Since sequencing techniques have become less expensive, larger sample sizes are applicable for microbiota studies. The aim of this study is to show how, and to what extent, different diversity metrics and different compositions of the microbiota influence the needed sample size to observe dissimilar groups. Empirical 16S rRNA amplicon sequence data obtained from animal experiments, observational human data, and simulated data were used to perform retrospective power calculations. A wide variation of alpha diversity and beta diversity metrics were used to compare the different microbiota datasets and the effect on the sample size.Our data showed that beta diversity metrics are the most sensitive to observe differences as compared with alpha diversity metrics. The structure of the data influenced which alpha metrics are the most sensitive. Regarding beta diversity, the Bray–Curtis metric is in general the most sensitive to observe differences between groups, resulting in lower sample size and potential publication bias.p-value < α. This way of proceeding is one of the many forms of the so-called p-value hacking. To this end, in our opinion, the only way to protect ourselves from (the temptation of) p-hacking would be to publish a statistical plan before experiments are initiated, describing the outcomes of interest and the corresponding statistical analyses to be performed.We recommend performing power calculations and to use multiple diversity metrics as an outcome measure. To improve microbiota studies, awareness needs to be raised on the sensitivity and bias for microbiota research outcomes created by the used metrics rather than biological differences. We have seen that different alpha and beta diversity metrics lead to different study power: because of this, one could be naturally tempted to try all possible metrics until one or more are found that give a statistically significant test result, i.e.,  For a few decades now, researchers have left culture-based methods and used molecular technologies, and more recently mostly sequencing-based approaches, to characterize microbial communities within a certain environment, referred to as the microbiome. In humans and animals, the microbiome has an important role in health and disease. For example, animals raised without or fewer microbes showed an underdeveloped immune system and are more susceptible to diseases . Microbii) the effect size, i.e., the quantification of the outcome of interest ; (ii) the sample size n, i.e., the number of samples (to be) collected; (iii) the power of tests 1 - β, i.e., the probability of the test of rejecting the null hypothesis when actually false; and (iv) the confidence level α, i.e., the probability of rejecting the null hypothesis when actually true.Hypothesis testing rests on the definition and choice of four parameters: , and α describes the false-positive error or Type I error. Once acceptable error rates α  and β  and the effect that one is interested to assess statistically are chosen, it is possible, at least in principle, to determine the optimal sample size, i.e., the number of samples that one needs to collect/analyze to obtain, with probability 1 −β, a statistically significant result with confidence α.Given the nature of microbiome data, it is possible to quantify differences between groups at two levels: the alpha (within-sample) and beta (between-sample) diversity . Alpha dThe choice of the diversity metrics affects the subsequent statistical testing and, as a result, how, and to which extent, power analysis can be performed.m taxa; thus, the problem of assessing differences between two (or more) groups can be addressed with a univariate test, like t-test, ANOVA, or a nonparametric test. The use of a beta diversity metric implies that all samples are to be considered simultaneously, and several methods to compare groups of samples measured on m > 1 have been proposed like analysis of similarities (ANOSIM)   and perm(ANOSIM)  to replathe test .t-test, the effect size can be expressed as Cohen’s δ  is sensitive to the particular diversity metric used to the point that sample size calculations can be severely affected.where μThe aim of this study is to show how, and to what extent, different diversity metrics influence the sample size needed to assess the statistical significance of dissimilarities between different microbial communities. Both simulated and empirical 16S rRNA gene amplicon sequence datasets are used to perform retrospective calculations of the empirical power for microbiota studies. A broad selection of alpha and beta diversity metrics was used to compare the different microbiota datasets.This study generated insight into the sensitivity and bias of certain statistical methods used in microbial ecology on microbiota research outcomes. We conclude with some recommendations for the reporting of power analysis and sample size calculations for microbiome studies.1 with a query: (microbiota [Title] OR microbiome [Title]) NOT Review [Publication Type] 2020/01:2020/02 [Date of Publication]). This strategy aimed to include a broad scope of microbiota studies. We limited our search to studies published in English with free full text.To support the choice of alpha and beta diversity metrics to consider in our comparison, we performed a literature search on PubMeds is the number of observed taxa, calculated as  or ASVs observed , where slated as s taxa  and two sequences . This metric assumes the number of organisms identified for a taxon to follow a Poisson distribution. The definition rests on the concept that rare taxa bring most information about the number of missing taxa. This index gives more weight to the low-abundance taxa, and only the singletons and doubletons are used to estimate the number of missing taxa  (Shannon’s index venness) . It is ds is the number of OTU/ASV and pi is the proportion of the community represented by the ith OTU/ASV. Basically, this index is the entropy associated with a given sample and quantifies the uncertainty in predicting the taxa identity of an individual selected at random from the sample. Shannon’s index uses the relative abundances of different taxa; thus, diversity depends on both taxa richness and evenness with which organisms are distributed among the different taxa. This index places a greater weight on taxa richness  and 1 (the two samples do not share any taxa). It gives more weight to common taxa , where J is the Jaccard index, which is defined asThe Jaccard distance which is the ratio between the number of members that are common between the two samples and the number of members that are distinct; it is a measure of similarity for the two communities and ranges between 0 (the communities are different) and 1 .UF and weighted UniFrac distances between two samples take into account the phylogenetic tree and thus phylogenetic distances between community members . In UF, bi as the length of branch i; Ai and Bi as the numbers of sequences that descend from branch i in communities A and B, respectively; and AT and BT as the total numbers of sequences in communities A and B, respectively. In order to control for unequal sampling effort, Ai and Bi are divided by AT and BT (T and BT .This dataset contains 16S rRNA gene amplicon sequence data obtained from a broiler chicken experiment. The dataset is described in detail in This dataset was obtained from the Human Microbiome Project (HMP) phase I . It contIn all datasets, ASVs were defined as unique sequences. All data were analyzed using NG-Tax . TaxonomWe simulated two different scenarios described by Simulated datasets 1 and 2. An overview of the characteristics of the simulated datasets is shown in X1 in the following. Datasets (named X2 for the sake of simplicity) were created where 2, 5, 10, 25, 50, and 75% of bacterial features were randomly removed. This simulation generates data under an exist–non-exist binary scenario where bacterial features are either present or absent in X1 and X2.Simulated dataset 1 is built starting from 1,995 microbial features observed in 169 stool samples from the HMP, indicated as X1 controls and X2 cases, with 1,995 features and 169 samples each) where one-fourth of bacterial features in X2 are 1, 2, 5, 10, 15, and 20% more abundant than in X1. Both simulated datasets have the same phylogenetic structure as the HMP dataset. This simulation thus generates data under a differential abundant scenario where bacterial features are present in the different compositions in X1 and X2.Simulated dataset 2 is a case–control scenario , Chao1, Simpson, and Shannon were assessed using the Kruskal–Wallis test . A signim > 1 variables. It constructs ANOVA-like test statistics from a matrix of resemblances  calculated among the sample units and assesses the significance of the observed differences using random permutations of observations among the groups (0 tested by PERMANOVA is that the centroids of the groups (in the space of the chosen resemblance measure) are the same for all groups. This test assumes that samples are exchangeable under the null hypothesis, are independent, and have similar multivariate dispersion. The PERMANOVA test statistic is a pseudo ANOVA F-ratio:The differences between groups using beta diversity as determined by using the BC, Jaccard, UF, and weighted UniFrac were assessed using the PERMANOVA . PERMANOe groups . The nulSSB is the total sum of squares of the (diss)similarities between groups, SSW is the total sum of squares of the (diss)similarities within groups, g is the number of groups, and n is the total number of samples.where F-statistics is calculated by means of permutations . The distribution of F under the null hypothesis is generated by permuting g times the sample group labels and recalculating F on the permuted data. Significance is expressed as a p-value calculated as the fraction of permuted F-statistics, which are equal to or greater than the pseudo F-ratio observed on the original data.The significance of the K random datasets of different sizes to take into account both data generation variability and the calculation of the empirical power. More specifically, from N1 × m and N2 × m data matrices X1 and X2 , we randomly sampled with replacements K = 1,000 n1 × m and n2 × m datasets for different sample sizes n1 and n2. For the sake of simplicity, we consider