所有图片

第 1 张图片，共 1 张：‘Figure showing the long table format on the left and wide table format on the right and arrows illustrating how the values in the 'sample' column on the left have become column names on the right and how the values in the 'expression' column on the left have become values on the the right. Below is the call to 'pivot_wider()' with annotations pointing to the 'sample' and 'expression' function arguments.’

Figure showing the long table format on the left and wide table format on the right and arrows illustrating how the values in the 'sample' column on the left have become column names on the right and how the values in the 'expression' column on the left have become values on the the right. Below is the call to 'pivot_wider()' with annotations pointing to the 'sample' and 'expression' function arguments. — Wide pivot of the `rna` data.

图 2

第 1 张图片，共 1 张：‘Figure showing the long table format on the left and wide table format on the right and arrows illustrating how the column names on the left have become a new column 'sample' on the left and the values in the wide table on the right have become a new column 'expression' on the left. Below is the call to 'pivot_wider()' with annotations pointing to the 'sample', 'expression' and the '-gene' arguments.’

Figure showing the long table format on the left and wide table format on the right and arrows illustrating how the column names on the left have become a new column 'sample' on the left and the values in the wide table on the right have become a new column 'expression' on the left. Below is the call to 'pivot_wider()' with annotations pointing to the 'sample', 'expression' and the '-gene' arguments. — Long pivot of the `rna` data.

图 3

A two by two table with X and Y row names and Female and Male column names showing the total counts for each X/Y and Female/Male combination. The Y/Female combinations shows 3 counts, while all other counts are above 2000 counts.

数据可视化

图 1

Default histogram produced by ggplot() and geom_histogram() for the expression data.

图 2

Histograms produced by ggplot() and geom_histogram() for the expression data with bin set of 15 (top) and binwith set to 2000 (bottom).

图 3

图 4

Histogram produced by ggplot() and geom_histogram() for the pre-computed log of expression.

图 5

Histogram produced by ggplot(), geom_histogram() and scale_x_log10() for the log of expression.

图 6

Scatter plot produced by ggplot() and geom_point() comparing the log-foldchanges computed above. All dots are black.

图 7

Scatter plot produced by ggplot() and geom_point() comparing the log-foldchanges computed above. All dots are semi-transparent black.

图 8

Scatter plot produced by ggplot() and geom_point() comparing the log-foldchanges computed above. All dots are semi-transparent blue.

图 9

Scatter plot produced by ggplot() and geom_point() comparing the log-foldchanges computed above. Dots are colour-coded based on the gene's biotype.

图 10

图 11

Scatter plot produced by ggplot() and geom_point() comparing the log-foldchanges computed above. Dots are colour-coded based on the gene's biotype. A black line of slope 1 crossing the origin was added by geom_abline().

图 12

图 13

Scatter plot produced by ggplot() and geom_hexbin() comparing the log-foldchanges computed above shows hexagons coloured based on the underlying dot density. A black line of slope 1 crossing the origin was added by geom_abline().

图 14

Figures showing a stretch of overlapping points indicating the log of expression + 1 for each sample. The points are coloured with different shades of blue for samples collected at different time points.

图 15

Boxplot showing the distribution of log expression + 1 values for each sample, as produced by geom_boxlpot(). Each boxplot is filled with white colour.

图 16

Boxplot and dots showing the distribution of log expression + 1 values for each sample, as produced by geom_boxlpot(). Each boxplot is transparent and the jittered dots are semi-transparent tomato-coloured and behind the boxpots.

图 17

图 18

图 19

第 1 张图片，共 1 张：‘Boxplot and dots showing the distribution of log expression + 1 values for each sample, as produced by geom_boxlpot(). On the first figure, each boxplot is transparent and the jittered dots are semi-transparent and coloured in different shares of blue. On the second figures, each boxplot is transparent and the jittered dots are semi-transparent and coloured red, green and blue.’