Cancer　is　an　omics　disease.　The　development　in　high-throughput　profiling　has　fundamentally　changed　cancer　research　and　clinical　practice.　Compared　with　clinical,　demographic　and　environmental　data,　the　analysis　of　omics　data-which　has　higher　dimensionality,　weaker　signals　and　more　complex　distributional　properties-is　much　more　challenging.　Developments　in　the　literature　are　often　＇scattered＇,　with　individual　studies　focused　on　one　or　a　few　closely　related　methods.　The　goal　of　this　review　is　to　assist　cancer　researchers　with　limited　statistical　expertise　in　establishing　the　＇overall　framework＇　of　cancer　omics　data　analysis.　To　facilitate　understanding,　we　mainly　focus　on　intuition,　concepts　and　key　steps,　and　refer　readers　to　the　original　publications　for　mathematical　details.　This　review　broadly　covers　unsupervised　and　supervised　analysis,　as　well　as　individual-gene-based,　gene-set-based　and　gene-network-based　analysis.　We　also　briefly　discuss　＇special　topics＇　including　interaction　analysis,　multi-datasets　analysis　and　multi-omics　analysis.