ACE provides a robust framework for identifying genes responsive to subtype-specific therapeutic targeting.High-throughput CRISPR-Cas9 knockout screens are widely used to evaluate gene essentiality in cancer research. Here we introduce a probabilistic modeling framework, Analysis of CRISPR-based Essentiality (ACE), that accounts for multiple sources of variation in CRISPR-Cas9 screens and enables new statistical tests for essentiality. We show using simulations that ACE is effective at predicting both absolute and differential essentiality. When applied to publicly available data, ACE identifies known and novel candidates for genotype-specific essentiality, including RNA mThe online version contains supplementary material available at (10.1186/s13059-021-02491-z). Pathogenic mutations and genotype-specific cancer liabilities can now be tested at unprecedented scales, owing to CRISPR-Cas9  technology and large panels of diverse cell lines –10. ThesIn a typical CRISPR-Cas9 screen Fig. a, the arWithin the general paradigm of a CRISPR-Cas9 screen, several variations of experimental design are possible. For example, many experiments evaluate the depletion of cells infected with lethal sgRNA by sequencing both the initial infected and the final population of cells –17. In cTP53. These context-dependent essential genes — sometimes called “non-oncogene addictions” — are of particular interest because they may offer druggable therapeutic targets even when irreversible loss-of-function mutations to oncogenes are present  denotes the editing efficiency of sgRNA g and ϕG≤1 denotes the essentiality of gene G. These initial and final numbers of infected cells are not observed directly (they are latent variables) but are indirectly sampled via the sequencing process, resulting in observed read counts of xsg and ysg, respectively, which are provided by the user as inputs to the inference procedure. We assume Poisson-distributed read counts xsg and ysg, and sum over possible values of the latent variable nsg in the likelihood calculation  to zero in the case of efficient editing, εg=1), whereas a value of ϕG=0 represents complete nonessentiality . Notably, however, our definition of ϕG also allows for negative values, reflecting increased cell growth after inactivation of gene G. Such an increase might occur, for example, if a tumor suppressor gene were deactivated by guide RNAs. In practice, negative estimates of ϕG are fairly frequently observed, either by random chance or from true increases in cell growth.A value of εg and ϕG, we determine εg by logistic regression based on a user-defined set of features, and treat the regression coefficients only as free parameters  likelihoods for each gene see the “” sectionTo evaluate the ability of ACE to infer sgRNA-, sample-, and gene-specific parameters, we devised a simulation method to generate artificial CRISPR-screen data sets. To ensure that our simulated data resemble data from real CRISPR screens as closely as possible, our simulator, called empiriCRISPR, mimics the variation in read counts observed in a reference experiment and makes minimal assumptions about the processes by which the data are generated (see the “ϕG=0) to complete essentiality (ϕG=1). To ensure a fair comparison with methods that rely on a median-based normalization method, we took care to include an adequate number of nonessential genes in each data set, simulating 3,150 genes with ϕG=0 . The remaining 40% of genes consisted of 300 genes at each of several intermediate values of ϕG and 600 genes at ϕG=0.99  in read counts see the “” sectionϕG, together with 3150 nonessential genes. For each of these experiments, we then compared the binary classification performance of ACE with BAGEL [ϕG, but in the harder scenarios where ϕG takes moderate to low values (ϕG<0.5), JACKS and ACE performed significantly better than the others. JACKS, however, slightly outperformed ACE on this task, apparently because it makes particularly efficient use of the negative controls in defining a null distribution. CRISPhieRmix performed substantially worse than the other methods in this test.Many methods do not provide a numerical estimate of the essentiality of a gene, but instead focus on a binary classification of genes into “essential” and “nonessential” categories. To compare ACE with these methods, we simulated data sets containing 300 genes at each of several values of th BAGEL , CRISPhith BAGEL , and a cϕG=0.99) and 300 genes at moderate (ϕG=0.5) essentiality, as well as a large collection (3150) of nonessential genes (ϕG=0). Similar to the previous tests, we compared the performance of ACE with BAGEL, JACKS, CRISPhieRmix, and a simple AFC-based method in the detection of the differentially essential genes. Because the other methods do not support direct tests for differential essentiality, we devised heuristic test statistics based on the results of two separate tests for essentiality. After some experimentation, we settled on using the ratio of p-values for the test and control panels for JACKS, as there is only one “sample type” in our simulation. For CRISPhieRmix, we used the ratio of predicted false discovery rates between test and control panels, and for BAGEL, we used the absolute difference in the Bayes Factors for the test and control panels  that included 300 genes essential only in the “test” sample with several different choices of see the “” sectionsee the “” sectionWe found that ACE’s likelihood ratio test for differential essentiality did result in substantially better power than the competing methods, on average Fig. . Most menes Fig. B.Fig. 3We next sought to evaluate the performance of ACE in detecting previously identified essential genes from real data. We focused on 688 genes that have been classified as essential in at least two previous studies –3, basedp=0.05), 90.5% of the positive controls were predicted to be essential. In an ROC analysis based on the same data, ACE was able to distinguish the positive and negative controls with high accuracy  or growth-suppressing activity  yielded both clearly elevated estimates of essentiality .Fig. 4To evaluate ACE’s ability to identify differential essentiality with real data, we applied it to genes evaluated in liver and lung tissue in the DepMap database. For our set of uniformly essential genes, we selected both a “uniformly active” set of 698 genes robustly expressed across lung- and liver-derived samples, and a “uniformly silent” set of 594 genes showing little or no evidence of expression across samples see the “” sectionWe found that ACE performed roughly as well as the best methods on this task . However, all methods performed, at best, only modestly well on this benchmark, likely owing to the disparity between gene expression levels and gene essentiality as evaluated by a knockout phenotype. The main outlier in this analysis was CERES, which showed considerably poorer performance than the other methods, possibly because it was optimized for uniformly essential and nonessential genes across all DepMap samples.TP53 and the proto-oncogene KRAS  with variants of two well-known cancer-related genes, the tumor suppressor see the “” sectionWe restricted ourselves to NSCLC samples in these cases, to avoid spurious signals unrelated to the mutations of interest. Moreover, we structured our test specifically to identify genes that were significantly more (rather than less) essential in the test samples than in the control samples. As in the previous analysis, we used data from Achilles DepMap , 25.TP53, we first defined the test samples as ones with “wildtype” copies of TP53, with no annotated mutations, and the control samples as ones in which TP53 has “damaging” mutations , the majority of which are activating gain-of-function mutations [KRAS SNP. Samples containing wildtype TP53 were additionally excluded to remove the signal from MDM2. As predicted, the KRAS gene itself emerged as having, by far, the strongest signal for a mutation-dependent increase in essentiality . These should all be well-powered tests, provided they are applied to sufficiently abundant data for the desirable asymptotic properties of likelihood ratio tests to become apparent.To our knowledge, ACE is the first method to support a direct likelihood-based test for differential essentiality between designated ‘test’ and ‘control’ sample panels. This test can be useful in a variety of applications, including tests for differences in essentiality in the presence of mutant and wild-type versions of known cancer genes. In comparison to indirect tests for differential essentiality — derived from separate analyses of “test” and “control” panels — ACE appears to offer improved power according to our simulation study see Fig. . NotablyWhereas most current statistical models for sequencing read counts allow for overdispersed counts, typically through the use of a negative binomial distribution, we settled on the use of the simpler Poisson distribution in this work. We initially experimented with a negative binomial distribution for read counts, allowing the gene-wise dispersion parameter to vary as a function of the mean, as in DEseq2 . HoweverϕG in ACE are also limited in that they reflect an “average” essentiality across all samples analyzed, so users must take care to choose a panel of samples that ensures a level of diversity appropriate for the biological question of interest. For example, a diverse collection of tissue samples or cell lines may be most suitable for the identification of universally essential genes, whereas a set of patient-derived tissue samples may be best for personalized tumor therapeutics. Notably, because it is a relative metric, differential essentiality may in some cases generalize better than absolute essentiality. As illustrated in Additional file As illustrated by the major revisions of “core essential gene” sets over the past 10 years, the definition of “essential genes” is inevitably somewhat subjective and arbitrary. In reality, essentiality occurs on a spectrum