Cancer subtype identification is important for the early diagnosis of cancer and the provision of adequate treatment. Prior to identifying the subtype of cancer in a patient, feature selection is also crucial for reducing the dimensionality of the data by detecting genes that contain important information about the cancer subtype. Numerous cancer subtyping methods have been developed, and their performance has been compared. However, combinations of feature selection and subtype identification methods have rarely been considered. This study aimed to identify the best combination of variable selection and subtype identification methods in single omics data analysis.p-values, and nonnegative matrix factorization (NMF) stably showed good performance in many cases unless the Dip test was used for feature selection. In terms of accuracy, the combination of NMF and similarity network fusion (SNF) with Monte Carlo Feature Selection (MCFS) and Minimum-Redundancy Maximum Relevance (mRMR) showed good overall performance. NMF always showed among the worst performances without feature selection in all datasets, but performed much better when used with various feature selection methods. iClusterBayes (ICB) had decent performance when used without feature selection.Combinations of six filter-based methods and six unsupervised subtype identification methods were investigated using The Cancer Genome Atlas (TCGA) datasets for four cancers. The number of features selected varied, and several evaluation metrics were used. Although no single combination was found to have a distinctively good performance, Consensus Clustering (CC) and Neighborhood-Based Multi-omics Clustering (NEMO) used with variance-based feature selection had a tendency to show lower Rather than a single method clearly emerging as optimal, the best methodology was different depending on the data used, the number of features selected, and the evaluation method. A guideline for choosing the best combination method under various situations is provided. In the era of precision medicine, cancer subtype identification, which aims to divide patients into subgroups with distinct clinical phenotypes such as survival time, is of the utmost importance. Since cancer is a heterogeneous disease, cancer types can have several subtypes with different phenotypic and molecular profiles . The claNumerous cancer subtyping methods have been developed, and these methods can be divided into two types: supervised and unsupervised learning. In supervised learning, classification takes the true class into account and classifies a new patient to the correct label, whereas clustering based on unsupervised learning does not use class labels when patients are grouped into similar types. One of the major limitations of classification analysis is that it cannot identify novel subtypes. The current subtypes of breast cancer were proven to be highly ambiguous, resulting in an inaccurate classification of new patients . In thisAn important procedure when identifying cancer subtypes is feature selection for detecting genes that contain important information about the cancer subtype , 20. FeaThere are various ways to classify feature selection algorithms. One approach is to consider this issue from the perspective of data, including statistical measure-based, probability measure-based, similarity measure-based, sparse learning-based, and evolutionary algorithm-based feature selection methods , 25. HowWrapper methods use specific learning algorithms to evaluate the performance of a feature subset . ForwardEmbedded methods differ from wrapper methods in that the optimal feature subset is built into the classifier construction. PSO-GWO is an example, which is a multi-objective feature selection method using Newton's law-based Particle Swarm with Grey Wolf optimization to minimize the classification error rate while performing feature selection . Deep FeSeveral attempts have been made to review and compare existing unsupervised clustering methods for subtype identification –43. A syIn this study, we compared the performance of combinations of feature selection and clustering methods, evaluated on four TCGA datasets of cancers with different characteristics. We considered six methods of feature selection and six clustering methods for subtype identification. The clustering methods are either state-of-the-art or commonly used in subtype identification, and only those available in the R programming language were chosen. Thus, a total of 24 combinations were compared in two mRNA gene expression datasets without labels, and 36 combinations were compared in two mRNA gene expression datasets with gold-standard labels using different settings. Furthermore, for each feature selection method, we compared the results of two different numbers of selected features along with the results when all features were used.In "The usual workflow for identifying cancer subtypes is as follows. After data preprocessing, which includes missing value imputation and normalization, we select informative genes using a feature selection method to solve the redundancy problem. A clustering method for subtype identification is then applied. The result is validated by checking the silhouette score or log-rank test. We briefly review the selected feature selection methods, clustering methods, and evaluation methods as follows.p-values are selected. The fourth criterion was based on the relevance of informative genes and their target variables for the datasets with true class labels. We selected genes with a high information criterion using two measures: mRMR [We considered six feature selection methods based on four criteria. First, we selected genes with high expression levels. We computed the median expression level (MED) for each gene across samples and selected the ones with high median values. The second criterion was based on variation. Genes with a large variability of expression are expected to contain variations caused by the subtype of cancer –47. We ses: mRMR  and Montes: mRMR . mRMR aies: mRMR . MCFS isD of m genes and n samples.We considered six popular subtype identification methods: CC, NMF, PINS, ICB, SNF, and NEMO. In all methods, the goal is to group the samples into th element of i and j belong to the same cluster and i and j are present in CC is a model-independent resampling-based method for single genomic datasets. CC achieves consensus across multiple clustering runs. It also involves determining the number of clusters and assessing the stability of the clusters . The algConsensusClusterPlus.To determine the number of clusters The goal of NMF is to find a