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=Cell Line Analysis= [[Daniel:Notebook/RNAFISH|Back to Calendar]] I've been using three cell lines to [[Daniel:Notebook/RNAFISH/2014-11-22|test the RNA FISH]], [[Daniel:CellLines/U87MG|U-87MG]], [[Daniel:CellLines/T98G|T98-G]], and [[Daniel:CellLines/BE2C|BE2-C]]. None of the results from the 11/22/2014 test were conclusive, so I also got some data from Dr. Zhang on NCBI's website. This data is meant to see whether or not we have gene expression of the 4 genes I have already (RBFOX3/NeuN, FOXP2, CUX2, and GAD1) in the 4 cell lines, since we just assumed before. Links to the raw data can be found on the cell line pages (links above). ==Gene Expression Analysis== I found two separate types of gene expression data sets. Two data sets (T98-G and U87-MG) were analyzed using Affymetrix microarrays, and one cell line (BE2-C) was analyzed using RNA-Seq. ===Affymetrix Microarray Data (T98-G and U87-MG)=== The first challenge of this is to understand the Affymetrix labeling system. With help from [http://david.abcc.ncifcrf.gov/ DAVID] I changed the Affymetrix notation to Entrez Genes and identified which labels (usually several) corresponded to my genes of interest. The data is summarized below. {| class="wikitable" <hiddentext>generated with [[:de:Wikipedia:Helferlein/VBA-Macro for EXCEL tableconversion]] V1.8</hiddentext> |- style="background-color:#D8E4BC;font-size:12pt;font-weight:bold" align="center" | width="65" height="50" | Gene | width="150" | Entrez Gene ID | width="158" | Gene List | width="145" | U87-MG Values | width="135" | T98-G Values |- style="font-size:12pt" |style="background-color:#E6B8B7" height="15" valign="bottom" | RBFOX3 | align="center" valign="bottom" | 146713 | valign="bottom" | 229406_at | align="center" | 1.654966 | align="center" | 1.756282 |- style="font-size:12pt" |style="background-color:#E6B8B7" height="15" valign="bottom" | RBFOX3 | align="center" valign="bottom" | 146713 | | 235468_at | align="center" | 2.087149 | align="center" | 2.173624 |- style="font-size:12pt" |style="background-color:#E6B8B7" height="15" valign="bottom" | RBFOX3 | align="center" valign="bottom" | 146713 | valign="bottom" | 240975_x_at | align="center" valign="bottom" | 3.09884 | align="center" | 3.058095 |- style="font-size:12pt" |style="background-color:#C5D9F1" height="14" valign="bottom" | FOXP2 | align="center" valign="bottom" | 93986 | valign="bottom" | 1562139_a_at | align="center" | 2.320036 | align="center" | 2.509934 |- style="font-size:12pt" |style="background-color:#C5D9F1" height="15" valign="bottom" | FOXP2 | align="center" valign="bottom" | 93986 | valign="bottom" | 1555516_at | align="center" | 1.644757 | align="center" | 1.88334 |- style="font-size:12pt" |style="background-color:#C5D9F1" height="15" valign="bottom" | FOXP2 | align="center" valign="bottom" | 93986 | valign="bottom" | 243278_at | align="center" | 1.420676 | align="center" | 2.523345 |- style="font-size:12pt" |style="background-color:#C5D9F1" height="15" valign="bottom" | FOXP2 | align="center" valign="bottom" | 93986 | valign="bottom" | 1552902_a_at | align="center" | 1.915386 | align="center" | 2.145663 |- style="font-size:12pt" |style="background-color:#C5D9F1" height="15" valign="bottom" | FOXP2 | align="center" valign="bottom" | 93986 | valign="bottom" | 235201_at | align="center" | 2.150443 | align="center" | 3.007517 |- style="font-size:12pt" |style="background-color:#C5D9F1" height="15" valign="bottom" | FOXP2 | align="center" valign="bottom" | 93986 | valign="bottom" | 1555647_a_at | align="center" | 2.238783 | align="center" | 2.606263 |- style="font-size:12pt" |style="background-color:#C5D9F1" height="15" valign="bottom" | FOXP2 | align="center" valign="bottom" | 93986 | valign="bottom" | 1555648_at | align="center" | 