Dataset

This dataset was created by Hemanth S

Contents

These json documents contain mappings for materials science entity normalization. Each entity is mapped onto the most frequently occurring synonym that is not an acronym.We provide entity normalization for materials science properties (pro), applications (apl), sample descriptors (dsc), symmetry/phase labels (spl), synthesis methods (smt), and characterization methods (cmt).Each term will have a "most common" entity to which it can be mapped. Sub entities are also included which have also been normalized.*Please note: entities that occur infrequently in our corpus are unlikely to be normalized (and less likely to be normalized correctly). In-line with Zipf's law for NLP, infrequently occurring entities make up the largest portion of unique entities in the corpus, and so a large fraction of entiites in these json files are not normalized. However, frequently occurring terms like "XRD" are very likely to be normalized and should be normalized correctly.

Background The Infinium EPIC array measures the methylation status of > 850,000 CpG sites. The EPIC BeadChip uses a two-array design: Infinium Type I and Type II probes. These probe types exhibit different technical characteristics which may confound analyses. Numerous normalization and pre-processing methods have been developed to reduce probe type bias as well as other issues such as background and dye bias.
Methods This study evaluates the performance of various normalization methods using 16 replicated samples and three metrics: absolute beta-value difference, overlap of non-replicated CpGs between replicate pairs, and effect on beta-value distributions. Additionally, we carried out Pearson’s correlation and intraclass correlation coefficient (ICC) analyses using both raw and SeSAMe 2 normalized data.
Results The method we define as SeSAMe 2, which consists of the application of the regular SeSAMe pipeline with an additional round of QC, pOOBAH masking, was found to be the best-performing normalization method, while quantile-based methods were found to be the worst performing methods. Whole-array Pearson’s correlations were found to be high. However, in agreement with previous studies, a substantial proportion of the probes on the EPIC array showed poor reproducibility (ICC < 0.50). The majority of poor-performing probes have beta values close to either 0 or 1, and relatively low standard deviations. These results suggest that probe reliability is largely the result of limited biological variation rather than technical measurement variation. Importantly, normalizing the data with SeSAMe 2 dramatically improved ICC estimates, with the proportion of probes with ICC values > 0.50 increasing from 45.18% (raw data) to 61.35% (SeSAMe 2). Methods

Study Participants and Samples

The whole blood samples were obtained from the Health, Well-being and Aging (Saúde, Ben-estar e Envelhecimento, SABE) study cohort. SABE is a cohort of census-withdrawn elderly from the city of São Paulo, Brazil, followed up every five years since the year 2000, with DNA first collected in 2010. Samples from 24 elderly adults were collected at two time points for a total of 48 samples. The first time point is the 2010 collection wave, performed from 2010 to 2012, and the second time point was set in 2020 in a COVID-19 monitoring project (9±0.71 years apart). The 24 individuals were 67.41±5.52 years of age (mean ± standard deviation) at time point one; and 76.41±6.17 at time point two and comprised 13 men and 11 women.

All individuals enrolled in the SABE cohort provided written consent, and the ethic protocols were approved by local and national institutional review boards COEP/FSP/USP OF.COEP/23/10, CONEP 2044/2014, CEP HIAE 1263-10, University of Toronto RIS 39685.

Blood Collection and Processing

Genomic DNA was extracted from whole peripheral blood samples collected in EDTA tubes. DNA extraction and purification followed manufacturer’s recommended protocols, using Qiagen AutoPure LS kit with Gentra automated extraction (first time point) or manual extraction (second time point), due to discontinuation of the equipment but using the same commercial reagents. DNA was quantified using Nanodrop spectrometer and diluted to 50ng/uL. To assess the reproducibility of the EPIC array, we also obtained technical replicates for 16 out of the 48 samples, for a total of 64 samples submitted for further analyses. Whole Genome Sequencing data is also available for the samples described above.

Characterization of DNA Methylation using the EPIC array

Approximately 1,000ng of human genomic DNA was used for bisulphite conversion. Methylation status was evaluated using the MethylationEPIC array at The Centre for Applied Genomics (TCAG, Hospital for Sick Children, Toronto, Ontario, Canada), following protocols recommended by Illumina (San Diego, California, USA).