small number of metagenes from thousands of genes based on decomposition by parts . Each meTo obtain mentclasspt{minimaThe NMF method builds on the CC algorithm to evaluate clusters quantitatively. NMF evaluates the robustness of decomposition quantitatively by assessing how much clusters vary in each run. Using the same concept of CC, the connectivity matrix and the resulting consensus matrix are obtained. The off-diagonal elements of consensus matrix represent the probability that a pair of samples belongs to the same cluster. The reordering of a consensus matrix using the average linkage hierarchical clustering provides a visual inspection of clustering stability. Furthermore, the quantitative measure of clustering robustness can be obtained as the cophenetic correlation coefficient of rank cument}k .NMF package in R. The NMF package provides several different NMF algorithms, published by different authors.This method can be implemented using the SNF is a non-Bayesian network-based method for integrating and finding cancer subtypes . In the patients . The eleThrough this process, weak similarities of low-weight edges are considered as noise and disappear, while only strong similarities of high-weight edges remain. The SNF algorithm uses full and sparse kernels to compute the fused matrix. The full kernel matrix carries the full information about similarity to all others, while the sparse kernel matrix encodes the similarity using the datasets , 17. Findatasets .SNFtool in R.SNF is generally used for integrating multi-omics data, but can also be used for clustering in single omics setting. This method can be implemented using the package The PINS algorithm, similar to the CC algorithm, uses the resampling and clustering technique to discover cancer subtypes. As it assumes that the true subtypes are stable with regards to small changes in features, new datasets are first obtained by repeatedly perturbing the data clusters .Perturbed datasets can be generated by adding Gaussian noise. PINS sets the variance of the perturbation noise equal to the median variance of the original data. The clustering stability is evaluated by comparing the partitions obtained from the original data to those obtained from the perturbed datasets. Using the concept of CC, the original connectivity matrix is obtained from the original data, and the perturbed connectivity matrix is obtained by calculating the average of the perturbed trials. The perturbed connectivity matrix will always reflect the true structure of the data, since PINS assumes that for truly distinct subtypes, the true connectivity between samples is recovered when the data are perturbed regardless of the number of clusters fference .PINSPlus.For each value of IClusterBayes is a Bayesian latent variable model that can jointly model omics data of continuous and discrete types . The intICB is known to overcome the limitations of iCluster in terms of statistical inference and computational speed. It adds an extra penalty term such as LASSO for the purpose of feature selection . In singVN0,Ik-1 . ThroughiClusterPlus.By applying i and j to i’s k nearest neighbors relative and to j’s k nearest neighbors.In real data, some patients have measurements for only a subset of omics. NEMO is an algorithm specialized in the clustering of these partial multi-omics datasets without having to impute missing data . In the cument}l . Then, bgithub repository: https://github.com/Shamir-Lab/NEMO or implemented using the R package NEMO. NEMO requires pre-installation of the R library SNFtool and uses parts of its code.The number of clusters is selected using the modified eigengap method . The nump-values from the log-rank test, the silhouette score, the Adjusted Rand Index (ARI), and Normalized Mutual Information (NMI). Computational complexity is also considered.To assess the performance of methods, we used several performance metrics: i, the silhouette for patient i is defined asa(i) is the average distance between each point within the same cluster and b(i) is the lowest average distance to all other patients in different clusters [First, the log-rank test is used to check the significance of differences in the survival profiles between the obtained clusters. The log-rank test assumes that the clusters of patients are different in a biologically meaningful way if the difference between their survival distributions is significant. The silhouette score was also used to check clustering robustness. The silhouette score is also used to measure compactness within clusters and separation across subtypes. It is often used as a measure of clustering in unsupervised learning , 18. Forclusters . We usedS of n elements, let S that belong to the same cluster in both C and C’, For datasets with true class labels, the performance was evaluated using additional measures of accuracy. The performance accuracy in the datasets was assessed by calculating ARI and NMI. Since both measures are normalized, it is possible to compare them between different clustering methods with different numbers of clusters , 55. ARIEmbrecht .4\documeARI is a correction of RI taking into account that some agreement between the two clusters may occur by chance, and defined asectively . The higNMI is a normalization of the MI score to scale the results between 0 and 1. 0 means no MI, while 1 means perfect correlation. MI accounts to the amount of information that can be extracted from a distribution regarding a second one. NMI is defined asectively , 57.Computational efficiency was also measured by calculating the total running time taken for each algorithm for all data types, with and without class labels.http://acgt.cs.tau.ac.il/multi_omic_benchmark/download.html [Combining the six feature selection methods and six subtype identification methods introduced above, the optimal combination of methods for clustering cancer patients into appropriate groups was considered. Thus, a total of 24 combinations of feature selection and clustering methods were taken into account in two datasets without true class labels, and a total of 36 combinations were taken into account for two datasets with gold-standard labels. For a fair comparison, we set the number of variables selected in all methods to be the same. Either 500 or 2000 informative genes were selected when feature selection was conducted, and we compared these results with those obtained when all genes are used. The datasets used are the preprocessed mRNA expression datasets of four different cancer types of TCGA benchmark analysis downloaded from oad.html . Among toad.html  has beenoad.html , 43.The datasets were preprocessed