and depends on the particular genetic background, cell-type, and conditions tested. As a result, candidate “essential” genes require carefully designed experiments for validation, and researchers should be cautious about relying upon published sets of genes labeled “essential”. Estimates of We have focused in this work on differential essentiality between previously defined groups of samples, but it is worth noting that ACE could be adapted to identify sample subgroups of interest de novo on the basis of differential essentiality. For example, one could iterate over all possible bipartitions of a set of samples and identify subgroups exhibiting the strongest signals of differential essentiality. If this approach proved too computationally intensive, candidate bipartitions could be identified based on pre-computed individual gene estimates of essentiality. We anticipate that subtype discovery may be a promising area for future applications of ACE.METTL3, METTL14, and METTL16 to be particularly interesting. RNA m6-A modifications have recently been found to be critical in the maintenance of leukemic cell growth [6-A methyltransferases in general have been a topic of recent interest in the new field of cancer epitranscriptomics, with several phase I trials targeting METTL3 due to begin in 2021 [TP53 mRNA and inducing drug resistance in a biochemical assay in SW48 colon cancer cells [METTL3 expression to be essential in NSCLC adenocarcinoma, although only two cell types were examined [METTL3 mRNA knockdown with siRNA has also been shown, by M6A-seq, to result in an enrichment of methylation changes in genes in the p53-signaling pathway [6-A regulatory genes have been found to have an increase in TP53 mutations, albeit only with sample sizes of n=5 [TP53. More generally, we anticipate that improved tests for differential essentiality, such as the ones introduced here, combined with rapidly growing CRISPR-screen data sets, will enable the detection of many new cases of genotype-dependent differences in essentiality, including both synthetic lethality and non-oncogene addiction.Among the genotype-dependent differentially essential genes identified by ACE, we found the RNA methyltransferase genes l growth . These ger cells . Anotherexamined . METTL3  pathway . Finallys of n=5 . Thus, tTP53-linked dependence on RNA methyltransferases in NSCLC lung samples. The ACE software is freely available for use by the scientific community.In this paper, we have introduced the ACE modeling framework for CRISPR/Cas9 screens. We show that ACE is competitive with the best available methods in predicting essentiality, and in addition, supports a powerful new test for differential essentiality. In addition, we show using real data how this test for differential essentiality can uncover unique genetic dependencies that might otherwise be overlooked, focusing in particular on the case of a nsg of each sgRNA g within sample s is assumed to be given by a Poisson distribution with mean μ1=csmg, where cs is the total number of cells infected in the screen and mg is the relative frequency of g in the master library: The initial abundance mg for each sgRNA g is estimated as the fraction of sequence reads corresponding to g in the sequence data for the master library. If multiple replicates are available, an average is taken. The parameter cs is either provided by the user or a default value of 1000 is assumed.The value xsg from the initial infected population nsg are assumed to follow a Poisson distribution with mean μ2=γsnsg, where γs is a scaling parameter that captures the relationship between sequencing depth and the number of cells infected: The sgRNA sequencing counts g is assumed to be dsg=nsg(1−εgϕG), where ϕG is the essentiality of the gene G that is targeted by g, and εg is the efficiency of g. This process is assumed to be deterministic; we assume that the number of infected cells is sufficiently large and sufficiently many cell divisions have occurred that stochastic effects can be ignored, and uncertainty in the value of dsg can be absorbed by the distributions for nsg and ysg. The final read counts ysg are then assumed to follow a Poisson distribution with mean μ3=γs′dsg, where γs′ is the final read count scaling factor: Following several rounds of cell division, the final abundance of each sgRNA γs′ not only captures the relationship between sequencing depth and the number of cells that remain after cell growth, but also can accommodate sample-specific differences in the growth rate of all cells (independent of sgRNA).The scaling factor εg is derived from a general feature vector by logistic regression: The sgRNA-specific efficiency Fg is a feature vector and ω is a corresponding vector of real-valued coefficients. In this paper, we used a ten-dimensional vector Fg whose elements are indicators (via “one-hot” encoding) for the decile of GC content of each sgRNA’s template guide. The corresponding 10-dimensional weight vector ω was treated as a free parameter and estimated by maximum likelihood. In this way, we effectively estimate a separate efficiency value for each GC decile, based on pooled information from all guides within that decile. As noted in the text, users may instead wish to use richer feature vectors such as those described in other recent studies [where  studies , 44–48.γs and γs′ are not treated as free parameters in the maximization of the likelihood, but instead are set in a preprocessing step based on the raw read counts xsg and ysg for negative controls. Specifically, the scaling parameter is set equal to the median of the ratios of each sgRNA’s read count to a guide-specific reference value, which in this case is the expected number of infected cells, csmg: Following the “median-of-ratios” approach used by DESeq2 , the samg∈Gneg indicates all sgRNAs targeting genes in the designated set of negative controls. If no master library is available, the initial abundance of sgRNA where S is the total number of samples. The user may choose to use the median ratio across all genes rather than only the negative controls, if desired. This heuristic method of estimation may prove insufficient for some experimental cases, such as misclassified negative controls or strong, sample-specific multipliers of essentiality affecting all genes in experiments with few samples, though we found these estimates satisfactory for the analyses and benchmarking presented in this work.where Substituting from Eqs. εg is implicitly a function of ω as defined by Eq. x.. = {xsg | for all sampless and sgRNAs g}).where nsg, which cannot be directly observed. This summation step can be accelerated by an approximation that bundles summands together, for example, into groups of 10 cells.The likelihood function must sum over the number of infected cells ω is held fixed, the full likelihood decomposes by gene, such that each ϕG can be estimated separately, and similarly, when the ϕG values are held fixed, the likelihood decomposes by the efficiency features f, owing to our “one-hot” design for the feature vector. Thus, we estimate each ϕG and component of ωf iteratively, to enable parallelization by gene and feature category. Specifically, we repeatedly carry out the following optimizations until convergence : When ϕG¬ denotes the set {ϕg | g≠G},ωf¬ denotes the set θ denotes the remaining parameter set. We initialize ω to treat all sgRNA as 100% effective.where ϕG is held fixed at zero. Specifically, we compute a test statistic T as, Finally, to evaluate whether an estimated value of L(ϕG) represents the component of the likelihood corresponding to gene G. We compute an empirical p-value for T based on the equivalent test statistics for the designated negative control genes.where x.. as missing data: The likelihood function can easily be adapted to settings in which data is unavailable for either the master library or the initial sgRNA abundances. In the case where the master library information is missing, we simply use a uniform prior for values of St, and a “control” set, Sc, we employ the following likelihood ratio test. Let G that has been maximized in the standard way, with one version of the ϕG parameter shared across both St and Sc. By contrast, let St and Sc, respectively. The likelihood ratio test statistic T′ is then given by, To identify genes with significant differences in essentiality between a “test” set of samples, γs and γs′, and the sgRNA-specific parameter εg; only T′. As with the test for absolute essentiality, an empirical p-value is computed for T′ based on the distribution of values computed for the negative controls. In some analyses, we performed a one-sided test by further requiring that T′ to 0.For this test, all samples are used to calculate the sample-specific parameters The set of essential genes used for validation was defined as those genes that were included in at least two of three previously published sets of essential genes –3. The sΔ) in all CCLE cell lines annotated as containing one or more of the following: single nucleotide polymorphisms (SNPs), deletions, or insertions in the start codon; deletions or insertions that introduce a frameshift mutation; mutations that introduce a premature stop codon or a de novo frameshifted start codon; or mutations that disrupt a splice site [For our analysis of real data from genome-wide CRISPR knockout screens, we used two large databases available from the Cancer Dependency Map (DepMap) Consortium: Project Achilles, produced by the Broad Institute , and Proice site , 33.For benchmarking with differential essential genes, we took three different approaches. For the first, we relied upon gene expression from the previously described ENCODE RNA-seq data sets to nominate our differentially essential gene set. The median expression of each gene was evaluated separately across the 206 unique lung samples and the 24 unique liver samples, with a total of 42 liver replicates and 42 lung replicates subsampled for data in the final analysis. 