Python

MACS is implemented in Python and freely available with an open source Artistic License at [16 ]. It runs from the command line and takes the following parameters: -t for treatment file (ChIP tags, this is the ONLY required parameter for MACS) and -c for control file containing mapped tags; --format for input file format in BED or ELAND (output) format (default BED); --name for name of the run (for example, FoxA1, default NA); --gsize for mappable genome size to calculate λ_BG from tag count (default 2.7G bp, approximately the mappable human genome size); --tsize for tag size (default 25); --bw for bandwidth, which is half of the estimated sonication size (default 300); --pvalue for p-value cutoff to call peaks (default 1e-5); --mfold for high-confidence fold-enrichment to find model peaks for MACS modeling (default 32); --diag for generating the table to evaluate sequence saturation (default off).
In addition, the user has the option to shift tags by an arbitrary number (--shiftsize) without the MACS model (--nomodel), to use a global lambda (--nolambda) to call peaks, and to show debugging and warning messages (--verbose). If a user has replicate files for ChIP or control, it is recommended to concatenate all replicates into one input file. The output includes one BED file containing the peak chromosome coordinates, and one xls file containing the genome coordinates, summit, p-value, fold_enrichment and FDR (if control is available) of each peak. For FoxA1 ChIP-Seq in MCF7 cells with 3.9 million and 5.2 million ChIP and control tags, respectively, it takes MACS 15 seconds to model the ChIP-DNA size distribution and less than 3 minutes to detect peaks on a 2 GHz CPU Linux computer with 2 GB of RAM. Figure S6 in Additional data file 1 illustrates the whole process with a flow chart.

Aside from the highly popular SCLVM (https://github.com/PMBio/scLVM) [46 (link), 47 (link)], which uses Gaussian process latent variable models for inferring hidden sources of variation, there are, among others, the visualization frameworks FASTPROJECT (https://github.com/YosefLab/FastProject) [48 (link)], ACCENSE (http://www.cellaccense.com/) [49 ], and SPRING (https://github.com/AllonKleinLab/SPRING) [15 ]—the latter uses the JavaScript package (http://d3js.org D3.js for the actual visualization and Python only for preprocessing—the trajectory inference tool SCIMITAR (https://github.com/dimenwarper/scimitar), the clustering tool PHENOGRAPH (https://github.com/jacoblevine/PhenoGraph) [19 (link)], the single-cell experiment design tool MIMOSCA (https://github.com/asncd/MIMOSCA)[50 (link)], UMIS (https://github.com/vals/umis) for handling raw read data [51 (link)], the tree-inference tool ECLAIR (https://github.com/GGiecold/ECLAIR) [52 (link)], and the framework FLOTILLA (https://github.com/yeolab/flotilla), which comes with modules for simple visualization, simple clustering, and differential expression testing. Hence, only the latter provides a data analysis framework that solves more than one specific task. In contrast to SCANPY, however, FLOTILLA is neither targeted at single-cell nor at large-scale data and does not provide any graph-based methods, which are the core of SCANPY. Also, FLOTILLA is built around a complicated class STUDY, which contains data, tools, and plotting functions. SCANPY, by contrast, is built around a simple HDF5-backed class ANNDATA, which makes SCANPY both scalable and extendable (law of Demeter).

We added a collection of new features and upgraded multiple previous features in GEPIA2. To widen the analysis usage from gene level to isoform level and to better delineate different pathological status, isoform expression data and cancer subtype information are made available. The features in GEPIA2 are divided into two major topics: Expression Analysis and Custom Data Analysis. The Expression Analysis contains eight tabs: General, Differential Genes, Expression DIY, Survival Analysis, Isoform Details, Correlation Analysis, Similar Genes Detection and Dimensionality Reduction. Custom Data Analysis contains two tabs: Cancer Subtype Classifier and Expression Comparison (Figure 1). These features allow users to analyze the existing data and upload their own data for analysis based on different interactive functions. In addition to the web-based interface, we also provide a python package, to allow easy access of GEPIA2 analyses from a command-line environment. An overview for each new and upgraded feature is given in the following sections.