as follows: patients and features with more than 20% missing values were removed, and missing values were imputed using mentclass2pt{minimmentclass2pt{minimThe suggested number of clusters entclass1pt{minimap-value for the log-rank test, CC consistently showed decent performance in clustering patients with different survival distributions. ICB had the lowest p-value in the GBM and BIC datasets. NEMO was the only clustering algorithm that showed a significant p-value under the 5% significance level in the COAD dataset. In terms of computational efficiency, NMF showed an overwhelmingly long running time, and ICB showed the second longest running time. On the contrary, SNF and NEMO took the shortest time to run.Table p-values of the four datasets for each combination of methods. This criterion shows whether the clustered subtypes actually showed significant differences in the survival profile. When using 500 selected genes, as shown in Fig. p-value) when used in combination with NEMO and CC, while DIP showed the second-best performance in combination with NEMO and CC. However, MAD outperformed the other feature selection methods when used in combination with all other clustering methods except for NEMO and CC. When using 2000 selected genes as shown in Fig. p-values for each dataset can be found in Tables p-values in general. We also noted that NEMO again showed a tendency to produce a large number of clusters when feature selection was done prior to clustering, as in the case when feature selection was not done a priori.Figure Figure Tables In the case of the COAD dataset, values of ARI and NMI were small in all methods Table . HoweverFigure Table Since subtype identification is performed after variable selection, the time required for each combination is the sum of the respective times.p-values were shown when CC or NEMO was used with VAR. However, when the number of selected genes was sufficiently large, NMF showed stable, small p-values unless DIP was used for feature selection. Third, in terms of computation time, MCFS does not seem to be suitable for large datasets. All other feature selection methods have very short running times, and there is no difference in time except for DIP. Among the subtype identification methods, NMF and ICB took much more time than others.Based on the above results, guidelines for the appropriate choice of a variable selection method and subtype identification method are summarized as follows. First, if there is sufficient information on the relevant phenotype and the dataset is not very large, mRMR or MCFS is good as a variable selection method in terms of the accuracy criterion. In this case, PINS, NMF or SNF are also good choices for subtype identification. Otherwise, when unsupervised feature selection is used due to insufficient phenotypic information, NMF and SNF are still good choices as subtype identification methods. In this case, it is recommended not to use NMF alone without variable selection, and not to use SNF together with the DIP and MED methods. Second, in terms of the significance test for survival times among groups, small In summary, as a feature selection method, we recommend VAR, which shows good performance for most subtype identifications. For subtype identification, we recommend NMF, which stably shows good performance in many cases.We compared six subtyping methods, two of which are used only for single-omics data sets, and four for both single-omics and multi-omics data sets. This study is different from previous comparative research in that it compared the performance of cancer subtyping methods combined with various feature selection methods. Rather than a single method dominating the others, the best methodology depended on the data used, the number of features selected, and the evaluation method.p-values for the log-rank test in differential survival [p-values in another study [p-values of the log-rank test [p-value for the log-rank test may not fully represent the clustering ability of the algorithms, since some patient groups may have a similar survival distribution even though they fall into different cancer subtypes or vice versa.This result is supported by the study of Dhal and Azad , which ssurvival , while tsurvival . PINS [1survival  and CIM er study . Howeverer study . The autank test . IClusteank test . Sensitiank test . Silhoueank test . MeanwhiThe number of clusters chosen for each clustering method was not set to be the same for all methods, as being able to evaluate the optimal number of clusters is also considered as an aspect of the algorithm’s performance. Except for CC and ICB, which require the user to evaluate the optimal number of clusters subjectively, all methods used their inherent algorithms to assess the number of clusters, with the maximum number of clusters set as 10 for all algorithms.p-values of the log-rank test in the two datasets of AML and BIC, and the combination of CC with DIP showed the best performance in the GBM dataset.Although the results of all four datasets tell us that there is no single combination of methods that outperforms others, the CC and NMF methods were generally good choices among the six clustering methods when informative genes were selected prior to clustering in terms of differences in the survival curves. Especially for CC, the combination of CC with variance as a feature selection method showed the best performance in terms of p-values of the log-rank test. The drawback of CC is that the number of clusters k is subject to the user’s opinion, and that its performance is sensitive to the value of k. Therefore, it is recommended to carefully set k using additional measures such as the silhouette score. Overall, the results of selecting 500 and 2000 genes differed by combinations and datasets.The variance method and DIP were often included in the best combinations. In the COAD dataset, the combination of DIP and the PINS clustering method showed the best performance in terms of We acknowledge that our study has some limitations. Firstly, we only utilized filter methods for feature selection; these methods have the advantage of reducing computational time and being efficient for high-dimensional datasets, but they may overlook certain relevant features. Secondly, our analysis employed only a subset of TCGA data , limiting the generalizability of our findings. Future research could explore the use of embedded feature selection methods or incorporate multi-omics data. In our opinion, it is also crucial to investigate more novel and recent feature selection methods to further enhance the performance of subtype identification in gene selection.