494 genes with a median expression difference ≥1.5 TPM were chosen to represent differentially essential genes .CDKN1A and MDM2, and randomly selected the various numbers of samples compared. To retain a similar genetic background between sample panels, samples were compared from the same tissue type and lineage, specifically cell lines originating from lung non-small cell lung cancer (NSCLC) adenocarcinoma. The Achilles Project dataset includes 220 replicates of 92 unique samples of these cell lines.For our second benchmarking analysis in real data, we focused on two known differentially essential genes, For our third benchmarking analysis in real data, we randomly selected three samples for each panel from cell lines across five tissues of origin. In this case, no replicates were used. We chose 19 differentially essential genes and their associated genetic backgrounds according to the RNAi-based findings of Project Drive , assuminTP53 (cell lines LU998 and H322M) with those from six screens from two of the cell lines with non-functional TP53 (LXF289 and H1650).The Project Score dataset has assayed 323 human cancer cell lines using the Human CRISPR Library . For ourWe designed the empiriCRISPR screen simulator to mimic the empirical properties of a template data set as closely as possible, while minimizing shared properties with our ACE inference model. empiriCRISPR simulates fractional sgRNA abundance sampled from a template screen and uses a series of gamma distributions to generate initial and final screen counts, X. and Y. are each sample’s target total read counts in the initial and final sequencing and m. is the abundance of each guide in the simulation’s template master library. The parameters ε. and ϕ. are the sgRNA efficiency and gene essentiality chosen for the simulation. empiriCRISPR simulates each sgRNA independently using α and β parameters derived from empirical estimates of variation from infection, initial, and final sequencing , fit across replicates of the provided template screen. empiriCRISPR calculates the gamma distribution parameters by minimizing the adjusted mean squared error of the summary statistics of simulated data with the median of 5 summary statistics of the template screens, shown in Additional file within a sample is wholly dependent on the number of sgRNA and template master library chosen.where ϕG across all sgRNA and samples for gene G.This implementation of empiriCRISPR is independent of the probabilistic model and parameter inference used in ACE, save for the general structure of the hierarchical model based on CRISPR screens. While the read counts in empiriCRISPR are derived from flexible gamma distributions designed to capture as much of the biological variation of template CRISPR screens as possible, ACE makes no attempt to infer specific variation parameters. CRISPR knockout screens from  were usedata.table [R6 [optim function with the “Brent” method. Both the ACE and empiriCRISPR github repositories can be found at https://github.com/CshlSiepelLab. ACE can be run across all genes in parallel. Optimization takes several seconds per gene  that usta.table  and R6 [able [R6 . OptimizG was calculated as follows: The “average fold change” (“AFC”) score for each gene xsg) and final (ysg) read counts to prevent undefined values. Each sample was normalized by λs to bring the total read count of each sample to a total of 10 million reads, and final fold changes were adjusted according to τneg, the mean fold change in all negative controls Gneg. Consistent with our ACE notation, an AFC value of zero has no impact on cell growth or proliferation.where a pseudocount of 0.5 was added to both initial  method  using sop-value in the case of only one sample type  method  version We used the CRISPhieRmix software version 0.1.0, using 100 points of integration (parameter “nMesh”) and the “BIMODAL” testing option. While the software is optimized for use in large pooled CRISPRi and CRISPRa screens, it may also be applied to knockout screens as demonstrated by the authors in . Differep-values were obtained for each Achilles DepMap sample used in the methods comparison. As in ref. [t-test to compare the by-sample log fold change effect sizes of the test and control sample panels. For Fig. t-test of the appropriate direction was separately performed for the statistics of MDM2 and CDKN1A, as CDKN1A is a tumor suppressor with a “negative” gene essentiality. In the case of bulk gene analysis in Additional file t-test was used.Previously published CERES-based  in ref. , the CERAdditional file 1 Supplemental figures and analysis.Additional file 2 Accession numbers of RNAseq assays used in nonessential and tissue-specific gene set curation.Additional file 3 Review history.


Smchd1 was analysed using a mix of long-read specific tools for preprocessing together with established short-read RNA-seq methods for downstream analysis. We used limma-voom to perform differential gene expression analysis, and the novel FLAMES pipeline to perform isoform identification and quantification, followed by DRIMSeq and limma-diffSplice (with stageR) to perform differential transcript usage analysis. We compared results from the sequins dataset to the ground truth, and results of the mouse dataset to a previous short-read study on equivalent samples. Overall, our work shows that transcriptomic analysis of long-read nanopore data using long-read specific preprocessing methods together with short-read differential expression methods and software that are already in wide use can yield meaningful results.Application of Oxford Nanopore Technologies’ long-read sequencing platform to transcriptomic analysis is increasing in popularity. However, such analysis can be challenging due to the high sequence error and small library sizes, which decreases quantification accuracy and reduces power for statistical testing. Here, we report the analysis of two nanopore RNA-seq datasets with the goal of obtaining gene- and isoform-level differential expression information. A dataset of synthetic, spliced, spike-in RNAs (‘sequins’) as well as a mouse neural stem cell dataset from samples with a null mutation of the epigenetic regulator  Short-read sequencing technology has underpinned transcriptomic profiling research over the past decade. The sequencing platforms offered by companies such as Illumina Inc. provide high read accuracy (>99.9%) and throughput which allows many samples to be profiled in parallel. One major limitation of short-read sequencing technology is the modest read lengths offered (currently up to 600 bases), which makes accurate isoform quantification and novel isoform discovery challenging. Long-read sequencing offers a distinct advantage in this regard, with the ability to generate reads that are typically in the 1–100 kilobase (kb) range   from 4 wild-type (WT) and 3 MommeD1 mutated (d1-null)  samples Technical replicates of synthetic ‘sequin’ RNA standards  from twoSynthetic ‘sequin’ RNA standards were obtained from the Garvan Institute of Medical Research.et al.  were constructed using 15 ng as input for cDNA synthesis. Samples were barcoded 1 to 4 using the supplied PCR barcodes. Transcripts were amplified by 14 cycles of PCR with a 6-min extension time.fast5 files were base-called by Guppy version 4.0.11 using configuration file dna_r9.4.1_450bps_hac.cfg to obtain fastq files, trim adaptor sequences and demultiplex barcoded reads. Guppy is only available to ONT customers via the community site (https://community.nanoporetech.com/).Sequencing libraries were individually purified using Beckman Coulter 0.8x AMPure XP beads and quantified using an Invitrogen Qubit 4.0 Fluorometer (ThermoFisher Scientific). Equimolar amounts of each sample were pooled to a total of approximately 160 fmol (assuming median transcript size is 1 kb), and quality control of the pooled library was performed using Agilent Technologies TapeStation 4200. The final library was loaded onto an R9.4.1 MinION flow cell and sequenced for 65 h with a buffer refuel at 24 h (using 250 ml buffer FB) using the ONT GridION platform. The http://lab.loman.net/protocols/) with extended incubation times (using SQK-LSK109 and EXP-NBD103 kits) for library preparation of the first batch, and used the updated kits SQK-DCS109 and EXP-NBD114 for the second batch (protocol PDCB_9093_v109_revA_04Feb2019). We loaded 100 ng of the final libraries on one PromethION flow cell (FLO-PRO002) per batch. The fast5 files were base-called by Guppy version 4.0.11 using configuration file dna_r9.4.1_450bps_hac_prom.cfg to yield fastq files. Guppy was also used to trim adaptor sequences from reads and demultiplex barcoded reads. The ‘other’ samples were removed in downstream analysis. For an overview of our analysis pipeline see Figure For the NSC dataset we prepared direct-cDNA libraries from 40 ng purified polyadenylated RNA. We combined the ONT direct-cDNA sequencing (SQK-DCS108) protocol (version DCB_9036_v108_revG_30Jun2017) with the one-pot native barcoding protocol  and HCC827 (mix B). The pooled RNA was subjected to the NEBNext Poly(A) mRNA Magnetic Isolation Module (E7490) and NEBNext Ultra II Directional RNA Library Prep (E7760) for Illumina following manufacturer’s instructions and default fragmentation conditions (15 min at 94°C), 5-fold adaptor dilution and a final amplification of nine PCR cycles. Indexed and pooled libraries were sequenced on a NextSeq500 (Illumina) High Output, 81 cycles paired-end.mm10 genome using minimap2 version 2.17-r943-dirty  annotation (chrIS_R using minimap2 with the arguments -ax splice –MD. The bam files were sorted and indexed using samtools version 1.6 (chrIS_R using Subread version 1.6.3 (sion 1.6 . The seqon 1.6.3 .featureCounts (https://www.R-project.org/) Bioconductor  and primaryOnly=TRUE to count primary alignments only were specified.Mapped reads were assigned to individual genes and counted by the reCounts  functiononductor  package n 1.34.4 ,28. We uMus.musculus (https://bioconductor.org/packages/Mus.musculus/ version 1.3.1) and read counts from technical