Processing and Analysis of DNA Methylation Data

The R/Bioconductor packages Meffil (version 1.1.0), RnBeads (version 2.6.0), minfi (version 1.34.0) and wateRmelon (version 1.32.0) were used to import, process and perform quality control (QC) analyses on the methylation data. Starting with the 64 samples, we first used Meffil to infer the sex of the 64 samples and compared the inferred sex to reported sex. Utilizing the 59 SNP probes that are available as part of the EPIC array, we calculated concordance between the methylation intensities of the samples and the corresponding genotype calls extracted from their WGS data. We then performed comprehensive sample-level and probe-level QC using the RnBeads QC pipeline. Specifically, we (1) removed probes if their target sequences overlap with a SNP at any base, (2) removed known cross-reactive probes (3) used the iterative Greedycut algorithm to filter out samples and probes, using a detection p-value threshold of 0.01 and (4) removed probes if more than 5% of the samples having a missing value. Since RnBeads does not have a function to perform probe filtering based on bead number, we used the wateRmelon package to extract bead numbers from the IDAT files and calculated the proportion of samples with bead number < 3. Probes with more than 5% of samples having low bead number (< 3) were removed. For the comparison of normalization methods, we also computed detection p-values using out-of-band probes empirical distribution with the pOOBAH() function in the SeSAMe (version 1.14.2) R package, with a p-value threshold of 0.05, and the combine.neg parameter set to TRUE. In the scenario where pOOBAH filtering was carried out, it was done in parallel with the previously mentioned QC steps, and the resulting probes flagged in both analyses were combined and removed from the data.

Normalization Methods Evaluated

The normalization methods compared in this study were implemented using different R/Bioconductor packages and are summarized in Figure 1. All data was read into R workspace as RG Channel Sets using minfi’s read.metharray.exp() function. One sample that was flagged during QC was removed, and further normalization steps were carried out in the remaining set of 63 samples. Prior to all normalizations with minfi, probes that did not pass QC were removed. Noob, SWAN, Quantile, Funnorm and Illumina normalizations were implemented using minfi. BMIQ normalization was implemented with ChAMP (version 2.26.0), using as input Raw data produced by minfi’s preprocessRaw() function. In the combination of Noob with BMIQ (Noob+BMIQ), BMIQ normalization was carried out using as input minfi’s Noob normalized data. Noob normalization was also implemented with SeSAMe, using a nonlinear dye bias correction. For SeSAMe normalization, two scenarios were tested. For both, the inputs were unmasked SigDF Sets converted from minfi’s RG Channel Sets. In the first, which we call “SeSAMe 1”, SeSAMe’s pOOBAH masking was not executed, and the only probes filtered out of the dataset prior to normalization were the ones that did not pass QC in the previous analyses. In the second scenario, which we call “SeSAMe 2”, pOOBAH masking was carried out in the unfiltered dataset, and masked probes were removed. This removal was followed by further removal of probes that did not pass previous QC, and that had not been removed by pOOBAH. Therefore, SeSAMe 2 has two rounds of probe removal. Noob normalization with nonlinear dye bias correction was then carried out in the filtered dataset. Methods were then compared by subsetting the 16 replicated samples and evaluating the effects that the different normalization methods had in the absolute difference of beta values (|β|) between replicated samples.

This dataset comprises 200,475 Mandarin Chinese sentences annotated for text normalization, transforming special symbols and Arabic numerals into Chinese characters. It is ideal for training and evaluating Text-to-Speech (TTS) systems and Natural Language Processing (NLP) models.

Normalization Ele

## Overview

Normalization Ele is a dataset for object detection tasks - it contains Elephant annotations for 1,477 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

To achieve high quality omics results, systematic variability in mass spectrometry (MS) data must be adequately addressed. Effective data normalization is essential for minimizing this variability. The abundance of approaches and the data-dependent nature of normalization have led some researchers to develop open-source academic software for choosing the best approach. While these tools are certainly beneficial to the community, none of them meet all of the needs of all users, particularly users who want to test new strategies that are not available in these products. Herein, we present a simple and straightforward workflow that facilitates the identification of optimal normalization strategies using straightforward evaluation metrics, employing both supervised and unsupervised machine learning. The workflow offers a “DIY” aspect, where the performance of any normalization strategy can be evaluated for any type of MS data. As a demonstration of its utility, we apply this workflow on two distinct datasets, an ESI-MS dataset of extracted lipids from latent fingerprints and a cancer spheroid dataset of metabolites ionized by MALDI-MSI, for which we identified the best-performing normalization strategies.