Cell-type composition is an important indicator of health. We present Guided Topic Model for deconvolution (GTM-decon) to automatically infer cell-type-specific gene topic distributions from single-cell RNA-seq data for deconvolving bulk transcriptomes. GTM-decon performs competitively on deconvolving simulated and real bulk data compared with the state-of-the-art methods. Moreover, as demonstrated in deconvolving disease transcriptomes, GTM-decon can infer multiple cell-type-specific gene topic distributions per cell type, which captures sub-cell-type variations. GTM-decon can also use phenotype labels from single-cell or bulk data to infer phenotype-specific gene distributions. In a nested-guided design, GTM-decon identified cell-type-specific differentially expressed genes from bulk breast cancer transcriptomes.The online version contains supplementary material available at 10.1186/s13059-023-03034-4. Cell-type composition and its relative proportions in a tissue is an indicator of health. For example, several studies have shown that type 2 diabetes is characterized by reduced beta cell mass and number in pancreatic tissue , 2. In aOn the other hand, bulk RNA-seq has been the workhorse behind transcriptome research over the past decades. Its falling costs and ease of experimental setup make it an attractive tool to work with any organism . SeveralMost of the deconvolution approaches require a set of gene markers for each cell type of interest. These marker genes are derived from expert knowledge or differential expression analysis of purified samples of specific cell types. Early methods that leverage these marker genes could achieve good performance in deconvolving mixtures with highly distinct cell types such as blood . CIBERSOIn this study, we present a guided topic model for cell-type deconvolution (GTM-decon). As an overview of our analysis, we first benchmark GTM-decon on deconvolving simulated and real bulk data in comparison with the state-of-the-art deconvolution methods. We then train GTM-decon on pancreatic and breast scRNA-seq datasets to deconvolve bulk RNA-seq datasets from pancreatic and breast tissues, respectively. When applied to deconvolving cancer bulk transcriptomes, GTM-decon successfully identifies the cell type of origin for pancreatic and breast cancer datasets. Interestingly, the results for human pancreatic cancer are recapitulated using the CTS topics inferred from the mouse pancreas scRNA-seq data, postulating cross-species deconvolution as an option where it is difficult to obtain scRNA-seq data due to technical or ethical challenges. Furthermore, our GTM framework also enables the inference of phenotype-specific topic distributions from bulk RNA-seq data by using the phenotypes  from single cell or bulk RNA-seq data as a guide for the topic inference. We leverage this capability to distinguish basal from estrogen receptor (ER) positive (ER +) breast tumor samples, not only achieving high classification accuracy but also identifying the genes and pathways that segregate the cancer subtypes. By fine-tuning the inferred CTS topic distributions guided by the breast cancer subtypes, we deconvolve the average differential gene expression into CTS expression changes, thereby enabling discovery of the subtype-specific aberrance of the gene regulatory programs.three methodological contributions. As our first and the main contribution, GTM-decon is a marker-free method and automatically infers the contribution of each gene for each cell type in the form of CTS categorical distributions, which we define as “topics”  . The modUpon convergence, the expected values for kth row of K-1 rows to relatively low values. For example, suppose we have 5 cell types and 3 sub-topics per cell type. If cell Suppose we use L values that correspond to the labeled cell type and relatively low expected value for the rest of the entries. We experimented modeling each cell type using The data generative process is identical to the basic GTM-decon except having K cell types .g under topic k, we computed the difference of its topic score K-1 topics K cell types and T topics per cell topic, the test statistic for cell type k and gene g isAfter GTM-decon topic inference, marker genes for each cell type were identified using permutation test. Specifically, for gene p-value is computed as fraction of permutations, where the test statistic is greater than the observed value.The significance of the observed statistic is compared against the same statistic calculated from 100,000 permutations. The empirical N − 1 subjects as the training examples and deconvolving the held-out subject as the validation example. The results are shown in Additional file We evaluated the deconvolution accuracy by comparing the inferred cell-type proportions against the ground truth values for the datasets. First, we simulated bulk RNA-seq data using the scRNA-seq dataset. We summed the gene expression counts of each sample from the scRNA-seq data to represent the bulk data of that sample . The ground-truth cell-type proportions are the fraction of cells for each cell type. To avoid information bias in the evaluation, we performed