n1 = n2 = n, and we varied n between 5 and 50 or 100 in steps of 5. We used K = 1,000 for the analysis of univariate analysis  and K = 100 for the analysis of multivariate analysis (beta), both simulated and experimental data. In total, more than 40,000 randomly generated datasets were analyzed.The experimental and simulated data were used to generate X1 and X2  at significance level α = 0.01 under the assumption of the null hypothesis being false was applied on the K randomly generated datasets for different sample sizes n. The empirical power of the test is defined as the empirical probability EPr of H0 being rejected, calculated asA statistical test to assess the difference between 0 is rejected.where # indicates the number of times that Hadonis function for the Vegan package. Other power calculations were performed using the G*power software  using a two-sample Kruskal–Wallis t-test.Let us suppose we want to plan an experiment to assess whether gut and oral microbial communities are different. A very simple and basic study design would be to collect d of a very similar effect size δ on data from HMP (d = 1.27 (PD), d = 0.3621 (Shannon), d = 0.58 (Chao1), and d = 0 (Simpson). These values are markedly different: fixing the power to 0.8 (β = 0.2) and confidence α = 0.05 will lead to dramatically different required total sample size (We can base our estimation from HMP . Using fple size . This cln = n1 + n2 = 50 + 50 = 100) and using different effect sizes  literature offers an indication of what the most used alpha and beta metrics are for the analysis of microbiome data. The results are not surprising and agree with the author’s experience. We can note that none of the papers screened mentioned the use of Hill’s numbers , a matheAs shown in the motivational example, the choice of a particular alpha (and beta) diversity metric determines the number of samples required to achieve a predetermined power. Based on this observation, we examined two simulated datasets using both alpha and beta diversity metrics to understand the relationship between the sample size, the observed power, and the diversity metrics, together with two experimental datasets (chicken and HMP datasets).As representatives of testing procedures using alpha and beta diversity measures to compare two groups, the Kruskal–Wallis test  and PERMANOVA (for beta metrics), respectively, were selected. The Kruskal–Wallis test is the nonparametric choice for comparing two groups when the normality assumption does not hold. When comparing two (or more) groups using beta diversity metrics, PERMANOVA and ANOSIM  are popuWhile in this study we focus only on inferential approaches for the analysis of microbiome data, as carried on using univariate and multivariate tests, we should comment that sample size is also relevant for exploratory approaches like principal component analysis (PCA), principal coordinate analysis (PCoA), and multidimensional scaling (MDS). These approaches are not inferential  (n) for different percentages of present/absent ASV. If 2%, 5% of the ASVs are deleted from the dataset, none of the alpha diversity metrics was able to capture the difference between datasets X1 and X2, irrespective of the sample size used  . The achize used . When moused  to observe differences between the data  of the datasets that were analyzed, using the Chao1 diversity measure in 28 of the 100 papers considered in the literature review. The distribution is highly skewed toward 0 with a median of 39 samples per group and a mode of 8 samples. Removing the two outlying studies with > 300 samples resulted in a median of 23 samples.Even considering that Chao1 was one of the best-performing measures in both simulated and experimental datasets, these numbers appear to be worryingly low: on experimental data, which have a complicated structure that is impossible to replicate in simulations, it is rarely possible to attain a power of 80% with less than 40 samples per group. Similar considerations hold when PERMANOVA is applied see , with a One of the studies we examined in the literary review reported that sample size and power analysis were performed: “Sample sizes were chosen on the basis of pilot experiments and on our experience with similar experiments.” This is commendable, but we believe that the way forward is to employ and report in full a standardized summary of sample size calculations performed. The software G*power  generateTogether with this, information should be provided on how effect size was determined, i.e., which pilot data were used and how the effect size was calculated.A similar reporting protocol could be devised if simulations are used in a PERMANOVA setting . Since sThe aim of this study was to assess how, and to what extent, different diversity metrics and compositions of the microbiota influence the needed sample size to observe statistically significant dissimilar groups. Based on our literature survey, we observed that Shannon’s and Bray–Curtis metrics are the most published metrics. This might be because they are often the most sensitive metrics to observe differences between groups, resulting in a lower sample size. Our results are in line with those of a previous literature that showed that the choice of distance metric may significantly influence the observed results .de novo reanalyzed.A well-known phenomenon that can hamper progress in every research field concerns publication biases in reporting mainly positive findings . In micrNone of the 100 microbiome studies that we have considered reported the effect size. A collaborative project aiming to investigate the reproducibility of 100 high-profile psychological studies reported that the average effect size observed in the replication studies was approximately half the magnitude of those given in the original studies, leading to a replication success of only 36% . The lacH test statistic is given by (in absence of ties)On the basis of this, reporting of effects and test statistics should be made compulsory in microbiome studies. For the highly used Kruskal–Wallis test, the N is the total number of samples, ni is the number of samples in group i, and Ri is the average rank of observations in the ith group. Note that H  obtainable from most software packages.where 2, which is defined asFor the Kruskal–Wallis, the most common effect is the ηH is the value obtained in the Kruskal–Wallis test and k is the number of groups. For instance, for the comparison of the two feedings  and Feed B (n2 = 35) samples were compared with Kruskal–Wallis test using the Chao1 metric: H (df) = H (1) = 14.68, p-value 0.0001, η2 = 0.66, δ = 0.58,Feed A  test . For theIn addition, performing power analysis for a Kruskal–Wallis is not a simple matter and requires the use of a rather advanced statistical machinery ; for insF-statistics (see Equation 10) and the effect size should be reported. Typical effect measures in ANOVA are Choen’s f2, η2, and ϖ2:The principle of reporting the effect size should also apply when testing is performed using beta diversity metrics, in which case the PERMANOVA pseudo n1 = 35) and Feed B (n2 = 35) samples were compared with PERMANOVA test using the Unweighted Unifrac metric: F = F = 6.27, p-value = 0.0001, f2 = 0.092, 1,000 permutations,Feed A : unfortunately, to the best of our knowledge, the package seems not to be maintained and lacks a proper manual. The original paper presents a table with effects calculated from different studies that could be used as a guide; however, this metric is not standard. It can be calculated from the PERMANOVA table asFor PERMANOVA, the matter complicates considerably: to estimate statistical power and calculate sample size, one must quantify the expected within-group variance and the effect to be expected when comparing two or more groups. A package like micropower  in princ2 values in the range 0.04–0.1, depending on the beta metrics, and these are consistent with those reported in Table 1 from For the chicken data, we observed ϖf2-value; i.e., the between-group to within-group ratio can be easily obtained by the ANOVA table provided by software like the R package Vegan by taking the ratio between the Treatment sum of squares and the Residual sum of squares. The f2-value is used for power calculation in the ANOVA setting; however, it should not be used to perform sample size calculation for PERMANOVA, not even to obtain a rough indication, since the corresponding F-statistics do not follow an F distribution. For instance, when comparing Feed A and B from the first chicken data with PERMANOVA, we can derive a Cohen’s f2 = 0.38; if this value is used to perform power calculation for an ANOVA with two groups with power 80% at α = 0.01, we obtain that 42 samples per group are needed. However, comparing the results in a priori power calculations using pilot data.A more common effect measure is Cohen’s Furthermore, there is a convention, more or less widely accepted, of classifying Cohen’s δ effect, which we have used in the power calculation for the Kruskal–Wallis, into trivial (δ < 0.2), small (δ = 0.2), medium (δ = 0.5), and large (δ > 0.8). However, this classification is based on what is observed in psychology and does not apply automatically to other fields of research . In micrp-value < α. This way of proceeding is one of the many forms of the so-called p-value hacking (p-hacking)  . p-Hackinference ) is the nference . p-Hackiion bias .p-hacking would be to publish, and we stress here the word publish, a statistical plan before experiments are initiated: this practice is customary for clinical trials where a statistical plan describing the endpoints and the corresponding statistical analyses must be disclosed before the start of the study and must be adhered to if results are going to be published  ublished . This ishttps://github.com/mibwurrepo/Microbial-bioinformatics-introductory-course-Material-2018/tree/master/input_data; http://doi.org/10.5281/zenodo.1436630 (https://www.ncbi.nlm.nih.gov/bioproject/ with accession number PRJNA553870. Simulated datasets are available at https://github.com/mibwurrepo/KersSaccenti-Power.HMP data used in this study are available at .1436630 . The pouJK and ES wrote the manuscript. Both authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Exhaled breath analysis for early disease detection may provide a convenient