Data-analysis strategies for image-based cell profiling

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.Peer reviewe

Data File S3. Genetic interaction profile similarity matrices

Matrix files containing genetic interaction profile similarity values (as measured by Pearson correlation) for every pair of mutant strains in the dataset. Similarity values were computed for essential (ExE), non-essential (NxN) and the global similarity network derived from a combined set of all genetic interactions (ExE, NxN, ExN) as described above (see "Constructing genetic interaction profile similarity networks"). Each matrix contains 2 sets of row and column headers, providing a unique allele name for every mutant strain (row & column header #1) as well as a systematic ORF name (row & column header #2)

Data File S4. GO bioprocess functions predicted by the nonessential and essential similarity networks using a K-nearest neighbor approach

This file reports the performance of gene function prediction for non-essential or essential genes based on genetic interaction profiles. For both classes of genes (either nonessential or essential), the performance of a KNN classifier is reported as the Precision at 25% Recall based on interactions derived from TS queries (PR_TSQ) or nonessential deletion queries (PR25_SN). Although analyses were performed using complete genetic interaction profiles (e.g. negative and positive genetic interactions), similar prediction performance was obtained using genetic interaction profiles based on negative interactions alone

Data File S7. Pleiotropic gene analysis

This file lists nonessential and essential query genes associated with high confidence pleiotropy scores based on their genetic interactions derived from the TSA (Essential derived pleiotropy) and DMA (Nonessential derived pleiotropy). The file also contains a second list of nonessential and essential query genes that participate in many genetic interactions but exhibited low pleiotropy scores indicating that these genes are more functionally specific

Data from: A global genetic interaction network maps a wiring diagram of cellular function

INTRODUCTION: Genetic interactions occur when mutations in two or more genes combine to generate an unexpected phenotype. An extreme negative or synthetic lethal genetic interaction occurs when two mutations, neither lethal individually, combine to cause cell death. Conversely, positive genetic interactions occur when two mutations produce a phenotype that is less severe than expected. Genetic interactions identify functional relationships between genes and can be harnessed for biological discovery and therapeutic target identification. They may also explain a considerable component of the undiscovered genetics associated with human diseases. Here, we describe construction and analysis of a comprehensive genetic interaction network for a eukaryotic cell. RATIONALE: Genome sequencing projects are providing an unprecedented view of genetic variation. However, our ability to interpret genetic information to predict inherited phenotypes remains limited, in large part due to the extensive buffering of genomes, making most individual eukaryotic genes dispensable for life. To explore the extent to which genetic interactions reveal cellular function and contribute to complex phenotypes, and to discover the general principles of genetic networks, we used automated yeast genetics to construct a global genetic interaction network. RESULTS: We tested most of the ~6000 genes in the yeast Saccharomyces cerevisiae for all possible pairwise genetic interactions, identifying nearly 1 million interactions, including ~550,000 negative and ~350,000 positive interactions, spanning ~90% of all yeast genes. Essential genes were network hubs, displaying five times as many interactions as nonessential genes. The set of genetic interactions or the genetic interaction profile for a gene provides a quantitative measure of function, and a global network based on genetic interaction profile similarity revealed a hierarchy of modules reflecting the functional architecture of a cell. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections associated with defects in cell cycle progression or cellular proteostasis. Importantly, the global network illustrates how coherent sets of negative or positive genetic interactions connect protein complex and pathways to map a functional wiring diagram of the cell. CONCLUSION: A global genetic interaction network highlights the functional organization of a cell and provides a resource for predicting gene and pathway function. This network emphasizes the prevalence of genetic interactions and their potential to compound phenotypes associated with single mutations. Negative genetic interactions tend to connect functionally related genes and thus may be predicted using alternative functional information. Although less functionally informative, positive interactions may provide insights into general mechanisms of genetic suppression or resiliency. We anticipate that the ordered topology of the global genetic network, in which genetic interactions connect coherently within and between protein complexes and pathways, may be exploited to decipher genotype-to-phenotype relationships

Data File S6. Genetic profile similarity-based hierarchy analysis

The first tab (“Gene to hierarchy cluster mapping”) lists the clusters identified at each level of the genetic interaction-based hierarchy and the deletion and TS allele array mutants assigned to each cluster. Examples of clusters described in the main text are highlighted. The subsequent 9 tabs indicate enrichment of clusters resolved at the specified profile similarity range for specific cell compartments (Cyclops_enrich), biological processes (GO BP_enrich), protein complexes (complex_enrich) and KEGG pathways (KEGG_enrich). The final tab in the file indicates the clusters used to map the functional distribution of negative and positive interactions shown in Fig. 5D