replicates of the same sample run across different batches were combined (i.e. summed together). For all datasets, we organized and preprocessed the count data using the R/Bioconductor package edgeR version 3.26.8 . Using a limma-voom pipeline, the study reported 1197 differentially expressed (DE) genes . We further restricted this list  to give us 218 up- and 54 down-regulated genes in Smchd1-null samples when compared to WT samples. This cutoff resulted in similar numbers of significant genes between the short- and long-read datasets. The DE genes were compared to that of the NSC long-read data using ROAST gene set testing  (TALON version 5.0 (commit c41b9dc) (FLAMES version 0.1.0 (available at https://github.com/XueyiDong/FLAMES) (FLAIR. TranscriptClean (TALON version 5.0. Transcripts identified by TALON were filtered using default setting.We used three different tools to perform isoform detection and quantification: 2f8df51)  and TALO/FLAMES)  for bothiptClean  version FLAMES  is a novel method and software tool developed for long-read RNA-seq data  and transcript start/end (<100 bp by default) to the reference transcript are merged with the reference. High-confidence isoforms are those identified with at least 10 supporting reads. This process will also collapse isoforms that are likely to be truncated transcripts, which are identified as having incomplete 3’ regions compared to the full-length forms. A draft transcript assembly is generated at this stage, and all reads are re-aligned to both the known and assembled transcripts and quantified. For reads that align to multiple transcripts, both the proportion of transcript covered by the read alignment and the percentage of read sequence that aligns to each transcript are considered, with the read assigned to the transcript with highest coverage and >80% of its sequence aligned. A configuration file (JSON format) is used to set all parameters mentioned above so that different parameters can used to adapt to different situations. For example, one could set more stringent parameters for merging splice junctions (i.e. decreasing the default from 5 to 1 bp) to better suit long-read data generated by the PacBio platform, which generally has a lower error rate.seq data , availabSQANTI  of 10 or more in every sample. A second filter required transcripts to have 10 or more counts in at least 3 samples in the NSC data, and in at least 2 samples in the sequins data.After running each isoform detection tool, SQANTI  version on 1.3.0  ‘mappingDRIMSeq version 1.12.0 . DiffSplice analyses exon-level counts in short-read data to indirectly call for differences in isoform proportions, and reports adjusted P-values at the gene-level (Simes adjustment and/or F-tests) and exon-level (t-tests). For long-read data, we applied the diffSplice to transcript-level counts rather than exon-level counts as carried out by Love et al.  and diffSplice (Simes and t-tests) methods for FDR control to give stageR gene- and transcript-level adjusted P-values.DTU analysis was performed using two methods: the R/Bioconductor package n 1.12.0 , and dife et al. . Additiohttps://github.com/XueyiDong/LongReadRNA.RNA-seq data can be accessed from Gene Expression Omnibus (GEO) under accession numbers GSE151984 (sequins long-read), GSE151841 (NSC long-read data) and GSE164598 (sequins short-read). All code used to perform these analyses are available from To assess the quality of our long-read datasets, raw long reads were pre-processed and assigned to gene-level counts using an appropriate reference genome. Figure In the NSC dataset, median read length is 752 bases. Figure 2-counts-per-million (log-CPM) values. Gene length is weakly associated with expression in both long-read datasets; Pearson correlation of 0.035 for the NSC dataset  are fragmented for sequencing, such that longer transcripts can be over-represented relative to transcripts that are shorter. As a consequence, DGE analyses are biased towards the detection of genes (or transcripts) that are relatively long . Also, Dt Figure  and -0.0t Figure , whereast Figure  is greatlimma-voom workflow designed for short-read DGE analysis to the long-read data. We first analysed the sequins data to check whether the approach was appropriate using the ground-truth available. The analysis was carried out using voomWithQualityWeights to account for sample-level heterogeneity by estimating sample-specific quality weights based on similarity of gene expression within the same group. The sample weights were combined with voom precision weights that are based on the mean-variance relationship estimated from the data. Even though there were only 69 genes present in the dataset , the mean-variance trend observed for the sequins data (We applied the ins data  was simiins data .2fold-change (logFC) values between mix A and B. Estimated values were highly correlated (R2 = 0.934) with expected logFCs  with the t-statistics from the long-read data  DTU genes that were lowly expressed . The a Figure . OverallSmchd1-null sample (sample 7) is positioned more closely to WT samples. The mean-variance trend for this dataset is again typical of what is observed in short-read RNA-seq experiments  was used to observe the relationships between NSC samples. Dimension 1 in the MDS plot roughly separates samples by genotype Figure , althougeriments  . The relatively large ROAST P-value and overall lack of power to detect differentially expressed genes is likely due to relatively low sequencing levels per sample and within-genotype sample heterogeneity in the long-read experiment. Smoothed scatter plots of the logFCs and t-statistic for each gene between the short- and long-read datasets are presented in In a previous short-read study on the same mouse NSC groups ,36, the FLAIR and TALON with our novel FLAMES pipeline on the sequins dataset. Ideally, all transcripts that appear in the sequins annotation should be detected, and there should be no novel isoforms. Our results showed that FLAMES detected the most sequin transcripts  and TALON (∼4.2 M) are similar, while FLAIR recovered a lower number (∼2.7 M). Results from the sequins dataset indicated that the novel FLAMES pipeline outperformed the other two methods.Transcript-level analysis of nanopore RNA-seq data usually starts with isoform detection. To test which tool is best suited to nanopore data, we compared two popular tools, y Figure , the majFLAMES and the quality of the dataset, we calculated the coverage fraction of transcripts by individual reads. Here, reads covering 95% or more of the bases of their corresponding transcript are defined as ‘full-length’. In our sequins data, 47% of reads were found to be full-length, which is similar to Gleeson et al   than expected. For transcript-level testing, stageR improved the TPR of both methods whilst maintaining the same FDR. However, stageR did not improve the performance in gene-level testing for either of the two methods  Figure . DRIMSeqP-value cutoff of 0.05, n most significant DTU genes. For both DRIMSeq and diffSplice, in the top 19 genes, which is the number of true DTU genes in the datasets, there were 16 genes in common between the two methods  values were also highly correlated with their expected abundances  among all the genes tested. Transcript ENSMUST00000201980.4 (‘Known5’) and ENSMUSG00000023452.19_32736305_32746312_1 (‘Novel2’) in Pisd was identified by DRIMSeq to have differential usage between the two groups  found in common by both methods. Transcript ENSMUST00000201980.4 in Pisd was identified by both methods to have a higher usage in Smchd1-null samples than in WT samples . We expect this to occur in the near future as throughput of long-read experiments increases.Our DGE analysis uses a FLAMES pipeline to be reliable in both isoform detection and quantification. The high false positive rate of FLAIR and TALON for isoform detection suggests that these algorithms need further improvement to adapt to the high error rates in long-read sequencing. Despite methods being designed originally for short-read data, diffSplice (with or without stageR) performed very well in DTU analyses of the sequins long-read data using transcript-level counts, with better FDR control observed in long-read DTU analysis than in the short-read DTU analysis. We believe these methods could be applied to other datasets with confidence, but may lack power to detect DTU genes if transcript counts are very low. Notably, relative to DGE analyses, a DTU analysis further splits gene-level counts into associated isoforms which reduces power for statistical testing. For this reason, the power to detect DTU genes in the NSC long-read dataset is reduced relative to the sequins dataset since the latter contains far fewer expressed genes and transcripts to begin with, such that transcripts have higher counts on average. Another potential issue in the application of these methods to our NSC dataset is that altered expression of the gene Smchd1 may not affect RNA splicing mechanisms, which could mean there is a biological explanation for why very little DTU was observed in these data across both the short and long-read analyses.We also looked into transcript-level analysis of long-read data and found our novel limma software. We expect that other short-read tools such as edgeR and DESeq2 may also be appropriate, as used in other studies (Our study is the first to test a pipeline for both gene-level DGE analysis and transcript-level DTU analysis of nanopore long-read RNA-seq data. Whilst the sequencing depth is relatively low, we are still able to obtain reasonable results using pre-existing methods designed for short reads, namely the  studies ,17 althoWe hope that our analysis will encourage further research into the potential for long-read RNA-seq to be used in place of short-read RNA-seq, allowing for the simultaneous exploration of gene-level and isoform-level changes within the same experiment in a more comprehensive way.lqab028_Supplemental_FileClick here for additional data file.