method for painless and non-invasive diagnosis. In this work, a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath sampling unit is presented. The developed analyzer system comprises a compact, low volume, temperature-controlled sensing chamber in three modules that can host any type of resistive gas sensor arrays. Furthermore, in this study three modular breath analyzers are explicitly tested for reproducibility in a real-life breath analysis experiment with several calibration transfer (CT) techniques using transfer samples from the experiment. The experiment consists of classifying breath samples from 15 subjects before and after eating a specific meal using three instruments. We investigate the possibility to transfer calibration models across instruments using transfer samples from the experiment under study, since representative samples of human breath at some conditions are difficult to simulate in a laboratory. For example, exhaled breath from subjects suffering from a disease for which the biomarkers are mostly unknown. Results show that many transfer samples of all the classes under study  are needed, although some CT methods present reasonably good results with only one class. The importance of exhaled breath gas analysis is increasing in medical diagnostics for early disease detection and therapy progress monitoring over the last decades ,2,3,4. ECurrently, the gold standard for detecting VOCs in exhaled air is gas chromatography coupled to mass spectrometry (GC-MS) ,24,25,26To reduce costs, chemical sensors integrated into electronic noses (eNoses) for breath analysis in medical point-of-care diagnosis have become an emerging field. Many research groups are pushing forward the frontier of non-invasive, rapid, portable and potentially low cost medical diagnosis tests for different diseases ,27. ElecAdvantages and limitations of eNose sensor techniques are associated with different parameters like specificity, response and recovery time, detection range, sensitivity, operating temperature, temperature as well as humidity effect on sensing technique, portability, cost and complexity of measuring circuitry.In the review by Röck et al., a list of commercially available eNoses is published . SeveralOver recent years, the field of breath analysis with MOX-based eNose systems is continuously progressing. Different studies of cancer detection via breath gas analysis with eNoses based on MOX sensors have been conducted by Yu et al. , Wang, DSpecial attention shall be given to the experiment design, most of the published studies on breath analysis by an eNose were conducted with one device. For larger scale studies, given the difficulties of obtaining breath measurements from patients with specific conditions, it would be desirable to extend the study to more devices and more places . However, sensor to sensor variability, time degradation (drift), cross-sensitivities to background and environmental conditions, etc., causes data models  built for one instrument at a given time and place to not be valid for measurements collected by another instrument, or the same instrument in another place or later in time. In other words, the data calibration models degrade when taking measurements under conditions other than those under which the calibration model was created. The effort in building a calibration model is costly and time consuming, and therefore limits the use of eNoses in many applications such as breath analysis.To reduce the impact of these limitations, data processing techniques exist that help in reducing the effort of full calibrations by transferring information from the main calibration model  to be applied to new measurements obtained under different conditions . These techniques are called calibration transfer (CT) or instrument standardization in chemometrics, and transfer learning tools in machine learning. CT methods have been largely applied in NIR spectrometry and also in eNoses ,55,56,57Calibration transfer methods can be grouped according to different criteria. For example, the following approach : (i) no Other classification of calibration transfer techniques can be made with regard to the domain of the transfer and the transfer samples. For the first one, we can have (a) transfer to the master space or the slave space, such as DS and PDS, (b) transfer to a common subspace such as OSC. Additionally, for the latter, CT methods regarding transfer samples; (a) methods that need transfer samples such as DS, PDS, OSC , Shenk’sAn important matter in calibration techniques that need transfer samples is the selection of these transfer samples. Such samples must be representative of the samples under study and the respective instruments, keeping other variables such that they can be linked between the instruments. The required transfer samples would be those measured at the same conditions by different instruments, for example, by having the instruments measuring the same sample at the same time; we call them standard samples. When standard samples are not available, we can use reference (or nonstandard) samples, which are measurements made under exact or similar conditions in all master and slave instruments.In addition, the fact that transfer samples must be representative of the samples under study may be a limitation in out-of-lab applications, such as breath analysis, because representative samples of given cases might be difficult to obtain. In an ideal situation, samples representative of the cases under study can be artificially made, such that measurements of these samples can be made in a laboratory under controlled conditions and thus be used as transfer samples. This way, all instruments would be referred to the same general samples under specified conditions. However, it is difficult to create synthetic samples representative of complex samples such as human breath samples, especially for patients suffering diseases for which the exhaled VOCs pattern is poorly known or even unknown and which is affected by numerous variables. Therefore, the use of on-site measured samples as transfer samples may be helpful, or alternatively hard-to-obtain sample classes could be excluded from the transfer sample set. In summary, for practical reasons we wonder whether on-site measured sample measurements can be used as transfer samples and whether the quality of the calibration transfer would decrease much if one sample class  is excluded from the transfer sample set. We can find an example in the literature where breath samples from electronic noses were used, although case breath samples were artificially made by mixing control breath samples with chemicals .In this work, we explore the performance of several CT techniques in a real-life breath analysis study using our recently published ,81 sensiThis study is mainly motivated by the difficulty of calibration of eNoses for breath analysis applications, the differences between instruments, the frequent recalibrations needed due to aging and drift and the environmental and other different conditions present in different hospitals which prevents obtaining a unified dataset for deep statistical studies. Another goal in this work, is to present our modular breath analyzer (MBA) platform shown in , a new uThe manuscript is organized as follows. The presented novel modular breath analyzer (MBA) platform is developed for laboratory and clinical use. The internal components of the MBA platform are shown in The two fluidic units—sampling unit as well as sensing unit—which are described below are integrated into a temperature-controlled aluminum body to ensure that all fluidic paths within the MBA platform are kept above body temperature to avoid temperature effects on the sensors to avoid influences towards the composition of the exhaled breath gas. Without thermostatic control the temperature within the fluidic paths will drop below body temperature and thus vapor will condense and trap water soluble volatile organic compounds (VOCs). For this reason, the temperature inside the sensing chamber of the presented modular breath analyzer platform is 45 °C ± 1 °C.During an exhalation into the device the volume of the sampling tube is exchanged several times. After the exhalation process has finished, the last 38 mL of the exhaled breath are buffered within the tube and remain there until the sample is transferred into the sensing chamber by a controlled pumping process. The buffered volume of approx. 30 mL allows the system to transfer several times the volume inside the sensor chamber, which can be set by the software. The internal exhalation monitoring unit is coupled to the sampling tube to operate the pump directly when the exhalation stops to draw selected parts of the alveolar air inside the sensor chamber. Sensors within the EMU allow real-time monitoring of the full exhalation process. EMU parameters  are recorded during the exhalation process of the volunteer or patient to ensure proper repeatable sampling and enable capturing relevant parts of the exhaled breath, which are different portions of the pulmonary volume.For a reliable breath analyzer platform, it is crucial that the EMU is not only recognizing the end of the exhalation, it is also important to ensure that the volume of the exhaled breath and the profile is within a certain variance. Direct feedback to the patient and the study nurses may help to improve the sampling process. This enables capturing relevant parts of the exhaled breath and allows a more accurate transfer of sample to sensors as well as selective sampling of different portions of the pulmonary volume. To avoid cross-infection between the patients the glass sampling tube can be exchanged and cleaned by sterilization. The disposable mouthpiece is exchanged for every volunteer or patient.To utilize the buffered end-tidal breath sampling method, we designed a specific valve-controlled inlet for our recently published sensing chamber ,81,82 toThe current setup contains many of the most relevant analog and digital surface mount devices (SMD) sensors on the market. A list of all integrated sensors, number of obtained signals from each sensor and used heater/supply voltages are summarized in The cleaning of the sensor chambers is done by a two-step process. First, the pump and the valve shown in A group of 15 generally healthy 17–18 year