A　gene　selection　algorithm　was　developed　using　Multiple　Principal　Component　Analysis　with　Sparsity　(MSPCA).　The　MSPCA　algorithm　is　used　to　analyze　normal　and　disease　gene　expression　samples　and　to　set　these　component　loadings　to　zero　if　they　are　smaller　than　a　threshold　for　sparse　solutions.　Next,　genes　with　zero　loadings　across　all　samples　(both　normal　and　disease)　are　removed　before　extracting　feature　genes.　Feature　genes　are　genes　that　contribute　differentially　to　variations　in　normal　and　disease　samples　and,　thus,　can　be　used　for　classification.　The　MSPCA　is　applied　to　three　microarray　datasets　to　select　feature　genes　with　a　linear　support　vector　machine　to　evaluate　its　performance.　This　method　is　compared　with　several　previous　gene　selection　results　to　show　that　this　MSPCA　gene　selection　algorithm　has　good　classification　accuracy　and　model　stability.　©　1996-2012　Tsinghua　University　Press.