Furthermore, we demonstrated clinically-relevant patient subtypes in four of five cancers, with most subtypes characterized by distinct driver somatic mutations, such as TP53, TTN, BRAF, HRAS, MET, KMT2D, and KMT2C mutations. Importantly, these subtyping results were frequently contributed by dysregulated biological processes, such as ribosome biogenesis, RNA binding, and mitochondria functions. PipeOne is efficient and accurate in studying different cancer types to reveal the specificity and cross-cancer contributing factors of each cancer.It could be easily applied to other diseases and is available at GitHub.Various abnormalities of transcriptional regulation revealed by RNA sequencing (RNA-seq) have been reported in cancers. However, strategies to integrate multi-modal information from RNA-seq, which would help uncover more disease mechanisms, are still limited. Here, we present PipeOne, a cross-platform one-stop analysis workflow for large-scale transcriptome data. It was developed based on Nextflow, a reproducible workflow management system. PipeOne is composed of three modules, data processing and feature matrices construction, disease feature prioritization, and disease subtyping. It first integrates eight different tools to extract different information from RNA-seq data, and then used random forest algorithm to study and stratify patients according to evidences from multiple-modal information. Its application in five cancers (colon, liver, kidney, stomach, or thyroid; total samples  RNA sequencing (RNA-seq) has been widely used in functional genomics studies ,2,3. VarPVT1 drives oncogene MYC expression in various types of cancer cells [Besides mRNA, a large number of non-coding RNAs can be detected by RNA-seq, such as circular RNAs (circRNA) , long noer cells . Furtherer cells  that haver cells ,27 and mer cells . In parter cells .https://github.com/nongbaoting/PipeOne. .Biological processes in the cell interact with each other. For example, RNA-editing can affect AS and circular RNA biogenesis , RNA reghttps://portal.gdc.cancer.gov/, accessed on 20 October 2019). Annotations from GENCODE v32 [https://www.gencodegenes.org/, accessed on 12 October 2019 and https://lncipedia.org/, accessed on 10 October 2019, respectively [Raw RNA sequencing reads of TCGA cancers  and associated sample clinical information were downloaded from the GDC portal  to find novel transcripts, which were assigned class_code ‘i’, ‘u’, or ‘x’. CPAT (v1.2.4)  [Raw sequencing reads for each sample were aligned against the human genome hg38) with HISAT2 (v2.1.0)  with HIS. All assNY, USA) , CPPred NY, USA) , and PLENY, USA)  were usehttps://github.com/suhrig/arriba, accessed on 25 March 2019), which was reported as fast and accurate [First, quality control for raw sequencing data was performed by FASTP  . Then, eaccurate . Tools wAfter preparation of the eight feature matrices, random forest algorithm (Python package ‘scikit-learn’) was used to select disease relevant features. Only the top 1000 (users may choose a different number) most variable features from each type of feature matrix were combined together and used for machine learning. Samples were randomly separated into training set (70% samples) and testing set (30%). First, random forest was applied to the training set to obtain all features importance using the leave-one-out validation strategy. Then, the top K  important features were validated using the testing set. For each chosen K, the subset training matrix with only those top K features were used as the input for retraining as in the first step, and this time the subset test matrix with only those top K features were used to evaluate the clustering accuracy. Finally, features with non-zero importance scores were selected and regarded as disease relevant, which can be further used for downstream analysis.p-values) to obtain the relative importance of all features contributing to subtyping.This module used non-negative matrix factorization (NMF) to obtain a latent feature matrix of the disease samples, clustered those samples, and evaluated those clusters as significant subtypes by using survival analysis. Similar to the feature selection module, the top 1000 most variable features were selected first. First, a robust NMF integration algorithm was applied to find latent features with corresponding feature weights. Then, the K-Means clustering algorithm (Python package ‘scikit-learn’) was performed on the latent feature matrix and clusters was evaluated by silhouette width . Next, chttps://toppgene.cchmc.org, accessed on 14 March 2021). Pathway enrichment for subtype-contributing genes shared across cancer types was performed by ToppGene webserver directly. Somatic mutation analysis and visualization for cancer subtypes were performed by MAFtools  [Pathway and disease enrichment analysis for cancer-contributing genes in each cancer type was performed by invoking ToppGene API (ngapore) .Studying diseases from multiple aspects proves to be effective in revealing disease mechanisms. We developed PipeOne that uses multi-modal information from RNA-seq data to comprehensively investigate transcriptomes. As shown in Compared with two previous RNA-seq pipelines, RNACocktail  and VIPEn = 325 , KIRP, n = 320 , LIHC, n = 421 , STAD, n = 403 , and THCA, n = 555 ). For each cancer, we observed that many non-mRNA expression cancer-associated features, especially AS, lncRNA expression and retrotranscriptome , and only one shared (KRT7 expression) with the gastrointestinal cancer COAD. KRT7 regulates cell differentiation and is involved in the regulating translation of human papillomavirus type 16 (HPV16) [ABI3BP in three tumors , compared to adjacent normal samples , LGALS3 (apoptosis), FBP1 (ferroptosis), and TMEM123 (oncosis), insulin growth factor gene IGF2, and macrophage inhibitory cytokine GDF15. The functionalities of those unexplored factors specifically contributing to individual cancer may be further investigated.We found 23 features as shared cancer-associate factors among different cancers B. In con (HPV16) , which i (HPV16) ,60. One  samples C–E. ABI3d cancer  and lungd cancer , and deper [TP53 . The exop = 1.2 × 10−5, FDR = 0.015) and ‘erythrocytes take up oxygen and release carbon dioxide’  for COAD, Eplerenone  and other drug metabolism pathways  for KIRP, ‘scavenging of heme from plasma’  for LIHC, ‘formation of tubulin folding intermediates by CCT/TriC’  for STAD, and extracellular matrix-associated and platelet degranulation pathways  for THCA   . Moreove × 10−5) G.p = 0.014), KIRP (p = 0.012), and THCA  (BRAF (69% vs. 53%) and HRAS (1% vs. 5%) (MET (13% vs. 4%), KMT2D (10% vs. 3%), and KMT2C (9% vs. 5%) somatic mutations in S0 compared to S1  A–D, but = 0.033) . This re vs. 5%) E. For KIed to S1 F. MET is with S1 G.p = 9.5 × 10−33 and RNA binding (p = 1.7 × 10−19 (Next, we examined subtype-contributing features in each cancer and across cancers. We observed 232 features  shared b × 10−19 H. Disrup × 10−19 I, possibDuring RNA-seq raw data processing, PipeOne not only included classical procedures of data analysis, e.g., quality control, alignment, transcriptome reconstruction, gene quantification, but also contained novel lncRNA prediction, circRNA prediction, RNA editing prediction, fusion prediction, retrotranscriptome quantification, alternative splicing event detection, variants calling. Compared to RNAcocktail and VIPER, PipeOne harbored more functions, including prediction of novel lncRNAs and circRNAs, retrotranscriptome, and alternative splicing event detection. These will greatly enrich the information derived from RNA-seq data for downstream analysis, in which PipeOne focused on integrating multi-modal information to perform feature prioritization and disease subtyping. To the best of our knowledge, existing tools did not utilize such broad aspect information from RNA-seq for integration analysis. PipeOne did not implement many functions provided in VIPER, however, most of those procedures in RNA-seq analysis are classical, and could be performed by using common tools. For example, differential expression analysis could be perform by edgeR  or DESeqOne limitation of PipeOne is that it currently focuses on RNA-seq data only. Still, it may also be expanded to include features from other high-throughput data, for example, genome sequencing, DNA methylation profiling by microarray or sequencing, and proteomics by mass spectrometry, where any of these data are available Another limitation is subtyping evaluation. In this study, we only assessed the subtyping clusters by Silhouette values and log-rank test-based overall survival analysis. In the future, more choices would be implemented to allow other ways to evaluate subtypes, for example, by comparing patient drug responses when this information is available. That will enable subtyping analysis for other diseases without survival information. In addition, future version of PipeOne could include features from other high-throughput data as mentioned above to perform multi-omics base subtype analysis and proper reduce noise effects may help to better subtyping. For example, DefFusion  can makeIn short, we presented a cross-platform analysis pipeline integrating all kinds of RNA-seq analysis to model diseases comprehensively and demonstrated the power of PipeOne in five cancer types. These results encourage us and other researchers to confidently apply PipeOne to more cancer types and other complex diseases with a large number of RNA-seq samples available. Our pipeline and analysis results provide new opportunities and clues for cancer research, and may inspire future multi-omics analysis to add more information from RNA-seq data.