old individuals were recruited for an experimental study using three modular breath analyzer prototypes, the first breath sample was obtained following a 12 h fasting period with all three MBA devices; then the participants were given a standardized meal and invited for a follow-up (second) breath sample 4 h thereafter. The test-meal was a hamburger with 0.5 L water. To avoid potential contaminants, on the day of testing the recruited study participants did not use mouthwash, chewing gum, furthermore they did not perform excessive physical activity, did not smoke or consume alcohol for 24 h before the breath test. The same restrictions were applied to the 4-h pause between two measurements. This experimental routine was repeated three times, each measurement day was one week apart from the previous one. The general scheme of the breath sample collection (measurement day) of the pilot study can be seen in In this study, the firmware of the used MBA devices was set to 10 s for baseline acquisition and 20 s for breath acquisition.In this work, we compare three methods for calibration transfer based on different principles: Correlation alignment (CORAL) , partialDS, PDS and PLSCT are based on adjusting the slave features to the master by using a set of labeled transfer samples measured in both the master and the slave instruments. These samples can be standard samples, i.e., samples related to both instruments, such as same samples measured at the same time, samples measured at the exact same conditions, etc., or non-standard samples if they are labeled but not related as the standard samples. In turn, CORAL is a simple method that transforms data from the master to the slave space using their covariance structure without the need of labelled samples. Finally, PLSDA finds a common master-slave space by removing components using unlabeled transfer samples, only the information about their membership to the master or the slave instrument is used.Direct standardization (DS) and piecewise direct standardization (PDS) methods—DS and PDS ,86,87 meDS and PDS are widely used methods which have provided good results in laboratory experiments using a relatively small number of samples ,79 and amct and Xsct are the data measured from the transfer samples at the master and slave instrument, respectively. Therefore, B can be estimated bysct)+ is the pseudo inverse of Xsct. The new samples from the slave instrument Xs can be projected onto the master instrument Xm:DS assumes a linear relationship between master and slave instruments such that:PDS has a diagonal structure:s can be projected on the master instrument by:In turn, PDS creates local PLS models relating the master instrument j-th variable to a sliding window of size w centered at the j-th variable in the slave instrument. The resulting transformation matrix BPartial least squares discriminant analysis-based calibration transfer (PLSDA)—the PLSDA-based method builds a PLSDA model relating transfer unlabeled samples from both master and slave instruments with a dummy vector containing their membership (master or slave) label. Furthermore, the predicted data from this model is removed from the original data set.m and Xs denote the original data from the master and the slave instrument, respectively, and Xm’ and Xs’ denote the transformed data from the master and the slave instrument, respectively:If W and P are notations for the resulting PLSDA weight and latent variable matrices, respectively, Xm’ and be used to predict Xs’.The number of components or latent variables (LVs) to be removed must be selected. Finally, a classification/regression method can be built on the XPartial least squares-based calibration transfer (PLSCT)—In PLSCT , a PLS mm and Ym are the calibration set data and label matrices in the master instrument, respectively, Wm, Pm and βm are the weight, latent variable and regression coefficient matrices of the PLS model for Xm and Ym in the master instrument (with selected number of latent variables (LVs)), the projection of the master’s transfer samples Xmct in the PLS model Tmts and the projection of the slave’s transfer samples Xsct in the PLS model Tsts are given by:If XAssuming a linear relationship between the projection matrices:Since in this work we use PLSDA for a classification problem, we will call this method PLSDA-CT instead of PLSCT.Correlation alignment (CORAL)—CORAL  is an unm and Xs subsets of unlabeled data from master and slave, respectively, having Nm and Ns number of features each (X matrix columns), the proposed transformation is:m and Cs are the master’s and slave’s data covariance matrices, respectively, adapted with a small regularization parameter λ that allows it to be full rank and thus the square root to be computed. Therefore, CORAL uses two steps to align both data distributions: whitening the master data and re-coloring it with the slave covariance.Notating XIn this work, we study the performance of several CT algorithms to transfer calibration models between pairs of 3 instruments in an experiment consisting of an on-site real breath analysis study for discrimination of breath samples from subjects before and after eating a specific meal. Thus, data is classified into classes “meal” and “no-meal”. We use device 1 as the master device, thus devices 2 and 3 are considered the slave devices. Denoting the master instrument as M and the slave instrument as S, two instrument pairs considered are: M1–S2 and M1–S3.To illustrate the effect of the different CT algorithms, we followed a procedure consisting of three steps. In the first step, dimensionality reduction and a classifier using data from M is built and evaluated with selection of their optimal parameters for each of the 3 devices. In the second step, a small number of samples (transfer samples) from both M and S are selected and the CT is performed. Finally, in the third step, the CT algorithm is evaluated using the classifier from the first step on S data. The result of the M classifier applied on the M data is considered the reference to be achieved, while the result of the M classifier applied on each S data is considered the threshold to be overcome.Since only two classes are involved in the classification task, the classification results are given in area under the receiver operating characteristic curve (AUC) and standard errors (SE). Results for each S device are compared before and after the application of the CT algorithms; if the results of the corrected S data (after CT) are similar to the M’s reference classification result we consider a successful CT, if the S’s result is higher than the one before the CT (threshold) we consider good CT.Classifier—First, the basic pre-processing step here is given by the ratio of the sensor’s conductance (1/R) with the baseline, which gives R0/R, where R0 is the sensor output resistance to room air (baseline) and R is the sensor’s response resistance to the breath exposure. The considered R is one value that summarizes the sensor’s response to the whole analyte exposure. It corresponds to the mean of the latest measurements before the cleaning step, in this case the last 5 s, when the sensor responses are most stable (steady state). Therefore, the resulting data sets from measurements with our 18 sensors have 18 columns, one per sensor.As specified above, the results of the classifier built in M and applied on each S is considered the reference for the evaluation of the CTs. The considered classifier is linear discriminant analysis (LDA) with a previous dimensionality reduction task using principal component analysis (PCA). Therefore, the number of PCA components (nPCs) for the PCA + LDA classifier is the parameter to be selected. For this, cross-validation is made on two random subsets of the M’s data: a training set containing 48 samples with equally represented classes (24 + 24), and a validation set with the remaining samples (~39). The procedure is repeated 20 times for each parameter value to obtain the optimal nPC.Transfer samples—Once the optimal nPCs for the classifier in M are selected, the CT task is carried out as follows. For every pair M–S, a sample subset (M-training set) is selected from M with both classes equally represented. The M’s transfer samples are selected from this subset. Then, the S’s transfer samples are selected according to the CT algorithm as described below, and the CT is performed.The CT algorithms need a number of samples (transfer samples), labeled or not, from both M and S. To select the transfer samples from M, we use first the Kennard–Stone algorithm (KS) , and theIn addition, for cases with labeled samples the selection of M and S transfer samples is made according to 2 class membership conditions: samples belonging to (a) both classes in the experiment  or (b) only one class corresponding to no-meal. As for the healthy class in a disease-control breath analysis experiment, subjects belonging to no-meal are easier to collect and thus, a CT method based on only such sample class would be more practical.CT evaluation—To assess the CT algorithms’ performance, the selected M classifier is built using the M’s training samples and the S’s transfer samples if they are labeled. The classifier is then applied on the corrected S’s samples excluding its transfer samples .An additional reference can be considered for the CT algorithms that need known labeled transfer samples from S; the calibration extension method (CEM) which consists of the LDA classifier built with M’s training samples plus original (non-corrected by CT) S’s labeled transfer samples, this is S’s samples without CT. Then, the CT can be considered good if its AUC overcomes the CEM’s AUC. The procedure is repeated 20 times and the AUC and SE are computed.The number of considered transfer samples is 10, 20, 30 and 40. If the transfer samples are labeled, as for DS, PDS and PLSDA-CT, the classes are equally represented within them.After removing few outliers using PCA, the data set composition of the breath analysis experiment is shown in The references to compare the performance of the CTs are the results of the classification of every device’s data using a PCA + LDA model built with M’s training set data (48 samples) before the CT. We can see in The selected reduced dimension obtained after cross-validation (13 PCs) corresponds to the model