leave-one-out cross-validation (LOOCV) by inferring topics from scRNA-seq data for a human pancreas   and mouse (GSE81433); b Peripheral blood mononucleocytes (PBMC) of healthy human (GSE132044); c human blood cells (HBC) of healthy humans (GSE149938); d post-mortem brain tissue of frontal cortex from adult human (GSE97930); e Breast tissue from healthy individuals (GSE113197). Apart from healthy individuals, scRNA-seq references from patients with a pancreatic cancer (CRA001160) and b breast cancer (GSE176078) were used to deconvolve cancer bulk RNA-seq datasets.In addition, we also used real bulk data with known cell-type proportion to benchmark our methods. To conduct different benchmarking and qualitative experiments, we used scRNA-seq datasets from several tissues for training GTM-decon , b PBMC from a cohort of 13 individuals (GSE107011), c PBMC from a cohort of 346 individuals (SDY67). For brain tissue, ground truth proportions from immunohistochemistry were available for a cohort of 41 individuals from the Religious Orders Study / Memory and Aging Project (ROSMAP) study (CortexCellDeconv). A unique dataset with both bulk RNA-seq data and scRNA-seq data from pancreas was accessed from E-MTAB-5060 and E-MTAB-5061, respectively, with the cell type proportions from scRNA-seq considered as ground truth values.Bulk RNA-seq datasets with known ground truth values were chosen for benchmarking  and brain prefrontal cortex (ROSMAP) bulk datasets, we used two independent single-cell references with the most closely matched tissues of origin  Pearson correlation coefficient (PCC), (2) Spearman correlation (SCC), (3) cross entropy (CE), and (4) residual mean squared error (RMSE) . Similarly, bulk RNA-seq data from the pancreas of a cohort of 89 normal and diabetic individuals was obtained from GSE50244.For the qualitative analyses on the cancer datasets, the bulk RNA-seq datasets for pancreatic cancer (PAAD) and breast cancer (BRCA) datasets from TCGA were downloaded from the GDC data portal (https://cibersortx.stanford.edu/) for Signature Matrix generation and Cell Fraction imputation. For BayesPrism, all genes were provided to the web portal (https://www.bayesprism.org/) for cell type composition estimation, with the metadata column for tumor status set to 0 for all cells.We compared GTM-decon with other state-of-the-art methods, including BSEQ-sc, BISQUE, MuSiC, CIBERSORTx, and BayesPrism. For BSEQ-sc, marker genes were selected from CellmarkerDB for the brain and immune datasets, while the built-in pancreatic marker genes were used for the Segerstolpe dataset. BISQUE requires at least two paired reference bulk and reference single-cell samples, which is the case for the Segerstolpe dataset. When using human blood cell (HBC) scRNA-seq data as a reference, in order to use BISQUE, artificial bulk samples were constructed using single-cell data. For the other 3 bulk datasets, since we used only one scRNA-seq reference, we left out BISQUE from the evaluation. For CIBERSORTx, all genes were provided to the web portal ((i)all: all genes without removal of any gene;(ii)pp: preprocessed genes to remove uninformative genes ;(iii)hvg: highly variable genes identified using the highly_variable_genes (HVG) function of scanpy [f scanpy .The gene expression profiles of scRNA-seq data were used as training data. Since the performance of GTM-decon may vary depending on how the scRNA-seq count data are processed, we explored different gene selection and transformations of the scRNA-seq data. Specifically, the following gene selection were considered:(i)raw count —scRNA-seq read counts per gene;(ii)normr —normalize counts  as counts divided by the sum of counts per cell multiplied by a scaling factor of 10,000 (a commonly used factor for scRNA-seq data [seq data ), and ro(iii)normr_log1p : log-transform normalized counts in (ii), and round the values to their nearest integer to make the input suitable for topic modeling.Furthermore, for each of these gene sets, the following transformation were considered:GSEA was performed using the fgsea package from R, on two different gene sets: (a) gene sets corresponding to specific cell types from CellMarkerDB ; (b) genp-value < 0.05 based on log-rank test.For TCGA datasets, after cell-type proportions are inferred using GTM-decon, survival analysis is performed using the survival and survminer R packages in order to assess if there is any correlation between survival probability of the cancer subjects and cell-type proportions for the cell types. Kaplan–Meier curves were generated for those cell types, where the association is deemed statistically significant at To identify phenotype-specific genes, we applied GTM-decon to directly infer phenotype-topics from the sparsified bulk RNA-seq data Fig. b. To evasettings .each phenotype d. For example, for In identifying phenotype-specific differentially expressed genes, we developed a way to leverage the CTS topics inferred from the scRNA-seq reference data by fine-tuning the CTS topics based on gene expression from the sparsified bulk RNA-seq data for different phenotypes Fig. c. Specifj, suppose his/her phenotype label is dK topics corresponding to the phenotype label d were set to 0.9, and the For subject mentclass2pt{minimg between phenotypes d and k were identified by subtracting the p-value < 0.05 by permutation test calculated from the 100,000 randomly shuffled p-value is the fraction of times the null statistic from the permutations is greater than the observed test statistic. As a reference, we compared our approach against the DE genes identified from the same samples using DESeq2 [From the learned matrix, the test statistic of a DE candidate gene g DESeq2 . We idenAdditional file 1. Supplementary Section S1 ; S2 ; S3 (Experimenting hyperparameters Additional file 2. Review history.