The use of RNA-sequencing has garnered much attention in recent years for characterizing and understanding various biological systems. However, it remains a major challenge to gain insights from a large number of RNA-seq experiments collectively, due to the normalization problem. Normalization has been challenging due to an inherent circularity, requiring that RNA-seq data be normalized before any pattern of differential (or non-differential) expression can be ascertained; meanwhile, the prior knowledge of non-differential transcripts is crucial to the normalization process. Some methods have successfully overcome this problem by the assumption that most transcripts are not differentially expressed. However, when RNA-seq profiles become more abundant and heterogeneous, this assumption fails to hold, leading to erroneous normalization. We present a normalization procedure that does not rely on this assumption, nor prior knowledge about the reference transcripts. This algorithm is based on a graph constructed from intrinsic correlations among RNA-seq transcripts and seeks to identify a set of densely connected vertices as references. Application of this algorithm on our synthesized validation data showed that it could recover the reference transcripts with high precision, thus resulting in high-quality normalization. On a realistic data set from the ENCODE project, this algorithm gave good results and could finish in a reasonable time. These preliminary results imply that we may be able to break the long persisting circularity problem in RNA-seq normalization.

Objective: Normalizing mentions of medical concepts to standardized vocabularies is a fundamental component of clinical text analysis. Ambiguity—words or phrases that may refer to different concepts—has been extensively researched as part of information extraction from biomedical literature, but less is known about the types and frequency of ambiguity in clinical text. This study characterizes the distribution and distinct types of ambiguity exhibited by benchmark clinical concept normalization datasets, in order to identify directions for advancing medical concept normalization research.

Materials and Methods: We identified ambiguous strings in datasets derived from the two available clinical corpora for concept normalization, and categorized the distinct types of ambiguity they exhibited. We then compared observed string ambiguity in the datasets to potential ambiguity in the Unified Medical Language System (UMLS), to assess how representative available datasets are of ambiguity in clinical language.

Results: We observed twelve distinct types of ambiguity, distributed unequally across the available datasets. However, less than 15% of the strings were ambiguous within the datasets, while over 50% were ambiguous in the UMLS, indicating only partial coverage of clinical ambiguity.

Discussion: Existing datasets are not sufficient to cover the diversity of clinical concept ambiguity, limiting both training and evaluation of normalization methods for clinical text. Additionally, the UMLS offers important semantic information for building and evaluating normalization methods.

Conclusion: Our findings identify three opportunities for concept normalization research, including a need for ambiguity-specific clinical datasets and leveraging the rich semantics of the UMLS in new methods and evaluation measures for normalization.

Methods These data are derived from benchmark datasets released for Medical Concept Normalization research focused on Electronic Health Record (EHR) narratives. Data included in this release are derived from:

SemEval-2015 Task 14 (Publication DOI: 10.18653/v1/S15-2051, data accessed through release at https://physionet.org/content/shareclefehealth2014task2/1.0/)
CUILESS2016 (Publication DOI: 10.1186/s13326-017-0173-6, data accessed through release at https://physionet.org/content/cuiless16/1.0.0/)

These datasets consist of EHR narratives with annotations including: (1) the portion of a narrative referring to a medical concept, such as a problem, treatment, or test; and (2) one or more Concept Unique Identifiers (CUIs) derived from the Unified Medical Language System (UMLS), identifying the reification of the medical concept being mentioned.

The data were processed using the following procedure:

All medical concept mention strings were preprocessed with lowercasing and removing of determiners ("a", "an", "the").
All medical concept mentions were analyzed to identify strings that met the following conditions: (1) string occurred more than once in the dataset, and (2) string was annotated with at least two different CUIs, when aggregating across dataset samples. Strings meeting these conditions were considered "ambiguous strings".
Ambiguous strings were reviewed by article authors to determine (1) the category and subcategory of ambiguity exhibited (derived from an ambiguity typology described in the accompanying article); and (2) whether the semantic differences in CUI annotations were reflected by differences in textual meaning (strings not meeting this criterion were termed "arbitrary").

For more details, please see the accompanying article (DOI: 10.1093/jamia/ocaa269).