with optimal results in M1 (89.26 ± 0.87). The same model applied to devices 2 and 3 gave 73.15 ± 1.15 and 75.72 ± 2.40, respectively. These values are the lower value reference for the evaluation of the methods  with samples from one or two classes, and using unlabeled transfer samples  knowing that they contain one or two classes. Since CORAL is based on covariance and in our data set the covariance of both classes are not dramatically different (so we can use LDA as classifier), its performance using a transfer sample set with one class is not much worse than using two classes. On the contrary, PLSDA needs both classes in the transfer sample set to find a component on a suitable direction to be removed. In turn, due to their nature, DS and PDS are very sensitive not only to the classes present in the transfer set but also to the type of samples. Therefore, standard samples of both classes are necessary for correct performance. Finally, results for PLSDA-CT are more robust and stable for standard samples of both classes, but for a small number of components and a high number of transfer samples, PLSDA-CT is giving good results for all cases.In this work, we have presented a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath sampling unit. Furthermore, we have tested the performance of four calibration transfer methods in a breath analysis experiment using real human breath measurements to classify breath samples of subjects before and after eating a specific meal. In our study the breath measurement is taken about 4 h after the food intake; this leads to the conclusion that the sensors are affected by every food intake. This can be viewed as a potential disorder in studies with healthy and sick people and should be considered when designing an appropriate sampling protocol.The measurements were made using three instruments. One of them (device 1) was selected as the master instrument, so that its measurements were used to build classification models along with transfer samples whenever their classes were known. The other two instruments were the slave instruments.The four CT methods tested follow very different approaches, especially with regard to the transfer samples they use. The test of these CT methods is in fact focused on the transfer samples they need for an acceptable performance as a response for a practical problem that arrives in on-field experiments, in our case in experiments using real human breath measurements with gas sensor-based instruments. In such experiments, measurements of samples at different locations and with different instruments are usual. The problem arrives when transferring the calibration from the master to the remaining slave instruments, because labeled samples from the slaves are needed and sometimes, they are difficult to obtain. We wondered firstly if real sample measurements (instead of lab-samples) could be used as transfer samples, and if so, how many and whether or not they must contain all classes under study in the classification problem.In the figures above, we have shown that real human breath measurements can be used as transfer samples, although in large numbers, much larger than in lab experiments, and with results that depend on the device. However, we could derive some general conclusions. First, in all cases that need labeled transfer samples, the best performance of all methods was obtained for two-class standard samples, and a decrease could be seen when the two-class samples were not standard. Methods like PLSDA, DS and PDS need transfer samples containing all the classes involved in the classification problem, although for PLSDA these samples do not need to be specifically known. However, PLSDA-CT gives good results for small LVs and large transfer samples which in our experiment could contain only one class. CORAL also shows good results for both one and two-class unlabeled transfer samples, although it depends on the device and a parameter. Therefore, calibration transfer methods such as CORAL and PLSDA-CT could be used in on-field experiments using transfer samples from the samples under study, without the need of laboratory samples specifically measured for calibration transfer tasks or for recalibration purposes.


Plasmodium falciparum, first in the Greater Mekong Subregion (GMS), and now in East Africa, is a major threat to global malaria elimination ambitions. To investigate the artemisinin resistance mechanism, transcriptome analysis was conducted of 577 P. falciparum isolates collected in the GMS between 2016–2018. A specific artemisinin resistance-associated transcriptional profile was identified that involves a broad but discrete set of biological functions related to proteotoxic stress, host cytoplasm remodelling, and REDOX metabolism. The artemisinin resistance-associated transcriptional profile evolved from initial transcriptional responses of susceptible parasites to artemisinin. The genetic basis for this adapted response is likely to be complex.The emergence and spread of artemisinin-resistant  Plasmodium falciparum) in the Greater Mekong Subregion of Southeast Asia identifies gene expression patterns that are correlated with resistance to a common anti-malaria drug, artemisinin.Transcriptomic analysis of isolates from the malaria parasite ( Loss of these drugs to resistance would be a disaster. Historically the Greater Mekong Subregion (GMS) has been the origin of antimalarial drug resistance in P. falciparum. In recent years the emergence and spread of artemisinin resistance and, subsequently, partner drug resistance has led to high failure rates of ACTs in several parts of the GMS3. Recently artemisinin resistance has arisen independently in East Africa (parts of Rwanda and Uganda)4. The phenotypic manifestation of artemisinin-resistant P. falciparum infections in vivo is slowed parasite clearance after treatment with artesunate. The slow clearance phenotype, defined by a parasite clearance half-life (PC½) > 5 h, is attributed to loss of sensitivity of P. falciparum to artemisinins during the early stage of the intraerythrocytic developmental cycle (IDC), ring stage6 and is causally associated with nonsynonymous mutations in the propeller region of the P. falciparum kelch 13 gene (PfK13)8. Artemisinin resistance was first reported in western Cambodia, Pailin province in 2009. Initially, over 20 different PfK13 mutations were associated with the slow parasite clearance phenotype10. However, since 2013 the soft sweeps resulting in multiple emergences of PfK13 mutations in the eastern part of the GMS were largely replaced by a selective sweep of a haplotype bearing a single nonsynonymous PfK13 SNP (C580Y)11. This single lineage of artemisinin-resistant P. falciparum spread and expanded through western and northern Cambodia, northeastern Thailand and southern Vietnam, and Lao PDR13. This was soon joined with molecular markers associated with resistance to the ACT partner drug piperaquine. PfK13-propeller domain mutations in artemisinin-resistant parasite lineages have also emerged independently and spread through Myanmar and western Thailand14. Moreover, PfK13-propeller domain mutations have been reported in Northern India15, and more recent foci include independent emergence in Papua New Guinea16, Rwanda4, Ethiopia17, and other parts of sub-Saharan Africa18.Artemisinin-based combination therapies (ACTs) have been critical to the success in reducing the global burden of falciparum malaria between 2000 and 2015PfK13 mutations confer artemisinin resistance is a subject of intense research using in vitro and in vivo models (reviewed in23). Collectively, these studies have proposed the involvement of multiple cellular and metabolic processes in artemisinin resistance including haemoglobin degradation, proteotoxic/unfolded protein stress response, vesicular biogenesis as well as oxidative stress response and mitochondrial functions. Translational suppression mediated by phosphorylation of eIF2α, linked to cell quiescence and slowing of the IDC can also confer artemisinin resistance in vitro24. Missense or loss-of-function alleles of other genes were also shown to contribute to artemisinin resistance in vitro. These include coronin25, falcipain2a/b26, ubiquitin hydrolase (pcubp1)27, and μ-subunit of the AP2 vesicular trafficking complex (pcap2)27. Thus, it appears that artemisinin resistance is mediated by a multifaceted mechanism that results from a concerted action of several metabolic and cellular factors. These may act in different, not mutually exclusive, combinations28. Undoubtedly these mechanisms drive artemisinin resistance of P. falciparum in in vitro conditions in which the parasites are supplied with superfluous amounts of nutrients, kept at uniform temperature, are not targeted by the host’s immune system and other ambient stresses exerted by the host’s environment29. The question now is what are the roles of each of these identified components of artemisinin resistance in natural infections, in vivo.The molecular mechanism by which the 31, and subsequently seven nonsynonymous SNPs associated with PfK13 SNPs32. A concurrent longitudinal study of the GMS parasites collected between 2001 and 2014 suggested that additional genes might be associated with artemisinin resistance including an additional kelch protein on chromosome 1033. Collectively, these studies detected genes that could be loosely linked with the artemisinin resistance-implicated biological functions, however, no experimental evidence of their role in the artemisinin resistance clinical phenotype has been reported so far.To investigate this, several genome-wide association studies (GWAS) were conducted. These identified large regions on chromosome 10 and 13, and 1434 to human37. This is based on the wealth of GWAS studies showing that the vast majority of genetic polymorphisms associated with complex genetic traits (such as genetic diseases) lay within the noncoding regions38. These are typically affecting DNA regulatory sequences and thus gene expression through which the phenotype is manifested39. This likely also applies to P. falciparum as suggested by our earlier study investigating expression quantitative trait loci (eQTL) in the TRACI parasite isolates40. We then carried out transcriptome analyses of the P. falciparum parasites from the Tracking Resistance to