Parkinson’s disease is a neurodegenerative disease that often comes with symptoms such as muscle stiffness, slowness of movement, and tremors at rest. Since this disease negatively influences the quality of life in patients, an early and accurate diagnosis is important for slowing the progression of the disease and providing effective treatment to patients. One of the quick and easy methods for diagnosing is the spiral drawing test and the differences between the target spiral picture and the drawing by patients can be used as an indicator of movement error. Simply, the average distance between paired samples of the target spiral and the drawing can be easily calculated and used as the level of movement error. However, finding the correct pair of samples between the target spiral and the drawing is relatively difficult, and the accurate algorithm to quantify the movement error has not been thoroughly studied. In this study, we propose algorithms applicable to the spiral drawing test, that ultimately can be used to measure the level of movement error in Parkinson’s disease patients. They are equivalent inter-point distance (ED), shortest distance (SD), varying inter-point distance (VD), and equivalent angle (EA). To evaluate the performance and sensitivity of the methods, we collected data from simulation and experiments with healthy subjects and evaluated the four methods. As a result, in normal (good drawing) and severe symptom (poor drawing) conditions, the calculated errors were 3.67/5.48 from ED, 0.11/1.21 from SD, 0.38/1.46 from VD and 0.01/0.02 from EA, meaning that ED, SD, and VD measure movement error with high noise while EA is sensitive to even small symptom levels. Similarly, in the experiment data, only EA shows the linear increase of error distance to changing symptom levels from 1 to 3. In summary, we found that EA is the most effective algorithm in finding the correct pair of samples between the spiral and the drawing, and consequently yields low errors and high sensitivity to symptom levels. Parkinson’s disease ,2 is a nThe methods for diagnosing this disease are mostly based on the patient’s medical history, symptoms of abnormal movement, and the degree of loss of living ability. The process of diagnosis often goes with questionnaires such as the Unified Parkinson’s Disease Rating Scale (UPDRS) or the Clinical Rating Scale for Tremor (CRST). However, these methods may be slow and could be subjective ,5. Thus,The purpose of this study is to present quantification algorithms from the spiral drawing test that can be used for an accurate diagnosis of movement error and to evaluate the performance of the algorithms. Therefore, a study was conducted to increase the accuracy of the ‘Error Distance’ in poor to good drawings . The ‘ErThere are various possible methods of calculating Error Distance. The most common method is to calculate the distance by sequentially comparing the baseline and the drawing. For accurate ‘Error Distance’ calculation, the same number of baseline data as In this study, we propose algorithms that can accurately measure the ‘Error Distance’ of movement error in the spiral drawing test by finding the optimal pairs of points between the base and the drawing of the spiral pattern. In the following sections, we introduce the methods for finding coordinate pairs and propose an algorithm. Afterward, the evaluation strategy with simulation and healthy subjects’ experiments is explained. Finally, we report the performance of the three methods and our proposed algorithm for estimating error distance.Measuring error distance can be modeled as follows. Let The easy and intuitive way to find the pairs of points between the base and drawing patterns is selecting by every Normally, the spacing between samples in the drawing is not constant, while it can be constant in the base pattern since this is generated. This mismatch may introduce large errors in finding pairs. Whereas pairing the closest point in the base with the target point in the drawing may be more reasonable and the whole sample  is presented within 3–15 Hz, and the motion component is likely under 3 Hz . This stWhen a real patient performs a drawing test, the measurement speed continuously changes due to bradykinesia or rigid movement. This feature was also reflected in the sample data. First, the whole series of sample points were divided into several sections, then different speeds were expressed for each section. This process was implemented by