Accurate cancer classification is essential for correct treatment selection and better prognostication. microRNAs (miRNAs) are small RNA molecules that negatively regulate gene expression, and their dyresgulation is a common disease mechanism in many cancers. Through a clearer understanding of miRNA dysregulation in cancer, improved mechanistic knowledge and better treatments can be sought.We present a topology-preserving deep learning framework to study miRNA dysregulation in cancer. Our study comprises miRNA expression profiles from 3685 cancer and non-cancer tissue samples and hierarchical annotations on organ and neoplasticity status. Using unsupervised learning, a two-dimensional topological map is trained to cluster similar tissue samples. Labelled samples are used after training to identify clustering accuracy in terms of tissue-of-origin and neoplasticity status. In addition, an approach using activation gradients is developed to determine the attention of the networks to miRNAs that drive the clustering. Using this deep learning framework, we classify the neoplasticity status of held-out test samples with an accuracy of 91.07%, the tissue-of-origin with 86.36%, and combined neoplasticity status and tissue-of-origin with an accuracy of 84.28%. The topological maps display the ability of miRNAs to recognize tissue types and neoplasticity status. Importantly, when our approach identifies samples that do not cluster well with their respective classes, activation gradients provide further insight in cancer subtypes or grades.An unsupervised deep learning approach is developed for cancer classification and interpretation. This work provides an intuitive approach for understanding molecular properties of cancer and has significant potential for cancer classification and treatment selection. Accurate cancer classification is the key to understanding and selecting effective treatments for this broad group of diseases. Typically, pathologists classify cancers using integrated morphologic, immunohistochemical, and/or targeted molecular analyses of tumor tissues. However, pathological diagnosis suffers from inter- and intra-observer variability . MoleculmicroRNAs (miRNAs) are a class of small  , 35 are In this study, we access sample hierarchy data  and comprehensive miRNA expression profiles from 2026 neoplastic and 1659 non-neoplastic tissue samples from an ongoing miRNA sequence curation and expression atlas project at Queen’s University, Canada and The Rockefeller University, US. Parts of the atlas data are available, including breast tissue samples  and neurOur end-to-end unsupervised deep learning approach is shown schematically in Fig. Autoencoder Pre-Training To reduce the dimensionality of the input features, the AE is pre-trained as follows. The weights for both the AE and SOM are randomly initialized between 0 and 1. The AE consists of three layers beginning with 984 inputs (corresponding to the filtered miRNAs), and successively reduced number of units in the following layers to form a geometric series leading to the final latent layer. We use Mean Squared Error (MSE) between the input and output of the AE as the loss function, a small regularization term, gradient descent with Adam optimization, a batch size of 64, and 3500 epochs for training.Self-Organizing Map Pre-Training The latent features of the AE are mapped to nodes on a 2D lattice. The weights associated with each node of the lattice are compared to an input sample from the AE using Euclidean distance. The node with the shortest distance to the input sample is labelled as the winning node, and the sample is topologically mapped to that location. During SOM pre-training, the weights of the winning node, and the weights of nodes surrounding the winning node  are modified to become more like the input sample. This ensures that similar input samples will eventually be mapped close to each other when training is complete, preserving the topological structure in the data. SOM weights are updated using the difference between the input from AE and the current SOM weights, along with a small learning rate and a Gaussian neighbourhood function. A time-decaying neighbourhood function is used to train the SOM; initially, the neighbourhood is larger to group all similar samples together, and slowly it shrinks to fine-tune the exact location of similar samples to a certain node , , and  .


Advancements in mass spectrometry‐based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much‐needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step‐by‐step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology. In mass spectrometry‐based proteomics, handling large sample sets introduces technical variability known as batch effects. This tutorial provides guidelines and tools for the assessment, normalization, and batch correction of proteomics data. As a consequence, large‐scale studies consisting of hundreds of samples are becoming increasingly common  across multiple samples, here referred to as “raw data matrix” and finishes with "batch‐adjusted" data, which are ready for downstream analyses . We split the workflow into five steps, shown in Fig In the context of this article, we will use the term “adjust for batch effects” when referring to the whole workflow and “correct for batch effects” when referring to the correction of normalized data  and a pre‐built Docker container , as well as a GitHub repository (https://github.com/symbioticMe/batch_effects_workflow_code) of the workflow with all code and data required to reproduce the case study analyses.In the accompanying “proBatch” package, we implemented several methods with proven utility in batch effect analysis and adjustment. We also provide tips for integrating other tools that might be useful in this context, and for making them compatible. proBatch is made available as a Bioconductor package  plotting the sample intensity average or median in order of MS measurement or technical batch, allows to estimate MS drift or discrete bias in each batch; (ii) boxplots allow to assess sample variance and outliers; and (iii) inter‐ vs. intrabatch sample correlation. A higher correlation of samples from the same batch compared with unrelated batches is a clear sign of bias. Optionally, a few proteins or peptides can be checked for signs of bias.et al, et al, et al, Heterogeneity of the data: If samples are fairly similar, the bulk of the proteome does not change, and thus, techniques such as quantile normalization  is a technique that identifies the leading directions of variation, known as principal components. The projection of data on two‐component axes visualizes sample proximity. Additional coloring of the samples by technical/biological factors, or by highlighting replicates, facilitates the interpretation of what drives sample proximity. This technique is particularly convenient to assess clustering by biological and technical factors or to check for replicate similarity. Visualization without sample point or label overlay effects works in our experience up to about 50–100 samples in a dataset.The diagnostic approaches can be divided into proteome‐wide and peptide‐level approaches. The main approaches for proteome‐wide diagnostics are as follows:One should be careful in interpreting proteome‐wide diagnostics because these methods were designed for data matrices without missing values. Proteomic datasets often contain missing values for technical or biological reasons. For more details, we refer the reader to Box et al, In proteomics, peptide‐level diagnostics are as useful as proteome‐wide diagnostics. As in other high‐throughput measurements, individual features, in this case, peptides, are visualized to check for batch‐related bias. In proteomic datasets, spike‐in proteins or peptides can be added as controls. In most DIA datasets, iRT peptides  removal of the bias (negative control) and (ii) improvement of the data (positive control).Typically, bias is considered removed if the similarity between samples is no longer driven by technical factors. This means that neither hierarchical clustering nor PCA shows clustering by batch, and the correlation of samples from the same batch is no longer stronger than the correlation of unrelated samples. Also, individual features should not show batch‐related biases. Thus, comparison of diagnostic plots for raw and adjusted data serves as the negative control.et al, et al, et al, Proving improvement achieved by batch correction is much harder. It is common to take “improved clustering by biological condition” or “higher number of differentially expressed proteins” as a positive control and generally, as a sign of data quality enhancement. However, both criteria are subjective: It is impossible to know beforehand, whether biological groups are separable in the proteomic space, especially if only a subset of proteins changes while the bulk of the proteome does not. Similarly, it is not possible to predict whether higher sensitivity for differential expression comes at the expense of added false‐positive hits. Therefore, we do not recommend using these criteria to assess normalization or batch effect correction. As described above, the choice of the method should rather be based on the properties of the samples. In general, since batch adjustment removes a certain portion of variance, the coefficient of variation for peptides and proteins in replicated samples should decrease. This is especially true for spike‐in peptides or proteins that are added to samples in controlled quantities. A stronger positive control is the assessment of reproducibility, such as comparison of lists of differentially expressed proteins or regression/classification models derived from two or more sample sets belonging to different batches , described in Table et al, et al, et al, The first study, called here "InterLab study", assessed the robustness of SWATH‐MS in a multi‐lab setting . They ask technical and biological questions about proteomes of varying complexity and present different degrees of sample‐to‐sample heterogeneity. In this respect, the InterLab study is very homogeneous, to the point that all the samples are essentially technical replicates. The PanCancer study is highly heterogeneous and comprises 205 proteins identified in samples originating from different hospitals and different tissues. Finally, the model‐organism‐based studies represent an intermediate case. On the one hand, its subjects were genetically related, but on the other hand, sampling introduces a certain amount of sample heterogeneity.The main goal of the initial assessment is to set a baseline for the magnitude and nature of batch effects in a particular dataset. At this stage, the data matrix is “raw”, and the quantities are reported as measured, without any calibration, normalization, or correction with regard to their values in other samples.Thus, it is essential to get a quick overview of the data by comparing global statistics, such as the average intensity, or correlation of samples and of few individual proteins with known expected abundance. In mass spectrometry, it is helpful to first plot these statistics by sample running order, as it is common for the measured signal to drift . This is clearly seen in Fig