Artemisinin Collaboration (TRACI) study conducted in the GMS countries between 2011 and 201341. This showed that artemisinin resistance is associated with broad transcriptional changes of many genes, some of which may be linked with inductions of the unfolded protein response (UPR) and with a general deceleration of the IDC. Here we present a transcriptome analysis of parasite isolates from a more recent cohort of P. falciparum natural infections collected in the GMS between 2016 and 2018; 5–7 years after the initial TRACI study8, and after the recent selective sweep of PfK13 C580Y2. We identified a spectrum of transcriptionally correlated genes that likely contribute to artemisinin resistance via their altered transcriptional levels. We termed this the artemisinin resistance-associated transcriptional profile (ARTP) and provide evidence that its constitutive expression may have evolved from the initial transcriptional responses of sensitive P. falciparum parasites to the artemisinins.Transcriptome-wide association analysis (TWAS) is currently emerging as the method of choice for identifying causative genetic variations of complex traits in a wide range of biological systems ranging from plantsPfK13 mutations, whose expression activity mediates/contributes to the physiological state that enables the parasite cell to withstand the parasiticidal effects of artemisinins. We conducted transcriptome analysis of P. falciparum parasites derived from the blood of patients with uncomplicated P. falciparum infections in 13 field sites across 6 GMS countries  analysis42. Overall, we analyzed 577 samples collected at study enrolment to characterize the baseline transcriptomic profiles 0 h sample set) 6 h sample set) 0 h and (tr)6 h samples respectively42 0 h sample set, we established the distribution of the P. falciparum life cycle developmental stages in the peripheral blood including the asexual IDC stages expressed as hours post invasion (hpi) and the fractions of sexual stages (gametocytes)40 0 h sample set represented synchronized ring stage parasites between 6 and 20 hpi, with ≤ 5% of the overall parasitemia corresponding to gametocytes. Principle component analysis (PCA) of the (bl)0 h transcriptome revealed parasite hpi composed the major transcriptome difference across samples as it is strongly correlated (Spearman rho = 0.87) with the first principal component (PC1) accounting for up to 32% of the overall transcriptome variation  of most of the studied genes measured by DNA microarrays (70%) and RNA-seq  of which 76% also carried multiple copies of plasmepsin II  directly; the approach we term Transcriptome-Phenotype Association Studies (TPAS) (see “Methods”). First, we conducted TPAS analysis on the (bl)0 h samples in order to identify genes whose steady state mRNA levels correlated with the level of artemisinin resistance represented by PC½. For this we applied a generalized additive model to relate each gene’s expression to the PC½ with a loess function along hpi  for each gene based on 1000 permutations  was applied to define robust expression-resistance associations beyond parasite lineage effect. By this we aimed to eliminate expression-lineage associations and identified transcripts whose expression levels are associated strictly with the PC½. One of the clearest examples was PHISTa (PF3D7_1372000) which displayed a strong association with artemisinin resistance at p = 7.06E-27 (FDR = 0) with FPR = 0 0 h samples and RNA-seq-derived data of 188 (bl)0 hr samples separately. We observed good correlation (Spearman rho = 0.68) between the TPAS results obtained from these two platforms  in upregulated and downregulated genes, including the PHISTa, KAHRP, FIKK, ATG5, and FKBP35 0 h samples. This approach identified six clusters that displayed a distinct geographical segregation (hypergeometric test p < 0.05)  resistance to artemisinins even in an PfK13-independat manner.Next, we aimed to investigate the utility of the ARTP as a marker of the spread and evolution of artemisinin resistance in the GMS and possibly beyond. Hence, we conducted a clustering analysis to the 323 resistant parasite samples (PC½ > 5 h). Specifically, the Ward’s method was applied for clustering based on the Euclidean distance matrix of similarity of the ARTP formed by the 156 transcript levels in each of the 05) Fig. . It yielion Fig. . Either ion Fig. . This fu 43 Fig. . Taken t(tr)6 h sample set to assess transcriptional responses of P. falciparum exposed to ACT in vivo. The (tr)6 h parasites exhibited a tighter distribution of the IDC stages compared to those in the (bl)0 h, falling between 8 and 14 hpi with a median ~12 hpi, which might reflect the more rapid loss of mature ring stage parasites because of their higher sensitivity to artemisinins 0 h and (tr)6 h was conducted to identify transcriptional responses for the resistant (KEL/PLA1 with PC½ > 5 h) and susceptible (WT with PC½ < 5 h) parasites separately (for details see “Methods”). In total, we identified 20 and 73 genes that were induced or repressed, respectively, after 6 h in vivo treatment in the KEL1PLA1/resistant parasites. Similarly, 33 and 106 genes were induced or repressed, respectively, in the WT/susceptible parasites  0 h and (tr)6 h parasite sets 0 h sample set while the only upregulated gene in both sample sets was PHISTa (PF3D7_1372000). There was a skewed distribution of the (tr)6 hr induced and repressed genes in the WT/susceptible parasites along the distribution of the transcriptional associations of the (bl)0 h parasites 0 h and the 106 drug-repressed genes towards downregulation (FDR = 0). Contrastingly, the (tr)6 h induced genes in the PfPailin (KEL1PLA1)/resistant parasites show no association with the (bl)0 h baseline ARTP but the (tr)6 h repressed genes exhibit a moderate overlap with upregulated genes in the (bl)0 h baseline ARTP .To complement the baseline TPAS, we evaluated the 14) Fig. . Highly ets Fig. , the nontes Fig. . SpecifiRTP Fig. . This suP. falciparum to artemisinin(s) 0 h TPAS as well as the in vivo transcriptional response of (tr)6 h parasites. There are good agreements between these results and earlier studies of in vitro transcriptional response of P. falciparum K1 strain exposed to dihydroartemisinin (DHA) for 1 h48, the FCR3 strain exposed to artesunate for 3 hours49 and the recently reported transcriptional responses of the Dd2wt strain exposed to DHA for 6 hours50. This is consistent with our observation showing that the basal level ARTP overlaps well with transcriptional responses of the susceptible parasites  Fig. . This aptes Fig. . We also23. These processes occur early in the asexual cycle and attenuate ring stage parasite killing. We found marked overlaps between the ARTP and the genes involved in the transcriptional response to the direct action of artemisinin observed in vivo  from the initial transcriptional response of P. falciparum parasites to the direct effect of the drug in which the artemisinin induced transcriptional changes became constitutive. Indeed, there are many overlaps on the gene-by-gene level between gene expression changes in the baseline ARTP and genes induced/suppressed by artemisinins in vivo and in vitro , and elongation factors EF-1-gamma whose proteins products were found to be direct artemisinin targets in the parasite cells53. Related to this, we observed significant transcriptional changes for several determinants of protein turnover and protein folding from which DnaJ proteins and the T-complex-protein 1 subunit are likely to be direct protein targets of the artemisinin drugs , spermidine synthase, pyruvate kinase, and hexokinase53. This also applies to the P. falciparum pyridoxal kinase, PDXK, whose mammalian orthologue, was shown to interact directly and being inhibited by artesunate54. Finally, the ARTP contains strong baseline-level upregulations of several transcripts encoding proteins exported to the host erythrocyte. These are implicated in host cell remodelling and/or host-parasite interactions that are paralleled by drug-induced transcriptional responses  of artemisinin resistance suggested by previous in vitro studies , translation, translational translocation, and protein transport , which subsequently leads to an increase in PI3P vesicles that alters the rate of hemoglobin endocytosis62. A set of genes involved in vesicular trafficking and the underlying lipid metabolism was also found to be transcriptionally associated with artemisinin resistance in this study  suppressed in susceptible parasites but also several downregulated transcripts pertaining to ECT  and TCA-metabolism  and eGMS (Cambodia Laos and Southern Vietnam), the latter of which likely provided the supporting genetic background for artemisinin resistance40. Moreover, substantial founder population effects were detected in eGMS also likely as a result of drug selection71. The main example represents the recent selective sweep of the KEL1/PLA1 parasite line throughout eGMS during 2013–2016; prior to the TRACII epidemiological survey43. Indeed, the TRACII P. falciparum cohort used in this study reflected both the w/eGMS segregation and KEL1/PLA1 selection sweep. This provided us with the unique opportunity to study artemisinin resistance-associated transitional changes at its origin, but also presented a major challenge due to the complex population structure. To overcome this, we implemented multiple steps during data analysis. In addition of the FPR-based differential gene expression pipeline . These results were reproduced in the TRACII only partially. However, multiple genes related to UPR, REDOX as well as IDC progression identified amongst the ARTP genes signal that these pathways still play a key role in artemisinin resistance. Future GWAS and TPAS using broader epidemiological cohorts will provide deeper insight into the multifaceted artemisinin resistance mechanism relevant to clinical settings.Since its emergence in 2009, artemisinin resistance exerted strong selection effects on the GMS ine Fig. , we carrine Fig. . Notwithine Fig.  that for; with the exception of the PfK13 SNP32. Although it is still possible that more mutations in the coding regions may contribute to artemisinin resistance they were not detected because of typical sample size limitations of GWAS39; in the context of a highly complex P. falciparum population