs. Finally, we calculate the accuracy, TPR, and TNR for all datasets.We used the published simulated single-cell sequencing dataset7 to test the effects of different methods on pseudotime analysis. The simulated single-cell dataset, which was generated with Splatter, consisted of 600 cells and 1000 genes. There were two cell tracks containing 250 simulated cells and 100 simulated doublets. The dataset without the doublets was used as a clean dataset, and the data containing the doublets were used as contaminated dataset. After evaluating the contaminated dataset through a doublet detection method, cells predicted to be doublets were deleted. After then, the R package monocle and Slingshot software were used for a pseudotime analysis of the dataset. We implemented the process described above for each method. In addition, we calculated the trajectories of the clean and contaminated datasets.We used the published dataset of simulated single-cell sequencingBased on random sampling from the real DM-2.1 dataset, we constructed test datasets of 1,000 to 12,000 cells with a gradient of 1,000 cells and tested various methods with the same processor. Then, the runtime of each method for the simulated datasets on the same computer server (E5-2678v3 CPU processor and 256 GB memory) was recorded.https://gbiomed.kuleuven.be/scRNAseq-NSCLC) including 19 samples from five patients.We obtained the Seurat object from SCope  was extracted, and a new Seurat object was created.After the data were normalised, 2000 variable genes were screened with the function FindVariableFeatures.23.PCA was used to reduces the dimension of the data to 50 dimensions, and PC1 to PC30 were used for clustering. Through the FindNeighbors function (resolution = 1.5), we divided the cells into 22 clusters, and we computed the UMAP embeddings to display the resultsAfter evaluating the predicted double cell rate for each sample based on the number of cells, we selected the 5 tumour samples  with the highest predicted double cell rate and applied Chord individually to each sample. Then we performed a standard Seurat analysis:We calculated the rogue (R package ROGUE) value for each cell type in each sample. Comparisons between two original datasets and filtered datasets were performed using paired two-tailed t-tests.The parameters of ROGUE were set as “platform = UMI” and “span = 0.6”.25) to perform cell type analysis on epithelial, endothelial, myeloid, T, and B cells before and after processing. The reference of human cell types were provided by SciBet (http://scibet.cancer-pku.cn/major_human_cell_types.csv).We used the function SciBet  with the following parameters: min.pct = 0.1, and test.use = ‘wilcox’, additionally, and the threshold of logFC was varied from 0.25 to 0.75 at an interval of 0.05. Then, we counted the number of DEGs at different logfc.threshold values.https://rpkgs.datanovia.com/ggpubr/) or build-in function of R 4.0.2 (https://www.r-project.org/). A P value less than 0.05 was considered as statistically significant.The details about the steps and statistics of each analysis were recorded in each part of methods. Statistical analyses were performed using R package ggpubr (Further information on research design is available in the Nature Research Reporting Summary linked to this article.Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary