▁profile ▁of ▁each ▁cell ▁to ▁be ▁affili ated ▁with ▁a ▁particular ▁perturb ation . ▁Further ▁down stream ▁anal ys es ▁on ▁the ▁trans cription al ▁profiles ▁will ▁depend ▁entirely ▁on ▁the ▁bi ological ▁question ▁of ▁interest . ▁T - dist ributed ▁Sto ch astic ▁Ne igh bor ▁Em bed ding ▁( t - S NE ) ▁is ▁a ▁commonly ▁used ▁machine ▁learning ▁algorithm ▁to ▁visual ize ▁the ▁high - dimensional ▁data ▁that ▁results ▁from ▁sc R NA - seq ▁in ▁a ▁ 2 - dimensional ▁scatter plot . ▁The ▁authors ▁who ▁first ▁performed ▁P ert urb - seq ▁developed ▁an ▁in - house ▁computational ▁framework ▁called ▁M IM OS CA ▁that ▁predict s ▁the ▁effects ▁of ▁each ▁perturb ation ▁using ▁a ▁linear ▁model ▁and ▁is ▁available ▁on ▁an ▁open ▁software ▁repository . ▁ ▁Adv antages ▁and ▁limitations ▁▁ ▁P ert urb - seq ▁makes ▁use ▁of ▁current ▁techn ologies ▁in ▁mole cular ▁bi ology ▁to ▁integrate ▁a ▁multi - step ▁workflow ▁that ▁cou ples ▁high - through put ▁screen ing ▁with ▁complex ▁phen ot yp ic ▁outputs . ▁When ▁compared ▁to ▁alternative ▁methods ▁used ▁for ▁gene ▁knock down s ▁or ▁knock outs , ▁such ▁as ▁R NA i , ▁z inc ▁finger ▁nuc leases ▁or ▁trans cription ▁activ ator - like ▁effect or ▁nuc leases ▁( TA LE N s ), ▁the ▁application ▁of ▁CR IS PR - based ▁perturb ations ▁enables ▁more ▁specific ity , ▁efficiency ▁and ▁ease ▁of ▁use . ▁Another ▁advantage ▁of ▁this ▁protocol ▁is ▁that ▁while ▁most ▁screen ing