Data normalization is a crucial preliminary step in analyzing genomic datasets. The goal of normalization is to remove global variation to make readings across different experiments comparable. In addition, most genomic loci have non-uniform sensitivity to any given assay because of variation in local sequence properties. In microarray experiments, this non-uniform sensitivity is due to different DNA hybridization and cross-hybridization efficiencies, known as the probe effect. In this paper we introduce a new scheme, called Group Normalization (GN), to remove both global and local biases in one integrated step, whereby we determine the normalized probe signal by finding a set of reference probes with similar responses. Compared to conventional normalization methods such as Quantile normalization and physically motivated probe effect models, our proposed method is general in the sense that it does not require the assumption that the underlying signal distribution be identical for the treatment and control, and is flexible enough to correct for nonlinear and higher order probe effects. The Group Normalization algorithm is computationally efficient and easy to implement. We also describe a variant of the Group Normalization algorithm, called Cross Normalization, which efficiently amplifies biologically relevant differences between any two genomic datasets.

Label-free proteomics expression data sets often exhibit data heterogeneity and missing values, necessitating the development of effective normalization and imputation methods. The selection of appropriate normalization and imputation methods is inherently data-specific, and choosing the optimal approach from the available options is critical for ensuring robust downstream analysis. This study aimed to identify the most suitable combination of these methods for quality control and accurate identification of differentially expressed proteins. In this study, we developed nine combinations by integrating three normalization methods, locally weighted linear regression (LOESS), variance stabilization normalization (VSN), and robust linear regression (RLR) with three imputation methods: k-nearest neighbors (k-NN), local least-squares (LLS), and singular value decomposition (SVD). We utilized statistical measures, including the pooled coefficient of variation (PCV), pooled estimate of variance (PEV), and pooled median absolute deviation (PMAD), to assess intragroup and intergroup variation. The combinations yielding the lowest values corresponding to each statistical measure were chosen as the data set’s suitable normalization and imputation methods. The performance of this approach was tested using two spiked-in standard label-free proteomics benchmark data sets. The identified combinations returned a low NRMSE and showed better performance in identifying spiked-in proteins. The developed approach can be accessed through the R package named ’lfproQC’ and a user-friendly Shiny web application (https://dabiniasri.shinyapps.io/lfproQC and http://omics.icar.gov.in/lfproQC), making it a valuable resource for researchers looking to apply this method to their data sets.

Vahadane_normalization

## Overview

Vahadane_normalization is a dataset for object detection tasks - it contains Mitosis annotations for 3,442 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Background Affymetrix oligonucleotide arrays simultaneously measure the abundances of thousands of mRNAs in biological samples. Comparability of array results is necessary for the creation of large-scale gene expression databases. The standard strategy for normalizing oligonucleotide array readouts has practical drawbacks. We describe alternative normalization procedures for oligonucleotide arrays based on a common pool of known biotin-labeled cRNAs spiked into each hybridization. Results We first explore the conditions for validity of the 'constant mean assumption', the key assumption underlying current normalization methods. We introduce 'frequency normalization', a 'spike-in'-based normalization method which estimates array sensitivity, reduces background noise and allows comparison between array designs. This approach does not rely on the constant mean assumption and so can be effective in conditions where standard procedures fail. We also define 'scaled frequency', a hybrid normalization method relying on both spiked transcripts and the constant mean assumption while maintaining all other advantages of frequency normalization. We compare these two procedures to a standard global normalization method using experimental data. We also use simulated data to estimate accuracy and investigate the effects of noise. We find that scaled frequency is as reproducible and accurate as global normalization while offering several practical advantages. Conclusions Scaled frequency quantitation is a convenient, reproducible technique that performs as well as global normalization on serial experiments with the same array design, while offering several additional features. Specifically, the scaled-frequency method enables the comparison of expression measurements across different array designs, yields estimates of absolute message abundance in cRNA and determines the sensitivity of individual arrays.