Not only do batch effects introduce shifts in the total sample intensity distributions, but they also lead to a spurious correlation between features . It is common, for samples belonging to the same batch to have strong correlation , which can be seen as the bimodal expression separation on the plot according to the two possible ACADS alleles of the inbred strain. However, as seen in Fig Optionally, one can complement the initial assessment with the analysis of a few specific features (peptides or proteins), for which prior information is known. Here, we plot a peptide from a representative protein Last but not least, intensity boxplots are extremely powerful at this stage of the analysis, as they visualize in a single plot: median, quantiles, and outliers. This allows one to see whether there are batch‐specific intensity patterns such as shifts  and Hierarchical Clustering are the best established. For instance, in the Aging mouse study, the following patterns became apparent see Fig : First, Hierarchical clustering, in contrast, allows one to visualize multiple factors at once Fig . OverallPrincipal Variance Components Analysis (PVCA) transforms the intuition of visualization brought about by PCA and Hierarchical Clustering into numbers. The weights of each factor in each Principal Component are combined, thus providing a concise summary of variance distribution between biological and technical factors and their combination. In the aging mouse dataset, we see in Fig It is important to point out that PCA, hierarchical clustering, and PVCA require matrices with complete feature measurements, or with imputed missing values. Since in MS proteomic missing values are often batch‐specific, this might lead to an overestimate of the batch‐related clustering (variance explained) and to over‐pessimistic assessment of data quality . Various factors, such as sample quality and composition, can contribute to the speed of this degradation. This is particularly relevant in large‐scale studies.To address this complex bias, we have developed a two‐step batch correction procedure shown for the aging mouse in Fig et al, et al, As a curve‐fitting algorithm for the first step, we have chosen LOESS, as it combines computational simplicity with relative flexibility of the fit characteristics. The procedure runs as follows: For each peptide and each batch, a unique curve is fit Fig  and thenet al, Datasets that do not exhibit MS signal drift require only discrete batch correction—second step of the aging mouse dataset. Here, the PanCancer Fig C–F and TWhen several batch factors affect the data , all factors should be accounted for during correction. This typically means that batch factors get combined. This, however, is not always possible . In the Aging mouse study, for instance, the two main technical factors, MS batch and digestion batch, were highly confounded. Thus, we opted to only correct for the MS batch.In conclusion, batch effect correction removes variance from known, annotated batch factors. Whether the adjustment procedures—normalization and batch effect correction—improved the data signal, remains, however, to be determined by a quality control step.The main goal of the quality control step is to determine whether the data quality has improved.Examples of negative controls are the plots discussed in the diagnostic sections such as individual peptide plots, PCA, hierarchical clustering, or PVCA. In most cases, these show that samples are no longer affected by batch‐specific patterns . As seen in Fig cis‐pQTLs passed the significance threshold in the raw data matrix, and extra 133 cis‐pQTLs have been detected after batch effect adjustment—100 after normalization and 33 additional cis‐pQTLs after batch effect correction. On peptide level, this corresponds to 993 peptide‐level pQTLs in raw data, additional 405 peptides after normalization and 352 after batch effect correction. Similarly, for the TMT mouse dataset 3,306 cis‐pepQTLs were detected at LOD ≥ 4 among the 8,774 peptides with complete measurements in the normalized data, with an additional 109 cis‐pepQTLs detected after correction for TMT batch factor. Note, that for the aging mouse dataset, normalization seems to alleviate most of the bias, as was already demonstrated in Fig Depending on the experimental setup, one can sometimes judge the data improvement through the presence of a better biological signal. In the Aging mouse study, this can be achieved via mapping protein quantitative trait loci (pQTL), particularly On the other hand, the sample and feature proximity methods we proposed in the “Quality control” section can be applied in almost any proteomic experimental context and thus are a more universal approach to quality control.Sample proximity measured as correlation distributions for intrabatch vs. unrelated samples, and for replicates vs. all other samples can be seen for the aging mouse dataset pre‐ and post‐adjustment in Figs The second method is based on assessing the proximity of feature sets. In bottom‐up proteomics, these are peptides or fragment ions. Similarly to the previous method, the distance between related peptides  is expected to be smaller than the distance between unrelated ones. This effect is true not only for selected proteins Fig  but alsoIn summary, through a combination of positive and negative quality controls, it is possible to assess improvements in data quality. It should, however, be stressed that quality control methods are not criteria for choosing batch adjustment methods. Their purpose is rather to provide metrics to control the quality of the data before and after adjustment.https://www.bioconductor.org/packages/release/bioc/html/proBatch.html). The "proBatch" package wraps multiple established techniques for data transformation and visualization with proven utility in the control of batch effects.To facilitate the practical application of the steps and principles described here, we have developed an R package, named "proBatch" and made it available in Bioconductor  and a sample annotation table with technical and biological factors.Consistent visual representation of factors at all steps of the analysis .Note that we have also deliberately avoided the inclusion of a "single‐click" batch adjustment function that would combine normalization and correction. As explained above, the choice of the specific algorithm for normalization and batch correction depends on the properties of a particular dataset, and thus, keeping these functions as separate exchangeable modules should prompt the researcher to check the properties of the data and the goals of the analysis and ultimately make an informed choice of appropriate batch adjustment methods.https://github.com/symbioticMe/batch_effects_workflow_code) of the workflow. An accompanying Docker container containing all the tools required to replicate the analyses is made available on DockerHub as a proBatch Docker container .To demonstrate how the package can be applied to different experimental setups, and how different parameters of the study can be adjusted, we make the code used for the analyses of the five case studies presented in this manuscript available as a GitHub repository  samples can be randomized. "Overcorrection'' can also potentially occur with small‐sized batches, where fitting a non‐linear curve or accurately estimating the mean and the variance becomes difficult. Hence, it has been suggested that batches should contain at least 25 samples  data. The workflow relies on having a quantitative data matrix with few assumptions on its structure and is applicable to most quantitative proteomic workflows. However, proteomic applications incorporating affinity purification, size exclusion chromatography, PTM‐centric protocols, or subcellular fractionation are typically more heterogeneous. These dataset types will likely require additional efforts for batch effect characterization and correction.While searching for publicly available datasets to present as case studies in this manuscript, we also noted that many of the available studies have been deposited with little to none of the technical meta‐data required to perform batch correction. We believe that this limits the re‐usability of proteomic data and would like to encourage future studies to include this information.In conclusion, mass spectrometry‐based proteomics has come a long way and is continuing to evolve. As throughput and reproducibility increase, so do batch effect‐related issues. Hence, we expect experimental design and batch effect correction methods to also grow in importance and to take center stage in large‐scale proteomic applications such as clinical proteomics and systems biology.JČ and PGAP designed the analysis workflow and wrote the manuscript; EGW introduced multiple components in the workflow and made major contributions to the manuscript outline and text. JČ, CHL, EGW, VSS, and GRK performed data analysis. JČ, CHL, and PGAP implemented back‐end R functionality; EGW helped with the code testing. EGW, TS, BCC, and GRK provided proteomic data and contributed to workflow conceptualization. FW, SG, and BW provided proteomic data measurements and analysis. MRM provided critical input on the project and assisted in analysis pipeline design. MRM and RA commented and edited the manuscript. PGAP and RA supervised the study.The authors declare that they have no conflict of interest.Box 1et al, et al, Proteomic experiments now routinely profile hundreds or thousands of proteins across hundreds of samples. However, detecting all proteins without missing values across the whole dataset is not yet feasible. The patterns of "missingness" are known to be batch‐specific , this problem can also arise due to fundamental peptide interference regardless of their abundance or the acquisition parameters.et al, et al, Missing values can also affect batch effect correction methodologies. For instance, the current implementation of ComBat  values inferred from MS elution traces are used, the correlation within batches seems higher than the correlation of replicates, while this problem is not observed when imputation is not used ("no requants"). Protein inference is also affected by the imputation on lower levels.Finally, provided that there are enough confidently quantified values, many downstream analysis techniques, such as differential expression or protein correlation analyses, can handle missing values. We therefore advise to avoid imputation, or at least suggest to perform it after batch correction whenever possible.Box Figure 1. The problem of missing values in batch effect diagnosis and correction: Aging mouse study. (A) Hierarchical clustering and heatmap of normalized data; missing values shown in black. The missing values are non‐randomly associated with the batch; (B) heatmap of selected sample correlation: Stronger correlation of samples within Batch 2 (blue) and Batch 3 (brown) is visible in the data with "requants", and replicate correlation is much more prominent in the data without "requants"; (C) distribution of selected sample correlation: same effect, as in (B) showing the distribution of sample correlation.AppendixClick here for additional data file.Expanded View Figures PDFClick here for additional data file.


