dataesr/scientific-paragraphs-categorization huggingface dataset:
Although many of the above theoretical methods are "parameter-free," selecting the best method for choosing an effective dimension for transcriptomic data can be challenging in the absence of a clearly defined validation strategy. One possible approach to overcome this limitation is to apply the same computational method to multiple transcriptomic datasets derived from the same tissue and disease. In this situation, it is reasonable to expect that a matrix factorization method should detect similar signals in all datasets. By taking advantage of the rich collection of public data such as The Cancer Genomic Atlas (TCGA)  [17]  and Gene Expression Omnibus  [18] , it is possible to compare and contrast the parameters of different gene expression analysis methods such as ICA. In this study, we used TCGA pan-cancer (32 different cancer types) transcriptomic datasets and a set of six independent breast cancer transcriptomic datasets to evaluate the effect of the number of computed independent components on reproducibility and biological interpretability of the obtained results.
We evaluated the reproducibility of ICA on three aspects: First, we analyzed the stability of the computed components with respect to multiple runs of ICA; second, we analyse the conservation of the computed components by varying the choice of the reduced data dimension; and third, we consider the reproducibility of the resulting set of ICA metagenes across multiple independent datasets. Our reproducibility analysis thus explores 13,027 transcriptomic profiles in 37 transcriptomic datasets, for which more than 100,000 ICA decompositions have been computed. We finally defined a novel criterion adapted for choosing the effective data dimension for ICA analysis of gene expression, which takes into account the global properties of transcriptomic multivariate data. The Maximally Stable Transcriptome Dimension (MSTD) is defined as the maximal dimension where ICA does not yet produce a large proportion of highly unstable signals.
Definition of component reproducibility measures used in this study Stability of an independent component, in terms of varying the initial starts of the ICA algorithm, is a measure of internal compactness of a cluster of matched independent components produced in multiple ICA runs for the same dataset and with the same parameter set but with random initialization. The exact index used for quantifying the clustering is documented in the Methods section. Conservation of an independent component in terms of choosing various orders of ICA decomposition is a correlation between matched components computed in two ICA decompositions of different orders (reduced data dimensions) for the same dataset. Reproducibility of an independent component is an (average) correlation between the components that can be matched after applying the ICA method using the same parameter set but for different datasets.
Maximally stable Transcriptome dimension (MSTD), a novel criterion for choosing the optimal number of ICs in transcriptomic data analysis We used 37 transcriptomic datasets to analyze the stability and reproducibility of the ICA results conditional on the chosen number of components. ICA has been applied separately to 37 cancer transcriptomic datasets following the ICA application protocols as described in Methods. The proposed protocol depends on a fundamental parameter M (effective dimension of the data and, at the same time, the number of computed independent components) whose effect on the stability of the ICs is investigated. For each transcriptomic dataset, the range of M values 2-100 has been considered. For each value of M, the data dimension is reduced to M by PCA and then data whitening is applied.
Subsequently, the actual signal decomposition is applied in the whitened space by defining M new axes, each maximizing the non-gaussianity of data point projections distribution. For transcriptomic data, ICA decomposition provides: (a) M metagenes ranked accordingly to their stability in multiple runs (n = 100) of ICA; and (b) a profile of stability of the components (set of M numbers in [0,1] range in descending order). Considering the largest dataset METABRIC as an example, the behavior of the stability profile as a function of M is reported in Fig. 1a. The results for stability analysis for other breast cancer datasets are similar (See Additional file 1: Figure  SF2 ). To recapitulate the behaviour of many stability profiles, the average stability of the first k top-ranked components S M (k) is used (See Fig.
We call this estimate Maximally Stable Transcriptome Dimension (MSTD) and in the following we investigated its properties. We note that, as in various information theorybased criteria (BIC, AIC), this estimate is free of parameters (thresholds), and it only exploits the property of the qualitative change in the character of the stability profile in higher data dimensions for transcriptomic data. In most of the cancer transriptomics datasets used in our analysis, MSTD was found to correspond roughly to the average stability profile S M total ≈ 0. 6 (Additional file 1: Figure  SF2 ). In Fig. 1d , the dependence of MSTD on the number of samples contained in the transcriptomic dataset is investigated for all the 37 transcriptomic datasets. As shown in Additional file 2: Figure  SF1 , MSTD increased with the number of samples; however, this trend was weaker than other estimates of an effective dimension such as Kaiser rule and broken stick distributionbased data dimension estimates.
Underestimating the effective dimension (M < MSTD) leads to a poor detection of known biological signals Previous large-scale ICA-based meta-analyses  [3]  have shown that some of the ICs derived from the decomposition of a cancer transcriptomic data were clearly and uniquely associated with known biological signals. For example, one of these signals was the one connected to proliferative status of tumors. Another example was given by the signals related to the infiltration of immune cells that were also strongly heterogeneous across cancer patients. We have checked the reproducibility of several metagenes obtained in previous meta-analyses  [3]  for all ICA decompositions as a function of M. For this analysis, we employed the METABRIC breast cancer dataset, which was not included in the input data of the previous publication  [3]  and thus it had not been used to derive the metagenes of that work.
In addition, we checked how the significance of intersections between the genes defining the components and several reference gene sets (produced independently of the ICA analyses) behaved as a function of M. We applied the previously developed correlation-based approach to match previously identified metagenes with the ones computed for a new METABRIC dataset (see Methods section). The components were oriented accordingly to the direction of the heaviest tail of the projection distribution. When matching an oriented component to the previously defined set of metagenes, we verified that the resulting maximal correlation should be positive, i. e. large positive weights in one metagene should correspond to large positive weights in another metagene. One of the most important case studies is reproducibility of the "proliferative" metagene in different data dimensions.
3a  and Additional file 1: Figure  SF2 ). To check the biological significance of the outlier genes, we considered as a case study the higher-order (M = 100) ICA decomposition of the METABRIC breast cancer dataset. We collected all those genes found to be drivers of at least one "small gene set-driven" component. We obtained in this way a set of 98 genes listed in Additional file 3: Table  ST2. This list appeared to be strongly enriched (p-value = 10 -12 after correction for multiple testing) in the genes of the signature DOANE_BREAST_CANCER_ESR1_UP "Genes upregulated in breast cancer samples positive for ESR1 compared to the ESR1 negative tumors" from Molecular Signature Database  [21]  and several other specific to breast cancer gene signatures. This analysis thus suggested that at least some of the identified "small gene set-driven" components are not the artifacts of the ICA decomposition, but they can be biologically meaningful and reproducible in independent datasets (Fig.
3a , right part). Most stable components with stability rank ≤ MSTD have more chances to be reproduced across independent datasets for the same cancer type It would be reasonable to expect that the main biological signals characteristic for a given cancer type should be the same when one studies molecular profiles of different independent cohorts of patients. Therefore, we expect that for multiple datasets related to the same cancer type, ICA decompositions should be somewhat similar; hence, reciprocally matching each other. We called this expected behavior "reproducibility," and here we studied this by applying ICA to six relatively large breast cancer transcriptomic datasets. Of note, these datasets were produced using various technologies of transcriptomic profiling (Additional file 4: Table  ST1 ).
To identify the reproducible components, we applied the same methodology as in the previously published ICA-based gene expression meta-analysis  [3]. We decomposed the six datasets separately and then constructed a graph of reciprocal correlations between the obtained metagenes. Correlation between two sets of components is called reciprocal when a component from one set is the best match (maximally correlated) to a component from another set, and vice versa (see Methods for a strict definition). Pseudo-cliques in this graph, consisting of several nodes, correspond to reproducible signals detected by ICA. As shown in Fig. 3 , multiple reproducible signals were identified in the analysis. Some of them correspond to signals already identified in  [3]  (e. g. , cell cycle, interferon signaling, microenvironment-related signals), and some correspond to newly discovered biological signals (e.
g. , ERBB2 amplicon-associated). Some other pseudocliques are associated with "small gene set-driven" components (frequently, one gene-driven), such as TFF1-3associated or SCGB2A1-2-associated components. The genes driver of reproducible and "small gene setdriven" components (S100P, TFF1, TFF3, SCGB2A1, SCGB1D2, SCGB2A2, LTF, CEACAM6, CEACAM5 being most remarkable examples) have been in detail, to further check their biological interest. They were found to be the genes known to be associated with breast cancer progression  [22]. For example, seven of the nine previously mentioned genes form a part of a gene set known to be up-regulated in the bone relapses of breast cancer (M3238 gene set from MSigDB). To quantify the reproducibility of the components, we computed a reproducibility score. It is a sum of correlation coefficients between the component and all reciprocally correlated components from other datasets.
By construction, the maximum value of the score is 5, which meant that a component with such a score would be perfectly correlated with the reciprocally related components from five other datasets. We studied the dependence of this score as a function of the relative to MSTD component stability-based rank (Fig. 3b ). From this study, it follows that even for the high-order ICA decompositions, the components ranked by their stability within MSTD range, have an increased likelihood of being reproduced in independent datasets collected for the same cancer type. To show that the stability-based ranking of genes is more informative compared with the standard rankings of independent components, we performed a computational analysis in which we compared the stability-based ranking with the rankings based on non-gaussianity (kurtosis) and explained variance.
These two measures are frequently used to rank the independent components  [6]. From Fig. 3b  it is clear that the stability-based ranking of independent components corresponds well to the reproducibility score, while two other simpler measures do not. It can also be shown that the total number of reciprocal correlations with relatively large correlation coefficients (|r| > 0. 3) between ICA-based metagenes computed for several independent datasets is significantly bigger when the component stabilization approach is applied (Additional file 5: Figure  SF4 ). This proves the utility of the applied stabilization-based protocol of ICA application to transcriptomic data.
Computing large number of components (M> > MSTD) does not strongly affect the most stable ones We lastly used ICA decompositions of 37 transcriptomic datasets to compare the ICA decompositions corresponding to M = MSTD with the higher-order decompositions, M = 50 or M = 100. It was found that the components calculated in lower data dimensions can be relatively well matched to the components from higher-order ICA decompositions (Fig. 4 ). More precisely, 90% of the components defined for M = MSTD had a reciprocal best matched component in the M = 100 ICA decomposition. Most stable components had a clear tendency to be reproduced with high correlation coefficient (r > 0. 8). Only 10% of the components had only non-reciprocal or too small correlations between two decompositions (in other words, not conserved in higher-order ICA decompositions).
This organization of the gene expression space is captured in the distribution of ICA stability profiles for varying M, which allowed us to define the Maximally Stable Transcriptome Dimension (MSTD) value, roughly reflecting the dimension of the dense part of the gene expression distribution. In one hand, when underdecomposing (compressing too much by dimension reduction, M < MSTD) a transcriptomic dataset, the resulting independent components are hard to interpret. In the other hand, overdecomposing transcriptomes (choosing the effective dimension much bigger than MSTD) is not dramatically detrimental: one can choose to explore a relatively multi-dimensional subspace of a transcriptomic dataset, taking into account that applying matrix factorization methods in higher dimensions becomes computationally challenging and prone to bad algorithm convergence.
Nevertheless, higher-order decompositions might allow capturing the behavior of some tissuespecific or cancer type-specific biomarker genes from the sparse part of the distribution, which can be found reproducible in other independent studies. In our computational experiments, we selected 100 as the maximum order of ICA decomposition (M) to test. However it is possible to examine even higher orders of ICA decompositions, reducing the data to more than 100 dimensions, but not more than the total number of samples, of course. In practice, computing ICA in such high dimension leads to significant deterioration of the fastICA algorithm convergence, so exploring M > 100 Fig. 4  Conservation or non-conservation of independent components in higher than MSTD dimensions. ICA decomposition in MSTD data dimension is compared with the one of dimension 50 and 100.
Conclusion By using a large body of data and comparing 0. 1 million decompositions of transcriptomic datasets into the sets of independent components, we have checked systematically the resulting metagenes for their reproducibility in several runs of ICA computation (measuring stability), for their reproducibility between a lower order and higher-order ICA decompositions (conservation), and between metagene sets computed for several independent datasets, profiling tumoral samples of the same cancer type (reproducibility). From the first of such analyses, we formulated a minimally advised number of dimensions to which a transcriptomic dataset should be reduced called Maximally Stable Transcriptome Dimension (MSTD). Reducing a transcriptomic dataset to a dimension below MSTD is not optimal in terms of the interpretability of the resulting ICA components.
We showed that for relatively large transcriptomic datasets, MSTD could vary from 15 to 30 and that the number of samples matters relatively weakly. From the second analysis, we concluded that the suggested protocol of ICA application to transcriptomic data is conservative, i. e. , the components identified in a higher dimension (for example, in one hundred dimensional space) can be robustly matched with those components obtained in the dimensions comparable with MSTD. Moreover, we described an effect of interpretable component splitting in higher dimensions, leading to detection of finer-grained signals (e. g. , related to the decomposition of the immune infiltrate in the tumor microenvironment). At the same time, the application of ICA in high dimensions resulted in a greater proportion of unstable components, many of them were driven by expression of small (one to three members) gene sets.
Yet, some of these small gene set-driven components were highly reproducible and biologically meaningful. From the third analysis, we established that the used protocol of ICA application, with ranking the independent components based on their stability, prioritized those components having more chances to be reproduced in independent transcriptomic datasets. Moreover, when ICA was applied in higher dimensions, the components within the MSTD range still have more chances to be reproduced. In sum, our results confirmed advantageous features of ICA applied to gene expression data from different platforms, leading to interpretable and quantifiably reproducible results. Comparing ICA analyses performed in various dimensions and multiple independent datasets for the same cancer types allow prioritizing of the most reliable and reproducible components which can be quantitatively recapitulated in the form of metagenes or the sets of top contributing genes.
Transcriptomics cancer data used in the analysis Expression data derived for 32 solid cancer types (ACC, BLCA, BRCA, CESC, CHOL, COAD, DLBC, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LGG, LIHC, LUAD, LUSC, MESO, OV, PAAD, PCPG, PRAD, READ, SARC, SKCM, STAD, TGCT, THCA, THYM, UCEC, UCS, UVM) were downloaded from the TCGA web-site and internally normalized. Normalized breast cancer datasets from CIT, BCR, WANG, BEKHOUCHE were re-used from the previous study  [3]. Normalized METABRIC breast cancer expression dataset was downloaded from cBioPortal at this link http://www. cbioportal. org/study? id=brca_metabric. When it was not already the case, the data values were converted into logarithmic scale. The list of breast cancer transcriptomic datasets used for reproducibility study is available in Additional file 4: Table  ST1.