structure in the GMS71. Our result strongly suggests that many causative mutations might be situated within DNA regulatory elements affecting the regulation of gene expression. It was previously shown that the majority of sequence polymorphisms in P. falciparum occurs in the noncoding regions accumulated particularly around core promoter regions as Insertion/Deletion within Short Tandem Repeats (STR)72. The high AT content (~85%) of the P. falciparum noncoding regions hampered most whole-genome sequencing efforts on field samples done up until now. However, the results of this study make a case for synergizing GWAS and TPAS (combined into eQTL studies) as a potential strategy to monitor the spread and evolution of malaria drug resistance in the future genomic epidemiological surveys.In contrast to transcriptome-based analyses, GWAS have until now failed to identify such factors making limited observations of direct links between the molecular effectors of artemisinin resistance in vivoAll the samples used in this study were collected with written informed consent from the patients or their legal guardians. All protocols were approved by the appropriate national ethics committees or the Oxford Tropical Research Ethics Committee.Plasmodium falciparum malaria were recruited in eight countries. In the presented study 680 patients’ samples from 6 countries and 13 sites across Southeast Asia Greater Mekong Region were analyzed . All details regarding samples collection, site locations, inclusion criteria, parasitemia assessment, and given treatments were published previously2. In brief, samples were collected from venous blood of malaria-infected patients at admission to the clinic  and 6 h after their respective treatment . Depending on their location, patients were randomly assigned different double or triple Artemisinin Combination Therapy (ACT) as follows: Thailand, Cambodia, Vietnam, and Myanmar - dihydroartemisinin-piperaquine or dihydroartemisinin-piperaquine plus mefloquine; Cambodia - artesunate-mefloquine or dihydroartemisinin-piperaquine plus mefloquine; Laos, Myanmar, Bangladesh - artemether-lumefantrine or artemether-lumefantrine plus amodiaquine. Following collection, blood was subsequently depleted of plasma and buffy coat. Hour 0 samples were additionally purified from white blood cells (WBC) using CF11 columns using the Worldwide Antimalarial Resistance Network (WWARN) protocols. 0.2–0.5 mL of packed red blood cells (pRBC) from each sample was homogenized by mixing with 10 volumes of TRIzol (Invitrogen), frozen at −80 °C, and then shipped to Nanyang Technological University, Singapore.All samples used in this study were collected from patients involved in Tracking Resistance to Artemisinin Collaboration 2 (TRAC2), a multi-site trial that took place between Aug 7, 2015, and Feb 8, 2018. The total number of 1100 patients with uncomplicated 42.All clinical samples mixed with TRIzol were supplemented with 5 volumes of chloroform (Merck) and processed as per the manufacturer’s instructions to obtain phase separation. Top (aqueous) phase was mixed 1:1 (v/v) with 100% analytical grade ethanol (Merck) and RNA was extracted using the Direct-Zol-96 extraction kit (Zymo) following the manufacturer’s guide. For this study, we have adopted the RNA extraction kit to a high-throughput EVO 200 robotic platform (Tecan). RNA integrity was assessed using Agilent Bioanalyzer (RIN) (Agilent), concentration was measured using Qubit RNA Broad Range kit (Invitrogen) and RNA contamination was estimated with Nanodrop (Thermo Fisher)73. In brief, oligo-dT30 (IDT Asia) was used as a primer to enrich for mRNA and avoid ribosomal rRNA amplification . The amplified product was purified using Ampure XP magnetic beads (Beckman Coulter) on Microlab Nimbus robotic platform (Hamilton) and 100 ng of cDNA was used for subsequent 10 rounds of amplification to generate aminoallyl-coupled cDNA for the hybridizations75. 17ul (~5 µg) of each Cy-5-labelled  cDNA of patient’s sample and an equal amount of Cy-3-labelled  cDNA of the reference pool were then hybridized together on our customized microarray chip using commercially available hybridization platform (Agilent) for 20 h at 70 °C with rotation at 10 rpm. Microarrays were washed and immediately scanned using Power Scanner (Tecan) at 10 µM resolution and with automated photomultiplier tubes gain adjustments to balance the signal intensities between both channels. The reference pool used for microarray was a mixture of 3D7 parasite strain RNA collected every 6 h during 48 h of the full intraerythrocytic developmental cycle.For each sample, 250 ng of total RNA was used for subsequent reverse transcription reaction followed by 19 rounds of PCR amplification using modified Smart-Seq2 methodPurified 1 ng of cDNA was used to generate sequencing libraries using Illumina Nextera XT kit as described in manufacturer’s protocol. Purified cDNA libraries were analyzed on the Bioanalyzer High-Sensitivity DNA chips (Agilent), subsequently pooled (20–24 samples per lane) and sequenced on Illumina HiSeq4000 platform generating 150 bp paired end reads with 110 Gb data output generated per lane.2, 3% O2, and 92% N2. Culture media were replenished every 24 h. Freshly washed pRBC were added to the culture when necessary. Both parasitemia and parasite morphology were assessed by microscopic examination of blood smears stained with Giemsa (Sigma).Plasmodium falciparum 3D7 strain was obtained from BEI Resources (MRA-102) and maintained in purified human pRBC in RPMI 1640 medium (Gibco) supplemented with Albumax I (Gibco) (0.25%), hypoxanthine (Sigma) (0.1 mM), Sodium bicarbonate (Sigma) (2 g/L), and gentamicin (Gibco) (50 μg/L). Cultures were kept at 37 °C with 5% CO42. In brief, prior to time-point experiment, parasites were double-synchronized with 5% sorbitol solution to achieve a synchrony of +/−6 h and cultured under constant agitation. For a sampling of highly synchronous parasites during the asexual life cycle, the first time point was considered as the TP1 (Time Point 1) when > 95% of early ring stage parasites (approx. 4 hpi) were present in the culture. Starting from TP1, parasites were collected every 2 h for 25 successive time points. The total of 24 time points were used here to build an asexual reference transcriptome generated using two different platforms: microarrays and RNA-seq.IDC reference time course data were obtained from the study published previouslyThe transcriptome was generated by quantifying the total RNAs for each parasite sample using two-colour microarray or RNA-seq or both. Additionally, control samples derived from ring stages 3D7 lab strain were quantified together with those clinical samples in microarray method. It was made up of a total of 30 samples for pre-treatment and 27 samples for post-treatment.76. Missing values were assigned to weak signal probes showing median foreground intensity less than 1.5-fold of the median background intensity at either Cy5 (sample RNA) or Cy3 (reference pool RNA) channel. Each gene expression was estimated as the average of log2 ratios (Cy5/Cy3) for probes representing it.The raw data of microarray was acquired using GenePix Pro v6.0 software (Axon Instruments). Then the signal intensities were Loess-normalized within arrays followed by quantile-normalization between samples/arrays using Limma package of R77 was used to perform alignment to the P.falciparum genome downloaded from geneDB database in version of March 2018. BEDTools78 was applied to calculate the read counts for each annotated transcript based on only uniquely mapped reads with pairs in proper orientation. At last, the transcriptional level was estimated for each gene by calculating the Fragments per kilo base per million mapped reads (FPKM) at the gene.For RNA-seq, raw reads were trimmed to remove sequencing adapters, amplification primers, and low-quality bases from 3′ ends. HISAT2 aligner79. In brief, the expression value of a gene, Eg, is assumed as a sum of the expression in asexual stage of hpi h, denoted as xg(h), and the average expression of sexual stages (from the 5th to 12th day during gametocytes developing), denoted as zg, mixed in certain proportions. The mixture model is presented in formula as:εg is the associated error term. xg(h) is estimated by the reference transcriptome generated during asexual intraerythrocytic cycle in P. falciparum 3D7 strain which has a total of 25 time points with 2 h intervals of 48 h, accessible via GEO accession number GSE149865. To obtain a higher resolution of the asexual reference transcriptome for stages estimation, 24 time points of the data (the 9th time point removed due to its big dissimilarity to others) were interpolated into 240 data points by smooth splines method. zg is estimated by the identical reference gametocyte transcriptome described in Zhu et al’s work9, accessible via GEO accession number GSE121505. In practice, samples were normalized by control samples across batches by adding a scaling factor for each batch before applying the prediction model. The scaling factor was estimated for each batch by calculating the average expression difference between controls and the reference at corresponding ages. Finally, we evaluated the log-likelihood values over a grid of mixtures for varying the gametocyte proportion α, and hpi h, for each sample. The results of estimated hpi and gametocytes proportion were listed in Supplementary Data For each parasite sample, we estimated the asexual age  and the proportion of gametocytes using the method described in Zhu et al’s and Lemieux et al’s worksrho. Second, we removed samples displaying extremely low intensities (mode of the intensity < 10) of Cy5 signal on the microarray 0 h transcriptome data set. We inspected the top 12 PCs for the following association analysis because each of the rest PCs contributed to < 1% of the overall transcriptome variations. The top 12 PCs were tested against all the clinical and technical factors collected during sample processing to assess the potential environmental influence on the population transcriptomic structure. For the factors represented in categorical variables like sampling sites, parasite lineages, and patient’s gender, ANOVA test was used for assessing the statistical significance of associations. For other factors in continues/discrete