Macenko_normalization

## Overview

Macenko_normalization is a dataset for object detection tasks - it contains Mitosis annotations for 3,442 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Overview

• The Naturalistic Neuroimaging Database (NNDb v2.0) contains datasets from 86 human participants doing the NIH Toolbox and then watching one of 10 full-length movies during functional magnetic resonance imaging (fMRI).The participants were all right-handed, native English speakers, with no history of neurological/psychiatric illnesses, with no hearing impairments, unimpaired or corrected vision and taking no medication. Each movie was stopped in 40-50 minute intervals or when participants asked for a break, resulting in 2-6 runs of BOLD-fMRI. A 10 minute high-resolution defaced T1-weighted anatomical MRI scan (MPRAGE) is also provided.
• The NNDb V2.0 is now on Neuroscout, a platform for fast and flexible re-analysis of (naturalistic) fMRI studies. See: https://neuroscout.org/

v2.0 Changes

• Overview
  • We have replaced our own preprocessing pipeline with that implemented in AFNI’s afni_proc.py, thus changing only the derivative files. This introduces a fix for an issue with our normalization (i.e., scaling) step and modernizes and standardizes the preprocessing applied to the NNDb derivative files. We have done a bit of testing and have found that results in both pipelines are quite similar in terms of the resulting spatial patterns of activity but with the benefit that the afni_proc.py results are 'cleaner' and statistically more robust.

• Normalization

  • Emily Finn and Clare Grall at Dartmouth and Rick Reynolds and Paul Taylor at AFNI, discovered and showed us that the normalization procedure we used for the derivative files was less than ideal for timeseries runs of varying lengths. Specifically, the 3dDetrend flag -normalize makes 'the sum-of-squares equal to 1'. We had not thought through that an implication of this is that the resulting normalized timeseries amplitudes will be affected by run length, increasing as run length decreases (and maybe this should go in 3dDetrend’s help text). To demonstrate this, I wrote a version of 3dDetrend’s -normalize for R so you can see for yourselves by running the following code:

  # Generate a resting state (rs) timeseries (ts)
  # Install / load package to make fake fMRI ts
  # install.packages("neuRosim")
  library(neuRosim)
  # Generate a ts
  ts.rs <- simTSrestingstate(nscan=2000, TR=1, SNR=1)
  # 3dDetrend -normalize
  # R command version for 3dDetrend -normalize -polort 0 which normalizes by making "the sum-of-squares equal to 1"
  # Do for the full timeseries
  ts.normalised.long <- (ts.rs-mean(ts.rs))/sqrt(sum((ts.rs-mean(ts.rs))^2));
  # Do this again for a shorter version of the same timeseries
  ts.shorter.length <- length(ts.normalised.long)/4
  ts.normalised.short <- (ts.rs[1:ts.shorter.length]- mean(ts.rs[1:ts.shorter.length]))/sqrt(sum((ts.rs[1:ts.shorter.length]- mean(ts.rs[1:ts.shorter.length]))^2));
  # By looking at the summaries, it can be seen that the median values become  larger
  summary(ts.normalised.long)
  summary(ts.normalised.short)
  # Plot results for the long and short ts
  # Truncate the longer ts for plotting only
  ts.normalised.long.made.shorter <- ts.normalised.long[1:ts.shorter.length]
  # Give the plot a title
  title <- "3dDetrend -normalize for long (blue) and short (red) timeseries";
  plot(x=0, y=0, main=title, xlab="", ylab="", xaxs='i', xlim=c(1,length(ts.normalised.short)), ylim=c(min(ts.normalised.short),max(ts.normalised.short)));
  # Add zero line
  lines(x=c(-1,ts.shorter.length), y=rep(0,2), col='grey');
  # 3dDetrend -normalize -polort 0 for long timeseries
  lines(ts.normalised.long.made.shorter, col='blue');
  # 3dDetrend -normalize -polort 0 for short timeseries
  lines(ts.normalised.short, col='red');
  

• Standardization/modernization

  • The above individuals also encouraged us to implement the afni_proc.py script over our own pipeline. It introduces at least three additional improvements: First, we now use Bob’s @SSwarper to align our anatomical files with an MNI template (now MNI152_2009_template_SSW.nii.gz) and this, in turn, integrates nicely into the afni_proc.py pipeline. This seems to result in a generally better or more consistent alignment, though this is only a qualitative observation. Second, all the transformations / interpolations and detrending are now done in fewers steps compared to our pipeline. This is preferable because, e.g., there is less chance of inadvertently reintroducing noise back into the timeseries (see Lindquist, Geuter, Wager, & Caffo 2019). Finally, many groups are advocating using tools like fMRIPrep or afni_proc.py to increase standardization of analyses practices in our neuroimaging community. This presumably results in less error, less heterogeneity and more interpretability of results across studies. Along these lines, the quality control (‘QC’) html pages generated by afni_proc.py are a real help in assessing data quality and almost a joy to use.