All analyses were performed using netZooPy v0.8.1, the Python distribution of the netZoo (netzoo.github.io). NetZoo methods are implemented in R, Python, MATLAB, and C. netZooR v1.3 is currently implemented in R v4.2 and available through GitHub (https://github.com/netZoo/netZooR) and Bioconductor (https://bioconductor.org/packages/netZooR) and includes PANDA, LIONESS, CONDOR, MONSTER, ALPACA, PUMA, SAMBAR, OTTER, CRANE, SPIDER, EGRET, DRAGON, and YARN. netZooPy v0.8.1 is implemented in Python v3.9 and includes PANDA, LIONESS, CONDOR, PUMA, SAMBAR, OTTER, and DRAGON. netZooM v0.5.2 is implemented in MATLAB 2020b (The Mathworks, Natick, MA, USA) and includes PANDA, LIONESS, PUMA, OTTER, and SPIDER. netZooC v0.2 implements PANDA and PUMA.

To find associations between TF targeting and promoter methylation status and copy number variation status, we selected 76 melanoma CCLE cell lines and we computed the significance of associations using ANOVA as implemented in the Python package statsmodels v0.13.2 [96 ]. Since we were mostly interested in finding strong associations and prominent regulatory hallmarks of melanoma, we discretized the input data by considering a gene to be amplified if it had more than three copies and to be deleted if both copies are lost. For promoter methylation data, promoters were defined in CCLE as the 1kb region downstream of the gene’s transcriptional start site (TSS). We defined hypermethylated promoter sites as those having methylation status with a z-score greater than three and we defined hypomethylated sites as those having methylation status with a z-score less than negative three; we considered a gene to be amplified if it had evidence of more than three copies in the genome and to be deleted if both copies are lost. We only computed the associations if they had at least three positive instances of the explanatory variable (for example, for a given gene at least three cell lines had a hypomethylation in that gene’s promoter) and corrected for multiple testing using a false discovery rate of less than 25% following the Benjamini-Hochberg procedure [97 ].
In all melanoma cell lines, for each modality (promoter hypomethylation, promoter hypermethylation, gene amplification, and gene deletion) and for each gene, we built an ANOVA model using TF targeting as the response variable across all melanoma cell lines while the status of that gene (either promoter methylation or copy number status) was the explanatory variable. For example, in modeling promoter hypermethylation, we chose positive instances to represent hypermethylated promoters and negative instances for nonmethylated promoters along with an additional factor correcting for the cell lineage. Similarly, for copy number variation analysis, we chose positive instance to represent amplified genes and negative instances for nonamplified genes while correcting for cell lineage. We only computed the associations if they had at least three positive instances of the explanatory variable (for example, promoter hypomethylation in at least three cell lines).
To predict drug response using TF targeting, we conducted a linear regression with elastic net [45 (link)] regularization as implemented in the Python package sklearn v1.1.3 using an equal weight of 0.5 for L1 and L2 penalties using regorafenib cell viability assays in melanoma cell lines as a response variable and the targeting scores of 1,132 TFs (Table S5) as the explanatory variable.
Finally, to model EMT in melanoma, we used MONSTER on two LIONESS networks of melanoma cancer cell lines, one representing a primary tumor (Depmap ID: ACH-000580) as the initial state and the other a metastasis cell line (Depmap ID: ACH-001569) as the end state. We modified the original implementation of MONSTER that implements its own network reconstruction procedure to take any input network, such as LIONESS networks. MONSTER identifies differentially involved TFs in the transition by shuffling the columns of the initial and final state adjacency matrices 1000 times to build a null distribution, which is then used to compute a standardized differential TF involvement score by scaling the obtained scores by those of the null distribution.