choosing several sample indices within each section and removing them from the total samples. Finally, we obtained the sample data This experiment aims to obtain a dataset at different movement error levels simulated by human subjects, thereby comparing and analyzing the performance of each algorithm described above for further evaluation. For this purpose, four healthy subjects  participated in the spiral drawing experiment. None of the subjects had participated in a spiral drawing experiment before this study. All subjects were informed of the purpose of the study, the subject’s rights, and possible rewards. A written consent form was acquired from all subjects, then the experiment was performed. For better simulation by subjects, we provided a sufficient explanation of motor symptoms in Parkinson’s disease, possible movement error, and the spiral drawing test. This experiment was approved by The Public Institutional Bioethics Committee designated by the Ministry of Health and Welfare approved study (P01-202012-13-002).The experiment was designed to acquire drawing samples of three different conditions . Thus, each subject was asked to draw a spiral pattern according to each condition and produced 15 sample drawings per condition on a tablet PC. The representative sample drawings of three conditions  are shown in We obtained 1000 instances of synthesized data (10 conditions and 100 samples per each condition) and 180 pieces of experiment data from four subjects; each subject conducted 15 times per each condition and there were 3 conditions. With these data, we obtained paired samples between the base spiral and the drawing and calculated the error distance by applying four algorithms . We then compared the performance and reliability of the methods. Throughout this study, sample groups were compared, and the significance was checked. We used the Wilcoxon Rank Sum Test as the statistical test in this study because we do not know if the samples follow the normal distribution.In this section, we present the representative drawing and its obtained pairs of samples from four algorithms, and the results from comparative analysis based on the above simulation data and experiment data. Next, we evaluated each method to check if the method well differentiates symptom levels from drawings. In In methods ED and VD, the pair values were significantly different in both cases, and SD seems to find partially correct pairs in the normal symptom, but not in severe symptoms. In comparison, EA finds pairs in both normal and severe symptoms with a small difference. The pair error was calculated per each method and presented in p < 0.05.As a result, in normal (a good drawing) and severe symptom (a poor drawing), the calculated errors are 3.67 ± 2.52/5.48 ± 2.33 from ED, 0.11 ± 0.04/1.21 ± 1.53 from SD, 0.38 ± 0.30/1.46 ± 1.19 from VD and 0.01 ± 0.01/0.02 ± 0.02 from EA. Statistical tests revealed that all methods differentiate the two levels of drawing quality (or symptoms), showing For additional verification of method performance, a comparative analysis was performed on simulation and experimental data. Error distance for four methods based on generated data and measurement data is shown in The drawing error in the spiral drawing test can be an effective measure for diagnosing movement disorders . In measGenerally, spiral drawing tests are conducted with a pen and paper. This requires manual checking by a clinical expert. With the help of various digital devices , the patient’s drawing data can be easily collected in digital format so that objective evaluation can be made by algorithms as proposed by this study. In this sense, we think that our study and results are meaningful. From the application perspective, our algorithm with spiral drawing test can be applied to understand the possible differences in movement error across different movement disorders, for example, Parkinson’s disease. In addition, the accurate error distance may be combined with brain MRI  to improWe believe that error distance with EA is effective for evaluating movement error. However, there are still limitations to this study. First, normal movement error may be caused by various factors like slowness or rigidity of movement and tremors. We did not consider each of these factors in designing the algorithm. With a more sophisticated algorithm based on an understanding of such error factors, the evaluation accuracy may be improved. Second, we conducted simulation and experiment studies for reliability testing, but the algorithms should be tested with true patient data. We will pursue these as our future research topics.We proposed algorithms to accurately quantify the error distance of a spiral drawing test that is important for the diagnosis of movement disorder. The algorithms were evaluated with simulation and experiment data, and we have shown the performances of the algorithms. As a result, EA, which uses an ‘Equivalent Angle’ in finding the correct pair of samples between two drawings, is proven to be the best among the proposed methods. We believe that this algorithm could be effectively used for objective diagnosis of movement disorder.


