Package: fCI 1.43.0

Introduction to fCI | Authors and Affliations | Abstract | Introduction | Installing fCI | Differential Expression Analysis using fCI | Reading the input data: | Integer raw read counts from NGS data or Spectrum counts from proteomics data | Normalized gene expression such as RPKM or FPKM, or peak intesntiy (height/area) in proteomics data | Ratio data from many experiments measuring relative gene expression with respect to control channels. | Data normalization | Total library normalization | Trimed sum normalization | Kernel density distribution centering | fCI analysis with the Spike-in microarray data | fCI DEG analysis Output | Print Differentially Expressed Genes | The Kernel Density Plot of Control-Control and Control-Case distributions | Alternative function to find DEGs | Testing fCI on a randomly generated simulated dataset | Finding Differentially Expressed Genes (no DEGs in this case): | Multi-dimensional (i.e.Pproteogenomics data) fCI analysis | Example of integrated proteogeonomics analysis | Specifying fCI runtime variables | Use only transcriptomics dataset in the proteogenomics data | Theory behind fCI