Study design is a critical aspect of any experiment, and sample size calculations for statistical power that are consistent with that study design are central to robust and reproducible results. However, the existing power calculators for tests of differential expression in single-cell RNA-seq data focus on the total number of cells and not the number of independent experimental units, the true unit of interest for power. Thus, current methods grossly overestimate the power.Hierarchicell is the first single-cell power calculator to explicitly simulate and account for the hierarchical correlation structure  that exists in single-cell RNA-seq data. Hierarchicell, an R-package available on GitHub, estimates the within sample correlation structure from real data to simulate hierarchical single-cell RNA-seq data and estimate power for tests of differential expression. This multi-stage approach models gene dropout rates, intra-individual dispersion, inter-individual variation, variable or fixed number of cells per individual, and the correlation among cells within an individual. Without modeling the within sample correlation structure and without properly accounting for the correlation in downstream analysis, we demonstrate that estimates of power are falsely inflated. Hierarchicell can be used to estimate power for binary and continuous phenotypes based on user-specified number of independent experimental units  and cells within the experimental unit.Hierarchicell is a user-friendly R-package that provides accurate estimates of power for testing hypotheses of differential expression in single-cell RNA-seq data. This R-package represents an important addition to single-cell RNA analytic tools and will help researchers design experiments with appropriate and accurate power, increasing discovery and improving robustness and reproducibility.The online version contains supplementary material available at 10.1186/s12864-021-07635-w. Robust and reproducible science depends on the quality of the experimental design. High quality experimental design revolves around focused research questions or hypotheses, appropriate and valid measures of the central variables related to these hypotheses, statistically sound analysis plans, and properly computed power analysis . While pHierarchicell, with two purposes: 1) it is a simulator of hierarchical single-cell RNA-seq data, and 2) it computes power estimates using a mixed-effects models for testing hypotheses of differential gene expression in single-cell RNA-seq data. Hierarchicell simulates single-cell RNA-seq data with a hierarchical structure that closely resembles that of real data and can be used by researchers to make informed choices on experimental design while balancing the trade-off between cost and power. Our R-package is user friendly and flexible to a variety of scenarios. It incorporates estimates from real data [Besides the classic, closed form, normal theory power calculations  that make too many overly simplistic assumptions , the power calculators for testing single-cell RNA differential expression all simulate cells independently, without the within-subject correlation structure –11. Preveal data  or alloweal data –15. Otheeal data . EstimatHierarchicell’s accompanying vignette. We encourage users to review this vignette  values, within sample variance, between sample variance, relationship between the grand mean and dispersion, dropout) that define the observed distribution of TPM values for a gene. To estimate these parameters, genes were pruned to a set of uncorrelated genes that captured the most representative patterns of detectable TPM values, without the resulting parameter estimates being primarily driven by dropout. Specifically, genes were sequentially sampled one at a time and any other gene having transcript abundances correlated (Spearman’s correlation coefficient > 0.25) with the gene were removed. To estimate the grand means independently from the hierarchical correlation structure, the grand means were estimated by sampling one cell from each individual and computing the mean TPM value 1000 times. The mean of each of those means was used to approximate the grand mean. To approximate the variance of the within-sample means , the means of all non-zero TPM values were computed across all cells within each individual and the variance between those values was subsequently computed. To estimate the within-sample dispersion values, the non-zero TPM values were first used to compute a within-sample variance and within-sample mean. Consistent with the classical definition of the Negative Binomial distribution’s dispersion parameter, the within-sample dispersion parameter was then computed as:αij represents the dispersion parameter, μij represents the within-sample mean, and i and individual j. The grand means and variances were computed empirically from the TPM values previously reported in six different cell types across three different single-cell studies [i, a random value was sampled from this gamma distribution to obtain a grand mean, μi. The variance of the within-sample means  was modeled as a linear function of the grand means, f1(μi) and the within-sample dispersion  was estimated as a logarithmic function of the within-sample means, f2(μi), and the probability of dropout was estimated independently as a bounded gamma distribution , a difference in means was drawn for each of the individuals j in the simulation. This difference was summed with the grand mean to obtain an individual mean, μij. Three different methods were used to simulate the number of cells per individual. To simulate scenarios where each of the individuals had the exact same number of cells, the number of cells desired for each individual was fixed at a constant value. In order to simulate scenarios where the number of cells per individual demonstrated slight imbalance, a Poisson distribution with a λ equal to the expected number of cells desired for each individual was then used to obtain the count of cells for each individual. To simulate scenarios where the number of cells per individual demonstrated greater imbalance, the number of cells per individual were modeled as a Negative Binomial random variable with a mean equal to the expected number of cells and a dispersion parameter of one. For each gene i and cell k assigned to an individual j, a read count value, Yijk, was drawn from a Negative Binomial distribution with an expected value equal to the individual’s assigned read count value, μij, and a dispersion parameter, αij, computed by the logarithmic function of the grand mean f2(μi).where mentclasspt{minima studies , 17. OncTo compute power, transcripts-per-million (TPM) values were simulated for each gene with the user-specified fold-change or ρ parameter Fig. . Fold-chWe applied a two-part hurdle model with a random effect for individual as directed in MAST’s reference manual . Specifically, a log(x + 1) transformation of the data was applied and the hurdle model computed to find genes exhibiting differences in expression. Using their same notation, the addition of random effects for differences among persons is as follows:Yig is the expression level for gene g and cell i, Zig is an indicator for whether gene g is expressed in cell i, Xi contains the predictor variables for each cell i, and Wi is the design matrix for the random effects of each cell i belonging to each individual j . βg represents the vector of fixed-effects regression coefficients and γj represents the vector of random effects . γj is distributed normally with a mean of zero and variance 2 null distributions; these statistics can be summed and remain asymptotically χ2 because Zg and Yg are defined conditionally independent for each gene. When summed together, these tests provide a single test for the two-part hurdle model. Our package also offers the ability to compute type 1 error rates (and thereby power) for a variety of different single-cell analysis approaches. New methods that properly handle within sample correlation will be integrated as they become available.where All simulations and data were compiled in RStudio using R-3.6.2 and is freely available on GitHub. The supplementing dataset that is included to run the R-package without user input data was significantly downsized by removing all of the genes correlated with a Spearman’s correlation coefficient > 0.25. This filtering is one of the first steps in our simulation procedure and doing so greatly reduced the size of the source package to 475 KB as well as the data structures held in memory during use. Currently, the simulation typically completes in less than 5 seconds, depending on user specifications. The simulation-based power calculations, however, can take much longer depending on the model that is used. For the recommended two-part hurdle mixed model , this can range anywhere from 1 to 20 min per simulation-based estimate of power for a given fold-change on a 64-bit Operating system with 8 CPUs and 16 GB of RAM. We note that these run times are heavily dependent on the number of genes, the sample size, the number of cells per individual specified, and the number of CPUs available.Previously, we demonstrated that our simulation recapitulates the most important aspects of single-cell gene expression data, particularly the hierarchical structure of single-cell RNA-seq data which is rarely accounted for in differential expression analysis . We alsoHierarchicell now also allows users to estimate power for detecting associations between continuous traits and single-cell gene expression  . Hierarcion Fig. . The simHierarchicell package. To consistently identify fold-change differences of at least 1.2 as statistically significant (power > 0.80), we approximate that researchers will need a minimum of 40 samples per group and 100 cells per sample in a classical case/control design. To consistently identify genes correlated with a correlation coefficient of 0.4 with a phenotype (power > 0.80), we approximate that researchers will need a minimum of 100 samples and 100 cells per sample.As expected, increasing the number of independent experimental units  in a study is the best way to increase power to detect true differences between traits measured at the individual and not individual cell level Fig. a. Power As experiments get larger, computational time will increase. Future work will parallelize the code. To more rapidly close in on plausible sample size options, a researcher can apply the aggregate (“pseudo-bulk) methods power estimates and as one approaches feasible design shift to refining the estimates using the two-part hurdle mixed model employed here. However, it is important to do this refining step given the differences between these two approaches and the types of scenarios where aggregation methods will be underpowered , 18.Future iterations of this package will incorporate any novel single-cell RNA-seq differential expression methods that properly account for the within sample correlation. In addition, we will parallelize the code and improve the speed of software by building components of the software in other languages (such as C++ via rcpp), and/or storing results of a large number of scenarios for quick and easy access to the necessary information. Future developments would be to incorporate the relationship between power and the variance explained by an effect, not simply fold-change between treatments. In real data, the variances explained by an effect fluctuate greatly among genes and cell types. While the simulated expression data herein have variances that are modeled after real data and are allowed to fluctuate by genes, simulating a direct relationship between the variance and an effect will be a meaningful addition to this work.Hierarchicell, with two purposes: 1) simulation of hierarchical single-cell RNA-seq data, and 2) computation of power estimates using a mixed-effects models for testing hypotheses of differential gene expression in single-cell RNA-seq data. Hierarchicell allows for a range of inputs and parameter settings and even the evaluation of various single-cell specific methods, but encourages using linear mixed models with individual as a random effect for both binary and continuous outcomes, as implemented in MAST [To date, none of the primary power calculation methods are directly applicable for differential expression analysis with single-cell RNA-seq data. Here, we present an R-package,  in MAST , 19. We Project name: hierarchicellhttps://github.com/kdzimm/hierarchicellProject home page: Operating system(s): Linux, Mac, and PCProgramming language: ROther requirements: NoLicense: CC0Any restrictions to use by non-academics: NoAdditional file 1.