• New afni_proc.py command line

  • The following is the afni_proc.py command line that we used to generate blurred and censored timeseries files. The afni_proc.py tool comes with extensive help and examples. As such, you can quickly understand our preprocessing decisions by scrutinising the below. Specifically, the following command is most similar to Example 11 for ‘Resting state analysis’ in the help file (see https://afni.nimh.nih.gov/pub/dist/doc/program_help/afni_proc.py.html): afni_proc.py \ -subj_id "$sub_id_name_1" \ -blocks despike tshift align tlrc volreg mask blur scale regress \ -radial_correlate_blocks tcat volreg \ -copy_anat anatomical_warped/anatSS.1.nii.gz \ -anat_has_skull no \ -anat_follower anat_w_skull anat anatomical_warped/anatU.1.nii.gz \ -anat_follower_ROI aaseg anat freesurfer/SUMA/aparc.a2009s+aseg.nii.gz \ -anat_follower_ROI aeseg epi freesurfer/SUMA/aparc.a2009s+aseg.nii.gz \ -anat_follower_ROI fsvent epi freesurfer/SUMA/fs_ap_latvent.nii.gz \ -anat_follower_ROI fswm epi freesurfer/SUMA/fs_ap_wm.nii.gz \ -anat_follower_ROI fsgm epi freesurfer/SUMA/fs_ap_gm.nii.gz \ -anat_follower_erode fsvent fswm \ -dsets media_?.nii.gz \ -tcat_remove_first_trs 8 \ -tshift_opts_ts -tpattern alt+z2 \ -align_opts_aea -cost lpc+ZZ -giant_move -check_flip \ -tlrc_base "$basedset" \ -tlrc_NL_warp \ -tlrc_NL_warped_dsets \ anatomical_warped/anatQQ.1.nii.gz \ anatomical_warped/anatQQ.1.aff12.1D \ anatomical_warped/anatQQ.1_WARP.nii.gz \ -volreg_align_to MIN_OUTLIER \ -volreg_post_vr_allin yes \ -volreg_pvra_base_index MIN_OUTLIER \ -volreg_align_e2a \ -volreg_tlrc_warp \ -mask_opts_automask -clfrac 0.10 \ -mask_epi_anat yes \ -blur_to_fwhm -blur_size $blur \ -regress_motion_per_run \ -regress_ROI_PC fsvent 3 \ -regress_ROI_PC_per_run fsvent \ -regress_make_corr_vols aeseg fsvent \ -regress_anaticor_fast \ -regress_anaticor_label fswm \ -regress_censor_motion 0.3 \ -regress_censor_outliers 0.1 \ -regress_apply_mot_types demean deriv \ -regress_est_blur_epits \ -regress_est_blur_errts \ -regress_run_clustsim no \ -regress_polort 2 \ -regress_bandpass 0.01 1 \ -html_review_style pythonic We used similar command lines to generate ‘blurred and not censored’ and the ‘not blurred and not censored’ timeseries files (described more fully below). We will provide the code used to make all derivative files available on our github site (https://github.com/lab-lab/nndb).

  We made one choice above that is different enough from our original pipeline that it is worth mentioning here. Specifically, we have quite long runs, with the average being ~40 minutes but this number can be variable (thus leading to the above issue with 3dDetrend’s -normalise). A discussion on the AFNI message board with one of our team (starting here, https://afni.nimh.nih.gov/afni/community/board/read.php?1,165243,165256#msg-165256), led to the suggestion that '-regress_polort 2' with '-regress_bandpass 0.01 1' be used for long runs. We had previously used only a variable polort with the suggested 1 + int(D/150) approach. Our new polort 2 + bandpass approach has the added benefit of working well with afni_proc.py.

  Which timeseries file you use is up to you but I have been encouraged by Rick and Paul to include a sort of PSA about this. In Paul’s own words: * Blurred data should not be used for ROI-based analyses (and potentially not for ICA? I am not certain about standard practice). * Unblurred data for ISC might be pretty noisy for voxelwise analyses, since blurring should effectively boost the SNR of active regions (and even good alignment won't be perfect everywhere). * For uncensored data, one should be concerned about motion effects being left in the data (e.g., spikes in the data). * For censored data: * Performing ISC requires the users to unionize the censoring patterns during the correlation calculation. * If wanting to calculate power spectra or spectral parameters like ALFF/fALFF/RSFA etc. (which some people might do for naturalistic tasks still), then standard FT-based methods can't be used because sampling is no longer uniform. Instead, people could use something like 3dLombScargle+3dAmpToRSFC, which calculates power spectra (and RSFC params) based on a generalization of the FT that can handle non-uniform sampling, as long as the censoring pattern is mostly random and, say, only up to about 10-15% of the data. In sum, think very carefully about which files you use. If you find you need a file we have not provided, we can happily generate different versions of the timeseries upon request and can generally do so in a week or less.

