▁approaches ▁can ▁only ▁ass ay ▁for ▁simple ▁phen ot ypes , ▁such ▁as ▁cell ular ▁vi ability , ▁sc R NA - seq ▁allows ▁for ▁a ▁much ▁rich er ▁phen ot yp ic ▁read out , ▁with ▁quant itative ▁measurements ▁of ▁gene ▁expression ▁in ▁many ▁cells ▁simultaneously . ▁ ▁However , ▁while ▁a ▁large ▁and ▁compreh ensive ▁amount ▁of ▁data ▁can ▁be ▁a ▁benefit , ▁it ▁can ▁also ▁present ▁a ▁major ▁challenge . ▁Single ▁cell ▁R NA ▁expression ▁read outs ▁are ▁known ▁to ▁produce ▁‘ no isy ’ ▁data , ▁with ▁a ▁significant ▁number ▁of ▁false ▁posit ives . ▁Both ▁the ▁large ▁size ▁and ▁noise ▁that ▁is ▁associated ▁with ▁sc R NA - seq ▁will ▁likely ▁require ▁new ▁and ▁powerful ▁computational ▁methods ▁and ▁bio in format ics ▁pip elines ▁to ▁better ▁make ▁sense ▁of ▁the ▁resulting ▁data . ▁Another ▁challenge ▁associated ▁with ▁this ▁protocol ▁is ▁the ▁creation ▁of ▁large ▁scale ▁CR IS PR ▁libraries . ▁The ▁prepar ation ▁of ▁these ▁extensive ▁libraries ▁depends ▁upon ▁a ▁compar ative ▁increase ▁in ▁the ▁resources ▁required ▁to ▁culture ▁the ▁massive ▁numbers ▁of ▁cells ▁that ▁are ▁needed ▁to ▁achieve ▁a ▁successful ▁screen ▁of ▁many ▁perturb ations . ▁ ▁In ▁parallel ▁to ▁these ▁single - cell ▁methods , ▁other ▁approaches ▁have ▁been ▁developed ▁to ▁re construct ▁gen etic ▁path ways ▁using ▁whole - organ ism ▁R NA - sequ encing . ▁These ▁methods ▁use ▁a ▁single ▁aggregate ▁stat istic , ▁called ▁the ▁trans cript ome - wide ▁ep istas is ▁coefficient , ▁to ▁guide ▁path way ▁reconst ruction .