Traditional radiotherapy and chemotherapy have been intensively studied for their role in the treatment of tumours. However, these therapies often cause side effects for patients, which calls for the development of novel treatment options for tumours. B-cell lymphoma-2 (Bcl-2)/adenovirus E1B 19 kDa-interacting protein 3 (BNIP3) reportedly apoptosis-inducing effects in tumour cells and is associated with the progression and treatment of multiple tumours. Nevertheless, little is known about its potential role in tumour diagnosis and targeted therapy.The results of the study demonstrated that the interaction of BNIP3 with HDAC1 may affect the progression of breast invasive cancer (BRCA), sarcoma (SARC), kidney renal clear cell carcinoma (KIRC), and low-grade glioma (LGG). BNIP3 seemed to exert its effects in BRCA and SARC primarily through gene silencing and integrator complex, and in KIRC and LGG, mainly by affecting olfactory function, suggesting that targeted therapy can be developed based on the above signalling pathway and downstream molecules.BNIP3 has emerged as a promising therapeutic and diagnostic target for BRCA, SARC, KIRC, and LGG, providing new insights into tumour molecular therapies in the clinic.The online version contains supplementary material available at 10.1186/s12943-023-01808-9. As a malignant disease, cancer is a serious global public health issue that has permanently affected human society. Cancer remains the leading cause of death in China despite the improvement in health care and increases in funding for cancer control over recent years . UnfortuBNIP3 is a mitochondrial pro-apoptotic protein in the Bcl-2 superfamily, which is vital for the induction of tumour cell apoptosis, as well as the regulation of mitochondrial autophagy, apoptosis, and other functions in tumour cells. Of note, Rossi et al. demonstrated that BNIP3 modulated melanoma development through gene silencing and autophagy . Common Accordingly, the present study used a multi-omics database and R language to conduct differential, survival, clinical correlation, microsatellite instability (MSI), and correlation analyses of BNIP3 expression in 33 pan-cancerous tumours, explicating the tumour mutational burden (TMB), tumour microenvironment, and immune cell infiltration. The results showed that BNIP3 expression in tumour samples was markedly lower than that in normal samples and that the low expression of BNIP3 was associated with the poor prognosis of BRCA, SARC, KIRC, and LGG. To investigate the molecular mechanism of BNIP3 in these tumours, the signalling pathways of BNIP3 were further analysed through protein–protein interaction (PPI) and gene set enrichment analyses (GSEA). Subsequently, we explored treatments that target BNIP3 by studying the molecules involved in its signalling pathways to provide new ideas for tumour diagnosis and treatment.http://xena.ucsc.edu/) database containing 10,327 cancer samples and 730 normal samples, which yielded 11,057 transcriptomic data. We used the R language to perform gene ID conversion on the downloaded gene data, extract the BNIP3 gene from the transcriptome data, and form tables of sample names, gene expression, sample types, and tumour types. The data were also analysed in series using R packages limma, ggpubr, etc. There were no available samples for some cancer types. Among these 33 cancers, 23 exhibited low BNIP3 expression, whereas 10 had extremely significantly high expression of BNIP3 , which are involved in several core processes regulating endothelial cell physiological functions, especially angiogenesis, inflammatory signalling, and redox homeostasis Fig. H–I. MostIn KIRC and LGG, the prognosis of patients with low BNIP3 expression was poorer than that of patients with high expression Fig. B–C, and Alterations in olfactory function can be affected by various factors, including environmental factors and variations. Therefore, biological correlation studies did not imply any causal relationship. Meanwhile, as a necessary condition for developing a detection method in tumour and eliminating inequality in cancer treatment, this study needs to encompass a broader population and include a wider range of cancer types, including less prevalent ones. Future validation of BNIP3 in large sample sizes is warranted prior to its clinical application as a diagnostic biomarker.BNIP3 exhibited abnormal expression in 33 tumours, which is associated with poor prognosis in BRCA, SARC, KIRC, and LGG. Clinical tumour prognosis evaluation through the exprssion of BNIP3 in BRCA, SARC, KIRC, and LGG revealed a potential for favourable immunotherapeutic efficacy of BNIP3 in these tumours. Furthermore, a correlation analysis was conducted on immune microenvironment indicators. Multi-omics GSEA and other approaches indicateds that BNIP3 interacted with HDAC1 and affected tumour prognosis by influencing olfactory function, suggesting that targeted regulation of olfactory function by BNIP3 and HDAC1 may serve as a potential clinical therapy prognostic diagnosis in BRCA, SARC, KIRC, and LGG.Below is the link to the electronic supplementary material.Fig. 1: Among the four types of tumours , the correlation between the three clinical features and BNIP3 expression was significant.Supplementary Material 1: Table 1: Total data of differential expression analysis and survival analysis of BNIP3 in 33 types of tumours.Supplementary Material 2: Table 2: The full names and abbreviations of 33 types of tumours correspond to the table.Supplementary Material 3: