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Tradict enables accurate prediction of eukaryotic transcriptional states from 100 marker genes

Transcript levels are a critical determinant of the proteome and hence cellular function. Because the transcriptome is an outcome of the interactions between genes and their products, it may be accurately represented by a subset of transcript abundances. We develop a method, Tradict (transcriptome predict), capable of learning and using the expression measurements of a small subset of 100 marker genes to predict transcriptome-wide gene abundances and the expression of a comprehensive, but interpretable list of transcriptional programs that represent the major biological processes and pathways of the cell. By analyzing over 23,000 publicly available RNA-Seq data sets, we show that Tradict is robust to noise and accurate. Coupled with targeted RNA sequencing, Tradict may therefore enable simultaneous transcriptome-wide screening and mechanistic investigation at large scales.

should be far less than the total number of genes it contains. If true to a large enough extent, it may be possible to faithfully compress and prospectively summarize entire transcriptomes by measuring only a 49 small, carefully chosen subset of it.
Indeed, previous studies have exploited this reduced dimensionality to perform gene expression imputation for microarray data for missing or corrupted values [10][11][12] . Others have extended these 52 intuitions to predict expression from probe sets containing a few hundred genes 13,14 . However,

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Furthermore, given that the expression of even the most abundant genes is highly skewed, sequencing effort is wastefully distributed compared to an approach that chooses which genes to measure more 66 wisely. Finally, it is still not clear from sample sizes and biological contexts previously studied whether 67 the low dimensionality of the transcriptome may be leveraged unconditionally (or nearly so) across 68 organism and application.

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In this work, we introduce Tradict (transcriptome predict), a robust-to-noise and probabilistically 70 sound algorithm, for inferring gene abundances transcriptome-wide, and predicting the expression of a 71 transcriptomically comprehensive, but interpretable list of transcriptional programs that represent the 72 major biological processes and pathways of the cell. Tradict makes its predictions using only the 73 expression measurement of a single, context-independent, machine-learned subset of 100 marker 74 genes. Importantly, Tradict's predictions are formulated as posterior distributions over unmeasured 75 genes and programs, and therefore simultaneously provide point and credible interval estimates over 76 predicted expression. Using a representative sampling of over 23,000 publicly available, transcriptome-77 wide RNA-Seq datasets for Arabidopsis thaliana and Mus musculus, we show Tradict prospectively 78 models program expression with striking accuracy. Our work demonstrates the development and large-79 scale application of a probabilistically reasonable multivariate count/non-negative data model, and 80 highlights the power of directly modeling the expression of a comprehensive list of transcriptional 81 programs in a supervised manner. Consequently, we believe that Tradict, coupled with targeted RNA 82 sequencing [19][20][21][22][23][24] , can rapidly illuminate biological mechanism and improve the time and cost of 83 performing large forward genetic, breeding, or chemogenomic screens.

Results
We downloaded all available Illumina

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In order to assess the quality and comprehensiveness of our training collection, we performed a 101 deep characterization of the expression spaced spanned by these transcriptomes. We found that the 102 transcriptome of both organisms was highly compressible and that the primary drivers of variation were Continuous-Poisson hierarchical model is used as a predictive model to capture covariance relationships between markers, transcriptional programs, and all genes. b) During prediction, Tradict predicts the expression of transcriptional programs and all genes in the transcriptome using the expression measurements of the marker genes. c) The Multivariate Normal Continuous-Poisson hierarchy enables Tradict to efficiently model statistical coupling between the non-negative expression measurements typical of sequencing experiments. This is done by assuming that associated with each observed, noisy TPM measurement, there is an unmeasured (denoised), latent abundance the logarithm of which comes from a Multivariate Normal distribution over all genes and transcriptional programs. tissue and developmental stage (Figure 1a-b, Figure S1), with many biologically realistic trends within each cluster (Supplemental Analysis 1). We additionally examined the distribution of submissions across the expression space, compared inter-submission variability within and between tissues, inspected expression correlations among genes with well-established regulatory relationships, and perturbations, and environmental stimuli represented in the SRA, these results, taken together, suggest 111 that our training collection is an accurate and representative sampling of the transcriptomic state space 112 that is of experimental interest for both organisms.        posterior predictive distribution for transcriptional programs may be slightly too wide at moderate 206 interval sizes (30-70%), which would make Tradict more conservative (higher type II error rate) than it 207 should be. However, in practice it is accurate (p-value = 0.24, t-test) for larger, more standard interval 208 sizes (e.g. 95%). We conclude that Tradict's probabilistic modeling assumptions capture unseen data 209 well.
that training-set expression variance and mean abundance correlated positively with both program and gene expression prediction performance (Supplemental Analysis 3.I, Supplemental Tables 3-4). annotation rates influence predictive performance revealed that program expression prediction 219 perfromance was robust up to a 20% mis-annotation rate and that gene expression prediction 220 performance was completely robust to any level of mis-annotation. The latter result is a consequence of 221 a statistical decoupling between gene and program expression prediction (Supplemental Analysis 3.III).

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Finally, Tradict's training time and peak memory requirements scaled linearly with training set size, and  Table 5, Supplemental Analysis 3.II).

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Furthermore, Tradict is robust to noisily defined transcriptional programs, and its computational 229 requirements scale well to large datasets.

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Case studies reveal the power of predicting and studying transcriptional program expression

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To demonstrate how Tradict may be applied in practice, we focused on two case studies related 233 to innate immune signaling --one performed using bulk A. thaliana seedlings (detailed below), and the 234 other using primary immune M. musculus cell lines (detailed in Supplemental Analysis 5; Figure S10).

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We trained Tradict on our full collection of training transcriptomes for each organism to produce two    Recall that the genes participating in each of our transcriptional programs are pre-defined, in to jasmonic acid" and "response to salicylic acid" transcriptional programs. Figure 4b

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In order to illustrate Tradict's use in hypothesis-free investigation, we performed a differential

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Transcriptional programs differentially expressed with respect to MeJA treatment included "response to 285 bacterium," "defense response to fungus", "response to wounding," and "response to jasmonic acid" as 286 expected. Transcriptional programs differentially expressed with respect to BTH treatment included 287 various abiotic stress responses, "defense response to fungus", "response to jasmonic acid" (via 288 antagonism), and "response to salicylic acid," again, as expected.

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Tradict is an accurate, robust-to-noise method for predicting the expression of a comprehensive,

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Tradict additionally provides expression predictions for all genes in the transcriptome. Though 301 its current gene expression prediction accuracy is less than ideal for more sensitive applications,

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Tradict's performance is superior to previous efforts and is improving logarithmically in the number of 303 samples. We attribute Tradict's performance gains over previous methods first to improved 304 measurement technology. Previous methods were developed for microarray, a substantially more noisy 305 technology than RNA-Sequencing 10-14 . Consequently, training efficiency was lower as well as 306 measurement accuracy of true expression, thus leading to modest prediction accuracy. By contrast,

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Tradict is meant to interface with sequencing based readouts of gene expression, a data type that is 308 popular and proliferating exponentially as the time and price of sequencing continues to fall. Second,

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we believe Tradict's probabilistic framework goes a step beyond previous efforts by modeling marker-310 gene and marker-program relationships not at the level of measured abundances, which are noisy, but 311 at the level of latent abundances. Working in this denoised space naturally improves accuracy and 312 affords robustness.

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Taken together, we believe that Tradict coupled with target RNA sequencing can enable 314 transcriptome-wide screening cheaply and at scale. Well-established commercial 19,20 and non-315 commercial 21,33 methods exist for targeted RNA sequencing in a multiplexed manner, and they are able 316 to measure the expression of 10's-100's of genes with accuracy, making their use immediately for thousands to tens of thousands of samples to close to $1 per sample. made cheap and scalable by monitoring an easily selectable phenotype, new phenotyping architectures 328 must be developed and optimized for each new screen (e.g. reporter lines, imaging hardware/software).

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Given the ubiquity of RNA, a transcriptome-wide screening approach would not suffer from such a 330 drawback. Furthermore, and more importantly, though quickly interpretable, the phenotype being 331 screened for is usually a uni-dimensional datum that offers little immediate insight into mechanism. In 332 contrast, using Tradict to help perform transcriptome-wide screening could couple the process of

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Tradict that will be made available before publication.