variables like parasitemia, hpi and patient enrolment time etc., linear regression was used to test the statistical relationship between each factor and each PC with Spearman’s rho calculated for each pair of variables as well. The results are visualized in Supplementary Fig. Principle Component Analysis (PCA) was applied to the p = 7.96e−283) correlated to the top PC (PC1) with showing a high Spearman’s rho as 0.87 in the (bl)0 hr data set by microarray and 0.85 by RNA-seq. PCA of all samples including both (bl)0 h and (tr)6 h resulted in similar high correlations with displaying rho as 0.78 in microarray and 0.76 in RNA-seq  can interact with other experimental or environmental conditions to drive the minor (compare to hpi) differences in parasites transcriptome. For this study, we considered only hpi as the major factor contributing to gene expression variation across nature parasites and used it as a major predictor variable in modelling gene expression for the further regression analysis. The potential for expression variation caused by other factors, like sampling site and parasite lineage, are analyzed for particular genes associated to resistance in the following studies.The regression analysis revealed that parasite age (hpi) significantly . The resulted PC1 clearly distinguishes the sexual and asexual stages, as well as the PC2, reflects the ring-stage parasite development as it obviously separates the 8 ring-stage transcriptomes with 2 h difference in between. With the space of PC1 vs PC2, parasite age can be visualized without bias by transcriptome projection. Therefore, we normalized all the clinical samples to the centre transcriptome derived from the above PCA and rotated it according to the PC1 and PC2 loadings. At last, the 577 pre-treatment samples and 459 post-treatment samples were projected onto the PC space showing an obvious age window shift in the parasites after drug treatment Fig. .The SNP information was provided by the Wellcome Sanger Institute. We extracted out a high-quality set of 7009 SNPs for the population stratification analysis here. The filtering criteria are as follow: (1) each polymorphism was covered by at least 10 reads; (2) the genotyping quality score was greater than 30 by GATK; (3) the minor allele count was at least 5 across the genotyped samples we studied, and the minor allele frequency was at least 0.05. (4) Only SNPs showing two alleles with our studied samples are included; (5) only SNPs in coding regions were included; (6) samples with more than 50% high quality SNPs missing were removed from this analysis. At last, 7009 SNPs and 528 isolate samples were used to determine the population stratification using Plink multidimensional scaling function with IBS pairwise distances. UMAP algorithm was applied to visualize the result in Fig. D(within-cluster), and between-cluster, D(between-cluster) individuals was used to indicate genetic differences between clusters and compare to the ratio calculated from transcriptional distance matrix.To measure the genetic differences between the ARTP clusters Fig. , we consE denotes gene expression across samples, f(hpi) denotes the function of hpi which is loess regression here, hf denotes the variable of PC½, β0,1,2 represents the parameters of intercept and slopes to predict and ε is the error terms. Therefore, the resulted p-value of this regression analysis reflects the relationship between expression residuals of age fitting to the half-lives (PC½), alternatively the expression-resistance association independent of age.Transcriptome-phenotype association study (TPAS) was carried out in order to call for the marker candidates whose mRNA levels were positively or negatively associated with artemisinin resistance. Since parasite age contributed the most to expression variation across clinical samples, we designed a generalized additive model with the age/hpi specified in a loess function to test the expression-resistance association over the dynamic relationship between expression and age. The regression analysis formula for each gene is represented as:p distribution based on testing the association of expression to randomized PC½ values  and FPR < 0.05.We applied this approach to microarray and RNA-seq data separately. The results agreed to each other with showing a high correlation (Pearson correlation coefficient = 0.68) of the average expression fold change of resistant/susceptible between the two datasets . This result significantly overlaps that from TRACII with 14 upregulation and 12 downregulation in common .We applied the same TPAS pipeline to the TRACI data and reanalyzed 824 samples collected during 2011–1013. It resulted in 61 expression upregulation and 63 downregulation associated to artemisinin resistance with the identical criteria above  for each parasite sample using the 156 marker genes. To obtain expression levels with hpi effects maximumly reduced, we second extracted out the expression residuals from the formula (2) with the hpi function fitting only for each gene. The expression residuals were normalized for the 323 resistant parasite samples (PC½ > 5 h and from eGMS) against that of 104 susceptible samples (PC½ < 5 h and from wGMS) by calculating z-scores to represent the number of standard deviations by which the expression of the studied gene in resistant parasite was above or below the mean in susceptible parasites. Next, Euclidean distance was applied to the similarity matrix of ARTP to construct the sample distance matrix and the Ward’s method was used to obtain the dendrogram of clustering tree for the 323 resistant parasite samples Fig. . The sixD(within-cluster), and between-cluster, D(between-cluster) individuals was used to indicate transcriptional differences between clusters and compare to the ratio calculated from a genetic distance matrix.To measure the transcriptional differences between clusters, we used the Pearson distance of the ARTP to construct the transcriptional distance matrix. And the pair-wise distance was calculated between individuals within each cluster and between clusters. The ratio of the average distance between within-cluster, P.falciparum parasites by comparing the (tr)6 h sample set to the (bl)0 h sample set. Grouping the (bl)0 h samples by lineage and resistance status level  revealed two largest groups which are KEL1PLA1 parasites with PC½ > 5 h  and PfK13 WT parasites with PC½ < 5 h (29% of 577). Excluding the lineage unknown samples, all the rest groups contain < 6% of the total samples. For this study, we performed the comparative transcriptional analysis specifically for the PfK13 WT samples with PC½ < 5 h (the susceptible parasites) and the KEL1PLA1 samples with PC½ > 5 h (the resistant parasites).The transcriptional response to artemisinin was inspected for the field (tr)6 h and (bl)0 h samples as:treatment indicates patient treatment condition which is pre-treatment ((bl)0 h) or post-treatment ((tr)6 h), and resistance_status indicates PC½ greater/smaller than 5 h. To discover the distinct transcriptional response for resistant and susceptible parasites individually, we performed a comparative analysis for each parasite group (resistant/susceptible). We aimed to define top drug-response genes for each parasite group independent of age and batch effects (due to the collection and transportation issue with the (bl)0 h and (tr)6 h sample set, the treatment condition here unavoidably confounded with the batch of transcriptome measurement). To achieve that, we first extracted the expression residuals from the model ((bl)0 h to 180 (tr)6 h samples for resistant parasites and 168 (bl)0 h to 130 (tr)6 h samples for susceptible parasites with microarray measurement. To balance the sample size differences, subsampling was applied to 130 samples 100 times per gene to obtain the p-value at 80% confident level. For the multiple test correction, we calculated FDR for each gene using the distribution of 500,000 null p-values generated from expression permutation based on all bl)0 h and (tr)6 h samples. To control the effect of treatment/batch, the structure of the treatment condition was maintained during each time permutation.We adjusted the above regression model of formula  to bette(bl)0 h to 55 (tr)6 h samples for resistant parasites and 57 (bl)0 h to 46 (tr)6 h samples for susceptible parasites. And the p-values were obtained by 100 subsampling of 46 samples per gene. The result was merged with that from microarray for susceptible and resistance group, respectively, at FDR < 0.05.We repeated the same analysis with the RNA-seq data. Mann–Whitney test was conducted to compare 67 p < 1e−14). Significant common response genes were observed between the resistant and susceptible parasite samples which included up to 12 induced and 34 repressed genes (Fig. By this approach, we identified 20 significantly induced genes and 73 repressed genes upon drug in the KEL1PLA1 resistant parasites and 33 induced genes and 106 repressed genes in the WT susceptible parasites (FDR < 0.05, corresponding p < 8.5e−6. For the differential expression analysis, Mann–Whitney test was applied for group comparison. Differentially expressed genes were defined at FDR < 0.05 and the corresponding p < 1e−14. Subsampling method was used to balance sample size difference and the p-values were obtained at 80% confidence level. All details regarding the statistics of this study can be found above in the description of each respective analysis.We analyzed 577 samples pre-treatment with 459 samples post-treatment using microarray method and 188 samples pre-treatment with 159 samples post-treatment using RNA-seq method. Due to the nature of this study as a part of the larger clinical trial, no biological replicates were used. In this study, binomial test or hypergeometric test was applied to the enrichment analyses; ANOVA test, Pearson Correlation Coefficient, and linear regression were performed for association or correlation testing between factors. We used FDR and FPR to help control for false positives that resulted in 156 resistance-associated marker genes defined at FDR < 0.05, FPR < 0.05, and the corresponding Further information on research design is available in the Supplementary InformationEditorial Assessment ReportList of Supplementary InformationSupplementary Data 1Supplementary Data 2Reporting Summary