• Effect on results

  • From numerous tests on our own analyses, we have qualitatively found that results using our old vs the new afni_proc.py preprocessing pipeline do not change all that much in terms of general spatial patterns. There is, however, an

Isobaric labeling has the promise of combining high sample multiplexing with precise quantification. However, normalization issues and the missing value problem of complete n-plexes hamper quantification across more than one n-plex. Here we introduce two novel algorithms implemented in MaxQuant that substantially improve the data analysis with multiple n-plexes. First, isobaric matching between runs (IMBR) makes use of the three-dimensional MS1 features to transfer identifications from identified to unidentified MS/MS spectra between LC-MS runs in order to utilize reporter ion intensities in unidentified spectra for quantification. On typical datasets, we observe a significant gain in quantifiable n-plexesMS/MS spectra that can be used for quantification. Second, we introduce a novel PSM-level normalization, applicable to data with and without common reference channel. It is a weighted median-based method, in which the weights reflect the number of ions that were used for fragmentation. On a typical dataset, we observe complete removal of batch effects and dominance of the biological sample grouping after normalization. This dataset is one of the datasets used for the study. It is TMT 10-plex with a reference channel.

Normalization of RNA-Seq data has proven essential to ensure accurate inferences and replication of findings. Hence, various normalization methods have been proposed for various technical artifacts that can be present in high-throughput sequencing transcriptomic studies. In this study, we set out to compare the widely used library size normalization methods (UQ, TMM, and RLE) and across sample normalization methods (SVA, RUV, and PCA) for RNA-Seq data using publicly available data from The Cancer Genome Atlas (TCGA) cervical cancer study. Additionally, an extensive simulation study was completed to compare the performance of the across sample normalization methods in estimating technical artifacts. Lastly, we investigated the effect of reduction in degrees of freedom in the normalized data and their impact on downstream differential expression analysis results. Based on this study, the TMM and RLE library size normalization methods give similar results for CESC dataset. In addition, the simulated datasets results show that the SVA (“BE”) method outperforms the other methods (SVA “Leek”, PCA) by correctly estimating the number of latent artifacts. Moreover, ignoring the loss of degrees of freedom due to normalization results in an inflated type I error rates. We recommend adjusting not only for library size differences but also the assessment of known and unknown technical artifacts in the data, and if needed, complete across sample normalization. In addition, we suggest that one includes the known and estimated latent artifacts in the design matrix to correctly account for the loss in degrees of freedom, as opposed to completing the analysis on the post-processed normalized data.

The different normalization methods applied in this study, and whether or not they account for lexical variation, synonymy, orthology and species-specific resolution. By creating combinations of these algorithms, their individual strengths can be aggregated.

Finding a good data source is the first step toward creating a database. Cardiovascular illnesses (CVDs) are the major cause of death worldwide. CVDs include coronary heart disease, cerebrovascular disease, rheumatic heart disease, and other heart and blood vessel problems. According to the World Health Organization, 17.9 million people die each year. Heart attacks and strokes account for more than four out of every five CVD deaths, with one-third of these deaths occurring before the age of 70 A comprehensive database for factors that contribute to a heart attack has been constructed , The main purpose here is to collect characteristics of Heart Attack or factors that contribute to it. As a result, a form is created to accomplish this. Microsoft Excel was used to create this form. Figure 1 depicts the form which It has nine fields, where eight fields for input fields and one field for output field. Age, gender, heart rate, systolic BP, diastolic BP, blood sugar, CK-MB, and Test-Troponin are representing the input fields, while the output field pertains to the presence of heart attack, which is divided into two categories (negative and positive).negative refers to the absence of a heart attack, while positive refers to the presence of a heart attack.Table 1 show the detailed information and max and min of values attributes for 1319 cases in the whole database.To confirm the validity of this data, we looked at the patient files in the hospital archive and compared them with the data stored in the laboratories system. On the other hand, we interviewed the patients and specialized doctors. Table 2 is a sample for 1320 cases, which shows 44 cases and the factors that lead to a heart attack in the whole database,After collecting this data, we checked the data if it has null values (invalid values) or if there was an error during data collection. The value is null if it is unknown. Null values necessitate special treatment. This value is used to indicate that the target isn’t a valid data element. When trying to retrieve data that isn't present, you can come across the keyword null in Processing. If you try to do arithmetic operations on a numeric column with one or more null values, the outcome will be null. An example of a null values processing is shown in Figure 2.The data used in this investigation were scaled between 0 and 1 to guarantee that all inputs and outputs received equal attention and to eliminate their dimensionality. Prior to the use of AI models, data normalization has two major advantages. The first is to avoid overshadowing qualities in smaller numeric ranges by employing attributes in larger numeric ranges. The second goal is to avoid any numerical problems throughout the process.After completion of the normalization process, we split the data set into two parts - training and test sets. In the test, we have utilized1060 for train 259 for testing Using the input and output variables, modeling was implemented.