ICA decompositions computation We applied the same protocol of application of ICA decomposition as in  [3]. In the ICA decomposition X ≈ AS, X is the gene expression (sample vs gene) matrix, A is the (sample vs. component) matrix describing the loadings of the independent components, and S is the (component vs. gene matrix) describing the weights (projections) of the genes in the components. To compute ICA, we used the fastICA algorithm  [1]  accompanied by the icasso package  [23]  to improve the components estimation and to rank the components based on their stability. ICA was applied to each transcriptomic dataset separately. For each analysed transcriptomic dataset, we computed M independent components (ICs), using pow3 nonlinearity and symmetrical approach to the decomposition, where M = [2…50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100].
In those cases, when M exceeded the total number of samples, the maximum M was chosen equal to 0. 9 multiplied by the number of samples (moderate dimension reduction improves convergence). We found that the MATLAB implementations of fastICA performs superior to other implementations (such as those provided in R  [24] ). The computational time required for performing all the 0. 1 million ICA decompositions used in this study is estimated in ~1500 single processor hours using MATLAB while other implementations would not make this analysis feasible at all. In our analysis, we used Docker with packaged compiled MATLAB code for fastICA together with MATLAB Runtime environment, which can be readily used in other applications and does not require MATLAB installed  [25]. C N ) compute the stability index using the following formula I q C k ð Þ ¼ 1 C k j j 2 X i;j∈C k |r ij |- 1 |C k | P l≠k |C l | X i∈C k X j∉C k |r ij | where |C k | denotes the size of the kth cluster.
3. 4) Compute the average stability index for M clusters: S M ð Þ ¼ 1 M X k I q C k ð Þ 4) Select the MSTD as the point of intersection of the two lines approximating the distribution of stability profiles (Fig. 1a ). The lines are computed using a simple k-lines clustering algorithm  [26]  for k = 2, implemented by the authors in MATLAB, with the initial approximations of the lines matching the abscissa and the ordinate axes of the plot. The index used in 3. 3 is a widely used index of clustering quality defined as a difference between the average intra-cluster similarity and the average inter-cluster similarity. In  [9]  this index was introduced to estimate the quality of clustering of independent components after multiple runs with random initial conditions, and tested in application to fMRI data.
Comparing metagenes computed for different datasets and in different analyses Following the methodology developed previously in  [3] , the metagenes computed in two independent datasets were compared by computing a Pearson correlation coefficient between their corresponding gene weights. Since each dataset can contain a different set of genes, the correlation is computed on the genes which are common for a pair of datasets. Note that this common set of genes can be different for different pairs of datasets. The same correlation-based comparison was done with previously defined and annotated metagenes. We computed the correlation only between those genes having projection value more than 3 standard deviations in the identified component. When comparing two sets of metagenes A = {A 1 ,…,A M } and B = {B 1 ,….
Molecular identification based on 16 s rRNA gene sequence comparison was performed  [6]  and showed that the isolate was Brevibacterium massiliense with 99. 60% of similarity with the genbank sequence NR116479. Susceptibility of the strain was tested using the disk diffusion method on Mueller-Hinton agar plates incubated at 37 C for 24 h. Our isolate was susceptible to amoxicillin, amoxicillin clavulanic acid, cefixime, imipenem, ofloxacin and vancomycin. The mother was contacted, and the boy had no fever and was in a good healthy state. No more antimicrobials were prescribed. We report here the second case of isolation with B. massiliense in human, in blood culture. B. massiliense was isolated and this new species was described for the first time in 2009  [8]  and his draft genome sequence was performed in 2012  [9].
Sequence analyses Sequences were deposited in GenBank and accession numbers are provided in Table  1. The VP1 genetic sequences were aligned in Seaview 4. 6  [10]  using Muscle algorithm  [9]. Best-fitting evolutionary models were determined by JModelTest 2. 1  [8]  or by ProtTest 2. 4  [1]  using the corrected version of the Akaike Information Criterion (AICc). The phylogeny of VP1 was performed by Maximum Likelihood (ML) inference using the model GTR + G + F with Seaview 4. 6  [10]. The robustness of nodes was assessed with 500 bootstrap replicates. ML analysis of the amino acid sequences was performed using the model JTT + I + G with Seaview 4. 6  [10]. The robustness of nodes was assessed with 500 bootstrap replicates. Sequence polymorphism was investigated using the DnaSP 5. 10. 01 package  [17].