1.967491 | align="center" | 2.01729 |- style="font-size:12pt" |style="background-color:#C5D9F1" height="15" valign="bottom" | FOXP2 | align="center" valign="bottom" | 93986 | valign="bottom" | 1564876_s_at | align="center" | 3.461954 | align="center" | 3.740007 |- style="font-size:12pt" |style="background-color:#C5D9F1" height="15" valign="bottom" | FOXP2 | align="center" valign="bottom" | 93986 | valign="bottom" | 1555352_at | align="center" | 1.765198 | align="center" | 2.079463 |- style="font-size:12pt" |style="background-color:#D8E4BC" height="15" valign="bottom" | CUX2 | align="center" valign="bottom" | 23316 | valign="bottom" | 1566528_at | align="center" | 2.388545 | align="center" | 2.225919 |- style="font-size:12pt" |style="background-color:#D8E4BC" height="15" valign="bottom" | CUX2 | align="center" valign="bottom" | 23316 | valign="bottom" | 213920_at | align="center" | 1.672316 | align="center" | 1.586615 |- style="font-size:12pt" |style="background-color:#D8E4BC" height="15" valign="bottom" | CUX2 | align="center" valign="bottom" | 23316 | valign="bottom" | 1566527_at | align="center" | 3.108661 | align="center" | 2.981597 |- style="font-size:12pt" |style="background-color:#FFFF00" height="15" valign="bottom" | GAD1 | align="center" valign="bottom" | 2571 | valign="bottom" | 205278_at | align="center" | 4.696471 | align="center" | 1.507815 |- style="font-size:12pt" |style="background-color:#FFFF00" height="15" valign="bottom" | GAD1 | align="center" valign="bottom" | 2571 | valign="bottom" | 206669_at | align="center" | 4.935924 | align="center" | 3.5153 |- style="font-size:12pt" |style="background-color:#FFFF00" height="15" valign="bottom" | GAD1 | align="center" valign="bottom" | 2571 | valign="bottom" | 206670_s_at | align="center" | 4.498899 | align="center" | 2.625633 |} Since this is microarray data, the numbers don't mean a lot on their own (they range from about 1 to 14). So I plotted, for each cell line, the total distribution (boxplot) and also the individual values for each of the four genes. <gallery widths=500px heights=500px> File:U87MG.jpg File:T98G_CellLineAnalysis.png </gallery> So the U87-MG line might have an enrichment of GAD1, since that is the only gene for either line that appears to be significantly above the mean. ===RNASeq Data (BE2-C)=== This data is a little more tame. Basically I just sorted through a large text file until I found the chromosomal locations of each gene (using [http://www.genecards.org/ Gene Cards]), then found the transcript name and FPKM. The table of results is below. {| class="wikitable" <hiddentext>generated with [[:de:Wikipedia:Helferlein/VBA-Macro for EXCEL tableconversion]] V1.8</hiddentext> |- style="background-color:#CCC0DA;font-size:12pt" align="center" | width="65" height="43" | Gene | width="138" | Gene ID | width="135" | FPKM | width="65" | Orientation | width="65" | Confirmed |- style="font-size:12pt" valign="bottom" | height="15" | RBFOX3 | ENST00000338834.4 | align="center" | 0 | - | No |- style="background-color:#D9D9D9;font-size:12pt" valign="bottom" | height="15" | FOXP2 | ENST00000393500.3 | align="center" | 0.0004195009 | + | No |- style="font-size:12pt" valign="bottom" | height="15" | CUX2 | ENST00000261726.5 | align="center" | 0.6117341499 | + | Maybe |- style="background-color:#D9D9D9;font-size:12pt" valign="bottom" | height="15" | GAD1 | ENST00000358196.3 | align="center" | 0 | + | No |} This one shows that the BE2-C line may contain appreciable amounts of CUX2. I'm basing this off the gene card for CUX2, which has the following information: [[Image:CUX2_GeneExpression.png|500px]] Using the same calculation for this (100*FPKM)^1/2, this would yield a score of 7.8, which for CUX2 seems to be about as high as it goes.
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