We performed CODEX (co-detection by indexing) multiplexed imaging on four sections of the human colon (ascending, transverse, descending, and sigmoid) using a panel of 47 oligonucleotide-barcoded antibodies. Subsequently images underwent standard CODEX image processing (tile stitching, drift compensation, cycle concatenation, background subtraction, deconvolution, and determination of best focal plane), and single cell segmentation. Output of this process was a dataframe of nearly 130,000 cells with fluorescence values quantified from each marker. We used this dataframe as input to 1 of the 5 normalization techniques of which we compared z, double-log(z), min/max, and arcsinh normalizations to the original unmodified dataset. We used these normalized dataframes as inputs for 4 unsupervised clustering algorithms: k-means, leiden, X-shift euclidian, and X-shift angular.

From the clustering outputs, we then labeled the clusters that resulted for cells observed in the data producing 20 unique cell type labels. We also labeled cell types by hiearchical hand-gating data within cellengine (cellengine.com). We also created another gold standard for comparison by overclustering unormalized data with X-shift angular clustering. Finally, we created one last label as the major cell type call from each cell from all 21 cell type labels in the dataset.

Consequently the dataset has individual cells segmented out in each row. Then there are columns for the X, Y position in pixels in the overall montage image of the dataset. There are also columns to indicate which region the data came from (4 total). The rest are labels generated by all the clustering and normalization techniques used in the manuscript and what were compared to each other. These also were the data that were used for neighborhood analysis for the last figure of the manuscript. These are provided at all four levels of cell type level granularity (from 7 cell types to 35 cell types).

It has been acknowledged that source databases recording experimentally supported human protein-protein interactions (PPIs) exhibit limited overlap. Thus, the reconstruction of a comprehensive PPI network requires appropriate integration of multiple heterogeneous primary datasets, presenting the PPIs at various genetic reference levels. Existing PPI meta-databases perform integration via normalization; namely, PPIs are merged after converted to a certain target level. Hence, the node set of the integrated network depends each time on the number and type of the combined datasets. Moreover, the irreversible a priori normalization process hinders the identification of normalization artifacts in the integrated network, which originate from the nonlinearity characterizing the genetic information flow. PICKLE (Protein InteraCtion KnowLedgebasE) 2.0 implements a new architecture for this recently introduced human PPI meta-database. Its main novel feature over the existing meta-databases is its approach to primary PPI dataset integration via genetic information ontology. Building upon the PICKLE principles of using the reviewed human complete proteome (RHCP) of UniProtKB/Swiss-Prot as the reference protein interactor set, and filtering out protein interactions with low probability of being direct based on the available evidence, PICKLE 2.0 first assembles the RHCP genetic information ontology network by connecting the corresponding genes, nucleotide sequences (mRNAs) and proteins (UniProt entries) and then integrates PPI datasets by superimposing them on the ontology network without any a priori transformations. Importantly, this process allows the resulting heterogeneous integrated network to be reversibly normalized to any level of genetic reference without loss of the original information, the latter being used for identification of normalization biases, and enables the appraisal of potential false positive interactions through PPI source database cross-checking. The PICKLE web-based interface (www.pickle.gr) allows for the simultaneous query of multiple entities and provides integrated human PPI networks at either the protein (UniProt) or the gene level, at three PPI filtering modes.